2018 Workshop on Metrology for Industry 4.0 and IoT最新文献_第2页

Deep Learning Based Machine Vision: First Steps Towards a Hand Gesture Recognition Set Up for Collaborative Robots 基于深度学习的机器视觉:迈向协作机器人手势识别的第一步

2018 Workshop on Metrology for Industry 4.0 and IoT Pub Date : 2018-04-16 DOI: 10.1109/METROI4.2018.8439044

Cristina Nuzzi, S. Pasinetti, M. Lancini, F. Docchio, G. Sansoni

{"title":"Deep Learning Based Machine Vision: First Steps Towards a Hand Gesture Recognition Set Up for Collaborative Robots","authors":"Cristina Nuzzi, S. Pasinetti, M. Lancini, F. Docchio, G. Sansoni","doi":"10.1109/METROI4.2018.8439044","DOIUrl":"https://doi.org/10.1109/METROI4.2018.8439044","url":null,"abstract":"In this paper, we present a smart hand gesture recognition experimental set up for collaborative robots using a Faster R-CNN object detector to find the accurate position of the hands in the RGB images taken from a Kinect v2 camera. We used MATLAB to code the detector and a purposely designed function for the prediction phase, necessary for detecting static gestures in the way we have defined them. We performed a number of experiments with different datasets to evaluate the performances of the model in different situations: a basic hand gestures dataset with four gestures performed by the combination of both hands, a dataset where the actors wear skin-like color clothes while performing the gestures, a dataset where the actors wear light-blue gloves and a dataset similar to the first one but with the camera placed close to the operator. The same tests have been conducted in a situation where also the face of the operator was detected by the algorithm, in order to improve the prediction accuracy. Our experiments show that the best model accuracy and Fl-Score are achieved by the complete model without the face detection. We tested the model in real-time, achieving good performances that can lead to real-time human-robot interaction, being the inference time around 0.2 seconds.","PeriodicalId":396967,"journal":{"name":"2018 Workshop on Metrology for Industry 4.0 and IoT","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116975959","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: 17

A Survey of Measurement Applications Based on IoT 基于物联网的测量应用综述

2018 Workshop on Metrology for Industry 4.0 and IoT Pub Date : 2018-04-16 DOI: 10.1109/METROI4.2018.8428335

P. Daponte, F. Lamonaca, F. Picariello, L. D. Vito, Gianluca Mazzilli, IOAN TUDOSA

引用次数: 28

Development and Characterization of a Safety System for Robotic Cells Based on Multiple Time of Flight (TOF) Cameras and Point Cloud Analysis 基于多飞行时间(TOF)相机和点云分析的机器人细胞安全系统的开发与表征

2018 Workshop on Metrology for Industry 4.0 and IoT Pub Date : 2018-04-16 DOI: 10.1109/METROI4.2018.8439037

S. Pasinetti, Cristina Nuzzi, M. Lancini, G. Sansoni, F. Docchio, Alberto Fornaser

{"title":"Development and Characterization of a Safety System for Robotic Cells Based on Multiple Time of Flight (TOF) Cameras and Point Cloud Analysis","authors":"S. Pasinetti, Cristina Nuzzi, M. Lancini, G. Sansoni, F. Docchio, Alberto Fornaser","doi":"10.1109/METROI4.2018.8439037","DOIUrl":"https://doi.org/10.1109/METROI4.2018.8439037","url":null,"abstract":"In this paper, a vision system for safety applications in human-robot collaboration is presented. The system is based on two Time-Of-Flight (TOF) cameras for 3D acquisition. The point clouds are registered in a common reference system, and human and robot recognition are then implemented. Human recognition is performed using a customized version of the Histogram of Oriented Gradient (HOG) algorithm. Robot recognition is achieved using a procedure based on the Kanade-Lucas-Tomasi (KLT) algorithm. Two safety strategies have been developed. The first one is based on the definition of suitable comfort zones of both the operator and the robot; the second implements virtual barriers between the operator and the robot. The vision system has been characterized in terms of (i) human and robot recognition performance, (ii) correctness of the detection of safety situations and (iii) evaluation of the time delays in the detection. The results show that the human operator is robustly recognized provided that he moves frontally with respect to the TOF cameras and the robot is always recognized. The safety situations are always identified correctly with an average time delay of 0.86 0.63 s (k=1).","PeriodicalId":396967,"journal":{"name":"2018 Workshop on Metrology for Industry 4.0 and IoT","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122663618","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: 11

Academic Fablab at University of Naples Federico II: New Research and Development Opportunities in the Fileds of IoT and Industry 4.0 那不勒斯大学费德里科二世学术实验室:物联网和工业4.0领域的新研究和发展机遇

2018 Workshop on Metrology for Industry 4.0 and IoT Pub Date : 2018-04-16 DOI: 10.1109/METROI4.2018.8439043

L. Angrisani, P. Arpaia, F. Bonavolontà, R. Schiano Lo Moriello

引用次数: 7

True Random Number Generators for Batch Control Sampling in Smart Factories 智能工厂批量控制抽样的真随机数生成器

2018 Workshop on Metrology for Industry 4.0 and IoT Pub Date : 2018-04-16 DOI: 10.1109/METROI4.2018.8428319

L. Ribeiro, Ana C. O. Marcelino, G. A. Garcia, Desiree S. Goncalves, L. Tarelho, Leandro P. Correa, W. A. Chapetta, Alan Oliveira de Sá, Raphael Machado

引用次数: 1

Measurements for Smart Manufacturing in an Industry 4.0 Scenario A Case-Study on A Mechatronic System 工业4.0场景下智能制造的测量——以机电一体化系统为例

2018 Workshop on Metrology for Industry 4.0 and IoT Pub Date : 2018-04-16 DOI: 10.1109/METROI4.2018.8428341

G. D'Emilia, A. Gaspari, E. Natale

引用次数: 16

Study for the Integration of a Measuring System to an Automated Platform for Monitoring the Growth of Bacterial Cultures 细菌培养物生长监测自动化平台测量系统集成研究

2018 Workshop on Metrology for Industry 4.0 and IoT Pub Date : 2018-04-16 DOI: 10.1109/METROI4.2018.8428321

Michele Bona, Paolo Bellitti, E. Sardini, M. Serpelloni

{"title":"Study for the Integration of a Measuring System to an Automated Platform for Monitoring the Growth of Bacterial Cultures","authors":"Michele Bona, Paolo Bellitti, E. Sardini, M. Serpelloni","doi":"10.1109/METROI4.2018.8428321","DOIUrl":"https://doi.org/10.1109/METROI4.2018.8428321","url":null,"abstract":"As bacterial infections are still a risk for human health, the market offers different systems able to detect bacterial growth in biological samples. One of them is the WASPLab automated platform, commercialized by COPAN Italia S.p.A., which combines growth detection capabilities with high levels of automation and connectivity, in compliance with Industry 4.0 principles. In this paper, we describe a study carried out on a system, whose operation relies on impedance measurements, to evaluate the possibility of its integration to the WASPLab. This integration would provide a larger quantity of data about bacterial growth to the user and would optimize analysis process. By using this system, we observed the growth of S. aureus in Petri dishes, while they were directly inside one of the WASPLab incubators. System provided enough information to successfully detect bacterial growth with detection time equal to three, four and a half, and six hours when initial pathogen concentration was in the order of 4.5... 108 CFU ml, 4.5... 107 CFU ml, and 4.5... 106 CFU/ml, respectively. Results highlight that the measuring system could work together with the WASPLab, enhancing its monitoring performances, connectivity, and flexibility. This contributes to the realization of an architecture compliant with the model of the Factory of the Future.","PeriodicalId":396967,"journal":{"name":"2018 Workshop on Metrology for Industry 4.0 and IoT","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132459446","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

Kinect-Based Micro-Behavior Sensing System for Learning the Smart Assistance with Human Subjects Inside Their Homes 基于运动学的微行为感知系统在人类受试者家中学习智能辅助

2018 Workshop on Metrology for Industry 4.0 and IoT Pub Date : 2018-04-16 DOI: 10.1109/METROI4.2018.8428345

Teruhiro Mizumoto, Alberto Fornaser, H. Suwa, K. Yasumoto, M. Cecco

{"title":"Kinect-Based Micro-Behavior Sensing System for Learning the Smart Assistance with Human Subjects Inside Their Homes","authors":"Teruhiro Mizumoto, Alberto Fornaser, H. Suwa, K. Yasumoto, M. Cecco","doi":"10.1109/METROI4.2018.8428345","DOIUrl":"https://doi.org/10.1109/METROI4.2018.8428345","url":null,"abstract":"The automatic sensing and understanding of the contexts are important to make the appropriate decision in Smart Systems. Especially, understanding and tracing human activities and behaviors are useful elements that enable the system to support or to imitate them. There are many studies that identify the activity or behavior with using the various sensors, but many of them could only identify rough activities, and some studies badly infringe on privacy. In this study, we propose a micro-behavior sensing system with Kinect sensor. To reduce the invasion of privacy with Camera device like Kinect sensor, our system identifies the micro-behavior by using features only extracted from the skeleton data. We deployed our system to actual Smart Home in Nara Institute of Science and Technology Japan. Then we conducted an experiment during 15 days to collect actual daily activities and confirmed the identification accuracy of the micro-behaviors related to the cooking activity, such as picking up a seasoning, cutting, mixing, washing etc. In the paper, 5 main micro-behaviors are considered. The achieved accuracy in the classification is 78%.","PeriodicalId":396967,"journal":{"name":"2018 Workshop on Metrology for Industry 4.0 and IoT","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125295943","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: 13

IOT-Based Measurement System for Wine Industry 基于物联网的葡萄酒行业测量系统

2018 Workshop on Metrology for Industry 4.0 and IoT Pub Date : 2018-04-16 DOI: 10.1109/METROI4.2018.8428308

G. Masetti, F. Marazzi, L. D. Cecilia, L. Rovati

引用次数: 9

Multimodal Computer Vision Framework for Human Assistive Robotics 人类辅助机器人的多模态计算机视觉框架

2018 Workshop on Metrology for Industry 4.0 and IoT Pub Date : 2018-04-16 DOI: 10.1109/METROI4.2018.8428330

E. Ivorra, Mario Ortega, M. Alcañiz, Nicolás García Aracil

引用次数: 8