Table of Content

20 January 2020, Volume 27 Issue 1

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Ball Mill Load Condition Recognition Model Based on Regularized Stochastic Configuration Networks

ZHAO Li-jie, ZOU Shi-da, GUO Shuo, HUANG Ming-zhong

2020, 27(1):  1-7. 

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Stochastic configuration network (SCN) is a universal approximator which can be automatically and quickly constructed under the supervision mechanism with inequality constraint. It has potential advantages in the field of large data modeling. In order to enhance the accuracy and stability of the model prediction, a stochastic configuration network model with L2 norm regularization (L2-SCN) based on the classical SCN is proposed to improve the algebraic properties of output weighted least squares analytical solutions and avoid the structural risk of the model overfitting. For the ball mill load operation status recognition under a wide range of non-stationary operating conditions, L2-SCN method was used to identify the ball mill load operating conditions. The experiment results on the ball mill show that the proposed L2-SCN model has relative advantages in terms of accuracy and stability compared with the classic SCN model and the random vector functional link network (RVFL).

Design and Simulation of Fractional Order Controller for Linear Inverted Pendulum

2020, 27(1):  8-14. 

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In order to solve the problem of stability control of the linear inverted pendulum, the fractional order proportional integral (FOPI and FO[PI]) controller is designed to correct the system. Firstly, the mathematical model of inverted pendulum is established according to the Newton mechanics method. Then, using the gain robustness fractional order controller parameter simplified algorithm based on the vector designed the fractional order proportional integral controller. Finally, the validity of the fractional order proportional integral controller parameter tuning method is verified and fractional order proportional integral controller and integer order PID (IOPID) controller are used to carry out stability control simulation experiment and then compared and analyzed the angle response curves of the pendulum .The results show that the fractional order proportional integral controller has better stability control effect than the IOPID controller for the inverted pendulum systems, and in the fractional order proportional integral controller, the FO[PI] controller has better stability control effect for the inverted pendulum systems, with robustness, faster response time and less oscillation amplitude.

Application of BDD Algorithm in Overhead Contact Line System Failure Risk Assessment

ZHAO Feng, CHEN Xian, WANG Ying

2020, 27(1):  15-21. 

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The possibility and the severity of the consequences for the failure of OCS(Overhead Contact Line System) is considered and analyzed accurately, and the failure risk is assessed in time to provide a theoretical basis for the development of risk control measures. Firstly, the failure fault tree of OCS is established, and then the BDD structure is generated by the ITE rules. The top event risk, the Birnbaum importance and the critical importance of the basic events are calculated by recursively accessing the nodes of the BDD structure from top to bottom. According to the BDD algorithm, the C# program is finished, thus the occurrence probability of failure accident for OCS and the key factors causing the accident can be obtained. Compared with the cut set method, the BDD method can not only get the exact values of the occurrence probability of top event and the importance of basic events, but also have the advantages of fast calculation speed and simple procedure.

Modeling and Detection of Stator Inter-turn Short Circuit Fault in Induction Motor#br#

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SHI Yong-qian, JIANG Bin, MAO Ze-hui

2020, 27(1):  22-27. 

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The stator inter-turn short circuit fault of asynchronous motor is one of the typical stator faults. To ensure the safe operation of the motor, it needs to detect the fault in time and take measures immediately. Mechanism modeling of asynchronous motor with stator inter-turn short circuit fault is built in the two-phase synchronous rotating coordinate system, which is transformed into the form of state equation. Based on the asynchronous motor of CRH2, simulation research is carried. It detects the fault by multiple model matching with inter-turn short circuit coefficient. The simulation results verify the accuracy and effectiveness of the model, and the fault of asynchronous motor is detected by multiple model matching.

Soft Sensor Modeling Based on Optimal Bounding Ellipsoid Algorithm with Penalty Factor

SUI Lu-lu, HAN Dong-sheng, CHENG Lan, YAN Gao-wei

2020, 27(1):  28-33. 

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The predictive accuracy and generalization performance of soft sensor model are two important indexes of soft measurement modeling. Extreme learning machine algorithm which is based on optimal bounding ellipsoid (OBE-ELM) can overcome the shortcomings of the traditional extreme learning machines, such as low prediction accuracy and unstable prediction results, but the traditional OBE algorithm only minimizes the model error and does not consider the complexity of the model, which leads to over-fitting of the model. Aiming at the above problems, firstly, an optimal bounding ellipsoid algorithm (POBE) with penalty term is proposed for nonlinear systems with unknown but bounded noise, and the penalty term added to the objective function is used to suppress the magnitude of parameter growth and drive the unimportant parameter to zero gradually. Then, POBE is used for the optimization of ELM model parameters. Finally, the experiments were carried out on the channel parameter estimation and the continuous stirred tank reactor data sets to validate the effectiveness of POBE and POBE-ELM respectively.

Simulation and Study on Fluid Loop of Space Utilization Bypass Control Algorithm#br#

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ZHENG Tong, ZHAO Li-ping, LIU Rong-hui

2020, 27(1):  34-41. 

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For the flow control problem of complex fluid looping system, a modified fuzzy PID controller which adapts different working conditions is designed. The lumped parameter method is used to build the simulation model. The flux and temperature model of fluid looping system is built based on fluid resistance and heat exchange theory. Other important modules of the system like centrifugal pump, stepping motor valve and liquid-to-liquid heat exchanger have been emulated at the same time. On basis of this, a fuzzy PID controller with valve status signal has been designed to decrease the effect of payload changing. Four different control algorithms, including the method described in this paper, have been compared together. The simulation results indicate the modified fuzzy PID controller has the best performance. Compared with traditional PID algorithm, it has excellent adaptability of nonlinearity, such us reducing 50 % maximum overshoot and 75 % setting time when valve of payloads has been closed.

Stabilization for Time-delay Rectangular Descriptor Systems

LI Jie, LIN Chong, CHEN Bing, ZHAO Xin

2020, 27(1):  42-48. 

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This paper focuses on the stabilization problem for time-delay rectangular descriptor systems. To begin with, time-delay dynamic compensator is introduced for feedback compensator, a suitable Lyapunov-krasovskii functional is constructed and advanced integral inequalities are used to deal with the integral terms of functional derivatives, furthermore new stabilization condition for time-delay rectangular descriptor systems is obtained in terms of strict linear matrix inequality (LMI). Based on this stabilization condition, utilizing iterative linear matrix inequality algorithm, the relevant parameter matrices of dynamic compensator and feedback gains are computed. Finally, numerical example is given to demonstrate the advantage and effectiveness of the proposed method in this paper.

Observer for Time-Varying Sliding Mode Synchronous Control of Double-Container Overhead Crane System

YUE Ya-wen, XU Wei-min, CHEN Tian-yu, CHEN Xi

2020, 27(1):  49-56. 

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To deal with the problems such as modeling inaccuracy, internal parametric perturbation and external disturbance commonly existing in double-container overhead crane system, this paper adopts the cross-coupling strategy and propose a method of nonlinear disturbance observer for time-varying sliding mode synchronous control of double-container overhead crane system. First, using time-varying sliding mode control ensures the global robustness of the controller. Then, the nonlinear disturbance observer to observe is employed the lumped disturbances and compensate the disturbances to the controller. Besides, a variable gain reaching law is proposed which can dynamically adapt to the variations of the control system, and effectively reduce chattering on control input and shorten approach time. Finally, the asymptotic stability of controller is verified by Lyapunov theory, the simulation results show the effectiveness of the proposed method, and the controller ensures good performance in the presence of unknown disturbances.

Establishment of BA Network Model Based on Adaptive Algorithms and Clustering Analysis of Network

DUAN Jia-yong, GUO Fang, ZHANG Xiao-yu, BAI Ke

2020, 27(1):  57-63. 

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BA model is a classic scale-free network model, which has some small world characteristics, but the clustering coefficient approaches zero with increasing number of points. In order to further optimize the average path length and clustering coefficient of BA network model, an improved scale-free network model based on adaptive algorithm is designed. The improved model optimizes the correlation degree of network nodes and the system. By calculating the optimal value of the correlation degree and the value of each parameter in the network Thus, the ideal network model is obtained. Through the mathematical analysis of the adaptive algorithm, the average path length of the system is the convergence state with conditions. The simulation results show that the improved network model is further optimized in terms of average path length and clustering coefficient. Unlike the BA scale-free network, the improved model has obvious clustering characteristics and is more accord with small world network characteristics.

Multi-model Soft Sensor Modeling Based on the DP-RFR Method

2020, 27(1):  64-69. 

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 A practical industrial process is often a large-scale complex system with muti-operating modes and nonlinearities, making it difficult to fully mine the data information through a single soft sensor model. To solve this problem, a multi-model soft sensor development approach based on the density peak (DP) clustering and the random forest regression (RFR) is proposed to estimate dominant variables. Firstly, classify the training data by means of the DP clustering algorithm; secondly, establish regression sub-models based on the samples of each category by using the RFR method; finally, apply the switching method for multi-model fusion. The proposed method has been utilized to develop soft sensors of the Tennessee Eastman process and the butane distillation process for estimating the contents of G and propane, respectively. The simulation results illustrate that the estimation accuracy has been improved, which can verify the effectiveness of the proposed method.

Three Dimensional Geometric Dynamic Modeling of Hazardous Chemicals Storage Based on Image

DAI Bo, LI Yan-fei, AN Hai-yang, ZHOU Ze-yu, LIU Xue-jun

2020, 27(1):  70-76. 

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Safety production supervision departments have strict regulations for the five-distance of the hazardous chemicals storage warehouse stacks (distance, pile wall distance, zenith distance, spacing and channel spacing), the dynamic supervision requirements can be achieved by describing the changes of the position of the stacks in the warehouse through the three-dimensional dynamic geometric modeling. Three-dimensional geometric dynamic modeling of the stacks based on image is proposed. In order to establish the corresponding relations between image points and objects, a five - element imaging model is constructed and two - point calibration method is proposed. On the basis of calibrating the parameters, the solution space of the coordinates of the point is obtained first, and then the real solution space is obtained by combining the constraint condition to realize the static  three - dimensional geometric reconstruction of the stacks. Then, the stacks are identified and reconstructed in the image sequences, and the dynamic geometric modeling is realized.

Improved VSG Control Method Based on Impedance Identification for Reactive Power Sharing in Parallel Inverter System

YANG Cai-ming, SUN Li-jing , WU Ming, YU Jie, TAO Hong-fei, LUO Gang, ZHANG Li-zong

2020, 27(1):  77-83. 

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As an effective supplement for smart energy in Internet, Microgrid (MG) could realize the frequency and voltage supporting functions and allocate the load power automatically when it runs in the island mode. As the Virtual Synchronous Generator (VSG) control with drooping feature can realize the automatic power distribution of the inverter according to the design parameters due to its inertia and damping links. However, due to the differences and changes in the impedance of the inverter connection lines in the microgrid, the VSG cannot only rely on the designed droop parameters to achieve the sharing of reactive power in full work conditions. Therefore, an improved reactive-voltage control method of VSG based on short-time pulse injection line impedance identification is proposed. The line voltage drop generated by each inverter is compensated by identifying the line impedance in advance, and a parallel inverter is realized common bus voltage-reactive droop control to compensate for the impact of line impedance voltage drop on the rational distribution of reactive power. The principle and implementation of the improved reactive power control under the control of VSG are analyzed, and the effectiveness of the method is verified by some simulations.

Coordination and Optimization of Operation Time Conflict in Steelmaking and Continuous Casting

LUO Xiao-chuan, SANG Mei-ning, DENG Meng-yi, FAN Yu-hao

2020, 27(1):  84-91. 

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 In the steelmaking and continuous casting process, some disturbances can cause time clash. Dynamic scheduling and casting speed adjustment can be applied to eliminate it. Considering the two problems, mathematical model is set up. The object functions are the minimum time of the broken pouring, redundant waiting, drawing speed adjustment and the smallest acceleration adjustment. The constraint functions are based on the production process. The steelmaking and continuous casting operation process consists of continuous and discrete processes, the problem model is difficult to solve, this paper divides the problem into two parts: the time clash elimination model and the casting speed optimization model, and put forward the solving method for the two models. Through the method of solving the whole model, the dynamic scheduling and casting speed adjustment are applied to achieve the coordinative optimization of the two models.  

Application of Kernel NPE for Fault Detection in Chemical Processes

LI Chun-yang, XIA Li-sha, LI Jun-xiang

2020, 27(1):  92-97. 

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Chemical production process has the characteristics of high dimension and strong nonlinearity. For the deficiency of traditional neighborhood preserving embedding (NPE) algorithm in feature extraction of non-linear data, a Gaussian kernel function is introduced to transform data from non-linear input space to linear feature space. Kernel neighborhood preserving embedding (KNPE) algorithm can extract the non-linear structure of data better on the basis of constructing local spatial feature structure. By a case study on the Tennessee Eastman (TE) simulation process,  and SPE statistics are constructed for fault detection, which proves that KNPE method can detect the occurrence of non-linear faults faster and more accurately than NPE and KPCA methods.

Defect Classification of Glass Fiber Fabric Based on Multi-feature Fusion

ZHENG Min, JING Jun-feng, ZHANG Huan-huan, SU Ze-bin

2020, 27(1):  98-103. 

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 Focusing on the problems of low efficiency and poor stability of the traditional glass fiber fabric defect classification method, a glass fiber fabric defect classification algorithm based on multi-feature fusion is proposed. Firstly, the median filter is used to preprocess the glass fiber fabric image to remove the detail noise and reduce the influence of background texture. Secondly, Canny edge detection is performed on the pre-processed image, and Hu invariant moment is used to extract the geometric features of defects. Then, the texture features of the image are extracted by scale invariant feature transform (SIFT). After K-means clustering, the bag of words model (BoW) of the glass fiber fabric image is constructed. Finally, the geometric features and texture features are merged and passed into the SVM for training, and the corresponding glass fiber fabric defect classification model is obtained. The experimental results show that the average classification accuracy can reach 97.22 %, which can meet the actual needs of enterprises.

Design of a Banknote Thickness Sensor Based on Eddy Current Principle

ZHAO Zuo-xi, LIU Xiong, XIAO Can, PAN Xiang, LAI Qi, KE Xin-rong

2020, 27(1):  104-108. 

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The counting machine mainly relies on magnetic safety line, magnetic ink, optical characteristics to identify counterfeit banknotes. Some counterfeit or old banknotes which stick transparent adhesive tapes with different sizes and positions should also be distinguished, but it’s difficult to identify them through existing technology. A multiple section mechanical thickness sensor is designed to convert the thickness of paper money into mechanical displacement, by using the precise displacement sensor based on the LDC1000 inductive digital converter provided by TI company to realize the positioning detection of the thickness of different parts of banknotes. Firstly, the structure and working principle of the mechanical thickness sensor are described. Then introduces the sensor circuit system that designed with the tool of WEBENCH ® Designer LDC1000 provided by TI company. The preliminary test results show that, the resolution of the sensor is better than 5μm in the range of 1 mm and the detection rate reaches 10kHz. The design meets the requirements of banknote thickness detection.

Iterative Learning Based Control for Wind Tunnel Mach Number

YI Fan, LI Xin-rui, DU Ning, YU Wen-shan

2020, 27(1):  109-113. 

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Robust Disturbance Rejection Control of SCR Denitration System

MA Zeng-hui, XU Hui-yi, ZHU Run-chao

2020, 27(1):  114-120. 

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Selective catalytic reduction (SCR) technology is widely used for flue gas denitration of thermal power plants. The SCR denitration system is very complex and SCR system has the characteristics of large inertia, dead-time, strong disturbance and uncertainty. So, it is almost impossible to achieve precise control of the amount of ammonia injection by the traditional PID control scheme. In this paper, a robust disturbance rejection control method for SCR denitration system is presented. Based on the design of robust PID controller, robust time-delay filter is used to suppress the strong disturbance of the system. The simulation results show that the addition of robust time-delay filter improves the dynamic performance of the system and makes the system has outstanding disturbance rejection performance. The scheme proposed in this paper has simple structure, easy parameter tuning and good robustness. It is worth popularizing in engineering.

Research of Electronic Cam Function Block Algorithm Based on PLCopen

CHEN Mei, WANG Shu-run

2020, 27(1):  121-126. 

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 The core of CNC machine tools and industrial robots is motion control system, in order to improve the performance of motion control system, based on the MC motion control specification defined by PLCopen, combined with SoftPLC and motion control program, design and implement the periodic and non-periodic position coordination of electronic cam, and spline interpolation algorithm is used to fit aperiodic curves in non-periodic electronic cam. First, C# is adopted as the programming language to design the framework of function block. Then, C/C++ is adopted as the programming language to carry out motion planning for master axis and combine the discrete points into a curve in the non-periodic condition, the whole control procedure is running in the ProConOS eClR kernel. Finally, the function block is called in the upper platform Multiprog to ensure master axis and slave axis to move cooperatively. The experimental results show that, this kind of cam function block can work not only in periodic condition according to scheduled trajectory but also in non-periodic condition according to key point position, its velocity will not change suddenly, and it has wider application scope.

Method on Nonlinear Adaptive Controller for Maglev Levitation Ball System

LV Zhi-guo, LONG Zhi-qiang

2020, 27(1):  127-133. 

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Aiming at the problem of designing adaptive controller for different plants for maglev ball system, a nonlinear adaptive control approach based on combination of feedback linearization and parameter identification is presented. Firstly the maglev ball system mathematical model is formulated by using the theory of state feedback exact linearization. Secondly a nonlinear controller is designed via system state feedback, and the method of controller parameter identification online is presented. The online experiments on MATLAB platform show the system using the presented approach has more advantages than backstepping sliding model control method, which can adaptively suspend different plants in equilibrium position. Furthermore it has ideal steady-state regulation performance.

PSO-based Weft Insertion System for Three-dimensional Tubular Loom

ZHOU Qi-hong, OU Si-fan, LI Qing-qing, SUN Zhi-hong, CHEN Ge

2020, 27(1):  134-137. 

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In order to meet the requirements of the strict motion relationship between the weft insertion mechanism and the opening mechanism of the three-dimensional tubular loom, a set of digital control adaptive weft insertion system was designed. The system drives mechanism of weft insertion motion of the shuttle, with the aid of a speed reducing mechanism based on servo motor and a synchronous and gear column driving mechanism. Encoder is used to get real-time angle, and the micro controller STM32F407 is the main control chip of the system. The adaptive control algorithm, with IATE accuracy standard and particle swarm optimization algorithm, can realize the parameter self-turning function. The system has been proved to meet the requirements of the synchronous control of the weft insertion mechanism and the opening mechanism of the three-dimensional tubular loom, and it can also satisfy the requirements of the three-dimensional tubular fabric weaving.

Design of Adaptive Sliding Mode Control for Four-rotor Aircraft

Gu xun, Zheng Ya-li, Chen Yu-qing

2020, 27(1):  138-142. 

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Aiming at the modeling error and external disturbance uncertainty of four-rotor aircraft attitude model, a nonlinear controller based on adaptive sliding mode is proposed. The parameter adaptive control method is used to approximate the modeling error term in the system, and the sliding mode control method can cancel out the system modeling error and the external uncertain disturbance term. The Lyapunov stability method is used to prove that the designed controller can achieve global asymptotic stability. Then, the validity of the control method proposed in this paper is verified by the real flight test, which can realize the attitude stabilization control of small four-rotor aircraft, and show that anti-disturbance performance is better than traditional PID control.

Rotor Position Detection of Permanent Magnet Synchronous Motor Based on Pulsating High Frequency Voltage Injection

CHAI Jun, JIANG Yan-yu, PENG Yan

2020, 27(1):  143-147. 

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The rotor position detection accuracy of permanent magnet synchronous motor directly determines whether the motor can start smoothly. In order to improve the accuracy of rotor position detection, a rotor position detection method for permanent magnet synchronous motor based on pulsating high frequency voltage injection is proposed. The high frequency voltage signal is injected into the stator windings of permanent magnet synchronous motor, and the rotor initial position is detected by the saturation degree of the magnetic circuit of the   axes of the motor. Experimental results show that the proposed method has high rotor position detection accuracy and it is suitable for sensorless start and low speed state operation of permanent magnet synchronous motor.

The Decision-making Model to Select Network Systems Based on the Hesitant Fuzzy Geometric Algorithm

GAO Ya-li

2020, 27(1):  148-154. 

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Aiming at multi-criteria group decision making (MCGDM) problems where criteria values are hesitant fuzzy information and criterions are associated with each other, a generalized hesitant fuzzy geometric Bonferroni mean (GHFGBM) operator is proposed on the basis of Archimedean T-norm and S-norm, and then a novel computer network system selection model is designed. The advantages of the developed method are not only its capability to capture the interrelationship among the input variables, it also enables the model method to be applied to other fields. Some desirable properties of the GHFGBM operator are discussed, including Permutation invariance, monotonicity, boundedness and idempotence and so on. Some special cases are obtained if the parameter and additive operator takes different values and functions. In the end, a numerical example about update alternative of computer network system selection to illustrate the rationality and effectiveness of the proposed model.

Multi-stages Dispatch Strategy Optimization for Flight Based on Discrete and Dynamic Programming

CHEN Hua-qun

2020, 27(1):  155-161. 

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Flight dispatch is the core of operational control. To realize global plan and quantitative assessment of operational dispatch release and control strategy, an multi-strategy framework is put forward based on minimum cost with shortest path. Qualitative technical means was changed just relying on traditional manual interpretation experience. Two-dimensional shortest path was constructed for flight state transition influenced by assignment strategy. Discrete dynamic programming mathematical model was made based on shortest path . Reverse recursive equation of minimum cost was established. Numerical calculation method was used to obtain the optimal dispatch strategy in tabular form. Quantitative evaluation and combinatorial optimization of non-linear and discrete composite dynamic programming problems such as flight operation decision-making were solved. Finally, feasibility and superiority of algorithm was test by simulation experiment. The experimental results show that, compared with time sequence operation mode of separate stage, the proposed algorithm is much better in global optimization for airlines operational control in whole course.

Research on Nash Equilibrium Based on Improved Quantum Particle Swarm Algorithm#br#

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ZHANG Lei

2020, 27(1):  162-167. 

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In the process of solving the Nash equilibrium problems related to N-person non-cooperative game, quantum uncertainty principle, co-evolution and antibody concentration suppression mechanism in immune algorithm were introduced into the classical particle swarm optimization, and a new improved quantum particle swarm optimization algorithm is designed to deal with Nash equilibrium problems. In the process of calculation, this algorithm utilizes antibody concentration and co-evolution to maintain the diversity characteristics of particle groups, and uses the uncertainty of quantum to decrease the time-consuming of iterative searching process. In addition, this algorithm not only effectively inherits the simplicity and convenience of particle swarm optimization, but also greatly improves the convergence speed and global search ability. The experimental results show that the improved algorithm can overcome the premature convergence of particles, and has better performance than genetic algorithm and immune particle swarm optimization.

YU Qi, ZHANG Ying-ping, WU Jiao, FENG Xiao-wen, SUN Yue-jin, SUN Wen-yong, JIANG Ya-tong

2020, 27(1):  168-173. 

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With the advent of the big data era, as well as information security issues highlighted, enterprises' "Remove IOE" voice continues to rise. Considering the actual situation, different countries and different industries have different ways of dealing with "Remove IOE". Since the concept of "Remove IOE” was officially put forward by Alibaba., after the successful practice, the research and application of "Remove IOE " has been increasing. The domestic enterprises represented by Lenovo, Ali and Huawei have an increasing competitiveness in the fields of server, database and storage, providing mature products and services for "Remove IOE ". This paper introduces the process of "Remove IOE "in different fields, analyzes the difficulties and applications of "Remove IOE ", and summarizes the main solutions. Finally, combining the trend of big data era, this paper looks forward to the future of "Remove IOE ".

Smart Energy Cloud Platform Based on MQTT and ILZ4 Compression Algorithm#br#

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LU A-li, GU De-lin, ZHANG Jian-shu, HUO Ying

2020, 27(1):  174-181. 

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In order to adapt to the smart energy application with large-scale data and fast response requirement, a smart energy cloud platform based on MQTT message transmission and ILZ4 algorithm is proposed. message queuing telemetry transport (MQTT) protocol is introduced into the data communication between Internet of things (IoT) and the cloud platform. The message queue architecture and uploading/downloading message flow process via the MQTT protocol are designed, and the ILZ4 compressor can be introduced and integrated into the tasks of information storage and message transport to achieve real-time data compression and transmission of large-scale monitoring flow. Three million monitored data points are recorded as input data stream of the cloud server. The experimental results show that, the proposed method gets better performance in compression ratio and throughputs, which can quickly reduce the storage cost and transmission overhead of large-scale data. So the proposed platform can provide a good, universal and scalable solution for smart energy applications.

Hesitant Fuzzy Kernel C-Means Clustering for Database System Selection

DENG Xiao-yan

2020, 27(1):  182-187. 

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In order to deal with clustering problems for hesitant fuzzy information, this paper normally solves them on sample space by using a certain hesitant fuzzy clustering algorithm, which is usually time-consuming or generates inaccurate clustering results. To overcome the issue, we propose a novel hesitant fuzzy clustering algorithm called hesitant fuzzy kernel C-means clustering(HFKCM) algorithm by means of kernel functions, which maps the data from the sample space to a high-dimensional feature space. As a result, the proposed HFKCM algorithm expands the differences between different samples, and makes the clustering results much more accurate. Finally, by conducting simulation experiments on the selection of database systems, and the results reveal the feasibility and availability of the proposed hesitant fuzzy kernel C-means clustering algorithm.

Research and Realization of Synchronous Robotic Arm with Autonomous Path Planning Functions#br#

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2020, 27(1):  188-193. 

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In order to solve the problem that elderly or patients in bed are unattended and improve their self-care ability, a synchronous robot arm with autonomous path planning is proposed. The user specifies the moving robotic arm to reach the destination through a mobile phone APP, and then guides the robot arm to perform the body arm behavior synchronously through a synchronization device on the arm such as object grasp. The system adopts RFID location systems with dense passive tags to arrange a 4*4m² RFID tag array indoors, and paths planning based on fuzzy logic. Experimental results show that the system is capable of helping a user to grasp ninety present of objects in daily life.

Multivariate Process Variables Abnormal Data Segments Detection Based on Correlation Coefficient

PANG Xiang-kun, HUANG Yue, WANG Zhen, YU Yan, GAO Song

2020, 27(1):  194-200. 

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The historical normal and abnormal data sets of process variables are premises of assessing and optimizing alarm performance and designing dynamic alarm trip-points of industrial alarm system. This paper proposes an improved abnormal data detection method, which is based on the correlation coefficients between process variables. The main idea is to divide multivariate time series of process variables on the basis of correlation coefficient values and suitable length of data segments, obtain the mutual variation directions of process variables through Spearman rank correlation coefficient and corresponding hypothesis test, and detect abnormal data segments that have inconsistent variation directions with prior knowledge for normal conditions. Simulation examples and industrial case are provided to validate this method.

Topology Verification of Low Voltage Distribution Network Based on ROF Outliers Detection Algorithm

GUO Shen, LIN Jia-ying, WANG Peng, ZHANG Ji-chuan, CHEN Lei, TANG Guo-jing

2020, 27(1):  201-206. 

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In the existing verification methods of low-voltage distribution network topology, most of smart meter voltage sequence data of customers in the most recent period of time are extracted from the Advanced Metering Infrastructure (AMI), and the correlation between the voltage sequence data is calculated to measure the similarity between voltage sequence profiles of different customers. However, the existing Local Outlier Factor (LOF) detection algorithm cannot detect the outliers. Therefore, a low-voltage distribution network topology verification algorithm based on ring outliers factor (ROF) detection algorithm is proposed in this paper. ROF algorithm verifies the customer with inaccurate connection of the transformer by correlation coefficient of voltage sequence data, and analyzes the degree of abnormality in the customer's ring domain, which can effectively verify the topology connection between the customer and the transformer in the GIS system.