Matching Items (1,190)
Filtering by

Clear all filters

150059-Thumbnail Image.png
Description
Dynamic loading is the term used for one way of optimally loading a transformer. Dynamic loading means the utility takes into account the thermal time constant of the transformer along with the cooling mode transitions, loading profile and ambient temperature when determining the time-varying loading capability of a transformer. Knowing

Dynamic loading is the term used for one way of optimally loading a transformer. Dynamic loading means the utility takes into account the thermal time constant of the transformer along with the cooling mode transitions, loading profile and ambient temperature when determining the time-varying loading capability of a transformer. Knowing the maximum dynamic loading rating can increase utilization of the transformer while not reducing life-expectancy, delaying the replacement of the transformer. This document presents the progress on the transformer dynamic loading project sponsored by Salt River Project (SRP). A software application which performs dynamic loading for substation distribution transformers with appropriate transformer thermal models is developed in this project. Two kinds of thermal hottest-spot temperature (HST) and top-oil temperature (TOT) models that will be used in the application--the ASU HST/TOT models and the ANSI models--are presented. Brief validations of the ASU models are presented, showing that the ASU models are accurate in simulating the thermal processes of the transformers. For this production grade application, both the ANSI and the ASU models are built and tested to select the most appropriate models to be used in the dynamic loading calculations. An existing application to build and select the TOT model was used as a starting point for the enhancements developed in this work. These enhancements include:  Adding the ability to develop HST models to the existing application,  Adding metrics to evaluate the models accuracy and selecting which model will be used in dynamic loading calculation  Adding the capability to perform dynamic loading calculations,  Production of a maximum dynamic load profile that the transformer can tolerate without acceleration of the insulation aging,  Provide suitable output (plots and text) for the results of the dynamic loading calculation. Other challenges discussed include: modification to the input data format, data-quality control, cooling mode estimation. Efforts to overcome these challenges are discussed in this work.
ContributorsLiu, Yi (Author) / Tylavksy, Daniel J (Thesis advisor) / Karady, George G. (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2011
150019-Thumbnail Image.png
Description
Currently Java is making its way into the embedded systems and mobile devices like androids. The programs written in Java are compiled into machine independent binary class byte codes. A Java Virtual Machine (JVM) executes these classes. The Java platform additionally specifies the Java Native Interface (JNI). JNI allows Java

Currently Java is making its way into the embedded systems and mobile devices like androids. The programs written in Java are compiled into machine independent binary class byte codes. A Java Virtual Machine (JVM) executes these classes. The Java platform additionally specifies the Java Native Interface (JNI). JNI allows Java code that runs within a JVM to interoperate with applications or libraries that are written in other languages and compiled to the host CPU ISA. JNI plays an important role in embedded system as it provides a mechanism to interact with libraries specific to the platform. This thesis addresses the overhead incurred in the JNI due to reflection and serialization when objects are accessed on android based mobile devices. It provides techniques to reduce this overhead. It also provides an API to access objects through its reference through pinning its memory location. The Android emulator was used to evaluate the performance of these techniques and we observed that there was 5 - 10 % performance gain in the new Java Native Interface.
ContributorsChandrian, Preetham (Author) / Lee, Yann-Hang (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2011
149977-Thumbnail Image.png
Description
Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from

Reliable extraction of human pose features that are invariant to view angle and body shape changes is critical for advancing human movement analysis. In this dissertation, the multifactor analysis techniques, including the multilinear analysis and the multifactor Gaussian process methods, have been exploited to extract such invariant pose features from video data by decomposing various key contributing factors, such as pose, view angle, and body shape, in the generation of the image observations. Experimental results have shown that the resulting pose features extracted using the proposed methods exhibit excellent invariance properties to changes in view angles and body shapes. Furthermore, using the proposed invariant multifactor pose features, a suite of simple while effective algorithms have been developed to solve the movement recognition and pose estimation problems. Using these proposed algorithms, excellent human movement analysis results have been obtained, and most of them are superior to those obtained from state-of-the-art algorithms on the same testing datasets. Moreover, a number of key movement analysis challenges, including robust online gesture spotting and multi-camera gesture recognition, have also been addressed in this research. To this end, an online gesture spotting framework has been developed to automatically detect and learn non-gesture movement patterns to improve gesture localization and recognition from continuous data streams using a hidden Markov network. In addition, the optimal data fusion scheme has been investigated for multicamera gesture recognition, and the decision-level camera fusion scheme using the product rule has been found to be optimal for gesture recognition using multiple uncalibrated cameras. Furthermore, the challenge of optimal camera selection in multi-camera gesture recognition has also been tackled. A measure to quantify the complementary strength across cameras has been proposed. Experimental results obtained from a real-life gesture recognition dataset have shown that the optimal camera combinations identified according to the proposed complementary measure always lead to the best gesture recognition results.
ContributorsPeng, Bo (Author) / Qian, Gang (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Spanias, Andreas (Committee member) / Arizona State University (Publisher)
Created2011
150050-Thumbnail Image.png
Description
The development of a Solid State Transformer (SST) that incorporates a DC-DC multiport converter to integrate both photovoltaic (PV) power generation and battery energy storage is presented in this dissertation. The DC-DC stage is based on a quad-active-bridge (QAB) converter which not only provides isolation for the load, but also

The development of a Solid State Transformer (SST) that incorporates a DC-DC multiport converter to integrate both photovoltaic (PV) power generation and battery energy storage is presented in this dissertation. The DC-DC stage is based on a quad-active-bridge (QAB) converter which not only provides isolation for the load, but also for the PV and storage. The AC-DC stage is implemented with a pulse-width-modulated (PWM) single phase rectifier. A unified gyrator-based average model is developed for a general multi-active-bridge (MAB) converter controlled through phase-shift modulation (PSM). Expressions to determine the power rating of the MAB ports are also derived. The developed gyrator-based average model is applied to the QAB converter for faster simulations of the proposed SST during the control design process as well for deriving the state-space representation of the plant. Both linear quadratic regulator (LQR) and single-input-single-output (SISO) types of controllers are designed for the DC-DC stage. A novel technique that complements the SISO controller by taking into account the cross-coupling characteristics of the QAB converter is also presented herein. Cascaded SISO controllers are designed for the AC-DC stage. The QAB demanded power is calculated at the QAB controls and then fed into the rectifier controls in order to minimize the effect of the interaction between the two SST stages. The dynamic performance of the designed control loops based on the proposed control strategies are verified through extensive simulation of the SST average and switching models. The experimental results presented herein show that the transient responses for each control strategy match those from the simulations results thus validating them.
ContributorsFalcones, Sixifo Daniel (Author) / Ayyanar, Raja (Thesis advisor) / Karady, George G. (Committee member) / Tylavsky, Daniel (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2011
149991-Thumbnail Image.png
Description
With the introduction of compressed sensing and sparse representation,many image processing and computer vision problems have been looked at in a new way. Recent trends indicate that many challenging computer vision and image processing problems are being solved using compressive sensing and sparse representation algorithms. This thesis assays some applications

With the introduction of compressed sensing and sparse representation,many image processing and computer vision problems have been looked at in a new way. Recent trends indicate that many challenging computer vision and image processing problems are being solved using compressive sensing and sparse representation algorithms. This thesis assays some applications of compressive sensing and sparse representation with regards to image enhancement, restoration and classication. The first application deals with image Super-Resolution through compressive sensing based sparse representation. A novel framework is developed for understanding and analyzing some of the implications of compressive sensing in reconstruction and recovery of an image through raw-sampled and trained dictionaries. Properties of the projection operator and the dictionary are examined and the corresponding results presented. In the second application a novel technique for representing image classes uniquely in a high-dimensional space for image classification is presented. In this method, design and implementation strategy of the image classification system through unique affine sparse codes is presented, which leads to state of the art results. This further leads to analysis of some of the properties attributed to these unique sparse codes. In addition to obtaining these codes, a strong classier is designed and implemented to boost the results obtained. Evaluation with publicly available datasets shows that the proposed method outperforms other state of the art results in image classication. The final part of the thesis deals with image denoising with a novel approach towards obtaining high quality denoised image patches using only a single image. A new technique is proposed to obtain highly correlated image patches through sparse representation, which are then subjected to matrix completion to obtain high quality image patches. Experiments suggest that there may exist a structure within a noisy image which can be exploited for denoising through a low-rank constraint.
ContributorsKulkarni, Naveen (Author) / Li, Baoxin (Thesis advisor) / Ye, Jieping (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
150353-Thumbnail Image.png
Description
Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find

Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find better solutions. In this thesis, a novel method is proposed which uses image registration techniques to provide better image classification. This method reduces the error rate of classification by performing image registration of the images with the previously obtained images before performing classification. The motivation behind this is the fact that images that are obtained in the same region which need to be classified will not differ significantly in characteristics. Hence, registration will provide an image that matches closer to the previously obtained image, thus providing better classification. To illustrate that the proposed method works, naïve Bayes and iterative closest point (ICP) algorithms are used for the image classification and registration stages respectively. This implementation was tested extensively in simulation using synthetic images and using a real life data set called the Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) dataset. The results show that the ICP algorithm does help in better classification with Naïve Bayes by reducing the error rate by an average of about 10% in the synthetic data and by about 7% on the actual datasets used.
ContributorsMuralidhar, Ashwini (Author) / Saripalli, Srikanth (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2011
150362-Thumbnail Image.png
Description
There are many wireless communication and networking applications that require high transmission rates and reliability with only limited resources in terms of bandwidth, power, hardware complexity etc.. Real-time video streaming, gaming and social networking are a few such examples. Over the years many problems have been addressed towards the goal

There are many wireless communication and networking applications that require high transmission rates and reliability with only limited resources in terms of bandwidth, power, hardware complexity etc.. Real-time video streaming, gaming and social networking are a few such examples. Over the years many problems have been addressed towards the goal of enabling such applications; however, significant challenges still remain, particularly, in the context of multi-user communications. With the motivation of addressing some of these challenges, the main focus of this dissertation is the design and analysis of capacity approaching coding schemes for several (wireless) multi-user communication scenarios. Specifically, three main themes are studied: superposition coding over broadcast channels, practical coding for binary-input binary-output broadcast channels, and signalling schemes for two-way relay channels. As the first contribution, we propose an analytical tool that allows for reliable comparison of different practical codes and decoding strategies over degraded broadcast channels, even for very low error rates for which simulations are impractical. The second contribution deals with binary-input binary-output degraded broadcast channels, for which an optimal encoding scheme that achieves the capacity boundary is found, and a practical coding scheme is given by concatenation of an outer low density parity check code and an inner (non-linear) mapper that induces desired distribution of "one" in a codeword. The third contribution considers two-way relay channels where the information exchange between two nodes takes place in two transmission phases using a coding scheme called physical-layer network coding. At the relay, a near optimal decoding strategy is derived using a list decoding algorithm, and an approximation is obtained by a joint decoding approach. For the latter scheme, an analytical approximation of the word error rate based on a union bounding technique is computed under the assumption that linear codes are employed at the two nodes exchanging data. Further, when the wireless channel is frequency selective, two decoding strategies at the relay are developed, namely, a near optimal decoding scheme implemented using list decoding, and a reduced complexity detection/decoding scheme utilizing a linear minimum mean squared error based detector followed by a network coded sequence decoder.
ContributorsBhat, Uttam (Author) / Duman, Tolga M. (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Li, Baoxin (Committee member) / Zhang, Junshan (Committee member) / Arizona State University (Publisher)
Created2011
148122-Thumbnail Image.png
Description

Coverage of Black soccer players by Italian media outlets perpetuate narratives rooted in anti-Black racism. These narratives reflect the country’s changing attitude toward immigration. Historically a country from which citizens emigrated, it is now a recipient of immigrants from Africa. These changing demographics have also caused a shift in the

Coverage of Black soccer players by Italian media outlets perpetuate narratives rooted in anti-Black racism. These narratives reflect the country’s changing attitude toward immigration. Historically a country from which citizens emigrated, it is now a recipient of immigrants from Africa. These changing demographics have also caused a shift in the focus of racism in Italy, from discrimination against southern Italians to anti-Black racism. As the country has explored what defines a unified Italian identity, Afro-Italians have been excluded. This study evaluates how these perceptions of Afro-Italian soccer players manifest according to various racial frames, and the frequency with which they do so in three Italian sports dailies: La Gazzetta dello Sport, Corriere dello Sport – Stadio, and Tuttosport. In this context, Afro-Italian refers to an Italian citizen of African descent, and anti-Black racism denotes any form of discrimination, stereotyping, or racism that specifically impacts those of African descent. For this study, a representative sample was collected consisting of website coverage published by the three sports dailies: articles devoted to Mario Balotelli that appeared between 2007 and 2014, and articles devoted to Moise Kean between 2016 and 2019. Three coders recorded the content of the sample articles on a spreadsheet organized by the type of racial frame applied to Black athletes. The analysis reveals that the players were frequently portrayed as being incapable of self-determination and of having an innate, natural athletic capability, rather than one honed through practice. The coders noted that in addition to explicit racial framing, there were also instances of implicit and subtle ways these racial frames manifest. In future research, the coding procedure will need to be adapted to account for these more layered and nuanced manifestations of anti-Black racism.

Created2021-05
148123-Thumbnail Image.png
Description

When examining the average college campus, it becomes obvious that students feel rushed from one place to another as they try to participate in class, clubs, and extracurricular activities. One way that students can feel more comfortable and relaxed around campus is to introduce the aspect of gaming. Studies show

When examining the average college campus, it becomes obvious that students feel rushed from one place to another as they try to participate in class, clubs, and extracurricular activities. One way that students can feel more comfortable and relaxed around campus is to introduce the aspect of gaming. Studies show that “Moderate videogame play has been found to contribute to emotional stability” (Jones, 2014). This demonstrates that the stress of college can be mitigated by introducing the ability to interact with video games. This same concept has been applied in the workplace, where studies have shown that “Gaming principles such as challenges, competition, rewards and personalization keep employees engaged and learning” (Clark, 2020). This means that if we manage to gamify the college experience, students will be more engaged which will increase and stabilize the retention rate of colleges which utilize this type of experience. Gaming allows students to connect with their peers in a casual environment while also allowing them to find resources around campus and find new places to eat and relax. We plan to gamify the college experience by introducing augmented reality in the form of an app. Augmented reality is “. . . a technology that combines virtual information with the real world” (Chen, 2019). College students will be able to utilize the resources and amenities available to them on campus while completing quests that help them within the application. This demonstrates the ability for video games to engage students using artificial tasks but real actions and experiences which help them feel more connected to campus. Our Founders Lab team has developed and tested an AR application that can be used to connect students with their campus and the resources available to them.

ContributorsKlein, Jonathan (Co-author) / Rangarajan, Padmapriya (Co-author) / Li, Shimei (Co-author) / Byrne, Jared (Thesis director) / Pierce, John (Committee member) / School of International Letters and Cultures (Contributor) / Department of Management and Entrepreneurship (Contributor) / Sandra Day O'Connor College of Law (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
148124-Thumbnail Image.png
Description

Before the COVID-19 pandemic, there was a great need for United States’ restaurants to “go green” due to consumers’ habits of frequently eating out. Unfortunately, COVID-19 has caused this initiative to lose traction. While the amount of customers ordering takeout has increased, there is less emphasis on sustainability.<br/>Plastic is known

Before the COVID-19 pandemic, there was a great need for United States’ restaurants to “go green” due to consumers’ habits of frequently eating out. Unfortunately, COVID-19 has caused this initiative to lose traction. While the amount of customers ordering takeout has increased, there is less emphasis on sustainability.<br/>Plastic is known for its harmful effects on the environment and the extreme length of time it takes to decompose. According to the International Union for Conservation of Nature (IUCN), almost 8 million tons of plastic end up in the oceans at an annual rate, threatening not only the safety of marine species but also human health. Modern food packaging materials have included a blend of synthetic ingredients, trickling into our daily lives and polluting the air, water, and land. Single-use plastic items slowly degrade into microplastics and can take up to hundreds of years to biodegrade.<br/>Due to COVID-19, restaurants have switched to takeout and delivery options to adapt to the new business environment and guidelines enforced by the Center of Disease Control (CDC) mandated guidelines. Some of these guidelines include: notices encouraging social distancing and mask-wearing, mandated masks for employees, and easy access to sanitary supplies. This cultural shift is motivating restaurants to search for a quick, cheap, and easy fix to adapt to the increased demand of take-out and delivery methods. This increases their plastic consumption of items such as plastic bags/paper bags, styrofoam containers, and beverage cups. Plastic is the most popular takeout material because of its price and durability as well as allowing for limited contamination and easy disposability.<br/>Almost all food products come in packaging and this, more often than not, is single-use. Food is the largest market out of all the packaging industry, maintaining roughly two-thirds of material going to food. The US Environmental Protection Agency reports that almost half of all municipal solid waste is made up of food and food packaging materials. In 2014, over 162 million tons of packaging material waste was generated in the states. This typically contains toxic inks and dyes that leach into groundwater and soil. When degrading, pieces of plastic absorb toxins like PCBs and pesticides, and then each piece will, in turn, release toxic chemicals like Bisphenol-A. Even before being thrown away, it causes negative effects for the environment. The creation of packaging materials uses many resources such as petroleum and chemicals and then releases toxic byproducts. Such byproducts include sludge containing contaminants, greenhouse gases, and heavy metal and particulate matter emissions. Unlike many other industries, plastic manufacturing has actually increased production. Demand has increased and especially in the food industry to keep things sanitary. This increase in production is reflective of the increase in waste. <br/>Although restaurants have implemented their own sustainable initiatives to combat their carbon footprint, the pandemic has unfortunately forced restaurants to digress. For example, Just Salad, a fast-food restaurant chain, incentivized customers with discounted meals to use reusable bowls which saved over 75,000 pounds of plastic per year. However, when the pandemic hit, the company halted the program to pivot towards takeout and delivery. This effect is apparent on an international scale. Singapore was in lock-down for eight weeks and during that time, 1,470 tons of takeout and food delivery plastic waste was thrown out. In addition, the Hong Kong environmental group Greeners Action surveyed 2,000 people in April and the results showed that people are ordering out twice as much as last year, doubling the use of plastic.<br/>However, is this surge of plastic usage necessary in the food industry or are there methods that can be used to reduce the amount of waste production? The COVID-19 pandemic caused a fracture in the food system’s supply chain, involving food, factory, and farm. This thesis will strive to tackle such topics by analyzing the supply chains of the food industry and identify areas for sustainable opportunities. These recommendations will help to identify areas for green improvement.

ContributorsDeng, Aretha (Co-author) / Tao, Adlar (Co-author) / Vargas, Cassandra (Co-author) / Printezis, Antonios (Thesis director) / Konopka, John (Committee member) / Department of Supply Chain Management (Contributor) / School of International Letters and Cultures (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05