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A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition systems

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A Novel Duo-Stage driven Deep Neural Network Approach for Mi...

2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Kulwa, Frank Samuel, Oluwarotimi Williams Asogbon, Mojisola Grace Oyemakinde, Tolulope Tofunmi Obe, Olumide Olayinka Li, Guanglin Cas Key Laboratory of Human-Machine Intelligence-Synergy Systems SIAT-CAS China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Guangdong Shenzhen518055 China University of Derby School of Computing and Engineering DerbyDE22 3AW United Kingdom Federal University of Technology Department of Computer Science Akure Nigeria

ISBN: (纸本)9781665493840

A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario ( © 2023 IEEE.

关键词： Electrodes

On the feasibility of producing cement-bonded particleboard panels containing agrowaste and silica fume 11

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On the feasibility of producing cement-bonded particleboard ...

11th International Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2022

作者： Meftahi, Farshad Hatami, Arman Majumdar, Kirty Dhemaied, Rahma Godbout, Stéphane Palacios, Joahnn Soliman, Ahmed Building Civil and Environmental Engineering Department Gina Cody School of Engineering and Computer Science Concordia University MontrealQCH3G 1M8 Canada Department of Civil Engineering Jadavpur University Kolkata700 032 India Energetic Systems and Advanced Technologies Department National School of Sciences and Advanced Technologies Borj Cedria Tunisia QuébecG1P 3W8 Canada

Agricultural waste disposal has become one of the most significant environmental challenges during the past decade. Consequently, agro-waste use in construction has become the subject of contemporary research. Implementing agro-waste materials for various elements in concrete mixtures had several implications on mechanical qualities, structural behavior, durability, and sustainability. Depending on its qualities, agro-waste can be replaced up to 100 percent with fine and coarse aggregates, serve as a filler, and operate as a reinforcing element. This study focused on producing cement-bonded agrowaste particle panels containing supplementary cementitious materials (silica fume and fly ash) to reduce the disposal problem while improving wall panels' surface quality significantly. For this reason, different panels with variations of agro-waste and types of supplementary cementitious materials were produced. The density of the panels and a visual inspection were carried out on the specimens. A novel method for eliminating agro-waste high-waste absorption and expansion was proposed. This paper can enlighten the in-situ engineers about the great potential of using agro-waste in cementitious wall panels. © 2022 International Conference on Structural Health Monitoring of Intelligent Infrastructure: Transferring Research into Practice, SHMII. All rights reserved.

关键词： Fly ash

A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements

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arXiv 2024年

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. The Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multi-player non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. Copyright © 2024, The Authors. All rights reserved.

关键词： Sensor nodes

Bayesian Optimization Framework for Efficient Fleet Design in Autonomous Multi-Robot Exploration

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arXiv 2024年

作者： Concha, David Molina Li, Jiping Yin, Haoran Park, Kyeonghyeon Lee, Hyun-Rok Lee, Taesik Sirohi, Dhruv Lee, Chi-Guhn The Dynamic Optimization and Reinforcement Learning Laboratory The Department of Mechanical & Industrial Engineering University of Toronto TorontoONM5S3G8 Canada The Department of Electrical & Computer Engineering University of Toronto TorontoONM5S3G8 Canada The Division of Engineering Science The Faculty of Applied Science and Engineering University of Toronto TorontoONM5S3G8 Canada The Complex System Design Laboratory The Department of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of The Department of Industrial Engineering Inha University Incheon22212 Korea Republic of

This study addresses the challenge of fleet design optimization in the context of heterogeneous multi-robot fleets, aiming to obtain feasible designs that balance performance and costs. In the domain of autonomous multi-robot exploration, reinforcement learning agents play a central role, offering adaptability to complex terrains and facilitating collaboration among robots. However, modifying the fleet composition results in changes in the learned behavior, and training multi-robot systems using multi-agent reinforcement learning is expensive. Therefore, an exhaustive evaluation of each potential fleet design is infeasible. To tackle these hurdles, we introduce Bayesian Optimization for Fleet Design (BOFD), a framework leveraging multiobjective Bayesian Optimization to explore fleets on the Pareto front of performance and cost while accounting for uncertainty in the design space. Moreover, we establish a sub-linear bound for cumulative regret, supporting BOFD’s robustness and efficacy. Extensive benchmark experiments in synthetic and simulated environments demonstrate the superiority of our framework over state-of-the-art methods, achieving efficient fleet designs with minimal fleet evaluations. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

SERVICE LEVEL AGREEMENTS AND SECURITY SLA: A COMPREHENSIVE SURVEY

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arXiv 2024年

作者： Nicolazzo, Serena Nocera, Antonino Pedrycz, Witold Department of Computer Science University of Milan Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Italy Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey

A Service Level Agreement (SLA) is a formal contract between a service provider and a consumer, representing a crucial instrument to define, manage, and maintain relationships between these two parties. The SLA’s ability to define the Quality of Service (QoS) expectations, standards, and accountability helps to deliver high-quality services and increase client confidence in disparate application domains, such as Cloud computing and the Internet of Things. An open research direction in this context is related to the possible integration of new metrics to address the security and privacy aspects of services, thus providing protection of sensitive information, mitigating risks, and building trust. This survey paper identifies state of the art covering concepts, approaches, and open problems of SLA management with a distinctive and original focus on the recent development of Security SLA (SecSLA). It contributes by carrying out a comprehensive review and covering the gap between the analyses proposed in existing surveys and the most recent literature on this topic, spanning from 2017 to 2023. Moreover, it proposes a novel classification criterium to organize the analysis based on SLA life cycle phases. This original point of view can help both academics and industrial practitioners to understand and properly locate existing contributions in the advancement of the different aspects of SLA technology. The present work highlights the importance of the covered topics and the need for new research improvements to tackle present and demanding challenges. © 2024, CC BY.

关键词： Quality of service

MF-MOS: A Motion-Focused Model for Moving Object Segmentation

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arXiv 2024年

作者： Cheng, Jintao Zeng, Kang Huang, Zhuoxu Tang, Xiaoyu Wu, Jin Zhang, Chengxi Chen, Xieyuanli Fan, Rui The School of Electronic and Information Engineering South China Normal University Foshan528225 China The Department of Computer Science Aberystwyth University AberystwythSY23 3DB United Kingdom The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The School of Internet of Things Engineering Jiangnan University Wuxi China The College of Intelligence Science and Technology National University of Defense Technology Changsha China The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China

Moving object segmentation (MOS) provides a reliable solution for detecting traffic participants and thus is of great interest in the autonomous driving field. Dynamic capture is always critical in the MOS problem. Previous methods capture motion features from the range images directly. Differently, we argue that the residual maps provide greater potential for motion information, while range images contain rich semantic guidance. Based on this intuition, we propose MF-MOS, a novel motion-focused model with a dual-branch structure for LiDAR moving object segmentation. Novelly, we decouple the spatial-temporal information by capturing the motion from residual maps and generating semantic features from range images, which are used as movable object guidance for the motion branch. Our straightforward yet distinctive solution can make the most use of both range images and residual maps, thus greatly improving the performance of the LiDAR-based MOS task. Remarkably, our MF-MOS achieved a leading IoU of 76.7% on the MOS leaderboard of the SemanticKITTI dataset upon submission, demonstrating the current state-of-the-art performance. The implementation of our MF-MOS has been released at https://***/SCNU-RISLAB/MF-MOS. © 2024, CC BY.

关键词： Semantics

A Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving:Deep Learning Approach

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 169-178页

作者： Mohammad Al-Sharman David Murdoch Dongpu Cao Chen Lv Yahya Zweiri Derek Rayside William Melek the Department of Electrical and Computer Engineering University of WaterlooWaterloo N2L 3G1Canada the Department of Mechanical and Mechatronics Engineering University of WaterlooWaterloo N2L 3G1Canada the Cognitive Autonomous Driving Lab University of WaterlooWaterloo N2L 3G1Canada the School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 999002Singapore the Faculty of Science Engineering and ComputingKingston University LondonLondon SW153DWUK the Khalifa University Center for Autonomous Robotic Systems Department of Aerospace EngineeringKhalifa UniversityAbu Dhabi 127788UAE

In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the *** a typical CPS,the braking system is crucial for the vehicle design and safe ***,precise state estimation of the brake pressure is desired to perform safe driving with a high degree of *** this paper,a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach.A deep neural network(DNN)is structured and trained using deep-learning training techniques,such as,dropout and rectified *** techniques are utilized to obtain more accurate model for brake pressure state estimation *** proposed model is trained using real experimental training data which were collected via conducting real vehicle *** vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving *** on these experimental data,the DNN is trained and the performance of the proposed state estimation approach is validated *** results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.

关键词： Brake pressure state estimation cyber-physical system(CPS) deep learning dropout regularization approach

Hidden Convexity-Based Distributed Operation of Integrated Electricity-Gas systems

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arXiv 2024年

作者： Liu, Rong-Peng Song, Yue Liu, Junhong Wang, Xiaozhe Guo, Jinpeng Hou, Yunhe The Department of Electrical and Computer Engineering McGill University MontrealQCH3A 0E9 Canada The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai China The Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong The Department of Energy and Electrical Engineering Ho-Hai University Jiangsu Nanjing China

This letter proposes a hidden convexity-based method to address distributed optimal energy flow (OEF) problems for transmission-level integrated electricity-gas systems. First, we develop a node-wise decoupling method to decompose an OEF problem into multiple OEF subproblems. Then, we propose a hidden convexity-based method to equivalently reformulate nonconvex OEF subproblems as semi-definite programs. This method differs from any approximation and convexification methods that may incur infeasible solutions. Since all OEF subproblems are originally convex or equivalently convexified, we adopt an ADMM to solve the hidden convexity-based distributed OEF problem with convergence analysis. Test results validate the effectiveness of the proposed method, especially in handling a large number of agents. Copyright © 2024, The Authors. All rights reserved.

关键词： Quadratic programming

A Data Completion Algorithm Based on Low-Rank Prior Knowledge for Data-Driven Applications

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IEEE Transactions on Network and Service Management 2024年

作者： Tang, Jun Guo, Bing Shen, Yan Garg, Sahil Kaddoum, Georges Hossain, M. Shamim Sichuan University College of Computer Science Chengdu China Chengdu University of Information Technology School of Computer Science Chengdu China École de Technologie Supérieure Electrical Engineering Department MontréalQCH3C 1K3 Canada Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India Lebanese American University Artificial Intelligence and Cyber Systems Research Center Beirut03797751 Lebanon King Saud University College of Computer and Information Sciences Department of Software Engineering Riyadh12372 Saudi Arabia

Low rank tensor ring based data recovery algorithms have been widely used in data-driven consumer electronics to recover missing data entries in the collecting data pre-processing stage for providing stable and reliable service. However, traditional recovery methods often fail to utilize the abundant prior knowledge of data and the non-local self-similarity of the data, thus leading to the failure to effectively capture the spatial relationships within high-dimensional data to recover them accurately. To address these problems, we present a novel Non-local Self-similarity and Low-rank Prior Knowledge based tensor ring completion method. Firstly, we incorporate the BM3D denoising operator within a Plug-and-Play framework to exploit the self-similarity in the data. Then a logarithmic determinant function is integrated to distinguish singular values in the cyclic unfolding matrix of the tensor and adopts a tensor ring completion approach based on weighted nuclear norms. Finally, in order to evaluate the effectiveness of our proposed method, we conducted a series of experiments by using the missing image dataset and the missing traffic data dataset respectively, and the experimental results show that our method achieves the highest level in terms of data recovery accuracy. © 2024 IEEE.

关键词： Data assimilation