Electrical and Computer Engineering Commons^™

A New Approach For Grasp Quality Calculation Using Continuous Boundary Formulation Of Grasp Wrench Space, Shuwei Qiu, Mehrdad Kermani Ph.D., P.Eng.

Electrical and Computer Engineering Publications

In this paper, we aim to use a continuous formulation to efficiently calculate the well-known wrench-based grasp metric proposed by Ferrari and Canny which is the minimum distance from the wrench space origin to the boundary of the grasp wrench space. Considering the L∞" role="presentation" style="box-sizing: border-box; margin: 0px; padding: 0px; display: inline-block; line-height: normal; font-size: 16.200000762939453px; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; position: relative;"> metric and the nonlinear friction cone model, the challenge of calculating this metric is to determine the boundary of the grasp …

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

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.

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

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 …

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.

Electrical and Computer Engineering Publications

No abstract provided.

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 …

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 …

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 …

‘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 …

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 …

Predicting Residential Energy Consumption Using Wavelet Decomposition With Deep Neural Network, Dagimawi Eneyew, Miriam A M Capretz, Girma Bitsuamlak, London Hydro

Electrical and Computer Engineering Publications

Electricity consumption is accelerating due to economic and population growth. Hence, energy consumption prediction is becoming vital for overall consumption management and infrastructure planning. Recent advances in smart electric meter technology are making high-resolution energy consumption data available. However, many parameters influencing energy consumption are not typically monitored for residential buildings. Therefore, this study’s main objective is to develop a data-driven energy consumption forecasting model (next-hour consumption) for residential houses solely based on analyzing electricity consumption data. This research proposes a deep neural network architecture that combines stationary wavelet transform features and convolutional neural networks. The proposed approach utilizes automatically …

Deep Neural Network For Load Forecasting Centred On Architecture Evolution, Santiago Gomez-Rosero, Miriam A M Capretz, London Hydro

Electrical and Computer Engineering Publications

Nowadays, electricity demand forecasting is critical for electric utility companies. Accurate residential load forecasting plays an essential role as an individual component for integrated areas such as neighborhood load consumption. Short-term load forecasting can help electric utility companies reduce waste because electric power is expensive to store. This paper proposes a novel method to evolve deep neural networks for time series forecasting applied to residential load forecasting. The approach centres its efforts on the neural network architecture during the evolution. Then, the model weights are adjusted using an evolutionary optimization technique to tune the model performance automatically. Experimental results on …

Noisy Importance Sampling Actor-Critic: An Off-Policy Actor-Critic With Experience Replay, Miriam A M Capretz, Norman Tasfi

Electrical and Computer Engineering Publications

This paper presents Noisy Importance Sampling Actor-Critic (NISAC), a set of empirically validated modifications to the advantage actor-critic algorithm (A2C), allowing off-policy reinforcement learning and increased performance. NISAC uses additive action space noise, aggressive truncation of importance sample weights, and large batch sizes. We see that additive noise drastically changes how off-sample experience is weighted for policy updates. The modified algorithm achieves an increase in convergence speed and sample efficiency compared to both the on-policy actor-critic A2C and the importance weighted off-policy actor-critic algorithm. In comparison to state-of-the-art (SOTA) methods, such as actor-critic with experience replay (ACER), NISAC nears the …

A Blockchain Approach To Social Responsibility, Augusto Bedin, Wander Queiroz, Miriam A M Capretz, London Hydro

Electrical and Computer Engineering Publications

As blockchain technology matures, more sophisticated solutions arise regarding complex problems. Blockchain continues to spread towards various niches such as government, IoT, energy, and environmental industries. One often overlooked opportunity for blockchain is the social responsibility sector. Presented in this paper is a permissioned blockchain model that enables enterprises to come together and cooperate to optimize their environmental and societal impacts. This is made possible through a private or permissioned blockchain. Permissioned blockchains are blockchain networks where all the participants are known and trust relationships among them can be fostered more smoothly. An example of what a permissioned blockchain would …

A Lightweight Magnetorheological Actuator Using Hybrid Magnetization, Masoud Moghani, Mehrdad Kermani Ph.D., P.Eng.

Electrical and Computer Engineering Publications

Copyright © 2020, IEEE

This paper presents the design and validation of a lightweight Magneto-Rheological (MR) clutch, called Hybrid Magneto-Rheological (HMR) clutch. The clutch utilizes a hybrid magnetization using an electromagnetic coil and a permanent magnet. The electromagnetic coil can adjust the magnetic field
generated by the permanent magnet to a desired value, and fully control the transmitted torque. To achieve the maximum torque to mass ratio, the design of HMR clutch is formulated as a multiobjective optimization problem with three design objectives, namely the transmitted torque, the mass of the clutch, and the
magnetic field strength within the clutch …

Edge-Cloud Computing For Iot Data Analytics: Embedding Intelligence In The Edge With Deep Learning, Ananda Mohon M. Ghosh, Katarina Grolinger

Electrical and Computer Engineering Publications

Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …

Deep Learning For Load Forecasting With Smart Meter Data: Online Adaptive Recurrent Neural Network, Mohammad Navid Fekri, Harsh Patel, Katarina Grolinger, Vinay Sharma

Electrical and Computer Engineering Publications

No abstract provided.

Pwd-3dnet: A Deep Learning-Based Fully-Automated Segmentation Of Multiple Structures On Temporal Bone Ct Scans, Western University, London Health Science Centre

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 crit- ical 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 …

Intrinsic Measures And Shape Analysis Of The Intratemporal Facial Nerve, Thomas Hudson, Bradley Gare, Daniel Allen, Hanif Ladak, Sumit Agrawal

Electrical and Computer Engineering Publications

Hypothesis: To characterize anatomical measurements and shape variation of the facial nerve within the temporal bone, and to create statistical shape models (SSMs) to enhance knowledge of temporal bone anatomy and aid in automated segmentation.

Background: The facial nerve is a fundamental structure in otologic surgery, and detailed anatomic knowledge with surgical experience are needed to avoid its iatrogenic injury. Trainees can use simulators to practice surgical techniques, however manual segmentation required to develop simulations can be time consuming. Consequently, automated segmentation algorithms have been developed that use atlas registration, SSMs, and deep learning.

Methods: Forty cadaveric temporal bones were …

Water Conservation Potential Of Self-Funded Foam-Based Flexible Surface-Mounted Floatovoltaics, Koami Soulemane Hayibo, Pierce Mayville, Ravneet Kaur Kailey, Joshua M. Pearce

Electrical and Computer Engineering Publications

A potential solution to the coupled water–energy–food challenges in land use is the concept of floating photovoltaics or floatovoltaics (FPV). In this study, a new approach to FPV is investigated using a flexible crystalline silicon-based photovoltaic (PV) module backed with foam, which is less expensive than conventional pontoon-based FPV. This novel form of FPV is tested experimentally for operating temperature and performance and is analyzed for water-savings using an evaporation calculation adapted from the Penman–Monteith model. The results show that the foam-backed FPV had a lower operating temperature than conventional pontoon-based FPV, and thus a 3.5% higher energy output per …

Generating Energy Data For Machine Learning With Recurrent Generative Adversarial Networks, Mohammad Navid Fekri, Ananda M. Ghosh, Katarina Grolinger

Electrical and Computer Engineering Publications

The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative adversarial network (R-GAN) for generating realistic energy consumption data by learning from real data. Generativea adversarial …

An Open-Source Integration Platform For Multiple Peripheral Modules With Kuka Robots, Mahyar Abdeetedal, Mehrdad Kermani Ph.D., P.Eng.

Electrical and Computer Engineering Publications

This paper presents an open-source software interface for the integration of a Kuka robot with peripheral tools and sensors, KUI: Kuka User Interface. KUI is developed based on Kuka Fast Research Interface (FRI) which enables soft real-time control of the robot. Simulink Desktop Real-Time™ or any User Datagram Protocol (UDP) client can send real-time commands to Kuka robot via KUI. In KUI, third-party tools can be added and controlled synchronously with Kuka light-weight robot (LWR). KUI can send the control commands via serial communication to the attached devices. KUI can generate low-level commands using data acquisition (DAQ) boards. This feature …

Similarity-Based Chained Transfer Learning For Energy Forecasting With Big Data, Yifang Tian, Ljubisa Sehovac, Katarina Grolinger

Electrical and Computer Engineering Publications

Smart meter popularity has resulted in the ability to collect big energy data and has created opportunities for large-scale energy forecasting. Machine Learning (ML) techniques commonly used for forecasting, such as neural networks, involve computationally intensive training typically with data from a single building or a single aggregated load to predict future consumption for that same building or aggregated load. With hundreds of thousands of meters, it becomes impractical or even infeasible to individually train a model for each meter. Consequently, this paper proposes Similarity-Based Chained Transfer Learning (SBCTL), an approach for building neural network-based models for many meters by …

Forecasting Building Energy Consumption With Deep Learning: A Sequence To Sequence Approach, Ljubisa Sehovac, Cornelius Nesen, Katarina Grolinger

Electrical and Computer Engineering Publications

Energy Consumption has been continuously increasing due to the rapid expansion of high-density cities, and growth in the industrial and commercial sectors. To reduce the negative impact on the environment and improve sustainability, it is crucial to efficiently manage energy consumption. Internet of Things (IoT) devices, including widely used smart meters, have created possibilities for energy monitoring as well as for sensor based energy forecasting. Machine learning algorithms commonly used for energy forecasting such as feedforward neural networks are not well-suited for interpreting the time dimensionality of a signal. Consequently, this paper uses Recurrent Neural Networks (RNN) to capture time …

Deep Learning: Edge-Cloud Data Analytics For Iot, Katarina Grolinger, Ananda M. Ghosh

Electrical and Computer Engineering Publications

Sensors, wearables, mobile and other Internet of Thing (IoT) devices are becoming increasingly integrated in all aspects of our lives. They are capable of collecting massive quantities of data that are typically transmitted to the cloud for processing. However, this results in increased network traffic and latencies. Edge computing has a potential to remedy these challenges by moving computation physically closer to the network edge where data are generated. However, edge computing does not have sufficient resources for complex data analytics tasks. Consequently, this paper investigates merging cloud and edge computing for IoT data analytics and presents a deep learning-based …

A Virtual-Reality Training Simulator For Cochlear Implant Surgery, Blake Jones, Seyed Alireza Rohani, Nelson Ong, Tarek Tayeh, Hanif M. Ladak, Ahmad Chalabi, Sumit K. Agrawal

Electrical and Computer Engineering Publications

No abstract provided.

Development Of Mr Clutch For A Prospective 5-Dof Robot, Sergey Pisetskiy, Mehrdad Kermani Ph.D., P.Eng.

Electrical and Computer Engineering Publications

This paper presents an improved design approach for the construction of a Magneto-Rheological (MR) clutch intended to be used in a prospective 5 degrees of freedom robot. The MR clutch features embedded Hall sensors for intrinsic torque control. After a brief description of the MR clutch principles, the details of the mechanical design are discussed. Simulation and preliminary experimental results demonstrate the main characteristics and advantages of the proposed MR clutch.

Grasp And Stress Analysis Of An Underactuated Finger For Proprioceptive Tactile Sensing, Mahyar Abdeetedal, Mehrdad Kermani Ph.D., P.Eng.

Electrical and Computer Engineering Publications

This paper presents the design and evaluation of a new sensorized underactuated self-adaptive finger. The design incorporates a two-degrees-of-freedom link-driven underactuated mechanism with an embedded load cell for contact force measurement and a trimmer potentiometer for acquiring joint variables. The utilization of proprioceptive (internal) sensors results in tactile-like sensations in the finger without compromising the size and complexity of the proposed design. To obtain an optimum finger design, the placement of the load cell is analyzed using finite element method. The design of the finger features a particular rounded shape of the distal phalanx and specific size ratio between the …