2023 3rd International Conference on Computer, Control and Robotics (ICCCR)最新文献

A Hybrid Autoscaling Algorithm for Microservices with Federated Load Balancing 基于联邦负载均衡的微服务混合自缩放算法

2023 3rd International Conference on Computer, Control and Robotics (ICCCR) Pub Date : 2023-03-24 DOI: 10.1109/ICCCR56747.2023.10194135

Chenghao Song, Lingyun Jiang

引用次数: 0

Research on Robot Blade Cleaning Method Based on Visual Servo 基于视觉伺服的机器人叶片清洗方法研究

2023 3rd International Conference on Computer, Control and Robotics (ICCCR) Pub Date : 2023-03-24 DOI: 10.1109/ICCCR56747.2023.10194139

Hao Xu, Xiansheng Qin, Minghao Dong, Yuru Fu, Di Wu, Jiwen Xie, Zhanxi Wang

引用次数: 0

A Compensation Method for Lost Trajectories of ORB-SLAM3 with DROID-VO 用DROID-VO补偿ORB-SLAM3丢失轨迹的方法

2023 3rd International Conference on Computer, Control and Robotics (ICCCR) Pub Date : 2023-03-24 DOI: 10.1109/ICCCR56747.2023.10194078

Kairong Liu, Yong Fan, Jianmei Su, Ling Wu, Kexin Zhang

引用次数: 0

Countersinking Accuracy Control Technology of Robot Drilling and Countersinking Integrated Drilling 机器人钻孔沉头精度控制技术与沉头一体化钻孔

2023 3rd International Conference on Computer, Control and Robotics (ICCCR) Pub Date : 2023-03-24 DOI: 10.1109/ICCCR56747.2023.10194032

Zhanxi Wang, Yuru Fu, Hao Xu, Minghao Dong, Mengge Shi, Banghai Zhang

引用次数: 0

Research on Image Lightweight Binocular Ranging 图像轻量化双目测距技术研究

2023 3rd International Conference on Computer, Control and Robotics (ICCCR) Pub Date : 2023-03-24 DOI: 10.1109/ICCCR56747.2023.10194107

Gang Gao, Lisheng Wei, Quangan Hu

引用次数: 0

Machine Learning Aided Classification of Noise Distribution in Scanning Electron Microscopy Images 扫描电镜图像中噪声分布的机器学习辅助分类

2023 3rd International Conference on Computer, Control and Robotics (ICCCR) Pub Date : 2023-03-24 DOI: 10.1109/ICCCR56747.2023.10194053

Sheikh Shah Mohammad Motiur Rahman, M. Salomon, S. Dembélé

{"title":"Machine Learning Aided Classification of Noise Distribution in Scanning Electron Microscopy Images","authors":"Sheikh Shah Mohammad Motiur Rahman, M. Salomon, S. Dembélé","doi":"10.1109/ICCCR56747.2023.10194053","DOIUrl":"https://doi.org/10.1109/ICCCR56747.2023.10194053","url":null,"abstract":"Noise estimation is a crucial part of any modern supervised denoiser. Various statistical approaches are studied to estimate the noise, but generally these depend on a manual analysis of the images. To remove the manual dependency, it is important to automate the noise estimation process. In this paper, the initial phase of noise estimation, which is to identify the types of noise distribution, is performed using machine learning (ML) techniques. To make the images workable with ML techniques, a feature extraction process was performed. Hu's moment invariants, Haralick's texture, and color histogram are extracted from the images and stacked horizontally by scaling with MinMax scale the three features into one. Label encoder is used for normalizing the labels. Multiple ML techniques are trained and validated, and then tested with unknown images. The result is that stacking multiple ML techniques can produce better results with an accuracy above 90%. Stacking with the test set produces the following scores for Precision, Recall, and F1-score: 0.98, 0.88, 0.93, and 0.89, 0.98, 0.93 for Gaussian and Poisson respectively, with an average precision of 88%. These promising results prove the capability of ML techniques for image noise classification tasks where noise is artificially added. However, in real case, i.e. when the images come from a Zeiss Auriga FE SEM, which is the initial target, the classification is not as efficient. Thus, it is not always possible to work in a real denoising scenario if the model is trained with synthetic data.","PeriodicalId":295437,"journal":{"name":"2023 3rd International Conference on Computer, Control and Robotics (ICCCR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122031029","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: 1

A 3D Positioning Method for Electric Robots Based on Binocular Vision 基于双目视觉的电动机器人三维定位方法

2023 3rd International Conference on Computer, Control and Robotics (ICCCR) Pub Date : 2023-03-24 DOI: 10.1109/ICCCR56747.2023.10194006

Wancai Zhang, Wenqing Yang, Zhen Sun, N. Zhang

引用次数: 0

Research on High Precision Point Cloud Registration Algorithm in Dynamic Environment 动态环境下高精度点云配准算法研究

2023 3rd International Conference on Computer, Control and Robotics (ICCCR) Pub Date : 2023-03-24 DOI: 10.1109/ICCCR56747.2023.10193893

Hao Li, Donghai Qian, Guangying Liu, Wei Zhao

{"title":"Research on High Precision Point Cloud Registration Algorithm in Dynamic Environment","authors":"Hao Li, Donghai Qian, Guangying Liu, Wei Zhao","doi":"10.1109/ICCCR56747.2023.10193893","DOIUrl":"https://doi.org/10.1109/ICCCR56747.2023.10193893","url":null,"abstract":"The localization technology based on Lidar is an important method to realize the precise localization and navigation of unmanned vehicle, and it is also the premise to realize the application of unmanned vehicle in more fields. In many application scenarios, dynamic objects may interfere with precise localization, resulting in degeneration of Lidar localization accuracy or even failure. This paper proposes an accurate point cloud registration algorithm for dynamic environment. Firstly, the point cloud is roughly registrated to obtain the rough pose estimation, and the potential dynamic object point cloud is segmented, then it is removed from the original point cloud. Finally, based on the corrected point cloud, the high-precision point cloud registration is performed with the pose obtained by the rough pose estimation as the initial pose, so as to obtain the accurate pose estimation of the unmanned vehicle. The method is applied to Lidar to realize the fast and high-precision lidar localization of Ackerman unmanned vehicle in dynamic environment. The algorithm is verified in the campus environment. The results show that the algorithm is reliable and effective in the dynamic environment, and is superior to the common point cloud registration algorithm.","PeriodicalId":295437,"journal":{"name":"2023 3rd International Conference on Computer, Control and Robotics (ICCCR)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128427440","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: 1

An Online Recognition Method of Badminton Stroke Based on Inertial Sensor 基于惯性传感器的羽毛球击球动作在线识别方法

2023 3rd International Conference on Computer, Control and Robotics (ICCCR) Pub Date : 2023-03-24 DOI: 10.1109/ICCCR56747.2023.10193896

Yitian Zhang, Ruichen Liu, Yi Yang, Shuning Gao, Jie Li

{"title":"An Online Recognition Method of Badminton Stroke Based on Inertial Sensor","authors":"Yitian Zhang, Ruichen Liu, Yi Yang, Shuning Gao, Jie Li","doi":"10.1109/ICCCR56747.2023.10193896","DOIUrl":"https://doi.org/10.1109/ICCCR56747.2023.10193896","url":null,"abstract":"Badminton is a sport that is very popular, and it has many benefits to the human body. At present the scope of the research on badminton movement using inertial senors is mainly focused on the relationship between the dynamic speed and speed of the racket, or the dynamic analysis of the swing arm of the badminton players.and There are currently few studies on badminton strokes identification, but a lot of study on the relationship between dynamic speed and racket speed or the dynamic analysis of the swing arm of the badminton players. In this research, they offer an initial online identification system for badminton strokes, and they show how to collect badminton data using an inertial sensor attached to the bottom of a racket. Extracting data on badminton strokes from video sequences is done using a threshold-based detection method and sliding window methodology. For the classification phase, they retrieved a total of twelve characteristics to make up the feature space, and they employ random forest (RF) to identify badminton shots. Experiments with a total of 12 participants show that the proposed algorithm is both successful and useful.","PeriodicalId":295437,"journal":{"name":"2023 3rd International Conference on Computer, Control and Robotics (ICCCR)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129537784","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

Reactively Following Equidistant Curves with Singularities by an Underactuated Nonholonomic Mobile Robot 欠驱动非完整移动机器人反应性跟踪奇异等距曲线

2023 3rd International Conference on Computer, Control and Robotics (ICCCR) Pub Date : 2023-03-24 DOI: 10.1109/ICCCR56747.2023.10194012

A. Matveev, V. Chernov

引用次数: 0