Matching Items (12)
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- Genre: Masters Thesis
- Creators: Sugar, Thomas
- Member of: Theses and Dissertations
Description
The ability to shift the photovoltaic (PV) power curve and make the energy accessible during peak hours can be accomplished through pairing solar PV with energy storage technologies. A prototype hybrid air conditioning system (HACS), built under supervision of project head Patrick Phelan, consists of PV modules running a DC compressor that operates a conventional HVAC system paired with a second evaporator submerged within a thermal storage tank. The thermal storage is a 0.284m3 or 75 gallon freezer filled with Cryogel balls, submerged in a weak glycol solution. It is paired with its own separate air handler, circulating the glycol solution. The refrigerant flow is controlled by solenoid valves that are electrically connected to a high and low temperature thermostat. During daylight hours, the PV modules run the DC compressor. The refrigerant flow is directed to the conventional HVAC air handler when cooling is needed. Once the desired room temperature is met, refrigerant flow is diverted to the thermal storage, storing excess PV power. During peak energy demand hours, the system uses only small amounts of grid power to pump the glycol solution through the air handler (note the compressor is off), allowing for money and energy savings. The conventional HVAC unit can be scaled down, since during times of large cooling demands the glycol air handler can be operated in parallel with the conventional HVAC unit. Four major test scenarios were drawn up in order to fully comprehend the performance characteristics of the HACS. Upon initial running of the system, ice was produced and the thermal storage was charged. A simple test run consisting of discharging the thermal storage, initially ~¼ frozen, was performed. The glycol air handler ran for 6 hours and the initial cooling power was 4.5 kW. This initial test was significant, since greater than 3.5 kW of cooling power was produced for 3 hours, thus demonstrating the concept of energy storage and recovery.
ContributorsPeyton-Levine, Tobin (Author) / Phelan, Patrick (Thesis advisor) / Trimble, Steve (Committee member) / Wang, Robert (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
Advancements in thermal interface materials (TIMs) allows for the creation of new and more powerful electronics as they increase the heat transfer from the component to the heat sink. Current industrial options provide decent heat transfer, but the creation of TIMs with higher thermal conductivities is needed. In addition, if these TIMs are elastic in nature, their effectiveness can greatly increase as they can deal with changing interfaces without degradation of their properties. The research performed delves into this idea, creating elastic TIMs using liquid metal (LM), in this case galinstan, along with other matrix particles embedded in Polydimethylsiloxane (PDMS) to create an easy to use, relatively inexpensive, thermally conductive, but electrically insulative, pad with increased thermal conductivity from industrial solutions.
The pads were created using varying amounts of LM and matrix materials ranging from copper microspheres to diamond powder mixed into PDMS using a high-speed mixer. The material was then cast into molds and cured to create the pads. Once the pads were created, the difficulty came in quantifying their thermal properties. A stepped bar apparatus (SBA) following ASTM D5470 was created to measure the thermal resistance of the pads but it was determined that thermal conductivity was a more usable metric of the pads’ performance. This meant that the pad’s in-situ thickness was needed during testing, prompting the installation of a linear encoder to measure the thickness. The design and analysis of the necessary modification and proposed future design is further detailed in the following paper.
The pads were created using varying amounts of LM and matrix materials ranging from copper microspheres to diamond powder mixed into PDMS using a high-speed mixer. The material was then cast into molds and cured to create the pads. Once the pads were created, the difficulty came in quantifying their thermal properties. A stepped bar apparatus (SBA) following ASTM D5470 was created to measure the thermal resistance of the pads but it was determined that thermal conductivity was a more usable metric of the pads’ performance. This meant that the pad’s in-situ thickness was needed during testing, prompting the installation of a linear encoder to measure the thickness. The design and analysis of the necessary modification and proposed future design is further detailed in the following paper.
ContributorsKemme, Nicholas (Author) / Rykaczewski, Konrad (Thesis advisor) / Wang, Robert (Thesis advisor) / Wang, Liping (Committee member) / Arizona State University (Publisher)
Created2017
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
Description
Electrostatic Discharge (ESD) is a unique issue in the electronics industry that can cause failures of electrical components and complete electronic systems. There is an entire industry that is focused on developing ESD compliant tooling using traditional manufacturing methods. This research work evaluates the feasibility to fabricate a PEEK-Carbon Nanotube composite filament for Fused Filament Fabrication (FFF) Additive Manufacturing that is ESD compliant. In addition, it demonstrates that the FFF process can be used to print tools with the required accuracy, ESD compliance and mechanical properties necessary for the electronics industry at a low rate production level. Current Additive Manufacturing technology can print high temperature polymers, such as PEEK, with the required mechanical properties but they are not ESD compliant and require post processing to create a product that is. There has been some research conducted using mixed multi-wall and single wall carbon nanotubes in a PEEK polymers, which improves mechanical properties while reducing bulk resistance to the levels required to be ESD compliant. This previous research has been used to develop a PEEK-CNT polymer matrix for the Fused Filament Fabrication additive manufacturing process
ContributorsChurchwell, Raymond L (Author) / Sugar, Thomas (Thesis advisor) / Rogers, Bradley (Committee member) / Morrell, Darryl (Committee member) / Arizona State University (Publisher)
Created2020
Description
Fine control of standing postural balance is essential for completing various tasks in daily activities, which might be compromised when interacting with dynamically challenging environments (e.g., moving ground). Among various biofeedback to improve postural balance control, vibrotactile feedback has an advantage of providing supplementary information about balance control without disturbing other core functions (e.g., seeing and hearing). This paper investigated the effectiveness of a waist vibrotactile feedback device to improve postural control during standing balance on a dynamically moving ground simulated by a robotic balance platform. Four vibration motors of the waist device applied vibration feedback in the anterior-posterior and medio-lateral direction based on the 2-dimensional sway angle, measured by an inertia measurement unit. Experimental results with 15 healthy participants demonstrated that the waist vibrotactile feedback is effective in improving postural control, evidenced by improvements in center-of-mass and center-of-pressure stability measures. In addition, this study confirmed the effectiveness of the waist vibrotactile feedback in improving standing balance control even under muscle fatigue induced by lower body exercise. The study further confirmed that the waist feedback is more effective in people with lower baseline balance performance in both normal and fatigue conditions.
ContributorsJo, Kwanghee (Author) / Lee, Hyunglae (Thesis advisor) / Sugar, Thomas (Committee member) / Peterson, Daniel (Committee member) / Arizona State University (Publisher)
Created2022
Description
Contact angle goniometer is one of the most common tools in surfaces science. Since the introduction of this instrument by Fox and Zisman1 in 1950, dispensing the liquid using a syringe has generated pendant drops. However, using such approach at conditions significantly deviating from standard pressure and temperature would require an elaborate and costly fluidic system. To this end, this thesis work introduces alternative design of a goniometer capable of contact angle measurement at wide pressure and temperature range. In this design, pendant droplets are not dispensed through a pipette but are generated through localized condensation on a tip of a preferentially cooled small metal wire encapsulated within a thick thermal insulator layer. This thesis work covers experimental study of the relation between the geometry of the condensation-based pendant drop generator geometry and subcooling, and growth rate of drops of representative high (water) and low (pentane) surface tension liquids. Several routes that the generated pendant drops can be used to measure static and dynamic contact angles of the two liquids on common substrates well as nanoengineered superhydrophobic and omniphobic surfaces are demonstrated.
ContributorsMohan, Ajay Roopesh (Author) / Rykaczewski, Konrad (Thesis advisor) / Herrmann, Marcus (Committee member) / Wang, Robert (Committee member) / Arizona State University (Publisher)
Created2015