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Neuromorphic Computing (ICNC), International Conference on

作者： Yonghuan Wang Leimin Wang Genping Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China

In this paper, by using the flux-controlled memristor model, the finite-time synchronization problem of delayed complex-valued memristive neural networks (MCNNs) is studied. Firstly, according to the proposed memristor model, we model the MCNNs as continuous systems on voltage-flux-time $(\wp, \varpi, t)$ domain. Then, we design a class of approaches to realize state synchronization between the drive and response systems, and the corresponding synchronization conditions are achieved. Finally, the effectiveness of results is illustrated with the simulations.

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Temperature Compensation Method of Fluxgate Sensor Based on Polynomial Fitting

arXiv

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arXiv 2024年

作者： Yang, Ruiping Liu, Huan Ge, Jian Chugo, Daisuke Dong, Haobin School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Faculty of Engineering Kwansai Gakuin University Osaka669-1330 Japan

Fluxgate sensors are widely used in the field of low frequency and weak vector magnetic field measurement because of their good performance, such as high resolution and low power consumption. However, during the long-term continuous observation, the drift errors of the fluxgate sensor will occur due to the variable ambient temperature. This paper proposes a temperature compensation method for fluxgate sensors based on polynomial fitting. First, a physical model of the temperature & fluxgate sensor was established on the COMSOL Multiphysics simulation platform, and the influence of temperature on the measurement performance of the fluxgate sensor was analyzed. Second, according to the existing temperature-magnetic field data, a temperature compensation model of the fluxgate sensor was constructed. And compared it with other temperature compensation method, the result shows that the proposed temperature compensation method is relatively simple and can better achieve real-time compensation for sensor application scenarios. Finally, to verify the effectiveness of the proposed method, numerous laboratory experiments were *** temperature drift is reduced from more than 500 nT before compensation to about 1 nT. The results show that the proposed method has a good temperature compensation effect on the data measured by the fluxgate sensor within a variable temperature background. Copyright © 2024, The Authors. All rights reserved.

关键词： Simulation platform

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Skeleton-Based Multi-Stream Adaptive Graph Convolutional Network for Indoor Scene Action Recognition

Skeleton-Based Multi-Stream Adaptive Graph Convolutional Net...

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2023 China automation Congress, CAC 2023

作者： Li, Jiazhuo Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru The School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan430074 China The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada The Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland The Department of Computer Engineering Istinye University Sariyer Istanbul34396 Turkey The Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798350303759

With the rapid advances in computer vision, human action recognition has gradually received attention, but the current methods still exhibit some problems in indoor environments. The human skeleton, as the framework of human motion, contains high-quality actional feature information, and the skeleton-based action recognition method effectively avoid the interference of interior background noise and has advantages in indoor action recognition. The outstanding effect of graph convolutional networks on graph structure data processing has led to its rapid development and wide application in skeleton-based action recognition. Second-order skeletal information also contains a large number of actional features but is not effectively utilized. The artificial predefined topology of the human skeleton map has limitations, and cannot reflect the interaction between limbs. To solve the above problems, this article designs an adaptive weighted multi-stream graph convolutional network (AM-GCN) based on skeletal information, using an attention mechanism to enhance the network's ability to extract actional features, and an adaptive layer to make the construction graph more flexible, incorporating second-order skeletal features through a dual-stream architecture. In this article, the NTU-RGB+D dataset has been used for the experiments, the results show that the method in this article has good results. © 2023 IEEE.

关键词： Convolution

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Plate Shape Prediction Based on Data-Driven in Roll Quenching Process

Plate Shape Prediction Based on Data-Driven in Roll Quenchin...

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2023 China automation Congress, CAC 2023

作者： Hu, Liu Chen, Luefeng Hu, Jie Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University The Department of Computer Engineering Istanbul Sariyer34396 Turkey Tokyo Institute of Technology Tokyo Yokohama226-8502 Japan

ISBN: (纸本)9798350303759

In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect types and flatness of the plate, providing theoretical support for setting process parameters in roller quenching production. First, the parameters of the quenching process are analyzed to identify their characteristics. Then, the K-Means clustering algorithm and correlation analysis are employed to process the quenching process parameters. A gradient boosting decision tree (GBDT) model is used to predict the defect types and flatness of the steel plates. Finally, industrial production data is utilized for experimental validation. The obtained experimental results verify the reliability of the proposed method. © 2023 IEEE.

关键词： decision trees

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Big Data Modeling Based on KNN-RF-SVM and Its Application in Product Sales Forecasting Field

Big Data Modeling Based on KNN-RF-SVM and Its Application in...

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intelligent Manufacturing and Industrial Big Data (ICIMIBD), International Conference on

作者： Chen Zhang Hongru Ren Xiuhua Lu Qunyao Yuan Renquan Lu School of Automation and Guangdong & Province Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou China

For the past few years, the big data prediction model has been extensively applied to our life. How to boost the performance of the big data prediction model has become a critical issue to be solved. At present, most of the big data prediction models are used single algorithm model. In this paper, K-nearest neighbor (KNN) algorithm, random forest (RF) algorithm and support vector machine (SVM) algorithm are used as the basic classifier, and the algorithm is combined through soft voting to obtain the KNN-RF-SVM combination model. Through the experiment test, the results indicate that the KNN-RF-SVM combined model is all the better than the single algorithm model in accuracy, precision, recall rate and F1 score.

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ADFNet: A two-branch robotic grasping network based on attention mechanism

ADFNet: A two-branch robotic grasping network based on atten...

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第40届中国控制会议

作者： Pengwei Xie Nan Hao Bo Chen Beijing Institute of Technology School of Automation Key Laboratory of Complex System Intelligent Control and Decision

One of the challenges of RGB-D-based robotic grasping technology is how to make full use of these two complementary heterogeneous data sources while ensuring real-time performance. The existing robotic grasping methods mainly extract information from single-mode images or use a one-branch network to process RGB and depth images. These methods are insufficient in fully fusing the effective information of the two modes, which limits the anti-jamming performance. In this work,we propose Attention Dense Fusion Network（ADFNet）, a novel RGB-D based robotic grasping system that directly predicts the optimal grasping pose from RGB-D images. Our system uses a two-branch network with a heterogeneous framework that processes RGB and depth image information separately and retain the original structure of each data source. After applying dense fusion networks at different scales, the high-dimensional features in the RGB and depth branches are embedded and fused at the pixel level. Besides, we incorporate attention mechanisms to effectively suppress the independent background regions of the feature map and enhance the significant features, which improves the prediction accuracy. We conduct qualitative and quantitative experiments on the standard Cornell Grasp Dataset. The experimental results show that ADFNet can effectively improve the prediction accuracy to 98.9 %, which is better than existing methods while ensuing real-time performance.

关键词： robotic grasping RGB-D attention mechanism

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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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2023 China automation Congress, CAC 2023

作者： Du, Sheng Ma, Xian Li, Xiang Wu, Min Cao, Weihua Pedrycz, Witold School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul Sariyer Turkey

ISBN: (纸本)9798350303759

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes. © 2023 IEEE.

关键词： Time series

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An attention-based RGBD dual-branch gesture recognition network

An attention-based RGBD dual-branch gesture recognition netw...

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第40届中国控制会议

作者： Bo Chen Pengwei Xie Nan Hao Beijing Institute of Technology School of Automation Key Laboratory of Complex System Intelligent Control and Decision

In this work,we use a hierarchical architecture based on detector-classifier for gesture recognition *** the operation of the architecture,the detector,which is essentially the switch of the classifier,is always *** the output of the detector is true,then the classifier is activated and returns a classification label for the input video *** work focuses on the improvement of detectors and *** the detector,we introduce an attention mechanism to guide the network to focus on the space and channel where the gesture is *** the classifier,based on the RGB information stream,we use an independent branch to extract the features of the depth stream,and finally merge the two *** gestures move in a three-dimensional space,depth information can make up for the lack of RGB *** show that on the Egogesture test set,our detector achieves 98.86% accuracy on RGB input,while the classifier achieves 93.85% *** the same time,our gesture recognition architecture can fully meet the real-time requirements.

关键词： Gesture Recognition RGBD feature fusion attention mechanism real-time

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Observer-based Data-driven Sliding Mode control for a Discrete-time Nonlinear Multiagent systems

Observer-based Data-driven Sliding Mode Control for a Discre...

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Data Driven control and Learning systems (DDCLS)

作者： Caiyun Yin Guohuai Lin Guangdeng Chen Hui Ma Hongyi Li School of Automation and the Guangdong Province Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou Guangdong China School of Mathematics and Statistics Guangdong University of Technology Guangzhou China

In this work, an observer-based sliding mode control strategy is proposed for a discrete-time nonlinear multiagent systems (MASs) with unknown disturbance. Only some agents are capable of acquiring the reference trajectory, and the dynamic models of the agents are unknown. Unlike the traditional model-based consensus control protocol, this method is data-driven and solely dependent on the input/output (I/O) data of the agents. The stability of the proposed control strategy is ensured by theoretical analysis and the simulation outcomes ultimately validate the viability of the developed approach.

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Isochronous Temporal Metric for Neighbourhood Analysis in Classification Tasks

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SN Computer Science 2023年第6期4卷 1-12页

作者： Kumar, Amit Krishan Mai, Nguyễn Ngọc Tian, Ke Xia, Yuanqing Institute of Research and Development Duy Tan University Da Nang Vietnam Faculty of Electrical-Electronic Engineering Duy Tan University Da Nang Vietnam Faculty of Chinese Language School of Foreign Language Duy Tan University Da Nang Vietnam State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

Machine learning, classification, and clustering techniques use the distance functions to evaluate the proximity between data entries and deduce the best neighbouring element and the closest matching entry. The best neighbour is not only the closest neighbour but a neighbour that is quick to respond. In view of that, a time-based isochronous metric is introduced to evaluate the best neighbours and form linkages by grouping similar entities. The proposed method uses parametric equations of the fastest descent and solves the time variables for attributes localised in curved space–time. The time metric is compared with commonly used distance metrics for accuracy in classification and clustering using benchmark and commonly used datasets. The nearest-neighbour technique is used for evaluating classification accuracy, and an adjusted random index (ARI) is used to evaluate clustering. The proposed method shows better accuracy and ARI in comparison to distance functions. It also assigns better weights to attributes of the dataset and easily identifies repeated patterns in noisy time series data.

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