2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)最新文献_第9页

Multi-Agent Reinforcement Learning for the Energy Optimization of Cyber-Physical Production Systems 面向信息物理生产系统能量优化的多智能体强化学习

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255795

Jupiter Bakakeu, Dominik Kißkalt, J. Franke, S. Baer, H. Klos, J. Peschke

{"title":"Multi-Agent Reinforcement Learning for the Energy Optimization of Cyber-Physical Production Systems","authors":"Jupiter Bakakeu, Dominik Kißkalt, J. Franke, S. Baer, H. Klos, J. Peschke","doi":"10.1109/CCECE47787.2020.9255795","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255795","url":null,"abstract":"The paper proposes an artificial intelligence-based solution for the efficient operation of a heterogeneous cluster of flexible manufacturing machines with energy generation and storage capabilities in an electricity micro-grid featuring high volatility of electricity prices. The problem of finding the optimal control policy is first formulated as a game-theoretic sequential decision-making problem under uncertainty, where at every time step the uncertainty is characterized by future weather-dependent energy prices, high demand fluctuation, as well as random unexpected disturbances on the factory floor. Because of the parallel interaction of the machines with the grid, the local viewpoints of an agent are non-stationary and non-Markovian. Therefore, traditional methods such as standard reinforcement learning approaches that learn a specialized policy for a single machine are not applicable. To address this problem, we propose a multi-agent actor-critic method that takes into account the policies of other participants to achieve explicit coordination between a large numbers of actors. We show the strength of our approach in mixed cooperative and competitive scenarios where different production machines were able to discover different coordination strategies in order to increase the energy efficiency of the whole factory floor.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"198 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":"115492062","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: 7

Dynamic Group Trip Planning Queries in Spatial Databases 空间数据库中的动态团体旅行计划查询

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255682

Farhana Aklam, W. Osborn

引用次数: 0

Grid-Connected Low-Voltage Power Supply to Equipment on Transmission Line Structures 输电线结构设备并网低压供电

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255707

H. Ahmadi, M. Armstrong

{"title":"Grid-Connected Low-Voltage Power Supply to Equipment on Transmission Line Structures","authors":"H. Ahmadi, M. Armstrong","doi":"10.1109/CCECE47787.2020.9255707","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255707","url":null,"abstract":"Electrical equipment installed on high-voltage (HV) transmission structures may require low-voltage (LV) electrical supply from the distribution network. For example, cell sites for communication antennas and warning lights are the most common applications in BC Hydro's system. Bringing the LV supply to the HV structures introduces a number of electrical concerns. The first concern is the transfer of ground potential rise (GPR) from the HV system to the LV system during a ground fault on the transmission structure. The second concern is the induction in the LV system due to the proximity to the HV transmission line. In addition, there could be system impacts that require special attention, such as reduction in circuit-to-circuit separation in multiple-circuit corridors, pole fire on the LV wood poles, etc. This paper discusses technical solutions to mitigate the identified concerns and system impacts. Amongst the possible recommendations, addition of appropriately rated isolation transformers to the LV feeder and improving the electrical grounding on the HV transmission structure are shown to be the most effective methods for preventing the transfer of hazardous potentials to the customers connected to the same LV feeder. The proposed isolation circuit has been tested in a HV laboratory to confirm its effectiveness.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"31 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":"125020368","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

Modified ESTOI for improving speech intelligibility prediction 改进的ESTOI用于提高语音可理解性预测

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255677

Ahmed Alghamdi, W. Chan

引用次数: 1

CloudMach: Cloud Computing Application Performance Improvement through Machine Learning CloudMach:通过机器学习提高云计算应用性能

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255686

Mohamed Abu Sharkh, Yong Xu, Eric Leyder

{"title":"CloudMach: Cloud Computing Application Performance Improvement through Machine Learning","authors":"Mohamed Abu Sharkh, Yong Xu, Eric Leyder","doi":"10.1109/CCECE47787.2020.9255686","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255686","url":null,"abstract":"Cloud computing is rapidly becoming the standard through which enterprises of all sizes fulfill their computing infrastructure demands. This work aims at exploring the impact that machine learning algorithms can have on Cloud application behavior profiling and prediction. Although classic machine learning algorithms have been used in Cloud Computing context before, cutting-edge algorithms like deep learning (DL) and reinforcement learning (RL) are yet to be convincingly exploited for this specific problem. Despite being a revelation with fields like image processing and speech recognition, these algorithms (deep neural networks for instance) face adoption challenges outside certain topics. There is a high demand for timely research work that dissects these algorithms and develops novel techniques to facilitate seamless adoption for Cloud providers and clients. In this work, we evaluate the efficiency of machine learning algorithms in the Cloud context by applying them to a large scale application resource utilization data set (TU Delft Bitbrains traces). The objective is to design a Cloud application behavior prediction technique based on machine learning predictors. Any improvement on prediction precision has direct impact on key performance indicators for both Cloud providers and Cloud tenants/clients. Experimental results show the potential of our approach to improve Cloud resource scheduling in a Cloud data center.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"1 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":"128721716","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

Mirrored and Rotated Letters in Children Spellings: An Automatic Analysis Approach 儿童拼写中的镜像和旋转字母:一种自动分析方法

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255765

J. Mombach, Cristiane B. R. Ferreira, J. P. Félix, R. Salvini, Fabrízzio Soares

引用次数: 0

Foreign Artifacts Detection on Pediatric Chest X-Ray 小儿胸片异物检测

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255768

Afonso U. Fonseca, Leandro L. G. Oliveira, J. Mombach, D. Fernandes, R. Salvini, Fabrízzio Soares

{"title":"Foreign Artifacts Detection on Pediatric Chest X-Ray","authors":"Afonso U. Fonseca, Leandro L. G. Oliveira, J. Mombach, D. Fernandes, R. Salvini, Fabrízzio Soares","doi":"10.1109/CCECE47787.2020.9255768","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255768","url":null,"abstract":"Chest radiography is one of the recommended imaging tests by the World Health Organization for childhood pneumonia diagnosis. In computer-aided diagnostic systems where radiography is the main input, its quality is crucial. The presence of foreign artifacts can, therefore, compromise the performance of these systems. In the radiography exam, foreign artifacts are very common, especially in children, due to the ingestion of objects and the need for immobilization of these patients by third parties. Identification tags, shirt buttons, catheters, tubes and in conventional scanned radiographs, fingerprints, tags, noise and inadequate brightness are some of the artifacts present. In this study, we present an efficient and very simple method for detecting and removing artifacts based on common digital image processing operations such as channel subtraction, edge detection, and morphological operations. We describe the proposed method and evaluate its performance in a database of 200 images. We show that it is robust to identify different types of artifacts regardless of their positions on the radiography. A visual inspection was used to measure the errors and the experimental results showed an accuracy of 0.98 and a processing time of about 375ms per image. As a result of this, the method demonstrates to be a very promising pre-processing tool.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"321 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":"116430146","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

Automatic QRS Detection and Segmentation Using Short Time Fourier Transform and Feature Fusion 基于短时傅里叶变换和特征融合的QRS自动检测与分割

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255676

A. Biran, A. Jeremic

引用次数: 3

Explainable Deep Learning for Referable Diabetic Retinopathy 可解释的深度学习治疗糖尿病视网膜病变

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255773

Mohamed Chetoui, Andy Couturier, M. Akhloufi

引用次数: 0

Visual Detection of Productive Crop and Pasture Fields from Aerial Image Analysis 基于航拍图像分析的生产作物和牧场视觉检测

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255827

G. S. Vieira, B. M. Rocha, H. Pedrini, N. M. Sousa, J. C. Lima, R. M. Costa, Fabrízzio Soares

{"title":"Visual Detection of Productive Crop and Pasture Fields from Aerial Image Analysis","authors":"G. S. Vieira, B. M. Rocha, H. Pedrini, N. M. Sousa, J. C. Lima, R. M. Costa, Fabrízzio Soares","doi":"10.1109/CCECE47787.2020.9255827","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255827","url":null,"abstract":"The use of unmanned aerial vehicles (UAV) is expanding rapidly throughout the world. Nowadays, it is common to find some useful applications that use them in both urban and rural environments. Especially in the second case, the UAV is promoting significant changes in traditional agricultural activities. Thus, current technologies have been incorporated into inspection, surveillance, and agricultural management. In this study, we investigated some practical uses of aerial images in rural areas. A new method for allowing a UAV to understand and interpret visual information from static imagery is presented. Tree detection and shadow segmentation are essential requirements for navigation and visual examination purposes. Therefore, our method deals with these tasks to be a starting point to enable a machine to perform visual inspections in production fields. The proposed method uses computer vision techniques such as visual color enhancement, morphological operations, and segmentation approaches. We performed an evaluation of our system based on a dataset with different types of crop areas and pasture lands. Moreover, we assessed our approach to verify tree canopy and shadow detection. We also verified delineating agricultural fields, and segmentation of sunlight exposed vegetation, as well as vegetation areas covered by shadows. Results demonstrate that our approach provides an exciting and robust approach to be adopted in the field.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"9 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":"130336738","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