Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255815
E. N. Sadjadi, M. Ebrahimi, Zahra Gachloo
The structure of fuzzy model impacts how well it approximates the nonlinear function, and how many rules are required to gain the desired accuracy. The most of the earlier works rely on diminishing the higher derivation of the fuzzy model in front of the higher derivatives of the real system. However, the smooth compositions are m-time differentiable and will not diminish. This has motivated to derive the relation of required fuzzy rules with the arbitrary accuracy for function approximation through the smooth fuzzy model. The originality of the work is that the approximation error and the number of required fuzzy rules in this paper, rely on the structure of the fuzzy model and the involved s-t compositions, beside the nonlinear properties of the real plant, through a reliable mathematical formulation. Hence, we have presented a prediction-correction algorithm to include all the main factors. It is proved that number of the required rules are lower than those of the earlier works to gain the same level of model accuracy.
{"title":"Discussion on Accuracy of Approximation with Smooth Fuzzy Models","authors":"E. N. Sadjadi, M. Ebrahimi, Zahra Gachloo","doi":"10.1109/CCECE47787.2020.9255815","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255815","url":null,"abstract":"The structure of fuzzy model impacts how well it approximates the nonlinear function, and how many rules are required to gain the desired accuracy. The most of the earlier works rely on diminishing the higher derivation of the fuzzy model in front of the higher derivatives of the real system. However, the smooth compositions are m-time differentiable and will not diminish. This has motivated to derive the relation of required fuzzy rules with the arbitrary accuracy for function approximation through the smooth fuzzy model. The originality of the work is that the approximation error and the number of required fuzzy rules in this paper, rely on the structure of the fuzzy model and the involved s-t compositions, beside the nonlinear properties of the real plant, through a reliable mathematical formulation. Hence, we have presented a prediction-correction algorithm to include all the main factors. It is proved that number of the required rules are lower than those of the earlier works to gain the same level of model accuracy.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"12 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131687992","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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255780
Mark Lipski, M. James, P. Spachos, S. Gregori
Low-power data-acquisition systems are instrumental in meeting the growing demand for Internet-of-things applications. Activity-aware wake-up circuits reduce power consumption by detecting activity in the analog domain and intelligently feeding that information to digital control systems. This paper investigates implementations of low-power audio systems with activity-aware wake-up and discrete components. Experiments are run to demonstrate the functionality of the wake-up function and estimate the power savings.
{"title":"Low Power Data Acquisition System for Noise Pollution Monitoring","authors":"Mark Lipski, M. James, P. Spachos, S. Gregori","doi":"10.1109/CCECE47787.2020.9255780","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255780","url":null,"abstract":"Low-power data-acquisition systems are instrumental in meeting the growing demand for Internet-of-things applications. Activity-aware wake-up circuits reduce power consumption by detecting activity in the analog domain and intelligently feeding that information to digital control systems. This paper investigates implementations of low-power audio systems with activity-aware wake-up and discrete components. Experiments are run to demonstrate the functionality of the wake-up function and estimate the power savings.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"144 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":"133895550","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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255705
J. Makaran
The following paper proposes a method for performing all facets of EMC Testing at temperatures other than ambient. An examination of operational requirements of electronic assemblies is presented through an examination of operating temperature requirements for electronic assemblies from different industrial sectors. This is followed by examination of the requirements of EMC specifications, followed by a proposal of the specifications required for an ideal device to perform EMC testing at temperatures other than ambient.
{"title":"EMC Testing at Temperatures Other than Ambient","authors":"J. Makaran","doi":"10.1109/CCECE47787.2020.9255705","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255705","url":null,"abstract":"The following paper proposes a method for performing all facets of EMC Testing at temperatures other than ambient. An examination of operational requirements of electronic assemblies is presented through an examination of operating temperature requirements for electronic assemblies from different industrial sectors. This is followed by examination of the requirements of EMC specifications, followed by a proposal of the specifications required for an ideal device to perform EMC testing at temperatures other than ambient.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"75 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":"133349445","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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255718
Yunfei Bai, A. Rajapakse
To provide more clean energy and satisfy the increasing power demand, microgrids using renewable energy sources (RES) are designed as additional power supplies. Recently, DC microgrids (DCMGs) have gained the attention for their higher power efficiency and simpler configuration compared to AC microgrids (ACMGs). Although DCMGs seem better than ACMGs, lack of protection standard is a critical problem when operating DCMGs. Since DC fault response is completely different as AC fault, AC protection methods cannot be used for DCMGs. In this paper, a ring bus DCMG model is simulated using computer software PSCAD. A combined protection scheme based on cable current derivatives is introduced. This protection scheme not only detects and localizes low resistance DC faults very fast, but also accurately handles high resistance DC faults. The reliability of this scheme is proved by the simulation results.
{"title":"Fault Detection and Localization in a Ring Bus DC Microgrid Using Current Derivatives","authors":"Yunfei Bai, A. Rajapakse","doi":"10.1109/CCECE47787.2020.9255718","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255718","url":null,"abstract":"To provide more clean energy and satisfy the increasing power demand, microgrids using renewable energy sources (RES) are designed as additional power supplies. Recently, DC microgrids (DCMGs) have gained the attention for their higher power efficiency and simpler configuration compared to AC microgrids (ACMGs). Although DCMGs seem better than ACMGs, lack of protection standard is a critical problem when operating DCMGs. Since DC fault response is completely different as AC fault, AC protection methods cannot be used for DCMGs. In this paper, a ring bus DCMG model is simulated using computer software PSCAD. A combined protection scheme based on cable current derivatives is introduced. This protection scheme not only detects and localizes low resistance DC faults very fast, but also accurately handles high resistance DC faults. The reliability of this scheme is proved by the simulation results.","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":"133384988","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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255700
Bamdad Vafaie, M. Shamsi, M. S. Javan, K. El-Khatib
Distributed computing systems are increasing in popularity and being widely used as a new way of large-scale data processing. However, to achieve a reliable and efficient performance in a distributed environment, it is important to deal with system anomalies as soon as they are encountered. In this paper, two novel anomaly detection algorithms will be introduced and compared with previous anomaly detection algorithms. These novel algorithms are devised based on data summarization and error prediction in comparison with previously extracted data. The result of our experiments show that the proposed methods exhibit higher performance in terms of precision and accuracy.
{"title":"A New Statistical Method for Anomaly Detection in Distributed Systems","authors":"Bamdad Vafaie, M. Shamsi, M. S. Javan, K. El-Khatib","doi":"10.1109/CCECE47787.2020.9255700","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255700","url":null,"abstract":"Distributed computing systems are increasing in popularity and being widely used as a new way of large-scale data processing. However, to achieve a reliable and efficient performance in a distributed environment, it is important to deal with system anomalies as soon as they are encountered. In this paper, two novel anomaly detection algorithms will be introduced and compared with previous anomaly detection algorithms. These novel algorithms are devised based on data summarization and error prediction in comparison with previously extracted data. The result of our experiments show that the proposed methods exhibit higher performance in terms of precision and accuracy.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"36 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":"132648215","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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255675
Y. Derbal
Cancer is a genetic disease whose growth and proliferation is driven by the dysregulation of cell signaling and an aberrant metabolism. A better understanding of signaling dysregulation dynamics in cancer cells would inform the development of more effective therapies. In this respect, an agent-based model of cellular pathways is developed to study the dynamics of the cell signaling circuit in closed loop with cell metabolism. The model focuses on signaling pathways that involve frequently altered cancer genes. This would support explorations of therapeutic strategies aimed at derailing cancer proliferation through disruptions of major oncogenic pathways.
{"title":"Agent-Based Model of Cell Signaling in Cancer","authors":"Y. Derbal","doi":"10.1109/CCECE47787.2020.9255675","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255675","url":null,"abstract":"Cancer is a genetic disease whose growth and proliferation is driven by the dysregulation of cell signaling and an aberrant metabolism. A better understanding of signaling dysregulation dynamics in cancer cells would inform the development of more effective therapies. In this respect, an agent-based model of cellular pathways is developed to study the dynamics of the cell signaling circuit in closed loop with cell metabolism. The model focuses on signaling pathways that involve frequently altered cancer genes. This would support explorations of therapeutic strategies aimed at derailing cancer proliferation through disruptions of major oncogenic pathways.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"102 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":"121586031","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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255721
Alexander Vucenovic, Osama Ali-Ozkan, Clifford Ekwempe, Ozgur Eren
Explainable models are a critical requirement for predictive analytics applications in the healthcare domain. In this work we develop a hypothetical clinical decision support system for the classification task of predicting hospital readmission within 30 days of discharge. We compare a baseline logistic regression model with an implementation of the coordinate descent algorithm known as lasso. We choose lasso because it inherently performs variable selection during optimization which leads to an explainable model. Using model evaluation data we achieve an area under the ROC curve score of 0.795 improving on the baseline score of 0.683 without inflating the feature space.
{"title":"Explainable AI in Decision Support Systems : A Case Study: Predicting Hospital Readmission Within 30 Days of Discharge","authors":"Alexander Vucenovic, Osama Ali-Ozkan, Clifford Ekwempe, Ozgur Eren","doi":"10.1109/CCECE47787.2020.9255721","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255721","url":null,"abstract":"Explainable models are a critical requirement for predictive analytics applications in the healthcare domain. In this work we develop a hypothetical clinical decision support system for the classification task of predicting hospital readmission within 30 days of discharge. We compare a baseline logistic regression model with an implementation of the coordinate descent algorithm known as lasso. We choose lasso because it inherently performs variable selection during optimization which leads to an explainable model. Using model evaluation data we achieve an area under the ROC curve score of 0.795 improving on the baseline score of 0.683 without inflating the feature space.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"139 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":"115042954","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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255806
U. Waqas, Nimra Akram, S. Kim, Donghun Lee, Ji-Yeol Jeon
Image-based vehicle insurance processing and loan management has large scope for automation in automotive industry. In this paper we consider the problem of car damage classification, where categories include medium damage, huge damage and no damage. Based on deep learning techniques, MobileNet model is proposed with transfer learning for classification. Moreover, moving towards automation also comes with diverse hurdles; users can upload fake images like screenshots or taking pictures from computer screens, etc. To tackle this problem a hybrid approach is proposed to provide only authentic images to algorithm for damage classification as input. In this regard, moiré effect detection and metadata analysis is performed to detect fraudulent images. For damage classification 95% and for moiré effect detection 99% accuracy is achieved.
{"title":"Vehicle Damage Classification and Fraudulent Image Detection Including Moiré Effect Using Deep Learning","authors":"U. Waqas, Nimra Akram, S. Kim, Donghun Lee, Ji-Yeol Jeon","doi":"10.1109/CCECE47787.2020.9255806","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255806","url":null,"abstract":"Image-based vehicle insurance processing and loan management has large scope for automation in automotive industry. In this paper we consider the problem of car damage classification, where categories include medium damage, huge damage and no damage. Based on deep learning techniques, MobileNet model is proposed with transfer learning for classification. Moreover, moving towards automation also comes with diverse hurdles; users can upload fake images like screenshots or taking pictures from computer screens, etc. To tackle this problem a hybrid approach is proposed to provide only authentic images to algorithm for damage classification as input. In this regard, moiré effect detection and metadata analysis is performed to detect fraudulent images. For damage classification 95% and for moiré effect detection 99% accuracy is achieved.","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":"115550346","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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255776
Islam I. Osman, M. Shehata
Moving object detection is a challenging task in computer vision. A class agnostic model is learned to detect moving objects in a video despite their category. This is done using the proposed MODSiam that takes a single background image of the scene and the current frame as input, then the model extracts features from both inputs and merges then to output the foreground objects. A comparison of using this model with three different backbone convolutional neural networks is presented. The evaluation is done using the metrics precision, recall, F1-measure, false-positive rate, false-negative rate, specificity, accuracy, and the number of frames per second. All models are tested on the benchmark dataset CDNet, which is a dataset of videos for moving objects under different conditions like low frame rate, shadows, and dynamic background. The results show that using ResNet as a backbone produced promising results compared to other models with respect to most of evaluation metrics.
{"title":"MODSiam: Moving Object Detection using Siamese Networks","authors":"Islam I. Osman, M. Shehata","doi":"10.1109/CCECE47787.2020.9255776","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255776","url":null,"abstract":"Moving object detection is a challenging task in computer vision. A class agnostic model is learned to detect moving objects in a video despite their category. This is done using the proposed MODSiam that takes a single background image of the scene and the current frame as input, then the model extracts features from both inputs and merges then to output the foreground objects. A comparison of using this model with three different backbone convolutional neural networks is presented. The evaluation is done using the metrics precision, recall, F1-measure, false-positive rate, false-negative rate, specificity, accuracy, and the number of frames per second. All models are tested on the benchmark dataset CDNet, which is a dataset of videos for moving objects under different conditions like low frame rate, shadows, and dynamic background. The results show that using ResNet as a backbone produced promising results compared to other models with respect to most of evaluation metrics.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"78 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":"128327739","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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255719
Rama Ferguson, Brody Voth, Zachary di Giovanni, Diego Felix de Almeida, Michal Aibin
The rapid changes and increase of modern and cloud-ready services “on-demand” increase the utilization of Content Delivery Networks (CDNs) to deliver service and content to end-users efficiently. In order to minimize the communication cost and the average waiting time, it is necessary to send the end-users' requests to the best available servers. In this paper, we design and implement a Honeybee algorithm that adapts quickly to possible servers' downtime to avoid communication delays. We then compare it to other algorithms available in the literature. Finally, the evaluation is performed using various scenarios with networking issues, such as single server failures or natural disasters consisting of multiple server issues.
{"title":"Honeybee Algorithm for Content Delivery Networks","authors":"Rama Ferguson, Brody Voth, Zachary di Giovanni, Diego Felix de Almeida, Michal Aibin","doi":"10.1109/CCECE47787.2020.9255719","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255719","url":null,"abstract":"The rapid changes and increase of modern and cloud-ready services “on-demand” increase the utilization of Content Delivery Networks (CDNs) to deliver service and content to end-users efficiently. In order to minimize the communication cost and the average waiting time, it is necessary to send the end-users' requests to the best available servers. In this paper, we design and implement a Honeybee algorithm that adapts quickly to possible servers' downtime to avoid communication delays. We then compare it to other algorithms available in the literature. Finally, the evaluation is performed using various scenarios with networking issues, such as single server failures or natural disasters consisting of multiple server issues.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"11 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":"131066911","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}
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