2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)最新文献_第5页

Design and Implementation of Android-based Smart Classroom Control System 基于android的智能教室控制系统的设计与实现

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/ICARCE55724.2022.10046561

Chenxi Jiang, Yan Yang, Baoping Han

引用次数: 0

Wavelet De-noising Method Analysis of Pipeline Magnetic Flux Leakage In-line Inspection Based on Coefficient of Variation 基于变异系数的管道漏磁在线检测小波降噪方法分析

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/ICARCE55724.2022.10046475

G. Shi, Peng Hu, Jinzhong Chen, Chunyu Li, Hanquan Zhou, Yilai Ma

引用次数: 1

Fruit Recognition Using Color Statistics 基于颜色统计的水果识别

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/ICARCE55724.2022.10046437

Qiang He, Kangli Xia, Hui Pan

{"title":"Fruit Recognition Using Color Statistics","authors":"Qiang He, Kangli Xia, Hui Pan","doi":"10.1109/ICARCE55724.2022.10046437","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046437","url":null,"abstract":"Because of aging and very low birthrate, agricultural labor force has become seriously insufficient. In order to solve this problem, researchers have developed a series of agricultural robots for different purposes, including fruit picking robots. For fruit picking robots, detection and recognition of fruits is an important task. Here a fruit recognition technique based on the statistical characteristics of HSV color was developed. First, the RGB color fruit images were converted into HSV color. Then the hue distribution of HSV color of fruit is approximated with a Laplace distribution. Further, this Laplace distribution can be adopted as the characteristic description of this fruit. The fruit was segmented out of the input image. If the segmented fruit image falls in some Laplace distribution with 90% confidence interval, then input fruit belongs to this special fruit. In practice, the Mahalanobis distance (MD) corresponding to the 90% confidence interval of the Laplace distribution for each fruit class was set as the reference evaluation. If the input fruit data has a smaller Mahalanobis distance than the reference evaluation, the input belongs to this type fruit. The experimental results have shown the good performance for this fruit recognition technique on different kinds of fruits.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131832019","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

Advances in the Study and Application of Robotic Navigation Technology in Pedicle Screw Fixation 机器人导航技术在椎弓根螺钉固定中的研究与应用进展

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/ICARCE55724.2022.10046515

Hui Chen, Xiangfu Wang

{"title":"Advances in the Study and Application of Robotic Navigation Technology in Pedicle Screw Fixation","authors":"Hui Chen, Xiangfu Wang","doi":"10.1109/ICARCE55724.2022.10046515","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046515","url":null,"abstract":"Objective: To review and evaluate the technical advantages, disadvantages and research progress of robotic navigation technology in pedicle screw fixation. Methods: An extensive review of domestic and international literature on robotic navigation technology in pedicle screw fixation was conducted to summarize the advantages and disadvantages of this technology and its clinical application, as well as to provide an outlook on its future development. Results: Robotic nailing has the advantages of improved accuracy, reduced intraoperative radiation and minimally invasive surgery compared to freehand nailing. However, as the application of robotic navigation technology is still in its infancy, there are disadvantages in terms of low coverage, high cost, unstable accuracy and long operative time. Conclusion: The application of robotic navigation technology in pedicle screw fixation is a combined solution that requires not only the improvement of robotic shortcomings, but also a good surgical foundation, strict control of the surgical indications and rational selection of the surgical approach in order to adequately ensure the effectiveness and safety of the procedure.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"516 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116229504","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 Novel RUL Prediction Method for Bearing Using IPVMD-LSTM 基于IPVMD-LSTM的轴承RUL预测新方法

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/ICARCE55724.2022.10046611

Shuangqing Lin, Kui Liang, Na An, Shiyu Peng

{"title":"A Novel RUL Prediction Method for Bearing Using IPVMD-LSTM","authors":"Shuangqing Lin, Kui Liang, Na An, Shiyu Peng","doi":"10.1109/ICARCE55724.2022.10046611","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046611","url":null,"abstract":"With the rapid development of high-end Computer Numerical Control (CNC) machine tools, aeroengines and other large-scale mechanical equipment towards high precision and intelligence, it is an extremely important task to carry out health management of equipment and ensure the equipment can work in safety and stability. The essential part of mechanical equipment are bearings, whose performance will directly determine the health of the equipment. Predicting the remaining life of bearings can provide effective decision support for equipment maintenance plans, so as to avoid safety accidents, which is significant for the health management of mechanical equipment. Currently, signal processing methods and data-driven methods are widely used in bearing life prediction. However, mechanical equipment has been in the background of strong noise for a long time, and its feature signal extraction is difficult, and the traditional regression prediction accuracy is low. Aiming at the above problems, a bearing residual life method based on Improved Parameter Adaptive Variational Mode Decomposition-Long Short Term Memory Networks (IPVMD-LSTM) model is proposed. IPVMD-LSTM has two characteristics: (1) Fully considering the characteristics of bearing cyclostationarity and impulsiveness, a synthetic index is constructed and used as the objective function, the parameters of VMD are optimized by Particle Swarm Optimization (PSO), so as to reduce noise effect influence. (2) Fully consider the temporal characteristics of the actual working condition data, and use the LSTM to extract the temporal characteristics for prediction. The experimental results show that the IPVMD-LSTM method in this paper has a significant improvement in the prediction accuracy, and its Root Mean Square Error (RMSE) is reduced by 2.81% compared with the traditional method.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130321869","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

Enhanced Adaptive Combined Ant Colony Algorithm 增强自适应组合蚁群算法

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/ICARCE55724.2022.10046631

Zelin Yao, Can Liu, Yu Wei, Xinyu Lian, Zehua Yang

引用次数: 0

Adaptive Genetic Algorithm Based Particle Swarm Optimization for Industrial Robotic Arm Obstacle Avoidance Trajectory Optimization 基于自适应遗传算法的粒子群优化工业机械臂避障轨迹优化

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/ICARCE55724.2022.10046592

Yu Chen, Liping Chen, J. Ding

引用次数: 0

ICARCE 2022 Cover Page ICARCE 2022封面页

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/icarce55724.2022.10046609

引用次数: 0

An End-to-End Uyghur Speech Recognition Based on Multi-task Learning 基于多任务学习的端到端维吾尔语语音识别

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/ICARCE55724.2022.10046434

Chuang Liu, Qiong Li, Zhiwei You

{"title":"An End-to-End Uyghur Speech Recognition Based on Multi-task Learning","authors":"Chuang Liu, Qiong Li, Zhiwei You","doi":"10.1109/ICARCE55724.2022.10046434","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046434","url":null,"abstract":"With the emergence of deep learning, speech recognition research for languages with a wide speaker base, such as English and Chinese, has become reasonably mature. However, Uyghur speech recognition research has only developed slowly, because the modeling is subpar since Uighur is a low-resource language. To solve the aforementioned problem, we propose an end-to-end Uyghur speech recognition model based on multi-task learning with Branchformer. Branchformer can capture both global and local contexts, allowing it to learn richer features from low volumes of data to improve model performance in resource-constrained situations. The multi-task learning model can fully utilize the data through multiple related tasks, enhancing the model’s generalizability under low resources. In this paper, the multi-task learning model is trained and decoded using modeling units including phoneme, sub-word, and word. The performance of the model is then evaluated using various datasets. The results show that the proposed model outperforms the mainstream models in terms of recognition, in the self-built database and open-source corpus Thuyg-20, respectively, the word accuracy of the proposed model reaches 94.48% and 91.16%, which is a significant improvement compared with each benchmark model.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115859020","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

Semi-supervised Fingerprint Construction and Localization System For Large Indoor Area 大型室内半监督指纹构建与定位系统

2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE) Pub Date : 2022-12-16 DOI: 10.1109/ICARCE55724.2022.10046610

Min Gao, Yuanyuan Fei, Zhou Wang, Chunming Ma, Li Luo

{"title":"Semi-supervised Fingerprint Construction and Localization System For Large Indoor Area","authors":"Min Gao, Yuanyuan Fei, Zhou Wang, Chunming Ma, Li Luo","doi":"10.1109/ICARCE55724.2022.10046610","DOIUrl":"https://doi.org/10.1109/ICARCE55724.2022.10046610","url":null,"abstract":"Many applications of location-based indoor navigation services require precise location information of a user. While Global Positioning System (GPS) loses reliability indoors, fingerprints-based localization technology (FBLT) embodies superiority regarding accuracy and robustness. In a Bluetooth-based fingerprint localization system, a radio map is constructed offline and used as a reference for subsequent real-time localization tasks. However, the quality of the fingerprint radio map could be problematic when it comes to a large, broad space with low beacon density. Data collection in such a space could be exhausting as well. Another main issue is that different mobile devices receive heterogeneous signal strength at the same location. In this article, we propose a highly practical localization system with a semi-supervised learning fingerprints construction method that provides an efficient solution for a large-scale localization system in a complex indoor environment. We also conducted a series of experiments to evaluate the performance of this system.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"135 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124992100","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