2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)最新文献_第5页

Mosquito Staging Apparatus for Producing PfSPZ Malaria Vaccines 用于生产PfSPZ疟疾疫苗的蚊子分期装置

2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) Pub Date : 2019-08-01 DOI: 10.1109/COASE.2019.8843147

Mengdi Xu, Shengnan Lyu, Yingtian Xu, Can Kocabalkanli, Brian K. Chirikjian, John S. Chirikjian, Joshua D. Davis, J. S. Kim, I. Iordachita, R. Taylor, G. Chirikjian

引用次数: 5

An Adaptive Interval Forecast CNN Model for Fault Detection Method 一种用于故障检测的自适应区间预测CNN模型

2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) Pub Date : 2019-08-01 DOI: 10.1109/COASE.2019.8843086

Junjie He, Junliang Wang, Lu Dai, Jie Zhang, Jin Bao

引用次数: 1

c-M2DP: A Fast Point Cloud Descriptor with Color Information to Perform Loop Closure Detection c-M2DP:一个带颜色信息的快速点云描述符，用于执行闭环检测

2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) Pub Date : 2019-08-01 DOI: 10.1109/COASE.2019.8842896

Leonardo Perdomo, Diego Pittol, Mathias Mantelli, R. Maffei, M. Kolberg, Edson Prestes e Silva

引用次数: 3

Automated Extraction of Surgical Needles from Tissue Phantoms 从组织幻影中自动提取手术针头

2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) Pub Date : 2019-08-01 DOI: 10.1109/COASE.2019.8843089

Priya Sundaresan, Brijen Thananjeyan, Johnathan Chiu, Danyal Fer, Ken Goldberg

{"title":"Automated Extraction of Surgical Needles from Tissue Phantoms","authors":"Priya Sundaresan, Brijen Thananjeyan, Johnathan Chiu, Danyal Fer, Ken Goldberg","doi":"10.1109/COASE.2019.8843089","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843089","url":null,"abstract":"We consider the surgical subtask of automated extraction of embedded suturing needles from silicone phantoms and propose a four-step algorithm consisting of calibration, needle segmentation, grasp planning, and path planning. We implement autonomous extraction of needles using the da Vinci Research Kit (dVRK). The proposed calibration method yields an average of 1.3mm transformation error between the dVRK end-effector and its overhead endoscopic stereo camera compared to 2.0mm transformation error using a standard rigid body transformation. In 143/160 images where a needle was detected, the needle segmentation algorithm planned appropriate grasp points with an accuracy of 97.20% and planned an appropriate pull trajectory to achieve extraction in 85.31% of images. For images segmented with $gt50$% confidence, no errors in grasp or pull prediction occurred. In images segmented with 25-50% confidence, no erroneous grasps were planned, but a misdirected pull was planned in 6.45% of cases. In 100 physical trials, the dVRK successfully grasped needles in 75% of cases, and fully extracted needles in 70.7% of cases where a grasp was secured.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"28 1","pages":"170-177"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84548324","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: 25

A Screen-Based Method for Automated Camera Intrinsic Calibration on Production Lines 基于屏幕的生产线摄像机内禀自动标定方法

2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) Pub Date : 2019-08-01 DOI: 10.1109/COASE.2019.8842956

Wenliang Gao, Jiarong Lin, Fu Zhang, S. Shen

引用次数: 1

Industrial Dataspace: A Broker to Run Cyber-Physical-Social Production System in Level of Machining Workshops 工业数据空间:机加工车间层面运行信息-物理-社会生产系统的中介

2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) Pub Date : 2019-08-01 DOI: 10.1109/COASE.2019.8843010

P. Jiang, Chao Liu, Pulin Li, Haoliang Shi

引用次数: 4

Visual-Guided Robot Arm Using Self-Supervised Deep Convolutional Neural Networks 基于自监督深度卷积神经网络的视觉引导机械臂

2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) Pub Date : 2019-08-01 DOI: 10.1109/COASE.2019.8842899

Van-Thanh Nguyen, Chao-Wei Lin, C. G. Li, Shu-Mei Guo, J. Lien

{"title":"Visual-Guided Robot Arm Using Self-Supervised Deep Convolutional Neural Networks","authors":"Van-Thanh Nguyen, Chao-Wei Lin, C. G. Li, Shu-Mei Guo, J. Lien","doi":"10.1109/COASE.2019.8842899","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842899","url":null,"abstract":"Perception-based learning approaches to robotic grasping have shown significant promise. This is further reinforced by using supervised deep learning in robotic arm. However, to properly train deep networks and prevent overfitting, massive datasets of labelled samples must be available. Creating such datasets by human labelling is an exhaustive task since most objects can be grasped at multiple points and in several orientations. Accordingly, this work employs a self-supervised learning technique in which the training dataset is labelled by the robot itself. Above all, we propose a cascaded network that reduces the time of the grasping task by eliminating ungraspable samples from the inference process. In addition to grasping task which performs pose estimation, we enlarge the network to perform an auxiliary task, object classification in which data labelling can be done easily by human. Notably, our network is capable of estimating 18 grasping poses and classifying 4 objects simultaneously. The experimental results show that the proposed network achieves an accuracy of 94.8% in estimating the grasping pose and 100% in classifying the object category, in 0.65 seconds.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"30 1","pages":"1415-1420"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82329121","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: 4

A New Electrostatic Gripper for Flexible Handling of Fabrics in Automated Garment Manufacturing 一种用于服装自动化生产中织物柔性处理的新型静电夹持器

2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) Pub Date : 2019-08-01 DOI: 10.1109/COASE.2019.8843149

Bin Sun, Xinyu Zhang

引用次数: 10

An improved Q-learning based rescheduling method for flexible job-shops with machine failures 一种改进的基于q学习的机器故障柔性作业车间重调度方法

2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) Pub Date : 2019-08-01 DOI: 10.1109/COASE.2019.8843100

Meng Zhao, Xinyu Li, Liang Gao, Ling Wang, Mi Xiao

{"title":"An improved Q-learning based rescheduling method for flexible job-shops with machine failures","authors":"Meng Zhao, Xinyu Li, Liang Gao, Ling Wang, Mi Xiao","doi":"10.1109/COASE.2019.8843100","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843100","url":null,"abstract":"Scheduling of flexible job shop has been researched over several decades and continues to attract the interests of many scholars. But in the real manufacturing system, dynamic events such as machine failures are major issues. In this paper, an improved Q-learning algorithm with double-layer actions is proposed to solve the dynamic flexible job-shop scheduling problem (DFJSP) considering machine failures. The initial scheduling scheme is obtained by Genetic Algorithm (GA), and the rescheduling strategy is acquired by the Agent of the proposed Q-learning based on dispatching rules. The agent of Q-learning is able to select both operations and alternative machines optimally when machine failure occurs. To testify this approach, experiments are designed and performed based on Mk03 problem of FJSP. Results demonstrate that the optimal rescheduling strategy varies in different machine failure status. And compared with adopting a single dispatching rule all the time, the proposed Q-learning can reduce time of delay in a frequent dynamic environment, which shows that agent-based method is suitable for DFJSP.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"57 1","pages":"331-337"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83350168","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: 16

A Labor-Efficient GAN-based Model Generation Scheme for Deep-Learning Defect Inspection among Dense Beans in Coffee Industry 一种高效的基于gan的咖啡豆深度学习缺陷检测模型生成方法

2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) Pub Date : 2019-08-01 DOI: 10.1109/COASE.2019.8843259

Chen-Ju Kuo, Chao-Chun Chen, Tzu-Ting Chen, Zhi-Jing Tsai, Min-Hsiung Hung, Yu-Chuan Lin, Yi-Chung Chen, Ding-Chau Wang, Gwo-Jiun Homg, Wei-Tsung Su

{"title":"A Labor-Efficient GAN-based Model Generation Scheme for Deep-Learning Defect Inspection among Dense Beans in Coffee Industry","authors":"Chen-Ju Kuo, Chao-Chun Chen, Tzu-Ting Chen, Zhi-Jing Tsai, Min-Hsiung Hung, Yu-Chuan Lin, Yi-Chung Chen, Ding-Chau Wang, Gwo-Jiun Homg, Wei-Tsung Su","doi":"10.1109/COASE.2019.8843259","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843259","url":null,"abstract":"Coffee beans are one of most valuable agricultural products in the world, and defective bean removal plays a critical role to produce high-quality coffee products. In this work, we propose a novel labor-efficient deep learning-based model generation scheme, aiming at providing an effective model with less human labeling effort. The key idea is to iteratively generate new training images containing defective beans in various locations by using a generative-adversarial network framework, and these images incur low successful detection rate so that they are useful for improving model quality. Our proposed scheme brings two main impacts to the intelligent agriculture. First, our proposed scheme is the first work to reduce human labeling effort among solutions of vision-based defective bean removal. Second, our scheme can inspect all classes of defective beans categorized by the SCAA (Specialty Coffee Association of America) at the same time. The above two advantages increase the degree of automation to the coffee industry. We implement the prototype of the proposed scheme for conducting integrated tests. Testin. results of a case study reveal that the proposed scheme ca] efficiently and effectively generating models for identifyin defect beans.Our implementation of the proposed scheme is available a https://github.com/Louis8582/LEGAN.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"1 1","pages":"263-270"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90099694","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: 5