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Modeling, analysis, and design of a 10 kVA, 20 kHz transformerFlory, Isaac Lynnwood 04 May 2010 (has links)
The design of a high-frequency transformer at levels above 1 kVA is limited by the winding and core materials which are available. This res~arch presents methods for the design and modeling of a 10 kVA transformer operating at a frequency of 20 kHz using readily available materials. A special winding technique is employed to increase both energy density and transformation efficiency by reducing leakage inductance and eddy current losses in the windings. The procedures for calculating the equivalent circuit parameters applicable to this design are outlined, and the calculated values compared with the measured quantities. A thermal analysis of the design is also explored using the equivalent circuit model as a basis for the calculation. Some of the calculations are specific to this particular design, whereas others are quite generic, however the overall concepts employed in the design and analysis of this device have widespread application within the area of high-frequency, high-power transformer design. / Master of Science
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Studies of Monitoring and Diagnosis Systems for Substation ApparatusLiang, Yishan 06 January 2006 (has links)
Substation apparatus failure plays a major role in reliability of power delivery systems. Traditionally, most utilities perform regular maintenance in order to prevent equipment breakdown. Condition-based maintenance strategy monitors the condition of the equipment by measuring and analyzing key parameters and recommends optimum maintenance actions. Equipment such as transformers and standby batteries which are valuable and critical assets in substations has attracted increased attentions in recently years.
An automated monitoring and diagnosis tool for power transformers based on dissolved gas analysis, ANNEPS v4.0, was developed. The new tool extended the existing expert system and artificial neural network diagnostic engine with automated data acquisition, display, archiving, and alarm notification functions.
This thesis also studied substation batteries types and failure mode and surveyed the market of current on-line battery monitors. A practical battery monitoring system architecture was proposed. Analysis rules of measured parameters were developed. The above study and results can provide basics for further designing of a simple battery monitoring system in industry applications. / Master of Science
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Transformer Networks for Smart Cities: Framework and Application to Makassar Smart Garden AlleysDeRieux, Alexander Christian 09 September 2022 (has links)
Many countries around the world are undergoing massive urbanization campaigns at an unprecedented rate, heralded by promises of economical prosperity and bolstered population health and well-being. Projections indicate that by 2050, nearly 68% of the world populace will reside in these urban environments. However, rapid growth at such an exceptional scale poses unique challenges pertaining to environmental quality and food production, which can negate the effectiveness of the aforementioned boons. As such, there is an emphasis on mitigating these negative effects through the construction of smart and connected communities (S&CC), which integrate both artificial intelligence (AI) and the Internet of Things (IoT). This coupling of intelligent technologies also poses interesting system design challenges pertaining to the fusion of the diverse, heterogeneous datasets available to IoT environments, and the ability to learn multiple S&CC problem sets concurrently. Attention-based Transformer networks are of particular interest given their success across diverse fields of natural language processing (NLP), computer vision, time-series regression, and multi-modal data fusion in recent years. This begs the question whether Transformers can be further diversified to leverage fusions of IoT data sources for heterogeneous multi-task learning in S&CC trade spaces. This is a fundamental question that this thesis seeks to answer. Indeed, the key contribution of this thesis is the design and application of Transformer networks for developing AI systems in emerging smart cities. This is executed within a collaborative U.S.-Indonesia effort between Virginia Tech, the University of Colorado Boulder, the Universitas Gadjah Mada, and the Institut Teknologi Bandung with the goal of growing smart and sustainable garden alleys in Makassar City, Indonesia. Specifically, a proof-of-concept AI nerve-center is proposed using a backbone of pure-encoder Transformer architectures to learn a diverse set of tasks such as multivariate time-series regression, visual plant disease classification, and image-time-series fusion. To facilitate the data fusion tasks, an effective algorithm is also proposed to synthesize heterogeneous feature sets, such as multivariate time-series and time-correlated images. Moreover, a hyperparameter tuning framework is also proposed to standardize and automate model training regimes. Extensive experimentation shows that the proposed Transformer-based systems can handle various input data types via custom sequence embedding techniques, and are naturally suited to learning a diverse set of tasks. Further, the results also show that multi-task learners increase both memory and computational efficiency while maintaining comparable performance to both single-task variants, and non-Transformer baselines. This demonstrates the flexibility of Transformer networks to learn from a fusion of IoT data sources, their applicability in S&CC trade spaces, and their further potential for deployment on edge computing devices. / Master of Science / Many countries around the world are undergoing massive urbanization campaigns at an unprecedented rate, heralded by promises of economical prosperity and bolstered population health and well-being. Projections indicate that by 2050, nearly 68% of the world populace will reside in these urban environments. However, rapid growth at such an exceptional scale poses unique environmental and food cultivation challenges. Hence, there is a focus on reducing these negative effects through building smart and connected communities (S&CC). The term connected is derived from the integration of small, low-cost devices which gather information from the surrounding environment, called the Internet of Things (IoT). Likewise, smart is a term derived from the integration of artificial intelligence (AI), which is used to make informed decisions based on IoT-collected information. This coupling of intelligent technologies also poses its own unique challenges pertaining to the blending of IoT data with highly diverse characteristics. Of specific interest is the design of AI models that can not only learn from a fusion of this diverse information, but also learn to perform multiple tasks in parallel. Attention-based networks are a relatively new category of AI which learn to focus on, or attend to, the most important portions of an arbitrary data sequence. Transformers are AI models which are designed using attention as their backbone, and have been employed to much success in many fields in recent years. This success begs the question whether Transformers can be further extended to put the smart in S&CC. The overarching goal of this thesis is to design and implement a Transformer-based AI system for emerging smart cities. In particular, this is accomplished within a U.S.-Indonesia collaborative effort with the goal of growing smart and sustainable garden alleys in Makassar City, Indonesia.
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Deep Learning Informed Assistive Technologies for Biomedical and Human Activity ApplicationsBayat, Nasrin 01 January 2024 (has links) (PDF)
This dissertation presents a comprehensive exploration and implementation of attention mechanisms and transformers on several healthcare-related and assistive applications. The overarching goal is to demonstrate successful implementation of the state-of-the-art approaches and provide validated models with their superior performance to inform future research and development. In Chapter 1, attention mechanisms are harnessed for the fine-grained classification of white blood cells (WBCs), showcasing their efficacy in medical diagnostics. The proposed multi-attention framework ensures accurate WBC subtype classification by capturing discriminative features from various layers, leading to superior performance compared to other existing approaches used in previous work. More importantly, the attention-based method showed consistently better results than without attention in all three backbone architectures tested (ResNet, XceptionNet and Efficient- Net). Chapter 2 introduces a self-supervised framework leveraging vision transformers for object detection, semantic and custom algorithms for collision prediction in application to assistive technology for visually impaired. In addition, Multimodal sensory feedback system was designed and fabricated to convey environmental information and potential collisions to the user for real-time navigation and grasping assistance. Chapter 3 presents implementation of transformer-based method for operation-relevant human activity recognition (HAR) and demonstrated its performance over other deep learning model, long-short term memory (LSTM). In addition, feature engineering was used (principal component analysis) to extract most discriminatory and representative motion features from the instrumented sensors, indicating that the joint angle features are more important than body segment orientations. Further, identification of a minimal number and placement of wearable sensors for use in real-world data collections and activity recognitions, addressing the critical gap found in the respective field to enhance the practicality and utility of wearable sensors for HAR. The premise and efficacy of attention-based mechanisms and transformers was confirmed through its demonstrated performance in classification accuracy as compared to LSTM. These research outcomes from three distinct applications of attention-based mechanisms and trans- formers and demonstrated performance over existing models and methods support their utility and applicability across various biomedical and human activity research fields. By sharing the custom designed model architectures, implementation methods, and resulting classification performance has direct impact in the related field by allowing direct adoption and implementation of the developed methods.
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Wavelet-enhanced 2D and 3D Lightweight Perception Systems for autonomous drivingAlaba, Simegnew Yihunie 10 May 2024 (has links) (PDF)
Autonomous driving requires lightweight and robust perception systems that can rapidly and accurately interpret the complex driving environment. This dissertation investigates the transformative capacity of discrete wavelet transform (DWT), inverse DWT, CNNs, and transformers as foundational elements to develop lightweight perception architectures for autonomous vehicles. The inherent properties of DWT, including its invertibility, sparsity, time-frequency localization, and ability to capture multi-scale information, present an inductive bias. Similarly, transformers capture long-range dependency between features. By harnessing these attributes, novel wavelet-enhanced deep learning architectures are introduced. The first contribution is introducing a lightweight backbone network that can be employed for real-time processing. This network balances processing speed and accuracy, outperforming established models like ResNet-50 and VGG16 in terms of accuracy while remaining computationally efficient. Moreover, a multiresolution attention mechanism is introduced for CNNs to enhance feature extraction. This mechanism directs the network's focus toward crucial features while suppressing less significant ones. Likewise, a transformer model is proposed by leveraging the properties of DWT with vision transformers. The proposed wavelet-based transformer utilizes the convolution theorem in the frequency domain to mitigate the computational burden on vision transformers caused by multi-head self-attention. Furthermore, a proposed wavelet-multiresolution-analysis-based 3D object detection model exploits DWT's invertibility, ensuring comprehensive environmental information capture. Lastly, a multimodal fusion model is presented to use information from multiple sensors. Sensors have limitations, and there is no one-fits-all sensor for specific applications. Therefore, multimodal fusion is proposed to use the best out of different sensors. Using a transformer to capture long-range feature dependencies, this model effectively fuses the depth cues from LiDAR with the rich texture derived from cameras. The multimodal fusion model is a promising approach that integrates backbone networks and transformers to achieve lightweight and competitive results for 3D object detection. Moreover, the proposed model utilizes various network optimization methods, including pruning, quantization, and quantization-aware training, to minimize the computational load while maintaining optimal performance. The experimental results across various datasets for classification networks, attention mechanisms, 3D object detection, and multimodal fusion indicate a promising direction in developing a lightweight and robust perception system for robotics, particularly in autonomous driving.
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Semantic search in historical documentationWiklund, Edvin, Maranan Hansson, Ivan Kelly January 2024 (has links)
Many organisations face problems with data digitisation and continuous data gathering. They often gather and store this data in outdated systems that are difficult to search through. In our thesis, we utilise the engineering method to investigate the feasibility of incorporating artificial intelligence to search a large corpus of data and find accurate answers. To achieve the thesis goal, we conducted a literature review, studying existing solutions that enhance flexibility and facilitate artificial intelligence operations to search in databases. This resulted in the choice of utilising OpenSearch. Within OpenSearch, we conducted an experiment investigating which sentence transformer for embedding the contextual meaning of sentences could be best utilised for semantic search in the database. We then evaluated the sentence transformers´s performance with the MS MARCO dataset measuring both speed and accuracy. Through the experiment we found two sentence transformers that outperformed the rest by a slight margin and that all the sentence transformers performed similarly overall. A notable result is that the sentence transformers specifically dedicated to semantic search and sentence transformers with larger dimensions did not perform better. Further, these results showed the easy combination of existing search engines that incorporate artificial intelligence to semantically search in the documentation and showed that this could be used within organisations to handle a large corpus of data.
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Adaptive Anomaly Prediction ModelsFarhangi, Ashkan 01 January 2024 (has links) (PDF)
Anomalies are rare in nature. This rarity makes it difficult for models to provide accurate and reliable predictions. Deep learning models typically excel at identifying underlying patterns from abundant data through supervised learning mechanisms but struggle with anomalies due to their limited representation. This results in a significant portion of errors arising from these rare and poorly represented events. Here, we present various methods and frameworks to develop the specialized ability of models to better detect and predict anomalies. Additionally, we improve the interpretability of these models by enhancing their anomaly awareness, leading to stronger performance on real-world datasets that often contain such anomalies. Because our models dynamically adapt to the significance of anomalies, they benefit from increased accuracy and prioritization of rare events in predictions. We demonstrate such capabilities on real-world datasets across multiple domains. Our results show that this framework enhances accuracy and interpretability, improving upon existing methods in anomaly prediction tasks.
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Toward Transformer-based Large Energy Models for Smart Energy ManagementGu, Yueyan 01 November 2024 (has links)
Buildings contribute significantly to global energy demand and emissions, highlighting the need for precise energy forecasting for effective management. Existing research tends to focus on specific target problems, such as individual buildings or small groups of buildings, leading to current challenges in data-driven energy forecasting, including dependence on data quality and quantity, limited generalizability, and computational inefficiency. To address these challenges, Generalized Energy Models (GEMs) for energy forecasting can potentially be developed using large-scale datasets. Transformer architectures, known for their scalability, ability to capture long-term dependencies, and efficiency in parallel processing of large datasets, are considered good candidates for GEMs. In this study, we tested the hypothesis that GEMs can be efficiently developed to outperform in-situ models trained on individual buildings. To this end, we investigated and compared three candidate multi-variate Transformer architectures, utilizing both zero-shot and fine-tuning strategies, with data from 1,014 buildings. The results, evaluated across three prediction horizons (24, 72, and 168 hours), confirm that GEMs significantly outperform Transformer-based in-situ (i.e., building-specific) models. Fine-tuned GEMs showed performance improvements of up to 28% and reduced training time by 55%. Besides Transformer-based in-situ models, GEMs outperformed several state-of-the-art non-Transformer deep learning baseline models in efficiency and efficiency. We further explored the answer to a number of questions including the required data size for effective fine-tuning, as well as the impact of input sub-sequence length and pre-training dataset size on GEM performance. The findings show a significant performance boost by using larger pre-training datasets, highlighting the potential for larger GEMs using web-scale global data to move toward Large Energy Models (LEM). / Master of Science / Buildings account for a large share of global energy use and emissions, which makes predicting their energy needs critical for better management. However, most research focuses on creating energy models for specific buildings or small groups, which limits their usefulness for larger-scale applications. Additionally, these models often face challenges such as relying on high-quality data, limited adaptability to different buildings, and inefficiencies when dealing with large amounts of data. This study aims to address these issues by developing Generalized Energy Models (GEMs), which use data from a large number of buildings to create more versatile and efficient energy forecasting tools. To achieve this, we used Transformer models, a type of machine learning approach known for handling large datasets efficiently and recognizing long-term patterns. We tested whether GEMs could provide better predictions than traditional models designed for individual buildings. Our analysis included data from over 1,000 buildings and used two strategies: zero-shot (using the model without further adjustments) and fine-tuning (adapting the model to specific data). The results showed that GEMs were more accurate than traditional models, improving prediction accuracy by up to 28% and reducing the time needed for training by over 50%. Additionally, GEMs outperformed other advanced methods of energy forecasting. We also examined how different factors, such as the amount of data and the length of the data sequences, influenced the model’s performance. The findings suggest that using even larger datasets could lead to further improvements, opening the possibility of creating Large Energy Models (LEMs) that can make predictions on a global scale.
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Flexible multimodal learning for whole slide image analysisSharma, Harsh 06 March 2025 (has links)
2024 / Whole slide imaging (WSI) has revolutionized digital pathology, enabling the digitization of entire tissue samples on glass slides into high-resolution images, providing a detailed view of tissue morphology essential for analysis and interpretation. However, practical challenges arise due to the varying availability and cost of different slide staining techniques. Hematoxylin and Eosin (H&E) staining is commonly performed and cost-effective, but it offers limited information compared to more expensive and less frequently conducted immunohistochemical (IHC) staining, which provides specific molecular insights critical for diagnosing conditions like Alzheimer’s disease (AD) and chronic traumatic encephalopathy (CTE). Addressing the need for a comprehensive analysis in the absence of complete modality sets, this research introduces ”MultiStainKD ,” a novel framework designed to handle multimodal WSI data effectively, even when certain modalities, such as IHC, are missing. MultiStainKD employs a combination of computational architectural design and training strategies, optimizing the use of available data to enhance diagnostic accuracy. It utilizes a blend of convolutional and transformer-based components to capture the nuanced interplay of local and global tissue features, thus ensuring robust performance without the necessity for costly feature generation processes traditionally used to compensate for missing modalities. Our extensive testing shows MultiStainKD’s superior capability in managing missing modalities and setting new benchmarks in AD and CTE prediction accuracy. By leveraging the inherent complementary information within accessible stains and slides, MultiStainKD demonstrates its practical value and effectiveness, aligning with the goal of maintaining diagnostic quality despite the unavailability of comprehensive staining modalities. This advancement signifies an important step forward in multimodal learning for digital pathology, potentially enhancing diagnostic accuracy and supporting patient care when full modality data is not available.
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Design of Tunable/Reconfigurable and Compact Microwave DevicesZhou, Mi 05 1900 (has links)
With the rapid development of the modern technology, radio frequency and microwave systems are playing more and more important roles. Since the time the first microwave device was invented, they have been leading not only the military but also our daily life to a new era. In order to make the devices have more practical applications, more and more strict requirements have been imposed. For example, good adaptability, reduced cost and shrank size are highly required. In this thesis, three devices are designed based on this requirement. At first, a symmetric four-port microwave varactor based 90-degree directional coupler with tunable coupling ratios and reconfigurable responses is presented. The proposed coupler is designed based on the modified structure of a crossover, where varactors are loaded. Then, a novel reconfigurable 3-dB directional coupler is presented. Varactors and inductors are loaded to the device to realize the reconfigurable performance. By adjusting the voltage applied to the varactors, the proposed coupler can be reconfigured from a branch-line coupler (90-degree coupler) to a rat-race coupler (180 degree coupler) and vice versa. At last, two types (Type-I and Type-II) of microwave baluns with generalized structures are presented. Different from the conventional transmission-line-based baluns where λ/2 transmission lines or λ/4 coupled lines are used, the proposed baluns are constructed by transmission lines with arbitrary electrical lengths.
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