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(1 - 3 of 3)
- Title
- A Complete Machine Learning Approach for Predicting Lithium-Ion Cell Combustion
- Creator
- Almagro Yravedra, Fernando
- Date
- 2020
- Description
-
The object of the herein thesis work document is to develop a functional predictive model, able to predict the combustion of a US18650 Sony...
Show moreThe object of the herein thesis work document is to develop a functional predictive model, able to predict the combustion of a US18650 Sony Lithium-Ion cell given its current and previous states. In order to build the model, a realistic electro-thermal model of the cell under study is developed in Matlab Simulink, being used to recreate the cell's behavior under a set of real operating conditions. The data generated by the electro-thermal model is used to train a recurrent neural network, which returns the chance of future combustion of the US18650 Sony Lithium-Ion cell. Independently obtained data is used to test and validate the developed recurrent neural network using advanced metrics.
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- Title
- Reconfigurable High-Performance Computation and Communication Platform for Ultrasonic Applications
- Creator
- Wang, Boyang
- Date
- 2021
- Description
-
In industrial and medical applications, ultrasonic signals are used in nondestructive testing (NDT), medical imaging, navigation, and...
Show moreIn industrial and medical applications, ultrasonic signals are used in nondestructive testing (NDT), medical imaging, navigation, and communication. This study presents the architecture of high-performance computational systems designed for ultrasonic nondestructive testing, data compression using machine learning, and a multilayer perceptron neural network for ultrasonic flaw detection and grain size characterization. We researched and developed a real-time software-defined ultrasonic communication system for transmitting information through highly reverberant and dispersive solid channels. Orthogonal frequency-division multiplexing is explored to combat the severe multipath effect in the solid channels and achieve an optimal bitrate solution. In this study, a reconfigurable, high-performance, low-cost, and real-time ultrasonic data acquisition and signal processing platform is designed based on an all-programmable system-on-chip (APSoC). We designed the unsupervised learning models using wavelet packet transformation optimized by convolutional autoencoder for massive ultrasonic data compression. The proposed learning models can achieve a compression accuracy of 98% by using only 6% of the original data. For ultrasonic signal analysis in NDT applications, we utilized the multilayer perceptron neural network (MLPNN) to detect flaw echoes masked by strong microstructure scattering noise (i.e., about zero dB SNR or less) with detection accuracy above 99%. It is of high interest to characterize materials using ultrasonic scattering properties for grain size estimation and classification. We successfully designed an MLPNN to classify the grain sizes of materials with an accuracy of 99%. Furthermore, a software-defined ultrasonic communication system based on the APSoC is designed for real-time data transmission through solid channels. Transducers with a center frequency of 2.5 MHz are used to transmit and receive information-bearing ultrasonic waves in solid channels where the communication bit rate can reach up to 1.5 Mbps.
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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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