Matching Items (15)
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- All Subjects: engineering
- Genre: Masters Thesis
- Creators: Sugar, Thomas
- Creators: Kashiwagi, Dean T.
- Resource Type: Text
Description
Utilizing the Arizona State University's Performance Based Studies Research Group, and their PIPS program, a roofing materials manufacturing company can evaluate performance of representatives, products and contractors. Service life of the systems can be tracked and customer satisfaction measured it provides an objective viable tool for the consumer to choose a quality product and contractor without the distractions of marketing, promises, or a salesman's hype. Facilities purchasing a new roof system, can benefit from the information gathered as a guide in making sound, value based decisions. Creating a historical, concise and accurate documentation of roofing systems is a benefit to all involved. The procurement process, installation and longevity of the roofing systems can be tracked and graded.
ContributorsGreenfeld, Larry (Author) / Kashiwagi, Dean T. (Thesis advisor) / Sullivan, Kenneth T. (Committee member) / Badger, William W. (Committee member) / Arizona State University (Publisher)
Created2013
Description
As a term and method that is rapidly gaining popularity, Building Information Modeling (BIM) is under the scrutiny of many building professionals questioning its potential benefits on their projects. A relevant and accepted calculation methodology and baseline to properly evaluate BIM's benefits have not been established, thus there are mixed perspectives and opinions of the benefits of BIM, creating a general misunderstanding of the expected outcomes. The purpose of this thesis was to develop a more complete methodology to analyze the benefits of BIM, apply recent projects to this methodology to quantify outcomes, resulting in a more a holistic framework of BIM and its impacts on project efficiency. From the literature, a framework calculation model to determine the value of BIM is developed and presented. The developed model is applied via case studies within a large industrial setting where similar projects are evaluated, some implementing BIM and some with traditional non-BIM approaches. Cost or investment metrics were considered along with benefit or return metrics. The return metrics were: requests for information, change orders, and duration improvements. The investment metrics were: design and construction costs. The methodology was tested against three separate cases and results on the returns and investments are presented. The findings indicate that in the tool installation department of semiconductor manufacturing, there is a high potential for BIM benefits to be realized. The evidence also suggests that actual returns and investments will vary with each project.
ContributorsBarlish, Kristen Caroline (Author) / Sullivan, Kenneth T. (Thesis advisor) / Kashiwagi, Dean T. (Committee member) / Badger, William W. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Commodity contracts are often awarded on the basis of price. A price-based methodology for making such awards fails to consider the suppliers' ability to minimize the risk of non-performance in terms of cost, schedule, or customer satisfaction. Literature suggests that nearly all risk in the delivery of commodities is in the interfacing of nodes within a supply chain. Therefore, commodity suppliers should be selected on the basis of their past performance, ability to identify and minimize risk, and capacity to preplan the delivery of services. Organizations that select commodity suppliers primarily on the basis of price may experience customer dissatisfaction, delayed services, low product quality, or some combination thereof. One area that is often considered a "commodity" is the delivery of furniture services. Arizona State University, on behalf of the Arizona Tri-University Furniture Consortium, approached the researcher and identified concerns with their current furnishing services contract. These concerns included misaligned customer expectations, minimal furniture supplier upfront involvement on large capital construction projects, and manufacturer design expertise was not being utilized during project preplanning. The Universities implemented a best value selection process and risk management structure. The system has resulted in a 9.3 / 10 customer satisfaction rating (24 percent increase over the previous system), for over 1,100 furniture projects totaling $19.3M.
ContributorsSmithwick, Jake (Author) / Sullivan, Kenneth T. (Thesis advisor) / Kashiwagi, Dean T. (Committee member) / Badger, William W. (Committee member) / Arizona State University (Publisher)
Created2012
Description
Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
Description
The sensor industry is a growing industry that has been predicted by Allied Market Research to be a multi-billion industry by 2022. One of the many key drives behind this rapid growth in the sensor industry is the increase incorporation of sensors into portable electrical devices. The value for sensor technologies are increased when the sensors are developed into innovative measuring system for application uses in the Aerospace, Defense, and Healthcare industries. While sensors are not new, their increased performance, size reduction, and decrease in cost has opened the door for innovative sensor combination for portable devices that could be worn or easily moved around. With this opportunity for further development of sensor use through concept engineering development, three concept projects for possible innovative portable devices was undertaken in this research. One project was the development of a pulse oximeter devise with fingerprint recognition. The second project was prototyping a portable Bluetooth strain gage monitoring system. The third project involved sensors being incorporated onto flexible printed circuit board (PCB) for improved comfort of wearable devices. All these systems were successfully tested in lab.
ContributorsNichols, Kevin William (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Brad (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2018
Description
Recent research and study have showed the potential of auto-parametric system in controlling stability and parametric resonance. In this project, two different designs for auto-parametrically excited mass-spring-damper systems were studied. The theoretical models were developed to describe the behavior of the systems, and simulation models were constructed to validate the analytical results. The error between simulation and theoretical results was within 2%. Both theoretical and simulation results showed that the implementation of auto-parametric system could help reduce or amplify the resonance significantly.
ContributorsLe, Thao (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Rogers, Brad (Committee member) / Arizona State University (Publisher)
Created2018
Description
A control method based on the phase angle is used to control oscillating systems. The phase oscillator uses the sine and cosine of the phase angle to change key properties of a mass-spring-damper system, including amplitude, frequency, and equilibrium. An inverted pendulum is used to show a further application of the phase oscillator. Two methods of control based on the phase oscillator are used for swing-up and balancing of the pendulum. The first control method involves two separate stages. The scenarios where this control works are discussed. The second control method uses variable coefficients to result in a smooth transition between swing-up and balancing.
ContributorsBates, Andrew (Author) / Sugar, Thomas (Thesis advisor) / Redkar, Sangram (Committee member) / Mignolet, Marc (Committee member) / Arizona State University (Publisher)
Created2015
Description
The performance of the Alpha Sprayed Polyurethane Foam (SPF) roofing system is perceived as not an economical option when compared to a 20-year modified bitumen roofing system. Today, the majority of roofs are being replaced, rather than newly installed. The coating manufacturer, Neogard, implemented the Alpha roofing program to identify the best contractors in the industry and to measure their roof performance. The Alpha roof system has shown consistent high performance on over 230 million square feet of surveyed roof. The author proposes to identify if the Alpha roof system is renewable, has proven performance that competes with the traditional modified roofing system, and is a more economical option by evaluating an Alpha roof system installation and the performance of a 29-year-old Alpha roof system. The Dallas Independent School District utilized the Alpha program for William Lipscomb Elementary School in 2016. Dallas Fort Worth Urethane installed the Alpha SPF roof system with high customer satisfaction ratings. This roofing installation showed the value of the Alpha roof system by saving over 20% on costs for the installation and will save approximately 69% of costs on the recoating of the roof in 20 years. The Casa View Elementary School roof system was installed with a Neogard Permathane roof system in 1987. This roof was hail tested with ten drops from 17 feet 9 inches of 1-3/4-inch steel ball (9 out of 10 passed) and four drops from 17 feet 9 inches with a 3-inch diameter steel ball (2 out of 4 passed). The analysis of the passing and failing core samples show that the thickness of the top and base Alpha SPF coating is one of the major differences in a roof passing or failing the FM-SH hail test. Over the 40-year service life, the main difference of purchasing a 61,000 square feet Alpha SPF roof versus modified bitumen roof are savings of approximately $1,067,500. Past hail tests on Alpha SPF roof systems show its cost effectiveness with high customer satisfaction (9.8 out of 10), an over 40-year service life after a $6.00/SF recoat and savings of over $1M for DISD.
ContributorsZulanas, Charles J., IV (Author) / Kashiwagi, Dean T. (Thesis advisor) / Kashiwagi, Jacob S (Thesis advisor) / Chong, Oswald (Committee member) / Arizona State University (Publisher)
Created2017
Description
The U.S. Army Medical Command has been testing a leadership based structure to increase the performance of delivering construction and facility services in its system of $600M of construction and 26 major hospital facilities in the U.S. The organizational requirement was to minimize the management and oversight of contractors and simultaneously increase project performance. The research proposes that a leadership based structure can supplement the perception, preplanning, and risk minimization capability of a contractor's project manager, thus increasing the project performance (on time, within budget, and meeting expectations) and decreasing client management requirement. The projects were delivered in a best value and low price environment. The major impact of this research was that proactive management by contractors was more effective than traditional management such as direction, control, and inspection by client's professional representatives. The results based on data collection and date analyses validated that a leadership based structure can increase the performance of an organization and reduce its management requirement.
ContributorsMalhotra, Neha (Author) / Kashiwagi, Dean T. (Thesis advisor) / Sullivan, Kenneth (Committee member) / Badger, William (Committee member) / Arizona State University (Publisher)
Created2010
Description
In nature, some animals have an exoskeleton that provides protection, strength, and stability to the organism, but in engineering, an exoskeleton refers to a device that augments or aids human ability. However, the method of controlling these devices has been a challenge historically. Depending on the objective, control systems for exoskeletons have ranged from devices as simple spring-loaded systems to using sensors such as electromyography (EMG). Despite EMGs being very common, force sensing resistors (FSRs) can be used instead. There are multiple types of exoskeletons that target different areas of the human body, and the targeted area depends on the need of the device. Usually, the devices are developed for either medical or military usage; for this project, the focus is on medical development of an automated elbow joint to assist in rehabilitation. This thesis is a continuation of my ASU Barrett honors thesis, Upper-Extremity Exoskeleton. While working on my honors thesis, I helped develop a design for an upper extremity exoskeleton based on the Wilmer orthosis design for Mayo Clinic. Building upon the design of an orthosis, for the master’s thesis, I developed an FSR control system that is designed using a Wheatstone bridge circuit that can provide a clean reliable signal as compared to the current EMG setup.
ContributorsCarlton, Bryan (Author) / Sugar, Thomas (Thesis advisor) / Aukes, Daniel (Committee member) / Hollander, Kevin (Committee member) / Arizona State University (Publisher)
Created2023