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检索条件"任意字段=2022 5th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2022"

共 264 条记录，以下是1-10 订阅

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2022 5th international conference on advanced algorithms and control engineering, icaace 2022

2022 5th International Conference on Advanced Algorithms and...

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2022 5th international conference on advanced algorithms and control engineering, icaace 2022

the proceedings contain 79 papers. the topics discussed include: interactive and fast low-light image enhancement algorithm and application;design of real-time target location algorithm for industrial scene;a computational optimization research on ant colony optimization for the traveling salesman problem;research on Bi-LSTM machine reading comprehension algorithm based on attention mechanism;the single train's punctual and energy-saving operation scheme based on dynamic programming algorithm;research on hybrid collision detection algorithm based on separation distance;a variable step-size normalized least mean square algorithm with orthogonal correction factors;research on optimizing multimodal transport path under the schedule limitation based on genetic algorithm;and optimization of flow shop scheduling based on genetic algorithm with reinforcement learning.

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5th international conference on Electrical engineering and control Applications, ICEECA 2022

5th International Conference on Electrical Engineering and C...

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5th international conference on Electrical engineering and control Applications, ICEECA 2022

ISBN: (纸本)9789819747757

the proceedings contain 50 papers. the special focus in this conference is on Electrical engineering and control Applications. the topics include: Sliding Mode control Based on Exponential Reaching Law Applied on a Five-Phase Permanent Magnet Synchronous Generator Based Wind System;indirect Field Oriented control and Synergetic control of Doubly Fed Induction Generator-Comparative Study;Storage Management Algorithm of a PV System;Energy Management of PV/WT/Battery Hybrid Power System Using Flatness-Based control;Solar Radiation Measurements in the Adrar Region of Algeria: Radiometric Stations, Statistics, and Comparison with MERRA2 Data;estimation of Solar Radiation on the Ground from Meteosat Satellite Images and Different Models: Case of the Ghardaïa Region;fuzzy Logic control Strategy for a Grid-Connected Wind System with a Battery Energy Storage;Impact of Distribution Generation on Transient Stability with PSAT;Ageing Impact on Reliability and Components Importance of an NPP’s Emergency Power Supply Model;Performance Improvement of a DFIG-Based Wind Energy Conversion System Using Fuzzy-MPPT and SVM-DTC;combined Machine Learning Techniques for Analyzing the Back Contact Influence on the Stability of Perovskite-Based Solar Cells;Test of advanced Functionalities of Photovoltaic Inverters Integrated in Algerian LV Distribution Network;Simulations of Silicon n-on-p Pixel Detectors for HEP Experiments;A Multiple-Areas Based on PID-Virtual Inertia control Strategy for Inverters;Characterization of Radiated Electromagnetic Fields Using Equivalent Sources with HGAGWO Method;fault Diagnosis of Photovoltaic Arrays Based on Support Vector Machine and t-Distributed Stochastic Neighbor Embedding;studying the Effect of Mn Doping on the Electrical and Optical Properties of ZnO/Ag/ZnO Multilayer.

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5th international conference on Electrical engineering and control Applications, ICEECA 2022

5th International Conference on Electrical Engineering and C...

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5th international conference on Electrical engineering and control Applications, ICEECA 2022

ISBN: (纸本)9789819700448

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Research on Asphalt Pavement Crack Detection using YOLOv5 Model 7

Research on Asphalt Pavement Crack Detection using YOLOv5 Mo...

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7th international conference on advanced algorithms and control engineering, icaace 2024

作者： Wu, Wenxiu Zou, Xiaoyong Fang, Zhihua Fang, Xiangzhen Song, Xiaohong Yang, Aiping Liu, Zhen Jinhua Highway Administration Bureau Jinhua China Lanxi Transportation Construction Investment Group Co. Ltd Jinhua China Lanxi Tongda Highway Maintenance Engineering Co. Ltd Jinhua China Jinhua Comprehensive Administrative Law Enforcement Bureau Jinhua China The Thomas D. Larson Pennsylvania Transportation Institute The Pennsylvania State University Park PA16802 United States

ISBN: (纸本)9798350361445

In the early diseases of asphalt pavement, cracks detection is very important, which can provide support for the follow-up highway maintenance work. this paper adopts the YOLOv5 algorithm of target detection in deep learning, and takes the road image data sets of seven cities as an example to detect pavement cracks. Firstly, the data set is transformed and split, then trained and predicted, and finally the results are analyzed. through comparison, the algorithm has achieved good detection results. © 2024 IEEE.

关键词： Asphalt pavements

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Pedestrian Detection Algorithm Based on Improved YOLOv5 7

Pedestrian Detection Algorithm Based on Improved YOLOv5

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7th international conference on advanced algorithms and control engineering, icaace 2024

作者： Li, Wenjie Zhao, Yongguo Zhang, Yanfang Gao, Jia Song, Xinran Shandong Provincial Key Laboratory Of Robot And Manufacturing Automation Technology Jinan China

ISBN: (纸本)9798350361445

In order to solve the problem that the object detector is difficult to identify pedestrians at different scales, this paper introduces the MSBlock module into the Backbone layer by improving the YOLOv5 algorithm, enhancing the ability of the real-time object detector to extract multi-scale features and the problem of missed detection and false detection caused by the large difference in the size of the character target, while maintaining the fast inference speed, adding the PConv module to the Head layer to reduce redundant computing and memory access, so as to extract spatial features more effectively. At the same time, the path aggregation structure of multi-feature fusion is realized by fusing the underlying features in the backbone network, and the CIoU loss function is used to reduce the regression error and further improve the accuracy of target detection, so as to achieve accurate identification of pedestrians. Experimental results show that compared with the YOLOv5s algorithm and the original YOLOv5n algorithm, the mAP of the improved YOLOv5 algorithm in the recognition experiment is increased by 1% and 5.3%, respectively. And the improved YOLOv5 algorithm is lighter than YOLOv5s. © 2024 IEEE.

关键词： Object detection

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Trajectory Planning Method for Underwater Mobile Robot in Nuclear Power Plant 7

Trajectory Planning Method for Underwater Mobile Robot in Nu...

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7th international conference on advanced algorithms and control engineering, icaace 2024

作者： Liu, Zhi Liang, Jun Hu, Jianqiang Chen, Li China Nuclear Power Technology Research Institute Co. Ltd Equipment Research Dept. Shenzhen China China Nuclear Power Operations Co. Ltd Static Primary Equipment Dept. Shenzhen China School Of Mechanical Engineering Hebei University Of Technology Tianjin China

ISBN: (纸本)9798350361445

Regularly inspect of underwater pinholes widely distributed among nuclear power equipments has became an important initiative to ensure the safety of nuclear power operation. In response to the problems of unreachable target point and insufficient obstacle avoidance ability of the current underwater operation robot, a local path planning algorithm based on Fuzzy Adaptive Dynamic Window Approach (FADWA) is proposed. the mathematical model is constructed based on the traditional dynamic window approach, and the coupling between the obstacle avoidance ability of the mobile robot and the reachability of the target point is solved by combining the fuzzy control with the dynamic window approach to realize the adaptive model weights. the simulation experiments results show that this local path planning algorithm enhances the environmental adaptability of the underwater mobile robot, and improves the obstacle avoidance ability and target reachability. © 2024 IEEE.

关键词： Fuzzy control

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Research on Tea Shoot Monitoring and Recognition Based on YOLOv5 Improved Algorithm 7

Research on Tea Shoot Monitoring and Recognition Based on YO...

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7th international conference on advanced algorithms and control engineering, icaace 2024

作者： Yu, Yongcai Huang, Wei Yang, Shuting Ming, Chen Chen, Hua Zhang, Gengfei Guizhou Light Industry Vocational And Technical College Department Of Information Engineering Guiyang China Guizhou Equipment Manufacturing Polytechnic Department Of Electrical Engineering Guiyang China

ISBN: (纸本)9798350361445

Tea as an important economic crop in China, the national demand in the growing, in order to meet the growing demand, this paper through in-depth research algorithms to form a solution to the characteristics of tea shoots, such as colour and shape. Although existing recognition models can distinguish the basic features of tea shoots, their recognition ability is often limited in complex real-world scenarios. therefore, to address this aspect, this paper first proposes how to accurately identify the quantity and judge the quality of tea shoots in complex scenes. While the problems of light, object coupling and occlusion are the research features of this paper, the neural network with YOLOv5 enhanced algorithmic model is mainly used to carry out the research of real-time monitoring of tea shoots. A complete software and hardware system of real-time monitoring system was constructed in the field environment by highly integrated WLAN SoC chip. through the software design, the real-time monitoring of tea shoots is achieved, and the acceleration and displacement functions of the monitoring system are carefully debugged and optimised, and the results show that the system accurately identifies and monitors the tea shoots in a variety of complex field environments, which provides powerful technical support for tea yield prediction and pest control, and has high application value. © 2024 IEEE.

关键词： Application programs

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Research and Implementation of Fuzzy Test Method Based on Multi-armed Bandit 7

Research and Implementation of Fuzzy Test Method Based on Mu...

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7th international conference on advanced algorithms and control engineering, icaace 2024

作者： Qin, Yiwen Jiang, Jincheng Li, Changzhi Lu, Futao Deng, Kaiwen Yang, Zengyu Network Control Lab Chongqing University Of Posts And Telecommunications Chongqing China

ISBN: (纸本)9798350361445

thereare a lot of vulnerabilities in industrial control protocols due to rothole in protocol implementation and problems with exception handling of function codes. In order to solve low effectiveness of generating test cases and the inability to adjust the test case generation by feedback from the device, fuzzy test method based on multi-armed bandit(MAB) algorithm is proposed. Firstly, the protocol unit is divided into feature sets and a matrix model is established by deeply analyzing the protocol specification, so as to initially preprocess the input data set. Secondly, the protocol constraint rule set is constructed to filter the invalid cases that violate the protocol specification. Finally, the exploration and utilization phase of the model is customized by using multi-armed bandit algorithm and fuzzy test method to increase the selection probability of mutation factors that easily trigger vulnerabilities, and establish a test case queue to save the fuzzy test cases, moreover, the probability distribution is adjusted by using the feedback of test *** results show that this method improves the abnormality rate of the test system, and the vulnerability mining ability is significantly improved. © 2024 IEEE.

关键词： Probability distributions

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Foreign Object Detection Method in Aerial Images of Power Transmission Lines Based on Scale Adaptive YOLOv5 7

Foreign Object Detection Method in Aerial Images of Power Tr...

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7th international conference on advanced algorithms and control engineering, icaace 2024

作者： Chen, Xudong Han, Lili Huang, Yali Gao, Junwei Xiao, Yinghao Xue, Baoguo Peng, Lingli Guangzhou Nansha Power Supply Bureau Of Guangdong Power Grid Co. Ltd. Guangzhou China

ISBN: (纸本)9798350361445

Foreign object detection is an important part of power inspection work. It aims to investigate objects that have not yet invaded transmission lines but are likely to threaten the operation of transmission lines. However, the complex environment of high-voltage transmission lines and the variable scale of foreign objects makes it difficult. therefore, a Scale Adaptive You Only Look Oncev5 (SA-YOLOv5) based foreign object detection method is proposed. Unmanned aerial vehicles collect aerial images containing two types of foreign objects: plastic film and house to build a dataset. the StemBlock module is introduced to downsample shallow features, reducing calculation parameters while maintaining strong feature expression capabilities. the scale-adaptive residuals module is designed to fit foreign object features with large scale differences. Finally, migrating pre-trained model weights strategy is adapted to accelerate model fitting. Experimental results prove that SA-YOLOv5 locates and identifies foreign objects more accurately, and meets the accuracy requirements for inspection of foreign objects in transmission lines to a certain extent. © 2024 IEEE.

关键词： Object detection

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Specific Emitter Identification Based on ACGAN and STFT 7

Specific Emitter Identification Based on ACGAN and STFT

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7th international conference on advanced algorithms and control engineering, icaace 2024

作者： Wang, Bin Xie, Jun Wang, Feng The 38th Research Institute Of China Electronics Technology Group Corporation China Key Laboratory Of Aperture Array And Space Application China Key Laboratory Of Intelligent Information Processing Anhui Hefei230088 China School Of Computer And Information Hefei University Of Technology Hefei230601 China

ISBN: (纸本)9798350361445

Deep learning methodologies are gaining popularity for enhancing the efficiency of Specific Emitter Identification (SEI). Nevertheless, the lack of labeled datasets continues to pose a challenge in most non-cooperative scenarios for SEI. Addressing this challenge, a framework for SEI is proposed, leveraging Short-Time Fourier Transform (STFT) in conjunction with auxiliary classifier GAN(ACGAN). the Short-Time Fourier Transform of emitters is utilized as radio-frequency fingerprint for the emitters. the input latent layer is infused with label information to steer the training process of the Generative Adversarial Network. through semi-supervised training, shared weight updates are implemented for the classification network. Experimental results demonstrate that our model can achieve superior or competitive performances in several datasets compared with the former models. © 2024 IEEE.

关键词： Deep learning

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