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International Conference on Automation and Computing (ICAC)

作者： Zhuangzhuang Dai Jinha Park Aleksandra Kaszowska Chen Li Dept. of Computer Science Engineering and Physical Science School of Engineering and Applied Science Aston University Birmingham United Kingdom SDU Robotics The Maersk Mc Kinney Moller Institute University of Southern Denmark Odense Denmark Department of Electronic Systems Aalborg University Aalborg Denmark The Department of Materials and Production Aalborg University Aalborg Denmark

The advent of industrial robotics and autonomous systems endow human-robot collaboration in a massive scale. However, current industrial robots are restrained in co-working with human in close proximity due to inability of interpreting human agents' attention. Human attention study is non-trivial since it involves multiple aspects of the mind: perception, memory, problem solving, and consciousness. Human attention lapses are particularly problematic and potentially catastrophic in industrial workplace, from assembling electronics to oper-ating machines. Attention is indeed complex and cannot be easily measured with single-modality sensors. Eye state, head pose, posture, and manifold environment stimulus could all play a part in attention lapses. To this end, we propose a pipeline to annotate multimodal dataset of human attention tracking, including eye tracking, fixation detection, third-person surveil-lance camera, and sound. We produce a pilot dataset containing two fully annotated phone assembly sequences in a realistic manufacturing environment. We evaluate existing fatigue and drowsiness prediction methods for attention lapse detection. Experimental results show that human attention lapses in production scenarios are more subtle and imperceptible than well-studied fatigue and drowsiness.

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Semi-parametric Memory Consolidation: Towards Brain-like Deep Continual Learning

arXiv

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arXiv 2025年

作者： Liu, Geng Zhu, Fei Feng, Rong Yi, Zhiqiang Wang, Shiqi Meng, Gaofeng Zhang, Zhaoxiang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences Hong Kong Department of Computer Science City University of Hong Kong Hong Kong School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The Department of Neurosurgery Peking University First Hospital Beijing China

Humans and most animals inherently possess a distinctive capacity to continually acquire novel experiences and accumulate worldly knowledge over time. This ability, termed continual learning, is also critical for deep neural networks (DNNs) to adapt to the dynamically evolving world in open environments. However, DNNs notoriously suffer from catastrophic forgetting of previously learned knowledge when trained on sequential tasks. In this work, inspired by the interactive human memory and learning system, we propose a novel biomimetic continual learning framework that integrates semi-parametric memory and the wake-sleep consolidation mechanism. For the first time, our method enables deep neural networks to retain high performance on novel tasks while maintaining prior knowledge in real-world challenging continual learning scenarios, e.g., class-incremental learning on ImageNet1. This study demonstrates that emulating biological intelligence provides a promising path to enable deep neural networks with continual learning capabilities. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep neural networks

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Curriculum Proximal Policy Optimization with Stage-Decaying Clipping for Self-Driving at Unsignalized Intersections

Curriculum Proximal Policy Optimization with Stage-Decaying ...

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International Conference on intelligent Transportation

作者： Zengqi Peng Xiao Zhou Yubin Wang Lei Zheng Ming Liu Jun Ma Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong SAR China HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Futian Shenzhen China

Unsignalized intersections are typically considered as one of the most representative and challenging scenarios for self-driving vehicles. To tackle autonomous driving problems in such scenarios, this paper proposes a curriculum proximal policy optimization (CPPO) framework with stage-decaying clipping. By adjusting the clipping parameter during different stages of training through proximal policy optimization (PPO), the vehicle can first rapidly search for an approximate optimal policy or its neighborhood with a large parameter, and then converges to the optimal policy with a small one. Particularly, the stage-based curriculum learning technology is incorporated into the proposed framework to improve the generalization performance and further accelerate the training process. Moreover, the reward function is specially designed in view of different curriculum settings. A series of comparative experiments are conducted in intersection-crossing scenarios with bi-lane carriageways to verify the effectiveness of the proposed CPPO method. The results show that the proposed approach demonstrates better adaptiveness to different dynamic and complex environments, as well as faster training speed over baseline methods.

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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Comparative analyses of multi-criteria methods in supplier selection problem 26

Comparative analyses of multi-criteria methods in supplier s...

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26th International Conference on Knowledge-Based and intelligent Information and Engineering systems, KES 2022

作者： Paradowski, Bartosz Szyjewski, Zdzislaw Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Zolnierska 49 Szczecin71-210 Poland Institute of Management University of Szczecin ul. Cukrowa 8 Szczecin71-004 Poland

The ever-growing interest in multi-criteria decision-making methods requires research that would compare them with each other as the decision-makers would need help choosing the appropriate method. It is important to highlight the differences between them, because using different methods may result in discrepancies in the obtained rankings. Additionally, it is worthwhile for the research to take into account the problems whose solution is extremely valuable. To this end, we present a comparison of five multi-criteria decision-making methods: TOPSIS, VIKOR, COMET, SPOTIS, and MARCOS in the supplier selection problem. The conducted research showed significant differences in the evaluation of the preferences of the alternatives and some discrepancies in the obtained rankings. © 2022 The Authors. Published by Elsevier B.V.

关键词： Decision making

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

arXiv

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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

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Automated Classification of Medical Images Using Convolutional Neural Networks

Automated Classification of Medical Images Using Convolution...

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International Conference on Computing and Networking Technology (ICCNT)

作者： N. Muthuvairavan Pillai S Manimala Aruna Sri Rongali Anchit Gugnani A Senthil Kumar G. V. Sriramakrishnan Computer Science and Business Systems R.M.D Engineering College Kavarapettai Thiruvallur district Computer science and engineering University SJCE JSS Science and technology University Mysore Karnataka India Department Information Technology Emphasis/Information Technology Ph.D University and city with the country University of The Cumberlands US Physiotherapy Amity University Jaipur Rajasthan Artificial Intelligent department Shri Vishnu Engineering College for Women Bhimavaram AP Department Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamilnadu

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Medical imagery has been increasingly used in diagnosing and treating various diseases, leading to advances in healthcare. While humans usually lead the qualitative interpretation of visual features, Automated Classification of Medical Images Using Convolution Neural Networks (CNNs) has gained traction for accurate image classification. CNNs are a type of deep learning algorithm that attempts to mimic the structure of the human brain and are ideal for image classification due to their convolution layers that can detect patterns within the image. These convolution layers enable the networks to learn the characteristics of the data through a series of convolution and pooling operations. Additionally, they can rapidly identify relationships and patterns within the dataset and extract features from the input image, thus allowing a more accurate classification. With the increasing availability of medical imaging data, advancements in the field of ML/AI has enabled the use of CNNs for automated medical image classification, helping clinicians to diagnose diseases accurately with less time and effort.

关键词： Training Image segmentation Accuracy Convolution Medical services Feature extraction Convolutional neural networks Medical diagnostic imaging X-ray imaging Image classification

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Design and Development of the FlexBE WebUI with Introductory Tutorials

Journal of Computing Sciences in Colleges

引用

Journal of Computing sciences in Colleges 2024年第3期40卷

作者： Samuel Raymond Grace Walters Josh Luzier David C. Conner Capable Humanitarian Robotics and Intelligent Systems Lab Department of Physics Computer Science and Engineering Christopher Newport University Newport News VA

Hierarchical Finite State Machines (HFSMs) continue to be a popular strategy for high-level behavioral control in robotics. Within the Robot Operating System (ROS) ecosystem, the Flexible Behavior Engine (FlexBE) provides an easy to use Python implementation with an intuitive user interface for runtime monitoring and "collaborative autonomy." In this paper, we describe the latest evolution of the FlexBE UI, that leverages FastAPI to provide a Python-based native ROS node interface, to a web-based user interface. Additionally, the paper describes a "quick-start" introductory tutorial to state machines using FlexBE. This system provides a natural introduction to HFSM-based techniques.

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SNE-RoadSeg+: Rethinking Depth-Normal Translation and Deep Supervision for Freespace Detection

SNE-RoadSeg+: Rethinking Depth-Normal Translation and Deep S...

引用

2021 IEEE/RSJ International Conference on intelligent Robots and systems, IROS 2021

作者： Wang, Hengli Fan, Rui Cai, Peide Liu, Ming The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering China College of Electronic and Information Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China

ISBN: (纸本)9781665417143

Freespace detection is a fundamental component of autonomous driving perception. Recently, deep convolutional neural networks (DCNNs) have achieved impressive performance for this task. In particular, SNE-RoadSeg, our previously proposed method based on a surface normal estimator (SNE) and a data-fusion DCNN (RoadSeg), has achieved impressive performance in freespace detection. However, SNE-RoadSeg is computationally intensive, and it is difficult to execute in real time. To address this problem, we introduce SNE-RoadSeg+, an upgraded version of SNE-RoadSeg. SNE-RoadSeg+ consists of 1) SNE+, a module for more accurate surface normal estimation, and 2) RoadSeg+, a data-fusion DCNN that can greatly minimize the trade-off between accuracy and efficiency with the use of deep supervision. Extensive experimental results have demonstrated the effectiveness of our SNE+ for surface normal estimation and the superior performance of our SNE-RoadSeg+ over all other freespace detection approaches. Specifically, our SNE-RoadSeg+ runs in real time, and meanwhile, achieves the state-of-the-art performance on the KITTI road benchmark. Our project page is at https://***/sne-roadseg-plus. © 2021 IEEE.

关键词： Data fusion

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Practical Study of Selected Multi-Criteria Methods Comparison 26

Practical Study of Selected Multi-Criteria Methods Compariso...

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26th International Conference on Knowledge-Based and intelligent Information and Engineering systems, KES 2022

作者： Wieckowski, Jakub Szyjewski, Zdzislaw Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Zolnierska 49 Szczecin71-210 Poland Institute of Management University of Szczecin ul. Cukrowa 8 Szczecin71-004 Poland

Multi-Criteria Decision Analysis (MCDA) methods enable a comprehensive analysis of decision options to identify the most preferred values. However, the abundance of these techniques, using different transformations in the calculations, can cause a divergence of results. However, the resulting rankings should be as consistent and reliable as possible. In this paper, we used the TOPSIS, COPRAS, SPOTIS, and COMET methods to examine the consistency of their results in the practical problem of laptop evaluation. The rankings were then compared with the reference ranking. The research showed that among the methods listed, the COPRAS method's results differed the most from the others. In turn, this technique ensured a ranking in line with the reference ranking. Based on the results, it can be established that the methods used are characterized by high consistency of results. © 2022 The Authors. Published by Elsevier B.V.

关键词： Preferred numbers

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