2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)最新文献

Prediction of clinicians' treatment in preterm infants with suspected late-onset sepsis — An ML approach 临床医生对疑似晚发型脓毒症的早产儿治疗的预测-一种ML方法

2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2018-06-28 DOI: 10.1109/ICIEA.2018.8397888

Yifei Hu, Vincent C. S. Lee, K. Tan

引用次数: 10

Neutral current reduction control for smart transformer under the imbalanced load in distribution system 配电网不平衡负荷下智能变压器中性点电流减小控制

2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2018-06-28 DOI: 10.1109/ICIEA.2018.8398108

Junru Chen, Rongwu Zhu, Marco Liserre, T. O’Donnell

引用次数: 6

Distributed dimensionality reduction of industrial data based on clustering 基于聚类的工业数据分布式降维

2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2018-06-28 DOI: 10.1109/ICIEA.2018.8397744

Yongyan Zhang, Guo Xie, Wenqing Wang, Xiaofan Wang, F. Qian, Xulong Du, Jinhua Du

引用次数: 0

SmartGrid technologies for flexible production: Initial explorations and laboratory case study 灵活生产的智能电网技术:初步探索和实验室案例研究

2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2018-06-26 DOI: 10.1109/ICIEA.2018.8397851

N. Galkin, V. Vyatkin

引用次数: 6

Qualitative analysis of a multimodal interface system using speech/gesture 使用语音/手势的多模态界面系统的定性分析

2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2018-06-26 DOI: 10.1109/ICIEA.2018.8398188

M. Z. Baig, M. Kavakli

引用次数: 10

Application of deep learning image-to-image transformation networks to GPR radargrams for sub-surface imaging in infrastructure monitoring 深度学习图像到图像转换网络在GPR雷达图中的应用，用于基础设施监测的地下成像

2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2018-06-26 DOI: 10.1109/ICIEA.2018.8397788

Jean Kyle Alvarez, S. Kodagoda

{"title":"Application of deep learning image-to-image transformation networks to GPR radargrams for sub-surface imaging in infrastructure monitoring","authors":"Jean Kyle Alvarez, S. Kodagoda","doi":"10.1109/ICIEA.2018.8397788","DOIUrl":"https://doi.org/10.1109/ICIEA.2018.8397788","url":null,"abstract":"The corrosion of reinforced concrete sewer pipes in aging infrastructure is a serious ongoing issue and as such, research into technologies that allow for autonomous site assessments are of major priority to wastewater managing utilities. The use of Ground Penetrating Radar (GPR) is being investigated for providing sub-surface images of sewer crowns. Due to the nature of GPRs, the analysis of a radargram for identifying sub-surface features is non-intuitive and usually require the use of an expert. Traditional methods to help ease analysis involve the use of Synthetic Aperture Radar (SAR) and migration techniques. These techniques refocus dipping and point reflectors to be closer to their true shape but require an accurate velocity model to be effective. This is not always readily available and difficult to estimate especially in regards to sewer conditions. We instead provide an alternative and present a deep learning framework for transforming ground penetrating radargrams into sub-surface permittivity maps. An evaluation of various state-of-the-art deep learning architectures is also conducted, comparing the performance of different objective functions and identifying current limitations. This work provides the base for further exploration of the application of deep learning for use in infrastructure monitoring.","PeriodicalId":140420,"journal":{"name":"2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121931200","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: 23

A solution to the inverse pulsed eddy current problem enabling 3D profiling 解决逆脉冲涡流问题，实现三维剖面

2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2018-06-26 DOI: 10.1109/ICIEA.2018.8397904

Nalika Ulapane, Linh V. Nguyen, J. V. Miró, G. Dissanayake

{"title":"A solution to the inverse pulsed eddy current problem enabling 3D profiling","authors":"Nalika Ulapane, Linh V. Nguyen, J. V. Miró, G. Dissanayake","doi":"10.1109/ICIEA.2018.8397904","DOIUrl":"https://doi.org/10.1109/ICIEA.2018.8397904","url":null,"abstract":"When a Pulsed Eddy Current (PEC) sensor assesses a metallic surface (i.e., a wall of finite thickness), the inverse problem involves quantification of the geometry and material properties of the wall. Once a PEC sensor is calibrated for a particular material, and the material under test happens to be considerably homogeneous, the inverse problem reduces to quantification of geometry alone. The state-of-the-art in the industry produces a quantification of this geometry only in the form of average wall thickness remaining underneath the sensor footprint, and produces a 2.5D map containing wall thickness information. Therefore, this paper contributes by proposing a solution that can jointly estimate the remaining wall thickness as well as lift-off (i.e., offset from the sensor to the surface of healthy material), in order to advance PEC sensing outputs by enabling estimation of wall condition in 3D. Since PEC maps are used as inputs for stress calculation and remaining life prediction of certain infrastructure like critical pipes, 3D profiles may become a richer form of input for such applications than 2.5D maps. Since PEC sensing is commonly used to assess ferromagnetic materials, this paper focuses on similar materials as well. The solution is demonstrated in simulation alone and future work should focus on experimental implementations.","PeriodicalId":140420,"journal":{"name":"2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)","volume":"34 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132441075","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

Adaptive sampling for spatial prediction in environmental monitoring using wireless sensor networks: A review 基于无线传感器网络的环境监测空间预测自适应采样研究进展

2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2018-06-26 DOI: 10.1109/ICIEA.2018.8397740

Linh V. Nguyen, Nalika Ulapane, J. V. Miró

引用次数: 10

Comparison of EKF based SLAM and optimization based SLAM algorithms 基于EKF的SLAM与基于优化的SLAM算法的比较

2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2018-06-26 DOI: 10.1109/ICIEA.2018.8397911

Yanhao Zhang, Teng Zhang, Shoudong Huang

引用次数: 18

Analysis of stator current of dual-three phase induction motor drive under broken bar fault condition 断条故障条件下双三相异步电动机驱动定子电流分析

2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) Pub Date : 2018-05-31 DOI: 10.1109/ICIEA.2018.8397779

Y. Maouche, M. Oumaamar, A. Khezzar, H. Razik

引用次数: 6