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Full-Text Articles in Engineering
Model Optimization And Applications In Deep Learning, Chengchen Mao
Model Optimization And Applications In Deep Learning, Chengchen Mao
Electrical Engineering Dissertations
ABSTRACT: Machine learning refers to a machine or an algorithm that draws experience from data. A certain pattern is found to build a model, which is used to solve real problems. Deep learning, an important branch and extension of machine learning, employs a neural network structure containing multiple hidden layers. It learns critical features of the data by combining lower-level features to form more abstract higher-level representations of attribute categories or features. In this dissertation, deep learning network models were applied to sense-through-foliage target detection and extended with Rake structure. The deep learning network models had a large number of …
Advancing The Radiation Oncology Clinic With Motion Management And Automatic Treatment Planning, Damon Anton Sprouts
Advancing The Radiation Oncology Clinic With Motion Management And Automatic Treatment Planning, Damon Anton Sprouts
Bioengineering Dissertations
The leading cause of premature death (death under the age of 70) is cancer. The top five cancers for both male and female are: lung, colorectum, pancreas, breast cancer, and prostate. In 2020 there was an estimated 19.3 million new cases with an estimated 9.9 million deaths. The cancer burden is expected to grow to 28.4 million by the year 2040. Surgery, chemotherapy, and radiotherapy are the three pillars in the modern clinic for cancer treatment. In radiotherapy, ionizing radiation particles can travel through the patient body, deposit energy along the way and damage the DNA Structure. There needs to …
Exploring Deep Learning In Finance, Abhijit Anand Anand Deshpande
Exploring Deep Learning In Finance, Abhijit Anand Anand Deshpande
Industrial, Manufacturing, and Systems Theses
Financial market analysis is process of analyzing market closely and predict the next move of market whether it will go up or down using historical data. Financial market is stochastic and has rapid changes over time, therefore it is very difficult to predict. The main goal of this work is to understand novel approaches of machine learning in finance, data parsing techniques, labelling the financial data. Furthermore, understand state of art Transformer model and implement and compare results with other traditional machine learning algorithms. Experiment carried out in python along with pytorch.
Efficient, Low-Cost Bridge Cracking Detection And Quantification Using Deep-Learning And Uav Images, Chao Sun, Xiangyu Meng, Joshua O. Ogbebor, Shaopan Guo
Efficient, Low-Cost Bridge Cracking Detection And Quantification Using Deep-Learning And Uav Images, Chao Sun, Xiangyu Meng, Joshua O. Ogbebor, Shaopan Guo
Data
Many bridges in the State of Louisiana and the United States are working under serious degradation conditions where cracks on bridges threaten structural integrity and public security. To ensure structural integrity and public security, it is required that bridges in the US be inspected and rated every two years. Currently, this biannual assessment is largely implemented using manual visual inspection methods, which is slow and costly. In addition, it is challenging for workers to detect cracks in regions that are hard to reach, e.g., the top part of the bridge tower, cables, mid-span of the bridge girders, and decks. This …
Resource Allocation And Capacity In Wireless Communications And Networks, Zikai Wang
Resource Allocation And Capacity In Wireless Communications And Networks, Zikai Wang
Electrical Engineering Dissertations
How to allocate resources in the era of Big Data in telecommunications becomes a new issue. Smartphone data could be a function of personality, as the smartphone supports interpersonal interaction, and the data collected from the smartphone usage often contains rich customer opinion and behavioral information. A bandwidth allocation method based on smartphone users' personality traits and channel condition is studied in a unified mathematical framework in this dissertation. Personalizing bandwidth allocation could be done by analyzing smartphone users' personality traits, resulting in business intelligence, a smarter and more efficient usage of the limited bandwidth, while taking channel fading conditions …
Multiscale Modeling And Simulation Of Clutter In Isar Imaging, Jon Mitchell
Multiscale Modeling And Simulation Of Clutter In Isar Imaging, Jon Mitchell
Electrical Engineering Dissertations
Clutter is common in applications of radar imaging and can adversely impact target imaging by contributing scattered energy that is not accounted for in target signal models. One potential source of clutter is moving foliage in the vicinity of the target, such as a target embedded in a forest. ISAR imaging of moving clutter results in an equivalent current image that changes over each imaging sample. The stochastic nature of this clutter equivalent current presents challenges in detecting and imaging a weak embedded target using traditional algorithms. This dissertation proposes a multiscale model and analysis method to characterize the multiscale …
Multivariate Time Series Pattern Recognition Using Machine Learning And Deep Learning Methods, Sai Abhishek Devar
Multivariate Time Series Pattern Recognition Using Machine Learning And Deep Learning Methods, Sai Abhishek Devar
Industrial, Manufacturing, and Systems Theses
In this research work, we have implemented machine learning & deep-learning algorithms on real-time multivariate time series datasets in the manufacturing & health care fields. The research work is organized in two case-studies. The case study-1 is about rare event classification in multivariate time series in a pulp and paper manufacturing industry, data was collected of multiple sensors at each stage of production line, the data contains a rare event of paper break that commonly occurs in the industry. For preprocessing we have implemented sliding window approach for calculating first order difference method to capture the variation in the data …
Towards Automated Understanding Of Laparoscopic Videos, Babak Namazi
Towards Automated Understanding Of Laparoscopic Videos, Babak Namazi
Electrical Engineering Dissertations
Despite the advantages of minimally invasive surgeries, the indirect access and lack of the 3D field of view of the area of interest introduce complications in the procedures. Fortunately, the recorded videos from the operation offer the opportunity for intra-operative and post-operative analyses of the procedures, to improve future performance and safety. Such analysis is essential to provide the tools for evaluation and assessment of the surgeries. In this dissertation, we investigate the potential of deep learning techniques in understanding the videos captured during laparoscopic surgeries. To this end, we describe new methods for identifying the surgical instruments and the …
Automated Multistep Classifier Sizing And Training For Deep Learner, Kanishka Tyagi
Automated Multistep Classifier Sizing And Training For Deep Learner, Kanishka Tyagi
Electrical Engineering Dissertations
Training algorithms for deep learning have recently been proposed with notable success, beating the start-of-the-art in certain areas like audio, speech and language processing. The key role is played by learning multiple levels of abstractions in a deep architecture. However, searching the parameters space in a deep architecture is a difficult task. By exploiting the greedy layer-wise unsupervised training strategy of deep architecture, the network parameters are initialized near a good local minima. However, many existing deep learning algorithms require tuning a number of hyperparameters including learning factors and the number of hidden units in each layer. Apart from this, …