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6th International Conference on Electrical, control and Instrumentation engineering, ICECIE 2024

作者： Asada, Daiki Oya, Hidetoshi Nagai, Shunya Hoshi, Yoshikatsu Graduate School of Integrative Science and Engineering Tokyo City University Tokyo Japan Tokyo City University The Department of Computer Science Tokyo Japan Niihama College Department of Electronics and Control Engineering Ehime Japan

ISBN: (纸本)9798350380040

In this paper, we propose a design method for a new event-trigger-based variable gain robust controller which achieves both consensus and guaranteed cost performance for a class of uncertain multi-agent systems (MASs) with the leader-follower structure. The proposed variable gain robust controller is composed of stabilizing state feedback laws, state feedback inputs for consensus, and compensation inputs with adjustable parameters, and is designed for giving consideration to relative positions explicitly. In this paper, we show that sufficient conditions for the existence of the proposed robust guaranteed cost formation controller are given in terms of Linear Matrix Inequalities (LMIs). Finally, a simple example is included. © 2024 IEEE.

关键词： State feedback

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Study of milk concentration detection based on the AC signal-modulated LED 2

Study of milk concentration detection based on the AC signal...

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2nd Conference on Biomedical Photonics and Cross-Fusion, BPC 2023

作者： Haonan, Xing Xiaoqi, Wei Lin, Gui Mingyu, Cao Xuhuix, Chen Cunliang, Cheng School of Intelligent Manufacturing and Control Engineering Shanghai Polytechnic University Shanghai201209 China School of Computer and Information Engineering Shanghai Polytechnic University Shanghai201209 China

ISBN: (数字)9781510667556

ISBN: (纸本)9781510667549

As milk consumption increases, rapid detection of milk concentration is becoming more and more crucial. In this research, we propose an LED-based milk concentration detection system, which is a new detection method that detects milk concentration by loading the LED with RF signals. The system includes an arbitrary waveform generator (AWG), LED, lampshade, silicon photo dioxide (PD), oscilloscope, and other components. After passing through different concentrations of milk solution, the light signal carrying a specific frequency is received by the PD, and after photoelectric conversion, the signal carrying a specific frequency in the light signal is converted out, and the concentration of the milk solution to be measured is obtained by reading the peak value measured by the oscilloscope. The experiments validated different brands of milk solutions with varying concentrations, demonstrating the feasibility of LED-based milk concentration detection, and the test analysis was performed for the same brand of milk solutions with varying concentrations, yielding a quadratic fit R2=0.9169, confirming that the system has good sensing performance for milk concentration and validating the method's feasibility. © 2023 SPIE.

关键词： Light emitting diodes

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An adaptive observer-based SOC estimation method for lithium-ion batteries against sensor measurement uncertainties 38

An adaptive observer-based SOC estimation method for lithium...

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38th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2023

作者： Xu, Xiaoyi Luo, Hao Chow, Mo-Yuen Huang, Cong-Sheng Jiang, Yuchen Harbin Institute of Technology Department of Control Science and Engineering Harbin China North Carolina State University Department of Electrical and Computer Engineering RaleighNC United States

ISBN: (纸本)9798350303636

Accurate estimation of battery state of charge (SOC) is critical for efficient and safe battery applications. The measurement uncertainties of sensors, including measurement noises and sensor bias will affect the estimation accuracy inevitably. Therefore, quantifying the relationship between the sensor measurement uncertainties and the SOC estimation errors and seeking better SOC estimation methods has been a hot topic of research. In this paper, model errors and SOC estimation errors under measurement noise and sensor bias are derived. The resulting analyses can be used to assess the robustness of the SOC estimates. In addition, an adaptive observer-based SOC estimation method for lithium-ion batteries is proposed to cope with the measurement uncertainty. Simulation experiments demonstrate the effectiveness of the proposed method. © 2023 IEEE.

关键词： adaptive observer lithium-ion battery sensor bias sensor noises State-of-Charge (SOC) estimation

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Research on multimodal relation extraction method based on contrast learning 3

Research on multimodal relation extraction method based on c...

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3rd International Conference on Electronic Information engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Zhao, Jingsheng Ni, Qihui Yan, Shuai Zhu, Qiaoming School of Information and Control Engineering Qingdao University of Technology Qingdao Shandong266000 China School of Computer Science and Technology Suzhou University Suzhou Jiangsu215006 China

ISBN: (纸本)9781510680449

Traditional relation extraction methods are usually based on single text data, and other modality information such as image and video can improve the effect of text relation extraction. Aiming at the problem of heterogeneity between multi-modal data, a multi-modal relation extraction model MRECL based on contrastive learning is constructed. Firstly, the feature representation of image and text is obtained through ViT and BERT models;secondly, contrastive learning is introduced to align image-text features to reduce modality heterogeneity in advance;then image is used as prefix guidance information, and multi-modal interaction fusion is carried out with text information through attention mechanism;finally, the probability of all relation classification is calculated through Softmax layer and the prediction relation is output. Experimental results show that the precision, recall, and F1 values of MRECL on the MNRE dataset reach 83.23%, 82.19%, and 82.70%, respectively, achieving better performance than the current SOTA model. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image enhancement

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Investigation of Factors, Affecting the Behaviour of Id-Vg Shift in MOSFET 6

Investigation of Factors, Affecting the Behaviour of Id-Vg S...

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6th International Youth Conference on Radio Electronics, Electrical and Power engineering, REEPE 2024

作者： Chukhraev, Igor V. Drach, Vladimir E. Bauman Moscow State Technical University Computer Science and Control Department Moscow Russia Sochi State University Department of Innovative Engineering and Digital Technologies Sochi Russia

ISBN: (纸本)9798350382891

This article investigates the I-V hysteresis (IVH) phenomenon in MOSFETs and the factors affecting it. The study focuses on the effects of oxide traps, interface traps, and 'border traps' on metal-oxide-semiconductor devices. The current paper also discusses a comprehensive method for separating the effects of interface traps and trapped oxide charge in metal-oxide-semiconductor transistors. According to the study border traps and bias-temperature instabilities in MOS devices are significant factors affecting IVH. The findings of this study can be useful in the MOSFET design and optimization of for various applications. © 2024 IEEE.

关键词： MOS devices

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Cloud-Edge Intelligent Collaborative Computing Model Based on Transfer Learning in IoT 9th

Cloud-Edge Intelligent Collaborative Computing Model Based o...

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9th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Long, Yang Li, Zhixin School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132022 China School of Computer Technology and Engineering Changchun Institute of Technology Changchun130012 China

ISBN: (纸本)9789819959679

The deep neural network is a reliable technical support for cloud computing and edge computing. It has excellent nonlinear approximation and generalization capabilities, making it suitable for classifying and predicting Internet of Things data in cloud computing and edge computing fields. However, the increasing size of neural networks poses a challenge for their deployment on devices with limited computing and storage resources. Traditional cloud computing services also suffer from high latency, which hinders real-time tasks. To address these challenges, this paper proposes a cloud-side cooperation model for deep learning based on migration learning technology. This model used migration learning technology to reduce the size of deep neural networks. Specifically, it deployed the deep neural network model (CDLM) in the cloud and the shallow neural network model (EDLM) at the edge. CDLM is used to help train EDLM and improve its performance, enabling it to run independently on edge devices with high accuracy and respond to real-time tasks. This approach reduced the amount of user data transmitted to the cloud, alleviated bandwidth pressure, and protected user privacy. Experimental results show that the proposed model improved the accuracy of EDLM by 19.58% compared with traditional neural network models. These findings provide a theoretical and experimental foundation for the study of cloud-edge collaborative models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Edge computing

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GPS Spoofing Attack Detection in Smart Grids Based on Improved CapsNet

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China Communications 2021年第3期18卷 174-186页

作者： Yuancheng Li Shanshan Yang School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China

This paper analyzes the influence of the global positionong system(GPS)spoofing attack(GSA)on phasor measurement units(PMU)*** propose a detection method based on improved Capsule Neural Network(CapsNet)to handle this *** the improved CapsNet,the gated recurrent unit(GRU)is added to the front of the full connection layer of the *** improved CapsNet trains and updates the network parameters according to the historical measurements of the smart *** detection method uses different structures to extract the temporal and spatial features of the measurements simultaneously,which can accurately distinguish the attacked data from the normal data,to improve the detection ***,simulation experiments are carried out on IEEE 14-,IEEE 118-bus *** experimental results show that compared with other detection methods,our method is proved to be more efficient.

关键词： smart grid detection method improved capsule neural network phasor measurement units global positioning system spoofing attack

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Virtual Flux-Based Scheme for Passivity Enhancement in MMC-HVDC System 7

Virtual Flux-Based Scheme for Passivity Enhancement in MMC-H...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Liu, Jiayi Zhang, Hengke Zhao, Bo Gu, Fangning Sun, Mingzhe Liu, Yiqi Zhao, Zheng Teng, Shangfu Northeast Forestry University College of Computer and Control Engineering Harbin China State Grid Economic and Technological Research Institute Co. Ltd. Beijing China

ISBN: (纸本)9798350359558

Inherent control delays pose significant challenges to the stability of the Modular multilevel converter-based high voltage direct current (MMC-HVDC) system, inducing the risk of high-frequency resonances (HFR). This paper provides the virtual flux-based passivity enhancement schemes to remove the influence of the time delay terms. The negative damping risks can be eliminated, improving the system's stability and safety. The impedance modeling and simulation results validate the effectiveness of the approach. © 2024 IEEE.

关键词： System stability

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A Document Ensemble Clustering Approach via Dimensionality Reduction 43

A Document Ensemble Clustering Approach via Dimensionality R...

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43rd Chinese control Conference, CCC 2024

作者： Li, Xingzhi Wang, Zhiwei Lang, Qi Liu, Xiaodong School of Control Science and Engineering Dalian University of Technology Dalian116024 China School of Computer Science and Information Technology Northeast Normal University Changchun130117 China

ISBN: (纸本)9789887581581

The internet's rapid expansion has generated abundant text data, posing a formidable challenge in extracting key information, where document clustering demonstrates its powerful role. Previously, many people commonly used a single clustering method, which may not be suitable for certain datasets. Additionally, documents are often represented as high-dimensional vectors, leading to the curse of dimensionality and sparsity issues, and clustering algorithms' effectiveness in high-dimensional spaces is limited. Therefore, this paper proposes a document ensemble clustering method based on dimensionality reduction, which can overcome the limitations caused by using a single clustering method, making it more suitable for the majority of text data. Initially, dimensionality reduction techniques are applied to preprocess document embeddings generated. Subsequently, these embedding vectors are fed into an ensemble clustering module, where distances between samples are computed using multiple clustering methods. By adjusting weights, the distance matrices are subjected to weighted summation to obtain an appropriate distance matrix. Finally, the K-means method is applied to cluster division, yielding the most suitable clusters for extracting the most relevant keywords. The proposed method was evaluated empirically, achieving excellent results on the 20Newsgroups and Reuters-21578 datasets. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Dimensionality reduction

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Research on Mixing of Virtual and Actual Reality for Motor Augmented Assembly 23

Research on Mixing of Virtual and Actual Reality for Motor A...

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6th International Conference on Image and Graphics Processing, ICIGP 2023

作者： Zhang, Haowei Shao, Xuqiang Wu, Xuan School of Control and Computer Engineering North China Electric Power University China Engineering Research Center of Intelligent Computing for Complex Energy Systems Ministry of Education China

ISBN: (纸本)9781450398572

The effect of the mixing of virtual and actual reality will directly affect the realism and immersion of the augmented assembly system. The correct occlusion relationship and collision interaction between virtual and real objects are crucial to the fusion effect. The existing virtual-real occlusion handling methods cannot be applied to the augmented assembly system with complex contours and changeable scenes. Therefore, an occlusion handling method based on "virtual avatar"of motor equipment is proposed, which has strong robustness and real-time performance. Aiming at the problem that the number of wrong matching pairs of feature points is too large in the 3D registration process, we combine the method of Cosine similarity measurement on the basis of ORB algorithm to improve the accuracy of feature point matching. The current virtual and real object collision detection methods have large computation and low detection accuracy. We propose an octree hierarchical collision detection optimization algorithm based on model surface voxelization representation and volume difference, which improves the detection accuracy on the basis of ensuring computational efficiency. The experimental results show that the proposed methods have high real-time performance and accuracy, and greatly improve the realism and immersion of the augmented assembly system. © 2023 ACM.

关键词： Augmented reality

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