International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)最新文献_第3页

Visual recognition and comparison system and method of intelligent watt hour meter chip based on convolutional neural network 基于卷积神经网络的智能电能表芯片视觉识别和比较系统及方法

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014476

Zhengang Shi, C. Wu, W. Fu, Peng Tao, Linhao Zhang, Bo Gao

引用次数: 0

Research on collaborative and integrated resource schedulingalgorithm in heterogeneous cloud environment 异构云环境中的协作与集成资源调度算法研究

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014391

Jiacheng Fu, Dengbin Liao, Chunzhi Meng, Anni Huang, Junbing Pan

{"title":"Research on collaborative and integrated resource schedulingalgorithm in heterogeneous cloud environment","authors":"Jiacheng Fu, Dengbin Liao, Chunzhi Meng, Anni Huang, Junbing Pan","doi":"10.1117/12.3014391","DOIUrl":"https://doi.org/10.1117/12.3014391","url":null,"abstract":"The current conventional collaborative resource scheduling algorithms in heterogeneous cloud environments mainly process the allocation through the quantified results of data characteristics of heterogeneous cloud resources, which leads to low integrated scheduling efficiency due to the differences in the attributes of resources. In this regard, a collaborative and comprehensive resource scheduling algorithm in heterogeneous cloud environment is proposed. Firstly, the heterogeneous cloud resource information data is sampled and processed, and the resource quality is graded. The scheduling task model is constructed by constructing the mapping function of scheduling task assignment sub-nodes, and the hierarchical scheduling strategy is proposed by combining with ant colony algorithm. In the experiments, the designed collaborative integrated scheduling algorithm is tested for the scheduling efficiency. The final results can prove that the algorithm has a lower average delay and a more desirable integrated scheduling efficiency when the proposed method is used for scheduling heterogeneous cloud resources.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"143 3","pages":"129690J - 129690J-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Naming conventions-based multi-label and multi-task learning for fine-grained classification 基于命名规则的多标签和多任务学习，实现精细分类

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014589

Qinbang Zhou, Kezhi Zhang, Feng Yue, Zhaoliang Zhang, Hui Yu

引用次数: 0

Embroidery style generation with machine learning 利用机器学习生成刺绣样式

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014501

Luojia Wang, Fei Guo

引用次数: 0

3D pipeline reconstruction and diameter measurement method based on target segmentation 基于目标细分的三维管道重建和直径测量方法

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014403

Guanghai Wu, Hao Zhang, Zhiqi Yan, Haoyu Wang, Zhihao Zhong, Ziao Yin

{"title":"3D pipeline reconstruction and diameter measurement method based on target segmentation","authors":"Guanghai Wu, Hao Zhang, Zhiqi Yan, Haoyu Wang, Zhihao Zhong, Ziao Yin","doi":"10.1117/12.3014403","DOIUrl":"https://doi.org/10.1117/12.3014403","url":null,"abstract":"3D reconstruction technology utilizes 3D data to create models of physical objects. Cameras, laser scanners, and other sensors can be used to gather 3D data of objects, which can be processed using computer graphics technology for creating 3D models through 3D reconstruction technology. In engineering, high-precision 3D reconstruction models can substitute physical pipes for automatic measuring of pipe diameters. This paper proposes a target segmentation-based optimization method for single-frame reconstruction, which enables precise diameter measurement of pipes. Experimental results show that single-frame reconstruction, based on target segmentation technology, produces excellent results in the current application scenario. The proposed method is better adapted to complex construction conditions than the complex reconstruction methods. Complex backgrounds include excessive and uneven distributed light and interfering objects. Using target segmentation technology based on image processing, the MIVOS user-interactive video can produce and distribute the target object mask based on the user's interaction with the video frame. Complex background removal can improve the quality of reconstructed sample images. MIVOS is used to segment the pipe area in the image and remove most of the background noise. Consequently, the process lessens the interference of background noise in the reconstruction results. The proposed method exhibits significant progress in measuring both the inner and outer diameters of pipes when compared to both multi-frame and single-frame reconstruction methods. Their measurements have an average error of no more than 1 mm. The proposed method provides technical guidance for measuring the inner and outer diameters of pipes under complex conditions.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"17 1","pages":"1296929 - 1296929-13"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-dimensional situation prediction of digital twin active power grid based on LSTM algorithm 基于 LSTM 算法的数字孪生有功电网多维形势预测

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014395

Xun Wang, Yinghui Tan, Tao Li, Chuang Liu, Guanghao Yang, Qian Wang

{"title":"Multi-dimensional situation prediction of digital twin active power grid based on LSTM algorithm","authors":"Xun Wang, Yinghui Tan, Tao Li, Chuang Liu, Guanghao Yang, Qian Wang","doi":"10.1117/12.3014395","DOIUrl":"https://doi.org/10.1117/12.3014395","url":null,"abstract":"In order to understand the multidimensional situation prediction of digital twin active power grid, a research on multidimensional situation prediction of digital twin active power grid based on LSTM algorithm is proposed. In this paper, firstly, a multi-dimensional situation prediction algorithm of power grid key indicators based on LSTM is established to realize the change prediction of key indicators attributes of digital twin active power grid. Secondly, the data of several key indicators such as load characteristics are collected, and a multi-dimensional system prediction model is established, which can control the state of active power grid; The LSTM prediction algorithm is proposed to fit the characteristics of multi-dimensional data, and the next stage of multi-dimensional data prediction is mapped to the power digital twin, so as to realize the synchronous implementation and intelligent regulation of smart energy system operation planning. Finally, a simulation test model is established, and an example shows that the multi-dimensional situation prediction method of digital twin power grid based on deep learning can better predict and distinguish the power grid situation, and provide decision support for accurate planning of energy system in the future.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":" 25","pages":"129690Y - 129690Y-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simulation study of brushless DC motor speed control system based on GA-PID 基于 GA-PID 的无刷直流电机速度控制系统仿真研究

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014483

Yang Tang, Hao Chen, Faxin Zhu

引用次数: 0

Research on face recognition algorithm based on FaceNet and coordinate attention 基于 FaceNet 和协调注意力的人脸识别算法研究

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014505

Tao Zhang, zewu ke

{"title":"Research on face recognition algorithm based on FaceNet and coordinate attention","authors":"Tao Zhang, zewu ke","doi":"10.1117/12.3014505","DOIUrl":"https://doi.org/10.1117/12.3014505","url":null,"abstract":"The development of information technology has made the field of deep learning face recognition develop rapidly. The traditional face detection and recognition algorithm can perform well under constrained conditions, but under unconstrained conditions, its effect will be greatly discounted when low quality images and partial occlusion of faces are encountered. Based on MTCNN and FaceNet, this paper adopts two strategies to solve the above two problems respectively. On the one hand, by introducing the face image quality assessment function to solve the problem of low quality pictures, before face detection, a quality assessment of the face image is done, and only the image whose quality score reaches the threshold can be input into the model. On the other hand, the Coordinate attention mechanism is introduced to deal with the problem of partial occlusion of the face, which improves the recognition ability of the model by adaptively enhancing the weight of the unocclusion area of the face. Experimental results show that compared with existing algorithms, the accuracy of the proposed algorithm is significantly improved.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"11 3","pages":"129690O - 129690O-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A dual population constrained multiobjective evolutionary algorithm with a feasible archive set 具有可行档案集的双群体约束多目标进化算法

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014412

Xinchang Yu, Yumeng Wang, Tong Zhang, Huaqing Xu

{"title":"A dual population constrained multiobjective evolutionary algorithm with a feasible archive set","authors":"Xinchang Yu, Yumeng Wang, Tong Zhang, Huaqing Xu","doi":"10.1117/12.3014412","DOIUrl":"https://doi.org/10.1117/12.3014412","url":null,"abstract":"Continuous updating and maintenance of feasible solutions is crucial when solving constrained multi-objective optimization problems (CMOPs). However, most existing constrained multi-objective evolutionary algorithms (CMOEAs) are not efficient enough in updating and preserving competitive feasible solutions, thus reducing population diversity. To address this issue, this paper proposes a dual-population (i.e., mainPop and auxPop) constrained multi-objective evolutionary algorithm with a feasible archive set for CMOPs, named DPFAS. The two populations have different functions in the algorithm. Specifically, the ݉ܽ݅݊ܲmainPop considers both objectives and constraints for solving the original CMOPs, while the ܽauxPop is used only for the optimization of objectives without considering constraints. In addition, a feasible archive set is used to store feasible solutions that are competitive in the ܽauxPop and provide useful information for the ݉ܽ݅݊ܲmainPop. Moreover, a fitness assignment strategy is designed to speed up the algorithm’s convergence. Particularly, the population converges faster by selecting better-nondominated solutions into the matching pool. Finally, experimental studies on 23 benchmark functions show that the proposed algorithm was more competitive compared with five state-of-the-art CMOEAs.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"590 1","pages":"1296904 - 1296904-5"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Pedestrian object detection algorithm based on lightweight YOLOv7 in complex street scenarios 复杂街道场景中基于轻量级 YOLOv7 的行人物体检测算法

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014518

Shangqi Cheng, Hongxia Niu

引用次数: 0