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Formation control and Obstacle Avoidance Design for Networked USV Swarm With Exogenous Disturbance Under Intermittent Communication

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IEEE Transactions on Network Science and engineering 2025年

作者： Chang, Zhenyu Zong, Guangdeng Wang, Wencheng Yue, Ming Zhao, Xudong Tiangong University School of Control Science and Engineering Tianjin300387 China Weifang University College of Machinery and Automation Weifang261061 China Dalian University of Technology School of Automotive Engineering Dalian116024 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China

This study addresses the formation control problem of under-actuated unmanned surface vehicle (USV) swarm systems and designs an obstacle avoidance strategy for navigating in obstacle environments. The intermittent communication scheme, consisting of the working period and the rest period, is introduced to reduce the communication burden and the consumption of control resources. A distributed formation control strategy is designed to guarantee the realization of the desired formation configuration. Considering the potential impact of obstacles in the operating environment, the artificial potential field repulsion obstacle avoidance method is embedded into the formation control strategy. To eliminate the impact of exogenous disturbances, a disturbance observer is established using the partially known disturbance information. Sufficient conditions are provided to ensure the uniformly ultimate boundedness of the formation error and disturbance observation error. In addition, an energy function related to the obstacle position information is designed to verify that the desired obstacle avoidance behavior can be achieved. The feasibility of theoretical analysis is validated by applying the developed control strategy to surface formation cruise missions. © 2013 IEEE.

关键词： Unmanned surface vehicles

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Output Current Pulsation Reduction with Multi-coil Receiver in the Dynamic Wireless Charging Systems for Electric Vehicles

Output Current Pulsation Reduction with Multi-coil Receiver ...

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International Conference on control, automation and Information Sciences (ICCAIS)

作者： Tran Duc Hiep Nguyen Huu Minh Nguyen Thi Diep Tran Trong Minh Nguyen Kien Trung School of Electrical and Electronic Engineering Hanoi University of Science and Technology Viet Nam Faculty of Electrical Engineering Hanoi University of Industry Viet Nam Faculty of Control and Automation Electric Power University Viet Nam

Dynamic wireless charging (DWC) systems for electric vehicles are an important solution for electric vehicles to be used more commonly in the future. However, when the electric vehicle is moving and charging in the DWC lane, the charging current is pulsated. This charging current pulse will affect the battery life. This paper proposes a multi-coil receiver structure to reduce the pulsation of the output current. The proposal has been verified by simulation and experimental results, the output current pulsation achieved is 3.1%.

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Design of Lowpass Filter and Wavelet Analysis for Electroretinogram in Dogs

Design of Lowpass Filter and Wavelet Analysis for Electroret...

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IEEE Region 10 International Conference TENCON

作者： Tunyawat Tuntrik Nalinee Tuntivanich David Banjerdpongchai Dept. of Electrical Engineering Center of Excellence in Intelligent Control Automation of Process Systems Faculty of Engineering Chulalongkorn University Bangkok Thailand Dept. of Veterinary Surgery Faculty of Veterinary Science Bangkok Thailand

In frequency domain, wavelet coefficient is one of the characteristics of ERG signal which is hidden in frequency-domain. This paper aims to develop a tool for analyzing the constituent frequencies and time of interest in the dog's ERG signal using wavelet analysis. We design a lowpass filter to reduce noise during the measurement of ERG response. ERG dataset was obtained for the light intensity series and their constituent frequencies are analyzed by using the continuous wavelet transform (CWT). The results show that, in time-domain, as light intensity increases, both amplitudes of a-wave and b-wave are higher. We can visualize the noise interference in the ERG signals. Then a lowpass filter is designed to attenuate the noise effect. The filtered signals have lower a-, and b-waves' amplitude which affect the important characteristics in time domain. Therefore, we choose to filter signal from 26ms onward, the main components are recovered, and the filtered signal becomes smoother. In frequency-domain, the peak magnitude of wavelet coefficients is increased as light intensity increases.

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Sewer Network Model of a City with a Medium-Sized Population

Sewer Network Model of a City with a Medium-Sized Population

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International Carpathian control Conference (ICCC)

作者： Iulian Vasiliev Laurentiu Luca Marian Barbu Ramon Vilanova Sergiu Caraman Department of Automation and Electrical Engineering Faculty of Automation Computers Electrical and Electronic Engineering Galati Romania Department of Automation Systems and Advanced Control Research Autonomous University of Barcelona Barcelona Spain

A model of a sewer network for a medium city located in Easter Romania with 250,000 inhabitants is presented in the paper. The model was developed using BSM Sewer software package. An influent was also determined considering the 5 collecting areas and defined on a time- horizon of 28 days. The influent has the following components: a domestic one calculated using the average wastewater production of each inhabitant with specific loads and considering daily, weekly and yearly variations, a pluviometric one proportional to the collecting area surface that includes a rain event and a storm one and an industrial influent (considered only for the 5 th collecting area). The model was tested in different operating conditions of the sewer network considering three operating points given by the values of the valve opening and pumps flows at the tank outlets. The results were analyzed based on 10 performance indicators implemented by researchers from Lund University for BSM Sewer. They showed the need for an optimization of the operating regime of the sewer network in terms of volume and quality of discharged water.

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Assessment of Depression Level Based on Multi-Scale 3D Convolution and Expression Videos 3

Assessment of Depression Level Based on Multi-Scale 3D Convo...

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3rd International Conference on Computer Vision and Pattern Analysis, ICCPA 2023

作者： Cao, Qishuang Gao, Junlong Lv, Zeying Li, Mi Department of Automation Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China

ISBN: (纸本)9781510667563

The automatic diagnosis of depression using deep learning has recently shown significant progress, and 3DCNN has been applied to assessing depression levels from videos. There are two problems to be encountered applying 3DCNN, including insufficient data to train the model and an insufficient diversity of features extracted on a single scale. An approach is presented in this paper to address these issues. Firstly, we augmented the raw data by adding noise and applying rotations to increase the amount of the training set. Secondly, we constructed a 3DCNN to extract more diverse features from multiscales. We evaluated our proposed method on the AVEC 2014. Our approach achieved MAE=7.13 and RMSE=9.16, demonstrating its effectiveness in improving the accuracy of depression assessment compared to existing approaches. © 2023 SPIE.

关键词： Deep learning

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Machine Learning in Predicting the Impact of Filter Parameters on the Accuracy of Biosignal Measurement

Machine Learning in Predicting the Impact of Filter Paramete...

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Digital Signal Processing and its Applications (DSPA), International Conference on

作者： Yeldos A. Altay Raisa K. Uskenbayeva Abu-Alim E. Ayazbay Alexey V. Fedorov Department of Robotics and Technical Means of Automation Satbayev University Almaty Kazakhstan Institute of Automation and Information Technologies Vice-Rector Satbayev University Almaty Kazakhstan Electronic engineering Almaty University of Power Engineering and Telecomunications Almaty Kazakhstan Faculty of Control Systems and Robotics ITMO University Saint-Petersburg Russian Federation

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

In this scientific article under consideration, a machine learning model has been developed that allows predicting the impact of filter parameters on the accuracy of measuring a biosignal - an ECG (electrocardiography) signal. For the first time, quantitative results have shown the degree of influence and the error introduced into the measured biosignal and described through dependence. The results of assessing the obtained relationship of indicators demonstrate an inverse correlation relationship that characterizes a decrease in the accuracy of measurement at the output of a high-order digital filtering system. The machine learning model is based on the linear regression method the dependence of which is approximated by the method based on least squares and visualized using a scattergram. The adequacy of the developed machine learning model was confirmed by calculating the coefficient of determination and checking its statistical significance. The developed model can be used to create new or improve existing biomedical data processing systems to increase the reliability of the results.

关键词： Accuracy Shape Filtering Biological system modeling Machine learning Biomedical measurement Electrocardiography Distortion Distortion measurement Digital filters

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Learning control laws in simulated environments for motion gestures of humanoid robots

Learning control laws in simulated environments for motion g...

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International Conference on control Systems and Computer Science (CSCS)

作者： Andrei Mateescu Ioana-Livia Stefan Dragos Constantin Popescu Ioan Stefan Sacala Silviu Raileanu Automatic Control and Systems Engineering Faculty of Automatic Control and Computers University Politehnica of Bucharest Splaiul Independentei No. 313 Bucharest Romania Automation and Industrial Informatics Faculty of Automatic Control and Computers University Politehnica of Bucharest Splaiul Independentei No. 313 Bucharest Romania

This article presents a way to implement a collection of fitness moves on an iCub (Cognitive Universal Body) robot, whose objective is to act as a fitness instructor and interact with humans. Due to the advantages of a simulation medium, the robot is simulated in the Gazebo simulator, on The Construct online platform. The control is done using Robot Operating System (ROS) with PID controllers for the robot joints, with the proportional, integral and derivative parameters tuned using a Genetic Algorithm. The scripts for the collection of moves and the algorithm used to tune the PID controllers are written in Python.

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Automated Depression Diagnosis Based on Facial Expression and Deep Dual-Stream Convolutional Neural Networks 3

Automated Depression Diagnosis Based on Facial Expression an...

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3rd International Conference on Computer Vision and Pattern Analysis, ICCPA 2023

作者： Chen, Yangyang Chen, Yingyu Li, Mi Department of Automation Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China

ISBN: (纸本)9781510667563

Depression is a common mental illness characterized by symptoms such as low mood, pessimism, and insomnia. In this study, we developed a deep Dual-Stream CNN to automatically diagnose and classify depression in expression video sequences. The network has two branches that extract static features and dynamic features from static and dynamic expressions, respectively, which are then fused for depression classification. The experiments were performed on the AVEC2014 database, and the results showed that the Dual-Stream model significantly improved the classification performance of depression, achieving an accuracy of 69.08% in depression categorization. © 2023 SPIE.

关键词： Convolutional neural networks

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Hardware Implementation of a Sliding Detection Algorithm for Robotic Hands Using Force Sensors

Hardware Implementation of a Sliding Detection Algorithm for...

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Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Jose Mendoza-Peñaloza Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Systems Graduate Program Automation and Control Group/GRACO Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

In this work, a sliding detection algorithm was proposed for a previously developed robotic hand through the utilization of force sensors. Previous works have designed several hardware architectures to filter sensor data, implement dynamic control, and machine learning models for controlling the robotic fingers using a single Field Programmable Gate Arrays (FPGA) chip. In this regard, the slip detection algorithm was developed to comprise three stages, each with a low computational cost, including a moving average filter, a first-order derivative, and a peak detection algorithm. A reference model was constructed and validated through an experimental protocol employing daily use objects to create a database. Subsequently, the slip detection algorithm was mapped onto hardware utilizing previously developed floating-point arithmetic IP cores and implemented using a Zynq 7020 device. The FPGA implementation was characterized in terms of resource occupation, power consumption, execution time, and numerical error with the reference model.

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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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