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第43届中国控制会议

作者： Nanyan Li Hao Chen Zhigang Sun Jing Gao Dewei Yi Cunjia Liu Jinya Su School of Automation Southeast University Key Laboratory of Measurement and Control of CSE Ministry of Education Southeast University State Grid Jiangsu Integrated Energy Service Co. Ltd. Department of Computing Science University of Aberdeen Department of Aeronautical and Automotive Engineering Loughborough University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper considers the real-time semantic segmentation of solar PhotoVoltaic(PV) arrays using down-facing cameras onboard Unmanned Aerial Vehicles(UAVs) to support autonomous navigation for ultra-low altitude industrial inspection. Various semantic segmentation algorithms, including SegNet, UNet, Deeplabv3+, and their respective variants, are compared to identify the most suitable one that strikes a balance between segmentation and real-time performance. Subsequently, each video frame is processed by the selected segmentation network to create a solar array mask. This mask is then optimally fitted to diverse solar array shapes to determine angular and horizontal deviations between the image center and the solar array center-line. The algorithms are validated on a self-constructed dataset of 594 solar PV array UAV images, encompassing varied environmental conditions and solar farm layouts. The top-performing network, lightweight vgg16bn-Unet, achieves 99.4% overall accuracy and 98.2% Intersection over Union(IoU), while the most balanced network, mobilev3-Deeplabv3+, attains 98.9% accuracy and96.5% IoU, with a 27.59 fps(using an NVIDIA GeForce MX150 GPU) suitable for real-time UAV flights.

关键词： CNN Machine Vision Solar Inspection UAV Navigation Real-time and Embedded systems

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Query-Centered Temporal Community Search via Time-Constrained Personalized PageRank

arXiv

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arXiv 2023年

作者： Lin, Longlong Yuan, Pingpeng Li, Rong-Hua Zhu, Chunxue Qin, Hongchao Jin, Hai Jia, Tao College of Computer and Information Science Southwest University Chongqing400715 China National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technololgy Beijing Institute of Technology Beijing100081 China

Existing temporal community search suffers from two defects: (i) they ignore the temporal proximity between the query vertex q and other vertices but simply require the result to include q. Thus, they find many temporal irrelevant vertices (these vertices are called query-drifted vertices) to q for satisfying their cohesiveness, resulting in q being marginalized;(ii) their methods are NP-hard, incurring high costs for exact solutions or compromised qualities for approximate/heuristic algorithms. Inspired by these, we propose a novel problem named query-centered temporal community search to circumvent query-drifted vertices. Specifically, we first present a novel concept of Time-Constrained Personalized PageRank to characterize the temporal proximity between q and other vertices. Then, we introduce a model called β-temporal proximity core, which can combine temporal proximity and structural cohesiveness. Subsequently, our problem is formulated as an optimization task that finds a β-temporal proximity core with the largest β. To solve our problem, we first devise an exact and near-linear time greedy removing algorithm that iteratively removes unpromising vertices. To improve efficiency, we then design an approximate two-stage local search algorithm with bound-based pruning techniques. Finally, extensive experiments on eight real-life datasets and nine competitors show the superiority of the proposed solutions. © 2023, CC BY.

关键词： Iterative methods

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Real-time Semantic Segmentation of Solar Photovoltaic Arrays for Autonomous UAV Flights 43

Real-time Semantic Segmentation of Solar Photovoltaic Arrays...

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43rd Chinese Control Conference, CCC 2024

作者： Li, Nanyan Chen, Hao Sun, Zhigang Gao, Jing Yi, Dewei Liu, Cunjia Su, Jinya Southeast University School of Automation Nanjing210096 China Southeast University Key Laboratory of Measurement and Control of Cse Ministry of Education Nanjing210096 China State Grid Jiangsu Integrated Energy Service CO. Ltd Nanjing210096 China Jiangsu Sudian Industry Management CO. Ltd Nanjing210096 China University of Aberdeen Department of Computing Science AberdeenAB24 3UE United Kingdom Loughborough University Department of Aeronautical and Automotive Engineering LoughboroughLE11 3TU United Kingdom

ISBN: (纸本)9789887581581

This paper considers the real-time semantic segmentation of solar PhotoVoltaic (PV) arrays using down-facing cameras onboard Unmanned Aerial Vehicles (UAVs) to support autonomous navigation for ultra-low altitude industrial inspection. Various semantic segmentation algorithms, including SegNet, UNet, Deeplabv3+, and their respective variants, are compared to identify the most suitable one that strikes a balance between segmentation and real-time performance. Subsequently, each video frame is processed by the selected segmentation network to create a solar array mask. This mask is then optimally fitted to diverse solar array shapes to determine angular and horizontal deviations between the image center and the solar array center-line. The algorithms are validated on a self-constructed dataset of 594 solar PV array UAV images, encompassing varied environmental conditions and solar farm layouts. The top-performing network, lightweight vgg 16bn-Unet, achieves 99.4% overall accuracy and 98.2% Intersection over Union (IoU), while the most balanced network, mobilev3-Deeplabv3+, attains 98.9% accuracy and 96.5% IoU, with a 27.59 fps (using an NVIDIA GeForce MX150 GPU) suitable for real-time UAV flights. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Semantic Segmentation

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An Effective Encrypted Traffic Classification Method Based on Pruning Convolutional Neural Networks for Cloud Platform

An Effective Encrypted Traffic Classification Method Based o...

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作者： Zilong Han Erxia Li Shichao Li Xiaoyong Li Chaoqun Kang Ruiwen Deng Yali Gao State Grid Shanghai Energy Interconnection Research Institute Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing University of Posts and Telecommunications

With the rapid development of cloud computing technology,the traffic of cloud platform also increases *** of these traffic adopt encryption technology,the original traffic classification method is no longer *** this paper,we propose an efficient encrypted traffic classification method based on pruning convolution neural network:*** method does not need to extract features manually,but automatically extracts high-level features through CNN,which makes the model have generalization ability and reduces the *** the pruning technology is adopted to prune the model and retain the parameters that are highly important to the model,which greatly reduces the size and computation of the model and will not affect the performance of the *** used the public network dataset ISCXVPN2016 to verify our proposed method,the experiments show that the F1 score of our proposed approach is 93% which is improved by 3%,and the precision is 94%,which is improved by 5%.

关键词： encrypted traffic classification convolutional neural network pruning end-to-end

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SCARE of Secret Ciphers under Rough Leakage Model

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Security and Communication Networks 2023年第1期2023卷

作者： Wang, Mengxing Tang, Ming Zhang, Xige Chen, Qi Ding, Haili Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Hubei Wuhan430000 China Beijing Smartchip Microelectronics Technology Co. Ltd. Beijing100192 China Marketing Service Center State Grid Electric Power Co. Ltd. Ningxia750002 China

Most previousside-channel analysis-based reverse engineering (SCARE) methods were based on collision attack. However, the collision-based methods generally need noise-free traces and can hardly be verified by realistic implementations. This paper proposes a novel SCARE, which is named as OSHDG-SCARE, targeting on secret S-box. We define a graph as OSHDG (one-step HD graph) to recover the parameters of any secret S-box. Due to the redundancy of OSHDG, the multiple paths can filter the correct results and improve the success rate of the reverse method even when the samples deviate from leakage model. We classify the deviation of side channel signal as two types, which are the gap between real leakage and leakage model, and measurement noise. Experiments are performed on real power traces satisfying the inaccurate HW (Hamming Weight) leakage model from a software implementation of AES-like cipher. OSHDG-SCARE recovers the secret S-box when the deviation rate of traces is 1.45%. © 2023 Mengxing Wang et al.

关键词： Side channel attack

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An Effective Encrypted Traffic Classification Method Based on Pruning Convolutional Neural Networks for Cloud Platform

An Effective Encrypted Traffic Classification Method Based o...

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International Conference on Electronics, Communications and Information Technology (CECIT)

作者： Zilong Han Erxia Li Shichao Li Xiaoyong Li Chaoqun Kang Ruiwen Deng Yali Gao State Grid Shanghai Energy Interconnection Research Institute Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665437585

With the rapid development of cloud computing technology, the traffic of cloud platform also increases rapidly. Most of these traffic adopt encryption technology, the original traffic classification method is no longer effective. In this paper, we propose an efficient encrypted traffic classification method based on pruning convolution neural network: PCNN. This method does not need to extract features manually, but automatically extracts high-level features through CNN, which makes the model have generalization ability and reduces the cost. Then the pruning technology is adopted to prune the model and retain the parameters that are highly important to the model, which greatly reduces the size and computation of the model and will not affect the performance of the model. We used the public network dataset ISCXVPN2016 to verify our proposed method, the experiments show that the F1 score of our proposed approach is 93% which is improved by 3%, and the precision is 94%, which is improved by 5%.

关键词： Cloud computing Computational modeling Data preprocessing Neurons Telecommunication traffic Feature extraction Data models

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GradSA: Gradient Sparsification and Accumulation for Communication-Efficient Distributed Deep Learning 15th

GradSA: Gradient Sparsification and Accumulation for Communi...

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15th International Conference on Green, Pervasive, and Cloud computing, GPC 2020

作者： Liu, Bo Jiang, Wenbin Zhao, Shaofeng Jin, Hai He, Bingsheng National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Huazhong University of Science and Technology Wuhan China Library Henan University of Economics and Law Zhengzhou China Department of Computer Science School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9783030642426

Large-scale distributed deep learning is of great importance in various applications. For distributed training, the inter-node gradient communication often becomes the performance bottleneck. Gradient sparsification has been proposed to reduce the communication overhead. However, the sparsification only arbitrarily selects a small fraction of gradients, while the efficiency of selection dimension has been overlooked. Furthermore, gradient staleness is inevitable after applying the sparsification which will ultimately lead to model divergence. In this paper, we propose a staleness-compensated sparse stochastic gradient descent algorithm, GradSA, to improve the training efficiency. Layer-level gradients are sparsified to reduce the communication overhead, which conform to the characteristics of the network structure, and historical accumulation of the approximated gradients is utilized to speed up convergence. We demonstrate the model convergence acceleration and the efficiency of our layer-level selection over existing state-of-the-art works, such as DGC, TernGrad, and 8-Bit quantization. © 2020, Springer Nature Switzerland AG.

关键词： Efficiency

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A Knowledge Graph-based Sensitive Feature Selection for Android Malware Classification

A Knowledge Graph-based Sensitive Feature Selection for Andr...

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Asia-Pacific Conference on Software Engineering

作者： Duoyuan Ma Yude Bai Zhenchang Xing Lintan Sun Xiaohong Li Tianjin Key Laboratory of Advanced Networking College of Intelligence and Computing Tianjin University Tianjin China Research School of Computer Science Australian National University Data61 CSIRO Australia State Grid Customer Service Center Tianjin China

ISBN: (数字)9781728195537

ISBN: (纸本)9781728195544

The rapid increase in Android malware has brought great challenges to malware analysis. To deal with such a severe situation, it has been proposed an effective way which groups malware with common behaviors into the same malware family. Although there are many methods for malware family classification, the most critical and primary step is always the definition of sensitive behavior in an application, which will be beneficial for the later classification task. Much existing literature has manually selected sensitive features, such as permission, or even designed graph-based features via the control flow graph. They heavily depend on expert knowledge and time-consuming malware application analysis, which means it has to focus on the mal ware itself to dig out valuable security knowledge at first. However, the zooming malware overwhelms such expensive feature definition methods. To overcome such a problem, we adopt a knowledge graph-based sensitive feature selection method for Android mal ware classification. Based on the Android Developer documentation, an Android API knowledge graph is constructed at first. We can obtain not only permission but also related critical API from this graph. Note that both hyperlink relation and similarity relation are used to find out the critical API. With the knowledge graph-based sensitive features, we represent each Android malware as a boolean feature vector and send it in to a machine learning classifier for malware classification. We evaluate our proposed methods on three well-known Android malware datasets, such as Genome, Drebin, and AMD. The experimental results show that: 1) our proposed sensitive API is advantageous for malware detection; 2) API chosen by similarity relation can marginally improve performance; 3) different permission groups also make an influence for classification.

关键词： Feature extraction Malware Security Task analysis Hypertext systems Smart phones Software engineering

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Asymptotic properties of zeros for discrete-time linear systems with backward triangle sampled-and-hold using delta operator

Asymptotic properties of zeros for discrete-time linear syst...

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Chinese Automation Congress (CAC)

作者： Minghui Ou Shan Liang Xiaobin Wu Zhiyong Yang Hehuan Ran School of Big Data and Internet of Things of Chongqing Vocational Institute of Engineering College of Automation of Chongqing University Chongqing China College of Automation of Chongqing University The Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing China State Grid Chongqing Shiqu Electric Power Supply Branch Chongqing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

This article focuses on a well-studied theoretical problem that, for rapid sampling, a continuous-time system with minimum-phase properties may become a non-minimum phase discrete-time system in the discretization process. The properties of zeros for discrete-time linear models in the field of digital control when using the special shift operator was considered in this paper. From the former relevant studies one can know that the backward triangle sample-and-hold (BTSH) as an alternative hold method to discretize the continuous-time systems has an advantage over ZOH in stabilizing sampling zeros for linear system with classical forward shift operator. In this article, we deduce the exact sampled-data model of linear system using delta operator to replace forward shift operator in the case of BTSH. Furthermore, the expressions and properties of zeros for the corresponding discrete-time system are derived.

关键词： Control systems Linear systems Discrete-time systems Process control Numerical models Signal reconstruction Internet of Things

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Short-term load forecasting for electric vehicle charging stations based on time series distance measuring 13

Short-term load forecasting for electric vehicle charging st...

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2017 13th IEEE International Conference on Electronic Measurement & Instruments (ICEMI)

作者： Shi Xin Qiu Lei Tian Li Liang Miaozhu Yi Lizheng Key Laboratory of Service Computing in Cyber Physical Society MOE College of Automation Chongqing University State Grid Chongqing Electric Power Company

ISBN: (纸本)9781509050369;9781509050352

To improve the efficiency and the precision of short-term load forecasting of the electric vehicle charging stations, this paper proposed a new algorithm for short-term load forecasting for electric vehicle charging stations based on time series distance measuring. Applying the editing distance to load the load sequence into the load segment and the time segment respectively to measure, so as to measure the distance between the load sequences accurately, And simplify the complexity of model which can improve the efficiency of prediction. Based on the actual historical load data of electric vehicle charging stations, the simulation analysis is given to verify that the proposed method has high prediction efficiency and accuracy.

关键词： Electric Vehicle Charging Station Short-term load forecasting Edit Distance Time Series distance measuring

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