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arXiv 2024年

作者： Xiang, Qiuchi Cheng, Jintao Luo, Jiehao Wu, Jin Fan, Rui Chen, Xieyuanli Tang, Xiaoyu School of Data Science and Engineering Xingzhi College South China Normal University Shanwei516600 China School of Electronic and Information Engineering South China Normal University Guangdong Foshan528225 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong College of Intelligence Science and Technology National University of Defense Technology Changsha China 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

Place recognition is the foundation for enabling autonomous systems to achieve independent decision-making and safe operations. It is also crucial in tasks such as loop closure detection and global localization within SLAM. Previous methods utilize mundane point cloud representations as input and deep learning-based LiDAR-based Place Recognition (LPR) approaches employing different point cloud image inputs with convolutional neural networks (CNNs) or transformer architectures. However, the recently proposed Mamba deep learning model, combined with state space models (SSMs), holds great potential for long sequence modeling. Therefore, we developed OverlapMamba, a novel network for place recognition, which represents input range views (RVs) as sequences. In a novel way, we employ a stochastic reconstruction approach to build shift state space models, compressing the visual representation. Evaluated on three different public datasets, our method effectively detects loop closures, showing robustness even when traversing previously visited locations from different directions. Relying on raw range view inputs, it outperforms typical LiDAR and multi-view combination methods in time complexity and speed, indicating strong place recognition capabilities and realtime efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

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Ensemble of CNN classifiers using Choquet Fuzzy Integral Technique for PCB Defect Classification

Ensemble of CNN classifiers using Choquet Fuzzy Integral Tec...

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IEEE International Conference on computer Communication and the Internet (ICCCI)

作者： Boonchana Purahong Woranidtha Krungseanmuang Kasi Tenghongsakul Tuanjai Archevapanich Parkpoom Khunthawiwone Nitjaree Satayarak Attasit Lasakul Department of Computer Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Robotics and Computational Intelligent Systems School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Electrical Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Electronics and Communication Engineering Faculty of Engineering and Architecture Rajamagala University of Technology Suvarnabhumi Nonthaburi Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

This paper presents a novel method for detecting defects in printed circuit boards (PCBs) using an ensemble of classifiers based on the Choquet fuzzy integral. Our approach employs convolutional neural network (CNN) models, specifically ResNet152, VGG19, and InceptionV3 as base classifiers to identify six types of PCB defects: spurs, mouse bites, short circuits, open circuits, spurious copper, and pinholes. Given the critical role of PCBs in ensuring electronic equipment reliability, effective defect detection methods like ours are essential. We employ pre-trained CNN models for feature extraction and classification of PCB defects. Following this, we combine the prediction scores using the Choquet fuzzy integral to derive more accurate final labels, exceeding the accuracy of standalone models. Our approach is tested on PCB images obtained from public repositories, captured using a linear scan CCD. The evaluation results demonstrate average precision, recall, F-score, and accuracy of 93.0%, 95.2%, 95.1%, and 95.1%, respectively.

关键词： Measurement Accuracy Electronic equipment Printed circuits Predictive models Feature extraction Internet

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Taming Cluster Synchronization

arXiv

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arXiv 2024年

作者： Tomaselli, Cinzia Gambuzza, Lucia Valentina Sun, Gui-Quan Boccaletti, Stefano Frasca, Mattia Department of Electrical Electronics and Computer Science Engineering University of Catania Italy Sino-Europe Complex Science Center North University of China Taiyuan Shanxi030051 China Complex Systems Research Center Shanxi University Taiyuan Shanxi030006 China Institute of Interdisciplinary Intelligent Science Ningbo Univ. of Technology Ningbo China CNR - Institute of Complex Systems Sesto FiorentinoI-50019 Italy

Synchronization is a widespread phenomenon observed across natural and artificial networked systems. It often manifests itself by clusters of units exhibiting coincident dynamics. These clusters are a direct consequence of the organization of the Laplacian matrix eigenvalues into spectral localized blocks. We show how the concept of spectral blocks can be leveraged to design straightforward yet powerful controllers able to fully manipulate cluster synchronization of a generic network, thus shaping at will its parallel functioning. Specifically, we demonstrate how to induce the formation of spectral blocks in networks where such structures would not exist, and how to achieve precise mastering over the synchronizability of individual clusters by dictating the sequence in which each of them enters or exits the synchronization stability region as the coupling strength varies. Our results underscore the pivotal role of cluster synchronization control in shaping the parallel operation of networked systems, thereby enhancing their efficiency and adaptability across diverse applications. © 2024, CC BY.

关键词： Synchronization

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Fairer Machine Learning Through the Hybrid of Multi-objective Evolutionary Learning and Adversarial Learning

Fairer Machine Learning Through the Hybrid of Multi-objectiv...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shenhao Gui Qingquan Zhang Changwu Huang Bo Yuan Research Institute of Trustworthy Autonomous Systems (RITAS) Southern University of Science and Technology (SUSTech) Shenzhen China Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology (SUSTech) Shenzhen China

With growing concerns about the unwanted bias or discrimination in machine learning, a number of fairness-aware machine learning algorithms have been developed to mitigate bias in the prediction. Because the objectives of accuracy and fairness are antagonistic, it is hard to balance the trade-off between them. Recently, Multi-objective Evolutionary Learning (MOEL) framework has been proposed to train a set of Pareto models with the consideration of accuracy and fairness simultaneously. However, this framework prefers to train more accurate models rather than fairer models due to the lack of gradient in terms of fairness. In this paper, MOEL is enhanced through introducing Adversarial Learning (AL). The MOEL-AL framework aims to maximize a set of predictors' ability to predict true labels and minimize the ability of an adversarial network to predict the sensitive attributes from the predictors' output. Specifically, the adversarial network can be regarded as a proxy of the undifferentiable fairness metrics, so it is possible to propagate gradients in terms of both accuracy and fairness for the predictors during the back-propagation process. Besides, the adversarial strength for different predictors is adjusted dynamically according to their fairness metric. Compared with the state-of-the-art methods, experimental studies on seven well-known datasets show that our method can provide a set of fairer Pareto models with little drop on accuracy.

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A Survey on Reconfigurable intelligent Surface-Assisted Orthogonal Time Frequency Space systems

IEEE Open Journal of Vehicular Technology

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IEEE Open Journal of Vehicular Technology 2025年

作者： Tao, Qin Li, Zhongjie Zhi, Kangda Li, Shuangyang Yuan, Weijie Zaniboni, Lorenzo Stanczak, Slawomir Viterbo, Emanuele Wang, Xianbin Hangzhou Normal University School of Information Science and Technology Hangzhou311121 China School of Automation and Intelligent Manufacturing Southern University of Science and Techonology Shenzhen518055 China Technical University of Berlin School of Electrical Engineering and Computer Science Berlin10623 Germany Technical University of Munich Institute for Communications Engineering Munich80333 Germany Fraunhofer Heinrich Hertz Institute Wireless Communications and Networks Department Berlin10587 Germany Monash University Department of Electrical and Computer Systems Engineering ClaytonVIC3800 Australia Western University Department of Electrical and Computer Engineering LondonONN6A 5B9 Canada

The vision for 6th-generation (6G) wireless communication systems emphasizes the need for robust and reliable communication in extremely high-mobility scenarios, while also addressing critical demands for energy and spectral efficiency. Under such scenarios, doubly time-frequency selective fading channels often significantly degrade the performance of orthogonal frequency-division multiplexing (OFDM) based systems due to the impact of large delay and Doppler shifts. Recently, orthogonal time frequency space (OTFS) modulation has emerged as a promising alternative. By processing signals in the delay-Doppler (DD) domain, OTFS offers several advantages, including quasi-static channel characteristics, full-time-frequency diversity, and low peak-to-average power ratio (PAPR), making it a promising candidate for high-mobility communications. Reconfigurable intelligent surfaces (RIS) are being further integrated to enhance the performance of OTFS systems cost-effectively. With their ability to dynamically reconfigure the wireless environment, the integration of RIS can offer significant performance improvements for OTFS systems. This survey offers a comprehensive review of RIS-assisted OTFS systems, including the fundamental principles, recent advances, and future research directions. Specifically, we first introduce the background of RIS-assisted OTFS systems, outlining the opportunities and challenges of their integration. To ensure the survey is self-contained, we provide a brief overview of the fundamental principles of OTFS and RIS technologies. Building on these foundations, we present a general input-output relationship and capacity characterization for RIS-assited MIMO-OTFS systems. Then, this survey further explores cutting-edge research in areas such as input-output analysis, RIS phase shift design, channel estimation, detection techniques, RIS-assisted integrated sensing and communication (ISAC), and other novel technologies. Finally, we outline some future rese

关键词： Orthogonal frequency division multiplexing

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A Feed-forward Switched Adaptive Filtering configuration for Underwater Acoustic Signal Denoising Technique with low-complexity

A Feed-forward Switched Adaptive Filtering configuration for...

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OCEANS

作者： Deekshitha Adusumalli Swapnil Maiti S. Hannah Pauline Gerard Dooly Samiappan Dhanalakshmi Department of Electronics and Communication Engg. College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Tech. SRM Nagar Kattankulathur Kanchipuram Chennai India Center for Robotics and Intelligent Systems (CRIS) University of Limerick Limerick Ireland

The Underwater acoustic communication is difficult by virtue of the noise that exists undersea. This can be caused by both natural phenomena, like waves and currents, as well as artificial sources such as ships and boats. In order to reduce or eliminate this interference, it is necessary to process the signal before further processing. One way of doing this is through denoising. However, because noise constantly fluctuates in intensity, predicting its future behavior becomes almost impossible. To address this problem, we propose a new method for filtering underwater communications using an superlative feedfoward switched adaptive filter model using Signed Error variant of Least Mean Square (SELMS) and Signed Data variant of Least Mean Square (SDLMS) algorithm that takes into account signed form LMS values. The effectiveness of the filter is tested using a clean fish sound that has been tainted by noises from underwater vessels from the ShipsEar database.

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Generating Realistic Arm Movements in Reinforcement Learning: A Quantitative Comparison of Reward Terms and Task Requirements

Generating Realistic Arm Movements in Reinforcement Learning...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Jhon P.F. Charaja Isabell Wochner Pierre Schumacher Winfried Ilg Martin Giese Christophe Maufroy Andreas Bulling Syn Schmitt Georg Martius Daniel F.B. Haeufle Hertie Institute for Clinical Brain Research and Centre for Integrative Neuroscience University of Tübingen Germany Institute of Computer Engineering (ZITI) Heidelberg University Germany Max Planck Institute for Intelligent Systems Tübingen Germany Institut for Industrial Manufacturing and Management (IFF) University of Stuttgart Germany Fraunhofer Institute for Manufacturing Engineering and Automation (IPA) Germany Institute for Visualization and Interactive Systems University of Stuttgart Germany Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Germany Department of Computer Science University of Tübingen Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Mimicking of human-like arm movement characteristics involves considering three factors during control policy synthesis: (a) task requirements, (b) noise during movement execution, and (c) optimality principles. Previous studies showed that when these factors (a-c) are considered individually, it is possible to synthesize arm movements that either kinematically match experimental data or reproduce the stereotypical triphasic muscle activation pattern. However, no quantitative comparison has assessed the realism of arm movements generated by each factor, nor has it been determined whether combining these factors results in movements with human-like kinematic characteristics and the triphasic muscle pattern. To investigate this, we used reinforcement learning to learn a control policy for a musculoskeletal arm model, aiming to discern which combination of factors (a-c) results in realistic arm movements according to four frequently reported stereotypical characteristics. Our findings indicate that incorporating velocity and acceleration requirements into the reaching task, employing reward terms that minimize mechanical work, hand jerk, and control effort, along with the inclusion of noise during movement, leads to realistic human arm movements by reinforcement learning. We expect that the gained insights will help in the future to better predict desired arm movements and corrective forces in wearable assistive devices.

关键词： Noise Reinforcement learning Kinematics Biomechatronics Muscles Manipulators Assistive devices Pattern matching

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Development and Analysis of a Versatile Dataset of Speech, Real and Synthesized, of Arabic Learners

Development and Analysis of a Versatile Dataset of Speech, R...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Mansour Alsulaiman Mohammed Algabri Hassan Mathkour Mohamed A. Bencherif Ghulam Muhammad Saad Al-Gahtani Mohammed Faisal Mohamed Amine Mekhtiche Taha Alfakih Computer Engineering Department College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Computer Science Department College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Centre of Smart Robotics Research (CS2R) King Saud University Riyadh Saudi Arabia Arabic Language Institute King Saud University Saudi Arabia Center of AI & Robotics Kuwait College of Science and Technology (KCST) Kuwait

computer-Aided Pronunciation Training (CAPT) systems are gaining popularity recently due to the advancements in deep neural networks (DNN) and machine learning and the availability of databases of speech of language learners. Unfortunately, research in Arabic CAPT systems suffer from the lack of CAPT datasets compared to other languages. In this paper, we present the details of and the ideas used in the development of a versatile dataset of speech, real and synthesized, of Arabic Learners. To develop the dataset, we utilized an existing Arabic speech corpus, King Saud University Speech Database (KSU-SD). KSU-SD's main application was a speaker recognition system, but it was designed to be useful in other applications such as CAPT systems. KSU-SD includes a large number of speakers from diverse nationalities (Saudis, Arabs, and Non-Arabs). KSU-SD contains the recording of about 60 non-native speakers from more than 20 nationalities, hence we selected it to build the non-native Arabic-CAPT corpus. The developed corpus consists of transcribed, segmented, and annotated speech, which makes it suitable for building Arabic CAPT systems. In addition to presenting the details of developing the dataset, we also present an analysis of the text and speech errors of the dataset. The dataset was verified by many CAPT systems.

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Robotic Control Optimization Through Kernel Selection in Safe Bayesian Optimization

Robotic Control Optimization Through Kernel Selection in Saf...

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IEEE International Conference on robotics and Biomimetics

作者： Lihao Zheng Hongxuan Wang Xiaocong Li Jun Ma Prahlad Vadakkepat School of Data Science The Chinese University of Hong Kong Research Intern at the Singapore Institute of Manufacturing Technology A*STAR Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Institute of Manufacturing Technology Agency for Science Technology and Research (A*STAR) Singapore Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Control system optimization has long been a fundamental challenge in robotics. While recent advancements have led to the development of control algorithms that leverage learning-based approaches, such as SafeOpt, to optimize single feedback controllers, scaling these methods to high-dimensional complex systems with multiple controllers remains an open problem. In this paper, we propose a novel learning-based control optimization method, which enhances the additive Gaussian process-based Safe Bayesian Optimization algorithm to efficiently tackle high-dimensional problems through kernel selection. We use PID controller optimization in drones as a representative example and test the method on Safe Control Gym, a benchmark designed for evaluating safe control techniques. We show that the proposed method provides a more efficient and optimal solution for high-dimensional control optimization problems, demonstrating significant improvements over existing techniques.

关键词： Additives Trajectory tracking Optimization methods Control systems Bayes methods Kernel Robots Tuning Standards Quadrotors

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iMTSP: Solving Min-Max Multiple Traveling Salesman Problem with Imperative Learning

arXiv

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arXiv 2024年

作者： Guo, Yifan Ren, Zhongqiang Wang, Chen The Flight Dynamics & Control/Hybrid Systems Lab Purdue University West LafayetteIN47907 United States Robotics Institute Carnegie Mellon University PittsburghPA15213 United States Lab Department of Computer Science and Engineering University at Buffalo NY14260 United States

This paper considers a Min-Max Multiple Traveling Salesman Problem (MTSP), where the goal is to find a set of tours, one for each agent, to collectively visit all the cities while minimizing the length of the longest tour. Though MTSP has been widely studied, obtaining near-optimal solutions for large-scale problems is still challenging due to its NP-hardness. Recent efforts in data-driven methods face challenges of the need for hard-to-obtain supervision and issues with high variance in gradient estimations, leading to slow convergence and highly sub-optimal solutions. We address these issues by reformulating MTSP as a bilevel optimization problem, using the concept of imperative learning (IL). This involves introducing an allocation network that decomposes the MTSP into multiple single-agent traveling salesman problems (TSPs). The longest tour from these TSP solutions is then used to self-supervise the allocation network, resulting in a new self-supervised, bilevel, end-to-end learning framework, which we refer to as imperative MTSP (iMTSP). Additionally, to tackle the high-variance gradient issues during the optimization, we introduce a control variate-based gradient estimation algorithm. Our experiments showed that these innovative designs enable our gradient estimator to converge 20× faster than the advanced reinforcement learning baseline, and find up to 80% shorter tour length compared with Google OR-Tools MTSP solver, especially in large-scale problems (e.g. 1000 cities and 15 agents). © 2024, CC BY.

关键词： Traveling salesman problem

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