2021 4th International Conference on Intelligent Robotics and Control Engineering (IRCE)最新文献

Verification and Analysis of Two-dimensional Path Planning Objective Function Optimization Based on Classical Particle Swarm Optimization Algorithm 基于经典粒子群优化算法的二维路径规划目标函数优化验证与分析

2021 4th International Conference on Intelligent Robotics and Control Engineering (IRCE) Pub Date : 2021-09-18 DOI: 10.1109/IRCE53649.2021.9570874

S. Li

{"title":"Verification and Analysis of Two-dimensional Path Planning Objective Function Optimization Based on Classical Particle Swarm Optimization Algorithm","authors":"S. Li","doi":"10.1109/IRCE53649.2021.9570874","DOIUrl":"https://doi.org/10.1109/IRCE53649.2021.9570874","url":null,"abstract":"The main task of mobile robot path planning is according to the environment model, the mobile robot finds a collision free path from the starting coordinate point to the target coordinate point in the motion space according to one or some optimization criteria, such as the minimum work cost, the shortest walking route, the shortest walking time and so on, Make the robot reach the destination safely. At present, mobile robot path planning methods can be divided into three categories according to the working mode. One is path planning based on environment model, which can deal with the path planning under the condition of completely knowing the position and automatically shape of obstacles. Specific methods such as a topological graph method [1–2], etc.; The second one is the local path planning method based on sensor information. Including: artificial potential field method [3], fuzzy logic method [4], etc.; The third one is the behavior-based path planning method [5–6], The paper shows Verification and analysis of two-dimensional path planning objective function optimization based on classical particle swarm optimization algorithm.","PeriodicalId":160003,"journal":{"name":"2021 4th International Conference on Intelligent Robotics and Control Engineering (IRCE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125873557","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

Remaining Useful Life Prediction based on Degradation Model: Application to a Scale Replica Assembly Plant 基于退化模型的剩余使用寿命预测:在规模复制装配厂中的应用

2021 4th International Conference on Intelligent Robotics and Control Engineering (IRCE) Pub Date : 2021-09-18 DOI: 10.1109/IRCE53649.2021.9571002

Islem Bejaoui, Dario Bruneo, M. Xibilia

引用次数: 0

Attention Guided Image Enhancement Network for Sewer Pipes Defect Detection 关注引导图像增强网络用于污水管道缺陷检测

2021 4th International Conference on Intelligent Robotics and Control Engineering (IRCE) Pub Date : 2021-09-18 DOI: 10.1109/IRCE53649.2021.9570948

Shangqin Yang, Zhengyun Zhao, Qingpeng Yang, Jun Wang

引用次数: 1

Breast Ultrasound Image Segmentation Model Based Residual Encoder 基于残差编码器的乳腺超声图像分割模型

2021 4th International Conference on Intelligent Robotics and Control Engineering (IRCE) Pub Date : 2021-09-18 DOI: 10.1109/IRCE53649.2021.9570898

Y. Alzahrani, B. Boufama

{"title":"Breast Ultrasound Image Segmentation Model Based Residual Encoder","authors":"Y. Alzahrani, B. Boufama","doi":"10.1109/IRCE53649.2021.9570898","DOIUrl":"https://doi.org/10.1109/IRCE53649.2021.9570898","url":null,"abstract":"Artificial intelligence algorithms show promise for various medical imaging applications. Modern computer-aided diagnosis (CAD) systems can potentially be used for the early diagnosis of breast tumors, a leading cause of death in women worldwide. Deep learning algorithms have been applied to readily available breast ultrasound (BUS) images and provided good segmentation and classification performance. Nonetheless, this task remains challenging because US images are very noisy with a class imbalanced data distribution and inhomogenous intensity. To address these issues, we proposed a new variant of the U-Net architecture, which is commonly used for medical image segmentation. We also increased the network depth by using residual blocks (RBs). To resolve the issue of the vanishing gradient while downsampling the features, we expanded the network width by adapting a convolution path instead of a concatenation path in the original U-net. Our proposed model includes a preprocessing stage, feature extraction based on RBs, convolution-concatenation path, and a simple decoder to reconstruct the extracted features. Our proposed model showed better performance than basic U-Net and most recent models like DAL, SK-U-Net, Mobile-U-Net, and Efficient-U-Net. In particular, our model achieved a Dice coefficient and IOU of 91.5% and 84.6% on BUSIS, respectively.","PeriodicalId":160003,"journal":{"name":"2021 4th International Conference on Intelligent Robotics and Control Engineering (IRCE)","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132006313","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

Multiple Fault Diagnosis Based on an Improved Differential Evolution Algorithm with Novel Major-minor Testing Sequences 基于改进差分进化算法的新型大小测试序列多故障诊断

2021 4th International Conference on Intelligent Robotics and Control Engineering (IRCE) Pub Date : 2021-09-18 DOI: 10.1109/IRCE53649.2021.9570894

Xiaohong Qiu, Ziying Ao, Jiali Chen

{"title":"Multiple Fault Diagnosis Based on an Improved Differential Evolution Algorithm with Novel Major-minor Testing Sequences","authors":"Xiaohong Qiu, Ziying Ao, Jiali Chen","doi":"10.1109/IRCE53649.2021.9570894","DOIUrl":"https://doi.org/10.1109/IRCE53649.2021.9570894","url":null,"abstract":"Optimal test sequence problem of fault diagnosis is an NP-complete problem, which inspires lots of studies. However, the multi-fault diagnosis of that is more critical and more challenging due to the overlapped signatures and the masking false failures, but this problem is very lacking in research. We propose a novel diagnostic strategy based on the major-minor testing sequences instead of the traditional fixed testing sequences. The major testing sequences lead the whole diagnosis which are evolved by an improved differential evolution algorithm (Ensemble of differential evolution variants, EDEV). Aiming at the different source faults, different minor testing sequences are generated from the major testing sequence by heuristic search algorithms. With the cooperation of these two sequences, the expected testing costs are reduced without losing the fault isolation rate. As for the hidden and masking false failures, we defined the concepts of the parent fault and fault link, so that the complexity of calculating the masking false failure is reduced from O(2mn) to O(n2), and the process of isolating multiple faults is more efficient. The study cases from real world show that the method is valid, and it is able to solve both the single-fault and multi-fault diagnosis effectively.","PeriodicalId":160003,"journal":{"name":"2021 4th International Conference on Intelligent Robotics and Control Engineering (IRCE)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132178566","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

Sample-based Frontier-Block Detection for Autonomous Robot Exploration 基于样本的自主机器人探索边界块检测

2021 4th International Conference on Intelligent Robotics and Control Engineering (IRCE) Pub Date : 2021-09-18 DOI: 10.1109/IRCE53649.2021.9570908

Yuguang Lu, Chengpeng Li, Bangyu Li, Wenchuan Qiao

引用次数: 0

Piku Prototype: Designing a Mobile Application for Community Cats Piku原型:为社区猫设计移动应用程序

2021 4th International Conference on Intelligent Robotics and Control Engineering (IRCE) Pub Date : 2021-09-18 DOI: 10.1109/IRCE53649.2021.9570884

Kaustubh Salgaonkar, Sumitra Padmanabhan

引用次数: 1

Research and Application of OPC Technology in Power Monitoring System OPC技术在电力监控系统中的研究与应用

2021 4th International Conference on Intelligent Robotics and Control Engineering (IRCE) Pub Date : 2021-09-18 DOI: 10.1109/IRCE53649.2021.9570991

Zhang Qingqing, Wang Zhiqiang

引用次数: 0

Design and Implementation of Improved Leader Follower Formation Control and Tracking Algorithm Based on Global Reference Framework in Network Environment 网络环境下基于全局参考框架的改进Leader - Follower群体控制与跟踪算法的设计与实现

2021 4th International Conference on Intelligent Robotics and Control Engineering (IRCE) Pub Date : 2021-09-18 DOI: 10.1109/IRCE53649.2021.9570881

S. Li

引用次数: 0

A Step-Size Adaptive Hill-Climbing Algorithm for Local Search 一种步长自适应爬坡局部搜索算法

2021 4th International Conference on Intelligent Robotics and Control Engineering (IRCE) Pub Date : 2021-09-18 DOI: 10.1109/IRCE53649.2021.9571016

Wenfen Zhang, Yaohui Liu, Hong Lv

引用次数: 0