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2024 8th international conference on Robotics, control and automation, ICRCA 2024

2024 8th International Conference on Robotics, Control and A...

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8th international conference on Robotics, control and automation, ICRCA 2024

ISBN: (纸本)9798350344721

The proceedings contain 85 papers. The topics discussed include: a hybrid method for INS/GPS integrated navigation system based on the improved Karman filter and back propagation neural network;development of Simulink-based physical experiment platform for EtherCAT bipedal robots;a biogeography-based optimization algorithm with heuristics for distributed multiproduct scheduling problem;research on low energy consumption AC system control strategy of battery electric vehicles;flyback converter modeling and overload control characteristics for power supply based on state space averaging;a simple confidence-supervised model for high resolution defect recognition;research and development of intelligent warehouse management system based on RFID technology;research on methods of vehicle image retrieval in expressway scenarios based on deep learning neural network;and motion simulation control system of detecting robot based on digital twin.

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GRU-Based Prediction of Muscle Activation Using IMU Data: Integrating VR for Rehabilitation Assessment

GRU-Based Prediction of Muscle Activation Using IMU Data: In...

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ieee international conference on cyber technology in automation, control, and intelligent systems

作者： Weishu Lin Chen Su Zida An Zhengyi Li Qi Jing Shengwei Zhang Fei Wang School of Robotics Northeastern University Shenyang Liaoning China School of Software Northeastern University Shenyang Liaoning China School of Robotics North-eastern University Shenyang Liaoning China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

In this paper, we propose a novel approach using IMU data to predict muscle activation, diverging from the conventional method of employing sEMG data for continuous angle estimation. We opt for Gated Recurrent Units (GRUs) over Long Short-Term Memory (LSTM) due to their simplified and efficient structure, achieving an accuracy of 0.7562 in predicting muscle activation. Additionally, we utilize VR to visualize the color changes in muscles, enhancing the effectiveness of rehabilitation assessments.

关键词： Accuracy automation Image color analysis Estimation Data visualization Muscles Logic gates control systems intelligent systems Long short term memory

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Blockchain-Integrated Deep Learning for Secure Health Data Sharing and Consent Management 2

Blockchain-Integrated Deep Learning for Secure Health Data S...

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2nd international conference on intelligent cyber Physical systems and Internet of Things (ICoICI)

作者： K Deepthika Shobana, G. Reddy, Kumbam Venkat Srimathi, S. Kumar, Binod Upadhyay, Shrikant Dayananda Sagar Univ CSE Cyber Secur Bangalore Karnataka India Takshashila Univ Sch Comp Sci Ongur India Vignana Bharati Inst Technol Dept IT Ghatkesar Medchal India SIMATS Saveetha Sch Engn Dept Bio Technol Chennai Tamil Nadu India Brainware Univ CSE Barasat WB India MLR Inst Technol Elect & Commun Engn Hyderabad India

ISBN: (纸本)9798331540661;9798331540678

The collection of medical data encompasses a variety of patient records that hold significant value for ongoing treatment and future research works. To ensure the privacy of the data, it is imperative to store and share it securely. Utilizing blockchain technology in managing healthcare data is prevalent due to its decentralized nature and ability to provide tamper-proof security measures. In healthcare's dynamic and ever-changing realm, the importance of securely sharing sensitive health data and managing consent effectively cannot be overstated. These factors are crucial in safeguarding patient privacy and promoting collaborative research efforts. This study aims to investigate the potential integration of blockchain technology and Long Short-Term Memory (LSTM) deep learning models to develop a comprehensive framework that ensures secure health data sharing and effective consent management. Integrating blockchain technology's decentralized and immutable ledger with the sequential learning capabilities of LSTM presents a promising approach to tackle the various obstacles related to data integrity, provenance, and patient-centric consent within healthcare ecosystems. The proposed system aims to improve the security and transparency of health data transactions while enabling dynamic consent management. This empowers individuals to have more control over their data. In this study, aims to assess the effectiveness of the blockchain-integrated LSTM model in ensuring health data security. Additionally, the investigated model facilitates smooth and privacy-preserving collaboration among various healthcare stakeholders. The experiments involved utilizing two publicly available data sources, CICIDS-2017 and NSL-KDD. These experiments evaluated the proposed model's performance compared to existing state-of-the-art approaches within non-blockchain and blockchain settings. The results demonstrated that the proposed model exhibited superior performance across both datasets, ach

关键词： Blockchain Deep Learning LSTM Health Data Sharing Consent Management Privacy Security

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Truncated Predictor Feedback control for Linear systems with Multiple Distributed Input Delays

Truncated Predictor Feedback Control for Linear Systems with...

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ieee international conference on cyber technology in automation, control, and intelligent systems

作者： Guangjie Wang Qingsong Liu School of Information Science and Engineering Wuhan University of Science and Technology Wuhan China

This paper aims to deal with the delay compensation problem for linear systems in the presence of three distributed input delays. A fresh state variable is instituted so as to design the state-based feedback controllers. Whereafter, truncated predictor feedback (TPF) approaches are built to make up for the three distributed input delays. It is displayed that the original system possessing three distributed input delays can be stabilized via the TPF controller. Additionally to demonstrating the efficacy of the built controller via an example.

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Advancing Industrial cybersecurity: Machine Learning-Based Detection and Mitigation of IIoT Attacks 5

Advancing Industrial Cybersecurity: Machine Learning-Based D...

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5th international conference on IoT Based control Networks and intelligent systems, ICICNIS 2024

作者： Kumar, S Gouri Kiran Prasad, Raavi Satya College of Computing and Information Sciences University of Technology and Applied Sciences Muscat Oman Dhanekula Institute of Engineering & Technology Department of Computer Science & Engineering A.P. Vijayawada India

ISBN: (纸本)9798331518097

A threatening entry or exploitation of vulnerabilities within an industrial network or system connected to IoT devices is an IIoT (Industrial Internet of Things) attack. Due to manufacturing delays, broken equipment, lost confidential data, and fines from the government, cyberattacks on IIoT systems can cause significant financial losses. Rapid reaction and recovery are made possible by efficient detection systems, which can lessen these losses. Many researchers have applied machine learning (ML) approaches to predict IIoT attacks in advance. Industrial networks can be highly effectively and dynamically protected against various cyber threats by utilizing MLthrough IIoT attack distinguishing. This ML methodology is a vital component of today's cybersecurity toolkit for IIoT contexts owing to its capacity for handling massive info, adjusting for novel threats, & delivering insights in real-time. Industrial networks can be highly effectively and dynamically protected against cyber threats by utilizing ML to IIoT attack identification. ML is a vital component of today's cybersecurity toolkit for IIoT contexts for its capacity to handle massive quantities of info, adjust to emerging risks, & deliver insights in real time. IIoT data can contain noise, missing values, or irrelevant information, negatively affecting model performance. Identifying and extracting relevant features from IIoT data was highly complex and time-consuming. The methodology's effectiveness is heavily related to features with high quality utilized. This paper discusses the various techniques, their limitations, and the gaps studied during the literature survey. Among the machine learning approaches, the model should adapt to the new infrastructure attacks, which is effectively possible only through meta-heuristic learning algorithms. © 2024 ieee.

关键词： cyber attacks

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Line Feature Based Visual Servo control with TDE for Underwater Vehicle Manipulator systems Target Grasping

Line Feature Based Visual Servo Control with TDE for Underwa...

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2024 ieee international conference on Unmanned systems, ICUS 2024

作者： Zhang, Yunfei Bian, Xinyu Sun, Yize Zhang, Zongyu Huang, Hai Yang, Xu National Key Laboratory of Autonomous Marine Vehicle Technology Harbin Engineering University Harbin China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350384185

Underwater vehicle manipulator systems (UVMSs) is becoming a research hotspot in the field of marine intelligent equipment. Introducing visual feedback equipped with cameras in the loop to guide UVMS to complete the grasping has comprehensive advantages of response speed, work efficiency and intelligence. In this paper, mapping between the feature error and the motion of the UVMS generalized degrees of freedom is established to realize the suspended grasping under the coordinated motion of the vehicle body and the manipulator. Then, a UVMS motion control algorithm based on time delay estimation (TDE) is constructed for visual servo autonomous grasping process, the TDE of which is used to estimate and compensate the lumped interference in real time. The Lyapunov theory is applied to analyze its stability and its control performance is verified through simulation experiments. The simulation results shew the effectiveness of the proposed visual servo control with TED method based on line features. © 2024 ieee.

关键词： Visual servoing

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Lightweight intelligent Fire Monitoring System Based on Flask and YOLOv8 2

Lightweight Intelligent Fire Monitoring System Based on Flas...

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2nd international conference on Artificial Intelligence and automation control, AIAC 2024

作者： Zheng, Zhihong Cai, Xiangyu Wang, Hongjie Fu, Xiaoyang School of Computing Zhuhai College of Science and Technology Zhuhai China

ISBN: (纸本)9798331529147

In recent years, with the impact of global warming, the incidence of fire has increased, and the prevention and control of fire has become an important topic of safety management. Traditional fire alarm systems rely on temperature and smoke sensors, but they often experience delays, making early fire detection challenging. Based on this requirement, this system aims to realize real-time fire monitoring and provide more efficient early fire warning by introducing computer vision technology. The system uses the improved YOLOv8 lightweight model as the core fire detection algorithm, and realizes the front-end transmission and display of video streams through the Web service constructed by the lightweight Flask framework. The system includes three core modules: front-end page, back-end server and model reasoning, to realize the automation of the whole process from video capture, image analysis to result display. Compared with YOLOv8n, the recall rate of our model is increased by 1.9% and the mAP is increased by 0.5%, while the model size is reduced by 15.4% and the calculation amount is reduced by 21.0%. In general, the system has a lightweight structure and fast reasoning, which ensures the real-time response ability of the system, and is also convenient for flexible deployment on various hardware environments. © 2024 ieee.

关键词： Fire alarm systems

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Adaptive Fuzzy Optimal control of Modular Robot Manipulators systems via Integral Reinforcement Learning-Based Value Iteration Algorithm

Adaptive Fuzzy Optimal Control of Modular Robot Manipulators...

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ieee international conference on cyber technology in automation, control, and intelligent systems

作者： Hucheng Jiang Tianjiao An Zhenguo Zhang Mingchao Zhu Yuanchun Li Bo Dong Department of Control Science and Engineering Changchun University of Technology Changchun China Changchun Institute of Optics Fine Mechanics and Physics Changchun China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

A novel adaptive fuzzy optimal compensation control approach is developed for modular robot manipulator (MRM). By incorporating integral reinforcement learning (IRL) to value iteration (VI), the adaptive fuzzy optimal control approach effectively addresses the trajectory tracking problem without accurate dynamic model information, and break the limitation of traditional policy iteration (PI). Moreover, this article approximate cost function with fuzzy logic system (FLS), which simplify the solution of the optimal control policy and better capture the important features of the cost function.

关键词： Fuzzy logic Adaptation models Adaptive systems Trajectory tracking Heuristic algorithms Optimal control Reinforcement learning Cost function intelligent systems Manipulator dynamics

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A Novel Hand Gesture Recognition Method Using sEMG-Based PSODE-BPNN 12

A Novel Hand Gesture Recognition Method Using sEMG-Based PSO...

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12th international conference on cyber technology in automation, control, and intelligent systems, cyber 2022

作者： Li, Ling-Long Cao, Guang-Zhong Zhang, Yue-Peng Chen, Jiang-Cheng College of Mechatronics and Control Engineering Shenzhen University Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots Shenzhen518060 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Aim to improve the deficiencies of slow convergence speed and prone to trapped into local minimum for back propagation neural network (BPNN) in sEMG-based hand gesture, a novel hand gesture recognition method associating particle swarm optimization (PSO) with differential evolution (DE) is proposed to optimize BPNN, namely PSODE-BPNN. Firstly, initial parameters of weights and biases are adjusted and updated to yield optimal values, via PSO and DE incorporation with the gradient descent method of BPNN. Secondly, the optimal values are utilized as the initial parameters to train the BPNN. Then the proposed method is evaluated on sEMG-based hand gesture dataset Ninapro DB4. The performance for the PSODE-BPNN is compared with the PSO-BPNN, the BPNN, and other related competing methods in literatures. The experimental results show that the PSODE-BPNN improves the inadequacy of slow convergence rate and local minimum of BPNN, and achieves higher gesture recognition accuracy than other comparison listed methods. The effectiveness and superiority of the proposed method is significantly validated. © 2022 ieee.

关键词： Optimal systems

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Remote Operation Human-Machine Interaction System for a Robotic Arm in Deep Ground Environments

Remote Operation Human-Machine Interaction System for a Robo...

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ieee international conference on cyber technology in automation, control, and intelligent systems

作者： Zhenming Zheng Junkai Huang Xinyi Wu Xinyao Wang Fei Wang Faculty of robot Northeastern University Bussiness School Liaoning University Department of Engineering Northeastern University

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

Developing deep underground energy resources is crucial for addressing our country's energy imbalance. To tackle unsafe conditions and high communication latency, we designed a Remote control Human-Machine Interaction System for Robotic Arms. Using dual MYO armbands to collect human arm joint data, Quaternion data is converted into Euler Angles for the robotic arm joints. This study focuses on Motion Mapping in the Joint Space from the master arm to the slave arm. Integrating Model Predictive control technology predicts the motion of the slave arm joints, achieving effective Remote control and significantly reducing latency.

关键词： Human computer interaction Energy resources Quaternions Arms Manipulators Main-secondary Hazards intelligent systems Remote control Predictive control

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