Matching Items (88)
Description
This thesis focuses on generating and exploring design variations for architectural and urban layouts. I propose to study this general problem in three selected contexts.
First, I introduce a framework to generate many variations of a facade design that look similar to a given facade layout. Starting from an input image, the facade is hierarchically segmented and labeled with a collection of manual and automatic tools. The user can then model constraints that should be maintained in any variation of the input facade design. Subsequently, facade variations are generated for different facade sizes, where multiple variations can be produced for a certain size.
Second, I propose a method for a user to understand and systematically explore good building layouts. Starting from a discrete set of good layouts, I analytically characterize the local shape space of good layouts around each initial layout, compactly encode these spaces, and link them to support transitions across the different local spaces. I represent such transitions in the form of a portal graph. The user can then use the portal graph, along with the family of local shape spaces, to globally and locally explore the space of good building layouts.
Finally, I propose an algorithm to computationally design street networks that balance competing requirements such as quick travel time and reduced through traffic in residential neighborhoods. The user simply provides high-level functional specifications for a target neighborhood, while my algorithm best satisfies the specification by solving for both connectivity and geometric layout of the network.
First, I introduce a framework to generate many variations of a facade design that look similar to a given facade layout. Starting from an input image, the facade is hierarchically segmented and labeled with a collection of manual and automatic tools. The user can then model constraints that should be maintained in any variation of the input facade design. Subsequently, facade variations are generated for different facade sizes, where multiple variations can be produced for a certain size.
Second, I propose a method for a user to understand and systematically explore good building layouts. Starting from a discrete set of good layouts, I analytically characterize the local shape space of good layouts around each initial layout, compactly encode these spaces, and link them to support transitions across the different local spaces. I represent such transitions in the form of a portal graph. The user can then use the portal graph, along with the family of local shape spaces, to globally and locally explore the space of good building layouts.
Finally, I propose an algorithm to computationally design street networks that balance competing requirements such as quick travel time and reduced through traffic in residential neighborhoods. The user simply provides high-level functional specifications for a target neighborhood, while my algorithm best satisfies the specification by solving for both connectivity and geometric layout of the network.
ContributorsBao, Fan (Author) / Wonka, Peter (Thesis advisor) / Maciejewski, Ross (Committee member) / Razdan, Anshuman (Committee member) / Farin, Gerald (Committee member) / Arizona State University (Publisher)
Created2014
Description
Advances in data collection technologies have made it cost-effective to obtain heterogeneous data from multiple data sources. Very often, the data are of very high dimension and feature selection is preferred in order to reduce noise, save computational cost and learn interpretable models. Due to the multi-modality nature of heterogeneous data, it is interesting to design efficient machine learning models that are capable of performing variable selection and feature group (data source) selection simultaneously (a.k.a bi-level selection). In this thesis, I carry out research along this direction with a particular focus on designing efficient optimization algorithms. I start with a unified bi-level learning model that contains several existing feature selection models as special cases. Then the proposed model is further extended to tackle the block-wise missing data, one of the major challenges in the diagnosis of Alzheimer's Disease (AD). Moreover, I propose a novel interpretable sparse group feature selection model that greatly facilitates the procedure of parameter tuning and model selection. Last but not least, I show that by solving the sparse group hard thresholding problem directly, the sparse group feature selection model can be further improved in terms of both algorithmic complexity and efficiency. Promising results are demonstrated in the extensive evaluation on multiple real-world data sets.
ContributorsXiang, Shuo (Author) / Ye, Jieping (Thesis advisor) / Mittelmann, Hans D (Committee member) / Davulcu, Hasan (Committee member) / He, Jingrui (Committee member) / Arizona State University (Publisher)
Created2014
Description
Laminated composite materials are used in aerospace, civil and mechanical structural systems due to their superior material properties compared to the constituent materials as well as in comparison to traditional materials such as metals. Laminate structures are composed of multiple orthotropic material layers bonded together to form a single performing part. As such, the layup design of the material largely influences the structural performance. Optimization techniques such as the Genetic Algorithm (GA), Differential Evolution (DE), the Method of Feasible Directions (MFD), and others can be used to determine the optimal laminate composite material layup. In this thesis, sizing, shape and topology design optimization of laminated composites is carried out. Sizing optimization, such as the layer thickness, topology optimization, such as the layer orientation and material and the number of layers present, and shape optimization of the overall composite part contribute to the design optimization process of laminates. An optimization host program written in C++ has been developed to implement the optimization methodology of both population based and numerical gradient based methods. The performance of the composite structural system is evaluated through explicit finite element analysis of shell elements carried out using LS-DYNA. Results from numerical examples demonstrate that optimization design processes can significantly improve composite part performance through implementation of optimum material layup and part shape.
ContributorsMika, Krista (Author) / Rajan, Subramaniam D. (Thesis advisor) / Neithalath, Narayanan (Committee member) / Mobasher, Barzin (Committee member) / Arizona State University (Publisher)
Created2014
Description
This research develops heuristics for scheduling electric power production amid uncertainty. Reliability is becoming more difficult to manage due to growing uncertainty from renewable resources. This challenge is compounded by the risk of resource outages, which can occur any time and without warning. Stochastic optimization is a promising tool but remains computationally intractable for large systems. The models used in industry instead schedule for the forecast and withhold generation reserve for scenario response, but they are blind to how this reserve may be constrained by network congestion. This dissertation investigates more effective heuristics to improve economics and reliability in power systems where congestion is a concern.
Two general approaches are developed. Both approximate the effects of recourse decisions without actually solving a stochastic model. The first approach procures more reserve whenever approximate recourse policies stress the transmission network. The second approach procures reserve at prime locations by generalizing the existing practice of reserve disqualification. The latter approach is applied for feasibility and is later extended to limit scenario costs. Testing demonstrates expected cost improvements around 0.5%-1.0% for the IEEE 73-bus test case, which can translate to millions of dollars per year even for modest systems. The heuristics developed in this dissertation perform somewhere between established deterministic and stochastic models: providing an economic benefit over current practices without substantially increasing computational times.
Two general approaches are developed. Both approximate the effects of recourse decisions without actually solving a stochastic model. The first approach procures more reserve whenever approximate recourse policies stress the transmission network. The second approach procures reserve at prime locations by generalizing the existing practice of reserve disqualification. The latter approach is applied for feasibility and is later extended to limit scenario costs. Testing demonstrates expected cost improvements around 0.5%-1.0% for the IEEE 73-bus test case, which can translate to millions of dollars per year even for modest systems. The heuristics developed in this dissertation perform somewhere between established deterministic and stochastic models: providing an economic benefit over current practices without substantially increasing computational times.
ContributorsLyon, Joshua Daniel (Author) / Zhang, Muhong (Thesis advisor) / Hedman, Kory W (Thesis advisor) / Askin, Ronald G. (Committee member) / Mirchandani, Pitu (Committee member) / Arizona State University (Publisher)
Created2015
Description
Substation ground system insures safety of personnel, which deserves considerable attentions. Basic substation safety requirement quantities include ground grid resistance, mesh touch potential and step potential, moreover, optimal design of a substation ground system should include both safety concerns and ground grid construction cost. In the purpose of optimal designing the ground grid in the accurate and efficient way, an application package coded in MATLAB is developed and its core algorithm and main features are introduced in this work.
To ensure accuracy and personnel safety, a two-layer soil model is applied instead of the uniform soil model in this research. Some soil model parameters are needed for the two-layer soil model, namely upper-layer resistivity, lower-layer resistivity and upper-layer thickness. Since the ground grid safety requirement is considered under the earth fault, the value of fault current and fault duration time are also needed.
After all these parameters are obtained, a Resistance Matrix method is applied to calculate the mutual and self resistance between conductor segments on both the horizontal and vertical direction. By using a matrix equation of the relationship of mutual and self resistance and unit current of the conductor segments, the ground grid rise can be calculated. Green's functions are applied to calculate the earth potential at a certain point produced by horizontal or vertical line of current. Furthermore, the three basic ground grid safety requirement quantities: the mesh touch potential in the worst case point can be obtained from the earth potential and ground grid rise; the step potential can be obtained from two points' earth potential difference; the grid resistance can be obtained from ground grid rise and fault current.
Finally, in order to achieve ground grid optimization problem more accurate and efficient, which includes the number of meshes in the horizontal grid and the number of vertical rods, a novel two-step hybrid genetic algorithm-pattern search (GA-PS) optimization method is developed. The Genetic Algorithm (GA) is used first to search for an approximate starting point, which is used by the Pattern Search (PS) algorithm to find the final optimal result. This developed application provides an optimal grid design meeting all safety constraints. In the cause of the accuracy of the application, the touch potential, step potential, ground potential rise and grid resistance are compared with these produced by the industry standard application WinIGS and some theoretical ground grid model.
In summary, the developed application can solve the ground grid optimization problem with the accurate ground grid modeling method and a hybrid two-step optimization method.
To ensure accuracy and personnel safety, a two-layer soil model is applied instead of the uniform soil model in this research. Some soil model parameters are needed for the two-layer soil model, namely upper-layer resistivity, lower-layer resistivity and upper-layer thickness. Since the ground grid safety requirement is considered under the earth fault, the value of fault current and fault duration time are also needed.
After all these parameters are obtained, a Resistance Matrix method is applied to calculate the mutual and self resistance between conductor segments on both the horizontal and vertical direction. By using a matrix equation of the relationship of mutual and self resistance and unit current of the conductor segments, the ground grid rise can be calculated. Green's functions are applied to calculate the earth potential at a certain point produced by horizontal or vertical line of current. Furthermore, the three basic ground grid safety requirement quantities: the mesh touch potential in the worst case point can be obtained from the earth potential and ground grid rise; the step potential can be obtained from two points' earth potential difference; the grid resistance can be obtained from ground grid rise and fault current.
Finally, in order to achieve ground grid optimization problem more accurate and efficient, which includes the number of meshes in the horizontal grid and the number of vertical rods, a novel two-step hybrid genetic algorithm-pattern search (GA-PS) optimization method is developed. The Genetic Algorithm (GA) is used first to search for an approximate starting point, which is used by the Pattern Search (PS) algorithm to find the final optimal result. This developed application provides an optimal grid design meeting all safety constraints. In the cause of the accuracy of the application, the touch potential, step potential, ground potential rise and grid resistance are compared with these produced by the industry standard application WinIGS and some theoretical ground grid model.
In summary, the developed application can solve the ground grid optimization problem with the accurate ground grid modeling method and a hybrid two-step optimization method.
ContributorsZhang, Qianzhi (Author) / Tylavsky, Daniel (Thesis advisor) / Undrill, John (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2014
Description
There will always be a need for high current/voltage transistors. A transistor that has the ability to be both or either of these things is the silicon metal-silicon field effect transistor (MESFET). An additional perk that silicon MESFET transistors have is the ability to be integrated into the standard silicon on insulator (SOI) complementary metal oxide semiconductor (CMOS) process flow. This makes a silicon MESFET transistor a very valuable device for use in any standard CMOS circuit that may usually need a separate integrated circuit (IC) in order to switch power on or from a high current/voltage because it allows this function to be performed with a single chip thereby cutting costs. The ability for the MESFET to cost effectively satisfy the needs of this any many other high current/voltage device application markets is what drives the study of MESFET optimization. Silicon MESFETs that are integrated into standard SOI CMOS processes often receive dopings during fabrication that would not ideally be there in a process made exclusively for MESFETs. Since these remnants of SOI CMOS processing effect the operation of a MESFET device, their effect can be seen in the current-voltage characteristics of a measured MESFET device. Device simulations are done and compared to measured silicon MESFET data in order to deduce the cause and effect of many of these SOI CMOS remnants. MESFET devices can be made in both fully depleted (FD) and partially depleted (PD) SOI CMOS technologies. Device simulations are used to do a comparison of FD and PD MESFETs in order to show the advantages and disadvantages of MESFETs fabricated in different technologies. It is shown that PD MESFET have the highest current per area capability. Since the PD MESFET is shown to have the highest current capability, a layout optimization method to further increase the current per area capability of the PD silicon MESFET is presented, derived, and proven to a first order.
ContributorsSochacki, John (Author) / Thornton, Trevor J (Thesis advisor) / Schroder, Dieter (Committee member) / Vasileska, Dragica (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2011
Description
Nowadays there is a pronounced interest in the need for sustainable and reliable infrastructure systems to address the challenges of the future infrastructure development. This dissertation presents the research associated with understanding various sustainable and reliable design alternatives for water distribution systems. Although design of water distribution networks (WDN) is a thoroughly studied area, most researchers seem to focus on developing algorithms to solve the non-linear hard kind of optimization problems associated with WDN design. Cost has been the objective in most of the previous studies with few models considering reliability as a constraint, and even fewer models accounting for the environmental impact of WDN. The research presented in this dissertation combines all these important objectives into a multi-objective optimization framework. The model used in this research is an integration of a genetic algorithm optimization tool with a water network solver, EPANET. The objectives considered for the optimization are Life Cycle Costs (LCC) and Life Cycle Carbon Dioxide (CO2) Emissions (LCE) whereby the system reliability is made a constraint. Three popularly used resilience metrics were investigated in this research for their efficiency in aiding the design of WDNs that are able to handle external natural and man-made shocks. The best performing resilience metric is incorporated into the optimization model as an additional objective. Various scenarios were developed for the design analysis in order to understand the trade-offs between different critical parameters considered in this research. An approach is proposed and illustrated to identify the most sustainable and resilient design alternatives from the solution set obtained by the model employed in this research. The model is demonstrated by using various benchmark networks that were studied previously. The size of the networks ranges from a simple 8-pipe system to a relatively large 2467-pipe one. The results from this research indicate that LCE can be reduced at a reasonable cost when a better design is chosen. Similarly, resilience could also be improved at an additional cost. The model used in this research is more suitable for water distribution networks. However, the methodology could be adapted to other infrastructure systems as well.
ContributorsPiratla, Kalyan Ram (Author) / Ariaratnam, Samuel T (Thesis advisor) / Chasey, Allan (Committee member) / Lueke, Jason (Committee member) / Arizona State University (Publisher)
Created2012
Description
A good production schedule in a semiconductor back-end facility is critical for the on time delivery of customer orders. Compared to the front-end process that is dominated by re-entrant product flows, the back-end process is linear and therefore more suitable for scheduling. However, the production scheduling of the back-end process is still very difficult due to the wide product mix, large number of parallel machines, product family related setups, machine-product qualification, and weekly demand consisting of thousands of lots. In this research, a novel mixed-integer-linear-programming (MILP) model is proposed for the batch production scheduling of a semiconductor back-end facility. In the MILP formulation, the manufacturing process is modeled as a flexible flow line with bottleneck stages, unrelated parallel machines, product family related sequence-independent setups, and product-machine qualification considerations. However, this MILP formulation is difficult to solve for real size problem instances. In a semiconductor back-end facility, production scheduling usually needs to be done every day while considering updated demand forecast for a medium term planning horizon. Due to the limitation on the solvable size of the MILP model, a deterministic scheduling system (DSS), consisting of an optimizer and a scheduler, is proposed to provide sub-optimal solutions in a short time for real size problem instances. The optimizer generates a tentative production plan. Then the scheduler sequences each lot on each individual machine according to the tentative production plan and scheduling rules. Customized factory rules and additional resource constraints are included in the DSS, such as preventive maintenance schedule, setup crew availability, and carrier limitations. Small problem instances are randomly generated to compare the performances of the MILP model and the deterministic scheduling system. Then experimental design is applied to understand the behavior of the DSS and identify the best configuration of the DSS under different demand scenarios. Product-machine qualification decisions have long-term and significant impact on production scheduling. A robust product-machine qualification matrix is critical for meeting demand when demand quantity or mix varies. In the second part of this research, a stochastic mixed integer programming model is proposed to balance the tradeoff between current machine qualification costs and future backorder costs with uncertain demand. The L-shaped method and acceleration techniques are proposed to solve the stochastic model. Computational results are provided to compare the performance of different solution methods.
ContributorsFu, Mengying (Author) / Askin, Ronald G. (Thesis advisor) / Zhang, Muhong (Thesis advisor) / Fowler, John W (Committee member) / Pan, Rong (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
Every year, more than 11 million maritime containers and 11 million commercial trucks arrive to the United States, carrying all types of imported goods. As it would be costly to inspect every container, only a fraction of them are inspected before being allowed to proceed into the United States. This dissertation proposes a decision support system that aims to allocate the scarce inspection resources at a land POE (L-POE), to minimize the different costs associated with the inspection process, including those associated with delaying the entry of legitimate imports. Given the ubiquity of sensors in all aspects of the supply chain, it is necessary to have automated decision systems that incorporate the information provided by these sensors and other possible channels into the inspection planning process. The inspection planning system proposed in this dissertation decomposes the inspection effort allocation process into two phases: Primary and detailed inspection planning. The former helps decide what to inspect, and the latter how to conduct the inspections. A multi-objective optimization (MOO) model is developed for primary inspection planning. This model tries to balance the costs of conducting inspections, direct and expected, and the waiting time of the trucks. The resulting model is exploited in two different ways: One is to construct a complete or a partial efficient frontier for the MOO model with diversity of Pareto-optimal solutions maximized; the other is to evaluate a given inspection plan and provide possible suggestions for improvement. The methodologies are described in detail and case studies provided. The case studies show that this MOO based primary planning model can effectively pick out the non-conforming trucks to inspect, while balancing the costs and waiting time.
ContributorsXue, Liangjie (Author) / Villalobos, Jesus René (Thesis advisor) / Gel, Esma (Committee member) / Runger, George C. (Committee member) / Maltz, Arnold (Committee member) / Arizona State University (Publisher)
Created2012
Description
The ASU COVID-19 testing lab process was developed to operate as the primary testing site for all ASU staff, students, and specified external individuals. Tests are collected at various collection sites, including a walk-in site at the SDFC and various drive-up sites on campus; analysis is conducted on ASU campus and results are distributed virtually to all patients via the Health Services patient portal. The following is a literature review on past implementations of various process improvement techniques and how they can be applied to the ABCTL testing process to achieve laboratory goals. (abstract)
ContributorsKrell, Abby Elizabeth (Co-author) / Bruner, Ashley (Co-author) / Ramesh, Frankincense (Co-author) / Lewis, Gabriel (Co-author) / Barwey, Ishna (Co-author) / Myers, Jack (Co-author) / Hymer, William (Co-author) / Reagan, Sage (Co-author) / Compton, Carolyn (Thesis director) / McCarville, Daniel R. (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05