2021 IEEE International Conference on Electro Information Technology (EIT)最新文献_第2页

Deep Learning optimization in remote sensing image segmentation using dilated convolutions and ShuffleNet 利用扩展卷积和ShuffleNet进行遥感图像分割的深度学习优化

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491910

Rahul Gomes, Papia F. Rozario, Nishan Adhikari

引用次数: 14

Mobile Device Threat: Malware 移动设备威胁:恶意软件

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491845

Nikolay Atanassov, M. Chowdhury

引用次数: 9

Self-Healing of Position Offset Error in Non-Salient PMSMs 非显著性永磁同步电机位置偏移误差的自修复

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491911

S. Kuruppu

引用次数: 0

Identify protein disorder from amino acid sequences with Machine learning 用机器学习从氨基酸序列中识别蛋白质紊乱

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491847

Shrinath Iyer

{"title":"Identify protein disorder from amino acid sequences with Machine learning","authors":"Shrinath Iyer","doi":"10.1109/EIT51626.2021.9491847","DOIUrl":"https://doi.org/10.1109/EIT51626.2021.9491847","url":null,"abstract":"Intrinsic protein disorder can predict a whole host of neurodegenerative diseases like Alzheimer's. Predicting protein disorder itself is best undertaken using computational methods. In this paper, a novel approach to predicting protein disorder using a Convolutional Neural Network (CNN) algorithm. The algorithm had found a 92% auc-roc score, which indicates the performance of a binary classification model. To extract features, the model had used OneHotEncoding, a technique that converts the sequential data into numerical values that is fed into the model. The data was also gathered from a variety of sources including the Protein Data Bank (PDB), The Disordered Protein Database (Disprot), and the Swiss protein database (Swissprot) [1]-[3]. This paper has a unique approach in the model built and distinguished itself from prior models based on the structure of the model and feature extraction that was performed. Using a tensorflow framework, the model used multiple convolutional layers of varying filter length and a final dense layer to enable the model to learn the features and predict associated outputs. Furthermore, the output from the initial stages was fed into a binary cross entropy classifier that gave the resulting judgement of order or disorder.","PeriodicalId":162816,"journal":{"name":"2021 IEEE International Conference on Electro Information Technology (EIT)","volume":"305 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131925847","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

Development Of A Flexible Printed Battery 柔性印刷电池的研制

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491880

J. Crosby, D. Maddipatla, Qingliu Wu, B. Bazuin, Matthew Stoops, M. Atashbar

引用次数: 0

What is a Digital Twin – A Mediation Approach 什么是数字孪生——一种调解方法

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491883

Ulrich Dahmen, J. Rossmann

{"title":"What is a Digital Twin – A Mediation Approach","authors":"Ulrich Dahmen, J. Rossmann","doi":"10.1109/EIT51626.2021.9491883","DOIUrl":"https://doi.org/10.1109/EIT51626.2021.9491883","url":null,"abstract":"In the context of digitalization, which has been developing rapidly since the early 2010s, and the related development of the Internet of Things, which enables the production of virtually controlled and networked products of all kinds with integrated services, one term is gaining strongly in importance - the digital twin. It is increasingly becoming an integral part of the development and operation of technical systems. The principle vividness of the term \"twin\" is an essential part of its success, but at the same time it is also the core of a problem that cannot be ignored. Since no special prior knowledge is required to have a basic understanding or at least a vague idea of a digital twin, the term works excellently at the marketing level. However, when it comes to concrete technical use, it turns out that there is still no precise understanding of it. The definitions that can be found differ significantly in some cases. This is related to the fact that the large number of application areas have led to different requirements for the digital twin. For example, in some cases the digital twin of a real machine is supposed to replicate the state of the machine as accurately as possible, in other cases it is supposed to extend the machine with digital services. In order to be able to use the digital twin as a key technology in the long term, the parallel developments must now be gradually brought together again. One hurdle here is the lack of a uniform linguistic basis. This paper presents an approach to address this problem and to facilitate the exchange of concepts around the digital twin.","PeriodicalId":162816,"journal":{"name":"2021 IEEE International Conference on Electro Information Technology (EIT)","volume":"12 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113938492","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

Optimal Allocation of EV Charging Stations in Distribution Systems Considering Discharging Economy and System Reliability 考虑放电经济性和系统可靠性的配电网电动汽车充电站优化配置

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491926

Md Shahin Alam, S. A. Arefifar

{"title":"Optimal Allocation of EV Charging Stations in Distribution Systems Considering Discharging Economy and System Reliability","authors":"Md Shahin Alam, S. A. Arefifar","doi":"10.1109/EIT51626.2021.9491926","DOIUrl":"https://doi.org/10.1109/EIT51626.2021.9491926","url":null,"abstract":"Electric vehicles have the potential to improve power distribution system’s operational performance. Integration of EV charging stations could have positive impacts on system losses and environmental emissions. This research presents a methodology to install different capacities of electric vehicle charging stations inside microgrids, integrated in distribution systems. The economic operation of electric vehicles in microgrids has been investigated in terms of system operational costs, system losses, and system environmental emissions. Electric vehicles’ uncertain behaviors creates a level of uncertainty, thus, the system reliability due to electric vehicle operation is also assessed in this research. A well-known PG&E 69-bus distribution system is chosen for simulations and case studies. Since microgrids have different distributed energy resources along with electric vehicles, which may generate uncertainty for distribution system operations, this research considers uncertain calculation modeling to get more accurate results in simulations and case studies. Sensitivity analysis has been performed for demand and price uncertainties and results are presented for corresponding system performances. The results validate the proposed methodology for an efficient operation of electric vehicles in microgrids integrated distribution systems.","PeriodicalId":162816,"journal":{"name":"2021 IEEE International Conference on Electro Information Technology (EIT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115404301","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

Generalization of Data Reliability Metric (DReM) Mechanism for Pulsatile Bio-signals 脉冲生物信号数据可靠性度量(DReM)机制的推广

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491839

Md Sabbir Zaman, B. Morshed

{"title":"Generalization of Data Reliability Metric (DReM) Mechanism for Pulsatile Bio-signals","authors":"Md Sabbir Zaman, B. Morshed","doi":"10.1109/EIT51626.2021.9491839","DOIUrl":"https://doi.org/10.1109/EIT51626.2021.9491839","url":null,"abstract":"Due to rapid development of wearable technologies used for health monitoring, a robust data reliability assessment technique is required. Choosing the right sets of Data reliability metrics (DReM) can improve the performance of data reliability assessment and thus, it can help in reliable data acquisitions of bio-signals. Traditional reliability assessment techniques rely significantly on the signal specific peak detection algorithms. It impedes the endeavor of generalizing signal independent data reliability assessment techniques. In this work, we explored nine signal independent statistical candidates for DReM and finalized five top features as our DReMs which can identify acceptable signal segments from unacceptable segments with 0.83 precision, 0.84 recall and 0.83 F-1 score on accurately identifying acceptable pulses (ECG, PPG and respiratory signal). Additionally, the five DReMs are capable of detecting unacceptable signal segments with 0.92 precision, 0.92 recall and 0.92 F-1 score. We proposed optimal Random Forest classifier model with excellent Receiver Operating Characteristics (ROC) with significant Area Under the Curve (AUC) value of 0.994.","PeriodicalId":162816,"journal":{"name":"2021 IEEE International Conference on Electro Information Technology (EIT)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126213570","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

3D Object Detection and Tracking Using Monocular Camera in CARLA 基于单目相机的三维目标检测与跟踪

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491905

Yanyu Zhang, Jiahao Song, Shuwei Li

引用次数: 0

Plant Species Image Recognition using Artificial Intelligence on Jetson Nano Computational Platform 基于Jetson纳米计算平台的人工智能植物物种图像识别

2021 IEEE International Conference on Electro Information Technology (EIT) Pub Date : 2021-05-14 DOI: 10.1109/EIT51626.2021.9491893

Shruti Chavan, John Ford, Xinrui Yu, J. Saniie

引用次数: 4