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arXiv 2020年

作者： Sun, Lijun Yang, Qian Chen, Xiao Chen, Zhenxiang College of Information Science and Technology Qingdao University of Science & Technology Qingdao266061 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan 250022 China School of Informatics University of Edinburgh EdinburghEH8 9AB United Kingdom

As the commercial use of 5G technologies has grown more prevalent, smart vehicles have become an efficient platform for delivering a wide array of services directly to customers. The vehicular crowdsourcing service (VCS), for example, can provide immediate and timely feedback to the user regarding real-time transportation information. However, different sources can generate spurious information towards a specific service request in the pursuit of profit. Distinguishing trusted information from numerous sources is the key to a reliable VCS platform. This paper proposes a solution to this problem called "RC-chain", a reputation-based crowdsourcing framework built on a blockchain platform (Hyperledger Fabric). We first establish the blockchain-based platform to support the management of crowdsourcing trading and user-reputation evaluating activities. A reputation model, the Trust Propagation & Feedback Similarity (TPFS), then calculates the reputation values of participants and reveals any malicious behavior accordingly. Finally, queueing theory is used to evaluate the blockchain-based platform and optimize the system performance. The proposed framework was deployed on the IBM Hyperledger Fabric platform to observe its real-world running time, effectiveness, and overall performance. © 2020, CC BY-NC-ND.

关键词： Crowdsourcing

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A lithium-ion battery health state assessment and remaining useful life prediction method based on multi slope features

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Journal of Energy Storage 2025年 130卷

作者： Gaige Chen Siqi Zhong Yahong Yang Xiaoshen Zhang Wei Deng Jinsong Liu School of Communications and Information Engineering & School of Artificial Intelligence Xi'an University of Posts and Telecommunications Xi'an Shaanxi 710121 China Shaanxi Union Research Center of University and Enterprise for 5G+ Industrial Internet Communication Terminal Technology Xi'an University of Posts and Telecommunications Xi'an Shaanxi 710121 China Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts & Telecommunications Xi'an Shaanxi 710121 China State Key Laboratory of Space Power Sources Shanghai Institute of Space Power-sources Shanghai 200245 China Xi'an Aviation Brake Technology Co. Ltd Xi'an Shaanxi 710065 China Contemporary Amperex Technology Co. Ltd. Ningde Fujian 352107 China

In the field of battery management, accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical to safeguard the safety of batteries and devices. However, current prediction methods are mainly based on sensor data such as battery voltage, current, temperature, time, and other data, and there is a challenge problem with neglecting the intrinsic connection between these data. To address this problem, a lithium-ion battery health state assessment and remaining useful life prediction method based on multi slope features of discharge curves is proposed. First, new slope features of discharge curve are proposed based on the discharge mechanism of lithium-ion batteries; second, slope features are utilized to estimate the state of health (SOH) of the batteries; then, unsupervised clustering is performed on the unlabeled SOH data to construct a health state assessment model; and finally, the RUL prediction model based on the gated recurrent unit with grey wolf optimizer (GWO-GRU) is constructed. The proposed method is validated using the aging data of lithium-ion batteries released by NASA Ames Research Center and the Oxford battery degradation dataset, and compared with other mainstream methods. The experimental results show that the method exhibits high accuracy in RUL prediction, the prediction accuracy is 99 % and 98 % respectively, which is significantly better than traditional methods. Therefore, the method can effectively assess the health state of lithium-ion batteries and accurately predict their RUL, which can provide support for the realization of lithium-ion battery health management and intelligent operation and maintenance.

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DPNet: Dynamic Pooling network for Tiny Object Detection

arXiv

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arXiv 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie School of Computer Science Beijing University of Posts and Telecommunications Beijing China Research Center for Space Computing System Zhejiang Lab Hangzhou China SWJTU-LEEDS Joint School Southwest Jiaotong University Chengdu China Guangdong Zhiyun City construction Technology Co. LTD Zhuhai China Department of Control Science and Engineering College of Electronic and Information Engineering Tongji University Shanghai China Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education Hohai University China

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed downsampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware downsampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection *** code will be made publicly available. Copyright © 2025, The Authors. All rights reserved.

关键词： Image coding

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Traffic Off-Loading over Uncertain Shared Spectrums with End-to-End Session Guarantee

Traffic Off-Loading over Uncertain Shared Spectrums with End...

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IEEE Conference on Vehicular technology (VTC)

作者： Ruyi Xiao Xuanheng Li Miao Pan Nan Zhao Fan Jiang Xianbin Wang School of Information and Communication Engineering Dalian University of Technology Dalian China University of Houston Houston TX USA Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts and Telecommunications Xian Shaanxi China University of Western Ontario London ON Canada

ISBN: (数字)9781728194844

ISBN: (纸本)9781728194851

As a promising solution of spectrum shortage, spectrum sharing has received tremendous interests recently. However, under different sharing policies of different licensees, the shared spectrum is heterogeneous both temporally and spatially, and is usually uncertain due to the unpredictable activities of incumbent users. In this paper, considering the spectrum uncertainty, we propose a spectrum sharing based delay-tolerant traffic off-loading (SDTO) scheme. To capture the available heterogeneous shared bands, we adopt a mesh cognitive radio network and employ the multi-hop transmission mode. To statistically guarantee the end-to-end (E2E) session request under the uncertain spectrum supply, we formulate the SDTO scheme into a stochastic optimization problem, which is transformed into a mixed integer nonlinear programming (MINLP) problem. Then, a coarse-fine search based iterative heuristic algorithm is proposed to solve the MINLP problem. Simulation results demonstrate that the proposed SDTO scheme can well schedule the network resource with an E2E session guarantee.

关键词： Vehicular and wireless technologies Uncertainty Simulation Heuristic algorithms Search problems Iterative algorithms Optimization

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Application of Blockchain and Internet of Things to Ensure Tamper-Proof Data Availability for Food Safety

arXiv

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arXiv 2020年

作者： Iftekhar, Adnan Cui, Xiaohui Hassan, Mir Afzal, Wasif Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China University of Glasgow Glasgow United Kingdom Mälardalen University Västerås Sweden

Food supply chain plays a vital role in human health and food prices. Food supply chain inefficiencies in terms of unfair competition and lack of regulations directly affect the quality of human life and increase food safety risks. This work merges Hyperledger Fabric, an enterprise-ready blockchain platform with existing conventional infrastructure, to trace a food package from farm to fork using an identity unique for each food package while keeping it uncomplicated. It keeps the records of business transactions that are secured and accessible to stakeholders according to the agreed set of policies and rules without involving any centralized authority. This paper focuses on exploring and building an uncomplicated, low-cost solution to quickly link the existing food industry at different geographical locations in a chain to track and trace the food in the market. Copyright © 2020, The Authors. All rights reserved.

关键词： Food safety

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Generalized association and deep understanding method for big video data processing

Generalized association and deep understanding method for bi...

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2019 IEEE International Conferences on Ubiquitous computing and Communications and Data Science and Computational Intelligence and Smart computing, networking and Services, IUCC/DSCI/SmartCNS 2019

作者： Xu, Lin Wu, Wei Lv, Wanghan Wang, Dong Science and Technology in Information System Engineering Lab Nanjing Research Institute of Electronic Engineering Nanjing China School of Information Science and Engineering University of Jinan China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan China

ISBN: (纸本)9781728152097

In Big Data Era, it is becoming more and more important to timely and efficient processing of massive videos, and mining of the value information contained in them. This paper studies chaotic compressed sensing theory and uses Long-term and short-term memory networks (LSTM) algorithm to generate a summary of the content of video behavior track. It has already to apply the proposed method in target recognition and tracking, and the recognition rate achieves more than 86%. © 2019 IEEE.

关键词： Big data

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Cyber security monitoring and data analysis at IHEP

Cyber security monitoring and data analysis at IHEP

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2019 International Symposium on Grids and Clouds, ISGC 2019

作者： Yan, Tian Hu, Hao An, Dehai Qi, Fazhi Jiang, Chen Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Inc. Beijing100085 China

Recently, cyber security threats becomes a noticeable challenge for academic institutes. In this paper, we present the security risk control model and the cyber security detection and monitoring system designed and deployed at Institute of High Energy Physics (IHEP) in China. security data collection and analysis plays the central role in this framework. In addition to the open-source solution like Zeek, MISP and ELK stack, we also deployed a commercial security Operation Center (SOC) as a supplement and cross-check solution. Copyright © 2019 the author(s).

关键词： Cybersecurity

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UR: A User-Based Collaborative Filtering Recommendation System Based on Trust Mechanism and Time Weighting

UR: A User-Based Collaborative Filtering Recommendation Syst...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Lei Zhang Zhijun Zhang Jufang He Zhenyu Zhang College of Intelligence and Computing Tianjin Key Laboratory of Advanced Network Technology and Application Tianjin China Key Laboratory of Grain Information Processing and Control Henan University of Technology Tianjin China

ISBN: (纸本)9781728125848

The technology of personalized recommendation is aimed at studying the behaviors of users, analyzing what they may be interested in and recommending suitable items to them. In other words, the personalized recommendation is to better solve the contradiction between the requirements of users and the explosive information on the Internet. The user-based collaborative filtering recommendation is one of the most successful technology for recommendation system. The most significant step of user-based collaborative filtering recommendation is comprehensive user similarity calculation. However, most recommendation systems ignore the indispensability of trust mechanism of the users and the time weighted users rating attributes in user similarity calculation, which leads to the inaccurate recommendation. Based on these issues, this paper proposes an optimized user-based collaborative filtering recommendation system called UR. UR not only validates the necessity of the trust mechanism of the users and the time weighted users rating in the comprehensive user similarity calculation, but also improves the recommendation accuracy.

关键词： User-based collaborative filtering recommendation Time weighted user rating User similarity calculation Recommender systems Consumers collaborative filtering User needs Nonprofit Organizations Trust trust

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FLBench: A Benchmark suite for federated learning

arXiv

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arXiv 2020年

作者： Liang, Yuan Guo, Yange Gong, Yanxia Luo, Chunjie Zhan, Jianfeng Huang, Yunyou Guangxi Key Lab of Multi-Source Information Mining & Security Department of Computer Science Guangxi Normal University Guilin541004 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China BenchCouncil R&D Lab - Guilin Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China

Federated learning is a new machine learning paradigm. The goal is to build a machine learning model from the data sets distributed on multiple devices-so-called an isolated data island-while keeping their data secure and private. Most existing federated learning benchmarks work manually splits commonly-used public datasets into partitions to simulate real-world isolated data island scenarios. Still, this simulation fails to capture real-world isolated data island's intrinsic characteristics. This paper presents a federated learning (FL) benchmark suite named FLBench. FLBench contains three domains: medical, financial, and AIoT. By configuring various domains, FLBench is qualified to evaluate federated learning systems and algorithms' essential aspects, like communication, scenario transformation, privacy-preserving, data distribution heterogeneity, and cooperation strategy. Hence, it becomes a promising platform for developing novel federated learning algorithms. Currently, FLBench is open-sourced and in fast-evolution. We package it as an automated deployment tool. The benchmark suite is available from https://***/*** Codes 68-04 Copyright © 2020, The Authors. All rights reserved.

关键词： Learning algorithms

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Study on the Digitalization Method of Intelligent Emergency Plan of Power System

Study on the Digitalization Method of Intelligent Emergency ...

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Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Zehao Zhang Zhen Yu Wei Weng Cheng Guan State Grid Key Laboratory of Information & Network Security Global Energy Interconnection Research Institute co. Ltd Research Institute of Information Technology &Communicatioon Beijing China Fujian Electric Power Emergency Technology Center State Grid FuJian Electric Power co. LTD Fuzhou China

ISBN: (数字)9781728191461

ISBN: (纸本)9781728191478

This paper puts forward a formalized method of emergency plan based on ontology, sums up the main concepts such as system, event, rule, measure, constraint and resource, and analyzes the logical relationship among concepts. A digital intelligent emergency plan storage scheme based on relational database model is proposed. In this paper, full-text search, data search and knowledge search are comprehensively used to adapt to the information needs and characteristics of different users' query plans. Finally, an example of emergency plan made by a power supply company is given to illustrate the effectiveness of the method.

关键词： Automation Power supplies Computational modeling Relational databases Companies Ontologies Emergency services

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