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检索条件"任意字段=2023 6th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2023"

共 292 条记录，以下是251-260 订阅

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Research on Dynamic Labels in Network Pruning

Research on Dynamic Labels in Network Pruning

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Industrial Cyber-Physical Systems (ICPS)

作者： Lijun Zhang Yaomin Luo Shiqi Xie Xiucheng Wu Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geosciences Wuhan China

Convolutional neural network compression technology plays an extremely important role in model transplantation and deployment, especially in mobile and embedded hardware platforms with small memory and low computing power, compression technology is even more critical. Convolutional neural network channel pruning technology has developed rapidly in recent years, and a number of excellent pruning algorithms have emerged. the channel pruning technology has gradually developed from the earliest static pruning to dynamic pruning, which adopts different pruning schemes for different inputs. However, the current dynamic pruning scheme needs to introduce multiple modules to predict the mask to prune the feature maps, and some schemes also introduce multiple hyperparameters in the loss function to balance the model accuracy and pruning rate, which leads to the model has difficulty converging during training. We propose a dynamic pruning method, each convolution structure configures a simple prediction module, and generating dynamic labels through the input's norm and similarity to guide the prediction module training, which will not bring new parameters to the loss function. We conducted related experiments on multiple models on the Cifar10 datasets. the experiments on ResNet56 show that our scheme is 1.3% higher than the most advanced scheme in terms of compression rate under the premise of the same accuracy.

关键词： Training Convolution Heuristic algorithms Computational modeling Predictive models Cyber-physical systems Prediction algorithms

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Optimal State Synchronization for Discrete-Time Nonlinear Multi-Agent Systems Under Switching Communication Graph

Optimal State Synchronization for Discrete-Time Nonlinear Mu...

引用

Industrial Cyber-Physical Systems (ICPS)

作者： Wenpeng He Xin Chen Sun Yipu Akinori Sekiguchi Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China School of Engineering Tokyo University of Technology Tokyo Japan

this paper proposes a new method to solve the optimal consensus control of discrete-time nonlinear multi-agent systems (MASs) under switching topology. To get a time-invariant control object for the switching MASs, a new augment local neighborhood tracking error is defined. then the hierarchical control strategy is used to decompose the nonlinear consensus problems into two sub-problems, i.e., the tracking problems between individuals and the reference models, and the distributed consensus control among reference models and the leader. For the former, a robust ADHDP method is used to make the states of the follower track that of the reference model. For the latter, the value iteration method is used to derive the distributed optimal reference control. A simulation is given to verify the effectiveness of the proposed method.

关键词： Switches Transforms Consensus control Cyber-physical systems control systems Topology Synchronization

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Forecasting COVID-19 Pandemic using Prophet, LSTM, hybrid GRU-LSTM, CNN-LSTM, Bi-LSTM and Stacked-LSTM for India

Forecasting COVID-19 Pandemic using Prophet, LSTM, hybrid GR...

引用

international conference on Information Systems and Computer Networks (ISCON)

作者： Satya Prakash Anand Singh Jalal Pooja Pathak Department of Computer Engineering and Applications GLA University Mathura UP INDIA

the COVID-19 Pandemic has been around for four years and remains a health concern for everyone. Although things are somewhat returning to normal, increased incidence of COVID-19 cases in some regions of the world (such as China, Japan, France, South Korea, etc.) has bred worry and anxiety in world, including India. the scientific community, which includes governmental organizations and healthcare facilities, was eager to learn how the COVID-19 Pandemic would develop. the current work makes an attempt to address this question by employing cutting-edge machine learning and Deep Learning algorithms to anticipate the daily incidence of COVID-19 for India over the course of the next six months. For the purpose famous timeseries algorithms were implemented including LSTM, Bi-Directional LSTM and Stacked LSTM and Prophet. Owing to success of hybrid algorithms in specific problem domains- the present study also focuses on such algorithms like GRU-LSTM, CNN-LSTM and LSTM with Attention. All these models have been trained on timeseries dataset of COVID-19 for India and performance metrics are recorded. Of all the models, the simplistic algorithms have performed better than complex and hybrid ones. Owing to this best result was obtained with Prophet, Bidirectional LSTM and Vanilla LSTM. the forecast reveals flat nature of COVID-19 case load for India in future six months.

关键词： COVID-19 Measurement Deep learning Machine learning algorithms Pandemics Bidirectional control Predictive models

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Multi-Objective Voltage and Reactive Power Coordinated control Strategy for Distribution Networks Utilizing Gaining-Sharing Knowledge Based Algorithm

Multi-Objective Voltage and Reactive Power Coordinated Contr...

引用

China international Electrical and Energy conference (CIEEC)

作者： Jinjin Ding Xunting Wang Bin Xu Mingxing Zhu Wei Liu State Grid Anhui Electric Power Research Institute State Grid Anhui Electric Power Co. Ltd. Hefei China Ministry of Education Power Quality Engineer Researcher Center Anhui University Hefei China School of Electrical Engineering and Automation Anhui University Hefei China

Existing heuristic optimization algorithms are prone to obtain a set of non-dominated solutions overconcentrated within an intermediate area in the objective space. It results in a poor diversity performance of the Pareto front when handling the problem on multi-objective voltage and reactive power coordinated control (MOVRPOC). For mitigating the aforementioned disadvantages, a newly developed heuristic algorithm, gaining-sharing knowledge based algorithm (GSK), is implemented to handle the problem of MOVRPOC. then, the minimum system losses, the minimum average voltage deviation and the minimum curtailment rate are treated as optimization objectives, and then the revised IEEE 33-bus distribution system is utilized as the benchmark networks. Grey wolf optimization (GWO) and equilibrium optimizer (EO) are taken as a comparison to validate the improvement on diversity of GSK. the results reveal that GSK is capable to obtain more diverse non-dominated solutions to MOVRPOC for distributed networks, which can be better applied to the practical scenarios on MOVRPOC distribution networks.

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A Rail Detection Method Using Bird's Eye View of LiDAR Point Clouds

A Rail Detection Method Using Bird's Eye View of LiDAR Point...

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international conference on Electronics Technology (ICET)

作者： Ming Yuan Wei Wang Quanli Liu Zichen Yu Xin Liu School of Control Science and Engineering Dalian University of Technology Dalian China

Rail detection is an important part of intelligent rail transportation, and research on rail recognition is of significant importance for both detecting foreign object intrusion in railways and realizing unmanned driving of trains. this paper proposes a new method for rail detection based on bird's-eye view (BEV) of LiDAR point clouds, which aims to solve the problems of traditional point cloud algorithms, such as weak robustness and low accuracy, as well as the problems of large computation and poor real-time performance in point cloud deep learning networks. the proposed method constructs a BEV by projecting the rail point cloud onto a 2D image and retaining the point cloud features, and then uses an anchor-based rail detection convolutional neural network (CNN) to detect the rail. Finally, the predicted rail are mapped back to the original point cloud to label the rail. the experiments on a self-built dataset show that the proposed method achieves good results in different rail scenarios.

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Research of Surface Floating Object Detection Method Based on Improved YOLOv4

Research of Surface Floating Object Detection Method Based o...

引用

6th international conference on Intelligent Computing and Signal Processing (ICSP)

作者： Wei Tang Dahai Xu School of Electrical and Control Engineering Shaanxi University of Science and Technology Xi’an China

this paper proposes an improved YOLOv4 water surface floating object detection method to address the problem that floating objects on the water surface are difficult to detect due to their small size and existing detection algorithms are computationally intensive and difficult to run in real time on embedded devices. the specific idea is to use the MobileNetV3 network to replace the backbone network in YOLOv4; at the same time, negative feedback is introduced, and the proportion of the loss of small-sized targets in the overall loss during the training process is used as feedback, so that selective data enhancement can be performed to improve the detection accuracy of small-sized targets. the experimental results show that compared with the traditional YOLOv4 algorithm, the model parameter number of the proposed algorithm is reduced by 82.4% and the detection speed is improved by 52%.

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Data-Driven Design for Model-Referenced Model-Free controller

Data-Driven Design for Model-Referenced Model-Free Controlle...

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international conference on control, Automation and Systems ( ICCAS)

作者： Shuichi Yahagi Itsuro Kajiwara 6th Research Department ISUZU Advanced Engineering Center Ltd. Fujisawa-shi Japan Division of Mechanical and Aerospace Engineering Hokkaido University Sapporo Japan

this paper presents the data-driven design of model-free control (MFC) based on an ultra-local model to realize model-matching. MFC can realize the desired system response even for nonlinear systems; however, the control performance relies on design parameters. the data-driven tuning approach has attracted attention because it provides optimized controller parameters without the system model to be controlled. In this paper, the control law of MR-MFC is derived by extending the traditional MFC based on the ultra-local model and introducing the data-driven design method to optimize the MFC parameters from single-experiment time-series data without repeated experiments and a system model to be controlled. Additionally, the processing cost is extremely low because the controller parameters can be obtained by using least square methods. the effectiveness of the proposed method was verified through simulations with a linear parameter varying (LPV) system.

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Risk Coupling Analysis of Railroad Accident Based on Two-Layer N-K Model 6th

Risk Coupling Analysis of Railroad Accident Based on Two-Lay...

引用

6th international conference on Electrical engineering and Information Technologies for Rail Transportation, EITRT 2023

作者： Li, Jiayin Ma, Xiaoping Wang, Xu Chen, Xiyuan Chen, Fei Wu, Zhaotian State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China Key Laboratory of Railway Industry of Proactive Safety and Risk Control Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819993147

Railroad accident risk analysis is crucial for railroad accident prevention and active safety assurance. In this paper, we adopt coupled risk theory to analyze the traffic safety risk of railroad transportation system, starting from the coupling of four types of main risk factors, namely human, machine, environment and management, and expanding them to secondary sub-factors, establishing a two-layer N-K model and quantifying their coupling degrees. the coupling degrees of primary main factors and secondary sub-factors of railroad accidents are calculated under different coupling modes of single-factor, two-factor and multi-factor respectively. the results show that the coupling degree of railroad transportation safety risk increases with the number of coupling factors;human factors are the main influencing factor and can influence other factors to some extent;the coupling degree of "human–machine" is the largest in the parallel coupling relationship. In addition to the management of personnel, it is also necessary to continuously improve the safety performance of the train itself and reduce the degree of risk of equipment;in the causal coupling relationship, the coupling degree of "locomotive staff-train signal-human management" is the largest. N-K model can provide some theoretical support for the prevention of safety accidents in railroad transportation, and bring practical significance to improve the safety prevention and control of railroad transportation. © 2024, Beijing Paike Culture Commu. Co., Ltd.

关键词： Railroads

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Design and Application of Wideband Monitoring Master Station in Power Systems

Design and Application of Wideband Monitoring Master Station...

引用

Energy, Electrical and Power engineering (CEEPE), international conference on

作者： Yiming Zeng Fang Shi Hengxu Zhang Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China

the use of power electronics to integrate increasing renewable energy resources into power grids has changed the wideband characteristic of the power system signals. the traditional wide-area measurement system (WAMS) based on synchronized phasor measurement unit (PMU) can only measure the fundamental frequency component, making it unable to meet the measurement requirements of harmonics and interharmonic components. To address this problem, the wideband synchronized measurement unit (WPMU) has been developed, which supports real-time measurement of harmonic and interharmonic components, in addition to the functionalities of PMU. As a result, upgrading the WAMS master station is necessary to make the integration of wideband measurement available. this paper analyzes the characteristics of wideband measurement data, explores its practical applications and discusses the idea of upgrading the WAMS master station for wideband measurement, including designing its data transmission architecture, selecting a suitable database, and outlining advanced application methods. Finally, the paper presents the analysis of one actual engineering application, where the wideband measurement devices are installed, and the data is sent to the master station in real time. this work provides a reference for the design and development of a wideband WAMS master station, and contributes to the safety and stability of the power grid operation by providing wideband measurement information.

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AG-UN-Net: U-shaped and N-shaped network with attention gate for liver segmentation

AG-UN-Net: U-shaped and N-shaped network with attention gate...

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international conference on Information and Computer Technologies (ICICT)

作者： Quchen Zou Xinde Li Chuanfei Hu Key Laboratory Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing China Nanjing Center for Applied Mathmatics Nanjing China

U-shaped network is the most widely used backbone architecture in the field of biomedical image segmentation in recent years. Meanwhile, it is widely used in segmentation of liver from computed tomography, since these volumes have heterogeneous and diffusive shapes. However, there are considerable performances drop in the case of detecting small region, finer details of surfaces and edges. We analyze this issue and address it by adding N-Net to backbone, where N-shaped network maps data to higher dimensions to segment small region and blurred boundaries. Meanwhile, we replace skip connection with attention gate in U-shaped net. the soft-attention can highlight specific target regions and avoid the model to repeatedly extract similar low-level features. the proposed method is validated on the public Liver Tumor Segmentation Challenge dataset. Experimental results demonstrate the superiority of the proposed method compared with the other advanced methods, where the dice scores of AG-UNet and AG-UN-Net are 96.7% and 97.8%, respectively.

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