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arXiv 2024年

作者： Yao, Hang Miao, Qiguang Zhao, Peipei Li, Chaoneng Li, Xin Feng, Guanwen Liu, Ruyi The School of Computer Science and Technology Xidian University Shaanxi Xi’an710071 China Xi’an Key Laboratory of Big Data and Intelligent Vision Shaanxi Xi’an710071 China Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi’an710071 China The School of Mechanical Engineering Yanshan University Qinhuangdao066004 China

Different from large-scale classification tasks, fine-grained visual classification is a challenging task due to two critical problems: 1) evident intra-class variances and subtle inter-class differences, and 2) overfitting owing to fewer training samples in datasets. Most existing methods extract key features to reduce intra-class variances, but pay no attention to subtle inter-class differences in fine-grained visual classification. To address this issue, we propose a loss function named exploration of class center, which consists of a multiple class-center constraint and a class-center label generation. This loss function fully utilizes the information of the class center from the perspective of features and labels. From the feature perspective, the multiple class-center constraint pulls samples closer to the target class center, and pushes samples away from the most similar nontarget class center. Thus, the constraint reduces intra-class variances and enlarges inter-class differences. From the label perspective, the class-center label generation utilizes class-center distributions to generate soft labels to alleviate overfitting. Our method can be easily integrated with existing fine-grained visual classification approaches as a loss function, to further boost excellent performance with only slight training costs. Extensive experiments are conducted to demonstrate consistent improvements achieved by our method on four widely-used fine-grained visual classification datasets. In particular, our method achieves state-of-the-art performance on the FGVC-Aircraft and CUB-200-2011 datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

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Transverse Velocity Field Measurement in High-Resolution Solar Images Based on Deep Learning

arXiv

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arXiv 2023年

作者： Shang, Zhen-Hong Mu, Si-Yu Ji, Kai-Fan Qiang, Zhen-Ping Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology Kunming650500 China Yunnan Observatories Chinese Academy of Sciences Kunming650216 China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming650224 China

To address the problem of the low accuracy of transverse velocity field measurements for small targets in high-resolution solar images, we proposed a novel velocity field measurement method for high-resolution solar images based on PWCNet. This method transforms the transverse velocity field measurements into an optical flow field prediction problem. We evaluated the performance of the proposed method using the Hα and TiO datasets obtained from New Vacuum Solar Telescope (NVST) observations. The experimental results show that our method effectively predicts the optical flow of small targets in images compared with several typical machine- and deep-learning methods. On the Hα dataset, the proposed method improves the image structure similarity from 0.9182 to 0.9587 and reduces the mean of residuals from 24.9931 to 15.2818;on the TiO dataset, the proposed method improves the image structure similarity from 0.9289 to 0.9628 and reduces the mean of residuals from 25.9908 to 17.0194. The optical flow predicted using the proposed method can provide accurate data for the atmospheric motion information of solar images. The code implementing the proposed method is available on https:// ***/lygmsy123/transverse-velocity-field-measurement. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV systems

arXiv

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arXiv 2025年

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fujian Key Laboratory of Network Fuzhou350002 China Computing and Intelligent Information Processing [Fuzhou University Fuzhou350116 China Department of Computer Science Faculty of Environment Science and Economy University of Exeter ExeterEX4 4QF United Kingdom School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Informatics University of Oslo Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Investigation of the Joint Automated Mobile Loading systems Two-Stage Vehicle Routing Problem Under the Consideration of Supply-Demand Imbalance, Fair Efficiency, and Demand Uncertainty

SSRN

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SSRN 2024年

作者： Xu, Jia Liu, Jian Pan, Nan Han, Yuhang Yin, Shi Liang, Weijie Han, Wei Lin, Cong Faculty of Civil Aviation and Aeronautics Kunming University of Science and Technology Yunnan650500 China Faculty of Transportation Engineering Kunming University of Science and Technology Yunnan650500 China Faculty of Science Hong Kong Baptist University 999077 Hong Kong Early Warning Data Section Yunnan Provincial National Defense Mobilization Command Information Assurance Center Yunnan650228 China Faculty of Command and Control Engineering Army Engineering University of PLA Jiangsu210007 China Yunnan Power Grid Co. Ltd. Measurement Center Yunnan650011 China Kunming Heli Intelligent Technology Co. Ltd. Yunnan650504 China

Efficient and fair material distribution is crucial in both supply chain management and emergency situations. Existing research is still immature in terms of issues such as the shortage of materials and uncertainty in customer *** paper proposes a new two-stage vehicle path planning method to address the imbalance between supply and demand of relief materials (e.g., food) and demand uncertainty in emergencies, such as war,public health crises, etc. Synergistic two-stage routing problem integrating automated mobile loading (AML) systems and trucks (A&V-TSRP) To optimize the fairness and efficiency of material distribution, a Mixed-Integer Non-Linear Programming (MINLP) model has been *** study innovatively considers distance factors and demand uncertainties, proposing a fair and efficient distribution strategy. The improved algorithm combines adaptive large neighborhood search as well as not allowed search with enhanced operators, greedy selection, and an exchange strategy. An empirical study, based in Shanghai Pudong, verifies the model and algorithm's effectiveness, showcasing a total cost reduction of 18.73% compared to the Gurobi solver and reductions of 27.28%, 20.96%, and 28.58% compared to three other heuristic algorithms. In addition, AMLs combined with trucks reduced the total costs by 37% and saved 42% in delivery time compared to using a single truck. © 2024, The Authors. All rights reserved.

关键词： Trucks

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Towards Few-shot Inductive Link Prediction on Knowledge Graphs: A Relational Anonymous Walk-guided Neural Process Approach

arXiv

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arXiv 2023年

作者： Zhao, Zicheng Luo, Linhao Pan, Shirui Nguyen, Quoc Viet Hung Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China Department of Data Science and AI Monash University Australia School of Information and Communication Technology Griffith University Australia Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education

Few-shot inductive link prediction on knowledge graphs (KGs) aims to predict missing links for unseen entities with few-shot links observed. Previous methods are limited to transductive scenarios, where entities exist in the knowledge graphs, so they are unable to handle unseen entities. Therefore, recent inductive methods utilize the sub-graphs around unseen entities to obtain the semantics and predict links inductively. However, in the few-shot setting, the sub-graphs are often sparse and cannot provide meaningful inductive patterns. In this paper, we propose a novel relational anonymous walk-guided neural process for few-shot inductive link prediction on knowledge graphs, denoted as RawNP. Specifically, we develop a neural process-based method to model a flexible distribution over link prediction functions. This enables the model to quickly adapt to new entities and estimate the uncertainty when making predictions. To capture general inductive patterns, we present a relational anonymous walk to extract a series of relational motifs from few-shot observations. These motifs reveal the distinctive semantic patterns on KGs that support inductive predictions. Extensive experiments on typical benchmark datasets demonstrate that our model derives new state-of-the-art performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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System Network Analytics: Evolution and Stable Rules of a State Series

arXiv

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arXiv 2022年

作者： Chaturvedi, Animesh Tiwari, Aruna Spyratos, Nicolas Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Karnataka Dharwad India Computer Science and Engineering Indian Institute of Technology Indore Indore India Computer Science Department LISN Lab University of Paris-Saclay Paris France

System Evolution Analytics on a system that evolves is a challenge because it makes a State Series SS = {S1, S2… SN} (i.e., a set of states ordered by time) with several inter-connected entities changing over time. We present stability characteristics of interesting evolution rules occurring in multiple states. We defined an evolution rule with its stability as the fraction of states in which the rule is interesting. Extensively, we defined stable rule as the evolution rule having stability that exceeds a given threshold minimum stability (minStab). We also defined persistence metric, a quantitative measure of persistent entity-connections. We explain this with an approach and algorithm for System Network Analytics (SysNet-Analytics), which uses minStab to retrieve Network Evolution Rules (NERs) and Stable NERs (SNERs). The retrieved information is used to calculate a proposed System Network Persistence (SNP) metric. This work is automated as a SysNet-Analytics Tool to demonstrate application on real world systems including: software system, natural-language system, retail market system, and IMDb system. We quantified stability and persistence of entity-connections in a system state series. This results in evolution information, which helps in system evolution analytics based on knowledge discovery and data mining. © 2022, CC BY.

关键词： Stability

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RETRACTED ARTICLE: Obstacles Uncovering System for Slender Pathways Using Unmanned Aerial Vehicles with Automatic Image Localization Technique

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International Journal of Computational Intelligence systems 2023年第1期16卷 1-14页

作者： Selvarajan, Shitharth Manoharan, Hariprasath Khadidos, Alaa O. Shankar, Achyut Khadidos, Adil O. Onyema, Edeh Michael Department of Computer Science Kebri Dehar University Kebri Dehar Ethiopia School of Built Environment Engineering and Computing Leeds Beckett University Leeds UK Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia Secure Cyber Systems Research Group (SCSRG) WMG University of Warwick Coventry UK School of Computer Science Engineering Lovely Professional University Phagwara India Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Mathematics and Computer Science Coal City University Enugu Nigeria

In this study, unidentified flying machines are built with real-time monitoring in mid-course settings for obstacle avoidance in mind. The majority of the currently available methods are implemented as comprehensive monitoring systems, with significant success in monitored applications like bridges, railways, etc. So, the predicted model is developed exclusively for specific monitoring settings, as opposed to the broad conditions that are used by the current approaches. Also, in the design model, the first steps are taken by limiting the procedure to specific heights, and the input thrust that is provided for take up operation is kept to a minimum. Due to the improved altitudes, the velocity and acceleration units have been cranked up on purpose, making it possible to sidestep intact objects. In addition, Advanced Image Mapping Localization (AIML) is used to carry out the implementation process, which identifies stable sites at the correct rotation angle. Besides, Cyphal protocol integration improves the security of the data-gathering process by transmitting information gathered from sensing devices. The suggested system is put to the test across five different case studies, where the designed Unmanned aerial vehicle can able to detect 25 obstacles in the narrow paths in considered routs but existing approach can able to identify only 14 obstacle in the same routes.

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Simulation of Urban Surface Temperature and Surface Heat Balance in the Tokyo Metropolitan Area

SSRN

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SSRN 2024年

作者： Hirano, Yujiro Ichinose, Toshiaki Ohashi, Yukitaka Shiraki, Yohei Onishi, Akio Yoshida, Yukiko Social Systems Division National Institute for Environmental Studies 16-2 Onogawa Ibaraki Tsukuba305-8506 Japan Faculty of Biosphere-Geosphere Science Okayama University of Science 1-1 Ridai-cho Kita-ku Okayama700-0005 Japan Faculty of Data Science Rissho University 1700 Magechi Saitama Kumagaya3600194 Japan School of Data Science Yokohama City University 22-2 Seto Kanazawa-ku Kanagawa Yokohama-shi236-0027 Japan Graduate School of Science and Engineering Ibaraki University 4-12-1 Nakanarusawa Ibaraki Hitachi316-8511 Japan

This study aimed to simulate ground surface temperature and surface heat balance in the Tokyo metropolitan area, utilizing thermal infrared remote sensing and an urban surface heat balance model. Previous studies have typically employed ground surface temperatures obtained through satellite remote sensing to analyze urban thermal environments. However, these studies were limited to using surface temperature data available only at the time of satellite passage. Therefore, in this study, we conducted a simulation for the Tokyo metropolitan area by integrating a one-dimensional heat balance model with ground surface temperatures observed by satellites. The novelty of this study lies in the utilization of parameters related to heat balance obtained through calculations of physical process, as opposed to relying on empirical formulas or parameters. Consequently, realistic ground surface characteristics with parameters such as evaporation efficiency and thermal inertia essential for the simulation were derived by the above combined method. Furthermore, diurnal variations in ground surface temperature distributions were accurately represented by using realistic surface temperature data based on satellite remote sensing to consider the effect of heat storage in urban areas—an aspect challenging to reproduce by using conventional local meteorological models. © 2024, The Authors. All rights reserved.

关键词： Surface temperature

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Proposal of Axial-gap type Magnetic Multiple Spur Gear for In-wheel Motor System of Electric Vehicle

Proposal of Axial-gap type Magnetic Multiple Spur Gear for I...

引用

IEEE International Conference on Electric Machines and Drives (IEMDC)

作者： Taiga Kamijo Kohei Aiso Kan Akatsu Yasuaki Aoyama Electrical Engineering and Computer Science Shibaura Institute of Technology Tokyo Japan Department of Electrical Engineering Shibaura Institute of Technology Tokyo Japan Department of Faculty of Engineering Division of Intelligent Systems Engineering Yokohama National University Yokohama Japan Center for Technology Innovation - Materials Hitachi Ltd. Research & Development Group Ibaraki Japan

Magnetic gears have the characteristics of maintenance-free, low noise, and low vibration due to non-contact power transmission. By taking advantage of the non-contact power transmission, the motor system can be made smaller and lighter by further increasing the motor speed. Magnetic Multiple Spur Gear (MMSG) has been proposed, the drive system of MMSG and multiple high-speed motors can reduce the size and weight of the drive system by converting the total power of multiple high-speed motors. Applying this drive system to in-wheel motors will increase the available interior space in electric vehicles. However, there is a risk that the high-speed rotor and low-speed rotor contact with each other due to the radial structure in the in-wheel motor system. In this research, an axial-gap MMSG is proposed to solve the problem and to obtain higher torque density, the performances such as torque density and efficiency are evaluated by finite element analysis.

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Imitation Learning for Nonprehensile Manipulation through Self-Supervised Learning Considering Motion Speed

arXiv

引用

arXiv 2022年

作者： Saigusa, Yuki Sakaino, Sho Tsuji, Toshiaki Faculty of Engineering Information and Systems University of Tsukuba Ibaraki305-8577 Japan Department of Intelligent Interactive Systems University of Tsukuba Ibaraki305-8577 Japan Graduate School of Science and Engineering Saitama University Saitama338-8570 Japan

Robots are expected to replace menial tasks such as housework. Some of these tasks include nonprehensile manipulation performed without grasping objects. Nonprehensile manipulation is very difficult because it requires considering the dynamics of environments and objects. Therefore imitating complex behaviors requires a large number of human demonstrations. In this study, a self-supervised learning that considers dynamics to achieve variable speed for nonprehensile manipulation is proposed. The proposed method collects and fine-tunes only successful action data obtained during autonomous operations. By fine-tuning the successful data, the robot learns the dynamics among itself, its environment, and objects. We experimented with the task of scooping and transporting pancakes using the neural network model trained on 24 human-collected training data. The proposed method significantly improved the success rate from 40.2% to 85.7%, and succeeded the task more than 75% for other objects. © 2022, CC BY.

关键词： Supervised learning

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