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(1 - 4 of 4)
- Title
- DATA-DRIVEN OPTIMIZATION OF NEXT GENERATION HIGH-DENSITY WIRELESS NETWORKS
- Creator
- Khairy, Sami
- Date
- 2021
- Description
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The Internet of Things (IoT) paradigm is poised to advance all aspects of modern society by enabling ubiquitous communications and...
Show moreThe Internet of Things (IoT) paradigm is poised to advance all aspects of modern society by enabling ubiquitous communications and computations. In the IoT era, an enormous number of devices will be connected wirelessly to the internet in order to enable advanced data-centric applications. The projected growth in the number of connected wireless devices poses new challenges to the design and optimization of future wireless networks. For a wireless network to support a massive number of devices, advanced physical layer and channel access techniques should be designed, and high-dimensional decision variables should be optimized to manage network resources. However, the increased network scale, complexity, and heterogeneity, render the network unamenable to traditional closed-form mathematical analysis and optimization, which makes future high-density wireless networks seem unmanageable. In this thesis, we study the design and data-driven optimization of future high-density wireless networks operating over the unlicensed band, including Radio Frequency (RF)-powered wireless networks, solar-powered Unmanned Aerial Vehicle (UAV)-based wireless networks, and random Non-Orthogonal Multiple Access (NOMA) wireless networks. For each networking scenario, we first analyze network dynamics and identify performance trade-offs. Next, we design adaptive network controllers in the form of high-dimensional multi-objective optimization problems which exploit the heterogeneity in users' wireless propagation channels and energy harvesting to maximize the network capacity, manage battery energy resources, and achieve good user capacity fairness. To solve the high-dimensional optimization problems and learn the optimal network control policy, we propose novel, cross-layer, scalable, model-based and model-free data-driven network optimization and resource management algorithms that integrate domain-specific analyses with advanced machine learning techniques from deep learning, reinforcement learning, and uncertainty quantification. Furthermore, convergence of the proposed algorithms to the optimal solution is theoretically analyzed using mathematical results from metric spaces, convex optimization, and game theory. Finally, extensive simulations have been conducted to demonstrate the efficacy and superiority of our network optimization and resource management techniques compared with existing methods. Our research contributions provide practical insights for the design and data-driven optimization of next generation high-density wireless networks.
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- Title
- SOLID-STATE SMART PLUG DEVICE
- Creator
- Deng, Zhixi
- Date
- 2022
- Description
-
Electrical faults are a leading cause of residential fire, and flexible power cords are particularly susceptible to metal or insulation...
Show moreElectrical faults are a leading cause of residential fire, and flexible power cords are particularly susceptible to metal or insulation degradation that may lead to a variety of electrical faults. Smart Plugs are a type of plug-in device controlling electrical loads via wireless communication for consumer market. However, there is lack of circuit protection features in existing Smart Plug products. Moreover, there is no previous product or research on Smart Plug with circuit protection features. This thesis introduces a new Smart Plug 2.0 concept which offers all-in-one protection against over-current, arc, and ground faults in addition to the smart features in Smart Plug products. It aims at preventing fire and shock hazards caused by degraded or damaged power cords and electrical connections in homes and offices. It offers microsecond-scale time resolution to detect and respond to a fault condition, and significantly reduces the electrothermal stress on household electrical wires and loads. A new arc fault detection method is developed using machine learning models based on load current di/dt events. The Smart Plug 2.0 concept has been validated experimentally. A 120V/10A solid-state Smart Plug 2.0 prototype using power MOSEFTs is designed and tested. It has experimentally demonstrated the comprehensive protection features against all types of electrical faults.
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- Title
- Exploiting contextual information for deep learning based object detection
- Creator
- Zhang, Chen
- Date
- 2020
- Description
-
Object detection has long been an important research topic in computer vision area. It forms the basis of many applications. Despite the great...
Show moreObject detection has long been an important research topic in computer vision area. It forms the basis of many applications. Despite the great progress made in recent years, object detection is still a challenging task. One of the keys to improving the performance of object detection is to utilize the contextual information from the image itself or from a video sequence. Contextual information is defined as the interrelated condition in which something exists or occurs. In object detection, such interrelated condition can be related background/surroundings, support from image segmentation task, and the existence of the object in the temporal domain for video-based object detection. In this thesis, we propose multiple methods to exploit contextual information to improve the performance of object detection from images and videos.First, we focus on exploiting spatial contextual information in still-image based object detection, where each image is treated independently. Our research focuses on extracting contextual information using different approaches, which includes recurrent convolutional layer with feature concatenation (RCL-FC), 3-D recurrent neural networks (3-D RNN), and location-aware deformable convolution. Second, we focus on exploiting pixel-level contextual information from a related computer vision task, namely image segmentation. Our research focuses on applying a weakly-supervised auxiliary multi-label segmentation network to improve the performance of object detection without increasing the inference time. Finally, we focus on video object detection, where the temporal contextual information between video frames are exploited. Our first research involves modeling short-term temporal contextual information using optical flow and modeling long-term temporal contextual information using convLSTM. Another research focuses on creating a two-path convLSTM pyramid to handle multi-scale temporal contextual information for dealing with the change in object's scale. Our last work is the event-aware convLSTM that forces convLSTM to learn about the event that causes the performance to drop in a video sequence.
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- Title
- Machine Learning (ML) for Extreme Weather Power Outage Forecasting in Power Distribution Networks
- Creator
- Bahrami, Anahita
- Date
- 2023
- Description
-
The Midwest region experiences a diverse range of severe weather conditions throughout the year. During the warmer months, thunderstorms,...
Show moreThe Midwest region experiences a diverse range of severe weather conditions throughout the year. During the warmer months, thunderstorms, heavy rain, lightning, tornadoes, and high winds pose a threat, while the colder season brings ice storms, snowstorms, high winds, and sleet storms, all of which can cause significant damage to the environment, properties, transportation systems, and power grids. The average climate in the Midwest is influenced by factors such as latitude, solar input, water systems' typical positions and movements, topography, the Great Lakes, and human activities. The combination of these conditions during different seasons contributes to the development of various types of storms. Therefore, it is crucial to predict the impacts of such atmospheric events on distribution and transmission lines, enabling utilities to assess and implement preventive measures and strategies to minimize the economic losses associated with these disasters. Additionally, the accurate classification of storm modes through an automated system allows operators to study trends in relation to climate change and implement necessary strategies to ensure grid reliability and resilience.In recent years, a significant number of power outages have occurred due to extreme ice formation on transmission and distribution networks, posing a threat to the power grid's resilience and reliability. To prepare power providers for snowstorms, extensive research has been conducted on snow accretion on power lines. Over the past two decades, many scientists have turned to machine learning (ML) algorithms for predicting ice accretion on overhead conductors, as ML models demonstrate superior accuracy compared to statistical forecasting models when it comes to forecasting challenging and fine-grained problems. However, most existing models primarily focus on predicting ice formation on power lines and fail to forecast the resulting damage to the distribution network. Therefore, this project proposes a model for predicting power outages caused by snow and ice storms in the distribution network. The goal is to aid in the planning process for disaster response and ensure the resilience and reliability of the power grid. The proposed outage prediction model incorporates statistical and machine learning techniques, taking into account features related to weather conditions, storm events, and information about the power network feeders.
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