Matching Items (88)
Description
Carbon emissions have become a major concern since the turn of the century. This has increased the demand of hybrid vehicles in United States market. Hence, there is a need to make these vehicles more efficient. This thesis focuses on creating a thermal model that could be used for optimization of these vehicles. The project was accomplished in collaboration with EcoCar3, and the temperature data obtained from the model was compared with the experimental temperature data gathered from EcoCar's testing of the vehicle they built. The data obtained through this study demonstrates that the model was accurately able to predict thermal behavior of the electric motor and the high-voltage batteries in the vehicle. Therefore, this model could be used for optimization of the powertrain in a hybrid vehicle.
ContributorsMuthuvenkatesh, Nikhil (Author) / Mayyas, Abdel (Thesis director) / Patel, Jay (Committee member) / W.P. Carey School of Business (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Every year, millions of guests visit theme parks internationally. Within that massive population, accidents and emergencies are bound to occur. Choosing the correct location for emergency responders inside of the park could mean the difference between life and death. In an effort to provide the utmost safety for the guests of a park, it is important to make the best decision when selecting the location for emergency response crews. A theme park is different from a regular residential or commercial area because the crowds and shows block certain routes, and they change throughout the day. We propose an optimization model that selects staging locations for emergency medical responders in a theme park to maximize the number of responses that can occur within a pre-specified time. The staging areas are selected from a candidate set of restricted access locations where the responders can store their equipment. Our solution approach considers all routes to access any park location, including areas that are unavailable to a regular guest. Theme parks are a highly dynamic environment. Because special events occurring in the park at certain hours (e.g., parades) might impact the responders' travel times, our model's decisions also include the time dimension in the location and re-location of the responders. Our solution provides the optimal location of the responders for each time partition, including backup responders. When an optimal solution is found, the model is also designed to consider alternate optimal solutions that provide a more balanced workload for the crews.
ContributorsLivingston, Noah Russell (Author) / Sefair, Jorge (Thesis director) / Askin, Ronald (Committee member) / Industrial, Systems and Operations Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Error correcting systems have put increasing demands on system designers, both due to increasing error correcting requirements and higher throughput targets. These requirements have led to greater silicon area, power consumption and have forced system designers to make trade-offs in Error Correcting Code (ECC) functionality. Solutions to increase the efficiency of ECC systems are very important to system designers and have become a heavily researched area.
Many such systems incorporate the Bose-Chaudhuri-Hocquenghem (BCH) method of error correcting in a multi-channel configuration. BCH is a commonly used code because of its configurability, low storage overhead, and low decoding requirements when compared to other codes. Multi-channel configurations are popular with system designers because they offer a straightforward way to increase bandwidth. The ECC hardware is duplicated for each channel and the throughput increases linearly with the number of channels. The combination of these two technologies provides a configurable and high throughput ECC architecture.
This research proposes a new method to optimize a BCH error correction decoder in multi-channel configurations. In this thesis, I examine how error frequency effects the utilization of BCH hardware. Rather than implement each decoder as a single pipeline of independent decoding stages, the channels are considered together and served by a pool of decoding stages. Modified hardware blocks for handling common cases are included and the pool is sized based on an acceptable, but negligible decrease in performance.
Many such systems incorporate the Bose-Chaudhuri-Hocquenghem (BCH) method of error correcting in a multi-channel configuration. BCH is a commonly used code because of its configurability, low storage overhead, and low decoding requirements when compared to other codes. Multi-channel configurations are popular with system designers because they offer a straightforward way to increase bandwidth. The ECC hardware is duplicated for each channel and the throughput increases linearly with the number of channels. The combination of these two technologies provides a configurable and high throughput ECC architecture.
This research proposes a new method to optimize a BCH error correction decoder in multi-channel configurations. In this thesis, I examine how error frequency effects the utilization of BCH hardware. Rather than implement each decoder as a single pipeline of independent decoding stages, the channels are considered together and served by a pool of decoding stages. Modified hardware blocks for handling common cases are included and the pool is sized based on an acceptable, but negligible decrease in performance.
ContributorsDill, Russell (Author) / Shrivastava, Aviral (Thesis advisor) / Oh, Hyunok (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2015
Description
A principal goal of this dissertation is to study wireless network design and optimization with the focus on two perspectives: 1) socially-aware mobile networking and computing; 2) security and privacy in wireless networking. Under this common theme, this dissertation can be broadly organized into three parts.
The first part studies socially-aware mobile networking and computing. First, it studies random access control and power control under a social group utility maximization (SGUM) framework. The socially-aware Nash equilibria (SNEs) are derived and analyzed. Then, it studies mobile crowdsensing under an incentive mechanism that exploits social trust assisted reciprocity (STAR). The efficacy of the STAR mechanism is thoroughly investigated. Next, it studies mobile users' data usage behaviors under the impact of social services and the wireless operator's pricing. Based on a two-stage Stackelberg game formulation, the user demand equilibrium (UDE) is analyzed in Stage II and the optimal pricing strategy is developed in Stage I. Last, it studies opportunistic cooperative networking under an optimal stopping framework with two-level decision-making. For both cases with or without dedicated relays, the optimal relaying strategies are derived and analyzed.
The second part studies radar sensor network coverage for physical security. First, it studies placement of bistatic radar (BR) sensor networks for barrier coverage. The optimality of line-based placement is analyzed, and the optimal placement of BRs on a line segment is characterized. Then, it studies the coverage of radar sensor networks that exploits the Doppler effect. Based on a Doppler coverage model, an efficient method is devised to characterize Doppler-covered regions and an algorithm is developed to find the minimum radar density required for Doppler coverage.
The third part studies cyber security and privacy in socially-aware networking and computing. First, it studies random access control, cooperative jamming, and spectrum access under an extended SGUM framework that incorporates negative social ties. The SNEs are derived and analyzed. Then, it studies pseudonym change for personalized location privacy under the SGUM framework. The SNEs are analyzed and an efficient algorithm is developed to find an SNE with desirable properties.
The first part studies socially-aware mobile networking and computing. First, it studies random access control and power control under a social group utility maximization (SGUM) framework. The socially-aware Nash equilibria (SNEs) are derived and analyzed. Then, it studies mobile crowdsensing under an incentive mechanism that exploits social trust assisted reciprocity (STAR). The efficacy of the STAR mechanism is thoroughly investigated. Next, it studies mobile users' data usage behaviors under the impact of social services and the wireless operator's pricing. Based on a two-stage Stackelberg game formulation, the user demand equilibrium (UDE) is analyzed in Stage II and the optimal pricing strategy is developed in Stage I. Last, it studies opportunistic cooperative networking under an optimal stopping framework with two-level decision-making. For both cases with or without dedicated relays, the optimal relaying strategies are derived and analyzed.
The second part studies radar sensor network coverage for physical security. First, it studies placement of bistatic radar (BR) sensor networks for barrier coverage. The optimality of line-based placement is analyzed, and the optimal placement of BRs on a line segment is characterized. Then, it studies the coverage of radar sensor networks that exploits the Doppler effect. Based on a Doppler coverage model, an efficient method is devised to characterize Doppler-covered regions and an algorithm is developed to find the minimum radar density required for Doppler coverage.
The third part studies cyber security and privacy in socially-aware networking and computing. First, it studies random access control, cooperative jamming, and spectrum access under an extended SGUM framework that incorporates negative social ties. The SNEs are derived and analyzed. Then, it studies pseudonym change for personalized location privacy under the SGUM framework. The SNEs are analyzed and an efficient algorithm is developed to find an SNE with desirable properties.
ContributorsGong, Xiaowen (Author) / Zhang, Junshan (Thesis advisor) / Cochran, Douglas (Committee member) / Ying, Lei (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2015
Description
All structures suffer wear and tear because of impact, excessive load, fatigue, corrosion, etc. in addition to inherent defects during their manufacturing processes and their exposure to various environmental effects. These structural degradations are often imperceptible, but they can severely affect the structural performance of a component, thereby severely decreasing its service life. Although previous studies of Structural Health Monitoring (SHM) have revealed extensive prior knowledge on the parts of SHM processes, such as the operational evaluation, data processing, and feature extraction, few studies have been conducted from a systematical perspective, the statistical model development.
The first part of this dissertation, the characteristics of inverse scattering problems, such as ill-posedness and nonlinearity, reviews ultrasonic guided wave-based structural health monitoring problems. The distinctive features and the selection of the domain analysis are investigated by analytically searching the conditions of the uniqueness solutions for ill-posedness and are validated experimentally.
Based on the distinctive features, a novel wave packet tracing (WPT) method for damage localization and size quantification is presented. This method involves creating time-space representations of the guided Lamb waves (GLWs), collected at a series of locations, with a spatially dense distribution along paths at pre-selected angles with respect to the direction, normal to the direction of wave propagation. The fringe patterns due to wave dispersion, which depends on the phase velocity, are selected as the primary features that carry information, regarding the wave propagation and scattering.
The following part of this dissertation presents a novel damage-localization framework, using a fully automated process. In order to construct the statistical model for autonomous damage localization deep-learning techniques, such as restricted Boltzmann machine and deep belief network, are trained and utilized to interpret nonlinear far-field wave patterns.
Next, a novel bridge scour estimation approach that comprises advantages of both empirical and data-driven models is developed. Two field datasets from the literature are used, and a Support Vector Machine (SVM), a machine-learning algorithm, is used to fuse the field data samples and classify the data with physical phenomena. The Fast Non-dominated Sorting Genetic Algorithm (NSGA-II) is evaluated on the model performance objective functions to search for Pareto optimal fronts.
The first part of this dissertation, the characteristics of inverse scattering problems, such as ill-posedness and nonlinearity, reviews ultrasonic guided wave-based structural health monitoring problems. The distinctive features and the selection of the domain analysis are investigated by analytically searching the conditions of the uniqueness solutions for ill-posedness and are validated experimentally.
Based on the distinctive features, a novel wave packet tracing (WPT) method for damage localization and size quantification is presented. This method involves creating time-space representations of the guided Lamb waves (GLWs), collected at a series of locations, with a spatially dense distribution along paths at pre-selected angles with respect to the direction, normal to the direction of wave propagation. The fringe patterns due to wave dispersion, which depends on the phase velocity, are selected as the primary features that carry information, regarding the wave propagation and scattering.
The following part of this dissertation presents a novel damage-localization framework, using a fully automated process. In order to construct the statistical model for autonomous damage localization deep-learning techniques, such as restricted Boltzmann machine and deep belief network, are trained and utilized to interpret nonlinear far-field wave patterns.
Next, a novel bridge scour estimation approach that comprises advantages of both empirical and data-driven models is developed. Two field datasets from the literature are used, and a Support Vector Machine (SVM), a machine-learning algorithm, is used to fuse the field data samples and classify the data with physical phenomena. The Fast Non-dominated Sorting Genetic Algorithm (NSGA-II) is evaluated on the model performance objective functions to search for Pareto optimal fronts.
ContributorsKim, Inho (Author) / Chattopadhyay, Aditi (Thesis advisor) / Jiang, Hanqing (Committee member) / Liu, Yongming (Committee member) / Mignolet, Marc (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2016
Description
Most engineers may agree that an optimum design of a particular structure is a proposal that minimizes costs without compromising resistance, serviceability and aesthetics. Additionally to these conditions, the theory and application of the method that produces such an efficient design must be easy and fast to apply at the structural engineering offices.
A considerable amount of studies have been conducted for the past four decades. Most researchers have used constraints and tried to minimize the cost of the structure by reducing the weight of it [8]. Although this approach may be true for steel structures, it is not accurate for composite structures such as reinforced and prestressed concrete. Maximizing the amount of reinforcing steel to minimize the weight of the overall structure can produce an increase of the cost if the price of steel is too high compared to concrete [8]. A better approach is to reduce the total cost of the structure instead of weight. However, some structures such as Prestressed Concrete AASHTO Girders have been standardized with the purpose of simplifying production, design and construction. Optimizing a bridge girder requires good judgment at an early stage of the design and some studies have provided guides for preliminary design that will generate a final economical solution [17] [18]. Therefore, no calculations or optimization procedure is required to select the appropriate Standard AASHTO Girder. This simplifies the optimization problem of a bridge girder to reducing the amount of prestressing and mild steel only. This study will address the problem of optimizing the prestressing force of a PC AASHTO girder by using linear programming and feasibility domain of working stresses. A computer program will be presented to apply the optimization technique effectively.
A considerable amount of studies have been conducted for the past four decades. Most researchers have used constraints and tried to minimize the cost of the structure by reducing the weight of it [8]. Although this approach may be true for steel structures, it is not accurate for composite structures such as reinforced and prestressed concrete. Maximizing the amount of reinforcing steel to minimize the weight of the overall structure can produce an increase of the cost if the price of steel is too high compared to concrete [8]. A better approach is to reduce the total cost of the structure instead of weight. However, some structures such as Prestressed Concrete AASHTO Girders have been standardized with the purpose of simplifying production, design and construction. Optimizing a bridge girder requires good judgment at an early stage of the design and some studies have provided guides for preliminary design that will generate a final economical solution [17] [18]. Therefore, no calculations or optimization procedure is required to select the appropriate Standard AASHTO Girder. This simplifies the optimization problem of a bridge girder to reducing the amount of prestressing and mild steel only. This study will address the problem of optimizing the prestressing force of a PC AASHTO girder by using linear programming and feasibility domain of working stresses. A computer program will be presented to apply the optimization technique effectively.
ContributorsRaudales Valladares, Eduardo Rene (Author) / Fafitis, Apostolos (Thesis advisor) / Zapata, Claudia (Committee member) / Hjelmstad, Keith (Committee member) / Arizona State University (Publisher)
Created2016
Description
Toward the ambitious long-term goal of a fleet of cooperating Flexible Autonomous Machines operating in an uncertain Environment (FAME), this thesis addresses various control objectives for ground vehicles.
There are two main objectives within this thesis, first is the use of visual information to control a Differential-Drive Thunder Tumbler (DDTT) mobile robot and second is the solution to a minimum time optimal control problem for the robot around a racetrack.
One method to do the first objective is by using the Position Based Visual Servoing (PBVS) approach in which a camera looks at a target and the position of the target with respect to the camera is estimated; once this is done the robot can drive towards a desired position (x_ref, z_ref). Another method is called Image Based Visual Servoing (IBVS), in which the pixel coordinates (u,v) of markers/dots placed on an object are driven towards the desired pixel coordinates (u_ref, v_ref) of the corresponding markers.
By doing this, the mobile robot gets closer to a desired pose (x_ref, z_ref, theta_ref).
For the second objective, a camera-based and noncamera-based (v,theta) cruise-control systems are used for the solution of the minimum time problem. To set up the minimum time problem, optimal control theory is used. Then a direct method is implemented by discretizing states and controls of the system. Finally, the solution is obtained by modeling the problem in AMPL and submitting to the nonlinear optimization solver KNITRO. Simulation and experimental results are presented.
The DDTT-vehicle used within this thesis has different components as summarized below:
(1) magnetic wheel-encoders/IMU for inner-loop speed-control and outer-loop directional control,
(2) Arduino Uno microcontroller-board for encoder-based inner-loop speed-control and encoder-IMU-based outer-loop cruise-directional-control,
(3) Arduino motor-shield for inner-loop speed-control,
(4) Raspberry Pi II computer-board for outer-loop vision-based cruise-position-directional-control,
(5) Raspberry Pi 5MP camera for outer-loop cruise-position-directional control.
Hardware demonstrations shown in this thesis are summarized: (1) PBVS without pan camera, (2) PBVS with pan camera, (3) IBVS with 1 marker/dot, (4) IBVS with 2 markers, (5) IBVS with 3 markers, (6) camera and (7) noncamera-based (v,theta) cruise control system for the minimum time problem.
There are two main objectives within this thesis, first is the use of visual information to control a Differential-Drive Thunder Tumbler (DDTT) mobile robot and second is the solution to a minimum time optimal control problem for the robot around a racetrack.
One method to do the first objective is by using the Position Based Visual Servoing (PBVS) approach in which a camera looks at a target and the position of the target with respect to the camera is estimated; once this is done the robot can drive towards a desired position (x_ref, z_ref). Another method is called Image Based Visual Servoing (IBVS), in which the pixel coordinates (u,v) of markers/dots placed on an object are driven towards the desired pixel coordinates (u_ref, v_ref) of the corresponding markers.
By doing this, the mobile robot gets closer to a desired pose (x_ref, z_ref, theta_ref).
For the second objective, a camera-based and noncamera-based (v,theta) cruise-control systems are used for the solution of the minimum time problem. To set up the minimum time problem, optimal control theory is used. Then a direct method is implemented by discretizing states and controls of the system. Finally, the solution is obtained by modeling the problem in AMPL and submitting to the nonlinear optimization solver KNITRO. Simulation and experimental results are presented.
The DDTT-vehicle used within this thesis has different components as summarized below:
(1) magnetic wheel-encoders/IMU for inner-loop speed-control and outer-loop directional control,
(2) Arduino Uno microcontroller-board for encoder-based inner-loop speed-control and encoder-IMU-based outer-loop cruise-directional-control,
(3) Arduino motor-shield for inner-loop speed-control,
(4) Raspberry Pi II computer-board for outer-loop vision-based cruise-position-directional-control,
(5) Raspberry Pi 5MP camera for outer-loop cruise-position-directional control.
Hardware demonstrations shown in this thesis are summarized: (1) PBVS without pan camera, (2) PBVS with pan camera, (3) IBVS with 1 marker/dot, (4) IBVS with 2 markers, (5) IBVS with 3 markers, (6) camera and (7) noncamera-based (v,theta) cruise control system for the minimum time problem.
ContributorsAldaco Lopez, Jesus (Author) / Rodriguez, Armando A. (Thesis advisor) / Artemiadis, Panagiotis K. (Committee member) / Berman, Spring M. (Committee member) / Arizona State University (Publisher)
Created2016
Description
Bioretention basins are a common stormwater best management practice (BMP) used to mitigate the hydrologic consequences of urbanization. Dry wells, also known as vadose-zone wells, have been used extensively in bioretention basins in Maricopa County, Arizona to decrease total drain time and recharge groundwater. A mixed integer nonlinear programming (MINLP) model has been developed for the minimum cost design of bioretention basins with dry wells.
The model developed simultaneously determines the peak stormwater inflow from watershed parameters and optimizes the size of the basin and the number and depth of dry wells based on infiltration, evapotranspiration (ET), and dry well characteristics and cost inputs. The modified rational method is used for the design storm hydrograph, and the Green-Ampt method is used for infiltration. ET rates are calculated using the Penman Monteith method or the Hargreaves-Samani method. The dry well flow rate is determined using an equation developed for reverse auger-hole flow.
The first phase of development of the model is to expand a nonlinear programming (NLP) for the optimal design of infiltration basins for use with bioretention basins. Next a single dry well is added to the NLP bioretention basin optimization model. Finally the number of dry wells in the basin is modeled as an integer variable creating a MINLP problem. The NLP models and MINLP model are solved using the General Algebraic Modeling System (GAMS). Two example applications demonstrate the efficiency and practicality of the model.
The model developed simultaneously determines the peak stormwater inflow from watershed parameters and optimizes the size of the basin and the number and depth of dry wells based on infiltration, evapotranspiration (ET), and dry well characteristics and cost inputs. The modified rational method is used for the design storm hydrograph, and the Green-Ampt method is used for infiltration. ET rates are calculated using the Penman Monteith method or the Hargreaves-Samani method. The dry well flow rate is determined using an equation developed for reverse auger-hole flow.
The first phase of development of the model is to expand a nonlinear programming (NLP) for the optimal design of infiltration basins for use with bioretention basins. Next a single dry well is added to the NLP bioretention basin optimization model. Finally the number of dry wells in the basin is modeled as an integer variable creating a MINLP problem. The NLP models and MINLP model are solved using the General Algebraic Modeling System (GAMS). Two example applications demonstrate the efficiency and practicality of the model.
ContributorsLacy, Mason (Author) / Mays, Larry W. (Thesis advisor) / Fox, Peter (Committee member) / Wang, Zhihua (Committee member) / Arizona State University (Publisher)
Created2016
Description
In order for assistive mobile robots to operate in the same environment as humans, they must be able to navigate the same obstacles as humans do. Many elements are required to do this: a powerful controller which can understand the obstacle, and power-dense actuators which will be able to achieve the necessary limb accelerations and output energies. Rapid growth in information technology has made complex controllers, and the devices which run them considerably light and cheap. The energy density of batteries, motors, and engines has not grown nearly as fast. This is problematic because biological systems are more agile, and more efficient than robotic systems. This dissertation introduces design methods which may be used optimize a multiactuator robotic limb's natural dynamics in an effort to reduce energy waste. These energy savings decrease the robot's cost of transport, and the weight of the required fuel storage system. To achieve this, an optimal design method, which allows the specialization of robot geometry, is introduced. In addition to optimal geometry design, a gearing optimization is presented which selects a gear ratio which minimizes the electrical power at the motor while considering the constraints of the motor. Furthermore, an efficient algorithm for the optimization of parallel stiffness elements in the robot is introduced. In addition to the optimal design tools introduced, the KiTy SP robotic limb structure is also presented. Which is a novel hybrid parallel-serial actuation method. This novel leg structure has many desirable attributes such as: three dimensional end-effector positioning, low mobile mass, compact form-factor, and a large workspace. We also show that the KiTy SP structure outperforms the classical, biologically-inspired serial limb structure.
ContributorsCahill, Nathan M (Author) / Sugar, Thomas (Thesis advisor) / Ren, Yi (Thesis advisor) / Holgate, Matthew (Committee member) / Berman, Spring (Committee member) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2017
Description
The thesis covers the development and modeling of the supervisory hybrid controller using two different methods to achieve real-world optimization and power split of a parallel hybrid vehicle with a fixed shaft connecting the Internal Combustion Engine (ICE) and Electric Motor (EM). The first strategy uses a rule based controller to determine modes the vehicle should operate in. This approach is well suited for real-world applications. The second approach uses Sequential Quadratic Programming (SQP) approach in conjunction with an Equivalent Consumption Minimization Strategy (ECMS) strategy to keep the vehicle in the most efficient operating regions. This latter method is able to operate the vehicle in various drive cycles while maintaining the SOC with-in allowed charge sustaining (CS) limits. Further, the overall efficiency of the vehicle for all drive cycles is increased. The limitation here is the that process is computationally expensive; however, with advent of the low cost high performance hardware this method can be used for the hybrid vehicle control.
ContributorsMaady, Rashad Kamal (Author) / Redkar, Sangram (Thesis advisor) / Mayyas, Abdel R (Thesis advisor) / Zhang, Wenlong (Committee member) / Arizona State University (Publisher)
Created2017