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Articles 1 - 17 of 17
Full-Text Articles in Engineering
Predicting Residential Energy Consumption Using Wavelet Decomposition With Deep Neural Network, Dagimawi Eneyew, Miriam A M Capretz, Girma Bitsuamlak, London Hydro
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
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 …
Inverse Mapping Of Generative Adversarial Networks, Nicky Bayat
Inverse Mapping Of Generative Adversarial Networks, Nicky Bayat
Electronic Thesis and Dissertation Repository
Generative adversarial networks (GANs) synthesize realistic samples (image, audio, video, etc.) from a random latent vector. While many studies have explored various training configurations and architectures for GANs, the problem of inverting a generative model to extract latent vectors of given input images/audio has been inadequately investigated. Although there is exactly one generated output per given random vector, the mapping from an image/audio to its recovered latent vector can have more than one solution. We train a deep residual neural network (ResNet18) architecture to recover a latent vector for a given target that can be used to generate a face …
Machine Learning Prediction Of Shear Capacity Of Steel Fiber Reinforced Concrete, Wassim Ben Chaabene
Machine Learning Prediction Of Shear Capacity Of Steel Fiber Reinforced Concrete, Wassim Ben Chaabene
Electronic Thesis and Dissertation Repository
The use of steel fibers for concrete reinforcement has been growing in recent years owing to the improved shear strength and post-cracking toughness imparted by fiber inclusion. Yet, there is still lack of design provisions for steel fiber-reinforced concrete (SFRC) in building codes. This is mainly due to the complex shear transfer mechanism in SFRC. Existing empirical equations for SFRC shear strength have been developed with relatively limited data examples, making their accuracy restricted to specific ranges. To overcome this drawback, the present study suggests novel machine learning models based on artificial neural network (ANN) and genetic programming (GP) to …
A New Approach For Homomorphic Encryption With Secure Function Evaluation On Genomic Data, Mounika Pratapa
A New Approach For Homomorphic Encryption With Secure Function Evaluation On Genomic Data, Mounika Pratapa
Electronic Thesis and Dissertation Repository
Additively homomorphic encryption is a public-key primitive allowing a sum to be computed on encrypted values. Although limited in functionality, additive schemes have been an essential tool in the private function evaluation toolbox for decades. They are typically faster and more straightforward to implement relative to their fully homomorphic counterparts, and more efficient than garbled circuits in certain applications. This thesis presents a novel method for extending the functionality of additively homomorphic encryption to allow the private evaluation of functions of restricted domain. Provided the encrypted sum falls within the restricted domain, the function can be homomorphically evaluated “for free” …
Ontology-Driven Semantic Data Integration In Open Environment, Islam M. Ali
Ontology-Driven Semantic Data Integration In Open Environment, Islam M. Ali
Electronic Thesis and Dissertation Repository
Collaborative intelligence in the context of information management can be defined as "A shared intelligence that results from the collaboration between various information systems". In open environments, these collaborating information systems can be heterogeneous, dynamic and loosely-coupled. Information systems in open environment can also possess a certain degree of autonomy. The integration of data residing in various heterogeneous information systems is essential in order to drive the intelligence efficiently and accurately. Because of the heterogeneous, loosely-coupled, and dynamic nature of open environment, the integration between these information systems in the data level is not efficient. Several approaches and models have …
Terramechanics And Machine Learning For The Characterization Of Terrain, Bryan W. Southwell
Terramechanics And Machine Learning For The Characterization Of Terrain, Bryan W. Southwell
Electronic Thesis and Dissertation Repository
An instrumented rover wheel can collect vast amounts of data about a planetary surface. Planetary surfaces are changed by complex geological processes which can be better understood with an abundance of surface data and the use of terramechanics. Identifying terrain parameters such as cohesion and angle of friction hold importance for both the rover driver and the planetary scientist. Knowledge of terrain characteristics can warn of unsafe terrain and flag potential interesting scientific sites. The instrumented wheel in this research utilizes a pressure pad to sense load and sinkage, a string potentiometer to measure slip, and records motor current draw. …
Optimized Machine Learning Models Towards Intelligent Systems, Mohammadnoor Ahmad Mohammad Injadat
Optimized Machine Learning Models Towards Intelligent Systems, Mohammadnoor Ahmad Mohammad Injadat
Electronic Thesis and Dissertation Repository
The rapid growth of the Internet and related technologies has led to the collection of large amounts of data by individuals, organizations, and society in general [1]. However, this often leads to information overload which occurs when the amount of input (e.g. data) a human is trying to process exceeds their cognitive capacities [2]. Machine learning (ML) has been proposed as one potential methodology capable of extracting useful information from large sets of data [1]. This thesis focuses on two applications. The first is education, namely e-Learning environments. Within this field, this thesis proposes different optimized ML ensemble models to …
Noisy Importance Sampling Actor-Critic: An Off-Policy Actor-Critic With Experience Replay, Miriam A M Capretz, Norman Tasfi
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 …
Edge-Cloud Iot Data Analytics: Intelligence At The Edge With Deep Learning, Ananda Mohon M. Ghosh
Edge-Cloud Iot Data Analytics: Intelligence At The Edge With Deep Learning, Ananda Mohon M. Ghosh
Electronic Thesis and Dissertation Repository
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 …
A Blockchain Approach To Social Responsibility, Augusto Bedin, Wander Queiroz, Miriam A M Capretz, London Hydro
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.
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 …
Geometric State Observers For Autonomous Navigation Systems, Miaomiao Wang
Geometric State Observers For Autonomous Navigation Systems, Miaomiao Wang
Electronic Thesis and Dissertation Repository
The development of reliable state estimation algorithms for autonomous navigation systems is of great interest in the control and robotics communities. This thesis studies the state estimation problem for autonomous navigation systems. The first part of this thesis is devoted to the pose estimation on the Special Euclidean group $\SE(3)$. A generic globally exponentially stable hybrid estimation scheme for pose (orientation and position) and velocity-bias estimation on $\SE(3)\times \mathbb{R}^6$ is proposed. Moreover, an explicit hybrid observer, using inertial and landmark position measurements, is provided.
The second part of this thesis is devoted to the problem of simultaneous estimation of the …
A Visual Analytics System For Making Sense Of Real-Time Twitter Streams, Amir Haghighatimaleki
A Visual Analytics System For Making Sense Of Real-Time Twitter Streams, Amir Haghighatimaleki
Electronic Thesis and Dissertation Repository
Through social media platforms, massive amounts of data are being produced. Twitter, as one such platform, enables users to post “tweets” on an unprecedented scale. Once analyzed by machine learning (ML) techniques and in aggregate, Twitter data can be an invaluable resource for gaining insight. However, when applied to real-time data streams, due to covariate shifts in the data (i.e., changes in the distributions of the inputs of ML algorithms), existing ML approaches result in different types of biases and provide uncertain outputs. This thesis describes a visual analytics system (i.e., a tool that combines data visualization, human-data interaction, and …
Water Conservation Potential Of Self-Funded Foam-Based Flexible Surface-Mounted Floatovoltaics, Koami Soulemane Hayibo, Pierce Mayville, Ravneet Kaur Kailey, Joshua M. Pearce
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 …
Deep Learning For Load Forecasting With Smart Meter Data: Online Adaptive Recurrent Neural Network, Mohammad Navid Fekri, Harsh Patel, Katarina Grolinger, Vinay Sharma
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.
Edge-Cloud Computing For Iot Data Analytics: Embedding Intelligence In The Edge With Deep Learning, Ananda Mohon M. Ghosh, Katarina Grolinger
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 …