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12th International Conference on Renewable Power Generation, RPG 2023

作者： Han, Siwei Wang, Liangyi Zhang, Chaobo He, Qian Pang, Rui Cui, Weiran Sun, Yilin Zhang, Ruozhu State Grid ChongQing Electric Power Company ChongQing400015 China Beijing Join Bright Digital Power Technology Co. Ltd. Beijing100085 China State Key Laboratory of Alternative Electrical Power System With Renewable Energy Sources North China Electric Power University Beijing102206 China

After a fault occurs in the grid, the short-circuit current of renewable energy sources presents a weak feedthrough, and strongly controlled fault characteristic and also has a large number of interharmonics due to the influence of fault ride-through control strategies and inverter current limiting, which is different from the fault characteristics of traditional synchronous generators, resulting in the decline of the reliability of conventional frequency distance protection. Recently, time domain distance protection has received more attention because it is less affected by the fault characteristics of the renewable energy sources. However, when the transmission line connected to the renewable energy station is long, the influence of line distributed capacitance cannot be ignored, resulting in large deviations in fault distance calculation. To solve the above problem, this paper proposes an improved time domain distance protection scheme for a long-distance transmission line, taking into account the influence of line distributed capacitance. The solution is based on a π-type equivalence model of the line, which improves the accuracy of the fault distance solution and forms the protection criterion by modifying the fault circuit equations to take into account of the effect of distributed capacitance. Finally, the effectiveness and feasibility of the proposed time-domain distance protection scheme are verified by extensive simulations based on PSCAD/EMTDC. © The Institution of Engineering & technology 2023.

关键词： Renewable energy resources

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Research on Operating system Runtime Jitter Control Method for Electromagnetic Transient Real time Simulation Calculation

Research on Operating System Runtime Jitter Control Method f...

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Computer technology and Information Science (ISCTIS), International Symposium on

作者： Qi Guo Tianyu Guo Yuanhong Lu Haiping Guo Libin Huang Liang Tu State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Operation Control for New Energy Power System Guangzhou China CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou China

Real-time electromagnetic transient simulation can quickly analyze and solve the problems in complex large-scale power systems, realize accurate prediction and diagnosis of faults, and is a necessary means to promote the implementation of new power systems. However, the real-time simulation of electromagnetic transient puts forward strict requirements for the real-time performance of the underlying operating system. In this paper, the test method of system runtime jitter on Linux platform is designed, and the causes of jitter are analyzed based on the test results, and the control method of runtime jitter is studied. The experimental results show that the jitter of the system can be controlled in nanosecond level after using the methods of interrupt shutdown, CPU core binding and configuration of NOHZ_FULL, which can meet the needs of real-time simulation of electromagnetic transient.

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Two Mapping Strategies for Autonomous Driving Accelerators

Two Mapping Strategies for Autonomous Driving Accelerators

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Computer Applications and Information technology (ISCAIT), International Symposium on

作者： Chunhua Tang Yang Jiang Wuyi Fu Haobing Shi Ping Huang Li Wang Meisong Huang Yiming Qiao Yan Zhou Penghui Guan State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing China Chongqing Changan Automobile Co. Ltd. Chongqing China Polar Bear Tech Xi’an China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

Many-core architectures hold tremendous potential in autonomous driving accelerators due to their significant energy efficiency advantages. However, effectively utilizing the vast computational resources of such architectures remains a major challenge and an open problem. To address this challenge, we propose two optimization strategies: Gluing Parallel Layers Dataflow (GPLD) and the Ouroboros-like resource allocation strategy. GPLD focuses on optimizing parallel layers, a dimension that has not yet been explored. We design a simple and elegant dataflow to directly glue parallel layers, allowing them to compute as a single layer. This approach reduces energy costs and improves the allocation of chiplet resources. The Ouroboros-like resource allocation strategy is employed to utilize idle resources during segment switching, thereby increasing overall throughput. Overall, these mapping optimizations improve 2.67× performance on average and reduce energy consumption by 50.68% compared to the state-of-the-art Tangram mapping strategy, optimized by us, across a wide range of DNNs.

关键词： Energy consumption Pipelines Computer architecture Switches Throughput Filling Resource management Information technology Optimization Autonomous vehicles

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LIBALCHEMY: A Two-Layer Persistent Summary Design for Taming Third-Party Libraries in Static Bug-Finding systems

LIBALCHEMY: A Two-Layer Persistent Summary Design for Taming...

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International Conference on Software Engineering (ICSE)

作者： Rongxin Wu Yuxuan He Jiafeng Huang Chengpeng Wang Wensheng Tang Qingkai Shi Xiao Xiao Charles Zhang Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Xiamen China The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Person Hang Zhou China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Despite the benefits of using third-party libraries (TPLs), the misuse of TPL functions raises quality and security concerns. Using traditional static analysis to detect bugs caused by TPL function is nontrivial. One promising solution would be to automatically generate and persist the summaries of TPL functions offline and then reuse these summaries in compositional static analysis online. However, when dealing with millions of lines of TPL code, the summaries designed by existing studies suffer from an unresolved paradox. That is, a highly precise form of summary leads to an unaffordable space and time overhead, while an imprecise one seriously hurts its precision or recall. To address the paradox, we propose a novel two-layer summary design. The first layer utilizes a line-sized program representation known as the program dependence graph to compactly encode path conditions, while the second layer encodes bug-type-specific properties. We implemented our idea as a tool called Libalchemy and evaluated it on fifteen mature and extensively checked open-source projects. Experimental results show that Libalchemy can check over ten million lines of code within ten hours. Libalchemy has detected 55 true bugs with a high precision of 90.16%, eleven of which have been assigned CVE IDs. Compared to whole-program analysis and the conventional design of path-sensitively precise summaries, Libalchemy achieves an 18.56× and 12.77× speedup and saves 91.49% and 90.51% of memory usage, respectively.

关键词： Codes Computer bugs Static analysis Libraries Security Software engineering

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Unsupervised Multivariate Time Series Anomaly Detection by Feature Decoupling in Federated Learning Scenarios

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： He, Yifan Ding, Xi Tang, Yateng Guan, Jihong Zhou, Shuigeng Fudan University School of Computer Science Shanghai200433 China Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China Tencent Weixin Group Guangzhou510308 China Tongji University Department of Computer Science and Technology Shanghai201804 China Ministry of Education Key Laboratory of Embedded System and Service Computing Tongji University Shanghai200092 China

Anomalies are usually regarded as data errors or novel patterns previously unseen, which are quite different from most observed data. Accurate detection of anomalies is crucial in various application scenarios. This paper focuses on unsupervised anomaly detection from Multivariate Time Series (MTS), as real-world data collected from sources such as wearable devices, medical equipment and industrial machines typically manifest as MTS and are often unlabeled. Anomaly detection in MTS represents a data-driven challenge that traditionally requires substantial centralized data for training models. However, in practice, data are frequently distributed among multiple institutions, with privacy concerns restricting unrestricted access. To address these issues, we introduce Feature Decoupling Federated Learning (FDFL), an approach designed to collaboratively train a representation learning network over multiple clients for unsupervised anomaly detection in MTS. Unlike previous methods that simply integrate MTS anomaly detection algorithms with federated learning strategies, FDFL specifically addresses heterogeneity among clients by decoupling the representation network into shared and private branches through a contrastive learning mechanism. This method aggregates shared parameters during each federated round while maintaining client-specific private parameters locally. Additionally, we develop a self-attention block that integrates the representations derived from both shared and private parameters to reconstruct MTS and identify anomalies based on reconstruction errors. Extensive experiments conducted on three publicly available datasets demonstrate that FDFL outperforms existing algorithms in most cases, highlighting the effectiveness and superiority of our proposed method in MTS anomaly detection. © 2020 IEEE.

关键词： Federated learning

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Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection 41

Size-Invariance Matters: Rethinking Metrics and Losses for I...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright 2024 by the author(s)

关键词： Object detection

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Forget less,count better:a domain-incremental self-distillation learning benchmark for lifelong crowd counting

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Frontiers of Information technology & Electronic Engineering 2023年第2期24卷 187-202页

作者： Jiaqi GAO Jingqi LI Hongming SHAN Yanyun QU James ZWANG Fei-Yue WANG Junping ZHANG Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Institute of Science and Technology for Brain-inspired Intelligence Fudan UniversityShanghai 200433China Shanghai Center for Brain Science and Brain-inspired Technology Shanghai 201210China School of Information Science and Technology Xiamen UniversityXiamen 361005China College of Information Sciences and Technology the Pennsylvania State UniversityUniversity ParkPA 16802USA State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Crowd counting has important applications in public safety and pandemic control.A robust and practical crowd counting system has to be capable of continuously learning with the newly incoming domain data in real-world scenarios instead of fitting one domain ***-the-shelf methods have some drawbacks when handling multiple domains:(1)the models will achieve limited performance(even drop dramatically)among old domains after training images from new domains due to the discrepancies in intrinsic data distributions from various domains,which is called catastrophic forgetting;(2)the well-trained model in a specific domain achieves imperfect performance among other unseen domains because of domain shift;(3)it leads to linearly increasing storage overhead,either mixing all the data for training or simply training dozens of separate models for different domains when new ones are *** overcome these issues,we investigate a new crowd counting task in incremental domain training setting called lifelong crowd *** goal is to alleviate catastrophic forgetting and improve the generalization ability using a single model updated by the incremental ***,we propose a self-distillation learning framework as a benchmark(forget less,count better,or FLCB)for lifelong crowd counting,which helps the model leverage previous meaningful knowledge in a sustainable manner for better crowd counting to mitigate the forgetting when new data arrive.A new quantitative metric,normalized Backward Transfer(nBwT),is developed to evaluate the forgetting degree of the model in the lifelong learning *** experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.

关键词： Crowd counting Knowledge distillation Lifelong learning

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Sea Surface Temperature Anomaly Spatio-temporal Data Generation Based on Separated Attention

Sea Surface Temperature Anomaly Spatio-temporal Data Generat...

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Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Junkai Wang Yu Zhang Ruowu Wu Lianlei Lin State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Luoyang China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Various natural disasters are closely related to the El Niño - Southern Oscillation (ENSO), the stable generation of accurate sea surface temperature anomaly (SSTA) data is key to exploring the occurrence patterns of ENSO and preventing disasters. In this study, we introduces the Separated Attention Data Generation (SADG) model, which efficiently generates SSTA data by capturing the long-range spatiotemporal relationship. SADG achieves high performance while reducing computational complexity compared to standard self-attention based models, generates up to 18 months of accurate SSTA data. Experiments on CMIP datasets show that its performance is better than other models on various lead times.

关键词： Accuracy Computational modeling Disasters Data collection Predictive models Data models Spatiotemporal phenomena Sea surface temperature Standards Oscillators

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Network Link Existence Prediction Method Based on Graph Autoencoder 12th

Network Link Existence Prediction Method Based on Graph Auto...

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12th International Conference on Computer Engineering and Networks, CENet 2022

作者： Yang, Guorui Feng, Tao Huo, Yonghua Tang, Shidang Qu, Zhenying The 54th Research Institute of CETC Hebei Shijiazhuang China National Key Laboratory of Science and Technology on Information System Security Institute of System Engineering Chinese Academy of Military Science Beijing China New China Life Insurance Co. LTD. Hebei Branch Hebei Shijiazhuang China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789811969003

With the development of computer technology, people have higher and higher requirements for network link quality, which brings new challenges to network management and analysis. By applying link prediction to computer network scenarios, link existence information in the network can be predicted, which facilitates network management and analysis. At present, graph autoencoder is one of the most important models for link prediction. However, the existing model does not take node centrality into account and may have the problem of over-fitting. Therefore, for the scenario of network link state prediction, we design a network link prediction model based on node centrality and noise injection in this paper. In this model, noise is injected before training, and then the decoder is improved based on the variational graph autoencoder model. Node importance index and the weights of the trained nodes are used to add coefficients to the decoder. Finally, the model is proved to be effective in link connectivity prediction. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Network management

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Real-time monitoring system of cantilever force deformation based on attitude perception 7

Real-time monitoring system of cantilever force deformation ...

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7th International Conference on intelligent Information Processing, ICIIP 2022

作者： Gan, Jinyu Li, Wenlong Deng, Jianzhi Fu, Fei College of Information Science and Engineering Guilin University of Technology Guilin Guangxi541004 China Guangxi Construction Engineering Group Construction General Contractor Co. Ltd Nanning Guangxi530012 China Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin University of Technology Guilin Guangxi541004 China

ISBN: (纸本)9781450396714

Based on the principle of attitude perception, a real-time detection system for force deformation of suspension beam arm using MPU-6050 module is *** constructing the mechanical model of cantilever arm, the bending deformation of cantilever beam model in the elastic deformation range is studied, that is, the Angle change value of cantilever at the free end fulcrum is collected, the deflection equation of cantilever beam is deduced, and then the parameters of the cantilever beam are ***, a new monitoring system can be designed to use the cantilever point Angle change to carry out the stress deformation of the beam. The system is integrated with software and hardware design, which is an integrated detection system composed of embedded monitoring platform and PC software monitoring *** experimental results show that the force error range of the cantilever beam is 0.3N, and the deflection error range of the free end is 1.2cm, which is only 6% compared with the length of the beam ***, the detection error of the system is small, easy to operate, and it can efficiently detect the force size of the beam arm bending deformation, the position of the force point, the maximum deflection value, the position of the maximum deflection point and other parameters in real time online. © 2022 ACM.

关键词： Nanocantilevers

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