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5th International Conference on Industrial Artificial Intelligence, IAI 2023

作者： Liu, Xianggang Liu, Zhi-Wei Chi, Ming Zhang, Yifan Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education College of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9798350325294

The integration of renewable energy sources into the power grid has led to new challenges in maintaining the stability of the system frequency. This paper proposes a novel approach to address the Optimal Demand Side Frequency control (ODFC) problem using Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method. The proposed method models the ODFC problem as a Markov game, with centralized training based on multi-agent cooperative self-learning and associative storage service. In the decentralized execution stage, each agent independently outputs control actions to the controlled plant using local observations. Numerical simulations show that the proposed method effectively addresses the ODFC problem with superior performance compared to traditional methods. © 2023 IEEE.

关键词： Numerical methods

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Source-free Unsupervised Domain Adaptation for Privacy-Preserving intelligent Fault Diagnosis 3

Source-free Unsupervised Domain Adaptation for Privacy-Prese...

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3rd International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2022

作者： Zhu, Mengliang Zeng, Xiangyu Liu, Jie Zhou, Kaibo MOE Key Laboratory of Image Information Processing and Intelligent Control School of Artificial Huazhong University of Science and Technology Wuhan China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665492812

Rolling bearing is of vital significance in industrial applications and intelligent fault diagnosis (IFD) have been widely exploited in this field. However, cross-machine variations hinder the model performance across different machines in varying working conditions. To solve this issue, many unsupervised domain adaptation (UDA) approaches have been proposed recently, requiring direct access to the fully labeled source domain. However, privacy concerns are raised in traditional UDA scenario, since raw signals contain various private information. To address this issue, the source-free unsupervised domain adaptation (SFUDA) scenario is considered, where only the pre-trained source model is required, instead of the fully labeled source domain. In this paper, a SFUDA approach for intelligent fault diagnosis (IFD) is proposed. It consists of two steps: 1) source model generalization, where virtual adversarial training, R-Drop and label smoothing techniques are adopted to improve the generalization ability of the source model;and 2) target model adaptation, where information maximization is used for cluster assumption and mean teacher training paradigm is utilized to alleviate the catastrophic forgetting phenomenon. The proposed approach is verified on the Case Western Reserve University (CWRU) dataset. Experimental results on the show that the proposed SFUDA approach outperforms several typical UDA approaches, and its effectiveness is verified. © 2022 IEEE.

关键词： intelligent fault diagnosis privacy concerns Rolling bearing source-free unsupervised domain adaptation

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Stability Analysis and controller Design of Positive Linear Systems Subject to Actuator Saturation

Stability Analysis and Controller Design of Positive Linear ...

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第三十二届中国控制会议

作者： ZHANG Jishi Wu Yan WANG Yanwu GAO Zhonghu School of Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan Institute of Marine Electric Propulsion CSIC

ISBN: (纸本)9781479900305

This paper addresses the stability and stabilization problems for a class of positive linear systems in the presence of saturating *** objective is to give conditions of the stability,and design state feedback control laws such that the closed-loop systems is asymptotically stable and positive at the origin with a large domain of *** sufficient conditions for stabilization and positivity are derived via the Lyapunov functions method and convex analysis in both the continuous-time and the discrete-time cases,*** state feedback controller design and the estimation of domain of attraction are presented by solving a convex optimization problem with LMIs constraints.A numerical example is given to show the effectiveness of the proposed methods.

关键词： Actuator Saturation Stability Analysis Positive System Domain of Attraction

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EMPIRICAL STUDIES ON THE PROPERTIES OF LINEAR REGIONS IN DEEP NEURAL NETWORKS 8

EMPIRICAL STUDIES ON THE PROPERTIES OF LINEAR REGIONS IN DEE...

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8th International Conference on Learning Representations, ICLR 2020

作者： Zhang, Xiao Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

A deep neural network (DNN) with piecewise linear activations can partition the input space into numerous small linear regions, where different linear functions are fitted. It is believed that the number of these regions represents the expressivity of the DNN. This paper provides a novel and meticulous perspective to look into DNNs: Instead of just counting the number of the linear regions, we study their local properties, such as the inspheres, the directions of the corresponding hyperplanes, the decision boundaries, and the relevance of the surrounding regions. We empirically observed that different optimization techniques lead to completely different linear regions, even though they result in similar classification accuracies. We hope our study can inspire the design of novel optimization techniques, and help discover and analyze the behaviors of DNNs. © 2020 8th International Conference on Learning Representations, ICLR 2020. All rights reserved.

关键词： Deep neural networks

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Spiking neural P systems for arithmetic operations

Spiking neural P systems for arithmetic operations

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6th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2011

作者： Zeng, Xiangxiang Song, Tao Pan, Linqiang Zhang, Xingyi Key Laboratory on Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China Key Lab of Intelligent Computing and Signal Processing School of Computer Science and Technology Anhui University Hefei 230039 China

ISBN: (纸本)9780769545141

Recently, Gutiérrez-Naranjo and Leporati considered performing basic arithmetic operations on a new class of bioinspired computing devices - spiking neural P systems (for short, SN P systems). However, the binary encoding mechanism used in their research looks like the encoding approach in electronic circuits, instead of the style of spiking neurons (in usual SN P systems, information are encoded as the time interval between spikes). In this work, three SN P systems are constructed as adder, subtracter and multiplier, respectively. In these devices, a number is inputted to the system as the interval of time elapsed between two spikes received by input neuron, the result of a computation is the time between the moments when the output neuron spikes. © 2011 IEEE.

关键词： Encoding (symbols)

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Object tracking in infrared image sequence by Monte-Carlo method

Object tracking in infrared image sequence by Monte-Carlo me...

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International Congress on image and Signal processing

作者： Ma, Qianli Wang, Min Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan China Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Wuhan China

ISBN: (纸本)9781424465149

This paper presents a robust tracking algorithm for infrared objects in the image sequence, which is based on particle filer. Particle filter is a powerful tool for tracking especially in non-Gaussian condition, but the selection of samples is still a challenging problem. According to the frame-to-frame correlation, two basic assumptions are proposed. Borrowing the idea from Sequence Importance Sampling, Monte-Carlo method will be applied to solve the well-known shortcomings of Particle filter in this paper. Technologically, the proposed algorithm could also track multiple objects successfully. The experimental result has demonstrated its feasibility and validity. ©2010 IEEE.

关键词： Infrared imaging

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Memory Analysis for Memristors and Memristive Recurrent Neural Networks

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IEEE/CAA Journal of Automatica Sinica 2020年第1期7卷 96-105页

作者： Gang Bao Yide Zhang Zhigang Zeng Key Laboratory of Cascaded Hydropower Stations Operation and Control Electrical Engineering and New EnergyChina Three Gorges UniversityYichang 443002and Hubei Key Laboratory of Applied Mathematics(Hubei University)Wuhan 430074China Department of Medical Engineering California Institute of TechnologyPasadenaCalifornia 91125 USA School of Artificial Intelligence and Automation Huazhong University of Science and Technologyand also with the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of ChinaWuhan 430074China

Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operational *** neural networks are constructed by replacing resistors with memristors. This paper focuses on the memory analysis,i.e. the initial value computation, of memristors. Firstly, we present the memory analysis for a single memristor based on memristors’ mathematical models with linear and nonlinear ***, we present the memory analysis for two memristors in series and parallel. Thirdly, we point out the difference between traditional neural networks and those that are memristive. Based on the current and voltage relationship of memristors, we use mathematical analysis and SPICE simulations to demonstrate the validity of our methods.

关键词： Dopant drift memory memristive neural networks memristor

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image retrieval using the modified intersecting cortical model

Image retrieval using the modified intersecting cortical mod...

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2011 IEEE International conference on intelligent Computation and Bio-Medical Instrumentation, ICBMI 2011

作者： Yuan, Wenjin Zhang, Xuming School of Life Science and Technology Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9780769546230

The intersecting cortical model(ICM) is driven from the pulse-coupled neural networks (PCNN), and a lot of works have been done on it. In this paper, we have proposed a method, which is modified from the PCNN, to retrieval the image. This method uses the iteration times of the ICM as the feature, and we do it repeatedly. So we will get the feature vectors and utilize the features to retrieval the image. And the experimental results have proved the effectiveness of the proposed method. While the proposed method shows some advantages like the feature curves are linear and steady. © 2011 IEEE.

关键词： image retrieval

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Prostate surgery path planning based on simplified delay-PCNN

Prostate surgery path planning based on simplified delay-PCN...

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2011 IEEE International conference on intelligent Computation and Bio-Medical Instrumentation, ICBMI 2011

作者： Li, He Zhang, Xuming Hou, Wenguang Ding, Mingyue Life School of Science and Technology Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9780769546230

This paper presents an algorithm to find the shortest path in 3D(three-dimensional) prostate surgery planning. Using a simplified delay pulse coupled neural network(S-DPCNN) model, a shortest path can be drawn automatically from the target position to the puncture point. Compared to the traditional pulse coupled neural network(PCNN), S-DPCNN needs much fewer neurons and therefore decreases complexity of computation. Experiments on computer simulations show the validity of this method. © 2011 IEEE.

关键词： Motion planning

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Knowledge-Data Fusion Based Source-Free Semi-Supervised Domain Adaptation for Seizure Subtype Classification

Knowledge-Data Fusion Based Source-Free Semi-Supervised Doma...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Peng, Ruimin An, Jiayu Wu, Dongrui School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control Wuhan430074 China

ISBN: (纸本)9781665410205

Electroencephalogram (EEG)-based seizure sub-type classification enhances clinical diagnosis efficiency. Source-free semi-supervised domain adaptation (SF-SSDA), which transfers a pre-trained model to a new dataset with no source data and limited labeled target data, can be used for privacy-preserving seizure subtype classification. This paper considers two challenges in SF-SSDA for EEG-based seizure subtype classification: 1) How to effectively fuse both raw EEG data and expert knowledge in classifier design? 2) How to align the source and target domain distributions for SF-SSDA? We propose a Knowledge-Data Fusion based SF-SSDA approach, KDF-MutuaISHOT, for EEG-based seizure subtype classification. In source model training, KDF uses Jensen-Shannon Diver-gence to facilitate mutual learning between a feature-driven Decision Tree-based model and a data-driven Transformer-based model. To adapt KDF to a new target dataset, an SF-SSDA algorithm, MutualSHOT, is developed, which features a consistency-based pseudo-label selection strategy. Experiments on the public TUSZ and CHSZ datasets demonstrated that KDF-MutualSHOT outperformed other supervised and source-free domain adaptation approaches in cross-subject seizure subtype classification. © 2024 IEEE.

关键词： Self-supervised learning

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