2019 4th International Conference on Control, Robotics and Cybernetics (CRC)最新文献_第4页

Android-Based Smartphone Authentication System Using Biometric Techniques: A Review 基于android的智能手机生物识别认证系统综述

2019 4th International Conference on Control, Robotics and Cybernetics (CRC) Pub Date : 2019-09-01 DOI: 10.1109/CRC.2019.00029

Xinman Zhang, Tingting He, Xuebin Xu

引用次数: 0

RGB Compensation Based on Background Shadow Subtraction for Low-Luminance Pill Recognition 基于背景阴影减法的低亮度药丸识别RGB补偿

2019 4th International Conference on Control, Robotics and Cybernetics (CRC) Pub Date : 2019-09-01 DOI: 10.1109/CRC.2019.00032

S. Chokchaitam, Phakdee Sukpornsawan, Nutcha Pungpiboon, Saowalak Tharawut

引用次数: 1

Research on Multi-Mode Control Strategy for Spaceborne Antenna Pointing Mechanism 星载天线指向机构多模态控制策略研究

2019 4th International Conference on Control, Robotics and Cybernetics (CRC) Pub Date : 2019-09-01 DOI: 10.1109/CRC.2019.00014

Yuan Li, Zhijuan Liu, Huanqiang Chen, Chengshan Liu, Ke Zhu

引用次数: 0

Time-Sensitive Network Profile Service for Enhanced In-Vehicle Stream Reservation 时间敏感的网络配置文件服务，增强车内流预订

2019 4th International Conference on Control, Robotics and Cybernetics (CRC) Pub Date : 2019-09-01 DOI: 10.1109/CRC.2019.00035

Juho Lee, Sungkwon Park

引用次数: 1

A Robot Augmented Environment Based on ROS Multi-Agent Structure 基于ROS多智能体结构的机器人增强环境

2019 4th International Conference on Control, Robotics and Cybernetics (CRC) Pub Date : 2019-09-01 DOI: 10.1109/CRC.2019.00020

S. Vorapojpisut, Matus Lhongpol, Ratchagree Amornlikitsin, Tienake Phuapaiboon

引用次数: 2

Autonomous Step Climbing Strategy Using a Wheelchair and Care Robot 使用轮椅和护理机器人的自主台阶攀登策略

2019 4th International Conference on Control, Robotics and Cybernetics (CRC) Pub Date : 2019-09-01 DOI: 10.1109/CRC.2019.00024

H. Ikeda, Takafumi Tohyama, Daisuke Maki, Keisuke Sato, E. Nakano

{"title":"Autonomous Step Climbing Strategy Using a Wheelchair and Care Robot","authors":"H. Ikeda, Takafumi Tohyama, Daisuke Maki, Keisuke Sato, E. Nakano","doi":"10.1109/CRC.2019.00024","DOIUrl":"https://doi.org/10.1109/CRC.2019.00024","url":null,"abstract":"This report describes a cooperative step climbing strategy using an electric wheelchair and autonomous robot. The robot, which was developed by our research group, has a wheeled travel mechanism and dual manipulators. The wheelchair is a commercially available model modified with added sensors, circuits, and batteries. When the wheelchair and robot encounter a step, the robot grasps the wheelchair and they help each other to ascend the step. In the step climbing process, the wheelchair front wheels are lifted using the difference between the wheelchair and robot velocities, and the front wheels are placed on the step. To make the rear wheels of the wheelchair climb the step, the robot upper arms push against the back of the wheelchair, which is like the motion of man pushing a wheelchair up the step. Similarly, the robot front and rear wheels climb the step using assistance from the wheelchair. We developed an automatic control system that realizes the cooperative step climbing of the wheelchair and robot and also simplifies the operation of both vehicles. An experiment was conducted to demonstrate that the wheelchair and robot can successfully maneuver up a step.","PeriodicalId":414946,"journal":{"name":"2019 4th International Conference on Control, Robotics and Cybernetics (CRC)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129306190","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: 2

Recognizing Malaysia Traffic Signs with Pre-Trained Deep Convolutional Neural Networks 用预训练的深度卷积神经网络识别马来西亚交通标志

2019 4th International Conference on Control, Robotics and Cybernetics (CRC) Pub Date : 2019-09-01 DOI: 10.1109/CRC.2019.00030

Tze How Dickson Neoh, K. Sahari, Yew Cheong Hou, Omar Gumaan Saleh Basubeit

{"title":"Recognizing Malaysia Traffic Signs with Pre-Trained Deep Convolutional Neural Networks","authors":"Tze How Dickson Neoh, K. Sahari, Yew Cheong Hou, Omar Gumaan Saleh Basubeit","doi":"10.1109/CRC.2019.00030","DOIUrl":"https://doi.org/10.1109/CRC.2019.00030","url":null,"abstract":"An essential component in the race towards the self-driving car is automatic traffic sign recognition. The capability to automatically recognize road signs allow self-driving cars to make prompt decisions such as adhering to speed limits, stopping at traffic junctions and so forth. Traditionally, feature-based computer vision techniques were employed to recognize traffic signs. However, recent advancements in deep learning techniques have shown to outperform traditional color and shape based detection methods. Deep convolutional neural network (DCNN) is a class of deep learning method that is most commonly applied to vision-related tasks such as traffic sign recognition. For DCNN to work well, it is imperative that the algorithm is given a vast amount of training data. However, due to the scarcity of a curated dataset of the Malaysian traffic signs, training DCNN to perform well can be very challenging. In this demonstrate that DCNN can be trained with little training data with excellent accuracy by using transfer learning. We retrain various pre-trained DCNN from other image recognition tasks by fine-tuning only the top layers on our dataset. Experiment results confirm that by using as little as 100 image samples for 5 different classes, we are able to classify hitherto traffic signs with above 90% accuracy for most pre-trained models and 98.33% for the DenseNet169 pre-trained model.","PeriodicalId":414946,"journal":{"name":"2019 4th International Conference on Control, Robotics and Cybernetics (CRC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127585917","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: 3

Detection of Bughole on Concrete Surface with Convolutional Neural Network 基于卷积神经网络的混凝土表面虫洞检测

2019 4th International Conference on Control, Robotics and Cybernetics (CRC) Pub Date : 2019-09-01 DOI: 10.1109/CRC.2019.00045

G. Yao, Fujia Wei, Yang Yang, Yujia Sun

{"title":"Detection of Bughole on Concrete Surface with Convolutional Neural Network","authors":"G. Yao, Fujia Wei, Yang Yang, Yujia Sun","doi":"10.1109/CRC.2019.00045","DOIUrl":"https://doi.org/10.1109/CRC.2019.00045","url":null,"abstract":"Bugholes are surface imperfections found on the surface of concrete structures. The presence of bugholes not only affects the appearance of the concrete structure, but may even affect the durability of the structure. Traditional measurement methods are carried out by in-situ manual inspection, and the detection process is time-consuming and difficult. Although various image processing technologies (IPT) have been implemented to detect defects in the appearance quality of concrete to partially replace manual on-site inspections, the wide variety of realities may limit the widespread adoption of IPTs. In order to overcome these limitations, this paper proposes a detector based on Convolutional Neural Network (CNN) to recognizing bugholes on concrete surfaces. The proposed CNN was trained on 4,000 images and tested on 800 images which were not used for training and validation; the recognition accuracy reached 94.37%. The image test results and comparative study with traditional methods showed that the proposed method exhibits excellent performance and indeed can detect the bugholes on the concrete surfaces under actual conditions.","PeriodicalId":414946,"journal":{"name":"2019 4th International Conference on Control, Robotics and Cybernetics (CRC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116662241","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

Message from the Local Chair 当地主席的讲话

2019 4th International Conference on Control, Robotics and Cybernetics (CRC) Pub Date : 2018-09-01 DOI: 10.1109/crc.2018.00006

引用次数: 0