Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Handwritten numeral recognition is a challenging problem in the character recognition field due to the large variation in the writing styles of different persons and high similarity in the contour of different numerals. To address this problem, an effective multi-task learning network (MTLN) for handwritten numeral recognition is presented in this paper. Based on the observation that the writing style could play an effective complementary role to the learned feature extracted from numerals, the proposed MTLN simultaneously performs the handwritten numeral learning module and the writing style learning module. Consequently, the determination of scratchy/non-scratchy in the writing style learning module can effectively assist the handwritten numeral learning module to obtain a more robust and distinguishable feature so as to improve the recognition performance. Extensive experiments on multiple existing handwritten numeral datasets have demonstrated that the proposed MTLN can effectively improve the recognition accuracy, and outperform multiple state-of-the-art methods.

This study proposes the digital fuzzy control based on Adaptive-Network-based Fuzzy Inference System (ANFIS) to overcome the tracking problem of a DC-AC and DC-DC boost converter and to implement on a digital computer. The proposed method eliminates the trial and error commonly associated with fuzzy systems. In the proposed design scheme, we take the existing continuous controller as training data of the ANFIS such that the proposed digital fuzzy control can replace original continuous one and can keep the original system performance. It is a sense of digital redesign that this paper proposes a new digital control such that the state trajectories of the sampled-data closed loop systems close to the continuous-time one. An existing boost converter circuit has demonstrated the feasibility of the proposed methodology.

According to the reports of the World Health Organization, the abnormal total cholesterol is one of the major risk factors for cardiovascular diseases. Supporting this, a number of prospective studies have examined that the risk of cardiac morbidity and mortality is directly associated with the concentration of plasma total cholesterol. Traditional Chinese medicine has been used to treat cardiovascular diseases since it creates milder healing effects and incurs fewer side effects than Western approaches. However, in real world, the traditional Chinese medicine diagnosis using the meridian system is highly complicated in nature so that it is difficult to create a general model. In our research, a hybrid evolutionary rule mining approach is proposed to assess the total cholesterol data patterns detected from the traditional Chinese medicine meridian energy. Based on the proposed approach, a rule-based decision-making model can be developed with maximum classification accuracy. Through a numerical experiment, the results of this study were compared with the commercial data mining software and the proposed approach is shown to be a promising method for improving prediction accuracy with fewer type II errors. The research outcomes may have benefits to help diagnose and treat cholesterol abnormalities in traditional Chinese medicine clinical therapy.

With the increasing of system scale and the growing of functional requirements and interactions within and/or among system hierarchies, there are more difficulties for safety engineers to carry out their works. Especially, the research on phased mission system (PMS) with probabilistic common cause failures (PCCFs) is still in the initial stage and has a lot of limitations, because the current researches have not yet considered the dynamic characteristics of PMSs. In this paper, a modular method is proposed as the theoretical basis to construct the components model in the PMS with PCCF. The relationships between common cause events were discussed and the probabilistic model of CCFs is extended, so that the model can fit for different statistical relations, and a module-based modeling and analysis method using binary decision diagram (BDD) and Markov model were proposed to deal with static and dynamic module in PMS respectively. Moreover, a standardization of description method for modular method using extensible markup language (XML) is given, based on which the system reliability model can be constructed by computer aided. Finally, the proposed method is demonstrated through a case study.

In this paper, a hill-climbing method is adopted to control the output of full bridge circuit to achieve desired constant voltage/current output. A novel technology also is presented to control the rising time of the DC converter output. The hardware implementation of this digital control is based on the Field Programmable Gate Array (FPGA) digital chip. The proposed hill-climbing control algorithm can automatically adjust the Pulse Width Modulation (PWM) control parameter to handle the DC converter output approaching desired one. High frequency power supply with DC switching circuit has been paying more attention to its small size, light weight, and small electromagnetic interference. Digital control has many different control modes than continuous circuit one, and digital control can be updated at any control mode, unlike continuous circuit design cannot be changed once it is completed. That is, the merit of digital control is flexible and programmable which is why the FPGA digital control used in this paper to control the DC switching circuit. We obtain the FPGA execution code by establishing its corresponding Matlab/Simulink simulation model. After meeting simulation test, the Matlab/Simulink model can be directly transformed into Hardware Description Language (HDL) code. This greatly saving the time of writing HDL code which often is a barrier to the electrical engineer. Finally, a high power, 7.2 KW, DC-DC converter experiment is presented to confirm the feasibility of the proposed control method and design procedure.

The conventional Iterative Learning Control (ILC) process can potentially excite rich frequency contents based on past error history and injects them into the updated learning process. Nevertheless, the learnable error signals should be extracted, and the non-learnable error signals should be separated by adaptive bandwidth filter before the updated command is injected into next repetition. This paper proposes a new particle swarm optimization (PSO) algorithm by combining several hybrid terms adopted from literatures for the new synergy properties of local and global searches based on cognitive and social terms. This algorithm is used to adjust the three proportional– integral– derivative controller gains, the Anticipatory Iterative Learning Control (AILC) learning gain, and the cutoff bandwidth of the Butterworth filter associated with AILC. Developed hybrid PSO algorithm is devised for the updating velocity terms. The new synthesis AILC control law with adaptive learning gain, bandwidth-tuning filter and PID control gains were optimized by PSO and facilitated the improvement of the learning process and positioning accuracy. Numerical simulations were conducted and compared with the literature’s P-type AILC for a linear synchronous motor. The tracking error through the adaptive learning processes was reduced successfully by shaping a new updated input trajectory at every repetition. The experimental results confirm the effectiveness of the new PSO– AILC for ultra-fine positioning in a one-axis linear motor.

The magnetic levitation systems of maglev vehicles face the problems of open-loop instability, strong nonlinearity, model uncertainty, and large external disturbances. In order to solve the problems of model uncertainty and exogenous disturbances simultaneously, a T-S fuzzy model of magnetic levitation system with exogenous disturbances and model uncertainties is constructed to obtain an overall control model. A fuzzy H_∞ robust state feedback controller for magnetic levitation systems is designed based on parallel distribution compensation (PDC) design method and the proposed T-S model. The quadratic stability of the closed-loop magnetic levitation system with fuzzy robust control law is proved. The linear matrix inequality (LMI) is utilized to obtain a controller which can satisfy the H_∞ performance index and the stability of the magnetic levitation system with the proposed control law is proved by Lyapunov method. Both simulation and experimental results are included to demonstrate that the proposed control law can ensure the stable suspension of the vehicle and can restrain the exogenous disturbance effectively. Compared with conventional PID controller, the presented controller can assure faster dynamic response, stronger robustness and smaller overshoot under both exogenous disturbances and model uncertainties.

Understanding factors affecting growth rates in swine is important in the productivity of pig farms. We herein propose machine learning-based schemes to predict the average daily gain (ADG) of pig weight using temperature, humidity, feed intake, and the current weight of the pig. In order to address the lack of available growth data for pigs, we generate a synthetic dataset describing the weight of swine in relation to environmental factors based on the theoretical growth model and experimentally measured data, in an attempt to facilitate the application of machine learning techniques. Using the generated growth data, linear regression, tree regression, adaptive boosting (AdaBoost), and a deep neural network (DNN) are applied to estimate ADG. By means of a performance evaluation, we confirm that the machine learning algorithms are capable of predicting the ADG of swine accurately even when the growth characteristics of pigs are heterogeneous, i.e., each pig follows a different growth curve. Moreover, we also find that DNN can provide a higher predictive accuracy than other machine learning-based schemes.

The initial crisis early warning index system of the supply chain quality has been built up according to crisis inducement, documentary research and corporate research. Then we evaluated the feasibility, importance, independence of the index system by using experts scoring methodology. The initial crisis early warning index system has been filtered by fuzzy inference system (FIS) and the final crisis early warning index system has been established.

One of the challenges for the fault feature extraction of a planetary gearbox is that the weak gear fault related vibration feature is often buried by the strong background noise in the gearbox. The minimum entropy deconvolution (MED) method for weak impulsive feature enhancement has been successfully applied to the feature extraction of gears and bearings in fixed-axis gearboxes. However, it is often failed if the result is converged to a single pulse rather than a periodic pulse sequence which corresponding to the gear or the bearing localized fault. In order to address this issue, the multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is employed in this paper to extract the vibration feature related to an individual planet gear with tooth-crack fault in a planetary gearbox. The effectiveness and advantages of the method are verified by experiments.

This paper presents a procedure for assigning multiple Level-of-Service (LOS) ratings to travel time data sets containing multiple (composite) distributions. Different LOS modes within a mixed data set, the travel time data were fitted to multiple gamma-type distributions using an Expectation Maximization (EM) iterative process. The EM process was enhanced with a Monte Carlo style method, wherein the EM process was run 100 times with different random starting values and the best fit according to an R-squared value was taken. The number of underlying distributions was determined by fitting 1 to 5 distributions and using the Akaike Information Criterion to determine which number of fits maximized the information content of the fitted function. The resulting final posterior probabilities were then used to separate the data into their respective distributions. Reported here are the results of applying this procedure to travel time data collected in the Metro Atlanta area. It is believed that this method can provide enhanced LOS information, especially when the data contain multiple overlapping travel time distributions. This multiple LOS rating method is not intended to replace the current method, instead it is being developed as a supplementary tool to provide more detailed LOS information, for example providing a more detailed view that captures the underlying performance of each subgroup in addition to a single aggregate LOS measure.

This study reviewed efficient bridge management using UAVs (Unmanned Arial Vehicles) that have much potential for facility maintenance such as the possibility of regular inspection on inaccessible parts of bridges, easy operation regardless of the operators’ specialty, and economic maintenance. For this study, images were obtained from a UAV and a 3D viewer was created with them for the work efficiency of bridge inspectors, so that the inspectors can see and check inaccessible areas regardless of inspector’s location. It compared various existing inspection methods for problems such as cracks, efflorescence and leakage with the UAV inspection method to analyze the work efficiency as well as sustainable economic benefit. The main purpose of this study was to figure out whether the proposed inspection using the images from the UAV and 3D viewer can replace the existing inspection methods that cannot be performed easily and analyze the economic benefit of the new method. The study result showed that the UAV image-based bridge maintenance and inspection is possible even though it was found inefficient when images of uneven quality were provided depending on operation of the UAV. Therefore, a UAV operation manual that can help generate images of even quality must be produced.

In this study, a four-degree of freedom (4-DOF) robot arm uses an innovative two-dimensional vision sensing method to grip a moving target on a moving platform. This study utilizes forward and inverse kinematics to establish a dynamic model of the 4-DOF robot arm. A computer as a controller and a single charge-coupled device (CCD) calculates the two-dimensional vision sensing method and sends commands to an Arduino Uno microcontroller to drive the robot arm. According to simulation results of transient and steady states in MATLAB SimMechanics, the response of the dynamic proportional-fuzzy controller is better than the response of proportional–integral–derivative controller. To demonstrate a precise control of the point-contact grip, this study utilizes a ping pong ball as a target on a moving platform. Using the dynamic proportional-fuzzy controller based on the two-dimensional vision sensing method, the 4-DOF robot arm can position, grip, and carry a moving ping pong ball to a designated place in three-dimensional space, which breaks through the previous two-dimensional limitation using a single CCD. This breakthrough can reduce the weight and cost of the robot arm. Therefore, this study aims to utilize the technology to grip moving targets on a moving platform for manpower cost reduction in the industry or agriculture domain in the future.

Allocating university resources, especially defining the number of necessary student groups and laboratory classes is a hard task without knowing the exact number of students who will enroll in the given courses. This number usually depends on the exam results of the prerequisite courses. However, the planning of the next term has to be done some months before the end of the actual term. This paper presents the creation of a fuzzy model that can predict the student results in case of the Visual Programming course with an acceptable accuracy based on nine input factors describing the relevant history of the student. The model has a low complexity rule base containing only 28 rules and predicts the exam result using fuzzy rule interpolation based inference. The position of the rule consequent sets as well as the rule weights were tuned by particle swarm optimization. The root mean squared error expressed in percentage of the output range was less than 13% in case of all the training, validation and test datasets, which gives a satisfactory level of information for the planning of the number of student groups and laboratory classes in the next term in case of the next course that follows the examined Visual Programming course.

 Regarding the real-time and content adaptation requirements for end-to-end delivery of compressed video codestreams, the proposed wavelength division multiplexing (WDM) system implements cross-layer functionality with video codestream parsing and shuffling for optical wavelength carriers to achieve format-compliant encryption and high computational efficiency. The proposed intelligent shuffling cryptography scheme is controlled by an arrayed waveguide grating (AWG) and an optical switch matrix; it is presented to change the carried optical wavelength of authorized users dynamically; this serves to protect against eavesdropping at the transmitted optical line terminal end. The approximate symmetric decryption is configured with an AWG/electrical switch matrix at the receiving optical network unit end to de-shuffle the wavelength and retrieve the video datastream in compressed format. In the proposed system, the presented chaos/binary algorithm (i.e., CBmA) generates the shuffling sequences required for implementing random and aperiodic effectiveness. The experimental video application layer results showed that the perceptibility of shuffled videos verifies the content protection ability of the proposed system on the basis of the peak signal-noise ratio degradation and perceptual quality.

In this paper, a novel multisignature scheme based on chaotic maps is proposed. The purpose of multisignatures is to manage the complexities of a document signed by numerous signatories. The chaotic system is an encryption method proposed in recent years that has received widespread attention in the field of cryptography. This paper first examines the methods of multisignatures and chaotic maps, and then proposes a multisignature scheme that was designed using chaotic maps. During the processing of a multisignature scheme, to authenticate the precision of the received document, the signatory must first obtain the signed document, and then utilize the characteristics of chaotic maps to recover the original document. If the recovered document is meaningful, this indicates that no errors exist in the signature, thus the signatures are valid and the document can be signed. If the recovered document is not sufficiently clear, signing is denied. At present, because no relevant literature exists that has proposed utilizing chaotic maps to establish a multisignature system; this paper aims to develop a novel research method.

The formation of mycotoxins and potentially allergenic spores associated with fungal growth can cause spoilage of food and animal feed. This study integrated an improved genetic algorithm (IGA) in an adaptive neuro-fuzzy inference system (ANFIS) for predicting the presence of foodborne fungi and modeling their growth. The IGA enhanced the performance of the ANFIS model in predictive microbiology. Based on temperature, pH, and water quantity, the proposed IGA-ANFIS model can accurately predict the maximum specific growth rate of the ascomycetous fungus Monascus ruber. The model uses Gaussian membership functions to minimize the root-mean-square error, which was used as a performance index. Experiments verified that the prediction accuracy of the proposed IGA-ANFIS model is higher than those of existing neural network models and neural fuzzy network models.

This study assesses the possible side effects of various doses of dapagliflozin. Dapagliflozin is a hypoglycemic agent that inhibits sodium-glucose co-transporter-2 (SGLT2) to remove excess glucose from the body through urination. Hence, dapagliflozin increases the risk for urinary tract infection (UTI) compared with other hypoglycemic agents. Clarifying the side effects of dapagliflozin at different doses and whether these effects are influenced by gender or other factors is significant. Through reviewing the literature on dapagliflozin treatment, we performed a meta-analysis of factors affecting UTI. We then determined the correlation of dapagliflozin treatment with UTI from the pooled data. Each dosing, gender group, or dapagliflozin added to other drugs for diabetes reported a percentage of contracting UTI. Basing on a reference number of pretreatment probability in UTI among patients with diabetes mellitus, we obtained the post-treatment probability in contracting the infection through the meta-analysis. We assessed the risk factors of UTI due to dapagliflozin administration on the basis of the results of odds ratio, regression analysis, and t-test. Considering the add-on-response relation, we recommend that the best add-on hypoglycemic agents to achieve low-risk UTI and control blood glucose are metformin or glimepiride. We also recommend that the best dosage of dapagliflozin is at 1 mg or 2.5 mg.

This paper aims to develop a realistic triage system to better quantify a patient’s disease severity for the evaluation of admission or discharging. A good triage can reduce loads of doctors and draw attention of staffs to critical conditions. However, existing systems score on readings of vital signs and the superficial scores usually are apart from doctors’ judgement. Instead of summing up rating score, we take a Bayesian network approach to estimate the source diseases that lead to the observed vital signs, such as temperature, lactate, HCT, and CRP, etc. Because the purpose of this assessment is not making a correct diagnosis, the source diseases are only stratified to four disease categories. Based on the reading of vital signs, Bayes belief network inferences the probability distributions of the severity for each one of the four disease categories. Finally, the four distributions are then sufficient to rank a patient’s final severity by a probabilistic decision framework. Diffing from traditional paper based evaluation, our method is required to use computer to perform the computation. Our triage results closely match doctors’ judgement. Sensitivity and specificity are improved significantly, comparing to traditional APACH II systems. Absolute and relative assessment gains were calculated and proved to be practical.

The underwater glider (UG) is an underwater exploration equipment that propels itself with very low power consumption by converting the vertical motion into a horizontal motion using a buoyancy control device and a wing. To enhance gliding performance of the UG, a hybrid underwater glider (HUG) has been developed to compensate for the disadvantages of slow speed and horizontal movement and limited navigation accuracy of the UG by attaching propellant to the UG [15].

In this paper, the structure, control system and control algorithm of the HUG are presented. Also, for the developed HUG, motion performance of the HUG is simulated by computer. To realize precise navigation of the developed HUG, an attitude reference system (ARS) composed of a ring laser gyroscope(RLG) and a geomagnetic sensor is developed with the Extended Kalman Filter (EKF) algorithm.

To control the HUG, the six degrees of freedom equations of the HUG with the hydrodynamic force coefficient were studied. A control algorithm based on the neural network was proposed to reduce tracking error of the HUG. To validate the proposed control algorithm, a computer simulation using Matlab / Simulink was performed [13].

Immersed tube tunnel serves as a preferred method of construction in large underwater tunnel engineering. In this work, modified quantum particle swarm optimization for translation control of immersed tunnel element with pontoons is studied aiming at its specific configuration. The translation control model is built based on the resistances of immersed tunnel element and two floating pontoons. To expand the search space, particles are coded according to Bloch coordinates. To make full use of three positions in each particle, they are selected with certain probabilities in accordance with the corresponding fitness values. Main dimension change and phase shift are implemented to improve the efficiency of velocity update for particles. Simulation results of Hong Kong-Zhuhai-Macao Bridge project delivers performance improvement of the proposed method.

A visual servo control system combines with the model-based image segmentation and an Ant Colony Optimization (ACO) algorithm to design an excellent six-Degree-of-Freedom (6-DOF) robot manipulator for solving the complicated combinations of pick-and-place tasks. A simple but efficient vision-based segmentation methodology is developed to extract the object information by getting appropriate feature of the controlled platform when the robot is tracking the manipulated image patterns. The evolutionary ACO learning algorithm explores the near-optimal path selections to drive the 6 ROF robot arm kinematics model for completing the Pick-and-Place tasks as soon as possible. Inverse orientation kinematic machine is proposed to successfully guide the robot manipulator into the desired position. Several software simulations include image segmentations, the shortest path selection, and the performance validation in various experiments. These results are described and presented to demonstrate that the designed image model-based robot manipulator wins the excellent Pick-and-Place task. Not only the software simulation, the practical robot synchronously performed in real-world to reach the higher feasible functions in the eye-to-hand experiments.

 This paper aims to propose a more efficient algorithm for the multi-dimensional classifier design. A novel model of wavelet fuzzy brain emotional learning neural network (WFBELNN) is proposed. This model comprises a wavelet function, a fuzzy inference system and a brain emotional learning neural network. As a result, the learning speed and the classifying accuracy can be effectively improved by the proposed model. The structure of WFBELNN is constructed first, and then the gradient-descent method is used to online tune the parameters of WFBELNN. Finally a medical pattern recognition system is studied to verify that the accurate multi-dimensional pattern recognition can be achieved by using the proposed model. A comparison between the proposed WFBELNN and other models shows that the proposed model can achieve the most accurate classification of the medical pattern recognition and it is also more suitable to deal with the influence of the uncertainties.

There are lots of line traces on the surface of the broken ends which left in the cable cutting case crime scene along the high-speed railway in China. The line traces usually present nonlinear morphological features and has strong randomness. It is not very effective when using existing image-processing and three-dimensional scanning methods to do the trace comparison, therefore, a fast algorithm based on wavelet domain feature aiming at the nonlinear line traces is put forward to make fast trace analysis and infer the criminal tools. The proposed algorithm first applies wavelet decomposition to the 1-D signals which picked up by single point laser displacement sensor to partially reduce noises. After that, the dynamic time warping is employed to do trace feature similarity matching. Finally, using linear regression machine learning algorithm based on gradient descent method to do constant iteration. The experiment results of cutting line traces sample data comparison demonstrate the accuracy and reliability of the proposed algorithm.

Multiple exposure fusion (MEF) is attracting considerable attention in research on high dynamic range (HDR) imaging: Eliminating the need to generate an intermediate HDR image, MEF directly expands an image’s dynamic range and thus provides greater detail enhancement than traditional HDR techniques. However, in the fusion stage, the optimal weights of each pixel in the images input to the final synthesized image are challenging to determine and usually required manual tuning of parameters. In addition, many MEF algorithms have been proposed, but most have lacked a self-regulation mechanism. To tackle the above disadvantages, we apply fuzzy theory and present a novel MEF framework with a fuzzy feedback structure. In this work, over- and under-exposed images are generated from a single input image using local histogram stretching. This avoids the creation of ghost artifacts when multiple exposed images are fused in the dynamic scene containing object motion. In the fusion stage, fuzzy logic is used to determine pixel weights based on gradient and chrominance analysis, and a guided image filter is used to suppress noise and enhance edges in the weight maps. To ensure detail enhancement without excessive or insufficient sharpness, we developed a simple sharpness measure named the edge-map overlapping rate (EOR). With EOR and the feedback structure, users are allowed to manipulate the output synthesized image to their preferred sharpness level, and the above weights are appropriately redesigned by automatically regulating the magnitude of the fuzzy input. From experimental results, this work demonstrated excellent image quality and outperformed other existing HDR/MEF methods.

 For dentists, it is very important to determine the color of the denture. Shade selection in dental practice is an important and difficult task. In the dental shade matching process, the shade selection will be affected by the observer’s physiological conditions such as age, mood, fatigue, and so on. These will make a difference on the judgement between the matching shade and the actual teeth color. In the past, dentists use shade tabs as a reference basis to match the teeth in the intra-oral environment. In this paper, an efficient color analysis methodology based on image processing and fuzzy decision techniques is proposed for dental shade matching. Since the color information is a very important index for the shade matching, the proposed methodology used the chrominance values Cb and Cr to increase the accuracy of color analysis. In order to improve the performance of the proposed methodology, three formulas, such as PSNR value of Cb, PSNR value of Cr, and S-CIELAB value, were selected by a fuzzy decision model. As shown in the results, the proposed efficient methodology based on fuzzy decision techniques improved at least 1.92 % in average accuracy and 0.59 in average score from the PSNR (Cb) and PSNR (Cr) in this work. In addition, the average values of the accuracies and scores in this work are 92.31% and 98.74, respectively, which are much better than the previous studies. To summarized, this work is the first study that applied fuzzy decision with the PSNR (Cb), PSNR (Cr) and S-CLIELAB information for dental shade matching. The results showed that the proposed methodology performs better than the previous work and other methods.

One difficulty that remains in image processing is the accurate location of key points in depth images. This paper presents an intelligent location method for identifying key points in depth images based on deep convolutional neural networks. This study used Kinect to process images, calculating the differences in depth as well as the directional gradient in subject depth images. The entirety of each depth image was traversed through a sliding window to identify the feature vector. Principal component analysis was used to reduce image dimensions. The random forest technique was used to select characteristics of strong classification as well as to actualize training and testing. A depth convolutional neural network was used to detect key points in images of pedestrians. During the study, an experimental test was conducted in a general environment under various conditions, including occlusion and low light. Even under these suboptimal conditions, the detection rate of the proposed method was 87.72%. Furthermore, this method was compared with the GEBCF and FCF algorithms, and proved to increase the detection rate by 0.92% and 0.68%, respectively. Using the depth convolutional neural network in the pedestrian key point positioning experiment, the average error obtained when comparing the predicted point coordinates to the sample mark coordinates was 2.102 pixels. These experimental results show that this method has good accuracy and robustness for the key point location problem of pedestrians in depth images.

Rotary tiller gearbox bears alternate and complex dynamic load. To provide accurate load for its design, use UG software to establish 3D parametric modeling of main parts. Import 3D parametric modeling of blade and shaft into EDEM software. Use Bonding model to establish simulation model of soil particles. Through numerical simulation of the impact load from blade and soil, the dynamic load parameters of blade and shaft are derived. Obtain the model of gearbox housing in ANSYS software and import it into ADAMS software as flexible body. Based on the dynamic load parameters of blade and the model of gearbox housing, multi-body dynamical rigid-flexible coupling simulation analysis for rotary tiller is done with ADAMS software. ADAMS software solves the model by adopting Lagrange dynamics equation, rigidity integral algorithm and sparse matrix technology, through which the load model of gearbox is derived. Finally, import the load model into ANSYS software, and make stress and strain analysis on the rotary tiller gearbox, by applying the load same as recorded in load file, to find out design defects and weakness of rotary tiller gearbox, which provide references for the design of rotary tiller gearbox, and help to optimize the design.

 There exists a huge amount of ECG data available in heart disease diagnosis which is found difficult in handing. Recently, many researchers focused on mining disease diagnosis to innovate the hidden patterns and their relevant features. Mining bio-medical data is one of the predominant research areas where clustering techniques are emphasized in heart disease diagnosis. But few people deal with large heart disease datasets and then classify disease data sets according to heart disease feature. We propose a method of anomaly threshold based on multiple classifiers can be well suited to datasets containing abnormal data, and use XGBoost algorithm as a sub-classifier to process massive ECG data. This research focuses on the heart disease classification problem. The data set is divided into two categories, and then it was classified into more specific categories, experimental results show that this method can improve classification accuracy. The experiments are conducted on massive instances of different heart disease obtained from the hospital actual cases and two data sets of UCI. In fact, we compared SVM, C4.5, Naive Bayes, Logistic, RandomForest and XGBoost algorithms, and found that tree-based model classifier is the best fit to predict arrhythmia. The method proposed in this paper is of great significance to the processing and forecasting system of large medical data sets, and promote the development of wisdom medical care.

 This study proposes a method that uses the Time-of-Flight (ToF) for an assisting device. A contactless Kinect device is used to obtain images of basketball players Field Goal Shooting (FGS) is used to detect posture. The method classifies the FGS postures of players into 3-stage routines for analysis. The ToF first sets the posture correcting conditions using the data provided by professional players and analyzes the postures with the highest and lowest FGS percentage and the most and least data from the statistical results. This gives a stable FGS reference value for the basketball player. The proposed technique has a higher correction rate for the FGS posture (for 100 continuous shots) for a player and allows processing in real-time, with a delay of only 0.4μs.

In this paper, we present a novel method for data-mining large informal product descriptions rather than extracting requirement features from proprietary project repositories. Our algorithm hybridizes deep-learning algorithms such as word2vec and recurrent neural network (RNN) with classical techniques to improve the performance of text analysis. Given the inaccuracy and incompleteness of the software requirement descriptions on websites, the instance-transfer learning method is utilized to construct a robust classifier and predict domain feature knowledge based on domain knowledge similar to the target domain. The bagging clustering algorithm is utilized with multiple clustering algorithms to help select transfer instances. [Author to confirm changes.]The RNN-based algorithm is utilized as a useful alternative to predict missing features by studying the requirement descriptions of a related software system, while word2vec is utilized to extract sensible feature keywords for the specific software domain. [Author to confirm changes.]Our RNN model for every subclass is based on the clustering result, and we construct subclass classifiers to recommend requirement keywords. Requirement features recommended by our algorithm potentially increase opportunities for requirement classification, promote software requirement quality, and deliver more reliable software products. We explain the details of implementation and perform experimental work on real requirement descriptions to establish its worth.

This paper presents a method to suppress the thrust disturbance for the permanent magnet linear synchronous motor (PMLSM), which is used as an actuator in an active vibration absorber. Because the characteristics of the working transmission structure of the PMLSM are relatively special, the thrust ripple and load disturbance will yield in the operation, and they will directly affect the control accuracy and positioning precision. Simultaneously, the thrust of the PMLSM directly acts on the object, and the thrust disturbance of the PMLSM will appear in the payload of the vibration absorber. Based on the actuator, the Proportional-integral-derivative (PID) control is investigated, and the transfer function of the actuator is obtained. Then, the thrust disturbance of the PMLSM is theoretical studied by analyzing the current flows through the PMLSM. A new method of trajectory overlay planning is presented, where the original input is divided into two inputs with different amplitudes and they suppress each other. Based on the method, the PID control can restrain the disturbance to achieve a predesigned output. The effectiveness of the proposed method is verified by simulation and experimental results.

A novel planar precise positioning stage on the rotating platform for calibration on raster scales of the rotary encoder is proposed in this paper. The monolithic structure is constructed with a flexible element and two ultra-fine adjustment screws. The structure for required motion is designed and optimized by software package of Solid Works Simulation, and then their performances are evaluated using numerical modelling approach. The mathematical model is then verified by resorting to finite element analysis (FEA) and experiment. The established analytical FEA models are helpful for optimizing a reliable architecture and improving performance of the precise positioning system.

In this study an adaptive on-line speed estimation approach has been proposed for a sensor-less indirect air-gap field orientation controlled (AGFOC) induction motor (IM) drive. The indirect AGFOC IM drive was established by utilizing the stator current and air-gap flux. The estimated synchronous speed was derived from the developed reactive power based adaptive air-gap flux estimator, and the estimation of rotor speed was made by subtracting the slip speed from the estimated synchronous speed. Speed estimation and control by the AGFOC IM drive could be extended to include constant power operation mode by utilizing the field weakening technique. The MATLAB^® ∖Simulink ^® toolbox was used to simulate this system and all the control algorithms were realized using a TI DSP 6713-and-F2812 card to generate pulse width modulation (PWM) signals to the power stage, actuate an IM to validate this approach (sensor-less AGFOC). Both simulation and experimental responses confirmed the effectiveness of the proposed system.

The chemical compound NOx is one of the biggest sources of air pollution, and presently Selective Catalytic Reduction (SCR) is the most frequently used method to dispose of NOx. A proportion of NH₃ as a reducer is injected into the system and mixed with the NOx waste gas so as to be treated thoroughly. With sufficient O₂, NOx is reduced to innocuous N₂ and H₂O by the catalytic reduction of the catalyst. The purpose of this study is to develop a monitoring system for blending the mixing ratio and concentration of NO automatically and for SCR catalyst De-NOx performance test analysis. This monitoring system enables the user to set various control parameters and instrument parameters, executes user calibration operations (e.g. zero calibration, full scale calibration and low concentration calibration), and tests the De-NOx performance of the plate type catalyst. In the catalyst De-NOx performance testing process, the system prepares the NO mixing ratio and concentration automatically according to the blending concentration and temperature control curves set by the user and then implements the catalyst bed multi-stage temperature control, so as to complete NO or NH₃ concentration detection and automatic data logging as the base of De-NOx performance test analysis.

Automated guided vehicle is the most important research issues for mobile robot development. The important research issue of the automated guided vehicle (AGV) is navigation system in recent years. Navigation system can be divided into self-localization, path planning and obstacle avoidance for indoor service execution. The application fields have security patrol or package delivery. Furthermore, recharging is necessary before the battery power has exhausted. The paper develops the automated guided vehicle that is designed and built with a 4WD mecanum wheel platform. Due to the laser ranger’s high precision, we applied the laser range finder to achieve the environment map construction, so the self-localization via particles filter (PF) and the path planning algorithms can be utilized with the map. The practical motion and safety avoidance strategies are also proposed for robot motion control. Finally, the sensory fusion methods are also integrated with the laser ranger and RGB-D camera for automated guided vehicle while performing the docking process. The experimental results show the successful demonstrations of autonomous patrol and docking for self-recharging.

A high sensory robot system conveniently controls the mobile robot action through the Natural human-machine interaction. Therefore, different hand motions are realized to approximate the required service tasks. Magnetic sensors are located at the defined path to guide the robot platform. Three ultrasonic sensors settle at the outside of mobile robot platform to detect possible blocks in an unknown environment. The RFID reader is proposed to understand the real position of mobile robot in the dynamic space. In data fusion machine, some matched position signals are selected into database for extracting the possible tracks in advance. Therefore, the appropriate path is remembered and will be reloaded again to guide the mobile robot into the desired target at the patrol mode. In practical experiments, people communicate with robot through the hand recognitions of Kinect sensor. Sensor information is suitable to handle various service tasks, i.e. pull the pallet in or extend it out, by the human-like movement. A fuzzy system with suitable rules is utilized to drive the server motor for achieving the great performance. The robot system is not only controlled by an interactive interface but also reached an autonomous navigation by extracting the appropriate mapping information. The high sensory robot system obtains interactive actions with the combinations of image recognitions, suitable path planning and obstacle detecting technologies to approach the guiding goal. In the implementation of hardware, the architectures of mobile robot and motor drivers are completely assembled to support the home tasks. The soft fuzzy system generates the robust robot regulation to automatically achieve the perfect feasibility of fetch-and-give tasks.

Bicycle-sharing systems are commonly established at geographically dispersed locations to create their rental service networks. To provide customers with flexibility and convenience, bicycle-sharing systems commonly allow them to pick up bicycles from one station and return them to a different one. However, allowing customers to return their rented bicycles to different stations can possibly lead to an imbalance in the bicycle rental network. One of the approaches to overcome the bicycle imbalance problem is to apply dynamic pricing to motivate consumers to return the rented bicycles to stations without a sufficient number of bicycles. This study developed a constrained dynamic pricing model to address the bicycle imbalance problem. Moreover, this study aimed to maximize the total revenues over a planning horizon through dynamic pricing strategies. We identify some necessary conditions for optimal sale prices. Using these conditions, we develop a heuristic algorithm based on linear programming and an evolutionary algorithm to efficiently produce comprise solutions. The proposed model and solution procedure were applied to analyze the bicycle system in Taiwan. Sensitivity analyses were also conducted to investigate the effects of various system parameters.

The purpose of this study is to integrate fuzzy sliding mode control (FSMC) into automatic landing system (ALS) to enhance aircraft safety during landing. FSMC can provide compensation signal to PID controller. The adaptive weight particle swarm optimization (AWPSO) and grey-based particle swarm optimization (GPSO) are applied to tune the matrix of controller parameters of the sliding surface. Fuzzy rules are applied to sliding mode controller to find the gain of the differential sliding function, sliding condition can be satisfied and stable control system can be achieved. PID controller is the main controller of the aircraft and it is also used for the FSMC controller in learning process. In this study, the proposed intelligent system can improve the ALS to against the wind disturbance and control aircraft landing in severe condition. Stability analysis is provided in the controller design by the use of Lyapunov theory.

This study is to investigate the nonlinearly constrained various signal detector location allocation problems in which the types of detectors and the corresponding numbers and locations can be determined at the same time so as to minimize the maximum detecting failure rate in a specified area. In other words, the objective of the detector location allocation problem is to minimize the maximum failure rate by determining the best possible conjunction of three types of decision variables, i.e., the type of detector, the numbers of each detector type and where to build up each of them with a limited resources. So, the quality of reliability of event detecting can be assured and consistent. By the way, the signal intensity usually disintegrates proportionally to some power of the distance from the detector. That makes the longer distance far away the detector, the bigger failure rate in detection of the event. The signal detector allocation problem is described as a mixed-integer nonlinear programming model, usually using math programming or heuristic optimization methods for finding the optimal solution or near optimal solution. While using the both methods, the difficulties encountered are the amount of decision variables and the difficulty of not violating the constraints. In this study, a two-phase evolutionary computation approach based on the immune algorithm and particle swarm optimization has been developed for overcoming the difficulties and finding the optimal solutions for the detector allocation problems effectively. Finally, the performance of the proposed methodology has been evaluated with the commercial optimization software. Numerical results illustrate that our approach is with well performance for the constrained detector allocation problems considered in this paper. As reported, solutions acquired by using our approach are as well as or better than those found by using LINGO^®.

Traditional digital image authentication is usually based on signature or fragile watermarks. This performs authentication without any secret hidden data. Until now, many image authentication schemes with error detection based on watermarks or signatures have been proposed. Tampering attack can be detected but the areas tampered with cannot be determined for these schemes. In order to improve this shortcoming, a verifiable data hiding scheme is proposed for digital images in this paper. The main idea of the proposed scheme is combining multi-bit encoding function and multi-group data hiding scheme to increase embedding capacity and strengthen security with parity check to verify the tampering of digital images having embedded secret messages. Therefore, there are three major contributions in our proposed scheme. First, it can achieve image tamper detection and find what has been modified. Second, it resists collage attack. Third, it can increase the embedding capacity. These contributions are discussed according to experimental results. The proposed scheme includes a high security to reduce detection of hidden data and the MSE analysis also proves this scheme has good image quality.

The rapid rates of industrialization and urbanization have induced considerable changes in family structures, increasing the number of older adults who voluntarily or involuntarily live alone. The rapid increase in the population of older adults living alone has raised many safety concerns, with fall-induced injuries and dementia presenting immediate dangers to older adults. Falls are prevalent in older adults, and not only cause injuries for the individuals, but also impose an extremely heavy burden on family members and caregivers. Furthermore, dementia is common among older adults in aging societies and is usually accompanied by dysfunctions in daily living activities, causing considerable difficulties for family members. The objective of this study was to develop a remote monitoring and control (M&C) smart floor system for detecting falls and wandering patterns in older adults with dementia in order to provide comprehensive care assistance. The proposed system integrates a floor detection sensor and Wi-Fi technology to analyze and determine the occurrence of falls and wandering in older adults with dementia. Conventionally, detection processes for falls and wandering in older adults with dementia are conducted using visual monitoring or wearable detectors, which may reduce the privacy, comfort, and convenience of older adults. By contrast, the proposed system maintains users’ privacy and eliminates the inconvenience associated with wearing detectors. The system determines fall behaviors and wandering patterns in older adults with dementia; when an accident occurs, the system can issue a warning and notify medical care units or relatives for immediate attention, thus reducing the occurrence of further accidents. The remote M&C and warning functions of the proposed system were verified through experiments.

Location selection for a freight village is extremely important since it has strong impacts on quality of life such as reduction of traffic congestion, reduction of carbon emission, and effective use of lands. The inclusion of such criteria in a location selection requires the fuzzy sets to be used in the decision making methodology. In this paper, we propose a novel integrated fuzzy decision model for the location selection of freight villages. In this integrated methodology, we use DEMATEL for determining the most effective criteria and their inner and outer dependencies; ANP for weighting the determined criteria; and TOPSIS for finding the best location alternative. The proposed model is applied to a case study for the city of Istanbul in Turkey.

In this paper, a new chaotic teaching learning based optimization (CTLBO) is proposed. TLBO is a rather newly proposed population-based algorithm. This algorithm has no control parameters for the tuning and has a simple structure. We improve its performance by chaotic maps. First, the presented CTLBO is tested on nine unimodal/multimodal benchmark functions. Then, chaotic sequences are applied as vectors with different initial values for design of a frequency reconfigurable antenna (FRA) as a practical example. Comparisons of the performance of this algorithm with those of the basic TLBO, genetic algorithm and particle swarm optimization show the ability of this algorithm in design of FRAs in terms of faster convergence and better performance. A prototype of the optimized antenna with CTLBO algorithm is fabricated and the simulation and measurement results agree suitably.

In this study, we propose a formulation of a Mond-Weir type dual program for a multiobjective nonlinear optimization problem under fuzzy environment. To deal with the multiobjectivity in the formulation, we consider the concept of weak Pareto optimal solution in the fuzzy sense. We use the Hukuhara metric/ difference to define the distance/difference between two fuzzy numbers. Further, we establish weak and strong duality theorems under fuzzy pseudo/quasi-convexity assumptions. Moreover, we also validate these duality relations using various numerical illustrations.

 Uncertainty plays an important role in project decision-making problems that involve incomplete and imperfect information of real-world situations. To completely considering the uncertainty of decision-making methods, Pythagorean fuzzy sets (PFSs) are used. PFSs in comparisons with classic fuzzy sets provide degrees of membership, non-membership and hesitancy, and in comparisons with intuitionistic fuzzy sets (IFSs), they prepare the larger space to explain the agreement, disagreement and hesitancy grades. In this paper, to tackle the uncertainty of real-world projects and determine the critical path of projects by considering efficient criteria, such as time, cost, risk, quality and safety, a new group decision methodology is extended based on concepts of technique for order of preference by similarity to ideal solution (TOPSIS) and complex proportional assessment (COPRAS) methods under PFSs. Furthermore, a new modified version of the proposed methodology is used to specify the weight of each expert. Finally, a case study from the literature, concerning workflow schema of marble processing plants project, is presented to better express the capability of the proposed methodology.

Linear Programming (LP) is an optimization problem, which deals with finding optimal solutions under set of linear inequality or linear equality constraints. Fuzzy Linear Programming (FLP) and Fuzzy Linear Programming problems (FFLP) have gained great importance in recent years due to the uncertainties that may arise in the parameters and variables of the problems. In this paper we provided an extension to Ozkok et al. [Ozkok, B. A., Albayrak, I., Kocken, H. G., & Ahlatcioglu, M. (2016). An approach for finding fuzzy optimal and approximate fuzzy optimal solution of fully fuzzy linear programming problems with mixed constraints. Journal of Intelligent & Fuzzy Systems, 31(1), 623-632.] to find fuzzy optimal and approximate fuzzy optimal solution of FFLP with trapezoidal fuzzy numbers.

 Traffic congestion is a big problem that influences the traffic flow in big cities, so better control of the traffic signals is always searched to solve this type of traffic problems. Fog computing is one of the most efficient paradigms for traffic system control as it enables connecting and analyzing big traffic data to help the control of traffic signals in the appropriate time. There are different optimization methods, which can be used to control traffic signal; one of these is Particle Swarm Optimization (PSO) algorithm, and there is correlation between PSO parameters (particle velocity, position) and traffic parameters (vehicle speed and location). Roundabouts with traffic signals is one of the modern roads infrastructures used to reduce traffic jam. Our objective is to minimize the average delay time in order to decrease the traffic congestion. This paper presents a control strategy called COTSD-PSO for optimizing traffic signaling based on PSO combined with three sub-controllers; this strategy depends on traffic control rules. These sub-controllers are PSO-Jump, PSO-Turn and PSO-Mix depend on two parameters; extension time and urgency degree for the different phases in the traffic cycle. PSO algorithm is applied to optimize the control of the traffic signal network for roundabouts model on fog computing environments using real data from Taif streets in KSA country. The PSO simulation results show that the PSO-Mix has the fastest convergence rate for the optimal solution and the best performance in minimizing the average delay time compared with the other combinations.

The Failure mode and effect analysis (FMEA) is an effective risk evaluation approach which has been widely used to assist in risk controlling in various workplaces. However, in practice, the conventional FMEA approach suffers from the drawbacks associated with the risk evaluation and priorization methods. In this paper, a novel risk priorization method for FMEA based on the extended MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the Full Multiplicative Form) method is proposed. First, the interval type-2 fuzzy sets are applied to deal with the uncertainty of risk evaluation in FMEA. Second, the distance-based method is used to calculate the importance weight of each risk factor. Then, an extended MULTIMOORA method is presented to rank risk priority of each failure mode, in which the distance measure for interval type-2 fuzzy number is incorporated. Finally, a practical case in steel company is selected to illustrate the application and feasibility of the proposed approach. A comparative analysis is conducted to demonstrate the effectiveness of the developed risk priorization method.

The exponential growth of Internet through sharing text content necessitates the analysis to convert them into useful information. The research areas such as Web mining, Opinion mining and Text mining focus on studies namely content mining, statistical analysis, prediction, and classification. Multinomial Naïve Bayes (MNB), the state of art of Bayesian classifier is the fastest and simplest text classifier. The objective of the proposed study is to enhance the classification by substituting the conditional probability of existing MNB with probability based frequency computation. A new combination that consists of Pointwise Mutual Information (PMI) and different normalized Term Frequency (TF) is used for computing the conditional probability. The new combinations provide weight to the words based on the information gain carried by the words related to the document that belongs to a class. The robustness of Similarity based Enhanced Conditional Probability MNB (SECP-MNB) is reflected in classification accuracy measurement.

In this paper, we intend to introduce the notion of the radical of a filter in a pseudo BL-algebra and express its characteristics and properties. We define dense, infinitesimal, nilpotent, and unity elements in a pseudo BL-algebra, and then, investigate the relationship between these elements and the radical in a pseudo BL-algebra. Our study revealed significant results in this regard.

A new artificial bee colony algorithm called modified artificial bee colony algorithm (MABC) is presented to solve the multi-objective fuzzy flexible job-shop scheduling problem (MFFJSP) in this paper. The objectives of MFFJSP are to minimize the maximum fuzzy completion time (fuzzy makespan), maximize the weighted agreement index and minimize the maximum fuzzy machine workload. The three-point satisfaction-degree model is adopted to calculate the agreement index and this model can indicate the degree of satisfaction between the due date and the processing time. An effective local search operator based on variable neighborhood search (VNS) and crossover operator are embedded in this algorithm for obtaining good searching performance. In order to make the novel algorithm valid, we texted it, five benchmark instances and a practical case for the sake of effectiveness. Then, the performance of the proposed MABC has been compared with other existing algorithms to prove the superiority of this algorithm. In the end, the Taguchi method is used to investigate the impact of three key parameters from the MABC.

An information system as a database that represents relationships between objects and attributes is an important model in the field of artificial intelligence. A three-source information system of this paper is an information system where there exist three data: categorical, boolean and set-valued data. This paper explores uncertainty measurement for this kind of information system. The concept of a three-source information system is first described by means of set matrices. Then, information structures in a three-source information system are presented and relationships between information structures are studied from the two aspects of dependence and separation. Next, properties of information structures in a three-source information system are given by using inclusion degree. Finally, as an application for information structures, uncertainty measurement for a three-source information system are investigated by means of its information structures. These results will be helpful for understanding the essence of uncertainty in a three-source information system.

 Quantitative methods for determining the quality of scientific publications evolved gradually from popularity methods to prestige methods. However, existing methods have some drawbacks, such as inability to account for important factors and mutual reinforcement between different entities, and limitation of using novel information techniques like artificial intelligence (AI) methods. This study proposes an intelligent time-aware mutual reinforcement ranking (TAMRR) model that accounts for mutual reinforcement, and temporal factors, such as the time of citation, to measure the prestige of scientific papers. The method also considers the distribution of the co-authors’ contributions, which indicates the credit allocation of citations. Moreover, mutual reinforcement which indicates interactive impact between different entities by means of the extension of an AI algorithm, i.e., Hyperlink-Induced Topics Search (HITS) algorithm, is adopted to further explore the interactions of papers, journals and authors. Another AI algorithm, i.e., PageRank, is also enhanced to measure the prestige of papers, journals, and authors in citation networks, which are then used as the inputs to the modified HITS. Experiments on temporal factors and heterogeneous networks reveal that these factors are likely to be informative in prestige measurements. Analysis of correlations suggests that our proposed intelligent ranking method is reasonable. This study offers an intelligent method for researchers, authors, and entrepreneurs to quantify the importance of scientific papers and the conclusions are likely to be of importance for researchers in both the academic and enterprise domains.

Data objects with both numeric and categorical attributes are prevalent in many real-world applications. However, most of the partitional clustering algorithms dealing with such data may trap into local optima. To further promote the performance, a novel clustering algorithm, called ABC-K-Prototypes (Artificial Bee Colony clustering based on K-Prototypes), is presented on the basis of the K-Prototypes algorithm, the search strategy of the artificial bee colony, and the chaos theory. In the presented approach, the one-step k-prototypes procedure is first given, and then this procedure is combined with the search strategy of the artificial bee colony to address the mixed numeric and categorical data. In the search process of scout bees, the chaotic map is utilized to generate chaotic sequences for substituting the random numbers. To accelerate the convergence of the ABC-K-Prototypes algorithm, the multi-source search is adopted in the search process of scout bees. Finally, the performance of the ABC-K-Prototypes algorithm is demonstrated by a series of experiments on mixed numeric and categorical data in comparison with that of the other popular algorithms.

The main focus of the present paper is to establish a common coincidence point theorem for a pair of L-fuzzy mappings and a non-fuzzy mapping under a generalized φ-contractive condition on a metric space in association with the Hausdorff distance on a class of L-fuzzy sets. A generalized common coincidence point theorem with the d L ∞ metric on 1- cuts of L-fuzzy sets is also obtained, which generalizes many recent results in literature. As applications, an analogous coincidence point theorem for crisp mappings is achieved to study some existence theorems of solution for a class of nonlinear integral equations. Even as direct application of coincidence of L- fuzzy mappings, an implicit function theorem is established which can be used to find an explicit restriction of a given implicit relation. For verification and elaboration of our results some interesting and non-trivial examples are presented as well.

Firefly algorithm (FA) is one of the most recently introduced stochastic, nature-inspired, meta-heuristic approaches that have seen countless applications in solving various types of optimization problems. The major source of inspiration leading to the development of FA is the phenomenon of light emission by fireflies that attract other fireflies for their potential mates. All the fireflies are unisexual and attract each other according to the intensities of their flash lights. Higher the flash light intensity, higher is the power of attraction and vice versa. For solving optimization problem, the brightness of flash is associated with the fitness function to be optimized. The firefly algorithm is advantageous over other optimization algorithms due to its flexibility, simplicity, robustness and easy implementation but a major drawback associated with the standard FA applied for solving different optimization problems is poor exploitation capability when the randomization factor is taken large during firefly changing position. This poor exploitation may lead to skip the most optimal solution even present in the vicinities of the current solution which results in poor local convergence rate that ultimately degrades the solution quality. To overcome this problem, the crossover operator of genetic algorithm (GA) is incorporated into firefly position changing stage that results in better exploitation capability which improves the local convergence rate resulting in better solution quality. The performance of the proposed approach has been compared with standard FA, GA, artificial bee colony (ABC) and ant colony optimization (ACO) algorithms in terms of convergence rate for various types of minimization and maximization optimization functions.

In this paper, we first introduce the concept of cubic intuitionistic sets in B-algebras. We consider the cubic intuitionistic subalgebras, the cubic intuitionistic ideals and also the cubic intuitionistic closed ideals of a B-algebra. Further properties and relationships of the above algebraic structures are discussed and investigated.

Schema matching is used for data integration, mediation, and conversion between heterogeneous sources. Nevertheless, mappings identified with an automatic or semi-automatic process can never be completely certain. In a process of concept alignment, it is necessary to manage uncertainty. In this paper, we present a fuzzy-based process of concept alignment for uncertainty management in schema matching problem. The ultimate goal is to enable interoperability between different electronic health records. Data integration of health information is done through the mediation of our ubiquitous user model framework. Results look promising and fuzzy theory proved to be a good fit for modeling uncertain schema matching. Fuzzy combined similarities can handle uncertainty in the schema matching process to enable interoperability between electronic health records improving the quality of mappings and diminishing the human error to verify the mappings.

 This paper introduces a fuzzy approach for classifying speech emotions in which a fuzzy inference system based on fuzzy associative memory (FAM-FIS) is used for recognizing speech emotions. Experiments on two databases of emotion speech Emo-DB in German and SAVEE in English, and feature of Mel-Frequency Cepstral Coefficients (MFCC) showed that the accuracy rates of the fuzzy inference system are better than that of Bayes and Support Vector Machine (SVM) on same kind of features and databases. Namely, with MFCC feature and 19 dimensions, Emo-DB is 74.31% and SAVEE is 97.29%.

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219434.

Classical rough set theory is based on the conventional indiscernibility relation. It is not suitable for analyzing incomplete information. Some successful extended rough set models based on different non-equivalence relations have been proposed. The data-driven valued tolerance relation is such a non-equivalence relation. However, when predicting the unknown attribute value of an object, it regards the frequency of an attribute value approximately as the probability of appearance of this value, without considering the effects of other known attribute values of this object on predicting the unknown attribute value. In this paper, considering both the frequency of the known attribute values and the influence weight to predict the unknown attribute values. Modified data-driven valued tolerance relation (MDVT) is defined. On this basis, an extended rough set model based on modified data-driven valued tolerance relation is proposed. Some properties of the new model are analyzed. Experimental results show that the MDVT can get better classification results than other generalized indiscernibility relations.

The exact location of faults in the electrical distribution systems is a problem that affects not only the users, but also the companies providing the electric service. With greater time invested in this period, the losses due to unbilled energy and inconvenience to users increases, thus decreasing the quality of service. One of the causes of the growth in time is the misunderstanding that might exist in the localization systems that act under the presence of distributed generation sources in the distribution networks. In this sense, the present research develops an intelligent diagnosis of faults in distribution systems with distributed generation. Three stages are defined: Identification of the type of fault, the location of the zone, and the exact point of fault. A mixed method based on artificial intelligence and mathematical algorithms is applied. Eleven different types of faults that can occur in a distribution system are considered with six different values of fault resistances ranging from 5 to 30 Ω. The errors found are less than 2% in the location of the fault point with robustness to variations in the load and the penetration of distributed generation.

An electronic glove is a sensor-equipped glove for detecting changes in the motion or the bending of a finger. The function of an electronic glove can replace the function of a remote control, especially for people with disabilities. In this study, we designed and realized a glove equipped with a flex sensor on each finger to detect the bending of the finger and used a mobile robot as the plant of the electronic glove. The system works by detecting finger-bending in the right hand and processing it into the commands stop, forward, backward, forward left and forward right. The flex sensor resistance value caused by the finger-bending is used as an input for fuzzy logic control and produces the output to control the mobile robot. The flex sensor is capable of varying values between normal conditions of 27.2 kΩ up to a maximum of 46 kΩ. The lowest speed of the mobile robot is 0.75 m/s, with a maximum speed of 2.17 m/s. The commands from the electronic glove are sent to the mobile robot wirelessly with a maximum distance between electronic glove and the mobile robot of 200 m in an obstacle-free situation. Based on the experimental results, 100% of the orders were executed according to the design. The use of fuzzy logic ensures that every bending change is accompanied by a change in speed, so that mobile robot movements become smoother.

The Z-number has become a new representation of evaluation information because of its superiority in describing reliability measure. Current studies focuses mainly on the unidimensional Z-number like Z = (about 25 min, usually). Studies on multidimensional cases have not been reported. However, because people often describe things from various aspects, using only one aspect of information to describe uncertain events fully is difficult. In this paper, we propose the concept of the multidimensional Z-number, such as ((about 20 miles, about 25 min), usually) to handle complex information. For this purpose, we first define the basic operations of the multidimensional Z-number. We then propose a feasible comparison method. The effectiveness of the proposed method is demonstrated by a series of numerical examples.

Fuzzy variable is a function from a credibility space to the set of real numbers. The convergence of fuzzy variables is important component of credibility theory, which can be applied into real problems in engineering and mathematical finance. Inspired by these, we will discuss some properties of convergence for fuzzy variables. At the same time, the conditions of convergence almost surely, convergence in credibility and convergence in mean for fuzzy variables will be given.

This paper provides a new connection between algebraic hyperstructures and fuzzy sets. We present the concept of I-fuzzy topological polygroups and prove some properties. The concept of I-fuzzy topological polygroups is a generalization of the concept of I-fuzzy topological groups. By considering the relative fuzzy topology on fuzzy subpolygroups we prove some related properties.

In this study, we have investigated the concepts of lacunary summable, lacunary statistical convergence and lacunary statistically Cauchy sequence for double sequences in fuzzy normed spaces. Also, we have investigated some properties and relationships between these concepts.

The modeling and prediction of short-term traffic flow can reflect the prediction results of the traffic state and traffic flow data. In this paper, first, we use a high-dimensional tensor to represent the multi-mode characteristics of traffic flow data, and we make use of the basic operations properties of tensors, such as Tucker decomposition, to study the methods for filling in data, such as ITRM. Additionally, we preprocess the lost traffic flow and abnormal data. At the same time, we study the short-term traffic flow based on the “week-day-time” multi-mode of the traffic flow data. Using the grey model (GM (1, 1)) to predict the same period of the weekly mode, the scrolling grey model (SGM) of the same time period is predicted. For the time mode, a neural network time series of wavelet analysis is used to predict the traffic flow forecast during the same period. Then, the prediction results of the three different models are weighted by the grey correlation analysis method, and then, the coupling prediction model of the three models is obtained. In the end, according to the traffic flow data of the main road of Shaoshan road in Changsha, Hunan, China, we first preprocess the lost data by using the filling algorithm for the tensor data, and then, we make the traffic flow data complete, use the three tensor data modes of traffic flow, and analyze the results. The experimental results show that the coupling prediction model with the tensor model is much better than the single GM (1, 1) model, the SGM and the neural network prediction model.

In this paper, the degree of which an L-subset is an L-convex fuzzy ideal is proposed and its some characterizations are given. Besides, an L-fuzzy convexity on a lattice is naturally proposed and its some properties are studied.

Fuzzy time series modeling has recently become an interesting topic to study. Among fuzzy time series models, the Abbasov-Mamedova (AM) model has advantages over the others because it can forecast the value that is outside the min-max range of the original data. However, the performance of the AM model strongly depends on three parameters that are user-defined. In previous studies, the optimal parameters of the fuzzy time series models have been identified with a global optimization method. Surprisingly, optimizing the parameters of the Abbasov and Mamedova model has not been solved in spite of its advantages over the others. This paper presents a new approach to improve the performance of AM model based on the evolutionary algorithm. Particularly, the objective function is calculated as the Mean absolute percentage error which will be minimized using the differential evolution (DE) algorithm. The experiments on Azerbaijan’s population, Vietnam’s GDP and rice production demonstrate the feasibility and applicability of the proposed methods.

Product redesign strategy can effectively shorten design lead time and reduce production cost of new variants development. Identification of function components is the basis of product redesign. In the existing methods to identify the function components, customer requirements are primarily considered while the failure knowledge, a critical information to improve product reliability, is often ignored. The objective of this research is to identify the to-be-improved components considering both customer requirements and product reliability. First, a two-stage fuzzy quality function deployment (QFD) is used to calculate the importance weight of each component considering customer requirements. Second, the fuzzy failure mode effects and analysis (FMEA) is adopted to measure the failure risk of each component. Different from traditional FMEA, the failure causality relationships are analyzed in this work to provide a means of making use of failure information more effectively for constructing a directed failure causality relationship diagram. Fuzzy permanent function is developed to quantify the failure risk of each component. Then, a modification necessity index is introduced to model the degree of modification necessity for each component considering customer requirements and failure risk. Finally, the optimal set of function components that need to be modified is identified by 0-1 objective programming and constrained optimization. A case study for identification of the function components for the operation device of a crawler crane is implemented to demonstrate the effectiveness of the developed approach.

Controlling a dynamic system to make its output identical to a user-desired reference trajectory given to the system’s input without any delay (i.e., perfect output tracking control) is an important and frequently encountered control requirement in industries (e.g., robotic control). When disturbances exist in a system, perfect output tracking control may be impossible to attain and alternatively asymptotical output tracking is sought. This paper presents an asymptotical output tracking control design method for a general class of discrete-time TS fuzzy systems with quadratic rule consequents, which offer better modeling capabilities than the linear rule consequents. The control problem is dealt with by utilizing the feedback linearization method. To guarantee asymptotical output tracking performance in the presence of square disturbance signal, an auxiliary PI controller is added to attenuate the disturbance. The feedback linearization method is known in the literature to fail to work for certain systems because it can make the tracking controller’s output unbounded. To address this issue, we put forward a full block S-procedure condition to check whether such failure will occur for any given quadratic TS fuzzy system. Applying feedback linearization to the quadratic TS fuzzy systems is innovative relative to the literature that has exclusively dealt with the TS fuzzy systems with linear rule consequents only. Two numerical examples are provided to illustrate the effectiveness and utility of our theoretical results.

Imaging techniques are the most rapidly growing area of computer vision, and the resolution has reached a new level. Super-resolution is a technique that enhances the resolution of images from the low-resolution input and help to accurately analyze and derive the data. Recently convolutional neural network are becoming mainstream in computer vision. Most existing CNN models based super-resolution either directly reconstruct the low-resolution input and then improve the resolution at the last layer, or another way is, to firstly enlarge the low-resolution input to high resolution (HR), then reconstruct the HR to obtain the desired output. These models encounter some major flows; large computational resources and losing information. In this paper, we adopt gradual process for training the CNN, to propose an efficient super-resolution model. The gradual strategy helps network to progressively magnify and reconstruct the LR image in each step, and thereby possibly avoid of losing information (second problem). In addition, we optimize the number of layers, add the residual network and skip connection to the proposed network to ease the difficulty of training (first problem). The proposed model not only achieves a compatible performance with the existing prominent methods but also, efficiently reduce the computational expenses.

In this paper, by using the fuzzy CESTAC method and the CADNA library a procedure is proposed to control the step size for solving the fuzzy differential equation with fuzzy boundary conditions based on the finite differences method under generalized H-differentiability (gH-differentiability). An algorithm is presented to implement the discrete stochastic arithmetic for solving the given fuzzy boundary value problem on the C++ code via the CADNA library. Also, a theorem is proved to show the accuracy of results based on the concept of the common proximity of two fuzzy numbers. Finally, some examples are solved by using the proposed algorithm to illustrate the effectiveness of applying the stochastic arithmetic (SA) in place of the floating-point arithmetic (FPA) to validate the results and find the optimal solution.

 The hesitant fuzzy linguistic term set (HFLTS) is usually used in the uncertain decision situation. In order to solve the problem of multi-criteria decision-making (MCDM) with HFLTS, a new MCDM method based on the cloud model and evidence theory is proposed in this paper. A new envelope, whose representation is a synthetic cloud generated by the multiple linguistic term in the HFLTS, is presented for HFLTS to facilitate the computing processes and take both the randomness and fuzziness of linguistic variables into consideration. As to overcome the drawbacks of tradition aggregation operator, the criteria values in the form of the belief degrees are obtained from the synthetic clouds and aggregated using the evidential reasoning algorithm. An illustrative example, which is given to confirm the feasibility and validity, also shows that with the proposed method more reasonable and accurate ranking results can be obtained. Moreover, the belief degree of each linguistic term as well as the hesitant degree of the assessment can be obtained simultaneously.

Task Scheduling is one of the most challenging problems in cloud computing. It is an NP-Hard and plays an important role in optimizing the use of available resources. Recently, Multi-Objectives Genetic Algorithm (MOGA) is proposed for cloud tasks scheduling. However, the execution time of the GA is higher than Particle Swarm Optimization (PSO), and the convergence is slower. PSO converges fast because it can be implemented without too many parameters and operators. In this paper, Multi-Objectives PSO (MOPSO) and MOPSO with Importance Strategy (IS) (MOPSO_IS) algorithms are proposed. MOPSO algorithm is integrated with the IS to select the global best leader. Furthermore, incorporating a mutation operator in MOPSO_IS resolved the problem of premature convergence to the local Pareto-optimal front. The performance of the proposed algorithms was compared with MOGA and produced better results. The results of the experiments showed that the proposed MOPSO and MOPSO_IS significantly minimized the total task time and average task time and obtained better distribution for tasks on the available resources in a minimal time.

This manuscript investigates speed and flux tracking control method based on quasi-continuous sliding mode control (SMC) for induction motor (IM). To overcome the problem of conventional SMC, a quasi-continuous second-order sliding mode controller (QC2SMC) is first designed. QC2SMC produces continuous control and less chattering. Chattering exists during the control action of QC2SMC when the system remains on the sliding manifold. However, it is difficult to select sliding mode (SM) controller gain that minimizes the reaching time on sliding manifold. So as to reduce the chattering, fuzzy-adapted QC2SMC (FQC2SMC) is designed, in which the fuzzy system is employed to adaptively tune the SM controller gain. Moreover, with the intention of reduce chattering and achieve high tracking accuracy, quasi-continuous third-order sliding mode controller (QC3SMC) is investigated. However, the transient response of QC3SMC is very slow. This is the main constraint of QC3SMC and overcome by applying a fuzzy-adapted QC3SMC (FQC3SMC) is intended. Control structure incorporates a state observer based on a super twisting algorithm to estimate the rotor flux and torque from stator currents and rotor speed. By using robust exact differentiator, estimation of the sliding surface derivative and designing of an observer for load torque are not needed. The convergence of controllers can be guaranteed and analyzed by using Lyapunov function. Here, simulation results are included to validate the effectiveness of the proposed strategy.

This study presents a new soft computing approach based on failure mode and effects analysis (FMEA)’s concept for sustainable supplier selection problem (SSSP) in the light of multi-attributes decision analysis. The approach determines weights of experts by considering interval-valued fuzzy sets (IVFSs) and asymmetric uncertainty information simultaneously. The presented group decision approach assesses the sustainable suppliers as indicated by risks of economic, social and environmental measurements. Ideas of fuzzy possibilistic statistical modeling are brought into the soft computing approach. New definitions are enhanced by fuzzy possibilistic statistical positive and negative ideal solutions. In addition, new relations of fuzzy evaluating and prioritizing of the risks are presented. Then, an application example is tackled by the presented soft computing approach to exhibit its capacity by three measurements of the sustainability for the evaluation of sustainable suppliers.

Real-time vehicle detection is one of the challenging problems for automotive and autonomous driving applications. Object detection using Deformable Parts Model (DPM) proved to be a promising approach providing higher detection accuracy. But the baseline DPM scheme spends 98% of its execution time in loop processing thus highlighting the drawback of higher computational cost for real time applications. In this paper, we have proposed a real time vehicle detection scheme for a low-powered embedded Graphics Processing Unit (GPU). The proposed scheme is based upon DPM approach using CUDA programming with different parallelization and loop unrolling schemes to reduce computational cost of DPM. Three loop unrolling schemes i.e. loosely unrolled, tightly unrolled and hybrid unrolled is proposed and implemented on two different datasets. Finally, we provided an optimal solution for vehicle detection with minimum execution time without having any impact on vehicle detection accuracy. We achieved a speedup of 3x to 5x as compared to state-of-the-art GPU implementation and 30x as compared to baseline CPU implementation of DPM on a low-powered automotive-grade embedded computing platform which features a Tegra K1 System on Chip (SOC), thus getting advantage of improved efficiency through parallel computation of CUDA.

For a multi-attribute group decision making (MAGDM) problem where the attribute values are intuitionistic uncertain linguistic variables (IULVs), we propose a novel decision making method based on hybrid aggregation operator of IULVs. Firstly, new operational rules of IULVs are proposed based on linguistic scale functions (LSFs) to overcome the existing shortcoming in which the operations on linguistic variables (LVs) directly based on the subscripts of linguistic terms (LTs) are not closed, then the expected value and accuracy function of IULVs are introduced. Further, some aggregation operators for IULVs are proposed, including the weighted geometric average operator for IULVs (IULWGA), ordered weighted geometric operator for IULVs (IULOWG), then the hybrid geometric operator for IULVs (IULHG) are developed, which could consider the weights of attributes and their ranking positions. However, because IULHG don’t meet some desirable properties, we proposed a new hybrid weighted geometric operator for IULVs (IULHWG) which can not only weight the importance of each argument and its ordered position but also maintain some desirable properties. Based on these operators, an approach to MAGDM with IULVs has been proposed. Finally, an illustrative example is provided to show the steps of the developed approach and to demonstrate its practicality and effectiveness.

The aim of this paper is to investigate information aggregation methods under Pythagorean trapezoidal fuzzy environment. Pythagorean fuzzy set, an extension of the intuitionistic fuzzy set which relax the condition of sum of their membership function to square sum of its non-membership functions is less than one. In this paper, we proposed some operational rules based on PTFNs and verified their some properties. we developed some aggregation operators to use the decision information represented by PTFNS, including the Pythagorean trapezoidal fuzzy weighted averaging (PTFWA) operator, Pythagorean trapezoidal fuzzy ordered weighted averaging (PTFOWA) operator and Pythagorean trapezoidal fuzzy hybrid averaging (PTFHA) operator. Furthermore, we proved their some desirable properties. Based on the (PTFWA) operator, the PTFOWA operator and the PTFHA operator, we presented some new method to deal with the multi-attribute group decision making (MAGDM) problems under the Pythagorean trapezoidal fuzzy environment. Finally, we used some practical example to illustrate the validity and feasibility of the proposed methods by comparing with other methods.

For a graph G = (V, E) the L (3, 1, 1)-labeling is a mapping μ from the vertex set V to the set of non-negative integers {0, 1, 2, …} such that |μ (x) - μ (y) |≥3 if d (x, y) =3 and |μ (x) - μ (y) |≥1 if d (x, y) =2 or 3, where d (x, y) is the distance between the vertices x and y. λ_3,1,1 (G) represents the L (3, 1, 1)-labeling number of the graph G and it is the largest non-negative integer used to label the graph G.

In the present article we have studied L (3, 1, 1)-labeling of squares of some simple graphs, namely square of paths ( P n 2 ) , square of complete graphs ( K n 2 ) and square of complete bipartite graphs ( K p , q 2 ) and we obtained good results for these graph classes. The results produced in this article are unique and exact. This is the first result about L (3, 1, 1)-labeling for these graph classes.

In the present paper we introduce the notion of M-fuzzifying median algebras, which is an extended notion of median algebras in van de Vel. It can further induce M-fuzzifying convex spaces and M-fuzzifying interval spaces, respectively. We study some mapping relations of these concepts. Some characterizations and properties of M-fuzzifying median algebras, M-fuzzifying convex spaces and M-fuzzifying interval spaces are given. Specially, an M-fuzzifying median algebra can not only induce an M-fuzzifying geometric interval operator but also obtain a special M-fuzzifying modular space.