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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Yu, Ye Wu, Yao Liang, Xi Ma, Cheng Lu, Qiang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei China School of Computer Science and Information Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (纸本)9781665405270

Academic collaboration has become one of the topics of large-scale social network analysis. The information extracted from academic collaboration also shows the contextual value in combination with other data. For academic collaborative social networks with intricate collaborative relationships, previous research methods mainly focused on network structure and community discovery, lacking in mining the characteristics of relationships in collaborative networks. There were some limitations in the research perspective. In this work, we propose a collaborative relationship representation method that is more suitable for academic collaborative social networks. It marks the attribute characteristics of nodes in collaborative relationships and highlights collaborative relationships in visualization. By adopting this new representation of collaborative relationships, we designed a visual analysis framework NcoVis, to visualize academic collaborative social networks. We further visually analyze the link relationship groups guided by nodes, and explore the potential representation of the structure and attribute characteristics of link relationships, so that analysts can explore the theme and structural change characteristics of link relationships, communities and the whole network in collaborative social networks. To prove the proposed framework, we conducted a case study on the academic collaboration literature data set, and analyzed the questionnaire with Likert scale through the user evaluation experiment, which verified the effectiveness of our method and the practicability of NcoVis. © 2022 IEEE.

关键词： Visualization

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Efficient time series anomaly detection by multiresolution self-supervised discriminative network

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NEUROCOMPUTING 2022年 491卷 261-272页

作者： Huang, Desen Shen, Lifeng Yu, Zhongzhong Zheng, Zhenjing Huang, Min Ma, Qianli South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China Hong Kong Univ Sci & Technol Informat Hub Artificial Intelligence Thrust Area Hong Kong Peoples R China South China Univ Technol Sch Software Engn Guangzhou Peoples R China South China Univ Technol Minist Educ Key Lab Big Data & Intelligent Robot Guangzhou Peoples R China

Time series anomaly detection aims to identify abnormal subsequences in time series that are markedly different from the temporal behaviors of the entire sequence. Although previous density-based or proximity-based anomaly detection methods are usually used for anomaly detection, they are still suffering from high computational costs due to the need of traversing the whole training dataset during testing. Recently, reconstruction-based deep learning methods are popular for time series anomaly detection. However, they may not work well because their objective is to recover all information appeared in time series, including high-frequency noises. In this paper, we propose a simple yet efficient method called Multiresolution Self-Supervised Discriminative Network (MS2D-Net) for efficient time series anomaly detection. Specifically, the MS2D-Net includes a multiresolution downsampling module, a feature extraction module, and a self-supervised discrimination module. The multiresolution downsampling module generates some multiresolution samples by downsampling the original time series with different sampling rates and creates different pseudo-labels representing multi-scale behaviors in time series. Then, in the feature extraction module, a shallow convolution network is used to extract temporal dynamics in time series at multiple resolutions. Finally, the self-supervised discrimination module uses the pseudo-labels obtained from the multiresolution downsampling module as the self-supervised information to help separate anomalies from the normal time series samples. Experimental results show that the proposed MS2D-Net can outperform recent strong deep learning baselines on 18 benchmarks for time series anomaly detection with a much lower computational cost. (c) 2022 Elsevier B.V. All rights reserved.

关键词： Self-supervised learning Discriminative network Time series Anomaly detection

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Aspect-Opinion Correlation Aware and Knowledge-Expansion Few Shot Cross-Domain Sentiment Classification

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IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 2022年第4期13卷 1691-1704页

作者： Ren, Haopeng Cai, Yi Zeng, Yushi Ye, Jinghui Leung, Ho-fung Li, Qing South China Univ Technol Sch Software Engn Guangzhou 510650 Peoples R China SCUT Key Lab Big Data & Intelligent Robot Guangzhou 510335 Peoples R China Pazhou Lab Guangzhou 510335 Peoples R China Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Cross-domain sentiment analysis has recently attracted significant attention, which can effectively alleviate the problem of lacking large-scale labeled data for deep neural network based methods. However, most of the existing cross-domain sentiment classification models neglect the domain-specific features, which limits their performance especially when the domain discrepancy becomes larger. Meanwhile, the relations between the aspect and opinion terms cannot be effectively modeled and thus the sentiment transfer error problem is suffered in the existing unsupervised domain-adaptation methods. To address these two issues, we propose an aspect-opinion correlation aware and knowledge-expansion few shot cross-domain sentiment classification model. Sentiment classification can be effectively conducted with only a few support instances of the target domain. Extensive experiments are conducted and the experimental results show the effectiveness of our proposed model.

关键词： Cross domain sentiment analysis knowledge graph few-shot learning

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Detent Force Reduction in Permanent Magnet Synchronous Linear Motor Based on Arc-teeth

Detent Force Reduction in Permanent Magnet Synchronous Linea...

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Chinese Control and Decision Conference, CCDC

作者： Yuxin Shen Rong Guo Hongyan Ma School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big Data Beijing Key Laboratory of Intelligent of Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Permanent Magnet Synchronous Linear Motors (PMSLM) has the advantages of high thrust force density, high acceleration, high speed, high precision, high efficiency etc. However, the detent force of PMSLM is an inevitable problem, caused by their inherent end-disconnection structure. Excessive detent force will cause thrust fluctuations and reduce the system’s stability. To resolve the above problems, an improved PMSLM structure based on arc-teeth is proposed to reduce detent force. Firstly, the motor permeability distribution is analyzed at different moments based on the flux-tube method, and the expression of detent force is derived. Second, the finite element model of PMSLM is established to optimize the height of arc-teeth, and the optimal structural parameters of the motor is obtained. Finally, the performance advantages of the proposed PMSLM are verified by comparing with traditional linear motor.

关键词： Fluctuations Shape Force Stators Motors Harmonic analysis Permanent magnets

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T3: Multi-modal Tailless Triple-Flapping-Wing robot for Efficient Aerial and Terrestrial Locomotion

arXiv

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arXiv 2025年

作者： Xu, Xiangyu Zheng, Zhi Wang, Jin Chen, Yikai Huang, Jingyang Wu, Ruixin Yu, Huan Lu, Guodong The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310058 China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

Flapping-wing robots offer great versatility;however, achieving efficient multi-modal locomotion remains challenging. This paper presents the design, modeling, and experimentation of T3, a novel tailless flapping-wing robot with three pairs of independently actuated wings. Inspired by juvenile water striders, T3 incorporates bio-inspired elastic passive legs that effectively transmit vibrations generated during wing flapping, enabling ground movement without additional motors. This novel mechanism facilitates efficient multi-modal locomotion while minimizing actuator usage, reducing complexity, and enhancing performance. An SE(3)-based controller ensures precise trajectory tracking and seamless mode transition. To validate T3’s effectiveness, we developed a fully functional prototype and conducted targeted modeling, real-world experiments, and benchmark comparisons. The results demonstrate the robot’s and controller’s outstanding performance, underscoring the potential of multi-modal flapping-wing technologies for future aerial-ground robotic applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Antenna grounds

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Research on Optimization Algorithm for Fire Emergency Evacuation in Experimental Buildings of Universities

Research on Optimization Algorithm for Fire Emergency Evacua...

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Chinese Control and Decision Conference, CCDC

作者： Xincheng Han Hongyan Ma Wei He Shun Wang School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big Data Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

College laboratory buildings have dense population, numerous electrical devices, and high fire risk. However, the existing evacuation A* algorithm has the disadvantages of long time and poor adaptability. In order to effectively solve the problems of long evacuation simulation time, low efficiency, and inability to change evacuation directions according to fire conditions in college laboratory buildings, this paper proposes an improved A* algorithm. This paper improves the heuristic function in the A* algorithm by introducing a logarithmic decay factor. By changing the weight of the estimated cost, it reduces the number of unnecessary search nodes and steers the search algorithm in the correct direction, thereby improving search efficiency. Secondly, it introduces a bidirectional A* search strategy to reduce search nodes and improve the efficiency of the algorithm. Simulation results show that the improved A* algorithm has fewer traversed nodes, shorter search paths, and higher search efficiency, which verifies the effectiveness and feasibility of the improved A* algorithm in experimental building fire evacuation. This study provides a solution for fire evacuation in college laboratory buildings.

关键词： Heuristic algorithms Simulation Buildings Sociology Laboratories Search problems Path planning

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Smaller and Faster robotic Grasp Detection Model via Knowledge Distillation and Unequal Feature Encoding

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IEEE robotICS AND AUTOMATION LETTERS 2024年第8期9卷 7206-7213页

作者： Nie, Hong Zhao, Zhou Chen, Lu Lu, Zhenyu Li, Zhuomao Yang, Jing Shanxi Univ Inst Big Data Sci & Ind Sch Comp & Informat Technol Taiyuan 030006 Peoples R China Shanxi Univ Minist Educ Key Lab Computat Intelligence & Chinese Informat P Taiyuan 030006 Peoples R China Cent China Normal Univ Sch Comp Sci Wuhan 430079 Peoples R China Hubei Engn Res Ctr Intelligent Detect & Identifica Wuhan 430079 Peoples R China Univ West England Bristol Robot Lab Bristol BS16 1QY England Shanxi Univ Sch Automat & Software Engn Taiyuan 030031 Peoples R China

In order to achieve higher accuracy, the complexity of grasp detection network increases accordingly with complicated model structures and tremendous parameters. Although various light-weight strategies are adopted, directly designing the compact network can be sub-optimal and difficult to strike the balance between accuracy and model size. To solve this problem, we explore a more efficient grasp detection model from two aspects: elaborately designing a light-weight network and performing knowledge distillation on the designed network. Specifically, based on the designed light-weight backbone, the features from RGB and D images with unequal effective grasping information rates are fully utilized and the information compensation strategies are adopted to make the model small enough while maintaining its accuracy. Then, the grasping features contained in the large teacher model are adaptively and effectively learned by our proposed method via knowledge distillation. Experimental results indicate that the proposed method is able to achieve comparable performance (98.9%, 93.1%, 82.3%, and 90.0% on Cornell, Jacquard, GraspNet, and MultiObj datasets, respectively) with more complicate models while reducing the parameters from MBs to KBs. Real-world robotic grasping experiment in an embedded AI computing device also prove the effectiveness of this approach.

关键词： robotic grasping light-weight modelling knowledge distillation generative network attention mechanism

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Specific Design and Implementation of the Characteristic Species database in Wuling Mountain Area

Specific Design and Implementation of the Characteristic Spe...

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Information Technology, Communication Ecosystem and Management (ITCEM), International Conference on

作者： Shaowei Deng Yujun Yang Yimei Yang College of Computer and Artificial Intelligence(Software College) Huaihua University Huaihua Hunan China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities Huaihua Hunan China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province

ISBN: (数字)9798331542696

ISBN: (纸本)9798331542702

This article aims to design and implement a database system for characteristic species in the Wuling Mountain area. The system will collect, organize, store, and analyze information on characteristic species in the Wuling Mountain area, providing data support for ecological research, biodiversity conservation, agricultural production, and other fields. This article will provide a detailed introduction to database requirements analysis, data model design, database structure implementation, data security management, and system testing and optimization.

关键词： System testing data security Ecosystems Production database systems data models Biodiversity Information technology Optimization

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Air quality prediction based on ICEEMDAN-SSA-LSTM

Air quality prediction based on ICEEMDAN-SSA-LSTM

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Chinese Control and Decision Conference, CCDC

作者： Yanling Qin Hongyan Ma Wei He School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing China School of Electrical and Information Engineering Institute of Distributed Energy Storage Safety Big Data Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

To address the problem that the Air Quality Index (AQI) is difficult to predict, a prediction model based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Sparrow Search Algorithm (SSA), and Long Short-Term Memory (LSTM) is proposed. First, the AQI sequence was decomposed by ICEEMDAN. Second, the initial learning rate, max training epoch and the number of hidden layers of the LSTM model are optimized by the SSA algorithm to establish the SSA-LSTM model. Last, the results decomposed were imported into the SSA-LSTM model to predict AQI. Compared with the prediction results of the LSTM, CEEMD-LSTM, and CEEMDAN-SSA-LSTM model, the accuracy (measured by correlation coefficients) of the CEEMDAN-SSA-LSTM is 98%. The result confirmed that the ICEEMDAN-SSA-LSTM can obtaining an improvement of the correlation coefficients and reduce the mean square error, which is valuable for engineering applications.

关键词： Training Correlation coefficient Adaptation models Accuracy Empirical mode decomposition Atmospheric modeling Predictive models

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SCALABLE ATTRIBUTION OF ADVERSARIAL ATTACKS VIA MULTI-TASK LEARNING

arXiv

引用

arXiv 2023年

作者： Guo, Zhongyi Han, Keji Ge, Yao Ji, Wei Li, Yun Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

Deep neural networks (DNNs) can be easily fooled by adversarial attacks during inference phase when attackers add imperceptible perturbations to original examples, i.e., adversarial examples. Many works focus on adversarial detection and adversarial training to defend against adversarial attacks. However, few works explore the tool-chains behind adversarial examples, which can help defenders to seize the clues about the originator of the attack, their goals, and provide insight into the most effective defense algorithm against corresponding attacks. With such a gap, it is necessary to develop techniques that can recognize tool-chains that are leveraged to generate the adversarial examples, which is called Adversarial Attribution Problem (AAP). In this paper, AAP is defined as the recognition of three signatures, i.e., attack algorithm, victim model and hyperparameter. Current works transfer AAP into single label classification task and ignore the relationship between these signatures. The former will meet combination explosion problem as the number of signatures is increasing. The latter dictates that we cannot treat AAP simply as a single task problem. We first conduct some experiments to validate the attributability of adversarial examples. Furthermore, we propose a multi-task learning framework named Multi-Task Adversarial Attribution (MTAA) to recognize the three signatures simultaneously. MTAA contains perturbation extraction module, adversarial-only extraction module and classification and regression module. It takes the relationship between attack algorithm and corresponding hyperparameter into account and uses the uncertainty weighted loss to adjust the weights of three recognition tasks. The experimental results on MNIST and ImageNet show the feasibility and scalability of the proposed framework as well as its effectiveness in dealing with false alarms. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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