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2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)最新文献

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ETX: A Flexible Simulation Framework for Design of Transactive Energy Systems ETX:一种用于交互能源系统设计的灵活仿真框架
2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861523
Shida Zhang, Daniel May, Peter Atrazhev, M. Gül, A. Leach, Timothy M. Weis, P. Musílek
{"title":"ETX: A Flexible Simulation Framework for Design of Transactive Energy Systems","authors":"Shida Zhang, Daniel May, Peter Atrazhev, M. Gül, A. Leach, Timothy M. Weis, P. Musílek","doi":"10.1109/CCECE.2019.8861523","DOIUrl":"https://doi.org/10.1109/CCECE.2019.8861523","url":null,"abstract":"The rapid growth in the installations of small scale, distributed energy resources in the recent years is astonishing. However, these installations are putting strain on the grid. This paper introduces the ETX simulation framework that allows for coordinated, repeatable studies of interactions between market, operations, and regulations. In addition, ETX is an excellent environment for artificial intelligence (AI) research that allows effective simulation of (AI)-based agents. This is crucial as the grid becomes more dynamic and complex, precluding direct human involvement in energy trading and management. Finally, through modeling behavior of individual participants in transactive energy systems, ETX provides quantitative comparisons instrumental for design of future electricity markets.","PeriodicalId":352860,"journal":{"name":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124882687","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
Defect Detection from X-Ray Images Using A Three-Stage Deep Learning Algorithm 基于三阶段深度学习算法的x射线图像缺陷检测
2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861944
Jing Ren, Rui Ren, Mark Green, Xishi Huang
{"title":"Defect Detection from X-Ray Images Using A Three-Stage Deep Learning Algorithm","authors":"Jing Ren, Rui Ren, Mark Green, Xishi Huang","doi":"10.1109/CCECE.2019.8861944","DOIUrl":"https://doi.org/10.1109/CCECE.2019.8861944","url":null,"abstract":"Defect detection is a crucial step in the process of manufacturing auto parts such as engines. Air bubbles are common defects in the engine which may result in engine failure leading to the breakdown of the car or even catastrophic accidents. Currently, X-ray images are used for air bubbles detection which adds complexity to the detection task due to the overlay of defects with complex engine 3D structures in 2D X-ray images. In this paper, we propose a three-stage deep learning algorithm to learn various patterns of the bubbles in engines. We then test the algorithm using normal and defected images. The results show that the proposed deep learning method can accurately identify bubbles in the X-ray engine images. This deep learning technique can also be extended to detect other surface level defects such scratches, missing components and physical damage. In this paper, we report that the accuracy of our defect detection method is above 90%.","PeriodicalId":352860,"journal":{"name":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122461828","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: 14
Neural Network Model for False Data Detection in Power System State Estimation 电力系统状态估计中假数据检测的神经网络模型
2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861919
Adel Tabakhpour, M. Abdelaziz
{"title":"Neural Network Model for False Data Detection in Power System State Estimation","authors":"Adel Tabakhpour, M. Abdelaziz","doi":"10.1109/CCECE.2019.8861919","DOIUrl":"https://doi.org/10.1109/CCECE.2019.8861919","url":null,"abstract":"False data injection is an on-going concern facing power system state estimation. In this work, a neural network is trained to detect the existence of false data in measurements. The proposed approach can make use of historical data, if available, by using them in the training sets of the proposed neural network model. However, the inputs of perceptron model in this work are the residual elements from the state estimation, which are highly correlated. Therefore, their dimension could be reduced by preserving the most informative features from the inputs. To this end, principal component analysis is used (i.e., a data preprocessing technique). This technique is especially efficient for highly correlated data sets, which is the case in power system measurements. The results of different perceptron models that are proposed for detection, are compared to a simple perceptron that produces identical result to the outlier detection scheme. For generating the training sets, state estimation was run for different false data on different measurements in 13-bus IEEE test system, and the residuals are saved as inputs of training sets. The testing results of the trained network show its good performance in detection of false data in measurements.","PeriodicalId":352860,"journal":{"name":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121542609","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: 6
Development of a GF(2)Math Coprocessor GF(2)数学协处理器的研制
2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861747
R. Tervo
{"title":"Development of a GF(2)Math Coprocessor","authors":"R. Tervo","doi":"10.1109/CCECE.2019.8861747","DOIUrl":"https://doi.org/10.1109/CCECE.2019.8861747","url":null,"abstract":"The mathematics of Galois fields GF(2) and the extension fields GF(2m) underpin applications in error control coding and cryptology yet these binary operations are not directly supported in most digital computer instruction sets. A set of hardware blocks to perform low level GF(2) operations as a math coprocessor is described. The circuits were elaborated in VHDL and integrated into an FPGA-based soft processor (Nios II). Performance tests and detailed timing measurements are reported for typical GF(2) calculations.","PeriodicalId":352860,"journal":{"name":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121701852","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
Interference Analysis for LoRa Chirp Spread Spectrum Signals LoRa调频扩频信号的干扰分析
2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861956
B. Dunlop, Ha H. Nguyen, Robert Barton, Jérôme Henry
{"title":"Interference Analysis for LoRa Chirp Spread Spectrum Signals","authors":"B. Dunlop, Ha H. Nguyen, Robert Barton, Jérôme Henry","doi":"10.1109/CCECE.2019.8861956","DOIUrl":"https://doi.org/10.1109/CCECE.2019.8861956","url":null,"abstract":"LoRa modulation is a modulation scheme patented by SemTech that uses chirp spread spectrum (CSS) signals to modulate data. This paper analyzes the interference between those signals for different spreading factors (SFs) and bandwidths (BWs). First, principles and properties of LoRa modulation and demodulation in discrete-time domain are presented, followed by theoretical analysis of the interference. Extensive simulation was performed to characterize the effect of interference on both detection signal-to-interference ratio (SIR) and the bit-error rate (BER) performance of the desired signals. Results demonstrate that the effect of interference is very serious if the desired and interfering signals have the same chirp rate (even though they might have different spreading factors and bandwidths), while it can be practically ignored for all other cases.","PeriodicalId":352860,"journal":{"name":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122070171","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: 12
Assessment of Photovoltaic Hosting Capacity of Existing Distribution Circuits 现有配电线路的光伏承载能力评估
2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861957
Mohammed Al-Saffar, Steven Zhang, A. Nassif, P. Musílek
{"title":"Assessment of Photovoltaic Hosting Capacity of Existing Distribution Circuits","authors":"Mohammed Al-Saffar, Steven Zhang, A. Nassif, P. Musílek","doi":"10.1109/CCECE.2019.8861957","DOIUrl":"https://doi.org/10.1109/CCECE.2019.8861957","url":null,"abstract":"Government initiatives have been mandating the increased integration of distributed energy resources (DERs), such as residential and commercial photovoltaic (PV) systems. However, high levels of PV penetration can negatively affect the operations of the distribution system, particularly during mid-day low demand periods. It is of vital importance to gain more understanding of the system and to prepare mitigation plans before the amount of PV installations reaches a critical level. Therefore, properly assessing the PV hosting capacity is necessary.In this paper, the hosting capacities of three real circuits in Alberta, Canada are evaluated using Monte Carlo simulation-based probabilistic power flow (MCS-based PPF) method. The examined circuits cover the cities of Fort McMurray, Lloydminster, and Drumheller. These areas represent circuits of different sizes and complexities. The hosting capacities of the three regions were determined to be 10%, 60%, and 70%, respectively. Buses impacted by PV penetration were found in all three distribution networks. Factors influencing the PV hosting capacity are also identified and analyzed.","PeriodicalId":352860,"journal":{"name":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122097438","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
Automated Identification and Localization of Premature Ventricle Contractions in Standard 12-Lead ECGs 标准12导联心电图中室性早搏的自动识别和定位
2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861527
A. Pereira, P. V. Dam, R. Abächerli
{"title":"Automated Identification and Localization of Premature Ventricle Contractions in Standard 12-Lead ECGs","authors":"A. Pereira, P. V. Dam, R. Abächerli","doi":"10.1109/CCECE.2019.8861527","DOIUrl":"https://doi.org/10.1109/CCECE.2019.8861527","url":null,"abstract":"It can take up to twelve hours to identify and precisely localize the origin of a premature ventricle contraction (PVC).This work is investigating a neural network (NN) as an automated alternative to a human expert for detecting and locating the arrhythmogenic zone—with the goal of accelerating the PVC detection process. The proposed shallow neural network contains one hidden layer with multiple hidden units. Three data sets consisting of a total of 328 samples of 12 lead resting ECGs were used to train as well as to evaluate the NN. After performing several iteration tests with different training sets, the most promising configuration was established. The first cohort consisted of a ratio of 1:1, the second cohort of a ratio of 25:4 (NO PVC:PVC).The study has resulted in high sensitivity and specificity values in NN’s performance given uniformly distributed training data. The proposed NN was shown to perform at a level comparable to that of a human expert.","PeriodicalId":352860,"journal":{"name":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123567935","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
Neural Network Music Genre Classification 神经网络音乐类型分类
2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861555
Nikki Pelchat, Craig M. Gelowitz
{"title":"Neural Network Music Genre Classification","authors":"Nikki Pelchat, Craig M. Gelowitz","doi":"10.1109/CCECE.2019.8861555","DOIUrl":"https://doi.org/10.1109/CCECE.2019.8861555","url":null,"abstract":"Music genre classification utilizing neural networks has achieved some limited success in recent years. Differences in song libraries, machine learning techniques, input formats, and types of neural networks implemented have all had varying levels of success. This paper reviews some of the machine learning techniques utilized in this area. It also presents some initial research work on music genre classification. The research uses images of spectrograms generated from time-slices of songs as the input into a neural network to classify the songs into their respective musical genres.","PeriodicalId":352860,"journal":{"name":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131475145","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: 35
A Passive Optical Transmitter Using LC Switches for IoT Smart Dusts 一种用于物联网智能粉尘的LC开关无源光发射器
2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861513
Jingjing Liu, Riqing Chen, Yuan Ren, Zhong-Ting Xu, G. Hu, Jun Pan, Chunlai Li, Jin He
{"title":"A Passive Optical Transmitter Using LC Switches for IoT Smart Dusts","authors":"Jingjing Liu, Riqing Chen, Yuan Ren, Zhong-Ting Xu, G. Hu, Jun Pan, Chunlai Li, Jin He","doi":"10.1109/CCECE.2019.8861513","DOIUrl":"https://doi.org/10.1109/CCECE.2019.8861513","url":null,"abstract":"Using optical wireless communications for sensors enables low power and small size of the nodes, such as smart dusts. This paper proposes a passive optical transmitter for smart dusts. Two liquid crystal (LC) modulator circuits have been designed for the switching the liquid crystal cells in the transmitter. The circuits are fabricated using a standard $0.18mu mathrm{m}$ CMOS process. Measurement results show that they can work under 0. 5V supply and successfully do the switching as designed. The transmission data rate can be 10bps, which is limited by the response speed of the LC cell.","PeriodicalId":352860,"journal":{"name":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126329244","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
Hardware Accelerator for Nuclei Detection in Histopathology Images 组织病理学图像中核检测的硬件加速器
2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861896
Raju Machupalli, Haonan Zhou, M. Mandal
{"title":"Hardware Accelerator for Nuclei Detection in Histopathology Images","authors":"Raju Machupalli, Haonan Zhou, M. Mandal","doi":"10.1109/CCECE.2019.8861896","DOIUrl":"https://doi.org/10.1109/CCECE.2019.8861896","url":null,"abstract":"In pathology based Computer Aided Diagnosis, accurate detection of cell nuclei is an important step. Accurate and efficient detection of cell nuclei in high resolution histopathological images is a highly compute intensive task. Hence, nuclei detection algorithms take a significant amount of processing time on general purpose processors. To reduce the processing time and assist the doctors in real-time, a special hardware accelerator which can process the complex computational tasks in parallel can be helpful. In this paper, we propose an FPGA based accelerator architecture for nuclei detection in Whole Slide Images using generalized Laplacian of Gaussian filters. The experimental results show that the implemented architecture provides a significant improvement in processing time without losing detection accuracy.","PeriodicalId":352860,"journal":{"name":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131676450","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
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