Kai Wang

Shanghai University

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i-10 index

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Yalin Wang

Central South University

Xiaofeng Yuan

Central South University

Chunhua Yang

Central South University

Lingjian Ye

Huzhou University

Chenliang Liu

Central South University

Junghui Chen

Chung Yuan Christian University

Chunhua Yang

Sixth Affiliated Hospital of Sun Yat-sen University

Weihua Gui

Central South University

Feifan Shen

Fudan University

Zhihuan Song

State Key Laboratory of Industrial Control Technology

Cooperative Institutions

Central South University

Chinese Academy of Sciences

Zhejiang University

University of Chinese Academy of Sciences

Huazhong University of Science and Technology

Shanghai Jiao Tong University

Beihang University

Beijing Institute of Technology

China University of Mining and Technology

Peking University

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Guaranteed cost reliable control for discrete systems based on LMIs

Ling Huang Shen Wang Kai Wang Bo You

The guaranteed cost reliable control for a class of discrete T-S fuzzy parameter uncertainty systems of actuator faults under a quadratic performance index is studied in the paper. The sufficient condition for the existence of a state feedback guaranteed reliable performance controller is deduced. Then the condition is converted to a linear matrix inequality group based on the Schur complement. The solutions to the LMIs are completely parameterized. Then the optimal reliable guaranteed cost control laws can be obtained by a convex optimization problem for the parameters. A numerical example is given to show the effectiveness of the proposed method.

Complement

Linear matrix inequality

10.1109/ifost.2012.6357504

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Citations (3)

Research of stacker speed control based on fuzzy control theory

Kai Wang

In the control system of stacker,if the stacker location is far away from the target,the stacker needs to operate at a high speed and to be tracked quickly while the stacker location is near to the target the speed of stacker needs to be slow down in order to locate accurately.Therefore,it needs to design a speed control system to meet the requirement of high-speed running,speed shifting steadily and High-precision positioning.After comparing a variety of control options,the results showed that multi-mode fuzzy controller which applied fuzzy control and traditional PID can effectively improve the system stability,accuracy and speed.

Stacker

Electronic speed control

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Citations (0)

Anomaly detection using large-scale multimode industrial data: An integration method of nonstationary kernel and autoencoder

Engineering Applications of Artificial Intelligence (2024)

Kai Wang Caoyin Yan Yanfang Mo Yalin Wang Xiaofeng Yuan

Kernel (algebra)

Autoencoder

Initialization

10.1016/j.engappai.2023.107839

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Citations (7)

Tongue image classification based on Universum SVM

2010 3rd International Conference on Biomedical Engineering and Informatics (2010)

Yue Jiao Xinfeng Zhang Zhuo Li Mingrui Chen Kai Wang

Tongue diagnosis is widely used in the Traditional Chinese Medicine (TCM) and tongue image classification based on pattern recognition plays an important role in the development of the modernization of TCM. However, due to labeled tongue samples are rare and costly or time consuming to obtain, most of the existing methods such as SVM utilize labeled training samples merely. Therefore the classifiers usually have poor performance. In contrast, Universum SVM is a promising method which incorporates a priori knowledge into the learning process with labeled data and irrelevant data (also called universum data). In tongue image classification, the number of irrelevant instances could be very large since there are many irrelevant categories for a certain tongue's type. But not all the irrelevant instances joined in training can improve the classifier's performance. So an algorithm of selecting the universum samples is also introduced in this paper. Experimental results show that the Universum SVM classifier is improved and the algorithm of selecting universum samples is effective.

Contextual image classification

10.1109/bmei.2010.5640046

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Citations (11)

Virtual Sensor Modeling for Nonlinear Dynamic Processes Based on Local Weighted PSFA

IEEE Sensors Journal (2022)

Xiaofeng Yuan Jiawei Rao Yalin Wang Lingjian Ye Kai Wang

Modern industrial processes are featured with complex dynamic, nonlinear, and noisy characteristics. It is of great significance to apply the probabilistic latent variable models (LVMs) to mine the pivotal features of the industrial processes. A probabilistic slow feature analysis (PSFA) can extract slowly varying features in rapidly changing data sequences as a dynamic LVM. However, the performance of PSFA is limited because of its linear assumption. In this article, a locally weighted PSFA (LWPSFA) is proposed for nonlinear dynamic modeling of industrial data with random noises. Two different kinds of weighting techniques are designed to approximate the nonlinear slow feature transition and emission functions. After that, the expectation maximum (EM) algorithm is adopted to estimate the parameters of LWPSFA with a weighted log-likelihood function (W-LLF). Eventually, a debutanizer column and a hydrocracking process are used to validate the effectiveness of LWPSFA.

Feature (linguistics)

Soft sensor

10.1109/jsen.2022.3196011

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Citations (21)

Deep Learning for Data Modeling of Multirate Quality Variables in Industrial Processes

IEEE Transactions on Instrumentation and Measurement (2021)

Xiaofeng Yuan Lu Feng Kai Wang Yalin Wang Lingjian Ye

Recently, deep-learning (DL)-based soft sensor has been widely applied to industrial processes, which plays a vital role for process monitoring, control, and optimization. However, most existing soft sensor models are established for only one quality variable or multiple quality variables with the same sampling rate. There are very few models focusing on prediction for multirate quality variables, especially with DL networks. To handle this problem, a novel DL strategy based on multirate stacked autoencoder (MR-SAE) is proposed. In MR-SAE, the network is composed of two parts: the cascade shared network for joint feature representations and the parallel quality-specific network for individual feature learning and quality prediction. The training procedure consists of three steps for MR-SAE. First, the available input data are used to pretrain the shared network. Then, all the multirate data are used to fine-tune the whole network. Finally, individual fine-tuning is further carried out for quality-specific subnetworks. The proposed MR-SAE method is used to build a unified soft sensor model for predicting both the 50% boiling point and cetane content of diesel oil. The results show that the performance of MR-SAE-based model is superior to SAE and deep belief networks. Moreover, the parameters and training time of MR-SAE model are less than the other two methods.

Autoencoder

Feature (linguistics)

Soft sensor

10.1109/tim.2021.3075754

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Citations (28)

Improved PID parameter setting method

Modern Electronics Technique (2012)

Kai Wang

A model need to be identified for the controlled plant when applying advanced strategies to determine the parameters of a PID controller.An online,model-free parameter tuning method is proposed based on simultaneous perturbation stochastic approximation in order to free the control system from plant model.The main idea for this tuning method is to use the highly efficient simultaneous perturbation approximation to the gradient of performance index function for PID controller.All parameters of PID controller can be tuned online by stochastic approximation.Simulation results show that satisfactory performances can be obtained using the proposed tuning method.

Source

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Citations (1)

Measurement and Control System Based on Wireless Senor Network for Granary

Advances in economics, business and management research/Advances in Economics, Business and Management Research (2015)

Jian Song Kai Wang Xiaochen Zhang

A wireless measurement and control system for granary is developed for the sake of overcoming the shortcoming of the wired measurement and control system such as complex wiring and low anti-interference capacity.In this system, Zigbee technology is applied with Zigbee protocol stack development platform by TI, and wireless senor network is used to collect and control the temperature and the humidity.It is composed of the upper PC, central control node based on CC2530, sensor nodes, sensor modules and the executive device.The wireless sensor node is programmed by C language in IAR Embedded Workbench for MCS-51 Evaluation environment.The upper PC control system software is developed based on Visual C++ 6.0 platform.It is shown by experiments that data transmission in the system is accurate and reliable and the error of the temperature and humidity is below 2%, meeting the functional requirements for the granary measurement and control system.

Granary

10.2991/emim-15.2015.50

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Citations (2)

A SIA-LSTM based virtual metrology for quality variables in irregular sampled time sequence of industrial processes

Chemical Engineering Science (2021)

Xiaofeng Yuan Zhenzhen Jia Lin Li Kai Wang Lingjian Ye

Sequence (biology)

Sampling interval

10.1016/j.ces.2021.117299

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Citations (34)

Research on harmonic detection method based on BP neural network used in induction motor controller

Kai Wang Fang Xie Changbao Zheng Bing Hang

Harmonic currents in a motor controller not only increase the loss of the motor but also reduce the efficiency of the motor. Harmonic detection is the precondition of harmonic control. General harmonic detecting scheme uses the Fast Fourier Transform (FFT) to detect all the harmonics. In the motor control, we often do not care about the specific values of all the harmonics, but only some key harmonics or several overall indicators. For the above reasons, the harmonic detecting scheme based on back propagation (BP) neural network is proposed. In this paper, the analysis of BP and FFT algorithm is presented, and the results of BP and FFT are compared. It is easy to see the superiority of the BP neural network in terms of calculation. The simulation results of the motor harmonic detection validate that the BP neural network scheme is feasible, and the BP neural network detecting accuracy is close to FFT.

Harmonic

Harmonic Analysis

10.1109/iciea.2017.8282910

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Citations (7)