2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)最新文献_第5页

Data Augmentation for Deep Learning based Cattle Segmentation in Precision Livestock Farming 基于深度学习的精准畜牧业牛类分割的数据增强

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216758

Yongliang Qiao, Daobilige Su, He Kong, S. Sukkarieh, S. Lomax, C. Clark

引用次数: 14

Machine Hearing for Industrial Fault Diagnosis 机器听觉用于工业故障诊断

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216787

Yu Zhang, Miguel Martínez-García

引用次数: 3

ACBI: An Alternating Clustering and Bayesian Inference approach for optimizing medical intervention budget under chance constraints 随机约束下优化医疗干预预算的交替聚类与贝叶斯推理方法

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216791

Chen He, B. Dalmas, Xiaolan Xie

{"title":"ACBI: An Alternating Clustering and Bayesian Inference approach for optimizing medical intervention budget under chance constraints","authors":"Chen He, B. Dalmas, Xiaolan Xie","doi":"10.1109/CASE48305.2020.9216791","DOIUrl":"https://doi.org/10.1109/CASE48305.2020.9216791","url":null,"abstract":"Unplanned readmissions to the emergency department (ED) have been identified as a key factor resulting in a negative effect on subjects’ health and healthcare resource scheduling. Often, the readmission prediction is modeled as a binary classification problem whose objective is to predict if a subject will be readmitted or not. Nevertheless, it ignores the uncertainty nature of readmission and usually results in poor prediction quality. In this paper, the problem is defined as a chance-constrained medical intervention rationing problem: at-risk subjects are targeted and given supplemental medical interventions, while the remaining subjects are treated as outpatients. The objective is to profile subjects, identify at-risk subjects, and select specific groups of subjects to which additional medical interventions are recommended, while addressing the unknown number of at-risk subjects and the unknown subjects’ readmission risks. We propose a white-box approach named Alternating Clustering and Bayesian Inference (ACBI) and investigate its efficiency on a real-life readmission data set. Results are promising and show the method could lead up to a 34.42% reduction in readmission rate.","PeriodicalId":212181,"journal":{"name":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114404137","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

Hospital Drugs Distribution with Autonomous Robot Vehicles 自动机器人车辆的医院药品配送

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9217043

M. P. Fanti, A. M. Mangini, M. Roccotelli, B. Silvestri

{"title":"Hospital Drugs Distribution with Autonomous Robot Vehicles","authors":"M. P. Fanti, A. M. Mangini, M. Roccotelli, B. Silvestri","doi":"10.1109/CASE48305.2020.9217043","DOIUrl":"https://doi.org/10.1109/CASE48305.2020.9217043","url":null,"abstract":"The hospital sector is implementing several services and procedures in order to improve the quality and assistances through new technologies. In this context, the drugs distribution is a very important activity to provide an efficient service to all departments that need supply. The spread of new viruses, such as COVID19, or other dangers, which requires the decrease of interactions between people even within the hospital sector, can also be limited using a fleet of autonomous robot vehicles. Drugs cross delivery in a hospital is an activity that can be performed through the use of these new vehicles. In this paper an innovative optimization approach of drugs cross distribution within a hospital is proposed, in order to reduce both number and length of trips and number of autonomous robot vehicles in the fleet, without significantly reducing the level of the provided service. The idea is based on the collaborative logistics concept in which a limited number of autonomous robot vehicles are used for time-scheduled delivery activities through a combination of two departments to be served for each delivery. This strategy is formalized by an Integer Linear Programming Problem to optimize the delivery tasks. Moreover, a case study simulation is presented to show the application of the methodology in a hospital.","PeriodicalId":212181,"journal":{"name":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115530254","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: 9

Fairness Control of Traffic Light via Deep Reinforcement Learning 基于深度强化学习的交通灯公平性控制

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216899

Chenghao Li, Xiaoteng Ma, Li Xia, Qianchuan Zhao, Jun Yang

引用次数: 8

Accelerating Grasp Exploration by Leveraging Learned Priors 利用已学习的先验知识加速掌握探索

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216740

Han Yu Li, Michael Danielczuk, A. Balakrishna, V. Satish, Ken Goldberg

引用次数: 8

Study of the Usage Life for a Robotic Arm Based on Reducer Diagnosis 基于减速器诊断的机械臂使用寿命研究

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216831

Y. Kao, Sheng-Jhe Chen, Feng-Jun Li

{"title":"Study of the Usage Life for a Robotic Arm Based on Reducer Diagnosis","authors":"Y. Kao, Sheng-Jhe Chen, Feng-Jun Li","doi":"10.1109/CASE48305.2020.9216831","DOIUrl":"https://doi.org/10.1109/CASE48305.2020.9216831","url":null,"abstract":"A robotic arm is an important equipment in an automated production line. The reducer in the robot arm is one of its important components but with the highest failure rate. The reducer is a complex system including input shaft, output shaft, gears and bearings, etc. When the reducer starts to be damaged, performance of the robotic arm will be affected, and even worse the system shut down and the production efficiency might be induced, to name only a few. Therefore, how to extend the useful life of the reducer has become an important issue. In general, the clamping jaws (grippers) are installed on the 6th axis in charge of the loading and unloading, which will inevitably higher the reducer failure rate than that of the other 5 axes. Therefore, this research aims at the useful life optimization of the 6th axis reducer. The machine learning algorithms were adopted to establish methodologies to find the key factors. In addition, since the movement path of the robot arm determines the life of the reducer, multiple paths with the same starting and ending position will be generated through the forward and reverse processing, and then the RMSF (Root Mean Squares of Features) values of various paths are calculated. The optimal path with the optimum useful life of the reducer will be the one with the minimum RMSF value. This study has successfully shown that significant differences exist among the various movement paths based on the healthy and abnormal data from the cooperated reducer manufacturing company. This means the developed methodology could be used as an helpful index to extend the useful life of the reducer and also to serve as the basis in futuristic predictive maintenance system.","PeriodicalId":212181,"journal":{"name":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125904464","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

Data-driven Online Group Detection Based on Structured Prediction 基于结构化预测的数据驱动在线群组检测

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216765

Yingli Zhao, Zhengxi Hu, Lei Zhou, Meng Liu, Jingtai Liu

引用次数: 1

A framework for personalized production based on digital twin, blockchain and additive manufacturing in the context of Industry 4.0 工业4.0背景下基于数字孪生、区块链和增材制造的个性化生产框架

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216732

Daqiang Guo, Shiquan Ling, Hao Li, Di Ao, Tongda Zhang, Yiming Rong, G. Huang

{"title":"A framework for personalized production based on digital twin, blockchain and additive manufacturing in the context of Industry 4.0","authors":"Daqiang Guo, Shiquan Ling, Hao Li, Di Ao, Tongda Zhang, Yiming Rong, G. Huang","doi":"10.1109/CASE48305.2020.9216732","DOIUrl":"https://doi.org/10.1109/CASE48305.2020.9216732","url":null,"abstract":"The booming customized and personalized demands call for new production paradigms that complies with that change. The ubiquitous connection, digitization and sharing in the context of Industry 4.0 present an opportunity for next-generation production paradigm-personalized production, to meet the booming personalized demands with individual needs and preferences. Personalized production refers to a customer-centric production paradigm, where individual needs and preferences are transformed into personalized products and services at an affordable cost, by maximizing the benefit of connection and sharing throughout the product life-cycle. This paper reviews and identifies the evolution of production paradigms. A framework for personalized production based on digital twin, blockchain and additive manufacturing in the context of Industry 4.0 is proposed. Besides, the impact of the implementation of personalized production is discussed from the aspects of customer-centric business model, social and environmental effects and challenges of data ownership. This paper provides helpful guidance and reference for personalized production paradigm.","PeriodicalId":212181,"journal":{"name":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","volume":"313 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115867444","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: 22

Sleep stages classification using cardio-respiratory variables 使用心肺变量对睡眠阶段进行分类

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9217045

Asma Gasmi, V. Augusto, Paul-Antoine Beaudet, J. Faucheu, C. Morin, Xavier Serpaggi, F. Vassel

{"title":"Sleep stages classification using cardio-respiratory variables","authors":"Asma Gasmi, V. Augusto, Paul-Antoine Beaudet, J. Faucheu, C. Morin, Xavier Serpaggi, F. Vassel","doi":"10.1109/CASE48305.2020.9217045","DOIUrl":"https://doi.org/10.1109/CASE48305.2020.9217045","url":null,"abstract":"Analysis of sleep is important in order to detect health issues and try to prevent them. In particular, sleep dysfunctions may be the first signs of cognitive frailties for elderly persons. The polysomnography (PSG) is considered the golden standard to perform a comprehensive sleep analysis, as it is based on several sensors placements. However, for longitudinal study of sleep that is required to prevent frailty for elderly persons, such medical equipment is not suitable since it is very invasive. Recent technological advances in sensors allow to gather data with a good precision with less intrusive equipment. The main objective of this study consists in developing a new algorithmic approach to analyse sleep using data from low intrusive sensors. In this study we focus on sleep phase detection, i.e. wake, Non-Rapid Eye Movement (NREM) and Rapid Eye Movement (REM). We consider the following sources of data: heart beat rate, as well as user data such as gender, age, etc. The problem is considered as a supervised classification machine learning problem. We propose a benchmark of several machine learning algorithms and compare their performances against the medical gold standard, the PSG. To do so, we use a data-set collected from a published clinical trial. Support Vector Machine (SVM) algorithm globally outperforms all other methods with a 76.5% agreement with the PSG. As a direct perspective of this study, we plan to add other sources of data using custom sensors to improve the performance of the prediction.Sleep stages, machine learning, supervised classification, sleep architecture, polysomnography","PeriodicalId":212181,"journal":{"name":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126637888","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