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

Reconfiguration of Distribution system during a contingency is a composite problem. The switching of loads between adjacent feeders to relieve the system from contingency could lead a trouble to the radiality of the system. These switching also lead to further overloading of the system feeders. To overcome from such a situation an algorithm based on ant colony optimization is presented to keep the system safe and maintain its radiality. The Ant Colony Optimization is a probabilistic approach which will seek the shortest path to switch the loads during the overload contingency while minimizing the power loss and maintaining the radiality of the system. To have the practical applicability, the algorithm is tested on the IEEE 30 bus system using the MI-Power software.

Inclusion of renewable energy resources with existing conventional generation resources summons revisit to optimization methods used in the field of generation scheduling. The Unit Commitment problem in itself is a highly convoluted problem governed by complex time varying constraints. It gets even more complicated when additional constraints are added due to inclusion of renewable generation backed up by battery storage system. An effort has been made in this paper to improve the model for solving the Unit Commitment problem of conventional thermal generation in conjunction with renewable energy based generation system with storage. A hybrid artificial intelligence based multiple stage solution methodology is envisaged to provide a techno-economical optimal solution to the problem. The proposed methodology provides economically better solution to the Unit Commitment problem of ten thermal generators when integrated with battery supported wind and solar generation. The overall operational cost gets reduced due to integration of renewable resources which gets further reduced by incorporating battery with a novel optimized charge/discharge scheduling technique.

We propose a fuzzy Reinforcement learning (FRL) framework for an efficient solution to the Economic thermal power dispatch (ETPD) considering multiple fuel options along with valve point loading effect concerning with thermal power generating units. The objective of ETPD is optimizing operating cost for specified power demand meet and to satisfy the generation capacity limits of each unit. In the presented work, We cast the ETPD as a multi agent FRL (MAFRL) problem wherein individual thermal generators act as players for minimizing operational cost and also satisfying the generation limits of each units to obtain a specified power demand. To prove supremacy and validity of proposed multi agent fuzzy reinforcement learning technique, two benchmark test systems involving 10 and 40 units integrated using numerous fuel systems with valve point loading effect have been simulated. Simulation results and comparison against several other existing solution approaches showcases the efficacy of MAFRL technique in solving the ETPD problem.

Present work introduces a most popular evolution based algorithm and application of two degree of freedom proportional integral Derivative (TDOFPID) Controller based multi area power system. Differential Evolution (DE) optimization technique is applied here to tune the TDOFPID gains. Each area consists of Automatic generation control with addition of non-linarites. In this model time delay, Generation rate constraints (GRC) and reheat turbine are added to introduce non-linearity. At first attempt simulation is being done in two areas with DE optimization technique. Further a series connected Flexible Alternating Current Transmission (FACT) device such as interline power flow controller (IPFC) is included into the system and simulated. DE is also used to get the optimum value of TDOFPID controller having Integral time absolute error (ITAE) as the objective function. At last robustness analysis is done with varying parameter and different loading conditions. It is seen that, TDOFPID with IPFC gives better response compared to others.

In this paper, a deregulated LFC scheme is considered. For successful operation of LFC scheme, remote measurements are transmitted to control center consisting delays. The existence of delays affects the performance of frequency control scheme and at worst can causes instability. Therefore, a better control scheme the must encounter the effects of transmission/communication delays. In this study, a test system is considered by two-area thermal system. GA based PID controller has been designed to settle down the frequency deviations in prescribed limit at steady state. The performance of the proposed controller is evaluated under various communication delays. The obtained results show the validity and success of the designed controller.

In this paper, policy iteration (PI) based adaptive critic approach has been utilized to design a controller for AGC scheme. PI approach consists policy evaluation and improvement steps to determine optimal control. Optimal controller based on linear quadratic regulator (LQR) design requires complete knowledge of the system dynamics, whereas PI technique provides optimal control of system without knowing the internal dynamics. PI based optimal controller has been tested on a power system having two area deregulated. The results are used to show the effective performance of the designed controller. A comparative study has also been given in between the performance of the PI based optimal controller and LQR.

Ancillary Services (AS) plays a vital role in a deregulated environment because these services act as the frontier of a power system. It helps to maintain the quality and safety of the supply. Operating Reserve (OR), as an important AS, has been considered in this work. This paper proposed a mixed Genetic Algorithm (GA)-Optimal Power Flow (OPF) mechanism can act as an effective tool for procurement of different services like energy and AS. The sequential clearing technique has been considered for procurement of Energy and OR an objective of cost minimization. Herein, the EM is cleared first in Energy Market (EM) followed by clearing of Operating Reserve Market (ORM). The proposed approach for obtaining the required service using mixed GA-OPF approach has been investigated by considering modified IEEE-30 bus test system.

Automatic Voltage Regulators (AVRs) are widely used to regulate the system voltage. For satisfactory performance of AVR system, optimal design of the Proportional Integral Derivative (PID) controllers is essential. Design tasks of controllers are multiple objective problems since the controller has to satisfy performance measures. The present paper reports the performance study of PID controller with multi-objective Non Dominated Shorting Genetic Algorithm-II (NSGA-II). The design objectives are to enhance the controller performance while minimizing integral error criteria, settling time and maximum overshoot of terminal voltage. Initially a simple AVR system has been considered for checking the superiority of the proposed multi-objective approach. Comparative analysis has been carried out with recently reported techniques. Additionally, the study is extended to an autonomous power system with synchronous and diesel generators. Finally, robustness analysis is also reported by varying the time constant of generator, exciter, amplifier and sensor.

Due to the recent advancement in power electronics devices in past few decades, HVDC system became mature but still has some protection issues, like tripping of the circuit breaker for a temporary fault as to load changes. Therefore, in this paper, a scheme of complete protection for fast, and accurate classification and detection of a fault in HVDC transmission line using support vector machine (SVM) is presented. In the proposed scheme, ac and dc side voltage and current at each converter station are measured and treated as the input of SVM binary classifier. For classification of fault, SVM module with multi-classification feature is used. For the normalization purposes of the signals, the standard deviation is used over half cycle before and after the occurrence of the fault. Features have been extracted through wavelet transform of predefined function for detection and classification of a fault. The proposed scheme is easy to use as it requires only one end data and a standard deviation over one cycle data.

With socio-economical development, power distribution systems are forced to work with both heavy as well as light loads, which causes voltage instability and power loss. This paper develops a Particle Swarm Optimization (PSO) based algorithm to find an optimal place and parameter of Unified Power Flow Controller (UPFC) to improve voltage profile with minimum system loss. UPFC is a most versatile Flexible Alternating Current Transmission System (FACTS) controller, which can control bus voltage; flow of active as well as reactive power through lines. The novelty of this work is to fix the location of UPFC at base load to make power distribution system more secure and efficient, by tuning its parameters at all possible load conditions. The developed methodology is employed in IEEE 30-bus system with different intensities of the load. Results prove the success of developed technique and verify the potential of UPFC for improvement of system stability with mitigation of losses.

Expansion planning of distribution system is the most significant tool which deal with the continuous increasing load demand. The main motive of the expansion planning is the minimization of the investment and operation cost of distribution network equipment which consider the installation/reinforcement cost of substation, feeders and Distribution Generation. In this paper, price and load uncertainties are taken in to expansion planning which gives the robust and reliable expansion planning. These uncertainties are molded as Normal Probability Distribution Function. By using Monte Carlo Simulation uncertainties are added in to planning. A 72 bus (Kian-pars Ahvaz 11 KV a practical distribution network in Iran) distribution network is used for case study of expansion planning. This multistage dynamic expansion planning problem is resolved by the Quantum Particle Swarm Optimization. The proposed algorithm is compared with the standard Particle Swarm Optimization and results shows the superiority of proposed algorithm over PSO.

A unified power quality conditioner (UPQC) has two converters interlinked with back to back DC voltage source. The UPQC is used to improve the multiple power quality (MPQ) of the AC distribution network. The quality of the UPQC depends on the selection of series and shunt control algorithms. The series converter injects the voltage and improves voltage quality whereas the shunt converter injects current and improves the current quality of the AC distribution network. The UPQC works properly if the DC link voltage maintains constant under abnormal or normal condition of AC grid system. Thus, in this paper, DC link voltage is controlled using intelligent model, adaptive neuro fuzzy inference system (ANFIS). The ANFIS result is compared with fuzzy model and conventional PI controller to enhance the mitigation capability of the UPQC. Moreover, the series and shunt converters are controlled by modified second order generalised integrator (SOGI) based algorithm. The performance of intelligent control model is verified by MATLAB based simulation results.

Development of power through wind with the enhancement of renewable energy resources, frolics/romps a principal role in a developing country like India due to its censorious locations. Wind speed prediction in long term scenario has become a key research area in distinct applications (i.e., management of energy, optimal designing of wind farm, restructuring of electricity marketing, load-shedding and load forecasting). However, forecasting of accurate wind speed data for installation of wind turbine is very difficult due to its deterministic and probabilistic characteristics. The presented technique in this study may bridge the research gap related with the long term wind speed forecasting as resolve the previously indicated problems. Thence, two basically distinct techniques, k-nearest neighbors (kNN) algorithm and artificial neural network (ANN), have been implemented to forecasting of monthly wind speed of Indian cities. The uniqueness of the presented paper is to predict the wind speed in common form of incoming month by implementing the kNN algorithm. A dataset of current wind speed recorded specimen from 168 cities of India is utilized to train and test the proposed approach. Obtained results through the proposed approach have been validated by using ANN technique, which shows very small MSE.

The power sector is experiencing comprehensive changes in its regulatory structure, sensing advancement and also prone to system security threats. To make the system more reliable the use of Ancillary Services (AS) become a must. The AS maintains the system security and reliability. With deregulation, the integration of Renewable Energy Sources (RES) in the power system has increased. To utilize RES at the maximum extent, the use of Energy Storage Systems (ESS) is required. ESS like Pumped Storage Plant (PSP) mainly adds great value to support renewable utilization. This paper proposes the simultaneous dispatch of energy and AS market such that the total procurement cost is minimized. The procurement of Operating Reserve (OR) as one of the principal AS is considered in the present work. The optimization problem is formulated and solved using Optimal Power Flow (OPF) technique. RES like Wind Power (WP) and Photo-Voltaic (PV), PSP as ESS with other conventional power generation units are considered to provide energy and AS. Four different cases considering various combinations of energy providers in the optimization problem have been studied and compared using modified IEEE-39 test bus system.

This paper proposes the solution to an optimal scheduling problem of a thermal-wind power system. Here, the power output from a wind energy generator (WEG) is assumed to be schedulable, therefore the wind power penetration limits can be determined by the system operator (SO). The intermittent nature of wind power and speed is modeled using the Weibull density function. Here, 3 objective functions i.e., total operating cost, voltage stability enhancement index and system losses are selected. The total generation cost minimization objective has the cost of power generated from thermal and WEGs, under and over estimation costs of wind power. In the present paper, a multi-objective optimal power flow (MO-OPF) problems are framed by considering different objective functions simultaneously, and they are solved using the multi-objective Glowworm Swarm Optimization (MO-GSO) technique. The proposed optimization problem is solved on a modified IEEE 30 bus test system with two wind farms situated at two different buses in the system. The obtained simulation results show the suitability of proposed MO-OPF method for large scale power systems.

In this paper, the fractional-order (FO) proportional-integral-derivative (PID) (FO-PID) controller is designed aiming at load-frequency control (LFC) for an isolated hybrid power system, involving a solar photovoltaic generator and a diesel engine generator. The FO-PID controller is a PID controller which has fractional order for integral and derivative. This paper engages’ particle swarm optimization (PSO) algorithm to carry out the above mentioned LFC for the considered wind-biomass isolated hybrid power system. A comparison of FO control strategy with conventional based controller techniques is made. The FO-PID controller outperforms the conventional PID controllers.

Wind turbine generator (WTG) and solar thermal power system (STPS) are growing field of renewable energy. These technologies are considered as the feasible options for the generation of electricity in future. However, the inclusion of these technologies affects the output power which intern affects the power frequency. Successful operation of a power system frequency requires frequency in operational limit, therefore, in this study a PID controller is designed for frequency control of a hybrid power system. Big bang big crunch (BBBC) optimization technique is used for determining the parameters of PID controller. The performance of controller has been checked with random variation of load demands. The obtained results demonstrate the effectiveness of the controller. Further, a comparative analysis of results obtained with BBBC has also been performed with genetic algorithm (GA) based controller.

Due to the liberalization of the electricity market, the traditional concepts and practices of the electrical systems have resulted in the introduction of Competitive Electricity Market (CEM). The recognition of CEM provides special consideration for the development of Renewable Energy (RE) throughout the world. The paper presents a mixed Genetic Algorithm (GA) and Optimal Power Flow (OPF) based model for determination of the optimal location and rating of Wind Power Generation (WPG). The optimization algorithm has been formulated and solved while considering the procurement cost minimization for obtaining the required energy by optimally locating the WPG in the system. The proposed mixed GA-OPF approach has been successfully applied to the modified IEEE 30-bus test system. The proposed algorithm also resulted in the prioritized list of optimal locations of WPG in the system.

The battery era has started to compensate the demand of the energy while the charging issues still exist. Thus, demand of reliable and optimized charging is required to charge cell/battery. In this paper novel optimized technique is proposed, based on gravitational search algorithm (GSA) to charge e-rickshaw battery using single sensor based maximum power point tracking (MPPT) of solar photovoltaic (SPV) module. There are various metaheauristic and heuristic techniques are available like Cauchy and Gaussian sine cosine optimization (CGSCO) intelligent technique, evolutionary algorithms, stochastic algorithms, Swarm optimization technique, ant colony technique, neural algorithms, fuzzy logic algorithms to optimize the charging current of cell/battery. These techniques take more iteration to give the optimal solution. Moreover, GSA is the high level intelligent technique which is used in multi area to optimize the various parameters in engineering fields. It is very ease to find the optimal solution in search space. This approach is novel in the field of e-rickshaw battery charging. Therefore, the mathematical algorithm based on GSA has been developed to optimize the current of charging cell/battery. The performance of GSA optimization technique is verified and compared with the metaheauristic based CGSCO optimization technique. It is observed that GSA is easy to design and reduce the cost of charger.

User activity classification is one of the most popular research topic in the domain of health care and social care, since this automated technology can provide monitoring and understanding of activities of patients. Smartphone inbuilt sensors based User Activity Classifier (UAC) recognizes user activities using features extracted from sensors like accelerometer and gyroscope in build in smartphones. In this research paper, we are proposing a new user activity classifier system using Layer Recurrent Neural Network (LRNN) which is Artificial Neural Network (ANN). We utilize synthesized data, containing features of user activity classification system, extracted from the raw data recorded in smartphones. With these derived features, we train and test Layer Recurrent Neural Network classifier for user activity classifier. In order to evaluate this system, we have compared the performance of this Layer Recurrent Neural Network based user activity classifier against the convention Multilayer Perceptron (MLP) and Naive Bayes based user activity classifier. Test results show that the proposed Layer Recurrent Neural Network -based user activity classifier is able to recognize user activities reliably and outperforms the Multilayer Perceptron based user activity classifier. We have achieved the classification accuracy of 98.56% for the activities. The results are much more accurate than Multilayer Perceptron based classifier and Naive Bayes classifier.

Selection of suitable features plays a pivotal role in Electromyography pattern recognition (EMG-PR) based system designing. Time-domain features are widely used in EMG-PR based application and show improved proficiency in the development of rehabilitation robotics. Even though, the performance of existing features is not satisfactory. In this study, we proposed four novel time-domain features obtained by using first-order differentiation of original surface electromyogram (sEMG) signals feature. Here, sEMG signals were acquired from ten healthy volunteers with the help of myotrace400 device for six different arm movements. The data acquisition and pre-processing stage were carried out followed by the feature extraction process for better classification results. Four different classifiers namely, k-nearest neighbors (KNN), Linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA) and Medium tree (MT) classifiers were utilized for the performance evaluation of proposed and conventional features. Experimental results demonstrate that proposed features extracted by using first-order differentiation of sEMG signals feature attained better classification accuracy with MT classifier as compared to the feature extracted from original sEMG signals with the conventional features. The accuracy of proposed feature based on first-order differentiation improved up to 6%. The results indicate that proposed features may be considered for developing the EMG-PR based system designing.

This work comes up with a reinforcement learning (RL) based neural classifier for elbow, finger and hand movements for a multitasking prosthetic hand. First, key statistical features are extracted from the Electromyogram (EMG) signals pertaining to elbow angles, finger movements for typing keys and hand movements. Next, these statistical features are fed to a neural network based reinforcement learning (NNRL) classifier for predicting elbow angle, typing key finger movements and hand movements. For the first task (elbow angle) EMG signals have been recorded with varying weights for different elbow positions; for the second task (typing keys) EMG data is for four tying keys and for the last task (hand movement) EMG data pertains to six hand movements. For the elbow angle prediction task, EMG signal for two channels: Biceps (channel 1) and Triceps (channel 2) has been recorded for 10 subjects and we extract 4 features from each channel. The classifier is able to achieve an average classification accuracy of 97.51%. For the typing keys and hand movement classification tasks, we have used two channels: right hand (channel 1) and left hand (channel 2) for EMG and extract 4 features for the typing keys and 10 features for hand movement. The classifier achieved an accuracy of 98.73% and 97.6% for the typing keys and hand movements tasks, respectively. NNRL gives superior results in comparison to the existing classifiers. High classification accuracy achieved by NNRL classifier shows that our approach could be used as a stepping stone for building a multi-tasking prosthetic hand.

Brain-computer interface may be delineated as the merger of machine and software through which brain activity is allowed to govern a peripheral device or computer. The major aim is to aid a critically paralyzed person to live a normal healthy life. This arrangement passes over numerous stages which include data acquisition, feature extraction, data classification and control. The present work emphasizes the use of selective wavelet based features and classifies them using an artificial intelligence based technique namely support vector machine for wrist movement in four different directions. The data base used is the data set-3 of Brain-computer interface competition-4, which pertains to MEG signals acquired from two healthy subjects performing wrist movement in four different directions. The signal was processed using both wavelet packet transform and discrete wavelet transform and thereafter statistical features were extracted. The best discriminating features were selected after ranking all the extracted features using Principle component analysis. These features were then fed to the support vector machine based classifier for classification. The accuracy achieved is better than most reported in theliterature.

Quality management is for providing better resources to the customers. It is applicable for hospitals for giving best services. QM has an initiative i.e. FMEA, which is applicable in the purchasing process of the hospital (PPH) for calculating the RPN, which determines the risks linked with the problems occurring in purchasing the particular equipment for the hospital. RPN is conquered from past experience and engineering decisions which leads to errors and discrepancies. In this study, Neuro-Fuzzy approach based technique is applied to improve the purchasing process in Indian private hospitals. Neuro-Fuzzy approach eliminates insufficiencies in the assessment of the RPN, resulting in saving of time. PPH in India has never been improved before by applying neuro-fuzzy scheme based FMEA technique. Analyzed results show that the applied neuro-fuzzy method is able to solve the problems which arise from conventional FMEA approach and will effectively find out RPN. Proposed method provides quality assurance in the process.

In industry loading on bearing and its fault severity in an induction motor is unpredictable. Hence, in the present article wide range of vibration data from induction motor bearing surface has been taken for extraction of fault features and classified for the detection of mechanical faults presents in the bearing so that condition based monitoring possible. The vibration data which is selected in this paper includes four different kinds of loading and three different types of fault with three different fault sizes. Firstly, Wavelet Packet Transform (WPT) is applied to decompose the vibration signal and develop Bearing Damage Index (BDI) from the decomposed signal to select the useful signal from the original recorded signal. This BDI based useful signal is further applied for extraction of statistical features and fed to the classifier. Total eleven time domain features has been calculated and Principal Component Analysis (PCA) is applied for the selection of significant features. The selected features further used as input to the Dendogram Support Vector Machine (DSVM) classifier to identify the faults. This proposed method shows significant improvement in classification rate as compared to conventional method, which is quite promising and encouraging.

Being the most widely used motor in all types of industrial and domestic applications, operation of the induction motor at optimal efficiency is paramount for conservation of energy. This paper presents simple and easily realizable techniques for implementation of a PI and Fuzzy Logic controller based efficiency optimization algorithm for a vector controlled induction motor drive. This is obtained by optimal control of the flux current component of the IM drive to reduce the core losses under light load conditions. A new approach to optimize the efficiency based on optimal control of iron losses only is introduced and compared with the optimal control of the total losses. The developed algorithm is simulated using MATLAB/Simulink and is tested under different operating conditions.

DC power supply is required by majority of power electronics devices such as Uninterrupted Power Supply, rectifier Switch Mode Power Supply etc. These draw highly distorted input current of nonlinear nature for a short time. This results in higher total harmonic distortion (THD) due to interference in other electrical equipments.

The problems related to poor power factor are mitigated by Power factor correction (PFC) techniques. A comparative study based on type of frequency, current amplifier IC’s used etc. This paper explains the development of fuzzy logic-based PFC operating in continuous conduction mode. Analysis has been carried out for different load and power factor is seen to improve with increase in the load.

This paper deals with MATLAB/SIMULINK simulation and analysis of a position sensor-less field oriented control of permanent magnet synchronous motor. Adaptive position estimators are required as the parameters of the machines like rotor resistance, inductance changes sometimes. Adaptive position and speed estimators viz. SMO, MRAS are much discussed in literature but the artificial neural network, adaptive neuro-fuzzy inference based estimators are least discussed. In this paper a MATLAB study of MRAS, ANN and ANFIS based position estimator in a Field oriented control of a permanent magnet synchronous motor drive is being done. MRAS, ANN, ANFIS estimators adaptive in nature so these estimators can adapt if there is any parameters change online. The performances of these three drives are analyzed, and results are compared. It is seen that ANFIS based system performance is better even when the parameters of the machines vary with time. This work is limited to analysis and simulation only and could be extended to a practical realization in future work.

Speed control of synchronous machines using Field Oriented Control (FOC) classically uses Proportional Integral (PI) or Integral Proportional (IP) regulators that allow to achieve satisfactory goals on the dynamics of speed and torque. However, the performance deteriorates with loss of one or more phases in multiphase machines with IP regulator. This paper present comparison between the use of IP regulator and Fuzzy Logic Regulator (FLR) under same conditions applied to Five Phase Permanent Magnet Synchronous Machine (FPSM). First, modeling and performance of the FPSM are presented. In the beginning, the control is ensured in healthy mode with an IP regulator then in degraded mode when one then two phases are opened. The performances of the FLR are compared to IP ones. Better performance of FLR is established in terms of faster dynamics.

Weight initialization is the most important component which affects the performance of artificial neural network during training the network using Back-propagation algorithm. The initial starting weights have significant effect on the training. If the weights are too large then the sigmoid will saturate, that makes learning slow. If weights are too small then gradients are also too small. In this paper a new weight initialization method has been proposed. The results for the proposed weight initialization technique are compared against the random weight initialization method. In this paper the proposed weight initialization method is statistically analyzed. Ten different data sets out of which five sets of data are taken from UCI machine learning repository and five sets of data are generated using function approximation problems that are used. Resilient Back Propagation training algorithm is used for training the feed forward artificial neural network. The proposed weight initialization method gives better results when compared with random weight initialization technique.

Software testing contributes a strategic role in software development, as it underrates the cost of software development. Software testing can be categorized as: testing via code or white box testing, testing via specification or black box and testing via UML models. To minimize the issues associated with object-oriented software testing, testing via UML models is used. It is a procedure which derives test paths from a Unified Modelling Language (UML) model which describes the functional aspects of Software Under Test (SUT). Thus, test cases have been produced in the design phase itself, which then reduces the corresponding cost and effort of software development. This early discovery of faults makes the life of software developer much easier. Also, there is a strong need to optimize the generated test cases. The main goal of optimization is to spawn reduced and unique test cases. To accomplish the same, in this research, a nature-inspired meta-heuristic, Moth Flame Optimization Algorithm has been offered for model based testing of software based on object orientation. Also, the generated test cases have been compared with already explored meta-heuristics, namely, Firefly Algorithm and Ant Colony Optimization Algorithm. The outcomes infer that for large object-oriented software application, Moth Flame Optimization Algorithm creates optimized test cases as equated to other algorithms.

This paper presents the opinion of intuitionistic fuzzy parameterized fuzzy soft set (IFP-FS set) by considering the images of approximate function in the fuzzy subsets of the set of universe of discourse rather than to a crisp subset. Some of the desired operations and relations of IFP-FS set are also considered in this study. A decision making approach based on the proposed IFP-FS set is used. Finally, an example is conducted which illustrates that the proposed approach can be use for many real life problems that include ambiguities and uncertainties.

Yager [1] introduced the concept of q-rung orthopair fuzzy sets (q-ROFSs) in which the sum of the q^th exponent of the support for membership and the q^th exponent of the support against membership is bounded by one. Thus, the q-ROFSs are an important way to express uncertain information in broader space, and they are superior to the intuitionistic fuzzy sets (IFSs) and the Pythagorean fuzzy sets (PFSs). However, in dealing with many real life situations, it is not appropriate for experts to precisely quantify their judgements with a crisp number due to insufficiency in available information. In such situation it is advisable for decision makers to provide their judgements by the subset of the closed interval [0, 1]. The notion of interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs) is presented in this paper, which allows decision makers to provide their satisfying degrees and non-satisfying degrees to a given set of alternatives by an interval value. Some of its important operations such as: negation, union and intersection are also given. Based on these operations, the aggregation of IVq-ROFSs is also studied.

Construction of robust regression learning models to fit training data corrupted by noise is an important and challenging research problem in machine learning. It is well-known that loss functions play an important role in reducing the effect of noise present in the input data. With the objective of obtaining a robust regression model, motivated by the link between the pinball loss and quantile regression, a novel squared pinball loss twin support vector machine for regression (SPTSVR) is proposed in this work. Further with the introduction of a regularization term, our proposed model solves a pair of strongly convex minimization problems having unique solutions by simple functional iterative method. Experiments were performed on synthetic datasets with different noise models and on real world datasets and those results were compared with support vector regression (SVR), least squares support vector regression (LS-SVR) and twin support vector regression (TSVR) methods. The comparative results clearly show that our proposed SPTSVR is an effective and a useful addition in the machine learning literature.

Copy-move forgery is one of the famous manipulation technique in digital image. Many block-based techniques have been proposed previously for forgery detection, but most of them have higher computational complexity due to higher number of feature vectors dimension. In this paper, we have tried to reduce the feature vectors dimension. This paper proposes a copy-move forgery detection (CMFD) technique based on circular blocks and discrete cosine transform (DCT) with fewer feature vectors than the prevalent methods. Initially an input image is taken and divided into overlapping blocks. To extract the features from each block, DCT transformation is used on each block. Then, these features are represented using a circle block to reduce the feature vectors dimension. The extracted feature vectors are then used for matching process to locate the manipulated regions. Experimental results depict the performance of the proposed method and robustness against the post-processing operations. The computational complexity of the proposed method is lower than the existing techniques due to fewer feature vectors dimension.

Numerous Fuzzy segmentation techniques have been proposed in the literature for Image segmentation. This paper proposes a new Novel Intuitionistic Fuzzy C-means (S-IFCM) incorporated with Spatial information to reduce noise/outliers influence. This new clustering algorithm uses City-block distance to compute the rank between two pixels. Yager’s type fuzzy complement is used to compute non-membership and further hesitation degree is calculated. The new intuitionistic membership obtained is incorporated with spatial information of image for robustness to noise. Experiments are performed on various noisy images including MRI brain image, to assess the performance of the proposed algorithm. Comparison is done with existing hard, fuzzy and intuitionistic methods on the basis of entropy based segmentation accuracy and validity index. Experimental results show the effectiveness of the proposed method in contrast with other conventional methods.

Invariant feature extraction under diverse illuminations is challenging for face recognition. Related face recognition techniques consider that illumination effect is predominant in low frequencies and involve various methods to segregate high frequency information. However, high frequency feature extraction results in loss of salient features that degrades performance. Thus, objective of this work is to extract illumination normalized robust facial features for face recognition under high illumination conditions. First, a new illumination normalization framework is proposed in which homomorphic filtering (HF) is applied for reducing illumination effect along with contrast enhancement and intensity range compression in face images. Then, illumination deviations are annulled by using reflectance ratio (RR), which yields appropriate texture smoothing and edge preservation. Further, selective feature extraction by discrete wavelet transform (DWT) is performed on HF and RR based face images that discards noise effect. It outcomes in illumination normalized significant facial features, on which subspace analysis (Principal component analysis) is performed to generate small size feature vectors for classification (k-nearest neighbour classifier). Experimental results on benchmark databases such as CMU-PIE, Yale B and Extended Yale B database, demonstrates that proposed face recognition technique yields high performance under diverse illuminations as compared to existing techniques.

Eye blink is a semi-autonomic rapid closing of eyelid. Eye blink is controlled by the autonomic nervous system of human brain. This normal and most important function of human eye can be embedded with some created (abnormal) sequences to indicate any unusual message. In this paper, a real time novel algorithm to reckon number of eye blinks (R_TREB) in a video sequence using eye facet correlation (EFC) is proposed. The proposed R_TREB approximates the eye landmark positions using EFC- an extricate variable value of eyelids clearly differentiating between opened and closed eyelid in a frame of a video sequence. The proposed R_TREB is classified with existing classifiers SVM, QSVM, KNN and DTREE. Increased accuracy to 99.8% validates the effectiveness and correctness of R_TREB.

In this paper, a robust fractional order fuzzy proportional derivative plus fractional order integrator (FOFPD+FOI) control structure is proposed to effectively control a nonlinear, coupled, multi-input multi-output, electrically driven three-link rigid robotic manipulator (EDRRM) system. The FOFPD+FOI controller is realized by using non-integer order differentiator and integrating operators in the integer order fuzzy proportional derivative plus integer order integrator (IOFPD+IOI) controller. A comparative study is carried out to assessed the performance of FOFPD+FOI controller with IOFPD+IOI controller, fractional order proportional, integral and derivative (FOPID) controller and integer order PID controller for reference trajectory tracking, noise suppression, disturbance rejection and model uncertainty. The gains of the controllers were tuned using a meta-heuristic optimization technique cuckoo search algorithm for objective function which is defined as the weighted sum of integral of absolute error and integral of absolute change in controller output. The simulation studies reveal that proposed FOFPD+FOI controller offers much superior performance over PID, FOPID and IOFPD+IOI controllers.

A two link planar rigid robotic manipulator is a highly nonlinear, coupled and multi-input multi-output system. For its effective control, an intelligent adaptive fractional order fuzzy sliding mode proportional integral and derivative controller (FOFSMCPID) has been presented in this work. Sliding mode controller (SMC) is designed by using exponential law and closed loop stability analysis is demonstrated by using Lyapunov theorem. The chattering in the SMC controller has been effectively reduced with the help of boundary layer along with the fuzzy logic. For tuning of the controller, a weighted sum of integral of absolute error and chatter has been considered as the objective function to be minimized using cuckoo search optimization algorithm. To demonstrate the efficacy of FOFSMCPID controller, the results have been compared with integer order fuzzy sliding mode proportional, integral and derivative controller (IOFSMCPID), integer order fuzzy sliding mode proportional and derivative controller (IOFSMCPD) and fractional order fuzzy sliding mode proportional and derivative (FOFSMCPD) controller for trajectory tracking, disturbance rejection, noise suppression and model uncertainties. The detailed presented investigations have demonstrated that FOFSMCPID controller exhibits much superior performance over IOFSMCPID, IOFSMCPD and FOFSMCPD controllers.

The robotic arm is one of the most prominent manipulators used in the field of robotics and therefore serves as a benchmark problem in control systems. Recent developments in the field of artificial intelligence demonstrate the use of intelligent control techniques for various systems. This paper presents a neuro-fuzzy based control scheme for a robotic arm with 6 degrees of freedom used for the sorting and placing of coloured balls. A webcam based image acquisition scheme is used to locate and identify various colored balls and subsequently the robotic arm is moved according to required trajectory. The performance of the proposed neuro-fuzzy controller is compared with a traditional PID control to illustrate the superiority of the intelligent control scheme. Experiments performed on a hardware test bench validate the improvement in performance achieved using the neuro-fuzzy control scheme.

The usage of water treatment technologies is increasing at a rapid rate due to scarcity of pure water in the world. This paper proposes controller design using grey wolf optimization (GWO) algorithm for water treatment plants. Doha reverse osmosis (RO) water treatment plant is considered for the experimental purpose. Proportional-integral-derivative (PID) controller is designed for the considered Doha RO plant. The proposed method can minimize the error criterion in such a way that good transient responses for flux and conductivity are obtained. To show the efficacy of GWO based controller, other state-of-art optimization techniques are also used for tuning of controller parameters. Comparative simulations show that GWO algorithm is more efficient and suitable over other algorithms for tuning the parameters of controller for Doha RO water treatment plant.

In this paper, a modified Artificial Bee Colony algorithm is proposed. Then estimation of the parameters of fractional order chaotic systems is performed using the proposed Artificial Bee Colony algorithm and Ant Colony algorithm. For the purpose of modeling, four fractional order chaotic systems viz. Financial System, Chen System, Lorenz’s system and 3 Cell Net system have been considered. Each chaotic system is defined by a set of fractional-order differential equations. These equations comprise of several variables – model parameters, derivative orders, and initial conditions. For the system’s entire state and future values to be known, the values of all the parameters have to be estimated to a reasonable degree of accuracy. It is a general practice to use modern evolutionary algorithms to solve such problems. Simulations on both nature inspired optimization algorithms are performed and estimated values of parameters determined. Comparisons with existing scheme of Artificial Bee Colony based parameter estimation are also performed. Observations reveal that the results of the modified ABC algorithm outperform those of other techniques for all the four cases.

In biometrics, feature extraction is an important step to extract the unique information from physiological or behavioural characteristics of individual such as palmprint, faceprint, speech and gait. In this paper, a novel feature extraction technique is proposed for person authentication using palmprint based on gradients of gaborized image called Oriented Gabor Gradients (OGG). To validate the proposed feature extraction method, palmprint recognition has been tested on both left and right palm of IITD database of 230 persons, PolyU palmprint database of 386 persons. The proposed OGG method is compared with histograms of oriented gradients (HOG), gabor transform, gaussian membership based features (GMF), absolute average deviation (AAD) and mean features. Experimental results show primacy of the proposed technique over the existing ones in the literature and achieved higher accuracy. Lastly, K-nearest neighbor is used to validate the matching stage.

Market Segmentation has been a key area of implementation of soft computing techniques in E-commerce applications. Various techniques have been used to achieve maximum results in the classification of the ecommerce market. From stochastic techniques to neural networks, there is a plethora of techniques that have been applied. In this paper, we use self organising Maps (SOMs) an unsupervised learning technique to study the various factors which can be used to segment the market. On the other hand supervised learning techniques such as Nearest Neighbour (NN) and Support vector machine (SVM) are used to quantitatively classify the purchase behaviour based on various factors. The better classification technique is identified through appropriate measures. Further, evolutionary algorithms are used to augment the performance of these classification techniques. Analysis of the results and various factors affecting it is also performed.

In cloud computing datacenter infrastructure, virtualization has enabled service providers to create abstraction of their physical resources and increased the infrastructure utilization. At the datacenter, cloud users services are implemented using Virtual Machines (VMs). With the popularity of cloud paradigm, numbers of enterprise applications deployed over cloud systems are increasing. This shift in cloud paradigm has brought new challenges of server consolidation, load balancing, resource management, and server fault situation. Cloud managers trigger VM migration to achieve all these challenges. VM migration allows migration of running VMs from one datacenter to another. In migration process to keep the VM alive, VM memory pages are transferred in multiple iterations. Thus, there is an increase in the requirement of network resources consumption during migration process. However, inaccurate bandwidth allocation for a VM migration request can lead to performance degradation. Therefore, there is a need to devise an efficient bandwidth allocation strategy. In this paper, we propose a multistage bandwidth allocation technique which aims to minimize the bandwidth allocation at each round of migration mechanism. The proposed technique is implemented by the simulation carried out in Matlab software. To ensure the applicability for real-time hosted applications, multistage scheme is evaluated using both low and high dirty rate conditions. We also considered distributed cloud deployment situation by using sequential and multiple VM migration test cases. The results demonstrated shows, as compared to existing VM migration bandwidth allocation schemes, our proposed technique outperform in terms of downtime, migration time, bandwidth provisioned and migration iterations count. Additionally, we also explored multistage technique with fuzzy logic to improve the VM selection process. The significance of this study is that, multistage technique allows bandwidth allocation in each iteration as variable to current dirty rate scenario, thereby increasing the revenue of cloud providers.

Most of the user authentication schemes are based on smart cards; however, a smart card requires additional infrastructure that includes card reader, leading to more deployment cost. Recently, Kumari et al. have discussed a user anonymous authentication scheme that uses common storage device such as USB stick, mobile phone, etc. rather than using a smart card. The common storage device is used to store some authentication information issued by the server. We cryptanalyze the Kumari et al.’s scheme and find that it is not resistant to the device stolen, privileged insider and denial of service attacks. In this paper, we propose a user authentication scheme based on public key cryptography to overcome its drawbacks. We show its formal security analysis using random oracle model and discuss its informal security analysis to show that it is resistant to the various known attacks. We simulate it using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool for its formal security verification. We compare our scheme with the related schemes and show that it has better communication cost and provides more security features.

Rolling bearing is an important mechanical element therefore its condition monitoring is necessary to ensure the steadiness of industrial machineries. In this paper, the open source vibration data have been processed using advanced signal processing techniques such as EMD method to extract more symmetric waves (IMFs) out of non-linear and non-stationary vibration signals. In addition to this, statistical time-domain and frequency-domain features are calculated and then J48 Decision Tree Algorithm is used for feature selection. The processed input signals have been used for comparative study of five different types of Artificial Neural Network (ANN) classifiers. The performance characteristics of MLP, PNN, GRNN, RBF and LVQ are shown in results and discussion section.

Strength of high performance concrete (HPC) is not depends upon water to cement ration only but it is also persuaded by the several components of the concrete. The HPC is a vastly compound material, which create its behavior property very difficult in modeling to analyze. The main aim of this paper is to provide the utilization possibilities of Gene Expression Programming (GEP) to predict the HPC compressive strength (HPCCS) at highly complex behavior. A set of 1030 samples of HPC was collected from open access repository that was developed in the laboratory and represented suitable experimental results, which includes eight attributes (i.e., age, blast furnaces slag, cement, fly ash, water, superplasticizer, fine aggregate and coarse aggregate). The obtained results are compared with other computational intelligence technique (i.e., RBF neural Network) to validate the performance analysis of the proposed approach. This method is used to predict the HPCCS in high strength level. Moreover, as per available literature, this is the first attempt to implement the GEP in this domain to predict the HPCCS.

Due to the fast growth of multimedia archives, the semantic gap is becoming a vital problem between machine learning based semantic concepts and local features of the image to retrieve images accurately. To address this issue, the proposed method of this article introduces two novel methods for effective image retrieval known as visual words integration after clustering (VWIaC) and feature integration before clustering (FIbC). These methods use complementary features of histograms of oriented gradients (HOG) and oriented FAST and rotated BRIEF (ORB) descriptors founded on the bag-of-words (BoW) model for salient objects within the images to build smaller and larger sizes of codebooks. To achieve higher efficiency in terms of specificity of the image retrieval system, the codebook of larger sizes are preferred, while larger sizes codebook produces low sensitivity and vice versa. The proposed method of VWIaC produces two smaller sizes codebooks to achieve higher sensitivity. After that visual words of both smaller size codebooks are integrated to produce larger size codebook, which improves the specificity of the proposed method. The performance of the proposed method is tested on three standard image benchmarks, which verifies its vigorous performance as compared to an FIbC method and recent CBIR methods.

Pythagorean Hesitant fuzzy set (PHFS) which permits the membership degree and non-membership degree of an element to a set represented by several possible values is deliberated as a powerful tool to express uncertain information in the process of multi-attribute decision making (MADM) problems. In this paper, we propose a novel approach based on TOPSIS method and the maximizing deviation method for solving MADM problems where the evaluation information provided by the decision makers (DMs) is expressed in form of Pythagorean hesitant fuzzy numbers and the information about attribute weights is incomplete. To determine the attribute weight we develop an optimization model based on maximizing deviation method. Finally we provide a practical decision-making problem to demonstrate the implementation process of the proposed method.

Convolutional neural networks (CNNs) are important methods in deep learning. They have presented up-to-date performance in different challenging areas, such as natural language processing and computer vision. These powerful and efficient neural networks implement training slowly under a massive number of network training parameters. The primary challenge is to reduce the training time for large volumetric data. CNNs have a small number of parameters at convolutional layers and a large number of parameters at fully connected layers. Training time can be reduced by the use of computing parallelism according to the characteristics of CNN layers. This paper presents an optimized parallelism algorithm using a communication strategy for CNN training in distributed graphic processing units (GPUs). We use the butterfly reduction communication strategy and apply data and model parallelisms at convolutional and fully connected layers respectively. A model is divided among distributed GPUs, and each division of the model works according to the characteristics of CNNs. This hybrid parallel approach is more desirable than previous parallelism alternatives, such as data parallelism only and model parallelism alone, which have been applied to modern CNNs. Experimental results reveal that this parallel approach with butterfly communication strategy can enhance accuracy and decrease training time.

In recent years, spectral clustering algorithm has been widely used in the field of pattern recognition and computer vision. How to construct an effective similarity matrix is the key issue of spectral clustering algorithm. In order to describe the uncertainty in the image and design the efficient similarity matrix for spectral clustering, an interval fuzzy spectral clustering ensemble algorithm for color image segmentation (IFSCE) is presented in this paper. Firstly, the color histogram is obtained by the just noticeable difference color threshold method. Then the interval fuzzy similarity measure based on color feature is constructed by utilizing the interval membership degree and the image are grouped by normalized cut criterion under the similarity matrix produced by interval fuzzy similarity measure. Finally, the segmentation results with different optimal fuzzy factors combination are integrated to get the final result. The experimental results on real images show that the proposed algorithm behaves well in the segmentation accuracy and visual segmentation result.

This paper presents a methodology for a high-resolution urban spatial load demand forecasting. This methodology is meant to improve the visualization, analysis and inference of load density information in the electric distribution systems in the near future. The proposed methodology converts input data into grid maps and then divides the grid map into larger regions, which will have their expected growth according to convolution matrices and weighting factors that search for characteristics in the history of this region. The definition of the characteristics of the region’s growth is obtained by processing the imperialist competitive algorithm that searches the best array of convolution, which will set the expected growth of the region. Thus, it is possible to obtain a spatial growth forecast of high resolution and with great precision, which are important factors for smart-grid planning.

The purpose of the current article is to introduce a propositional linear time temporal logic of common knowledge. This logic can be utilized for reasoning when the agents should commonly know the information that may change over time. This is the main advantage of our logic over the existing temporal logics of knowledge in the literature. We provide a language, as well as appropriate semantics for our logic. We also introduce a resolution-based proof method for this logic by adopting the approach proposed by Dixon et al. [11, 14]. This resolution system is based upon a separated normal form. We prove that our resolution system is sound and complete. More importantly, to justify our approach, we apply our resolution method to specify and verify the anonymity properties of Chaum’s dining cryptographers protocol [4].

Kumar and Garg (Applied Intelligence, 2017, 10.1007/s10489-017-1067-0) pointed out the limitations of some existing methods for solving intuitionistic fuzzy multi-attribute decision-making (MADM) problems. Also, to overcome the limitations, Kumar and Garg proposed a connection number (CN) based method for solving intuitionistic fuzzy MADM problems. In this paper, it is shown that the ranking method, used in Step 5 of Kumar and Garg’s method for comparing connection numbers (CNs), fails to compare two distinct CNs. Hence, Kumar and Garg’s method fails to rank the alternatives of intuitionistic fuzzy MADM problems. Furthermore, to overcome the limitation of Kumar and Garg’s method, a new ranking method (named as Mehar ranking method) is proposed for comparing CNs.

Texture spaces were introduced by L.M. Brown as a means of representing fuzzy sets in a point-based setting. The concept of ditopological texture space was defined on a texture as a generalization of topological, bitopological and fuzzy topological spaces. This paper provides an introduction to the concept of diframe. In particular, we describe the morphisms of the category of diframes and establish a connection between texture spaces and frame theory.

In view of intelligent Minkowski metric Weighted K-means (iMWK) sensitive to feature weighting, a novel clustering technique called intelligent Minkowski metric feature weights subspace clustering algorithms through hybrid dissimilarity measure (iMWK-HD) is presented. First, a new optimization objective function is constructed by incorporating the Minkowski distance and Cosine dissimilarity in the subspace. Based on this objective function, the corresponding update rules for clustering are then derived, followed by the development of the novel iMWK-HD algorithm. The properties of this algorithm are investigated and the performance is evaluated experimentally using synthetic and UCI datasets. The experimental studies demonstrate that the accuracy of the proposed iMWK-HD algorithm outperforms three existing clustering algorithms, i.e., iK-means, iWK-means and iMWK-means. In addition, the proposed algorithms are immune to irrelevant features in cluster subspace.

It is easy to apply artificial intelligence methods and address scientific problems with concrete rules. However, it is often challenging to apply these methods to art creation problems of weak regularity. To automatically generate musical melodies in the prairie songs of northern China, this paper proposes a formal method for melody creation based on theme development and fuzzy inference. First, we analyze the features of mode and scale in the prairie songs and construct an algorithm to generate a theme phrase according to the inner fuzzy relations among the prairie-song phrases to obtain seed materials. Then, concerning the fuzzy relations between two phrases in the prairie songs, this paper adopts fuzzy inference to manage the progression of the phrase relations and generates developmental phrases. Finally, many complete melodies of the prairie songs are generated. Compared with existing rule-based approaches, the proposed method can improve the global structure of music and can make the output compositions more musical and interesting.

The main goal of this paper is to introduce the notions of derivations on EQ-algebras and obtain several special types of them. In this paper, we discuss some properties of useful derivations and give some characterisations of them. Then, we also show that the fixed point set of useful derivations is a good EQ-algebra. Moreover, we study simple ⊙-derivations and their adjoint derivations and prove that the fixed point set of simple ⊙-derivations and that of their adjoint derivations are order isomorphism. Furthermore, the quotient algebras induced by d-filter are studied.

Sub-vocal speech (SVS) recognition is highly desirable for silent communications among defence personnel and underwater operations. The SVS of Hindi phoneme has a great role to transform the sub-auditory signals into textual information for deaf Indians. Electromyography (EMG) has been applied to record signals of Hindi phoneme. EMG signals are picked up by placing electrodes over the neck areas below the chin of the subject. The SVS of Hindi phoneme recorded for four Hindi alphabets क (Ka), ख (Kha), ग (Ga) and घ (Gha) for 10 healthy Indian subjects. Two types of features; Wavelet based features and Auto Regressive (AR) coefficient features were extracted for these phonemes. Analysis has been made using three classifiers namely linear classifier, quadratic classifier and Support Vector Machine (SVM). Performances of all three classifiers are also evaluated in terms of accuracies. The classification accuracies averaged on 10 subjects with SVM classifier are found to be 75.00%, 78.05%, 80.50% and 81.30 % corresponding to phoneme क, ख, ग and घ respectively. Results also indicated that the wavelet based features with SVM classifier are best suited among three classifiers for accuracy of SVS Hindi phonemes discrimination. Myoelectric signals proved to have an important role for classification of sub-vocal Hindi phonemes in speech pattern recognition.

States data are among the most vital transferred data in power systems. Therefore, if the attacks to these states is not recognized, the system performance will get noisy and in the worst case, it will lead to widespread power outages. In this paper, three indicators will be presented to detect false data injection attack (FDI). These indicators use three factors to detect the attacks. The first indicator has been designed based on the performance of traditional power systems. The second indicator, in addition to the first factor, uses a relationship between angle and voltage changes in each bus to detect the attacks. The third one has been designed for detecting the attacks based on the neighbor buses performance. In this paper, FDI attacks have been classified into two categories: Manipulating attack, and PMU attack, in order to investigate the indicators performance. Using two standards of IEEE 14bus and New England 39bus, the indicators performance was analyzed. Our results show that all the three indicators have unique properties and all are successful in detecting attacks on smart grids.

Decision making on allocation of limited financial resources and determining the service quality level and facilities to the customers is an important issue for banking industries and financial enterprises. In this research by using neural networks, customer’s credibly behavior is modeled and clustered in order to optimize the allocation of financial resources and enhance the quality of banking services. By using Analytic Hierarchy Process (AHP), weighting coefficients of each input variable is determined and then these coefficients are used as primarily weights in fuzzy neural networks (FNN). This approach has increased training speed and accuracy of FNN considerably. Also the customers credibly behavior is predicted by using neural networks clustering models. The fuzzy neural networks model with the same data is also carried out without using AHP weights. The comparison of both approaches show the fuzzy neural networks clustering models with AHP weightings is more accurate with higher prediction speed. The model is implemented for a real application of Iranian National Bank. The case study shows 98% increase in prediction speed and 12% increase in accuracy.

To reduce fossil fuel consumption, carbon dioxide emissions, and greenhouse gas emissions, countries all over the world have been gradually directing their attention toward the development and application of microgrids (MGs) that run on renewable energy sources. The MG concept has been gaining increased interest, particularly with respect to distribution systems. On the other hand MGs are equipped with new technologies such as plug-in electric vehicles (PEVs) and plug-in hybrid electric vehicles, which have become viable alternatives to traditional combustion-engine cars. In this paper the novel optimization method for efficiency maximization in smart MGs in the presence of demand response, was proposed. This method combines a hybrid shuffled frog leaping algorithm (SFLA) and intelligent water drop optimization. In this situation, the EV energy storage system (ESS) state of health (SOH) model was considered to adjust the ESS temperature set point. SFLA is a new member of intelligent algorithms and a new member in the family of memetic algorithms. For this purpose, simulation results were made in MATLAB software environment to demonstrate the effectiveness of the proposed methodology. In order to verify proposed algorithm, simulations were made along with some conventional optimization methods. The results show that the proposed optimization method, can effectively improve the performance of MG power flow, when it is compared with other methods.

In this paper, we introduce the concepts of fuzzy congruence relation and fuzzy coset relation on vector spaces and find some their properties. We define some operations on fuzzy coset relations and show that the set of all fuzzy coset relations on a vector space is a vector space, too. We study the union of two fuzzy congruence relations and investigate some its properties. Finally, by using the concepts of a generated subspace of a set and a congruence relation on a vector space, we introduce the notion of a fuzzy congruence relation generated by a fuzzy relation in a vector space.

It is clear to characterize logic algebras, ideals and filters play important role, therefore in this research, we survey the structure of various ideals and find some types of ideals such as positive implicative ideals, strong ideals and MV-ideals in residuated lattices. We also introduce the concept of quasi ideals and show that any ideal is a quasi ideal, but the converse does not hold in general. We clarify the relations between these ideals in residuated lattices, strong residuated lattices and BL-algebras. For instance, we prove that in strong residuated lattices the concept of quasi ideals and ideals are the same and in BL-algebras the concepts of quasi ideals, ideals, strong ideals and MV-ideals coincide, whereas they are different in residuated lattices and MTL-algebras. We detect the relation between these ideals with MV-algebras, strong algebras and Boolean algebras.

Over the last decade, a lot of attentions have been drawn in the problem of multiple attribute decision-making associated with incomplete information tables. The filling missing value method and minimum decision cost are two important challenges for incomplete information tables. But most published studies focus on the optimization of data reckoning without considering the risk appetite of decision makers and decision-making environments. In this paper, considering the above factors, we presented an attribute weight determination method and an effective computing method for dealing with intuitionistic fuzzy incomplete information tables. Then, delay-refused decision in the boundary region and the risk assessment based on cross-entropy were proposed. Finally, a secondary decision strategy based on the probability entropy of delay-accepted decision and the application algorithms were given for improving the quality of decisions.

The theory of rough sets is an efficient mathematical tool for dealing and reasoning with uncertainty information systems. The measures of traditional rough sets are applicable to discrete-valued information systems, but not suitable to real-valued data sets. In this paper, by introducing a distance matrix to granulate these real-valued data, a granulated fuzzy rough set model is proposed, which combines fuzziness and roughness into a rough set theoretical framework. By constructing a fuzzy similar relation with a distance matrix form, real-valued data sets can be deal with. We also define some operations on the fuzzy relations and fuzzy granules. Furthermore, two kinds of measures of fuzzy granules are proposed, which are information entropy measure and information granularity measure. These measures are calculated by a novel representation with a fuzzy granule matrix. As a result, uniform representations of fuzzy rough sets and their information measures are formed in this work.

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

A knowledge base is one basic notion in rough set theory. In a knowledge base, one can approximately describe target notions by using existing knowledge structures. This paper investigates invariant characteristics of knowledge structures in a knowledge base under homomorphisms and their uncertainty measures. First, partial dependence knowledge structures in the same knowledge base is proposed and the concept of knowledge structure bases is presented. Then, invariant and inverse invariant characteristics of knowledge structures in a knowledge base under homomorphisms are obtained. Next, measuring uncertainty of knowledge structures in the same knowledge base is investigated. Finally, two examples are employed to illustrate that knowledge granulation, rough entropy, knowledge entropy and knowledge amount of knowledge structures, and knowledge distance between knowledge structures are neither invariant nor inverse invariant under homomorphisms, and third example shows the feasibility of the proposed measures for uncertainty of knowledge structures in the same knowledge base. These results will be helpful for building a skeleton of granular computing in knowledge bases.

We introduce the notions of pointwise and uniform ideal convergence and kind of convergence lying between aforementioned convergence methods, namely, equi-ideal convergence of sequences of fuzzy valued functions and obtain various results related to these kinds of convergence and their representations of sequences of α-level cuts. We prove the ideal version of the Egorov’s theorem for sequences of fuzzy valued measurable functions defined on a finite measure space ( X , M , μ ) . We also define the notion of ideal convergence in measure for sequences of fuzzy valued functions and prove some interesting results.

In this paper, a novel learning frameworks–multiple rank multi-linear twin support matrix classification machine (MRMLTSMCM) is outlined, as an extension of twin support vector machine (TWSVM). Different from TWSVM, MRMLTSMCM uses two pairs of projecting matrixes to construct the pair of functions, which are used to establish decision function. Compared with the vector-based method, the matrix-based could not only keep the structure of the matrix data but also reduce computational complexity. In addition, a regularization term is considered adding to improve the performance of MRMLTSMCM. Moreover, a novel algorithm for MRMLTSMCM is introduced. Finally, experimental results show the effectiveness of the method by classification accuracy, convergence behavior and computation time.

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

With movement toward complication and automation, modern machinery equipment encounters the problems of diversity and complex origination of faults, incipient weak faults, complicated monitoring systems, and massive monitoring data, which are all challenging current fault diagnosis technologies. Conventional machine learning techniques, such as support vector machine and back propagation, have disadvantages in handling the non-linear relationships and complicated structure of massive data. Deep learning (DL) methods have a greater capability to address complex and heterogeneous machinery signals, and identify faults more accurately. This paper presents a review of DL methods in emerging research in the machinery fault diagnosis field. First, common DL models are briefly described. Then, the application of DL to machinery fault diagnosis is described in detail, including the problems DL aims to solve and the achievements it has accomplished thus far. To demonstrate the capability of DL to handle the multiplicity and complexity of equipment faults and massive data, we examine experimental results for typical reciprocating compressor and bearing. Finally, the limitations and trends of further DL development are discussed.

This paper applies uncertain theory to establish an uncertain differential equation (UDS) SIS epidemic model, comparing with deterministic and stochastic SIS models. Solution of the UDE model is obtained. Threshold conditions are derived for permanence and extinction of disease by the corresponding α-paths, which reveal relationships of three models. Numerical simulations are given to illustrate these results.