2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)最新文献_第8页

Demand Charges Minimization for Ontario Class-A Customers Based on the Optimization of Energy Storage System 基于储能系统优化的安大略省a类客户需求费用最小化

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255750

Abdeslem Kadri, F. Mohammadi

{"title":"Demand Charges Minimization for Ontario Class-A Customers Based on the Optimization of Energy Storage System","authors":"Abdeslem Kadri, F. Mohammadi","doi":"10.1109/CCECE47787.2020.9255750","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255750","url":null,"abstract":"Demand charges (DC) is one of the major utility charges especially in the case of large electricity customers. The Energy Storage System (ESS) can be optimized to minimize these charges. For instance, a large consumer can minimize his DC by incorporating an ESS that charges during his low-consuming hours and discharges during his high-consuming hours. In other words, the ESS can shave the high peak powers of the customer's load profile to ensure lower DC. In this way, the large electricity customer will be able to save a big portion of his/her energy bill of which the DC is a part. This paper presents an optimization formulation for the sizing and scheduling of the ESS to minimize the energy monthly bill through the minimization of DC. Based on the market regulations of Ontario (Canada), this study investigates the potential of using the ESS for DC minimization based on real data for a large class-A Canadian electricity customer in Ontario. The results demonstrate the effectiveness of the proposed ESS deployment algorithm in minimizing the overall energy bills of the class-A customer.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128024811","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: 3

Extraction of Fetal ECG Signal with Ectopic Beats using Blind Source Separation Based Null Space Approach 基于零空间盲源分离的胎儿异位心电信号提取

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255816

L. Taha, E. Abdel-Raheem

引用次数: 0

Automation of Thermal Energy Storage in Homes Using Artificial Neural Networks 利用人工神经网络实现家庭热能储存自动化

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255680

B. Venkatesh

引用次数: 1

Supporting Delay-Sensitive IoT Applications: A Machine Learning Approach 支持延迟敏感物联网应用:一种机器学习方法

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255800

A. Alnoman

引用次数: 4

Determination of SWIR Features for Noninvasive Glucose Monitoring Using Machine Learning 利用机器学习确定无创血糖监测的SWIR特征

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255775

Khoa Nguyen, A. Dinh, F. Bui

{"title":"Determination of SWIR Features for Noninvasive Glucose Monitoring Using Machine Learning","authors":"Khoa Nguyen, A. Dinh, F. Bui","doi":"10.1109/CCECE47787.2020.9255775","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255775","url":null,"abstract":"The use of infrared (IR) light for noninvasive glucose monitoring is a potential solution to reduce infection-related mortality rate for diabetic patients. However, IR spans a wide band and the relevant wavelengths need to be chosen. This paper presents an automated and computationally efficient model, capable of examining a large number of wavelengths, to determine the suitable ones for monitoring, based on feature selection and other machine learning techniques. The study examined wavelengths from 1300-2600nm which cover the majority of short-wave infrared (SWIR) band. For reliable ground truth, two datasets, D1 and D2, were used with 100 observations and 1000 observations respectively. In term of optimal performance with limited time and computational resources, Sequential Forward Floating Selection (SFFS) technique was chosen as a core feature selection algorithm due to its high accuracy and reasonable speed. Classifiers based on Support Vector Machine (SVM) were used to evaluate the performance of selected wavelengths. Principal Component Analysis (PCA) was used to enhance the accuracy. Pipeline and nested cross-validation techniques were adopted to prevent information leakage and biased results. The proposed approach managed to reduce the number of wavelengths by 65% for D1 and 58% for D2 while achieving accuracy and f1 score above 90%, which are 10% higher compared to other work in the literature. The feature selection results also suggest that suitable wavelengths fall in the range 1600–2600 nm.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123930459","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

Stereo Matching Optimization with Multi-baseline Trinocular Camera Model 多基线三联眼相机模型立体匹配优化

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255786

Jie Wang, Chenglei Peng, Ming Li, Xudong Chen, S. Du, Yang Li

引用次数: 6

Toward Smart Internet of Things (IoT) Devices: Exploring the Regions of Interest for Recognition of Facial Expressions using Eye-gaze Tracking 迈向智能物联网(IoT)设备:探索使用眼球追踪识别面部表情的兴趣区域

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255696

Abdallah S. Abdallah, L. Elliott, Daniel Donley

{"title":"Toward Smart Internet of Things (IoT) Devices: Exploring the Regions of Interest for Recognition of Facial Expressions using Eye-gaze Tracking","authors":"Abdallah S. Abdallah, L. Elliott, Daniel Donley","doi":"10.1109/CCECE47787.2020.9255696","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255696","url":null,"abstract":"A significant portion of the internet of things (IoT) devices will become reliable products in our daily life if and only if they are equipped with strong human computer interaction (HCI) technologies, specifically visual interaction with users through affective computing. One of the major challenges faced in affective computing is recognizing facial expressions and the true emotions behind them. Despite numerous studies performed, current detection systems are ineffective at correctly identifying facial expressions with reliable accuracy, especially in case of negative expressions. Several research projects attempted to extract the recognition process that humans follow to identify facial expressions in order to replicate in smart machines without a significant success. This paper describes our interdisciplinary project whose goal is to extract and define the recognition process that humans follow when identifying the facial expressions of others. We monitor this process by identifying and analyzing the regions of interest participants look at when they are shown static emotions samples under a specific experimental setup. This paper reports the current status of data collection, experimental setup, and initial data visualization.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114684897","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: 3

Robotic Sanding of Wooden Bowls with Hybrid Force/Position Impedance Control 基于混合力/位置阻抗控制的木碗机器人磨砂

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255797

S. Sudhagar, B. Surgenor, K. Hashtrudi-Zaad

引用次数: 3

A Study of Jacobi-Fourier Moments via Image Reconstruction 基于图像重构的雅可比-傅立叶矩研究

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255726

Yubing Liang, S. Liao

引用次数: 1

Conditional Probabilistic Relative Visual Localization for Unmanned Aerial Vehicles 无人机条件概率相对视觉定位

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255691

Andy Couturier, M. Akhloufi

{"title":"Conditional Probabilistic Relative Visual Localization for Unmanned Aerial Vehicles","authors":"Andy Couturier, M. Akhloufi","doi":"10.1109/CCECE47787.2020.9255691","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255691","url":null,"abstract":"Unmanned aerial vehicles (UAV) are now used for a large number of applications in everyday life. These applications require autonomous navigation which is enabled by the self-localization solution integrated to the UAV. To perform self-localization, most UAVs are relying on a series of sensors combined with a global navigation satellite system (GNSS) in a sensor fusion framework. However, GNSS are using radio signals which are subjected to a large range of outages and interferences. This paper presents a relative visual localization (RVL) approach for GPS-denied environments using a down-facing 2D monocular camera and an inertial measurement unit (IMU). The solution is embedded in an adapted particle filter and use feature points to match images and estimate the localization of the UAV. A new conditional RVL measure is developed in order to leverage spare computation resources available during the data collection when the UAV is still receiving a GNSS signal. An evaluation of six feature point extraction methods is performed using real-world data while varying the number of feature points extracted. The results are promising and the approach has shown to be more efficient and to have fewer limitations than similar approaches in the literature.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114997184","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: 3