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作者： Talukdar, Upasana Hazarika, Shyamanta M. Gan, John Q. Biomimetic and Cognitive Robotics Lab Department of Computer Science and Engineering Tezpur University Tezpur India Mechatronics and Robotics Lab Department of Mechanical Engineering Indian Institute of Technology Guwahati India School of Computer Science and Electronic Engineering University of Essex United Kingdom

Objective. Electroencephalogram (EEG) signals are non-stationary. This could be due to internal fluctuation of brain states such as fatigue, frustration, etc. This necessitates the development of adaptive brain-computer interfaces (BCI) whose performance does not deteriorate significantly with the adversary change in the cognitive state. In this paper, we put forward an unsupervised adaptive scheme to adapt the feature extractor of motor imagery (MI) BCIs by tracking the fatigue level of the user. Approach. Eleven subjects participated in the study during which they accomplished MI tasks while self-reporting their perceived levels of mental fatigue. Out of the 11 subjects, only six completed the whole experiment, while the others quit in the middle because of experiencing high fatigue. The adaptive feature extractor is attained through the adaptation of the common spatial patterns (CSP), one of the most popular feature extraction algorithms in EEG-based BCIs. The proposed method was analyzed in two ways: offline and in near real-time. The separability of the MI EEG features extracted by the proposed adaptive CSP (ADCSP) has been compared with that by the conventional CSP (C-CSP) and another CSP based adaptive method (ACSP) in terms of: Davies Bouldin index (DBI), Fisher score (FS) and Dunn's index (DI). Main results. Experimental results show significant improvement in the separability of MI EEG features extracted by ADCSP as compared to that by C-CSP and ACSP. Significance. Collectively, the results of the experiments in this study suggest that adapting CSP based on mental fatigue can improve the class separability of MI EEG features. © 2020 IOP Publishing Ltd.

关键词： Electroencephalography

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Federated Learning While Providing Model as a Service: Joint Training and Inference Optimization

arXiv

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arXiv 2023年

作者： Han, Pengchao Wang, Shiqiang Jiao, Yang Huang, Jianwei School of Information Engineering Guangdong University of Technology Guangzhou China IBM T. J. Watson Research Center Yorktown HeightsNY United States Department of Computer Science and Technology Tongji University Shanghai China School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Shenzhen China

While providing machine learning model as a service to process users’ inference requests, online applications can periodically upgrade the model utilizing newly collected data. Federated learning (FL) is beneficial for enabling the training of models across distributed clients while keeping the data locally. However, existing work has overlooked the coexistence of model training and inference under clients’ limited resources. This paper focuses on the joint optimization of model training and inference to maximize inference performance at clients. Such an optimization faces several challenges. The first challenge is to characterize the clients’ inference performance when clients may partially participate in FL. To resolve this challenge, we introduce a new notion of age of model (AoM) to quantify client-side model freshness, based on which we use FL’s global model convergence error as an approximate measure of inference performance. The second challenge is the tight coupling among clients’ decisions, including participation probability in FL, model download probability, and service rates. Toward the challenges, we propose an online problem approximation to reduce the problem complexity and optimize the resources to balance the needs of model training and inference. Experimental results demonstrate that the proposed algorithm improves the average inference accuracy by up to 12%. Copyright © 2023, The Authors. All rights reserved.

关键词： Constrained optimization

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Comparative Analysis of Real-Time Multi-Person Pose Detection in Electrical Industrial Safety Scenarios Using YOLOv8-Pose and OpenPose

Comparative Analysis of Real-Time Multi-Person Pose Detectio...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Shang-Te Tsai Zong-Rong Wu Pin-Hsuan Lin Chin-Hsiung Chen Wei Chien Yu-Cheng Chang School of Art and Design Sanming University Fujian China Department of Technical and Vocational Program in Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan Department of Business Administration National Central University Taoyuan Taiwan Department of Electrical Engineering New Taipei Municipal Tamsui Commercial Industrial Vocational Senior High School New Taipei Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan Taiwan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

We reviewed the application of modern technology for rapid and accurate multi-person real-time pose detection in the hazardous field of electrical engineering. We focused on two leading pose detection technologies: YOLOv8 and OpenPose. To optimize performance, we integrated these two techniques into the LSTM model for training and investigated frame rates and accuracy.

关键词： Training Accuracy Microcontrollers Neural networks Real-time systems Safety Internet of Things

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Learning Terminal State of the Trajectory Planner: Application for Collision Scenarios of Autonomous Vehicles

Learning Terminal State of the Trajectory Planner: Applicati...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Joonhee Lim Kibeom Lee Jangho Shin Dongsuk Kum Robotics Program Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea Department of Mechanical Engineering Gachon University Seongnam Republic of Korea Research & Development Division Hyundai Motor Company Hwaseong Republic of Korea The Cho Chun Shik Graduate School of Mobility Korea Advanced Institute of Science and Technology (KAIST) Daejeon Republic of Korea

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Collision Avoidance/Mitigation System (CAMS) for autonomous vehicles is a crucial technology that ensures the safety and reliability of autonomous driving systems. Conventional collision avoidance approaches struggle in complex and various scenarios by avoiding collisions based on rules for specific collision scenarios. This has led to learning-based methods using neural networks for adaptive collision avoidance. However, the approaches directly outputting control inputs through neural networks have drawbacks in interpretability and stability. To address these limitations, we propose a trajectory planning method for CAMS that combines deep reinforcement learning (DRL) and quintic polynomial (QP) trajectory planning. The proposed method determines the terminal state and confidence of the trajectory using DRL and plans a QP trajectory based on them. By utilizing the terminal state and confidence of the trajectory rather than direct control inputs as the output of the neural network, it generates a more realistic and continuous path. Moreover, this approach considers collision avoidance and mitigation in an integrated manner through the reward function of RL. Our experimental results demonstrate that the proposed method not only improves interpretability and stability compared to existing learning-based methods but also upholds performance in complex and various collision scenarios.

关键词： Learning systems Trajectory planning Neural networks Trajectory Cams Safety Reliability

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Physics-Model-Regulated Deep Reinforcement Learning Towards Safety & Stability Guarantees

Physics-Model-Regulated Deep Reinforcement Learning Towards ...

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IEEE Conference on Decision and Control

作者： Hongpeng Cao Yanbing Mao Lui Sha Marco Caccamo Hongpeng Cao is with School of Engineering and Design Technical University of Munich Munich Germany Yanbing Mao is with Engineering Technology Division Wayne State University Detroit MI USA Department of Computer Science University of Illinois at Urbana-Champaign Urbana IL USA School of Engineering and Design and Institute of Robotics and Machine Intelligence Technical University of Munich Munich Germany

Deep reinforcement learning (DRL) has demonstrated impressive success in solving complex control tasks by synthesizing control policies from data. However, the safety and stability of applying DRL to safety-critical systems remain a primary concern and challenging problem. To address the problem, we propose the Phy-DRL: a novel physics-model-regulated deep reinforcement learning framework. The Phy-DRL is novel in two architectural designs: a physics-model-regulated reward and residual control, which integrates physics-model-based control and data-driven control. The concurrent designs enable the Phy-DRL the mathematically provable safety and stability guarantees. Finally, the effectiveness of the Phy-DRL is validated by an inverted pendulum system. Additionally, the experimental results demonstrate that the Phy-DRL features remarkably accelerated training and enlarged reward.

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AirVO: An Illumination-Robust Point-Line Visual Odometry

AirVO: An Illumination-Robust Point-Line Visual Odometry

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Kuan Xu Yuefan Hao Shenghai Yuan Chen Wang Lihua Xie School of Electrical and Electronic Engineering Nanyang Technological University Singapore Robot R&D Department Geekplus Corp. Beijing China Spatial AI & Robotics Lab at The Department of Computer Science & Engineering State University of New York at Buffalo Buffalo NY USA

This paper proposes an illumination-robust visual odometry (VO) system that incorporates both accelerated learning-based corner point algorithms and an extended line feature algorithm. To be robust to dynamic illumination, the proposed system employs the convolutional neural network (CNN) and graph neural network (GNN) to detect and match reliable and informative corner points. Then point feature matching results and the distribution of point and line features are utilized to match and triangulate lines. By accelerating CNN and GNN parts and optimizing the pipeline, the proposed system is able to run in real-time on low-power embedded platforms. The proposed VO was evaluated on several datasets with varying illumination conditions, and the results show that it outperforms other state-of-the-art VO systems in terms of accuracy and robustness. The open-source nature of the proposed system allows for easy implementation and customization by the research community, enabling further development and improvement of VO for various applications.

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PFS3F: Probabilistic Fusion of Superpixel-wise and Semantic-aware Structural Features for Hyperspectral Image Classification

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zhang, Ying Duan, Puhong Liang, Lianhui Kang, Xudong Li, Jun Plaza, Antonio Hunan University of Technology College of Railway Transportation Zhuzhou412007 China Hunan University School of Robotics Changsha410082 China Guangxi University School of Electrical Engineering Nanning530004 China China University of Geosciences Hubei Key Laboratory of Intelligent Geo-Information Processing the School of Computer Science Wuhan430078 China Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politecnica University of Extremadura CaceresE-10071 Spain

Processing high-dimensional data cubes and developing high-performance classifiers are core objectives in the field of hyperspectral image classification (HSIC). Superpixel-based methods are widely used in HSIC due to their efficacy in reducing redundant information and enhancing local features. However, imprecise segmentation, especially in complex structures and textures of hyperspectral images (HSIs), may lead to inconsistencies in the regions extracted by superpixels and the boundaries between different ground objects. Such inconsistencies significantly degrade the classification performance of HSIs. Alternatively, when parameter settings are inaccurate, edge-aware feature extraction methods often introduce sharpening artifacts at the image boundaries, resulting in a decrease in classification accuracy. To effectively address these challenges, we propose a novel probabilistic fusion method for HSIC. This method consists of the following stages. First, spatial information is extracted by a multiscale superpixel segmentation method and then probabilistically optimized by the extended random walk (ERW) method. Next, semantic-aware structural features (S2Fs) are extracted along with edge information of different objects. Lastly, a probabilistic framework is proposed to fuse the class probabilities of superpixel-based spatial information and semantic-aware structural features. Experimental results on three real datasets show state-of-the-art classification performance, even with limited training sets. ©1991-2012 IEEE.

关键词： Superpixels

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Enabling AI-Generated Content (AIGC) Services in Wireless Edge Networks

arXiv

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arXiv 2023年

作者： Du, Hongyang Li, Zonghang Niyato, Dusit Kang, Jiawen Xiong, Zehui Shen, Xuemin Kim, Dong In The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Information and Communication Engineering University of Electronic Sciences and Technology of China Chengdu China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of

Artificial Intelligence-Generated Content (AIGC) refers to the use of AI to automate the information creation process while fulfilling the personalized requirements of users. However, due to the instability of AIGC models, e.g., the stochastic nature of diffusion models, the quality and accuracy of the generated content can vary significantly. In wireless edge networks, the transmission of incorrectly generated content may unnecessarily consume network resources. Thus, a dynamic AIGC service provider (ASP) selection scheme is required to enable users to connect to the most suited ASP, improving the users’ satisfaction and quality of generated content. In this article, we first review the AIGC techniques and their applications in wireless networks. We then present the AIGC-as-a-service (AaaS) concept and discuss the challenges in deploying AaaS at the edge networks. Yet, it is essential to have performance metrics to evaluate the accuracy of AIGC services. Thus, we introduce several image-based perceived quality evaluation metrics. Then, we propose a general and effective model to illustrate the relationship between computational resources and user-perceived quality evaluation metrics. To achieve efficient AaaS and maximize the quality of generated content in wireless edge networks, we propose a deep reinforcement learning-enabled algorithm for the optimal ASP selection. Simulation results show that the proposed algorithm can provide a higher quality of generated content to users and achieve fewer crashed tasks by comparing with four benchmarks, i.e., overloading-avoidance, random, round-robin policies, and the upper-bound schemes. © 2023, CC BY.

关键词： Reinforcement learning

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ACO With Reinforcement Learning Applied to Rescues Operations on Urban Forests

ACO With Reinforcement Learning Applied to Rescues Operation...

引用

Congress on Evolutionary Computation

作者： Cláudio Alves Israel Mendonça Vanessa de Almeida Guimarèes Pedro Henrique González System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan COPPEAD Graduate Business School Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

In the context of smart cities where green infras-tructure is incentived, besides important benefits like regulating temperatures and absorbing pollutants among others, tour by urban forests is a way to experience closer contact with nature near of big urban centers. Eventually, visitors get lost, and helping these people with velocity is important to avoid severe incidents. Normally, rescue operations mobilize firefighters, ex-pensive equipment like helicopters and public resources. Following that idea of reducing search time in rescue operations, this paper considers the Data Mule Routing Problem with Limited Autonomy (DMRP-wLA). To find high-quality solutions, this paper proposes an Ant Colony Optimization algorithm enhanced with Reinforcement Learning to create an adaptive decision-making algorithm.

关键词： Ant colony optimization Forests Smart cities Helicopters Decision making Reinforcement learning Evolutionary computation

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Enhance Local Consistency in Federated Learning: A Multi-Step Inertial Momentum Approach

arXiv

引用

arXiv 2023年

作者： Liu, Yixing Sun, Yan Ding, Zhengtao Shen, Li Liu, Bo Tao, Dacheng The Department of Electrical and Electronic Engineering The School of Engineering The University of Manchester ManchesterM13 9PL United Kingdom The School of Computer Science The Faculty of Engineering The University of Sydney SydneyNSW2006 Australia JD Explore Academy *** Beijing100000 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China

Federated learning (FL), as a collaborative distributed training paradigm with several edge computing devices under the coordination of a centralized server, is plagued by inconsistent local stationary points due to the heterogeneity of the local partial participation clients, which precipitates the local client-drifts problems and sparks off the unstable and slow convergence, especially on the aggravated heterogeneous dataset. To address these issues, we propose a novel federated learning algorithm, named FedMIM, which adopts the multi-step inertial momentum on the edge devices and enhances the local consistency for free during the training to improve the robustness of the heterogeneity. Specifically, we incorporate the weighted global gradient estimations as the inertial correction terms to guide both the local iterates and stochastic gradient estimation, which can reckon the global objective optimization on the edges' heterogeneous dataset naturally and maintain the demanding consistent iteration locally. Theoretically, we show that FedMIM achieves the (Equation presented). convergence rate with a linear speedup property with respect to the number of selected clients S and proper local interval K in communication round T without convex assumption. Empirically, we conduct comprehensive experiments on various real-world datasets and demonstrate the efficacy of the proposed FedMIM against several state-of-the-art baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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