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

In this paper, we highlight an intelligent system to properly construct a function between a pair of generalized-fractal spaces: the d-cube [0, 1] ^d endowed with its natural fractal structure, and the closed unit interval endowed with a natural-like fractal structure. Such a function allows the definition of space-filling curves by levels as well as the calculation of the box dimension of higher dimensional subsets through the box dimension of subsets on the real line.

For multi-attribute decision-making (MADM) problems with temporal characteristics and attribute values of interval-valued intuitionistic normal fuzzy numbers, dynamic interval-valued intuitionistic normal fuzzy weighted averaging (DIINFWA) operators are presented, and their properties are proved. Since attribute weights and time weights have both been unknown in MADM problems, we propose a dynamic interval-valued intuitionistic normal fuzzy MADM method. In this method, a combination weighting method of gray correlation analysis and the maximum deviation method are used to solve for attribute weights, comprehensively considering the subjective experience of decision-makers and objectives of decision data; time weights are decomposed into time-constant and time-variable weight vectors. We determine time weights using the time function, combining information entropy and a logistic function. According to the algorithm of interval-valued intuitionistic normal fuzzy numbers, decision-making information in different time sequences are aggregated using the proposed DIINFWA operators. We construct a dynamic interval-valued intuitionistic normal fuzzy comprehensive decision matrix and use the VIKOR (Vlsekriterijumska Optimizacija I Kompromisno Resenje) method to obtain the optimal solution. Finally, the feasibility and significance of the presented method compared to existing methods are verified through analysis of numerical examples.

Let G = (V, E) be a connected graph. An edge set S ⊂ E is a 3-restricted edge cut, if G - S is disconnected and every component of G - S has at least three vertices. The 3-restricted edge connectivity λ₃ (G) of G is the cardinality of a minimum restricted edge cut of G. A graph G is λ₃-connected, if 3-restricted edge cuts exist. A graph G is called λ′-optimal, if λ₃ (G) = ξ₃ (G), where ξ 3 ( G ) = min { | [ X , X ¯ ] | : X ⊂ V , | X | = 3 , G [ X ] is connected}. Furthermore, if every minimum 3-restricted edge cut is a set of edges incident to a set of three vertices, then G is said to be super 3-restricted edge connected or super-λ₃ for simplicity. Inverse degree of G is R ( G ) = ∑ v ∈ V 1 d ( v ) , where d (v) denotes the degree of the vertex v. In this paper, we study the relation of inverse degree and super 3-restricted edge connected graphs.

This paper consider the design approach of guaranteed cost control for discrete uncertain networked systems with time delay. A novel sliding mode manifold that can compensate time delay has been designed by introducing a discrete compensator. Then, a guaranteed cost controller is designed to make the sliding mode manifold stable. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method.

Taking the dynamic risk identification as the research object, propose a risk identification model of dynamic memory and variable fuzzy identify based on the immune mechanism extension, DRIBIEM. According to the dynamic risk characteristics which is complex and uncertain, DRIBIEM dynamically maps the intensity and frequency of risk to the concentration of antigen, based on the cell death pattern, stimulates immune memory, guides the evolution of antibodies and controls life cycle of identifier by antigen concentration which solves the problem that the traditional immune identification algorithm takes too long time, realizes the distributed automatic updating of identifiers and improves the dynamic risk identification ability. Simulation results show that DRIBIEM fully reflects the dynamic characteristics of immune memory and can effectively identify the complex dynamic risks. Its feasibility can be verified in the practical application of dynamic risk identification.

Urban distribution is regard as a time- and labor-consuming logistics activity which is critical to the residents’ daily life and municipal administration. To address this challenging practical problem, a novel multi-objective location-routing problem with simultaneous pickup and delivery is proposed. The problem seeks to minimize distribution operation cost and maximize service level by constructing a more effective logistics network. In the problem formulation, two types of delivery service modes and a fuzzy satisfaction function are use to describe the characteristics of urban distribution in real world application. To solve this specific problem, a hybrid heuristic algorithm combining tabu search and efficient procedures is presented. Through evaluating the effects of time window and service mode, and comparing heuristics performance on solving problem instances, the experimental results demonstrate the validity of the proposed model formulation and heuristic approach.

In order to improve the performance of semi-supervised learning, a kind of safe semi-supervised classification algorithm based active learning sampling strategy is proposed. First, an active learning sampling method based on uncertainty and representativenes is designed. The weighted algorithm combining the uncertainty and representativenesss is used to select the unlabeled samples with rich information and representation, providing for semi-supervised learning. Second, a method of label prediction based on grouping verification is designed. Prelabeling is executed on unlabeled sample selected by active learning. The sample with pseudo-label is added into the labeled sample set to carry out grouping, training and testing. The corresponding errors of various pseudo-labels are calculated and the pseudo-label making the accuracy least is selected as the candidate label of the unlabeled sample. Third, a method of security verification is designed. Only the label making the accuracy lower than before is selected as the final label of the unlabeled sample to expand the number of labeled samples. Iterations are repeatedly executed until a certain precision is met. Finally, the classifier is trained using the final labeled set. The experiments are carried out on semi-supervised datasets and UCI datasets, and the results show that the proposed algorithms are effective.

In this paper, we first give a new proof and a complement of the Hadamard-Fischer inequality, then present some results related to positive definite 3 × 3 block matrix and matrices whose numerical ranges are contained in a sector.

The current massive data storage and sharing algorithm based on file system and NIOT have not considered the heterogeneous and real-time characteristics of data in distributed and heterogeneous environment, which has the problems of high storage capacity, high time delay of data sharing, and poor security of data. To address this problem, massive data storage and sharing algorithm in distributed heterogeneous environment is proposed in this paper. Hierarchical analysis of distributed heterogeneous environment is carried out. By using the file system, database technology, and hybrid technology, with the heterogeneous and real-time characteristics of massive data in distributed heterogeneous architecture, a three-level storage framework is obtained. Distributed storage algorithm is applied to save massive data. Through HBase-based massive data sharing algorithm in distributed heterogeneous environment, based on data integration mode of data warehouse, massive data sharing is achieved based on blackboard system to reduce data sharing delay. The node encryption unit is used to improve data security. Experimental results show that the proposed algorithm can reduce the occupancy rate of storage capacity, with stable storage speed and low sharing time delay. It can play a good role for data security.

The overturning stability issue of continuous girder bridges is critical so that it is necessary to obtain the true overturning stability performance. At present, the parameters uncertainties in the structure were neglected in the stability evaluation method of the long-span continuous girder bridges, which leads to the unknown safety level of the continuous girder bridges during the cantilever construction. Therefore, a calculating method for overturning stability safety factors of long-span continuous girder bridges in cantilever construction based on inverse reliability theory is presented in this paper. The proposed method is extended from the traditional deterministic form of safety factor, which considered influence of uncertainty factors among structure parameters was used to obtain safety factors through target reliability index based on inverse reliability theory. Overturning stability safety factor of long-span continuous girder bridges in cantilever construction and parameter sensitivity were assessed using the proposed method, as well as the reasonableness of longitudinal overturning stability safety factors was discussed. The results show that parameter uncertainties have a major effect on overturning stability safety factors of long-span continuous girder bridges in cantilever construction, ignoring parameter uncertainties will result in overestimation of overturning stability safety factors of long-span continuous girder bridges in cantilever construction, reasonable safety factor should be obtained based on target performance. The sum of the self-weight of the travelling form and the pouring segment has the most significant effect on the safety factor. It’s critical to ensure a reasonable situation of the travelling form during the construction stage in case of falling. The resistant moment of the temporary support and the eccentric distance of the support also need to be handled carefully because of the remarkable effect. The proposed method is stable and reliable, which will be convergent to the same result from different initial value in spite of different iteration progress.

The traditional Gauss-Newton iterative method is highly dependent on the initial value when locating the multimode GNSS receiver. If the difference between the initial value and the true value is higher, the algorithm has the problem of increasing number of iterations, and the algorithm lacks the self main monitoring process of the GNSS receiver, which leads to a great reduction in the positioning accuracy. A high precision multi-mode GNSS positioning algorithm is proposed. It is based on the composition and working principle of multimode GNSS multimode receiver, and the pseudo distance positioning distance is obtained by using GNSS multi constellation combined location algorithm. It uses a direct algorithm without initial value and iteration through the new algorithm of high precision positioning. After linearizing the pseudo range location distance equation, the user’s general position is calculated. After the pseudo range location distance equation is carried out in the general position of the user, the user’s position correction is calculated by weighted least squares, and the exact location of the user is obtained. The receiver autonomous integrity monitoring (RAIM) algorithm based on the least square residual method of GNSS receiver is used to realize the self-improvement monitoring of GNSS, and to further improve the precision of the multi-mode GNSS positioning algorithm. Experimental results show that the proposed location algorithm has high location accuracy and stability.

The traditional credibility-based security performance analysis method of physical layer transmission link for millimeter wave communication system applies single analytic hierarchy process and the built evaluation index system has limitation. To address this problem, a security performance evaluation method for physical layer link of millimeter wave communication system based on fuzzy AHP is proposed in this paper. Combined with fuzzy evaluation and analytic hierarchy process, the safety performance evaluation index system of physical layer transmission link for millimeter wave communication system is built from 4 aspects: asset, threat, vulnerability, and security. Experimental results show that the proposed method can obtain valuable evaluation results, and it is reliable and accurate for analyzing the security performance of the physical layer transmission link for millimeter wave communication system.

Nowadays, the multi-sensor information fusion algorithm of the integrated power grid operation system based on Bayesian network is disturbed by the high flow data, causing that the single data fusion level and large convergence error. Therefore, an intelligent fusion algorithm of multi-sensor information in integrated power grid system is proposed. According to the asynchronous aggregation distribution construction algorithm based on hierarchical clustering, and in accordance with the hierarchical clustering, all nodes are put to aggregate and construct a collection tree according to the distance, then calculate the optimal grouping number. Then based on the number of grouping, grouping is implemented. According to asynchronous distributed strategy, selection of the optimal aggregation nodes and construction of the optimal transmission topology are carried out, to quickly find the aggregation mode of sensor data in power grid with minimal overhead, in order to reduce the data flow of power grid. In the aggregation distribution environment of multi-sensor, based on the principle of multi-sensor information fusion and detection in the integrated power grid operation system, the information fusion abstract model of the integrated power grid operation system is applied. The multi-sensor information fusion is divided into three levels: data level, feature level and decision level. The functional structure of multi-sensor information fusion can realize the effective fusion of multi-sensor information. The experimental results show that the proposed algorithm has a high accuracy and stability of information fusion, and can reduce the loss of the power grid.

The small fillet aluminum alloy cavity is a typical structure. The analysis and experiment have been conducted on the chatter at the corner of the aluminum alloy during milling process, the results show that continuity changing of spindle speed acceleration and size effect (special ploughing effect) are the main factors of chatter. The Axial Depth of Cut–Spindle Speed Analysis Method is not effective to reflect the chatter, the previous experiments have proved this point. This study investigated the cutting chatter at the corner during the circular milling process by using a new polar coordinates geometric model. The mechanics of the process are modeled by considering size effect, while regarding the ploughing effect as a new important factor. The chatter of circular milling is verified and tested both at the reduced speed of spindle and reduced feed per teeth. Acceleration of spindle speed can cause chatter and ploughing effect for enhanced chatter stability because the changed process damping occured at small size.

Through fuzzy membership function, the fuzzy algorithm of image boundary detection based on power function can transform ordinary space into generalized fuzzy space. However, the algorithm has a large amount of operation and slow speed, and it will lose the boundary information of some low gray value in the image, thus the quality of the image boundary detection is poor. Therefore, a bilinear fast enhancement fuzzy algorithm for image boundary detection is proposed in this paper. Based on the defined generalized fuzzy set GFS and the generalized fuzzy operator LGFO, the linear left half trapezoid fuzzy distribution function is first used as the generalized membership transformation of the image.The general space of grayscale image is transformed into generalized fuzzy space, and then boundary detection algorithm based on bilinear fast image enhancement is used to transform color image into gray scale and transform to generalized fuzzy set. The generalized fuzzy operator LGFO is used to enhance the contrast of the generalized fuzzy sets. The generalized fuzzy set after the enhancement is transformed into an ordinary fuzzy subset. The boundary extraction is carried out for the ordinary fuzzy subset after processing, and the image boundary detection is realized. The experimental results show that the proposed algorithm greatly improves the speed and quality of image boundary detection.

In this paper, the changes of suspended concentration of particles with different particle sizes were studied in different speed and height. Indoor human activities can cause resuspension of particles. In this paper, a miniature room model is adopted, using electric draw stem to control the forward movement of the footstep and the upper and lower motion to study the influence of different footstep motions in the small space on the resuspension of particles. There are three kinds of speeds, including 0.05 m/s, 0.1 m/s, 0.15 m/s, and three kinds of lifting height, including 0.06 m, 0.12 m, 0.18 m. Suspended ratio r _p value in the range of 0–10^–6, when lifting heights are 0.06 m or 0.12 m, and speed is increasing, the particles suspension rate continuous growth, when lifting height is 0.18 m, particulate suspension rate presents the trend of increased and then reduced. When speeds are 0.05 m/s or 0.1 m/s, with lifting height increasing, the particles suspension rate increase. When speeds is 0.15 m/s, with lifting height increasing, particulate matter suspended the first rise and fall, including PM10 suspension rate has been a declining trend.

Traditional MESH-based high-voltage transmission line condition data acquisition and communication systems collect all types of transmission line related condition data using the wireless monitoring device, and transmit condition data to the information center point through the wireless mesh node by wireless multi-hopping. The traditional methods are easy to generate lagging response and the high energy consumption, which result in high system condition data loss rate and low comprehensive utilization value. Therefore, smart distribution network transmission line condition data acquisition and communication system is designed based on the overall structure of the system, including data acquisition module, data communication module, transmission line condition monitoring communication module, and wireless transmission module of transmission line condition data. Tension, ambient temperature, solar radiation temperature, and wind direction signals collected by the data acquisition module are transmitted to the data communication module. After the collected signals are packaged to wake up G24, and establish a good GPRS network connection for data transmission. The transmission line condition monitoring communication module adopts an embedded operating system, which can combine its own functions to cut down the operating system, to speed up the response to the interruption event. The MCU in the transmission line condition data acquisition and communication system of smart distribution network realizes the command control of G24 by sending AT commands through the UART port. Data exchange between terminal and master station and addition of data items ensure the normal and smooth data communication. The experimental results show that the designed system can significantly reduce the loss rate of transmission line condition data and improve the system’s comprehensive utilization capability.

In the past, intelligent system often realized reasoning operation by interpolation method for one-dimensional sparse rule base, and could not analyze fuzzy reasoning of multi-dimensional sparse rule condition, which greatly improved the error and volatility of reasoning results. Therefore, a multiple multi-dimensional fuzzy reasoning algorithm based on CMAC neural network weighting is proposed. Through the CMAC neural network, the influence weight of each variable is extracted. CMAC neural network is applied to train weights of multi-dimensional variables in multiple multi-dimensional fuzzy reasoning rules, and local correction weights are made, so that the weights of each modification are very few. After fast learning, the influence weights of the multi-dimensional variables on the reasoning result are obtained. A multiple multi-dimensional fuzzy reasoning algorithm based on CMAC neural network weighting is applied to input the given neighboring rules into CMAC neural network, and the weights of the variables in the neighboring rules are obtained. According to the linear interpolation and the sequence of interpolation cardinal numbers, the influence weights of the variables in the observation value are determined. According to the linear interpolation reasoning method, a new fuzzy rule is constructed. Based on the approximation between the new fuzzy rules and the observed values, the similarity between the predicted values and the new fuzzy rules is constructed. The result of fuzzy inference is obtained according to the similarity. The experimental results show that the proposed algorithm has high reasoning precision and stability, and the practical application effect is good.

The traditional time series data clustering for landslide displacement prediction is based on Euclidean distance measure. The time series data is clustered by distance calculation of two vectors. The correlation between components is not considered. The multiple components with single feature will interfere with the clustering results, and the accuracy of clustering results is greatly reduced. To address this problem, an intelligent clustering algorithm for time series data in landslide displacement prediction based on nonlinear dynamic time bending is proposed in this paper. By reconstructing the phase space of the landslide displacement time series, the phase space transposed matrix is obtained as the time series reconstruction matrix. After embedding dimension processing, the time series of landslide displacement is predicted by SVM data mining model. Dynamic time warping calculation is based on the correlation of time series sequence and the components. The local optimal solution is obtained by recursive search, and the whole curve path is obtained. Clustering calculation of time series data set is carried out by using hierarchical clustering algorithm according to bending path. The intelligent clustering results of time series data in landslide displacement prediction is obtained. Experimental results show that the proposed algorithm has better clustering effect and higher clustering accuracy.

The traditional fault detection system of complex electronic equipment based on image analysis theory only analyzes the image characteristics of complex electronic equipment for artificial intelligent fault diagnosis. It cannot deal with the system diagnosis problem of qualitative fault data and has the problems of low accuracy and long time consuming of fault detection. To address these problems, an artificial intelligent fault diagnosis system of complex electronic equipment based on BP neural network is designed in this paper. BP neural network model for artificial intelligent fault diagnosis of complex electronic equipment is built based on system overall structure. The structure of BP neural network and learning algorithm is determined according to the actual fault problem. Learning and training of BP neural network are carried out by using sample data of fault. Artificial intelligent fault diagnosis algorithm of complex electronic equipment based on BP neural network and qualitative fault data is used, which combines the BP neural network and qualitative fault data. The preprocessing method is applied to quantify the fault data. Fault diagnosis is achieved by BP neural network technology. The system database and the implementation process of the BP neural network are designed. Experimental results show that the designed system can significantly improve the accuracy of fault detection of complex electronic equipment, improve the effect of fault detection, and reduce the time consuming of fault detection.

The traditional data query algorithm based on clustering strategy library ignores the association features of social network data, characteristic data acquisition exist a large number of redundant features and frequent relationship among features is low, resulting in the social network data query efficiency and the accuracy is poor, so a fast query algorithm for social network data based on fuzzy degree function based on association features is proposed, it is based on Apriori algorithm for data association feature mining of social network to obtain the maximum frequent association feature set; for association feature preprocessing, it reduce the maximum frequent association feature set by feature dimension reduction and de redundancy algorithm, to obtain better social network maximal frequent associated feature set; when using fuzzy function to query social network data quickly, it uses data of a single gene ambiguity function to build a fast data query diagram, input the best frequent feature set of social network, and output the query results of social network data with the highest priority. The experimental results show that the proposed algorithm has the advantages of high efficiency and high accuracy in social network data query.

In traditional multi-infeed AC/DC transmission systems, decentralized and coordinated controllers are usually used to achieve AC/DC transmission control without considering the state and output of the system. Therefore, it cannot reasonably regulate the state of output based on the demand of multi-target control, which leads to poor control effect and weak adaptability. Therefore, a multi- sliding mode adaptive fuzzy controller is designed for the multi-infeed AC/DC transmission system. When the controller is designed, the state equation and the output equation of the multi-infeed AC/DC transmission system are considered. Based on the three different design parameters and the multi-sliding mode surface of the thickness of the saturated layer, the adaptive controller based on multi-sliding mode is designed. This controller is used to set up the dynamic characteristics of some observable measurements in the multi-infeed AC/DC transmission system. Based on the setting results, the results of the comprehensive decision of the system are obtained by the adaptive fuzzy controller. According to the results of a comprehensive decision, the disturbance degree of feedback point is judged. Through the fuzzy algorithm based onthe second component function, the weighting matrices of the output feedback gain matrix are modified, so that the optimal control feedback gain is variable gain, to ensure that the control effect of the system meet the multi-objective control of engineering, and realizing the multi-sliding mode adaptive fuzzy controller ofthe multi-infeed AC/DC transmission system. The experimental results show that the designed multi-sliding mode adaptive fuzzy controller has good control effect on multi-infeed AC/DC transmission system, and has strong adaptability, and it can improve the dynamic performance of the system.

For the current shipborne anti-collision sounding system, when multiple detection signals are transmitted, it is difficult to avoid collision with each other. In addition, there are shortcomings of insufficient energy consumption, low sounding precision, and slow response. To address this problem, a shipborne anti-collision sounding system based on ACT algorithm and Internet of things is designed in this paper. With ZigBee wireless communication technology and embedded technology, the function of anti-collision and sounding is realized by modular design. For the problem of the signal collision of each node of the wireless network, the ACT algorithm is introduced for system optimization to prevent signals from conflict when receiving, and ensure the synchronization and accuracy of the whole system. STM32F103 VET6 embedded chip is used as the control core of the system. CC2530 is responsible for the implementation of ZigBee wireless network communication. Experimental results show that the designed system has the advantages of low energy consumption, fast response, and high precision.

The current resource integration algorithm lacks the consideration of users’ needs, which can cause high violation of service-level agreement and poor data quality after integration. It affects the energy consumption and service quality of data center. To address this problem, a financial resource integration algorithm of virtual enterprise based on improved artificial bee colony in big data environment is proposed in this paper. The improved PageRank algorithm is used to extract the financial resource of virtual enterprise. The extracted resource is transformed. From the unified data resource centralization after transformation, service resources that satisfy users’ needs and constraints are selected and combined. An improved artificial bee colony algorithm is applied to dynamically integrate service resources for different needs. Experimental results show that the proposed algorithm can effectively reduce the energy consumption of the data center, improve the data quality and user service satisfaction. The advantages and feasibility of the proposed algorithm in the integration of virtual enterprise financial resources under the big data environment are verified.

At present, weak signal detection algorithm detects parallel weak signals under Gauss noise interference, which has the problems of low denoising performance, inaccurate detection results and low detection efficiency. To this end, a parallel weak signal detection algorithm based on Gauss noise interference is proposed. Wavelet transform is applied to detect weak signals with Gauss noise by wavelet threshold denoising method, and the weak signal is denoised based on the set threshold function and threshold. The EMD decomposition method is used to decompose the weak signal after denoising, and the weak signal is filtered through the imitation Cauchy convergence filter stopping criterion to extract the characteristics of weak signal. The weak signal detection under the interference of Gauss noise is completed based on the Doffing oscillator and the characteristic of the weak signal extracted. The experimental results show that the proposed method has high signal-to-noise ratio, accurate detection of weak signal, and the time of detection is below 8 s. The results show that the proposed method has high denoising performance, high detection accuracy and high detection efficiency.

The current traffic evacuation path control system has high risk coefficient and path congestion, and low efficiency and system error coefficient. For this problem, a fuzzy control system of traffic evacuation path based on genetic method is proposed and designed in this paper. The data server, geographic information server, computing server, and application server are used to construct the system framework. The logical structure is divided into data source layer, data access layer, scheduling layer, computing model layer, and application interface layer. The function module is mainly composed of static data management module, emergency management module, dynamic data interface module, dynamic traffic assignment module, guidance information release module, and user management module. The system hardware is designed by using the logical structure in combination with the function module. In the system software, the coordinator-operator mode is introduced into the real-time computing operation mechanism. The interaction of the coordinator and the operator is to implement the user specified operational function. Traffic data is forecast by autoregressive model. It is substituted into the objective function of intelligent traffic evacuation and the genetic method is used to solve the objective function. At last, fuzzy control result of optimal traffic evacuation path is obtained. Experimental results show that the average risk coefficient in the evacuation process is about 0.27, the average time consuming is 0.3 h, and the congestion of the evacuation path is relatively low, so the fault tolerance coefficient of the system can be controlled within a reasonable range. The system has a good overall operation effect and is feasible.

When the network resource information is scheduled with the current algorithm, the execution time of the resource scheduling task cannot be improved. The utilization of network resources is reduced in the case of the heavy scheduling task. To address this problem, a network resource information scheduling based on non-convex function optimization algorithm is proposed in this paper. The network resource is modeled as a non-convex function. The execution interval of task is divided into subspaces of multiple units. Task density is introduced into network resource scheduling model. In this model, computing resources and storage resources of the network are considered. Ant colony particle swarm optimization algorithm is used for scheduling with the built network resource scheduling model. The initial solution is obtained by initial search with the particle swarm algorithm. Then the initial solution is transformed into the initial pheromone distribution of the ant colony. The resource information is searched by using ant colony algorithm until the optimal solution is found, so as to achieve network resource information scheduling. Experimental results show that the proposed algorithm can reduce the execution time of task and improve the utilization rate of network resource information.

The current method does not take full account of multiple hybrid tasks in fuzzy control system and the problems of the balance between the requirements of the system reliability and the maximum completion time of the scheduling and the high cost of resource occupancy. To address these problems, a fault-tolerant scheduling algorithm of multiple hybrid tasks based on supporting multilevel criticality is proposed in this paper. The models of fuzzy control system and multiple hybrid tasks are built respectively. Multiple hybrid tasks in the model are divided into periodic task and non-periodic task, task with fault-tolerant requirement and with no fault tolerance requirement. According to the priority of each task and the relationship of the response time and time limit of each task, whether to start its supplementation task and the fault tolerance priority allocation is determined. The worst response time of each task in the model is calculated and fault-tolerant scheduling for multiple hybrid tasks is realized. Experimental results show that the proposed algorithm can further reduce the maximum completion time of task scheduling on the basis of satisfying the reliability requirements of the fuzzy control system. The cost of computer resource occupancy and the overhead of communication resources have been greatly reduced.

Currently, the fuzzy clustering algorithm of customer group behavior data had the poor effect of data clustering. Therefore, a fuzzy clustering algorithm of Internet customer group behavior data based on fuzzy C means clustering was proposed. By constructing the feature vector of behavior data, this algorithm realized the feature extraction of behavior data, and then it used the nearest neighbor chain to extract data features for the reduction and sample equilibrium. The classification of Internet customer group behavior data was achieved. According to the fuzzy C means clustering algorithm, the interval estimation of classification results of behavior data was carried out. Meanwhile, the membership values of each data sample were updated. Finally, the classification interval was adjusted. Thus, the fuzzy clustering of Internet user group behavior data was completed. Experiment results show that the proposed algorithm has high accuracy in data classification, short execution time in clustering, less memory footprint and low computational complexity, which improves clustering effect.

The traditional algorithm does not take account of the authentication problem of terminal and server. It has poor security, heavy computation of encryption or decryption, and low efficiency. To address these problems, a new intelligent encryption algorithm for network communication parallel data of information release terminal is proposed in this paper. After users’ registration, the registered ID, user password, and two random numbers are entered. The first authentication data is obtained by calculating and then transferred through a secure channel to the server for the first authentication. After the success of the identity authentication in the information release terminal and the server, the user of the information release terminal obtains the release authority. Self-inverse key matrix is generated with MapReduce parallel mechanism. Source release information data file is divided into blocks in the communication process, and each block is encrypted with key matrix. After dividing the plaintext matrix and the key matrix, the plaintext is encrypted according to the Hill encryption principle. After obtaining the ciphertext and key matrix, the plaintext is decrypted according to the principle of Hill decryption principle. Experimental results show that the proposed algorithm has high security and efficiency.

The key algorithm of the traditional system is aimed at the minimum of a certain factor, but does not consider the uncertain conditions and various modes of transportation, and the result of the scheduling is not excellent. To this end, a new fuzzy scheduling optimization system based on ant colony algorithm for multi-objective transportation path is designed. Based on the GPS module, a fuzzy scheduling optimization system based on ant colony algorithm for multi-objective transportation path is designed, and the overall structure of the system is given. The scheduling optimization problem of freight transport lines is described, and the volume of demand, the total volume of delivery and the remaining number of vehicles are made fuzzy processing. The goal is to minimize the total time of the advance or tardiness of the transportation and the total cost, so that the fuzzy scheduling model of transportation path is built. According to the principle of ant colony algorithm, the built multi-objective model will be transformed into a single objective model, and combined with the objective function, the index heuristic information and the performance of ant colony algorithm are set, and the optimal solution of that the deviation is minimum with the ideal solution is calculated by using ant colony algorithm, so as to achieve the multi-objective transportation path scheduling. The experimental results show that the total transportation distance of the designed system is short, the total cost is low, and the goods can be delivered in time.

In traditional clustering algorithm, the number of classes must be set beforehand and it is difficult in setting parameters. For uncertain environment, the precision of clustering is low and the scalability is poor. To address these problems, a new fuzzy clustering algorithm for interactive multi-sensor probabilistic data is proposed in this paper. The optimal hierarchical fusion algorithm with no prior knowledge is used to sort the sensors used for fusion according to the quality and the importance of information. The fusion of the first layer is the fusion of probabilistic data of two interactive sensors. The fusion of the second layer is the fusion of the fusion results of the first layer and the probability data of the other sensor to obtain the final fusion results. On this basis, the fuzzy C mean clustering algorithm is proposed to cluster the interactive multi-sensor probabilistic data. Wireless sensor networks are dynamic, and it is difficult to determine the number of classes beforehand. Subtraction clustering algorithm is used to adaptively determine the number of classes and the initial cluster center though building mountain function as the data density index. Thus, the convergence speed of the algorithm is accelerated and the local optimum is avoided. Experimental results show that the proposed algorithm has high clustering accuracy and good scalability.

The traditional algorithms reduce the recognition accuracy because of the influence of the fluctuation of the camera position during the walking of the robot. For this reason, a new intelligent recognition algorithm for color vision image position of soccer robot is proposed. The structure of the soccer robot vision system is designed. The panoramic visual sensor VS-C450 N-RC and the image acquisition device based on the IEEE 1394 standard are used to obtain color visual images, and the acquired distorted images are processed. Comparing color patches, an effective color patches scheme is proposed based on practice. RGB space is converted into HIS space, color, saturation and brightness are used to represent colors. According to the principle of contour extraction, an effective color patch extraction and recognition algorithm is proposed to match the robots on the actual field so as to obtain information such as the position of the soccer robot. The pose information of the robot is represented by the pose information of the color patches, and the position of the color visual image of the soccer robot is determined. Experimental results show that the proposed algorithm has high recognition accuracy.

There are many local optimums for the non-convex function. The traditional algorithm is easy to fall into the local optimum and cannot obtain the optimal solution of non-convex function. To address this problem, a new intelligent optimization algorithm for non-convex function based on genetic algorithm is proposed in this paper. A proximal point sequence is obtained by using the idea of proximal point algorithm. Two simple and easily solved non-convex function subproblems are constructed by convexity technique, cutting plane method, and alternating linearization method. The basic operation process of genetic algorithm is analyzed. The combination selection operator, the initial population molding, the cross probability and the mutation probability are improved to ensure the global optimum. The processing result of the non-convex function is taken as the objective function. The mapping relationship between the fitness function and the objective function is constructed. Intelligent optimization of non-convex function is achieved by optimized genetic algorithm. Experimental results show that the proposed algorithm can obtain the global optimal solution of the non-convex function, and the optimization performance is better.

The traditional abnormal location algorithm ignores the uncertainty of wireless sensor networks, which is not suitable for practical applications, and has low accuracy of location. To address this problem, a new fuzzy weighted location algorithm for abnormal target in wireless sensor networks is proposed in this paper. For the characteristics of spatiotemporal association and association of non-spatiotemporal attribute, the abnormal target is identified by multi-attribute association algorithm. Considering that Bayesian networks can effectively express dependencies between variables, Bayesian networks are used to establish the dependency model of non-spatiotemporal attribute. The dependence structure of non-spatiotemporal attributes is obtained by structure learning. The parameter learning of each node of the network structure is carried out to obtain the conditional probability table. The confidence degree of attribute association is used to judge whether the attribute association pattern of the point to be detected is an abnormal pattern. The abnormal target location problem is described. The coordinates of sensor node with abnormal target are identified by the weighted location algorithm. The circles with the centers of three points not on a straight line and the diameter of the signal intensity indicator distance are drawn to obtain the abnormal target position. The weights for weighted location are obtained by fuzzy algorithm. Experimental results show that the proposed algorithm has high accuracy of location.

When the current algorithm encrypts cloud computing user behavior data, it cannot effectively resist external attacks. When there are many feature data, the encryption performance is poor. To solve this problem, a secondary encryption algorithm for data based on coupled control game mechanism is proposed. The piecewise linear chaotic maps and Fibonacci sequence perturbations are utilized to obtain pseudo-random numbers and improve the key’s mapping space, and can effectively defend against threats and attacks. Based on the piecewise linear chaotic map encryption algorithm, the discrete chaotic integrated map encryption algorithm based on the coupled control game mechanism is adopted. After group-based encryption, the user behavior feature data is mapped into the encryption source-optimization evolution structure, and encrypted mapping is performed piecewisely. The encrypted data is used as the seed-derived set in the coupled control game mechanism, and the competition mechanism is adopted to perform the second discrete chaotic optimization on the encrypted data. The encrypted data ciphertext with the lowest chaotic discrete coefficient and the best game performance is selected as the output results of the coupled control game. Experimental results show that the proposed algorithm can effectively improve the encryption performance and improve the operation security of cloud computing network.

In order to reduce energy consumption of network nodes, it is necessary to design a monitoring system for energy consumption of network nodes. When the current network nodes energy consumption monitoring system is used to monitor and control energy consumption of nodes, there are problems of low monitoring efficiency and poor energy saving. A fuzzy system design method is proposed in this paper for energy consumption monitoring of wireless sensor network nodes. The energy consumption monitoring daemon on sensor nodes, the energy consumption monitoring program on gateway nodes and the control program on the host PC in the fuzzy system for energy consumption monitoring of wireless sensor network nodes are designed and analyzed. The change of energy in nodes is regarded as an important condition for selecting work or sleep. The fuzzy power control algorithm is used to control the node sleep mechanism and the node wake-up mechanism in the wireless sensor network to complete the design of the fuzzy system for energy consumption monitoring of wireless sensor network nodes. The experimental results show that the proposed method has high monitoring efficiency and energy saving performance.

At present, special domain image encryption and compression algorithms have problems such as poor encryption and image compression, long time consuming of encryption and compression, and no guarantee of image compression quality. In this regard, this paper proposes an encryption and compression algorithm for spatial domain image selection based on hyperchaotic system. The hyperchaotic Chen system is selected to decompose the dynamics of the hyperchaotic system. The decomposition result is replaced by image scrambling, and the chaotic sequence output from the hyperchaotic Chen system is preprocessed. The two groups of sequences are used to complete the image scrambling so that the image is encrypted for the first time. The discrete cosine basis is applied to make sparse representation of the original image after scrambling. The partial Hadamard matrix, which is controlled by the Logistic chaotic map, is used as the measurement matrix in the compressed sensing, and the two-dimensional projection measurement of the image is done to complete the image compression. The hyperchaotic Chen system is used to cyclically shift the projection results to change the pixel value of the image, and the final cipher image is obtained. The experimental results show that the algorithm anti-attack coefficient is 0.99, the average compression time is 7 s, and the compressed image has high resolution and strong confidentiality. The proposed algorithm is superior to the current algorithm in security and other performance, and can provide support for this field.

Current methods lack generality and self-adaptation, resulting in large difference in throughput between networks, high packet loss rate, and long page response time. To solve these problems, an online resource sharing system design method is proposed based on fuzzy control for enterprise networks. A four-layer online resource balanced sharing system is designed, including three parts: management plane, control plane and forwarding plane. The fuzzy control method is adopted to design an online resource balanced sharing system controller for enterprise networks, and an adaptive method is used to adjust the variable parameters of the fuzzy controller and calculate the controlled quantity of system server. Through the obtained controlled quantity, the amount of requests should be distributed to each server is calculated. With these requests as the standard, online resources are modified to achieve the balanced sharing of online resources of enterprise networks. Experimental results show that the proposed method can effectively reduce the packet loss rate, reduce the load difference between networks, and better realize the balanced sharing of resources between networks in the system.

Due to the poor image quality and complex enhancement process in the current image contrast fuzzy enhancement algorithm, a multilevel image contrast fuzzy enhancement algorithm in multimedia network based on homogeneity measurement was put forward. This algorithm used the minimum fuzzy entropy to detect noise in multilevel image and remove noise through improved Shannon entropy, so as to achieve restoration of multilevel image. According to the membership degree of restored image, the local feature of image was determined to realize and the homogeneity expression of image. Then, the nonlinear transformation was introduced to optimize the image homogeneity. Thus, the multilevel image contrast fuzzy enhancement in multimedia network was realized. Experimental results show that the proposed algorithm can effectively guarantee the image quality after the contrast enhancement and reduce the computational complexity.

At present, network abnormal data detection algorithm has low efficiency and accuracy, and the false negative rate is very high. Therefore, the location accuracy of abnormal data is not ideal. An intelligent detection method of network abnormal data based on space-time nearest neighbor and likelihood ratio test was proposed. The time interval adjustment algorithm based on the change smoothness judgement strategy and the adaptive data change rule was used to adaptively adjust data acquisition time interval according to network performance parameters and achieve network data acquisition. The grid partition was used to convert source data points into appropriate granularity to complete the data preprocessing. Based on the maximum a posteriori probability, we selected the measured values of data to be detected at several moments as the time nearest neighbor points. The abnormal degree of data was quantified. Meanwhile, the likelihood ratio test was used to determine whether the data was abnormal. The abnormal alarm information was aggregated. All alarm information was arranged according to the size. The two alarm times with maximum difference value are used as the boundary, and the multi-point dislocation combined abnormal location method was used to locate the detection result. Experiment results show that the average detection time of proposed algorithm is 0.21 s. The average false negative rate is 2.8%. The accuracy of abnormal data detection and the positioning accuracy are high. The proposed algorithm can detect network abnormal data efficiently, which lays a foundation for the development of this field.

Using the current recognition system to recognize dynamic scene cannot effectively speed up the target recognition. When target recognition increases, the accuracy of target recognition is relatively low. In order to solve this problem, a target recognition system of dynamic scene based on DSP was designed. Combined with the idea of DSP system design, the design process and composition of target recognition system was expounded. The recognition algorithm based on spatial-temporal condition information was used to realize the designed recognition system. By introducing the visual attention mechanism, the spatial-temporal domain model based on visual significance was built. The pixel neighborhood weighted condition information was used as classification features to enhance the linear separability for target and background and improve the recognition accuracy of dynamic scene moving target. Finally, combined with image block modeling strategy, the efficient and real-time recognition of moving target in dynamic scene was realized. Experimental results show that the proposed target recognition system can effectively improve the accuracy of target recognition.

Currently, the method was not applicable to the requirement of feature extraction of different types of grayscale images, resulting in the feature extraction results with low accuracy, long time consumption, low clarity and poor flexibility. In this article, a method of extracting feature of gray image based on fuzzy clustering algorithm was proposed. The grayscale, the median filtering, the edge detection and mathematical morphology processing were carried out for the color image of CCD camera collected by acquisition card. Then, sample feature object of target object gray level image and object of target feature were obtained. The similarity between sample feature object of target object gray level image and object of target feature was obtained through calculation. Moreover, the feature conforming to the set threshold was selected. Meanwhile, the grayscale image feature extraction results with different requirements were obtained through adjusting gray level image matrix and similarity parameters. From comparison and analysis of experimental result, we can see that the correctness, effectiveness and flexibility of proposed method are proved for different types of gray level image feature extraction. The extraction result has high definition and short running time.

At present, obstacle avoidance systems of robots cannot avoid obstacles with high efficiency, high stability and high precision. Thus, a self-adaptive obstacle avoidance fuzzy system for mobile robots based on ultrasonic range measurement is proposed and designed. An upper computer, a motor drive module, an ultrasonic ranging sensor module, an infrared sensor module, an electronic compass module, a communication module, a power supply module and peripheral circuits are connected to form the system hardware. After the system is initialized, the robot starts to work according to instructions of the upper computer and enters the self-adaptive obstacle avoidance subroutine. In the subroutine, the ultrasonic sensor scans the infrared sensor output at the corresponding position. After receiving reflection information of the ultrasonic wave, the counter is stopped, and reflection time of the ultrasonic wave is simply calculated and cached into the buffer, so as to determine whether there is an obstacle in front, and the result is fed back to the upper computer through the RS485 bus. If there is an obstacle, then the interrupt program will be called, and the electronic compass program is utilized to determine the direction to avoid the obstacle; if there is no obstacle, the robot will continue to move following instructions of the upper computer to complete the system software design. Experiments show that the average time to avoid obstacles using this system is 0.40 s, and the obstacle avoidance accuracy is high and the stability is good. Under the data comparison and analysis, the proposed system is obviously superior to current systems in the time-consuming and accuracy of obstacle avoidance, and has great reliability.

When using the current authentication code recognition system to identify the character authentication code, there are the problems of low integrity and low recognition accuracy. In this regard, a design method of artificial intelligence recognition system for cracking character type authentication code is proposed in this paper. The denoising algorithm based on the connected domain is used to remove the noise in the character type authentication code, and the character authentication code after the denoising is normalized. The feature extraction module is used to extract color moments, color correlation diagrams and LBP texture features of character authentication codes, and complete the feature extraction of character authentication codes. The similarity matching module is used to match the characters of the character authentication code. In the recognition module, the character authentication code is classified by the classification algorithm based on multi-feature SVM, and the recognition of the character authentication code is completed. The experimental results show that the proposed method has high information integrity and high recognition accuracy.

In order to solve the problem of storing large amounts of data in the wireless sensor network space, the design method of data storage system of wireless sensor network space should be studied. When the current method is used to design a data storage system for wireless sensor network space, there are problems of low storage efficiency and low data storage quality. We propose a design method of data storage system for wireless sensor network space based on fuzzy control. The C/S mode is used to design the client module, transmission module and server module in the data storage system of wireless sensor network space according to the concept of level and modularity. The flow control method based on module control is used to forward or discard data in the network space to complete the design of data storage system of wireless sensor network space. Experimental results show that the proposed method has high data transmission rate and high accuracy of the decision function. It is verified that the proposed method has extraordinary storage efficiency and great data storage quality.

The traditional re-recognition algorithm needs to find or design the characteristics with better robustness to light, scale, and deformation. The quality of the feature directly affects the recognition performance and the uncertainty is high. In addition, it needs supervision and training, and has the higher training time and space complexity. To address this problem, a new intelligent re-recognition algorithm for specific ship target in busy waters under the actual scene is proposed in this paper. Combining the existing feature extraction model and graph model, the graph structure is used to describe the identity relationship between the samples. Two points with side connections have the same identity label. Then the multi-layer graph structure is built. After obtaining the block of the divided area, the similarity between the two samples of the link is calculated and the weight of the edge is obtained. Labeled samples are built according to the selected initial area. The energy loss of the graph model is obtained by estimating the pixel likelihood energy function with different labels of pixels and areas. A graph structure is obtained by minimizing the energy loss, which is the intelligent recognition result of specific ship target. For the large-scale data, the problem of incremental processing is solved by incremental maintenance. Experimental results show that the proposed algorithm has high recognition precision.

Aiming at the problem that the active queue management algorithm can not explicitly control the queue length and the relationship between throughput and delay, a fuzzy information control algorithm based on Active Queue Management for digital substation communication network congestion is proposed. After packet grouping is entered into the router cache queue, the packets that first enter the cache area of the router are preprocessed, and the data packet is processed fairly by using the geometric distribution function. The combination of Smith predictive control and adaptive fuzzy control is used to compensate the network delay of packets, eliminate the negative impact of time delay on active queue jitter and delay jitter, and control congestion according to fuzzy rules intelligently. The experimental results showed that the proposed algorithm can maintain smaller queue oscillations, especially when the network conditions change. It can effectively eliminate the impact of time delay on queue jitter and delay jitter, and improve the overall performance of the network.

At present, the intelligent control system of robots is closed, which has the disadvantages of poor fault tolerance, unstable operation and low positioning accuracy. Aiming at these deficiencies, a Petri net model of the intelligent control system for open architecture robots based on PMAC is designed. Starting from the kinematics of robots, the forward and inverse kinematics model of open architecture robots are established according to DH method; then the trajectory planning is performed from Cartesian space linear interpolation algorithm and circular interpolation algorithm respectively, and the basic function of robot path planning is constructed. Finally, a PMAC-based open architecture robot intelligent control system is established. The control system adopts dual-microcomputer hierarchical control mode and modular structure design. Real-time communication between the upper computer and the lower computer can be realized by calling the Pcomm32 dynamic link library; based on the robot’s forward and inverse kinematics model and trajectory interpolation algorithm, the modular control software for the robot system is developed. The control software realizes functions such as security check, parameters setting, kinematics analysis, and teaching reproduction. Combined with the principle of hierarchical Petri nets, various modules of open architecture robot control system based on PMAC are modeled. Experiments show that the designed system runs smoothly, has high positioning accuracy, good openness and scalability.

The traditional global convergence optimization algorithm is prone to premature convergence and slow convergence in the face of complex non-convex function. To this end, a new intelligent optimization algorithm based on improved fuzzy algorithm for global convergence of non-convex function is proposed. The general model of the optimal problem is designed and the general model of non-convex function is established. The genetic algorithm is used to optimize the non-convex function, and the global convergence of the current non-convex function is analyzed. It is found that the global convergence of non-convex function is actually based on the optimization of crossover probability and mutation probability to decide the convergence of genetic algorithm, so as to improve the global convergence. A fuzzy controller is designed, which determines the input and output variables and their membership functions, establishes fuzzy rules and anti-fuzzing process to control the crossover rate. The fuzzy control of mutation rate is similar to the crossover rate, but the difference is that the new fuzzy control rule is needed. The experimental results show that the proposed algorithm can effectively optimize the global convergence of non-convex function.

The current e-business information retrieval system ignores the threat of distributed denial of service attack from computer network, which results in low retrieval recall, accuracy, and efficiency. For this problem, a design method of fuzzy retrieval system for e-business information based on block chain technology is proposed in this paper. A fuzzy retrieval system of e-business information is designed, which includes three layers of client, application server and data server. The construction rules of the rule library used by the system are researched. The keyword expansion method of association word list, compound word list and synonymous word list is given. The dynamic knowledge base of the system is built and updated in real-time. Security control of e-business information documents in the system is implemented by using anti-attack performance of block chain technology. Experimental results show that the proposed method improves the recall, accuracy and average accuracy of the system and the retrieval efficiency is high.

In the NFV network, the availability of resource scheduling can be transformed to the existence of the fractional factor in the corresponding NFV network graph. Researching on the existence of special fractional factors in network structure can help to construct the NFV network with the efficient application of resources. The concept of fractional (g, f, n′, m)-critical deleted graph is corresponding to the structure of NFV network where certain sites and channels are occupied in some period of time. In this paper, we present several sharp degree conditions for a graph to be an all fractional (g, f, n′, m)-critical deleted graph. These results extend the corresponding conclusions raised in Gao et al. [9].

Network function virtualization (NFV) can be regarded as the latest trick development in the provisioning of network service. Software programs are running on virtual machines and industry standard servers to replace the traditional hardware middleboxes, and thus lead to flexibility, service agility and cost decreasing. A basic problem in NFV service chain provisioning is the ability of resource scheduling which equals to the existence of fractional factor. The concept of all fractional (g, f, n′, m)-critical deleted graph is the extension of fractional (g, f, n′, m)-critical deleted graph. In this paper, we consider the resource scheduling problem in NFV networks using graph theory, and an independent set degree condition and an independent set neighborhood union condition for all fractional (g, f, n′, m)-critical deleted graphs are determined. Furthermore, we show that the result are tight on independent set condition.

As a data utility and aided tool, ontology has been widely used in many areas of the computer. Owing to its great efficiency, ontologies have also been introduced into various engineering disciplines. In this paper, we present the fundamental ideas of how to deal with similarity measuring problem in ontology learning algorithms. The mathematical basis of ontology learning algorithms is also introduced from a statistical learning theory point of view. Finally, we present two ontology learning algorithms in multi-dividing setting and ontology sparse vector learning setting, respectively.

The core problem of Ontology mapping and various kinds of ontology engineering applications is the calculation of similarity between concepts in ontology. From the machine learning point of view, by means of learning the sample set, it gets the optimal ontology similarity calculation function, so that each pair of concepts mapped to a positive real number, thus reflected the similarities between concepts. After representing the ontology using graph, the goal of ontology learning is to obtain a real-valued function, which maps each pair of vertices into real axes and uses distances to reflect the similarities between concepts of vertices. In this paper, we present an ontology learning algorithm in view of ontology geometry distance computation and deep learning tricks. The iteration procedure is designed and the experiments show the effectiveness of given ontology algorithm.

From the mathematical point of view, the goal of ontology learning is to obtain the dimensionality function f : ℝ p → ℝ , and the p-dimensional vector corresponding to the ontology vertex is mapped into one-dimensional real number. In the background of big data applications, the ontology concept corresponds to the high complexity of information, and thus sparse tricks are used in ontology learning algorithm. Through the ontology sparse vector learning, the ontology function f is obtained via ontology sparse vector β, and then applied to ontology similarity computation and ontology mapping. In this paper, the ontology optimization strategy is obtained by coordinate descent and dual optimization, and the optimal solution is obtained by iterative procedure. Furthermore, the greedy method and active sets are applied in the iterative process. Two experiments are presented where we will apply our algorithm to plant science for ontology similarity measuring and to mathematics ontologies for ontology mapping, respectively. The experimental data show that our primal dual based ontology sparse vector learning algorithm has high efficiency.

In this paper, we explore the fractal dimension of Cone Beam Computed Tomography images to analyze the trabecular bone structure of healthy subjects. That quantity, computed throughout three distinct approaches, provided us accurate values of normality concerning the radiographic density of this kind of bones and will allow us to establish comparisons with respect to the fractal dimension from patients with different pathologies that may affect the density of trabecular bones.

Identifying the number of niches in multimodal optimization is vital to enhancement of efficiency of algorithms. This paper presents a genetic algorithm (GA)-based clustering method for multiple optimal determinations. The approach uses self-organizing map (SOM) neural networks to detect clusters in GA population. After clustering all population and recognizing the number of niches, the phenotypic space is partitioned. Within each partition, a simple GA is independently running to evolve to the actual optima. Before the SOM starts, we allow GA to run several generations until the borders of clusters are identified. Our proposed algorithm is easy to implement, and does not require any prior knowledge about the fitness function. The algorithm was tested for seven multimodal functions and four constrained engineering optimization functions, and the results have been compared with the other related algorithms based on three performance criteria. We found that the present algorithm has acceptable diversification and function evaluation number.

The main goal of this paper is to study ordered semihypergroups in the framework of uni-soft left (right) hyperideals. The notions of uni-soft semihypergroups, uni-soft left (right) hyperideals are introduced, and the related properties are investigated. The concept of exclusive set is provided and the characterizations of ordered semihypergroups in terms of uni-soft left (right) hyperideals are studied. The notions of uni-soft product is considered and their properties related to uni-soft left (right) hyperideals are studied. Using the notions of uni-soft left (right) hyperideals, the main theorems for a regular and intra-regular ordered semihypergroups are investigated. The concepts of a concave soft set and critical soft point in ordered semihypergroups are provided and the basic properties of these notions are given.

Due to increasing the demand of electrical energy and non-economic development of the centralized power plant, the electrical industry has been tended toward using the decentralized power plant and distributed generation sources (DGS). In this paper, a three-area re-structured power system with the participation of DGS, hydro power plant, thermal power plant, wind turbine with doubly fed induction generator (DFIG) and photovoltaic system have been studied. The DGS due to the low capacity production and lack of cost effectiveness will not be participating in the electricity marketing. Injection the energy to power system by the DGS and how to participate these resources in the automatic generation control (AGC) is controlled by a separate controller (not main controller). This controller is designed based on the optimal fuzzy logic controller (OFLC). Load of this system has been considered as a random variable that continuously and randomly has the fluctuation number of 0.3 around nominal amount. Fluctuations occurred because of considering the load as a random variable and participation of the wind-powered turbine with DFIG, photovoltaic system and DGS as a source of generation in power system. Since the wind turbine and photovoltaic system don’t produce power in the whole day, in this study capacitive energy storage (CES) units will be used parallel with this renewable energy source. To improve the response of power system, PID and OFLC will be utilized. Controller parameters can be optimized with three intelligent algorithms such as Imperialist Competitive Algorithm (ICA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Algorithm and finally the results will be compared with each other.

Development of digital content has increased the necessity of copyright protection using watermarking. Imperceptibility and robustness are two important features of watermarking algorithms. The goal of watermarking methods is to satisfy the tradeoff between these two contradicting characteristics. Recently, watermarking methods in transform domains have displayed favorable results. In this paper, we present an adaptive blind watermarking method, which has high imperceptibility in areas that are important to the human visual system. We propose a fuzzy system to control the embedding strength factor adaptively. Image saliency, intensity, and edge-concentration are shown to be important to a human observer and are hence used as fuzzy attributes. Embedding is performed in the discrete cosine transform of the wavelet domain to achieve high imperceptibility and acceptable robustness. Experimental results show the superiority of the proposed algorithm over comparable methods.

This paper presents a Feedback Visual Background extractor (FViBe) method for salient foreground detection in urban traffic scenes to efficiently resolve deficiencies that the background subtraction model is easily contaminated by temporarily stopped or slow motions objects. The background template is constructed based on the history of recently observed pixel samples and each pixel is assigned counters to describe the corresponding traffic state and stability. The threshold for salient foreground decision is set adaptively according to stability of scenes, and model update depends on the feedback current traffic state and the stability. The overall results obtained with the real-world urban traffic videos are presented to demonstrate that the FViBe achieves better performance of both visual comparison and quantitative evaluation than other state-of-the-art methods, particularly in the slow motions or temporarily stopped objects traffic scenario. Moreover, the experimental results show that the FViBe is suitable for real-time implementation in salient foreground detection of the urban traffic scenario.

With the rapid proliferation of information technology, researchers find it more and more difficult to rapidly find appropriate reference papers for an authoring paper. Citation recommendation aims to overcome this problem by providing a list of reference papers given a query document. There exist various aspects in bibliographic literature acting as paper’s scholarly roles, such as paper’s content, paper’s author, citation behavior, paper’s topic. We argue that combining different kinds of paper’s scholarly roles can enhance citation recommendation performance. Based on it, we propose a network correlation based query-oriented citation recommendation approach. We first construct a semantic network and a citation network, these two networks consist of the same vertices but different edge connection. Then we build correlations of these two networks and select the top features to calculate the semantic similarities of the query paper and scientific papers. Finally, we choose the top ranked scientific papers as the recommended citation list. When evaluating on the AAN dataset, the experimental results demonstrate the efficacy of the proposed approach.

In this study, the performance analysis of Fuzzy Self Tuning (FST) control method for Bridgeless Power Factor Correction (PFC) converter is proposed. The Bridgeless PFC converter based on FST method operates AC line rectification to result in DC output voltage without full-bridge rectifier. The proposed method generates the duty cycles to provide higher Unity Power Factor (UPF) and lower Total Harmonic Distortion (THD) of input current, even if AC line voltage consists of harmonics. Although Ziegler-Nichols method is very important to tune the coefficients of PI control method, it cannot guarantee to be always effective. For this reason, this study investigates the design of FST control method that tunes the coefficients of K_p, and K_i gains that are used into inner and outer control loops. These parameters are self-tuned to make them more general, to achieve minimum steady-state error, and to improve the dynamic behavior such as overshoot. Performance of proposed system was implemented for 1 kW Bridgeless PFC converter board with a TMS320F2812 DSP. The simulation and experimental results are compatible with the limits for harmonic current emissions, IEC 61000-3-2 and show that the proposed method is better than traditional ones.

In this paper, (2, M)-double fuzzifying topology as a generalized (2, M)-fuzzifying topology introduced by Höhle [7] is introduced. The relations between (2, M)-double fuzzy topology and (2, M)-double fuzzifying topology are studied. Also, we provided the definition of (2, M)-double fuzzifying preproximity structure. Not only that we studied the properties of these new notions, but we have also generated (2, M)-double fuzzifying topology from (2, M)-double fuzzifying preproximity. Finally, we introduced (2, M)-double fuzzifying preproximially continuous mapping which it used in more properties.

In this paper, a new definition of M-fuzzifying derived operators on matroids is introduced and their characterizations are given. What’s more, it is shown that there is a one-to-one correspondence between M-fuzzifying matroids and M-fuzzifying derived operators, and there is also a one-to-one correspondence between M-fuzzifying closure operators and M-fuzzifying derived operators. Besides, we can obtain an M-fuzzifying closure operator induced by an M-fuzzifying matroid directly.

As the dynamic kernel of natural gas pipeline infrastructure, centrifugal compressor unit and its status of health hold a crucial impact on the regular operation of the pipeline system. In order to keep track of the state of the centrifugal compressor unit, monitoring parameters are taken as the indexes for state evaluation. However, with the increasing complexity of monitoring systems, how to select appropriate parameters among numerous monitoring parameters and to establish a rational index system becomes an urgent problem in the process of state evaluation. In traditional, the indexes are selected with expert experience, ignoring the correlation between the monitoring parameters, which results in information redundancy in state evaluation. Besides, the monitoring parameters of auxiliary systems are usually not taken into consideration. For improving the performance of the index system, namely to reduce the information redundancy and to broaden the coverage, a time lag based correlation analysis model (TLCAM) is proposed in this paper for index selection. In the TLCAM, Erdem correlation coefficient (ECC), which takes the lag difference into account, is used to depict the coupling correlation between the monitoring parameters. Meanwhile, a neighborhood extreme value method (NEVM) is proposed to calculate the ECC. Hereafter, the monitoring parameters are classified through incorporating the advantage of information entropy into the density-peak based clustering (CDP). Consequently, an index system with less redundancy and wider coverage is established for the state evaluation of centrifugal compressor unit. At the end, correlation analysis and sensitivity analysis are performed on the two index systems respectively obtained from expert knowledge and TLCAM to compare their performance. The result shows that the index system established by using TLCAM is more sensitive to some early faults and contains less information redundancy. Moreover, it covers more critical components of the whole unit.

In this paper, we introduce double-framed soft bi-ideals (briefly DFS bi-ideals) and double-framed soft generalized bi-ideals (briefly DFS generalized bi-ideals) in AG-groupoids and some properties of them are investigated. Several characterizations of intra-regular AG-groupoids in terms of DFS left (resp. right) ideals, DFS bi-ideals, DFS generalized bi-ideals and semiprime class of them are provided.

Employee performance evaluations are one type of decision-making process that is embedded with uncertainty and ambiguity before concluding the final index. After reviewing different approaches to achieving such target, this paper introduces the implementation of fuzzification mechanism termed as “Fuzzimetric Sets” as a method of defining the minimum and maximum tolerance possibilities within pre-defined fuzzy sets. Decision-making evaluation process would be dependent on the inferred minimum to maximum defuzzified differences (spectrum). Based on this concept, a prototype was built to measure the employee performance level allowing much more flexibility when taking a decision under uncertainty. This application was termed as “Fuzzimetric Employee Evaluation System” (FEES). Comparative study of FEES was done by comparing the results of the work of another researcher investigated the same field of Fuzzy-employee-evaluation.

In this paper, L-concave structures are characterized by L-concave internal relations and L-convex enclosed relations. Then L-concave bases are introduced and their relations with L-concave structure are discussed. As an application of L-concave bases, L-topological-concave spaces are introduced and some characterizations of them are obtained.

Typical process is a sample process which can reflect processes of a group of similar parts. As a kind of process knowledge it can be referred to for the process planning of new parts. In this paper a methodology of typical process discovery for body-in-white (BIW) parts, based on the distance (i.e. dissimilarity) between processes, is proposed. The process for BIW part is divided into assembly positioning, joining, and quality inspection operations, in accordance with the typical assembly; the assembly oriented typical process is extracted based on these three operations. The distances of assembly positioning, joining, and quality inspection are calculated respectively using different measuring methods. The distance between processes is calculated as the sum of the assembly positioning, joining, and quality inspection distances. Furthermore, the clustering algorithm is applied to form the process clusters according to the distances between processes. The mean variances of the distance between processes in the cluster are calculated. The process with the minimum mean variance in the cluster is selected as the typical process. Finally, a case study is used to show the procedure of the typical processes acquisition for BIW and validate the effectiveness of the proposed method.

Micro-expressions reveal a person’s true feelings and motivations, and they can be so subtle that they are difficult to recognize. Micro-expression recognition can be used for criminal justice and national security. The existing methods usually extract the same micro-expression features from three different orthogonal planes, but cannot accurately reflect the characteristics of different space features, so the actual recognition rate is low. According to the motion and appearance information of micro-expression sequences in the temporal domain and spatial domain, this paper proposes a micro-expression recognition method based on spatiotemporal features selection. The Improved Local Directional Number Pattern (ILDNP) in the spatial domain and the Pyramid of Histograms of Orientation Gradients without Edge Extraction (PHOG-WEE) in the temporal domain are extracted individually and fused in the spatiotemporal domain to be a micro-expression feature, and then classified by the Support Vector Machine (SVM). Compared with the state-of-the-art LBP-TOP, HOG-TOP, HIGO-TOP, LBPSIP and Gabor algorithms on the SMIC, CASME and CASME II databases, experiments show that the proposed method has a better performance in describing the texture features of different planes, as well as a higher recognition rate on micro-expressions.

Telecommunication system plays a vital role nowadays. It is important for the service provider to retain a customer in his network. Churning of customer is a major problem in telecommunication network. A graph theoretic approach for identification of such customer in a communication network is proposed. The presence of interior node and end node in a fuzzy graph and their properties are discussed based on sum distance metric. The effect of interior node and end node on blocks is analyzed. The condition for existence of interior node in complete fuzzy graph is discussed. Interior node acts as a sensory node which loses its property if there is a decrease in number of outgoing calls or duration of calls decreases. A fuzzy telecommunication network model with interior node is presented and its advantage over usual networking system is given.

The aim of this paper is mainly to find the existence of a common coincidence point for three intuitionistic fuzzy set-valued maps in the context of (α, β) - level sets of an intuitionistic fuzzy set. The main result has been applied to derive the solution of a system of nonlinear integral equations. Moreover, an interesting example is put forth to demonstrate the applicability of the method.

Despite the surge of deep learning, deploying the deep learning-based pedestrian detection into the real system faces hurdles, mainly due to the huge resource usages. The classical feature-based detection system still becomes feasible option. There have been many efforts to improve the performance of pedestrian detection system. Among many feature set, Histogram of Oriented Gradient seems to be very effective for person detection. In this research, various machine learning algorithms are investigated for person detection. Different machine learning algorithms are evaluated to obtain the optimal accuracy and speed of the system.

The main result obtained in this paper is constructed the fractional Chebyshev operational matrix based on generalized shifted fractional-order Chebyshev functions of the first and second kind, is applied this operational matrix to the problem for numerically solving fuzzy fractional differential equations of order 0 < ν < 1 with fuzzy initial condition. We shown through numerical result that a new tau method is effective to the good approximate solution of Kelvin-Voiget equation, the model of viscosity behavior for non-Newtonian fluid and fuzzy fractional differential equation with variable coefficient. The numerical accuracy are compared with the results obtained by generalized fractional-order Legendre functions, Chebyshev polynomials and Jacobi polynomials.

An AG -groupoid can be referred to as a non-associative semigroup, as the main difference between a semigroup and an AG -groupoid is the switching of an associative law. In this paper, we define the smallest one-sided ideals in an AG -groupoid and use them to characterize a strongly regular class of a unitary AG -groupoid along with its semilattices and SI-l-ideals (SI-r-ideals) and SI-bi-ideals. As an application, we get some interesting and new characterizations which we usually do not find in semigroups. Finally we give the concept of an AG * * * -groupoid and give an example to show that this class is the generalization of a unitary AG -groupoid.

In this paper, our aim is to introduce and study some new spaces of lacunary I - convergence for sequence of fuzzy real number which are defined by compact operator (T) on the real space ℝ . i.e., c F I θ ( T ) ,   c F 0 I θ ( T ) ,   ℓ F ∞ I θ ( T ) ,   ℓ F ∞ θ ( T ) and examine some basic properties and prove some inclusions relation on these spaces.

The concept of lattice ordered fuzzy soft groups(l- FSGs) was instigated by J. Vimala and J. Arockia Reeta. In this present work, we define anti- lattice ordered fuzzy soft groups (anti- l-FSGs) and extend anti- l-FSGs matrix with appropriate examples. Also we derive the properties relevant to anti- l-FSGs matrices with its distinctive operations. Furthermore, we have applied the bounded sum and bounded difference operations in real life deciding process to find the best solution.

The aim of this paper is to discuss the uncertainty in soft graphs and a new type of graphs, called Z-soft rough fuzzy graphs is introduced. The notions of Z-soft rough vertex graphs, Z-soft rough edge graphs and Z-soft rough fuzzy graphs are given, and their related properties are explored. The theory of graph approximation space is developed. To validate the applicability of Z-soft rough fuzzy graphs, we put forward an innovative approach to multicriteria group decision making problems.