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Articles 1 - 10 of 10
Full-Text Articles in Engineering
Decoding Usage And Adoption Behavior Of The Low-Carbon Transportation Market: An Ai-Driven Exploration, Vuban Chowdhury
Decoding Usage And Adoption Behavior Of The Low-Carbon Transportation Market: An Ai-Driven Exploration, Vuban Chowdhury
Graduate Theses and Dissertations
The transportation sector stands as a significant contributor to greenhouse gas emissions in the United States, with its environmental impact steadily escalating over the past few decades. This has prompted government agencies to facilitate the adoption and usage of low-carbon transportation (LCT) options as alternatives to fossil-fuel-powered transportation. LCTs include modes of transportation that minimize the overall carbon footprint of the transportation sector by relying on energy sources that are environmentally sustainable. These sustainable transportation options have also garnered significant interest in the transportation research community. For government agencies and researchers alike, a comprehensive understanding of the adoption and usage …
Improving Classification In Single And Multi-View Images, Hadi Kanaan Hadi Salman
Improving Classification In Single And Multi-View Images, Hadi Kanaan Hadi Salman
Graduate Theses and Dissertations
Image classification is a sub-field of computer vision that focuses on identifying objects within digital images. In order to improve image classification we must address the following areas of improvement: 1) Single and Multi-View data quality using data pre-processing techniques. 2) Enhancing deep feature learning to extract alternative representation of the data. 3) Improving decision or prediction of labels. This dissertation presents a series of four published papers that explore different improvements of image classification. In our first paper, we explore the Siamese network architecture to create a Convolution Neural Network based similarity metric. We learn the priority features that …
Using Reinforcement Learning To Improve Network Reliability Through Optimal Resource Allocation, Henley Wells
Using Reinforcement Learning To Improve Network Reliability Through Optimal Resource Allocation, Henley Wells
Graduate Theses and Dissertations
Networks provide a variety of critical services to society (e.g. power grid, telecommunication, water, transportation) but are prone to disruption. With this motivation, we study a sequential decision problem in which an initial network is improved over time (e.g., by adding or increasing the reliability of edges) and rewards are gained over time as a function of the network’s all-terminal reliability. The actions during each time period are limited due to availability of resources such as time, money, or labor. To solve this problem, we utilized a Deep Reinforcement Learning (DRL) approach implemented within OpenAI-Gym using Stable Baselines. A Proximal …
Supervised Representation Learning For Improving Prediction Performance In Medical Decision Support Applications, Phawis Thammasorn
Supervised Representation Learning For Improving Prediction Performance In Medical Decision Support Applications, Phawis Thammasorn
Graduate Theses and Dissertations
Machine learning approaches for prediction play an integral role in modern-day decision supports system. An integral part of the process is extracting interest variables or features to describe the input data. Then, the variables are utilized for training machine-learning algorithms to map from the variables to the target output. After the training, the model is validated with either validation or testing data before making predictions with a new dataset. Despite the straightforward workflow, the process relies heavily on good feature representation of data. Engineering suitable representation eases the subsequent actions and copes with many practical issues that potentially prevent the …
Deep Learning Strategies For Pool Boiling Heat Flux Prediction Using Image Sequences, Connor Heo
Deep Learning Strategies For Pool Boiling Heat Flux Prediction Using Image Sequences, Connor Heo
Graduate Theses and Dissertations
The understanding of bubble dynamics during boiling is critical to the design of advanced heater surfaces to improve the boiling heat transfer. The stochastic bubble nucleation, growth, and coalescence processes have made it challenging to obtain mechanistic models that can predict boiling heat flux based on the bubble dynamics. Traditional boiling image analysis relies on the extraction of the dominant physical quantities from the images and is thus limited to the existing knowledge of these quantities. Recently, machine-learning-aided analysis has shown success in boiling crisis detection, heat flux prediction, real-time image analysis, etc., whereas most of the existing studies are …
Statistical Modeling, Learning And Computing For Stochastic Dynamics Of Complex Systems, Mohammadmahdi Hajiha
Statistical Modeling, Learning And Computing For Stochastic Dynamics Of Complex Systems, Mohammadmahdi Hajiha
Graduate Theses and Dissertations
With the recent advances in sensor technology, it is much easier to collect and store streams of system operational and environmental (SOE) data. These data can be used as input to model the underlying behavior of complex engineered systems and phenomenons if appropriate algorithms with well-defined assumptions are developed. This dissertation is comprised of the research work to show the applicability of SOE data when fed into proposed tailored algorithms. The first purposes of these algorithms are to estimate and analyze the reliability of a system as elaborated in Chapter 2. This chapter provides the derivation of closed-form expressions that …
Signal Processing And Data Analysis For Real-Time Intermodal Freight Classification Through A Multimodal Sensor System., Enrique J. Sanchez Headley
Signal Processing And Data Analysis For Real-Time Intermodal Freight Classification Through A Multimodal Sensor System., Enrique J. Sanchez Headley
Graduate Theses and Dissertations
Identifying freight patterns in transit is a common need among commercial and municipal entities. For example, the allocation of resources among Departments of Transportation is often predicated on an understanding of freight patterns along major highways. There exist multiple sensor systems to detect and count vehicles at areas of interest. Many of these sensors are limited in their ability to detect more specific features of vehicles in traffic or are unable to perform well in adverse weather conditions. Despite this limitation, to date there is little comparative analysis among Laser Imaging and Detection and Ranging (LIDAR) sensors for freight detection …
Statistical Machine Learning For Breast Cancer Detection With Terahertz Imaging, Tanny Andrea Chavez Esparza
Statistical Machine Learning For Breast Cancer Detection With Terahertz Imaging, Tanny Andrea Chavez Esparza
Graduate Theses and Dissertations
Breast conserving surgery (BCS) is a common breast cancer treatment option, in which the cancerous tissue is excised while leaving most of the healthy breast tissue intact. The lack of in-situ margin evaluation unfortunately results in a re-excision rate of 20-30% for this type of procedure. This study aims to design statistical and machine learning segmentation algorithms for the detection of breast cancer in BCS by using terahertz (THz) imaging. Given the material characterization properties of the non-ionizing radiation in the THz range, we intend to employ the responses from the THz system to identify healthy and cancerous breast tissue …
Analog Spiking Neural Network Implementing Spike Timing-Dependent Plasticity On 65 Nm Cmos, Luke Vincent
Analog Spiking Neural Network Implementing Spike Timing-Dependent Plasticity On 65 Nm Cmos, Luke Vincent
Graduate Theses and Dissertations
Machine learning is a rapidly accelerating tool and technology used for countless applications in the modern world. There are many digital algorithms to deploy a machine learning program, but the most advanced and well-known algorithm is the artificial neural network (ANN). While ANNs demonstrate impressive reinforcement learning behaviors, they require large power consumption to operate. Therefore, an analog spiking neural network (SNN) implementing spike timing-dependent plasticity is proposed, developed, and tested to demonstrate equivalent learning abilities with fractional power consumption compared to its digital adversary.
Operating System Identification By Ipv6 Communication Using Machine Learning Ensembles, Adrian Ordorica
Operating System Identification By Ipv6 Communication Using Machine Learning Ensembles, Adrian Ordorica
Graduate Theses and Dissertations
Operating system (OS) identification tools, sometimes called fingerprinting tools, are essential for the reconnaissance phase of penetration testing. While OS identification is traditionally performed by passive or active tools that use fingerprint databases, very little work has focused on using machine learning techniques. Moreover, significantly more work has focused on IPv4 than IPv6. We introduce a collaborative neural network ensemble that uses a unique voting system and a random forest ensemble to deliver accurate predictions. This approach uses IPv6 features as well as packet metadata features for OS identification. Our experiment shows that our approach is valid and we achieve …