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IEEE Conference on Wireless Communications and Networking

作者： Zhiwen Hu Tiankuo Song Kaigui Bian Lingyang Song National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing 101 Middle School Beijing China Department of Computer Science Peking University China

ISBN: (数字)9781728131061

ISBN: (纸本)9781728131078

Confronted with the severe indoor air pollution nowadays, we propose the usage of multiple robots to detect the indoor air quality (IAQ) cooperatively for fewer sensors and larger sensing area. To acquire the complete real-time IAQ distribution map, we exploit the real statistical data to construct the IAQ data model and adopt Kalman Filter to obtain the estimation of the unmeasured area. Since the movement of the robots affects the estimation accuracy, a proper movement strategy should be planned to minimize the total estimation error. To solve this optimization problem, we design a deep Q-learning approach, which provides sub-optimal movement strategies for real-time robot sensing. By simulations, we verify the adopted IAQ data model and testify the effectiveness of the proposed solution. For application considerations, we have deployed this system in Peking University since Dec. 2018 and developed a website to visualize the IAQ distribution.

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Local global information aggregation graph convolution for skeleton-based action recognition

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Neurocomputing 2025年 640卷

作者： Xie, Shichong Li, Shengze Chen, Peng Wang, Bing Zhang, Jun School of Artificial Intelligence Anhui University Hefei230601 China School of Electrical Engineering and Automation Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province National Engineering Research Center for Agro-Ecological Big Data Analysis & Application Institute of Health Sciences and Technology & School of Internet Anhui University Hefei230601 China School of Management Science and Engineering Anhui University of Finance & Economics Bengbu233030 China Anhui Rocvision Intelligent Technology Co. Ltd Hefei230001 China

In the field of human action recognition based on skeletal data, how to capture the complex features of skeletal sequences in both spatial and temporal dimensions is a significant challenge. Existing Graph Convolutional Network (GCN) methods typically focus on extracting spatial features using various approaches, while neglecting the temporal characteristics inherent in human actions. In this paper, we propose the L(ocal) G(lobal)-GCN, a novel approach that emphasizes the under-explored temporal dimension by segmenting time to capture both local and global information. We also introduce a relative position embedding method tailored for skeletal data to enhance the representation capability of graph convolution. Additionally, we propose a partitioning method that reduces computational complexity while retaining all information, leveraging the sequential nature of skeletal data. Our LG-GCN achieves and surpasses the state-of-the-art accuracy across three widely employed datasets: NTU RGB+D with 97.3% (X-View), NTU RGB+D 120 with 91.1% (X-Set), and NW-UCLA with 96.6%. © 2025

关键词： Network theory (graphs)

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Gradient descent meets shift-and-invert preconditioning for eigenvector computation 18

Gradient descent meets shift-and-invert preconditioning for ...

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Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Zhiqiang Xu Big Data Lab (BDL-US) Baidu Research National Engineering Laboratory for Deep Learning Technology and Applications

Shift-and-invert preconditioning, as a classic acceleration technique for the leading eigenvector computation, has received much attention again recently, owing to fast least-squares solvers for efficiently approximating matrix inversions in power iterations. In this work, we adopt an inexact Riemannian gradient descent perspective to investigate this technique on the effect of the step-size scheme. The shift-and-inverted power method is included as a special case with adaptive step-sizes. Particularly, two other step-size settings, i.e., constant step-sizes and Barzilai-Borwein (BB) step-sizes, are examined theoretically and/or empirically. We present a novel convergence analysis for the constant step-size setting that achieves a rate at Õ(√λ1/λ1 – λp+1), where λi represents the i-th largest eigenvalue of the given real symmetric matrix and p is the multiplicity of λ1. Our experimental studies show that the proposed algorithm can be significantly faster than the shift-and-inverted power method in practice.

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Optimal Energy Dispatch of Grid-Connected Electric Vehicle Considering Lithium Battery Electrochemical Model

arXiv

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arXiv 2024年

作者： Chen, Yuanbo Zheng, Kedi Gu, Yuxuan Wang, Jianxiao Chen, Qixin State Key Laboratory of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China

The grid-connected electric vehicles (EVs) serve as a promising regulating resource in the distribution grid with Vehicle-to-Grid (V2G) facilities. In the day-ahead stage, electric vehicle batteries (EVBs) need to be precisely dispatched and controlled to ensure high efficiency and prevent degradation. This article focuses on considering a refined battery model, i.e. the electrochemical model (EM), in the optimal dispatch of the local energy system with high penetration of EVs which replenish energy through V2G-equipped charge station and battery swapping station (BSS). In this paper, to utilize the EM efficiently, recursive EVB constraints and a corresponding matrix-based state update method are proposed based on EM power characterization. The charging EV state distribution is profiled and a multi-layer BSS model along with binary aggregation is proposed, in order to overcome the computation complexity of combining the refined battery constraints with the mixed integer optimization. Finally, a local energy system scenario is investigated for evaluation. The efficiency and effectiveness of EM consideration are assessed from the perspective of both the system and battery. Copyright © 2024, The Authors. All rights reserved.

关键词： Charging stations

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Well-balanced fifth-order finite volume WENO schemes with constant subtraction technique for shallow water equations

arXiv

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arXiv 2024年

作者： Zhao, Lidan Tao, Zhanjing Zhang, Min School of Mathematics Jilin University Changchun130012 China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China Chongqing Research Institute of Big Data Peking University Chongqing401121 China

In this paper, we propose a new well-balanced fifth-order finite volume WENO method for solving one- and two-dimensional shallow water equations with bottom topography. The well-balanced property is crucial to the ability of a scheme to simulate perturbation waves over the "lake-at-rest" steady state such as waves on a lake or tsunami waves in the deep ocean. We adopt the constant subtraction technique such that both the flux gradient and source term in the new pre-balanced form vanish at the lake-at-rest steady state, while the well-balanced WENO method by Xing and Shu [Commun. Comput. Phys., 2006] uses high-order accurate numerical discretization of the source term and makes the exact balance between the source term and the flux gradient, to achieve the well-balanced property. The scaling positivity-preserving limiter is used for the water height near the dry areas. The fifth-order WENO-AO reconstruction is used to construct the solution since it has better resolution than the WENO-ZQ and WENO-MR reconstructions for the perturbation of steady state flows. Extensive one- and two-dimensional numerical examples are presented to demonstrate the well-balanced, fifth-order accuracy, non-oscillatory, and positivity-preserving properties of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Finite volume method

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Power Grid Critical State Search Based on Improved Particle Swarm Optimization 6th

Power Grid Critical State Search Based on Improved Particle ...

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6th International Conference on Advanced Intelligent Systems and Informatics, AISI 2020

作者： Luo, Jie Deng, Hui-Qiong Li, Qin-Bin Zheng, Rong-Jin Li, Pei-Qiang Chang, Kuo-Chi School of Information Science and Engineering Fujian University of Technology Fuzhou350108 China Fujian Provincial University Engineering Research Center of Smart Grid Simulation Analysis and Integrated Control Fuzhou350108 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Electrical Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9783030586683

This article proposes a method to find the closest critical initial running state of power grid, aiming at the possible chain reaction failure caused by branch fault in power system. First of all, assuming the initial failure of power grid, considering the specific performance of relay protection, analyze the critical state of the branch in cascading trip in detail, the electrical distance between the current running state and the interlocking fault expressed in the form of nodal injection power is given. Secondly, on the basic of the relationship between the nodal injections power and the system safety, a safety level model that the system will not trigger cascading failures is established. Finally, the improved simplified mean particle swarm optimization algorithm is used to solve the model, and an example is analyzed on IEEE-14 node system. An example shows the effectiveness and feasibility of the proposed method, which provides a reference for further prevention of cascading trip accidents. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Outages

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Image Recognition of Concrete Electromagnetic Flaw Detection Based on Improved YOLOv7 24

Image Recognition of Concrete Electromagnetic Flaw Detection...

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7th International Conference on Image and Graphics Processing, ICIGP 2024

作者： Yuan, Shuai Yuan, Ze He, Kai School of Computer Science and Engineering Shenyang Jianzhu University China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction China Shenyang Branch of National Special Computer Engineering Technology Research Center China School of Electrical and Control Engineering Shenyang Jianzhu University China

ISBN: (纸本)9798400716720

Owing to the invisibility characteristics of the interiors of concrete structures, nondestructive testing technologies are commonly employed to detect internal damage. Electromagnetic flaw detection technology, as a prominent nondestructive testing method, offers capabilities of strong penetration and precise imaging. However, the output images produced by this detection technology are usually reliant on subjective human judgment, and existing automatic detection algorithms have limitations in terms of accuracy. To enhance the accuracy of electromagnetic flaw detection images, this paper proposes improved YOLOv7 based detection method. First of all, the ACBlock convolution module is introduced to replace the original 3×3 convolution kernel for alleviating the loss of edge feature information caused by the sampling operation of the backbone network MP module. Next, in order to have more focus on the semantic features of small targets, a global attention mechanism (GAMAttention) is incorporated into the algorithm. Then, the FReLU activation function is adopted to enhance the algorithm sensitivity to image space. Finally, experiments are conducted on a self-created concrete flaw detection image dataset to illustrate the efficiency of the proposed algorithm. The results show that the mean Average Precision (mAP) value achieved by the improved algorithm is 95.85%. which has a significant improvement of 13.89% in comparison with the pre-improved YOLOv7 algorithm and surpasses other algorithms in the same domain. These findings represent that the proposed algorithm effectively enhances the accuracy of concrete electromagnetic flaw detection image detection. © 2024 ACM.

关键词： Nondestructive examination

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bbTopk: Bandwidth-Aware Sparse Allreduce with Blocked Sparsification for Efficient Distributed Training

bbTopk: Bandwidth-Aware Sparse Allreduce with Blocked Sparsi...

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International Conference on Distributed Computing Systems

作者： Chang Chen Min Li Chao Yang Center for Data Science Peking University School of Mathematics Sciences Peking University National Engineering Laboratory for Big Data Analysis and Applications Peking University PKU-Changsha Institute for Computing and Digital Economy

Communication overhead is one of the major bottlenecks for large-scale distributed model training. Sparse gradient has been proposed to reduce the communication volume dramatically without affecting the model accuracy. However, high performance implementation of sparse gradient is still hindered by the overheads of gradient sparsification and inefficient implementation of sparse allreduce. For a sparse allreduce operation, the density of intermediate results may dynamically increase as summations progress. Meanwhile, high-performance sparse allreduce is further complicated with heterogeneous bandwidths. To tackle these challenges, we propose bbTopk for efficient sparse gradient training, which includes a new blocked top $k$ sparsification technique and a novel bandwidth-aware sparse allreduce algorithm. In particular, to alleviate the sparsification overhead, we design a blocked top- $k$ method, which can reduce the overhead without sacrificing model accuracy. We then build a heterogeneity-aware communication model combined with the dynamic workload feature of the sparse allreduce. Guided by that, a new sparse allreduce algorithm is proposed that can take advantage of the network resources by improving bandwidth utilization, reducing cross-node hops, and adjusting the round order. Experiments are conducted on a variety of typical neural networks and distributed environments. Results show that bbTopk can substantially outperform the previous state-of-the-art work in most test cases with up to 2.57x speedup while achieving similar accuracy with the dense model empirically.

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A New Approach to the Position of Acupoints on the Back of Humans

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Recent Patents on Mechanical engineering 2023年第2期16卷 150-162页

作者： Xie, Shenglong Liu, Zijing Zhang, Liangan Song, Fengfeng Key Laboratory of Intelligent Manufacturing Quality Big Data Tracking and Analysis of Zhejiang Province China Jiliang University Zhejiang Hangzhou310018 China Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Ma’anshan243032 China Hangzhou Qiandaohu Ruichun Robot Research Institute Co. Ltd Zhejiang Hangzhou310018 China Zhejiang Shusi Robot Co. Ltd Zhejiang Huzhou313000 China

Aim: In order to realize the position of acupoints on the back of humans, a new acupoints positioning approach combined with machine vision technology is proposed in this paper. Background: The acupuncture point (acupoint) is an important reference in the treatment of Traditional Chinese Medicine (TCM). However, the traditional acupoints positioning method mainly depends on manpower, which has the disadvantages of low efficiency and relying on practical experience heavily. Objective: In order to realize the position of acupoints on the back of humans, a new acupoints positioning approach combined with machine vision technology is proposed in this paper. Methods: Firstly, the distribution of acupoints on the back of humans are determined according to the meridian theory, and the coordinate of each acupoint are established based on the approach of fingercun measurement, after review of previous literature and patents. Secondly, the principle of the proposed acupoints positioning method is introduced based on the skeleton model of humans. Finally, the effec-tiveness of the proposed approach is validated by experimental testing. Results: The value of emax is around 3.5mm and those of emae and erms are smaller than 0.25mm and 0.45mm, respectively. Conclusion: The experimental results prove that the proposed approach possesses the capability of good adaptive capability and high. © 2023 Bentham Science Publishers.

关键词： Computer vision

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data Preprocessing Technology in Network Traffic Anomaly Detection

Data Preprocessing Technology in Network Traffic Anomaly Det...

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作者： Duan, Xueyuan Fu, Yu Wang, Kun Department of Information Security Naval University of Engineering Wuhan430033 China College of Computer and Information Technology Xinyang Normal University Xinyang464000 China Henan Key Laboratory of Analysis and Applications of Education Big Data Xinyang464000 China School of Mathematics and Information Engineering Xinyang Vocational and Technical College Xinyang464000 China

Computer network plays a very important role in today’s world. With the development of network technology and the increasing popularity of network topology, network management is facing huge challenges. Among them, finding network traffic anomalies is one of the most important tasks in network monitoring, and is an important technology in network intrusion, security monitoring, and performance maintenance. This paper aims to study data preprocessing techniques in network traffic anomaly detection. This paper firstly introduces the network traffic measurement characteristics completely, and then selects and applies the data preprocessing algorithm, constructs a complete data preprocessing framework, and conducts experiments for the abnormal detection of network multi-dimensional characteristic traffic and the abnormal detection of unit-level characteristic traffic. The superiority of the data preprocessing technology in this paper, by reducing the data, determines the data preprocessing threshold of this paper to ensure that the data preprocessing is maximized. Experiments show that the data preprocessing technology constructed in this paper has about 20% more data filtering than traditional data filtering, which shortens the amount of detection data. The threshold is around 0.6, and data preprocessing can maximize the reduction of data without destroying the integrity of the attacked data. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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