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Enabling Stateful TCP Performance Profiling with key Event Capturing

IEEE Transactions on Network and Service Management 2025年

作者： Geng, Ruopeng Lu, Jianyuan Fang, Chongrong Zhang, Shaokai Zhao, Jiangu Zong, Zhigang Lyu, Biao Zhu, Shunmin Cheng, Peng Chen, Jiming Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China Alibaba Group Hangzhou310052 China Ministry of Education of China Key Laboratory of System Control and Information Processing China Shanghai Jiao Tong University Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China Zhejiang University College of Control Science and Engineering Hangzhou310027 China

TCP ensures reliable transmission through its stateful implementation and remains crucial today. TCP performance profiling is essential for tasks like diagnosing network performance problems, optimizing transmission performance, and developing new TCP variants, etc. Existing profiling methods lack enough attention to TCP state transition to provide detailed insights on TCP performance. Thus, we build TcpSight, a tool focusing on TCP state transition throughout connection lifetimes. TcpSight conducts stateful analysis by capturing key events using an efficient per-connection lock-free data management mechanism. Besides, TcpSight enhances profiling by integrating application layer information collected from the TCP stack. With the profiling results, users can identify the culprit of TCP performance degradation, and evaluate the performance of TCP algorithms. We design optional modules and filtering mechanisms to reduce TcpSights overhead. Our evaluation presents that TcpSight incurs an additional CPU consumption of about 16.6% (without filtering) and 10.6% (with filtering) when the server's load is 55.7%, and generates storage consumption about 1.88 KB per connection on average. We also give application cases of TcpSight and the deployment experiences in Alibaba Cloud. TcpSight helps in revealing meaningful findings and insights into exploiting TCP in the production deployment. © 2004-2012 IEEE.

关键词： information management

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Decentralized coordination between economic dispatch and demand response in multi-energy systems

arXiv

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arXiv 2021年

作者： Liu, Zishun Zhu, Shanying Xu, Jinming Chen, Cailian The Department of Automation Shanghai Jiaotong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China College of Control Science and Engineering Zhejiang University HangzhouZhejiang310027 China

In this paper, we investigate the problem of coordination between economic dispatch (ED) and demand response (DR) in multi-energy systems (MESs), aiming to improve the economic utility and reduce the waste of energy in MESs. Since multiple energy sources are coupled through energy hubs (EHs), the supply-demand constraints are nonconvex. To deal with this issue, we propose a linearization method to transform the coordination problem to a convex social welfare optimization one. Then a decentralized algorithm based on parallel Alternating Direction Method of Multipliers (ADMM) and dynamic average tracking protocol is developed, where each agent could only make decisions based on information from their neighbors. Moreover, by using variational inequality and Lyapunov-based techniques, we show that our algorithm could always converge to the global optimal solution. Finally, a case study on the modified IEEE 14-bus network verifies the feasibility and effectiveness of our algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Electric load dispatching

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Down-Level control Based on Level Prediction of Landslide Evolutionary State

Down-Level Control Based on Level Prediction of Landslide Ev...

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International Conference on information Science and Technology (ICIST)

作者： Shu Sun Cheng Lian Xiaoping Wang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Automation Wuhan University of Technology Wuhan China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

Accurate prediction and effective control are the key to reducing the impact of landslide disasters. In practice, most of recent studies focus on landslide displacement prediction and control. In this paper, we propose a new control method based on the level prediction of landslide evolution state, namely down-level control. The core components are level predictor and interval predictor in this method. Specially, we first transform the traditional displacement regression prediction problem into a level classification prediction problem by labeling discrete category information for landslide sample points. Then the level predictor based on Multi-task learning-Stacked long-short time memory network (MTL-SLSTM) is established to predict the state of the landslide and judge whether the system needs to be controlled. Finally, we design a safe rainfall interval predictor based on bootstrap method to obtain the safe value of control variation. The effectiveness of the proposed control method is verified on Baishuihe and Shiliushubao landslides. The results show the proposed down-level control method is valid and more intuitive.

关键词： information science Geology Transforms Predictive models Terrain factors Complexity theory Labeling

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Uncertainty-guided cross teaching semi-supervised framework for histopathology image segmentation with curriculum self-training

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Applied Soft Computing 2025年 180卷

作者： Rui Xu Nan Zhou Siyang Feng Zhenbing Liu Huahu Deng Yajun An Jian Li Chu Han Zaiyi Liu Rushi Lan Cheng Lu Xipeng Pan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin Guangxi 541004 China Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou Guangdong 510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou Guangdong 510080 China International Joint Research Laboratory of Spatio-temporal Information and Intelligent Location Services Guilin University of Electronic Technology Guilin Guangxi 541004 China

Histopathology image segmentation is crucial in disease diagnosis, therapeutic response evaluation, and prognosis. However, manually annotating pixel-level labels for histopathology images is both time-consuming and labor-demanding task. In this study, we propose a novel semi-supervised semantic segmentation framework called UTCS ( U ncertainty-guided cross T eaching and C urriculum S elf-training) to address the challenges of limited labeled data. UTCS effectively harnesses the benefits of consistency regularization and self-training in semi-supervised learning. Our approach introduces a mutual consistency network, where one network’s prediction is used as a pseudo mask to supervise the other network and vice versa. Addressing the issue of unreliable pseudo labels, we propose a dynamically re-weighted loss function that leverages uncertainty to perform pixel-level selection during the mutual teaching process, referred to as uncertainty-guided cross teaching. Furthermore, inspiring from curriculum learning, we incorporate an self-training strategy, focusing on image-level selection, that prioritizes reliable images during the re-training stage and aims to generate high-quality pseudo-labels for less reliable images. Extensive experiments on two publicly available histopathology datasets, BCSS and LUAD-HistoSeg, demonstrate the superior performance of our method compared to state-of-the-art semi-supervised semantic segmentation methods.

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Research on Infrared image processing Algorithm of Generator Carbon Brush Based on Adaptive Stochastic Resonance Array

Research on Infrared Image Processing Algorithm of Generator...

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2021 China Automation Congress, CAC 2021

作者： Jiao, Shangbin Huang, Weichao Geng, Bo Zhang, Youmin Wu, Xiaohui Wu, Pengyue Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi 'an University of Technology Xi'an710048 China Department of Mechanical Industrial and Aerospace Engineering Concordia University MontrealQCH3G 1M8 Canada Huaneng Weihai Power Generation Co. Ltd Weihai264200 China Xi'an Thermal Power Research Institute Co. Ltd. Xi'an710048 China

ISBN: (纸本)9781665426473

The safe and stable operation of the generator set is related to the national economy and people's livelihood. As an important part of generator excitation system, carbon brush and slip ring temperature monitoring can effectively evaluate the state of the generator, which plays a vital role. Most of the current researches use infrared images to monitor carbon brush temperature, but there are often problems such as high noise and unclear focus. According to the requirements of infrared image display, this paper combines the method of bistable stochastic resonance to denoise the image, and on this basis, introduces the adaptive stochastic resonance array method to denoise the gray image. Experimental results show that compared with common image denoising methods and classic bistable stochastic resonance, the adaptive stochastic resonance array method has improved both visual effects and peak signal-to-noise ratio (PSNR), which further proves the good application of stochastic resonance in weak signal detection and extraction. © 2021 IEEE

关键词： Stochastic systems

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Efficient 3D Deep LiDAR Odometry

arXiv

引用

arXiv 2021年

作者： Wang, Guangming Wu, Xinrui Jiang, Shuyang Liu, Zhe Wang, Hesheng Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of Artificial Intelligence of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China

An efficient 3D point cloud learning architecture, named EfficientLO-Net, for LiDAR odometry is first proposed in this paper. In this architecture, the projection-aware representation of the 3D point cloud is proposed to organize the raw 3D point cloud into an ordered data form to achieve efficiency. The Pyramid, Warping, and Cost volume (PWC) structure for the LiDAR odometry task is built to estimate and refine the pose in a coarse-to-fine approach. A projection-aware attentive cost volume is built to directly associate two discrete point clouds and obtain embedding motion patterns. Then, a trainable embedding mask is proposed to weigh the local motion patterns to regress the overall pose and filter outlier points. The trainable pose warp-refinement module is iteratively used with embedding mask optimized hierarchically to make the pose estimation more robust for outliers. The entire architecture is holistically optimized end-to-end to achieve adaptive learning of cost volume and mask, and all operations involving point cloud sampling and grouping are accelerated by projection-aware 3D feature learning methods. The superior performance and effectiveness of our LiDAR odometry architecture are demonstrated on KITTI, M2DGR, and Argoverse datasets. Our method outperforms all recent learning-based methods and even the geometry-based approach, LOAM with mapping optimization, on most sequences of KITTI odometry dataset. We open sourced our codes at: https://***/IRMVLab/EfficientLO-Net. Copyright © 2021, The Authors. All rights reserved.

关键词： Iterative methods

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A Multi-Channel Multi-Head CNN Framework for Fault Classification in Industrial Process

A Multi-Channel Multi-Head CNN Framework for Fault Classific...

引用

Data Driven control and Learning Systems (DDCLS)

作者： Hui Wu Yan Wang Junyu Lin Weidong Yang Yanwei Wang Ying Zheng The Key Laboratory of Image Information Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China Zhengzhou University of Light Industry Zhengzhou China Wuhan Institute of Technology Wuhan China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

This paper proposes a novel fault classification method via convolutional neural network with multi-channel and multi-head along the time dimension, which is defined as MM-CNN. The MM-CNN extracts features of industrial process data by convolutional layers from local to global level. Unlike traditional methods, this method can capture the independent features of every process variable and the relevant characteristics from sensor data to learn more useful latent fault patterns. Besides, a data preprocessing approach is proposed to transform original data for convolutional neural network. Finally, for all the all 21 faults in Tennessee Eastman(TE) process, this paper compares the proposed method with the published methods and a architecture which combines the convolutions of multi-head and a bidirectional Long Short-Term Memory (Bi-LSTM) layer in fault classification. The simulation results show that the method has better classification performance than the state-of-the-art methods.

关键词： Feature extraction Time series analysis Deep learning Data models Data mining Convolutional neural networks Convolution

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Grid-forming converters control based on DC voltage feedback

arXiv

引用

arXiv 2020年

作者： Gao, Yuan Ren, Hai-Peng Li, Jie Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’anShaanxi710048 China

The renewable energy is connected to the power grid through power electronic converters, which are lack of make the inertia of synchronous generator/machine (SM) be lost. The increasing penetration of renewable energy in power system weakens the frequency and voltage stability. The Grid-Forming Converters(GFCs) simulate the function of synchronous motor through control method in order to improve the stability of power grid by providing inertia and stability regulation mechanism. This kind of converter control methods include virtual synchronous machine, schedulable virtual oscillator control and so on. These control method mainly use AC side state feedback and do not monitor the DC side state. This paper analyzes the control strategy of GFC considering power grid stability, including Frequency Droop control, Virtual Synchronous Machine control and dispatchable Virtual Oscillator control. The DC side voltage collapse problem is found when a large load disturbance occurs. The control methods of GFC considering DC side voltage feedback are proposed, which can ensure the synchronization characteristics of grid connection and solve the problem of DC side voltage collapse. The proposed method is verified by IEEE-9 bus system, which shows the effectiveness of the proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： State feedback

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Research on Improved Multiple Users Detection Algorithm in Pattern Division Multiple Access System

SSRN

引用

SSRN 2022年

作者： Tang, Wanwei Wang, Lixia Wang, Zedai Han, Guofeng Zhao, Jinlong Yue, Xinwei The Key Laboratory of Intelligent Data Information Processing and Control of Hebei Province Tangshan University Tangshan063000 China The School of Engineering Hong Kong University of Science and Technology Hong Kong999077 Hong Kong The Guofeng Han is with the Tangshan employment service center Tangshan human resources and Social Security Bureau Tangshan063000 China The School of Electronic and Information Engineering Beihang University Beijing100191 China The School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China

Receiver detection algorithm has always been a research hotspot in communication system and the key point to determine the performance of 5G system. Both industry and academia have been committed to finding detection algorithms with lower complexity and higher detection performance. This paper will study how to solve the problems of low detection performance and high complexity of classical algorithms. Taking Pattern Division Multiple Access (PDMA) as an example, this paper improves the existing receiver detection algorithms such as Minimum Mean Square Error-Serial Interference Cancellation(MMSE-SIC), Message Passing Algorithm (MPA) and Expectation Propagation Algorithm (EPA), and puts forward corresponding improved algorithms with specific formulas, i.e. Enhanced-MMSE-SIC, Turbo-like MPA (MPA-Turbo) and Turbo-like EPA with hybrid parallel interference cancellation (EPA-Turbo-PIC). Link-level simulation results and analysis show that the proposed improved algorithms have performance advantages over the corresponding traditional algorithms. © 2022, The Authors. All rights reserved.

关键词： Mean square error

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Early Forest Fire Segmentation Based on Deep Learning

Early Forest Fire Segmentation Based on Deep Learning

引用

Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Mengna Li Youmin Zhang Jing Xin Lingxia Mu Ziquan Yu Han Liu Guo Xie Shangbin Jiao Yingmin Yi Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an Shaanxi China Concordia University Montreal Quebec Canada College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing 211106 China

ISBN: (纸本)9781665401166

Fire segmentation is very important for fire rescue. It can make firefighters get the information on fire area, spread direction and so on, and then help them make quick and effective fire-fighting plan. Therefore, this paper proposes an early forest fire segmentation algorithm based on a deep learning model, named F-Unet, which mainly uses the architecture idea of Unet for reference. F-Unet consists of contraction path, feature fusion layer and expansion path. The contraction path is composed of the first 13 layers of VGG16, which is used to obtain feature maps with different scales. In order to improve the segmentation accuracy of the model, the feature fusion network proposed in this paper is added to the Unet architecture to fuse these feature maps with different scales. The expansion path is used for upsampling these feature maps to restore the size of the original input image and obtain the fire segmentation results. The experimental testing results on the FLAME dataset show that F-Unet can significantly improve the fire segmentation precision, and also prove that the proposed feature fusion network is effective to improve the performance of fire segmentation.

关键词： Deep learning image segmentation Fuses Fault detection Fires Forestry Safety

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