Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Institution
- Keyword
-
- Generative adversarial networks (2)
- Adaptive neural fuzzy inference system (1)
- Anomaly detection (1)
- Arms (1)
- Artificial intelligence (1)
-
- Balanced-weighting (1)
- Brushless doubly-fed machine (BDFM) (1)
- Brushless doubly-fed reluctance machine (BDFRM) (1)
- Brushless machines (1)
- CNN (1)
- Class imbalance (1)
- Computer vision (1)
- Constant power load (CPL) (1)
- Cyber-attacks (1)
- Cyber-attacks; cyber-attacks detection; cyber-attacks identification; cybersecurity; False Data Injection (FDI) attacks; cyber resilience; smart grid (1)
- Cyber-physical power systems (1)
- DC Circuit Breakers (CBs) (1)
- DC Faults (1)
- Damage detection (1)
- Dc microgrid (MG) (1)
- Decision-making (1)
- Deep Q-Network (1)
- Deep learning (1)
- Deep neural networks (1)
- Doubly-fed induction machine (DFIG) (1)
- Dust accumulation (1)
- Dynamic state estimation (1)
- Electrical machinery (1)
- Evolutionary algorithms; short term load forecasting; neural architecture search; transfer learning; sensitivity analysis (1)
- Extended Kalman filter (EKF) (1)
Articles 1 - 20 of 20
Full-Text Articles in Engineering
Machine Learning For Stock Prediction Based On Fundamental Analysis, Yuxuan Huang, Luiz Fernando Capretz, Danny Ho
Machine Learning For Stock Prediction Based On Fundamental Analysis, Yuxuan Huang, Luiz Fernando Capretz, Danny Ho
Electrical and Computer Engineering Publications
Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks’ historical data. Most of these existing approaches have focused on short term prediction using stocks’ historical price and technical indicators. In this paper, we prepared 22 years’ worth of stock quarterly financial data and investigated three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) for …
Reliability Models For Smartphone Applications, Sonia Meskini, Ali Bou Nassif, Luiz Fernando Capretz
Reliability Models For Smartphone Applications, Sonia Meskini, Ali Bou Nassif, Luiz Fernando Capretz
Electrical and Computer Engineering Publications
Smartphones have become the most used electronic devices. They carry out most of the functionalities of desktops, offering various useful applications that suit the user’s needs. Therefore, instead of the operator, the user has been the main controller of the device and its applications, therefore its reliability has become an emergent requirement. As a first step, based on collected smartphone applications failure data, we investigated and evaluated the efficacy of Software Reliability Growth Models (SRGMs) when applied to these smartphone data in order to check whether they achieve the same accuracy as in the desktop/laptop area. None of the selected …
Precision Grasp Using An Arm-Hand System As A Hybrid Parallel-Serial System: A Novel Inverse Kinematics Solution, Shuwei Qiu, Shuwei Qiu Ph.D., P.Eng.
Precision Grasp Using An Arm-Hand System As A Hybrid Parallel-Serial System: A Novel Inverse Kinematics Solution, Shuwei Qiu, Shuwei Qiu Ph.D., P.Eng.
Electrical and Computer Engineering Publications
In this letter, we present a novel inverse kinematics (IK) solution for a robotic arm-hand system to achieve precision grasp. This problem is kinematically over-constrained and to address the issue and to solve the problem, we propose a new approach with three key insights. First, we propose a human-inspired thumb-first strategy and consider one finger of the robotic hand as the “thumb” to narrow down the search space and increase the success rate of our algorithm. Second, we formulate the arm-thumb serial chain as a closed chain such that the entire arm-hand system is controlled as a hybrid parallel-serial system. …
Reinforcement Learning Algorithms: An Overview And Classification, Fadi Almahamid, Katarina Grolinger
Reinforcement Learning Algorithms: An Overview And Classification, Fadi Almahamid, Katarina Grolinger
Electrical and Computer Engineering Publications
The desire to make applications and machines more intelligent and the aspiration to enable their operation without human interaction have been driving innovations in neural networks, deep learning, and other machine learning techniques. Although reinforcement learning has been primarily used in video games, recent advancements and the development of diverse and powerful reinforcement algorithms have enabled the reinforcement learning community to move from playing video games to solving complex real-life problems in autonomous systems such as self-driving cars, delivery drones, and automated robotics. Understanding the environment of an application and the algorithms’ limitations plays a vital role in selecting the …
A Hybrid Framework For Detecting And Eliminating Cyber-Attacks In Power Grids, Arshia Aflaki, Mohsen Gitizadeh, Roozbeh Razavi-Far, Vasile Palade, Ali Akbar Ghasemi
A Hybrid Framework For Detecting And Eliminating Cyber-Attacks In Power Grids, Arshia Aflaki, Mohsen Gitizadeh, Roozbeh Razavi-Far, Vasile Palade, Ali Akbar Ghasemi
Electrical and Computer Engineering Publications
The work described in this paper aims to detect and eliminate cyber-attacks in smart grids that disrupt the process of dynamic state estimation. This work makes use of an unsupervised learning method, called hierarchical clustering, in an attempt to create an artificial sensor to detect two different cyber-sabotage cases, known as false data injection and denial-of-service, during the dynamic behavior of the power system. The detection process is conducted by using an unsupervised learning-enhanced approach, and a decision tree regressor is then employed for removing the threat. The dynamic state estimation of the power system is done by Kalman filters, …
Generative Adversarial Network-Based Scheme For Diagnosing Faults In Cyber-Physical Power Systems, Hossein Hassani, Roozbeh Razavi-Far, Mehrdad Saif, Vasile Palade
Generative Adversarial Network-Based Scheme For Diagnosing Faults In Cyber-Physical Power Systems, Hossein Hassani, Roozbeh Razavi-Far, Mehrdad Saif, Vasile Palade
Electrical and Computer Engineering Publications
This paper presents a novel diagnostic framework for distributed power systems that is based on using generative adversarial networks for generating artificial knockoffs in the power grid. The proposed framework makes use of the raw data measurements including voltage, frequency, and phase-angle that are collected from each bus in the cyber-physical power systems. The collected measurements are firstly fed into a feature selection module, where multiple state-of-the-art techniques have been used to extract the most informative features from the initial set of available features. The selected features are inputs to a knockoff generation module, where the generative adversarial networks are …
Dynamic Planning Networks, Norman Tasfi, Miriam A M Capretz
Dynamic Planning Networks, Norman Tasfi, Miriam A M Capretz
Electrical and Computer Engineering Publications
We introduce Dynamic Planning Networks (DPN), a novel architecture for deep reinforcement learning, that combines model-based and model-free aspects for online planning. Our architecture learns to dynamically construct plans using a learned state-transition model by selecting and traversing between simulated states and actions to maximize information before acting. DPN learns to efficiently form plans by expanding a single action conditional state transition at a time instead of exhaustively evaluating each action, reducing the number of state-transitions used during planning. We observe emergent planning patterns in our agent, including classical search methods such as breadth-first and depth-first search. DPN shows improved …
Arm-Hand Systems As Hybrid Parallel-Serial Systems: A Novel Inverse Kinematics Solution, Shuwei Qiu, Mehrdad Kermani Ph.D., P.Eng.
Arm-Hand Systems As Hybrid Parallel-Serial Systems: A Novel Inverse Kinematics Solution, Shuwei Qiu, Mehrdad Kermani Ph.D., P.Eng.
Electrical and Computer Engineering Publications
No abstract provided.
Hvdc Circuit Breakers: A Comprehensive Review, Fazel Mohammadi, Kumars Roubehi, Masood Hajian, Kaveh Niayesh, Gevork B. Gharehpetian, Hani Saas, Hasan Ali Mohd, Vijay K. Sood
Hvdc Circuit Breakers: A Comprehensive Review, Fazel Mohammadi, Kumars Roubehi, Masood Hajian, Kaveh Niayesh, Gevork B. Gharehpetian, Hani Saas, Hasan Ali Mohd, Vijay K. Sood
Electrical and Computer Engineering Publications
High Voltage Direct Current (HVDC) systems are now well-integrated into AC systems in many jurisdictions. The integration of Renewable Energy Sources (RESs) is a major focus and the role of HVDC systems is expanding. However, the protection of HVDC systems against DC faults is a challenging issue that can have negative impacts on the reliable and safe operation of power systems. Practical solutions to protect HVDC grids against DC faults without a widespread power outage include (1) using DC Circuit Breakers (CBs) to isolate the faulty DC-link, (2) using a proper converter topology to interrupt the DC fault current, and/or …
Cleaning Of Floating Photovoltaic Systems: A Critical Review On Approaches From Technical And Economic Perspectives, Rafi Zahedi, Parisa Ranjbaran, Gevork B. Gharehpetian, Fazel Mohammadi, Roya Ahmadiahangar
Cleaning Of Floating Photovoltaic Systems: A Critical Review On Approaches From Technical And Economic Perspectives, Rafi Zahedi, Parisa Ranjbaran, Gevork B. Gharehpetian, Fazel Mohammadi, Roya Ahmadiahangar
Electrical and Computer Engineering Publications
There are some environmental factors, such as ambient temperature, dust, etc., which cause a reduction in the efficiency of Photovoltaic (PV) systems. Installation of PV panels on the water surface, commonly known as Floating Photovoltaic (FPV) systems, is one solution to employ PV panels in a cooler environment, achieve higher efficiency, and reduce water evaporation. FPV systems open up new opportunities for scaling up solar generating capacity, especially in countries with high population density and valuable lands, as well as countries with high evaporation rates and water resources deficiency. Since the FPV system is an almost new concept, its cleaning …
A Novel Stochastic Predictive Stabilizer For Dc Microgrids Feeding Cpls, Elham Kowsari, Jafar Zarei, Roozbeh Razavi-Far, Mehrdad Saif, Tomislav Dragicevic, Mohammad Hassan Khooban
A Novel Stochastic Predictive Stabilizer For Dc Microgrids Feeding Cpls, Elham Kowsari, Jafar Zarei, Roozbeh Razavi-Far, Mehrdad Saif, Tomislav Dragicevic, Mohammad Hassan Khooban
Electrical and Computer Engineering Publications
In this work, a novel nonlinear approach is proposed for the stabilization of microgrids (MGs) with constant power loads (CPLs). The proposed method is constructed based on the incorporation of a pseudo-extended Kalman filter (EKF) into stochastic nonlinear model predictive control (MPC). In order to achieve high-performance and optimal control in dc MGs, estimating the instantaneous power flow of the uncertain CPLs and the available power units is essential. Thus, by utilizing the advantages of the stochastic MPC and the pseudo-EKF, an effective control solution for the stabilization of dc islanded MGs with CPLs is established. This technique develops a …
Emerging Challenges In Smart Grid Cybersecurity Enhancement: A Review, Fazel Mohammadi
Emerging Challenges In Smart Grid Cybersecurity Enhancement: A Review, Fazel Mohammadi
Electrical and Computer Engineering Publications
In this paper, a brief survey of measurable factors affecting the adoption of cybersecurity enhancement methods in the smart grid is provided. From a practical point of view, it is a key point to determine to what degree the cyber resilience of power systems can be improved using cost-effective resilience enhancement methods. Numerous attempts have been made to the vital resilience of the smart grid against cyber-attacks. The recently proposed cybersecurity methods are considered in this paper, and their accuracies, computational time, and robustness against external factors in detecting and identifying False Data Injection (FDI) attacks are evaluated. There is …
A Comprehensive Review On Brushless Doubly-Fed Reluctance Machine, Omid Sadeghian, Sajjad Tohidi, Behnam Mohammadi-Ivatloo, Fazel Mohammadi
A Comprehensive Review On Brushless Doubly-Fed Reluctance Machine, Omid Sadeghian, Sajjad Tohidi, Behnam Mohammadi-Ivatloo, Fazel Mohammadi
Electrical and Computer Engineering Publications
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. The Brushless Doubly-Fed Reluctance Machine (BDFRM) has been widely investigated in numerous research studies since it is brushless and cageless and there is no winding on the rotor of this emerging machine. This feature leads to several advantages for this machine in comparison with its induction counterpart, i.e., Brushless Doubly-Fed Induction Machine (BDFIM). Less maintenance, less power losses, and also more reliability are the major advantages of BDFRM compared to BDFIM. The design complexity of its reluctance rotor, as well as flux patterns for indirect connection between the two windings mounted …
Transfer Learning By Similarity Centred Architecture Evolution For Multiple Residential Load Forecasting, Santiago Gomez-Rosero, Miriam A M Capretz, Syed Mir
Transfer Learning By Similarity Centred Architecture Evolution For Multiple Residential Load Forecasting, Santiago Gomez-Rosero, Miriam A M Capretz, Syed Mir
Electrical and Computer Engineering Publications
The development from traditional low voltage grids to smart systems has become extensive and adopted worldwide. Expanding the demand response program to cover the residential sector raises a wide range of challenges. Short term load forecasting for residential consumers in a neighbourhood could lead to a better understanding of low voltage consumption behaviour. Nevertheless, users with similar characteristics can present diversity in consumption patterns. Consequently, transfer learning methods have become a useful tool to tackle differences among residential time series. This paper proposes a method combining evolutionary algorithms for neural architecture search with transfer learning to perform short term load …
‘Digits’ App - Smartphone Augmented Reality For Hand Telerehabilitation, Hongdao Dong, Edward Ho, Herbert Shin, Tania Banerjee, Geoffrey Masschelein, Jacob Davidson, Sandrine De Ribaupierre, Roy Eagleson, Caitlin Symonette
‘Digits’ App - Smartphone Augmented Reality For Hand Telerehabilitation, Hongdao Dong, Edward Ho, Herbert Shin, Tania Banerjee, Geoffrey Masschelein, Jacob Davidson, Sandrine De Ribaupierre, Roy Eagleson, Caitlin Symonette
Electrical and Computer Engineering Publications
Hand telerehabilitation currently has limitations for accurate and remote assessment of range of motion (ROM) in small finger joints. ‘DIGITS’ application utilises the front smartphone camera to measure finger ROM in a reliable and rapid assessment protocol. Our initial beta-phase testing examined the consistency of our software measurements to in-person goniometry. 6 to 9 degrees of difference existed between the smartphone application recorded data versus the in-person measurements. This range is within acceptable 7 to 9 degree tolerance for interrater goniometry measurements. The effect of environmental factors such as hand distance, lightings and hand orientation was evaluated. The intraclass correlation …
Adversarial Learning On Incomplete And Imbalanced Medical Data For Robust Survival Prediction Of Liver Transplant Patients, Ehsan Hallaji, Roozbeh Razavi-Far, Vasile Palade, Mehrdad Saif
Adversarial Learning On Incomplete And Imbalanced Medical Data For Robust Survival Prediction Of Liver Transplant Patients, Ehsan Hallaji, Roozbeh Razavi-Far, Vasile Palade, Mehrdad Saif
Electrical and Computer Engineering Publications
The scarcity of liver transplants necessitates prioritizing patients based on their health condition to minimize deaths on the waiting list. Recently, machine learning methods have gained popularity for automatizing liver transplant allocation systems, which enables prompt and suitable selection of recipients. Nevertheless, raw medical data often contain complexities such as missing values and class imbalance that reduce the reliability of the constructed model. This paper aims at eliminating the respective challenges to ensure the reliability of the decision-making process. To this aim, we first propose a novel deep learning method to simultaneously eliminate these challenges and predict the patients' survival …
A Systematic Review Of Convolutional Neural Network-Based Structural Condition Assessment Techniques, Sandeep Sony, Kyle Dunphy, Ayan Sadhu, Miriam A M Capretz
A Systematic Review Of Convolutional Neural Network-Based Structural Condition Assessment Techniques, Sandeep Sony, Kyle Dunphy, Ayan Sadhu, Miriam A M Capretz
Electrical and Computer Engineering Publications
With recent advances in non-contact sensing technology such as cameras, unmanned aerial and ground vehicles, the structural health monitoring (SHM) community has witnessed a prominent growth in deep learning-based condition assessment techniques of structural systems. These deep learning methods rely primarily on convolutional neural networks (CNNs). The CNN networks are trained using a large number of datasets for various types of damage and anomaly detection and post-disaster reconnaissance. The trained networks are then utilized to analyze newer data to detect the type and severity of the damage, enhancing the capabilities of non-contact sensors in developing autonomous SHM systems. In recent …
Micro-Ct Of The Human Ossicular Chain: Statistical Shape Modeling And Implications For Otologic Surgery, Western University, London Health Sciences Centre
Micro-Ct Of The Human Ossicular Chain: Statistical Shape Modeling And Implications For Otologic Surgery, Western University, London Health Sciences Centre
Electrical and Computer Engineering Publications
The ossicular chain is a middle ear structure consisting of the small incus, malleus and stapes bones, which transmit tympanic membrane vibrations caused by sound to the inner ear. Despite being shown to be highly variable in shape, there are very few morphological studies of the ossicles. The objective of this study was to use a large sample of cadaveric ossicles to create a set of three-dimensional models and study their statistical variance. Thirty-three cadaveric temporal bone samples were scanned using micro-computed tomography (μCT) and segmented. Statistical shape models (SSMs) were then made for each ossicle to demonstrate the divergence …
Deep Learning For High-Impedance Fault Detection: Convolutional Autoencoders, Khushwant Rai, Firouz Badrkhani Ajaei, Farnam Hojatpanah, Katarina Grolinger
Deep Learning For High-Impedance Fault Detection: Convolutional Autoencoders, Khushwant Rai, Firouz Badrkhani Ajaei, Farnam Hojatpanah, Katarina Grolinger
Electrical and Computer Engineering Publications
High-impedance faults (HIF) are difficult to detect because of their low current amplitude and highly diverse characteristics. In recent years, machine learning (ML) has been gaining popularity in HIF detection because ML techniques learn patterns from data and successfully detect HIFs. However, as these methods are based on supervised learning, they fail to reliably detect any scenario, fault or non-fault, not present in the training data. Consequently, this paper takes advantage of unsupervised learning and proposes a convolutional autoencoder framework for HIF detection (CAE-HIFD). Contrary to the conventional autoencoders that learn from normal behavior, the convolutional autoencoder (CAE) in CAE-HIFD …
Pwd-3dnet: A Deep Learning-Based Fully-Automated Segmentation Of Multiple Structures On Temporal Bone Ct Scans, Soodeh Nikan, Kylen Ann Van Osch, Mandolin Li Bartling, Allen Gregory Daniel, Seyed Alireza Rohani, Ben Connors, Sumit Kishore Agrawal, Hanif M. Ladak
Pwd-3dnet: A Deep Learning-Based Fully-Automated Segmentation Of Multiple Structures On Temporal Bone Ct Scans, Soodeh Nikan, Kylen Ann Van Osch, Mandolin Li Bartling, Allen Gregory Daniel, Seyed Alireza Rohani, Ben Connors, Sumit Kishore Agrawal, Hanif M. Ladak
Electrical and Computer Engineering Publications
The temporal bone is a part of the lateral skull surface that contains organs responsible for hearing and balance. Mastering surgery of the temporal bone is challenging because of this complex and microscopic three-dimensional anatomy. Segmentation of intra-temporal anatomy based on computed tomography (CT) images is necessary for applications such as surgical training and rehearsal, amongst others. However, temporal bone segmentation is challenging due to the similar intensities and complicated anatomical relationships among critical structures, undetectable small structures on standard clinical CT, and the amount of time required for manual segmentation. This paper describes a single multi-class deep learning-based pipeline …