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International Conference on Automation in Manufacturing, Transportation and Logistics (iCaMaL 2023)

作者： Yue Su Liang Qi MengChu Zhou Remigiusz Wiśniewski Faculty of Innovation Engineering Macau University of Science and Technology Macau People's Republic of China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao People's Republic of China Institute of Control and Computation Engineering University of Zielona Góra Poland

ISBN: (数字)9781837242054

Petri nets (PNs) are graphical and mathematical tools used to model various discrete event systems and analyze their properties. Reachability is their fundamental property. When we use a state equation to determine a state’s (marking’s) reachability, the existence of legal firing sequences (LFSs) corresponding to nonnegative integer solutions (NISs) of the state equation needs to be determined. Our previous work has proposed an algorithm to determine the LFSs’ existence in polynomial time. However, when a state equation has an infinite number of NISs, we cannot check all NISs to determine a marking’s reachability. Hence, this work studies the relationship between the structure of an ordinary PN and the number of NISs of its state equation. Then, a method is proposed to modify the structure of the PN such that the state equation has no more than one NIS. The modified PN is used as the final model of the system, which maintains the functions of its modeled system. Its reachability can be determined in polynomial time. This work shows the application of the proposed method to a flexible manufacturing system’s modeling and analysis.

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Clustering with adaptive graph learning and spectral rotation

Clustering with adaptive graph learning and spectral rotatio...

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7th International Conference on Vision, Image and Signal Processing (ICVISP 2023)

作者： G. Yuan J. Zhou C. Huang C. Gao Y. Wang X. Shen National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 518060. Guangdong People's Republic of China College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 Guangdong People's Republic of China College of Mathematics and Statistics Shenzhen University Shenzhen 518060 Guangdong People's Republic of China

Prototype-based clustering algorithms have garnered considerable attention in the field of machine learning due to their efficiency and interpretability. Nonetheless, these algorithms often face performance degradation when confronted with high-dimensional or non-ellipsoidal data distributions. To surmount these challenges, this study introduces a novel clustering approach, dubbed Clustering with Adaptive Graph learning and Spectral Rotation (CAGSR). In CAGSR, the imposed spectral rotation operation mitigates the discrepancy between the membership matrix, which adheres to the notion of fuzzy clustering, and the spectral representations derived from an adaptive graph rather than a predefined one. This enables the generation of a comprehensive representation of the data across multiple spaces. Furthermore, the clustering and graph learning tasks are jointly optimized in a projected subspace, which can effectively reduce the adverse impact caused by irrelevant features in the original space. The proposed method seamlessly integrates fuzzy clustering, graph structure learning, and spectral rotation into a unified model, facilitating the detection of intrinsic structures. Experimental evaluations conducted on benchmark data sets substantiate the effectiveness of CAGSR when compared to related clustering approaches.

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Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders

arXiv

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arXiv 2023年

作者： Zha, Yaohua Ji, Huizhen Li, Jinmin Li, Rongsheng Dai, Tao Chen, Bin Wang, Zhi Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University China Research Center of Artificial Intelligence Peng Cheng Laboratory China College of Computer Science and Software Engineering Shenzhen University China Harbin Institute of Technology Shenzhen China

Learning 3D representation plays a critical role in masked autoencoder (MAE) based pre-training methods for point cloud, including single-modal and cross-modal based MAE. Specifically, although cross-modal MAE methods learn strong 3D representations via the auxiliary of other modal knowledge, they often suffer from heavy computational burdens and heavily rely on massive cross-modal data pairs that are often unavailable, which hinders their applications in practice. Instead, single-modal methods with solely point clouds as input are preferred in real applications due to their simplicity and efficiency. However, such methods easily suffer from limited 3D representations with global random mask input. To learn compact 3D representations, we propose a simple yet effective Point Feature Enhancement Masked Autoencoders (Point-FEMAE), which mainly consists of a global branch and a local branch to capture latent semantic features. Specifically, to learn more compact features, a share-parameter Transformer encoder is introduced to extract point features from the global and local unmasked patches obtained by global random and local block mask strategies, followed by a specific decoder to reconstruct. Meanwhile, to further enhance features in the local branch, we propose a Local Enhancement Module with local patch convolution to perceive fine-grained local context at larger scales. Our method significantly improves the pre-training efficiency compared to cross-modal alternatives, and extensive downstream experiments underscore the state-of-the-art effectiveness, particularly outperforming our baseline (Point-MAE) by 5.16%, 5.00%, and 5.04% in three variants of ScanObjectNN, respectively. Code is available at https://***/zyh16143998882/AAAI24-PointFEMAE. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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Semantic Communication-Empowered Physical-layer Network Coding

arXiv

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arXiv 2022年

作者： Yang, Shuai Pan, Haoyuan Chan, Tse-Tin Wang, Zhaorui College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong Shenzhen China

In a two-way relay channel (TWRC), physical-layer network coding (PNC) doubles the system throughput by turning superimposed signals transmitted simultaneously by different end nodes into useful network-coded information (known as PNC decoding). Prior works indicated that the PNC decoding performance is affected by the relative phase offset between the received signals from different nodes. In particular, some "bad" relative phase offsets could lead to huge performance degradation. Previous solutions to mitigate the relative phase offset effect were limited to the conventional bit-oriented communication paradigm, aiming at delivering a given information stream as quickly and reliably as possible. In contrast, this paper puts forth the first semantic communication-empowered PNC-enabled TWRC to address the relative phase offset issue, referred to as SC-PNC. Despite the bad relative phase offsets, SC-PNC directly extracts the semantic meaning of transmitted messages rather than ensuring accurate bit stream transmission. We jointly design deep neural network (DNN)-based transceivers at the end nodes and propose a semantic PNC decoder at the relay. Taking image delivery as an example, experimental results show that the SC-PNC TWRC achieves high and stable reconstruction quality for images under different channel conditions and relative phase offsets, compared with the conventional bit-oriented counterparts. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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High-Altitude Pedestrian Detection Based on Improved YOLOv3 1

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6th International Conference on Data Mining and Big Data, DMBD 2021

作者： Tian, Qing Cao, Pengfei Fan, Haoyi Hu, Rong Li, Zuoyong School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China University of Sevilla Sevilla Spain School of Computer Science and Technology Harbin University of Science and Technology Harbin150080 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9789811674761

ISBN: (纸本)9789811674754

As one of the main tasks in the field of computer vision, pedestrian detection aims to find out all pedestrians in the image or video. The existing YOLOv3 is a relatively mature object detection method. However, for the long-distance pedestrian detection task in high-altitude scenes, YOLOv3 has the limitations of low detection speed and low detection accuracy. This paper proposes an improved YOLOv3 method briefly called YOLOv3-M for the high-altitude pedestrian detection, which replaces the feature extraction module called darknet53 in YOLOv3 with MobileNetv1. Specifically, YOLOv3-M first constructs the dataset with the small objects of high-altitude pedestrians as the detection object. Then, it uses the K-means + + algorithm to re-cluster the high-altitude pedestrian dataset. Next, it uses the Distance Intersection over Union (DIoU) loss function to alleviate the problem of high-altitude pedestrian overlapping. Experimental results show that the proposed YOLOv3-M improves the detection precision and the detection speed compared to YOLOv3. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Object detection

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An Energy-Aware UAVs Path Coverage for Critical Infrastructure Inspections

An Energy-Aware UAVs Path Coverage for Critical Infrastructu...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Yifan Wang Wei Zeng Guanyu Li Chenglong Xiong Zicheng Wang Yingchi Mao College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China Huaneng Lancang River Hydropower Co. CHINA HUANENG Group Kunming China Power China Kunming Engineering Co. Yunnan China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

Critical infrastructures increasingly rely on unmanned aerial vehicles (UAVs) for inspection tasks. The significance of different components within these infrastructures is subject to variability and can be influenced by external factors. The principal aim of routine UAV inspections is to ensure differentiated coverage of pivotal sections with minimal energy consumption. However, extant research on UAV path coverage fails to fully account for the variability and dynamic shifts in regional significance and their impact on coverage efficacy. This paper presents an energy-aware collaborative coverage policy for UAVs, designated as ***-MATD3 employs a dynamic weight region partitioning method tailored to real-world environments and addresses the action selection challenge for UAVs using a discrete Partially Observable Markov Decision Process (Dec-POMDP). By amalgamating MATD3 with stacked LSTM, this approach mitigates redundant path overlaps and unnecessary action replication across multiple agents, thus optimizing coverage and diminishing energy usage. Simulation outcomes demonstrate that EA-MATD3 reduces energy consumption by an average of 9.65% relative to the Greedy, MADDPG, and MATD3 algorithms while sustaining a superior coverage rate.

关键词： Energy consumption Simulation Markov decision processes Termination of employment Inspection Autonomous aerial vehicles Critical infrastructure Vehicle dynamics Long short term memory Optimization

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DDBO: Discrete Dung Beetle Optimizer for Optical Communication Simulation Task Allocation

DDBO: Discrete Dung Beetle Optimizer for Optical Communicati...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Zheng Wang Weiwei Xing Yue Cheng Weibin Liu Zhiyuan Zou Zhi Wang Genxiang Chen Wei Lu School of Software Engineering Beijing Jiaotong University Beijing China Institute of Information Science Beijing Jiaotong University Beijing China Computer School Beijing Information Science and Technology University Beijing China School of Physical Sciences and Engineering Beijing Jiaotong University Beijing China College of Science Minzu University of China Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Optical Communication Simulation Task Allocation (OCSTA) constitutes an interdisciplinary quandary. This paper proposes a swarm intelligence approach harnessing a Discrete Dung Beetle Optimizer (DDBO) algorithm with a globally equilibrated strategy to offer a resolution avenue for this intricate conundrum. Firstly, we provide a comprehensive exposition of the mathematical model underpinning the OCSTA, which employs the spatial positioning of the population to articulate diverse allocation solutions. Then, a weighted random selection technique is used to generate initial solutions. Thirdly, a predator avoidance strategy is introduced to facilitate updates in the dung beetle position. Finally, the global equilibrium mechanism with the swap operator is exploited to harmonize the exploratory and exploitative capabilities, thereby further augmenting the quality of the solutions. We conducted several simulation experiments 1 across various distinct task load scenarios, and statistical tests are employed to evaluate the significant differences between the proposed algorithm and other state-of-the-art methods. The outcomes revealed that the DDBO solution yielded an improvement of approximately 15.8%, which underscores the competitiveness and robustness of solving the OCSTA.

关键词： Distributed processing Heuristic algorithms Dynamic scheduling Optical fiber communication Robustness Mathematical models Resource management Spatial resolution Particle swarm optimization Optimization

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Adapting Site Selection of Sewage Treatment Plant to Urban Planning: A Hesitant Fuzzy Linguistic Mcdm Model

SSRN

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SSRN 2022年

作者： Liu, Jun Xie, Mingyue Lin, Mingwei Chen, Shuyu School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China College of Computer and Cyber Security Fujian Normal University Fujian Fuzhou350117 China School of Big Data and Software Engineering Chongqing University Chongqing400044 China

The sewage treatment plant is the basic facility of urban construction. However, selecting an appropriate site to construct a sewage treatment plant is challenging task. Therefore, this paper puts forward a novel hesitant fuzzy linguistic multi-criteria decision-making (HFLMCDM) model for the sewage treatment plants siting decision. For this purpose, the hesitant fuzzy linguistic term set (HFLTS) is introduced to accurately evaluate the alternative sites. Then, a novel score function based on trigonometric semantic value (Score-TSV) and distance measure based on trigonometric semantic value and dispersion (Dis-TSVD) are proposed for HFLTSs. A hesitant fuzzy linguistic VIKOR (HFLV) method combining the novel Score-TSV and Dis-TSVD is utilized to rank the alternative sites for sewage treatment plants. The proposed HFLMCDM model is applied to determine the best site for a sewage treatment plant in Chongqing. Ultimately, the superiority of the HFLMCDM model is elaborated by comparative analysis. © 2022, The Authors. All rights reserved.

关键词： Site selection

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Applications of Knowledge Distillation in Remote Sensing: A Survey

arXiv

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arXiv 2024年

作者： Himeur, Yassine Aburaed, Nour Elharrouss, Omar Varlamis, Iraklis Atalla, Shadi Mansoor, Wathiq Al Ahmad, Hussain College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates MBRSC Lab University of Dubai Dubai2713 United Arab Emirates Department of Computer Science and Software Engineering United Arab Emirates University United Arab Emirates Department of Informatics and Telematics Harokopio University of Athens AthensGR-17778 Greece

With the ever-growing complexity of models in the field of remote sensing (RS), there is an increasing demand for solutions that balance model accuracy with computational efficiency. Knowledge distillation (KD) has emerged as a powerful tool to meet this need, enabling the transfer of knowledge from large, complex models to smaller, more efficient ones without significant loss in performance. This review article provides an extensive examination of KD and its innovative applications in RS. KD, a technique developed to transfer knowledge from a complex, often cumbersome model (teacher) to a more compact and efficient model (student), has seen significant evolution and application across various domains. Initially, we introduce the fundamental concepts and historical progression of KD methods. The advantages of employing KD are highlighted, particularly in terms of model compression, enhanced computational efficiency, and improved performance, which are pivotal for practical deployments in RS scenarios. The article provides a comprehensive taxonomy of KD techniques, where each category is critically analyzed to demonstrate the breadth and depth of the alternative options, and illustrates specific case studies that showcase the practical implementation of KD methods in RS tasks, such as instance segmentation and object detection. Further, the review discusses the challenges and limitations of KD in RS, including practical constraints and prospective future directions, providing a comprehensive overview for researchers and practitioners in the field of RS. Through this organization, the paper not only elucidates the current state of research in KD but also sets the stage for future research opportunities, thereby contributing significantly to both academic research and real-world applications. © 2024, CC BY.

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Application of various CNNs for the Identification of Hypertrophic Cardiomyopathy

Application of various CNNs for the Identification of Hypert...

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Artificial Intelligence, Computational Electronics and Communication System (AICECS), International Conference on

作者： Jimcymol James Anjan Gudigar Raghavendra U Jyothi Samanth Simran Kumari Rathore Aashna Baheti Rhea Tiwari Mukund A Prabhu Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Cardiovascular Technology Manipal College of Health Professions Manipal Academy of Higher Education Manipal India Department of Cardiology Kasturba Medical College Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798350391244

ISBN: (纸本)9798350391251

Hypertrophic Cardiomyopathy (HCM) is an abnormal condition of the heart due to the thickening of heart muscles. Early diagnosis and prognosis of HCM is appreciable as it can be a threaten to life if left untreated. The structural definitions of the heart are easily captured by Echocardiography. An automated system utilizing Echocardiography for the identification of HCM is thus beneficial to the well-being of the society. This study performs a comparative analysis of various Convolutional Neural Networks (CNNs), that are used for extracting the features which are relevant for the classification of HCM. Heart ultrasound images from 35 subjects each of HCM and normal are utilized for the study. Variants of Support Vector Machine (SVM) are used to perform the classification. Further, significant features are analyzed by feature ranking methods. Finally, it is observed from the result that ResNet50 and AlexNet are the best CNN models towards the identification of HCM.

关键词： Support vector machines Heart Deep learning Training Ultrasonic imaging Echocardiography Feature extraction Convolutional neural networks Testing Residual neural networks

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