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第三十九届中国控制会议

作者： Yi Tang Dan Ling Hong Zhang Yanewei Wang Ying Zheng the Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology School of Electrical and Information Engineering Zhengzhou University of Light Industry School of Mechanical & Electrical Engineering Wuhan Institute of Technology

Model mismatch is one of the main factors of control performance degradation. In this paper, a new model mismatch detection approach with parity space-based methods is proposed for linear discrete time-invariant（LDTI） systems. This paper establishes a parity space-based residual evaluation function for the detection of model mismatch. The problem of parity space-based model mismatch is formulated on a state space model of a LDTI system. A standard evaluation function and its thresholds for the detection of model mismatch are proposed by providing the parity model-based residuals. It is shown the proposed approach results in an optimal fault detectability without any assumptions on the probability distribution of unknown disturbances. The effectiveness of the proposed methods is demonstrated by the simulation study of the paper machine head box control example.

关键词： Parity space Model mismatch detection LDTV systems model-based residual generation

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Simulation study and characteristics analysis of the ERT sensor

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ICIC Express Letters, Part B: Applications 2011年第4期2卷 865-871页

作者： Wang, Pai Guo, Baolong Li, Nan Institute of Intelligent Control and Image Engineering Xidian University No. 2 South Taibai Road Xi'an 710071 China Department of Mechanical Engineering and Applied Electronics Technology Beijing University of Technology No. 5 South Zhongguancun Street Haidian District Beijing 100081 China

3D simulation of an ERT sensor field is established using COMSOL and MATLAB 7.0 to study the limitations of an ERT system's sensor field and determine the influence of electrode size and geometry on the established field. As a result of the analysis, a pointed electrode array and a rectangular-shaped electrode array were designed and their frequency characteristics were tested by measuring conductance in milk flows. The results indicate that increasing the width and height of the electrode is beneficial to improving the axial and radial distribution of the sensing field. Increasing the electrode area can reduce "connection resistance" and enable the ERT sensor to have more stable frequency characteristics and improve the SNR of the ERT system. © 2011 ISSN 2185-2766.

关键词： MATLAB

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Capacitive sensor design for an automatic car-wiper system

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ICIC Express Letters, Part B: Applications 2011年第4期2卷 879-884页

作者： Li, Nan Han, Ying He, Cunfu College of Mechanical Engineering and Applied Electronics Technology Beijing University of Technology No. 5 South Zhongguancun Street Haidian District Beijing 100081 China Institute of Intelligent Control and Image Engineering Xidian University No. 2 South Taibai Road Xi'an 710071 China

A new structure of capacitive sensor used for an automatic car-wiper system is presented. It aims to develop a low-cost sensor with high sensitivity and robustness. The working principles of the sensors used for liquid on the surface detection are introduced. Three types of proximity capacitive sensors of different shapes are designed under the same outer diameter. The parameters affecting the sensor signal strength and measurement sensitivity are discussed based on the performance analyzing of the sensors. The electric field distributions of the sensors are simulated and the fringe capacitance of the sensors is measured and analyzed. For the flower-shaped sensor, the noise analysis is presented and the measurement results are given. The effects of the excitation's frequency are experimented and the optimum frequency of the excitation is determined. The experimental results indicate the validity and importance of the capacitive sensing technique which can be used for liquid detection on windshield of vehicles, and the new structure of the sensor has greater measurement performance, especially in signal strength and the sensitivity. © 2011 ISSN 2185-2766.

关键词： Capacitive sensors

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Path planning for multiple mobile robots under double-warehouse

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Information Sciences 2014年 278卷 357-379页

作者： Yong Ma Hongwei Wang Yong Xie Min Guo School of Navigation Wuhan University of Technology Wuhan 430063 China Hubei Key Laboratory of Inland Shipping Technology Wuhan 430063 China Key Laboratory of Image Processing and Intelligent Control Institute of Systems Engineering Huazhong University of Science and Technology Wuhan 430074 China

This work investigates the conflict-free path planning problems for efficient guidance of multiple mobile robots under the dynamic double-warehouse environment, a challenging problem that appears recurrently in a wide range of applications such as the service robots moving in a multistory building. United with two symmetrical transfer elevators, double-warehouse consists of two parallel warehouses. Within each warehouse, the polynomial based paths are subject to constraints such as motion boundaries, kinematics constraints, obstacle-avoidance, limited resource of elevators and smoothness. We formulate the shortest path planning problems as one time-varying nonlinear programming problem (TNLPP) while restricted to the above constraints, and apply the multi-phase strategy to reduce their difficulty. We present the new variant algorithms of PSO named constriction factor and random perturb PSO (Con-Per-PSO) and the simulating annealing PSO (SA-PSO) to achieve the solution. Numerical simulations verify that, our approach can fulfill multiple mobile robots path planning problems under double-warehouse successfully.

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Reinforcement learning based predictive maintenance for a machine with multiple deteriorating yield levels

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Journal of Computational Information Systems 2014年第1期10卷 9-19页

作者： Wang, Xiao Wang, Hongwei Qi, Chao Sivakumar, Appa Iyer State Key Laboratory of Education Ministry for Image Processing and Intelligent Control Institute of Systems Engineering Huazhong University of Science and Technology Wuhan 430074 China School of Safety Engineering Shenyang Aerospace University Shenyang 110136 China Division of Systems and Engineering Management School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore Singapore

This paper proposes a predictive maintenance methodology for a machine in manufacturing with deteriorating quality states represented by multiple deteriorating yield levels. Imperfect minor maintenance and perfect major repair are considered. The underlying yield level cannot be directly obtained. Instead, product quality inspection information is used as the observed system state. The optimal maintenance policy associated with each possible observed system state is learnt by modeling the problem as hidden semi-Markov decision processes and solving it using policy iteration based Q-P learning. Then the future maintenance time can be estimated by re-simulating the system model using the learned maintenance policy. A set of experimental studies is conducted to testify the effectiveness of the proposed methodology and to investigate the impacts of involved system parameters. Copyright © 2014 Binary Information Press.

关键词： Markov processes

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Temporal pattern recognition using spiking neural networks for cortical neuronal spike train decoding

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IFAC Proceedings Volumes 2008年第2期41卷 5203-5208页

作者： Huijuan Fang Yongji Wang Jiping He Shan Liu Key Laboratory for Image Processing and Intelligent Control of Education Ministry of China Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China (Tel: 86-27-87557284 The Harrington Department of Bioengineering and the Center for Neural Interface Design of The Biodesign Institute Arizona State University Tempe AZ 85287 USA

Most experimental and decoding algorithm studies of brain neural signals assume that neurons transmit information as a rate coding, but recent studies on the fast cortical computations indicate that temporal coding is probably a more biologically plausible scheme used by neurons. We introduce spiking neural networks (SNN) which consist of spiking neurons propagate information by the timing of spikes to analyze the cortical neural spike trains directly without temporal information lost. The SNN based temporal pattern classification is compared with the conventional artificial neural networks (ANN) based firing rate analysis. The results show that the SNN algorithm can achieve higher accuracy, which demonstrates that temporal coding is a viable code for fast neural information processing and the SNN approach is suitable for recognizing the temporal pattern in the cortical neural signals.

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

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Simulating evolutional symport/antiport by evolution-communication and vice versa in tissue p systems with parallel communication 18th

Simulating evolutional symport/antiport by evolution-communi...

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18th International Conference on Membrane Computing, CMC 1018

作者： Adorna, Henry Alhazov, Artiom Pan, Linqiang Song, Bosheng Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Wuhan430074 China University of the Philippines Diliman Quezon City1101 Philippines Institute of Mathematics and Computer Science Academy of Science of Moldova Academiei 5 Chişinău2028 Moldova School of Electric and Information Engineering Zhengzhou University of Light Industry Zhengzhou450002 China

We aim to compare functionality of symport/antiport with embedded rewriting to that of symport/antiport accompanied by rewriting, by two-way simulation, in case of tissue P systems with parallel communication. A simul... 详细信息

ISBN: (纸本)9783319733586

关键词： Tissue

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Multiple index optimization method based on orthogonal design and fuzzy analysis for electrical resistance tomography sensor design

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ICIC Express Letters 2012年第9期6卷 2301-2307页

作者： Wang, Pai Guo, Baolong Li, Nan The Institute of Intelligent Control Image Engineering Xidian University P.O. Box 284 No. 2 Taibai Road Xi'an 710071 China College of Mechanical Engineering and Applied Electronics Technology Beijing University of Technology No. 100 Pingleyuan Chaoyang District Beijing 100124 China

A multiple index optimization method for ERT sensors is put forward which combines an orthogonal design and fuzzy analysis. This method first establishes a scientific and proper fuzzy evaluation method using fuzzy mathematics to define ERT index satisfaction (ERTIS) and overall ERT index satisfaction (OERTIS) and to construct their satisfaction functions. It unifies all of ERT sensor indices into a single comparable satisfaction value. Then, the OERTIS analysis is developed which allows a set of multiple index orthogonal experiments to be transferred into a single index orthogonal experiment, where, the uniformity index and the correlation coefficient index of the ERT sensor are set as the optimization objectives. The experiments are set up based on multi-index orthogonal design. The experimental results indicate that the method can derive an evenly distributed sensitivity field and a better image with the optimized sensors, and improve the OERTIS of the ERT sensors by 13.75%. © 2012 ICIC International.

关键词： Functions

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