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arXiv 2024年

作者： Xiao, Yubin Wang, Di Wu, Xuan Wu, Yuesong Li, Boyang Du, Wei Wang, Liupu Zhou, You Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University 639956 Singapore WeBank-NTU Joint Research Institute on Fintech Nanyang Technological University 639956 Singapore College of Computing and Data Science Nanyang Technological University 639956 Singapore

Neural models produce promising results when solving Vehicle Routing Problems (VRPs), but may often fall short in generalization. Recent attempts to enhance model generalization often incur unnecessarily large training cost or cannot be directly applied to other models solving different VRP variants. To address these issues, we take a novel perspective on model architecture in this study. Specifically, we propose a plug-and-play Entropy-based Scaling Factor (ESF) and a Distribution-Specific (DS) decoder to enhance the size and distribution generalization, respectively. ESF adjusts the attention weight pattern of the model towards familiar ones discovered during training when solving VRPs of varying sizes. The DS decoder explicitly models VRPs of multiple training distribution patterns through multiple auxiliary light decoders, expanding the model representation space to encompass a broader range of distributional scenarios. We conduct extensive experiments on both synthetic and widely recognized real-world benchmarking datasets and compare the performance with seven baseline models. The results demonstrate the effectiveness of using ESF and DS decoder to obtain a more generalizable model and showcase their applicability to solve different VRP variants, i.e., travelling salesman problem and capacitated VRP. Notably, our proposed generic components require minimal computational resources, and can be effortlessly integrated into conventional generalization strategies to further elevate model generalization. Copyright © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

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Preface

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Annals of data Science 2015年第3期1卷 281-282页

作者： Ying Liu Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing China Key Research Laboratory on Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

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Research on the motivation to contribution and influencing factors of university students——a semi-structured interview based on qualitative research

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IOP Conference Series: Materials Science and engineering 2019年第5期563卷

作者： Yueyao Li Sanhong Deng Yue Zhang School of Information Management Nanjing University Nanjing 210023 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing University Nanjing 210023 China

Due to the lack of theoretical knowledge and practical experience, the university students have many deficiencies in contribution. We conducted semi-structured interviews with 28 undergraduate and postgraduate students of Nanjing Agricultural University. At the same time, we used descriptive statistics and cross-analysis methods to analyse the motivations of college students' initial contribution and the influencing factor of their choice. Based on this, this paper puts forward some suggestions for the initial submission of college students, and the journals are also provided for reference in the selection of manuscripts.

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Research on Influencing Factors of College Students' Intention of Online Health Information Behavior Based on Social Cognitive Theory

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Journal of Physics: Conference Series 2019年第2期1213卷

作者： Jia Kong Sanhong Deng Yue Zhang School of Information Management Nanjing University Nanjing 210023 China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing University Nanjing 210023 China

Based on the Social Cognition Theory, we constructed the influencing factors model of college students' intention of online health information behavior from three levels of individual, society and information systems, which was analyzed by using the PLS-SEM. Research shows that self-efficacy, information service quality, social support, information literacy and information quality significantly influence college students' intention of online health information behavior. However, the influence of perceived risk and information system quality is not significant.

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On the Stability and Generalization of Triplet Learning

arXiv

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arXiv 2023年

作者： Chen, Jun Chen, Hong Jiang, Xue Gu, Bin Li, Weifu Gong, Tieliang Zheng, Feng College of Informatics Huazhong Agricultural University Wuhan430070 China College of Science Huazhong Agricultural University Wuhan430070 China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China Key Laboratory of Smart Farming for Agricultural Animals Wuhan430070 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates School of Computer Science and Technology Xi'an Jiaotong University Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Ministry of Education Xi’an710049 China

Triplet learning, i.e. learning from triplet data, has attracted much attention in computer vision tasks with an extremely large number of categories, e.g., face recognition and person re-identification. Albeit with rapid progress in designing and applying triplet learning algorithms, there is a lacking study on the theoretical understanding of their generalization performance. To fill this gap, this paper investigates the generalization guarantees of triplet learning by leveraging the stability analysis. Specifically, we establish the first general high-probability generalization bound for the triplet learning algorithm satisfying the uniform stability, and then 1 obtain the excess risk bounds of the order (Equation presented). for both stochastic gradient descent (SGD) and regularized risk minimization (RRM), where 2n is approximately equal to the number of training samples. Moreover, an optimistic generalization bound in expectation as fast as O(n−1) is derived for RRM in a low noise case via the on-average stability analysis. Finally, our results are applied to triplet metric learning to characterize its theoretical underpinning. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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Multi-objective Aerial Collaborative Secure Communication Optimization via Generative Diffusion Model-enabled Deep Reinforcement Learning

arXiv

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arXiv 2024年

作者： Zhang, Chuang Sun, Geng Li, Jiahui Wu, Qingqing Wang, Jiacheng Niyato, Dusit Liu, Yuanwei The College of Computer Science and Technology Jilin University Changchun130012 China The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

Due to flexibility and low-cost, unmanned aerial vehicles (UAVs) are increasingly crucial for enhancing coverage and functionality of wireless networks. However, incorporating UAVs into next-generation wireless communication systems poses significant challenges, particularly in sustaining high-rate and long-range secure communications against eavesdropping attacks. In this work, we consider a UAV swarm-enabled secure surveillance network system, where a UAV swarm forms a virtual antenna array to transmit sensitive surveillance data to a remote base station (RBS) via collaborative beamforming (CB) so as to resist mobile eavesdroppers. Specifically, we formulate an aerial secure communication and energy efficiency multi-objective optimization problem (ASCEE-MOP) to maximize the secrecy rate of the system and to minimize the flight energy consumption of the UAV swarm. To address the non-convex, NP-hard and dynamic ASCEE-MOP, we propose a generative diffusion model-enabled twin delayed deep deterministic policy gradient (GDMTD3) method. Specifically, GDMTD3 leverages an innovative application of diffusion models to determine optimal excitation current weights and position decisions of UAVs. The diffusion models can better capture the complex dynamics and the trade-off of the ASCEE-MOP, thereby yielding promising solutions. Simulation results highlight the superior performance of the proposed approach compared with traditional deployment strategies and some other deep reinforcement learning (DRL) benchmarks. Moreover, performance analysis under various parameter settings of GDMTD3 and different numbers of UAVs verifies the robustness of the proposed approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Energy efficiency

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Pioneering case: Robot-assisted remote radical distal gastrectomy for gastric cancer based on 5G communication technology

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Intelligent Surgery 2024年第1期7卷 22-26页

作者： Honghai Guo Yuan Tian Jia Shi Peigang Yang Jiaxuan Yang Pingan Ding Xuefeng Zhao Zhidong Zhang Qun Zhao The Third Department of Surgery The Fourth Hospital of Hebei Medical University Shijiazhuang Hebei 050011 China Hebei Provincial Key Laboratory of Precision Diagnosis and Comprehensive Treatment of Gastric Cancer Shijiazhuang Hebei 050011 China Big Data Analysis and Mining Application for Accurate Diagnosis and Treatment of Gastric Cancer Hebei Provincial Engineering Research Center Shijiazhuang Hebei 050011 China Department of Academic Research The First Affiliated Hospital of Shandong First Medical University Jinan Shandong 250014 China

Background The aim of this study is to describe technical considerations and short-term outcomes of the first officially published robot-assisted remote radical distal gastrectomy for gastric cancer with 5G technology. Methods A 51-year-old gastric cancer patient with clinical stage T2N0M0 underwent 5G-enabled robot-assisted remote radical distal gastrectomy on September 11, 2023 at the Third Department of Surgery, the Fourth Hospital of Hebei Medical University. Results The surgery procedure was successfully performed with an average total intraoperative delay of 225 ms, average round-trip delay of 30 ms and no packet loss rate. The operative time was 140 min and intraoperative blood loss was 20 ml. The patient was discharged on postoperative day 6 without any postoperative complications. Conclusion This case has preliminarily verified the feasibility and safety of 5G-enabled robot-assisted remote radical distal gastrectomy for patients with gastric cancer. The initial study fills in the gaps in related fields and lays a solid foundation for subsequent research. To verify its superior operative outcomes, further clinical trials are needed.

关键词： 5G technology Robotic surgery Radical gastrectomy Remote surgery

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Road network modeling with layered abstraction for path discovery in vehicle navigation systems

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Journal of Information Hiding and Multimedia Signal Processing 2016年第4期7卷 791-801页

作者： Pan, Jeng-Shyang Wang, Chen Sung, Tien-Wen Fuzhou College of Foreign Studies and Trade No. 28 Yuhuan Road Changle Fuzhou CityFujian Province350202 China Innovative Information Industry Research Center Harbin Institute of Technology Shenzhen Graduate School University Town Shenzhen City518055 China College of Information Science and Engineering Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No. 3 Xueyuan Road Fuzhou CityFujian Province350118 China

Algorithms for path planning have recently drawn renewed attention from researchers due to the advances of intelligent transportation applications. The difference on path planning between past and nowadays is the complexity of dynamic transportation network. This paper focuses on the road network modeling to speed up real time path discovery in dynamic and complex road networks. On the basis of community detection (partitioning), an agglomerative hierarchical clustering algorithm with greedy strategy is proposed in this paper. The algorithm performs a hierarchical network abstraction to make a layered road network with largest community modularity. Static and dynamic information of the real time road network conditions is concerned in the process of network abstraction. Based on the road network layering, the performance of path discovery can be improved due to the reduction of unnecessary searching. The performance of the proposed algorithm was evaluated and compared with the well-known Dijkstra and A-star algorithms. The result shows that the proposed algorithm performs better than the other two algorithms. © 2016.

关键词： Motion planning

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Real-time Energy Dispatching Strategy for Microgrid Based on Improved SD3 Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1338-1357页

作者： Hang, Tian-Xiao He, Wen-Wu Pei, Bo-Yu Lin, Mei-Li Li, Pei-Qiang School of Computer Science and Mathematics Fujian University of Technology Xuefu South Road Fujian Province Fuzhou City350118 China School of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Xuefu South Road Fujian Province Fuzhou City350118 China School of Electronic Electrical Engineering and Physics Fujian University of Technology Xuefu South Road Fujian Province Fuzhou City350118 China

Deep reinforcement learning algorithms has attracted attention in the field of energy management in recent years, which can effectively interact with the environment and learn relevant policies to achieve specific goals. To eliminate Q-value estimation bias, in this paper we propose a real-time energy dispatching strategy for microgrid based on SD3 algorithm, and introduce prioritized experience replay and Huber loss, to develop an improved algorithm, called SD3 with loss-adjusted prioritized experience replay (SD3-LAP). A grid-connected microgrid that includes photovoltaic system, wind turbine, micro-turbine, energy storage system and electrical load is built to simulate the environment and validate the proposed methods. The problem of real-time energy dispatching for microgrid is recast as a Markov Decision Process, which aims to minimize the operating costs under the condition that the constraints of power balance and the upper and lower limits of SOC of the energy storage system are satisfied. Experimental results show that compared with baseline models, the energy dispatching strategies provide by SD3 algorithm and SD3-LAP algorithm are more effective and robust, and the mechanism of LAP is a general purposed plug-in that can effectively improve the performance of related reinforcement learning algorithm. The daily average operating cost of SD3-LAP algorithm and SD3 algorithm are respectively 37.9% and 22.89% lower than TD3 algorithm, while the daily average operating cost of of TD3 algorithm is 21.9% lower than DQN. In addition, compared with the original algorithms, namely SD3, TD3 and DQN, the LAP mechanism reduces respectively the daily average operating cost by 19.5%, 11.7%, and 20.1%. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Reinforcement learning

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Constructing the 3d Cloud Amount from Advanced Geostationary Imager Observations

SSRN

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SSRN 2024年

作者： Lin, Han Li, Jun Min, Min Zhang, Feng Wang, keyue Wu, Qunyong Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education Fuzhou University Fuzhou350108 China National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology Fuzhou University Fuzhou350108 China National Satellite Meteorological Centre China Meteorological Administration Beijing100081 China School of Atmospheric Sciences Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Sun Yat-sen University Southern Laboratory of Ocean Science and Engineering Zhuhai519082 China Department of Atmospheric and Oceanic Sciences Institute of Atmospheric Sciences Fudan University Shanghai200438 China

Accurate information on cloud amount vertical structure is crucial for weather monitoring and understanding climate systems. Active sensors from satellites can provide three-dimensional (3D) cloud structure but with limited geographical coverage, passive sensors from satellites have expanded observation coverage but with limited capability on profiling the clouds. Combing active and passive observations from satellites, together with atmospheric reanalysis data, this study proposes a machine learning approach (CLANN, CLoud Amount Neural Network) to construct three-dimensional (3D) cloud amounts at passive observational coverage. Independent validation is conducted for cloud amount estimates derived from the Advanced Geostationary Radiation Imager (AGRI) onboard Fengyun-4A and ERA5 using CLANN against CALIPSO/CALIOP lidar product, results indicate significant correlations (Pearson's r = 0.73). The cloud-amount-weighted height showed a high consistency in terms of height positioning between CLANN estimations and CALIOP data, with an RMSE of 1.89 km and a Pearson's r of 0.92. key features such as water vapor band brightness temperature and upper-layer temperature significantly enhanced model accuracy, as revealed by permutation importance analysis. Sensitivity tests highlighted the critical role of the 1.375 µm band in cirrus altitude detection, justifying the model's reliance on daytime observations. Additionally, CLANN's application to tropical cyclone (TC) dynamics was exemplified through Typhoon Lekima's rapid intensification process, emphasizing its potential in forecasting TC behavior based on cloud structure analysis. And the 3D statistical results from CLANN in 2019 reveal the seasonal variation details of cloud distribution, further demonstrating its application value in climate analysis. © 2024, The Authors. All rights reserved.

关键词： Tropical cyclone

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