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Virtual Source-Based Particle Swarm Optimizer for Multi-Source Location Using Mobile Swarm Robotics

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Virtual Source-Based Particle Swarm Optimizer for Multi-Sour...

第33届中国过程控制会议

作者： JunQi Zhang YuXuan Lin Qi Kang Department of Computer Science and Technology the Key Laboratory of Embedded System and Service ComputingTongji UniversityMinistry of EducationShanghai Electronic Trans actions and Information Service Collaborative Innovation Center IEEE

Source location is an interesting topic in the field of swarm ***-source location is a vital branch of *** with a single-source location problem,the main difficulty for the multi-source location lies in that the number and distribution of sources are *** Swarm Optimizer(PSO) has been widely adopted for the single-source location ***,its performance deteriorates greatly when it faces multiple sources because it is designed to find the single global best solution,not multiple *** handle such a challenge,a virtual source mechanism is proposed to help PSO overcome this *** key idea of the virtual source mechanism is to divide the search area into multiple cells,each of which has a virtual *** virtual source leads the swarm to cover each cell such that particles can cover all cells in a search space before they *** results demonstrate that the virtual source mechanism-based PSO effectively locates multiple sources.

关键词： Particle swarm optimizer virtual source mobile swarm robotics multi-source location

Distributed satellite information networks: Architecture, enabling technologies, and trends

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arXiv 2024年

作者： Zhang, Qinyu Xu, Liang Huang, Jianhao Yang, Tao Jiao, Jian Wang, Ye Shi, Yao Zhang, Chiya Zhang, Xingjian Zhang, Ke Gong, Yupeng Deng, Na Zhao, Nan Gao, Zhen Han, Shujun Xu, Xiaodong You, Li Wang, Dongming Jiang, Shan Zhao, Dixian Zhang, Nan Hu, Liujun He, Xiongwen Li, Yonghui Gao, Xiqi You, Xiaohu Shenzhen518055 China Pengcheng Laboratory Shenzhen518055 China Dalian University of Technology Dalian116024 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China State Key Laboratory of CNS/ATM Beijing Institute of Technology Beijing100081 China National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China ZTE Corporation Algorithm Department Shenzhen518057 China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China Beijing Institute of Spacecraft System Engineering Beijing100076 China University of Sydney SydneyNSW2006 Australia

Driven by the vision of ubiquitous connectivity and wireless intelligence, the evolution of ultra-dense constellation-based satellite-integrated Internet is underway, now taking preliminary shape. Nevertheless, the entrenched institutional silos and limited, nonrenewable heterogeneous network resources leave current satellite systems struggling to accommodate the escalating demands of next-generation intelligent applications. In this context, the distributed satellite information networks (DSIN), exemplified by the cohesive clustered satellites (CCS) system, have emerged as an innovative architecture, bridging information gaps across diverse satellite systems, such as communication, navigation, and remote sensing, and establishing a unified, open information network paradigm to support resilient space information services. This survey first provides a profound discussion about innovative network architectures of DSIN, encompassing distributed regenerative satellite network architecture, distributed satellite computing network architecture, and reconfigurable satellite formation flying, to enable flexible and scalable communication, computing and control, fundamentally enhancing network resilience. The DSIN faces challenges from network heterogeneity, unpredictable channel dynamics, sparse resources, and decentralized collaboration frameworks. To address these issues, a series of enabling technologies is identified, including channel modeling and estimation, cloud-native distributed MIMO cooperation, new waveform design, grant-free massive access, non-orthogonal multicast, distributed phased array antennas, high-speed inter-satellite communication, network routing, and the proper combination of all these diversity techniques. Furthermore, to heighten the overall resource efficiency, the cross-layer optimization techniques are further developed to meet upper-layer deterministic, adaptive and secure information services requirements. In addition, emerging research dire

关键词： Antenna phased arrays

Intelligent Inspection Combining Db and Svtr Algorithms to Identify Equipment Selection Errors and Installation Position Errors in Petrochemical Plants

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SSRN

SSRN 2024年

作者： Wang, Feng Huang, Hehua Yin, Hong Li, Xiaozhi Shan, Xinru Gu, Xinyue National Foundation Research Laboratory of Fault Prevention and Control in Hazardous Chemicals Production System Beijing University of Chemical Technology Beijing China Certification Services Co. Ltd. Tianjin Energy and Chemical Explosion-proof Safety Academician Expert Collaborative Innovation Center Tianjin Key Laboratory of Energy and Chemical Explosion-proof Safety Tianjin China

In petrochemical plants, it is important that petrochemical equipment is placed in the correct explosion-proof area. Making the incorrect choice could result in equipment failure or even catastrophic fire and explosion incidents. By identifying the nameplate information on the equipment, the conventional manual inspection approach verifies the installation site and equipment selection;nevertheless, it has issues with low recognition efficiency and potential bias. This study constructed a petrochemical equipment nameplate dataset and determined the text features of the equipment nameplates. Using the DB algorithm model, the labeled dataset features were trained, and a method for combing DB and SVTR algorithms for intelligent inspection to identify equipment selection errors and installation position errors in Petrochemical Plants was proposed. According to the selection standards for petrochemical equipment types in explosion-proof areas, discrimination programs for equipment type selection errors and installation position errors in explosion-proof areas were established. By adjusting and optimizing the parameters of the SVTR algorithm model, accurate text detection and recognition of nameplate information were achieved. The recognized information was compared and analyzed against the standard content to determine equipment selection errors and installation position errors. Additionally, the model was integrated with the NCNN framework to develop a portable program for recognizing equipment selection errors and installation position errors based on the Android system in petrochemical plants. This program can be installed on explosion-proof phones, tablets, and explosion-proof AR helmets. Through intelligent inspection, automatic recognition of important information from petrochemical equipment nameplates can be achieved, thereby identifying equipment selection and installation position errors in explosion-proof areas and ensuring the safe operation of petrochemical e

关键词： Nameplates

TS-ENAS:Two-Stage Evolution for Cell-based Network Architecture Search

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arXiv 2023年

作者： Zou, Juan Wu, Shenghong Xia, Yizhang Jiang, Weiwei Wu, Zeping Zheng, Jinhua Engineering Research Center of Hunan Province for Optimization and Security of Intelligent System Key Laboratory of Intelligent Computing and Information Processing Ministry of Education of China Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Province Xiangtan411105 China The College of Aerospace Science and Engineering National University of Defense Technology Hunan Changsha410073 China

Neural network architecture search provides a solution to the automatic design of network structures. However, it is difficult to search the whole network architecture directly. Although using stacked cells to search neural network architectures is an effective way to reduce the complexity of searching, these methods do not able find the global optimal neural network structure since the number of layers, cells and connection methods is fixed. In this paper, we propose a Two-Stage Evolution for cell-based Network Architecture Search(TS-ENAS), including one-stage searching based on stacked cells and second-stage adjusting these cells. In our algorithm, a new cell-based search space and an effective two-stage encoding method are designed to represent cells and neural network structures. In addition, a cell-based weight inheritance strategy is designed to initialize the weight of the network, which significantly reduces the running time of the algorithm. The proposed methods are extensively tested and compared on four image classification dataset, Fashion-MNIST, CIFAR10, CIFAR100 and ImageNet and compared with 22 state-of-the-art algorithms including hand-designed networks and NAS networks. The experimental results show that TS-ENAS can more effectively find the neural network architecture with comparative performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

Dynamics-Based Algorithm-Level Privacy Preservation for Push-Sum Average Consensus

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arXiv 2023年

作者： Cheng, Huqiang Xie, Mengying Yang, Xiaowei Lü, Qingguo Li, Huaqing Key Laboratory of Dependable Services Computing in Cyber Physical Society-Ministry of Education College of Computer Science Chongqing University Chongqing401331 China School of Software Engineering South China University of Technology Guangzhou510006 China Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China

In the intricate dance of multi-agent systems, achieving average consensus is not just vital–it is the backbone of their functionality. In conventional average consensus algorithms, all agents reach an agreement by individual calculations and sharing information with their respective neighbors. Nevertheless, the information interactions that occur in the communication network may make sensitive information be revealed. In this paper, we develop a new privacy-preserving average consensus method on unbalanced directed networks. Specifically, we ensure privacy preservation by carefully embedding randomness in mixing weights to confuse communications and introducing an extra auxiliary parameter to mask the state-updated rule in initial several iterations. In parallel, we exploit the intrinsic robustness of consensus dynamics to guarantee that the average consensus is precisely achieved. Theoretical results demonstrate that the designed algorithms can converge linearly to the exact average consensus value and can guarantee privacy preservation of agents against both honest-but-curious and eavesdropping attacks. © 2023, CC BY.

关键词： Consensus algorithm

Deep Multi-Instance Learning with Adaptive Recurrent Pooling for Medical Image Classification

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Deep Multi-Instance Learning with Adaptive Recurrent Pooling...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yi Ding Lu Zhao Liming Yuan Xianbin Wen School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China School of Computer and Information Engineering Tianjin Chengjian University Tianjin China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Recently, deep multi-instance neural networks have been successfully applied for medical image classification, where only image-level labels rather than fine-grained patch-level labels are available for use. One key issue for these multi-instance neural networks is how to aggregate all patch (instance) features into an entire image (bag) representation, referred to as multi-instance pooling, e.g., max, mean, and attention based pooling. Nevertheless, these multi-instance pooling operations do not take the structural information within an image into account. This is obviously inappropriate for medical image classification since there often exist certain dependencies among regional patches/lesions. We propose an adaptive recurrent pooling based deep multi-instance neural network in this paper. In this network, we first extract multi-view global structural features from every bag using the self-attention mechanism, and then aggregate these multi-view features into a whole bag representation based on the adaptive recurrent pooling operation in order to further capture the contextual information within the bag. Moreover, we introduce the cross-normalization operation used in the Unit Force Operated Vision Transformer into the self-attention module to reduce its computational complexity. We have experimentally evaluated the performance of the proposed network on three medical image datasets, namely UCSB breast, Messidor, and Colon cancer. The results demonstrate the advantage of our network over current state-of-the-art deep multi-instance networks in terms of classification accuracy and interpretability.

关键词： Technological innovation Adaptive systems Aggregates Neural networks Force Feature extraction Transformers

Multi-View Representation Learning for Multi-Instance Learning with Applications to Medical Image Classification

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Multi-View Representation Learning for Multi-Instance Learni...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lu Zhao Liming Yuan Zhenliang Li Xianbin Wen School of Computer and Information Engineering Tianjin Chengjian University Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Multi-Instance Learning (MIL) is a weakly supervised learning paradigm, in which every training example is a labeled bag of unlabeled instances. In typical MIL applications, instances are often used for describing the features of regions/parts in a whole object, e.g., regional patches/lesions in an eye-fundus image. However, for a (semantically) complex part the standard MIL formulation puts a heavy burden on the representation ability of the corresponding instance. To alleviate this pressure, we still adopt a bag-of-instances as an example in this paper, but extract from each instance a set of representations using $1 \times1$ convolutions. The advantages of this tactic are two-fold: i) This set of representations can be regarded as multi-view representations for an instance; ii) Compared to building multi-view representations directly from scratch, extracting them automatically using $1 \times1$ convolutions is more economical, and may be more effective since $1 \times1$ convolutions can be embedded into the whole network. Furthermore, we apply two consecutive multi-instance pooling operations on the reconstituted bag that has actually become a bag of sets of multi-view representations. We have conducted extensive experiments on several canonical MIL data sets from different application domains. The experimental results show that the proposed framework outperforms the standard MIL formulation in terms of classification performance and has good interpretability.

关键词： Training Representation learning Retinopathy Supervised learning Buildings Diabetes Bioinformatics

PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples

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arXiv 2022年

作者： Hu, Shengshan Zhang, Junwei Liu, Wei Hou, Junhui Li, Minghui Zhang, Leo Yu Jin, Hai Sun, Lichao School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China Cluster and Grid Computing Lab China National Engineering Research Center for Big Data Technology and System Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Services Computing Technology and System Lab China City University of Hong Kong Hong Kong Deakin University Australia Lehigh University United States

Point cloud completion, as the upstream procedure of 3D recognition and segmentation, has become an essential part of many tasks such as navigation and scene understanding. While various point cloud completion models have demonstrated their powerful capabilities, their robustness against adversarial attacks, which have been proven to be fatally malicious towards deep neural networks, remains unknown. In addition, existing attack approaches towards point cloud classifiers cannot be applied to the completion models due to different output forms and attack purposes. In order to evaluate the robustness of the completion models, we propose PointCA, the first adversarial attack against 3D point cloud completion models. PointCA can generate adversarial point clouds that maintain high similarity with the original ones, while being completed as another object with totally different semantic information. Specifically, we minimize the representation discrepancy between the adversarial example and the target point set to jointly explore the adversarial point clouds in the geometry space and the feature space. Furthermore, to launch a stealthier attack, we innovatively employ the neighbourhood density information to tailor the perturbation constraint, leading to geometry-aware and distribution-adaptive modification for each point. Extensive experiments against different premier point cloud completion networks show that PointCA can cause a performance degradation from 77.9% to 16.7%, with the structure chamfer distance kept below 0.01. We conclude that existing completion models are severely vulnerable to adversarial examples, and state-of-the-art defenses for point cloud classification will be partially invalid when applied to incomplete and uneven point cloud data. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

Memory-Based Contrastive Learning with Optimized Sampling for Incremental Few-Shot Semantic Segmentation

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Memory-Based Contrastive Learning with Optimized Sampling fo...

IEEE International Symposium on Circuits and systems (ISCAS)

作者： Yuxuan Zhang Miaojing Shi Taiyi Su Hanli Wang Department of Computer Science & Technology Tongji University Shanghai P. R. China Key Laboratory of Embedded System & Service Computing Ministry of Education Tongji University Shanghai P. R. China Department of Control Science & Engineering Tongji University Shanghai P. R. China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Incremental few-shot semantic segmentation (IFSS) aims to incrementally expand a semantic segmentation model’s ability to identify new classes based on few samples. However, it grapples with the dual challenges of catastrophic forgetting (due to feature drift in old classes) and overfitting (triggered by inadequate samples in new classes). To address these issues, a novel approach is proposed to integrate pixel-wise and region-wise contrastive learning, complemented by an optimized example and anchor sampling strategy. The proposed method incorporates a region memory and pixel memory designed to explore the high-dimensional embedding space more effectively. The memory, retaining the feature embeddings of known classes, facilitates the calibration and alignment of seen class features during the learning process of new classes. To further mitigate overfitting, the proposed approach implements an optimized example and anchor sampling strategy. Extensive experiments show the competitive performance of the proposed method. The source code of this work can be found in https://***.

关键词： Circuits and systems Semantic segmentation Source coding Semantics Calibration