Matching Items (238)
Description
Semiconductor manufacturing facilities are very complex and capital intensive in nature. During the lifecycle of these facilities various disciplines come together, generate and use a tremendous amount of building and process information to support various decisions that enable them to successfully design, build and sustain these advanced facilities. However, a majority of the information generated and processes taking place are neither integrated nor interoperable and result in a high degree of redundancy. The objective of this thesis is to build an interoperable Building Information Model (BIM) for the Base-Build and Tool Installation in a semiconductor manufacturing facility. It examines existing processes and data exchange standards available to facilitate the implementation of BIM and provides a framework for the development of processes and standards that can help in building an intelligent information model for a semiconductor manufacturing facility. To understand the nature of the flow of information between the various stakeholders the flow of information between the facility designer, process tool manufacturer and tool layout designer is examined. An information model for the base build and process tool is built and the industry standards SEMI E6 and SEMI E51 are used as a basis to model the information. It is found that applications used to create information models support interoperable industry standard formats such as the Industry Foundation Classes (IFC) and ISO 15926 in a limited manner. A gap analysis has revealed that interoperability standards applicable to the semiconductor manufacturing industry such as the IFC and ISO15926 need to be expanded to support information transfers unique to the industry. Information modeling for a semiconductor manufacturing facility is unique in that it is a process model (Process Tool Information Model) within a building model (Building Information Model), each of them supported more robustly by different interoperability standards. Applications support interoperability data standards specific to the domain or industry they serve but information transfers need to occur between the various domains. To facilitate flow of information between the different domains it is recommended that a mapping of the industry standards be undertaken and translators between them be developed for business use.
ContributorsPindukuri, Shruthi (Author) / Chasey, Allan D (Thesis advisor) / Wiezel, Avi (Committee member) / Mamlouk, Michael (Committee member) / Arizona State University (Publisher)
Created2011
Description
Concrete columns constitute the fundamental supports of buildings, bridges, and various other infrastructures, and their failure could lead to the collapse of the entire structure. As such, great effort goes into improving the fire resistance of such columns. In a time sensitive fire situation, a delay in the failure of critical load bearing structures can lead to an increase in time allowed for the evacuation of occupants, recovery of property, and access to the fire. Much work has been done in improving the structural performance of concrete including reducing column sizes and providing a safer structure. As a result, high-strength (HS) concrete has been developed to fulfill the needs of such improvements. HS concrete varies from normal-strength (NS) concrete in that it has a higher stiffness, lower permeability and larger durability. This, unfortunately, has resulted in poor performance under fire. The lower permeability allows for water vapor to build up causing HS concrete to suffer from explosive spalling under rapid heating. In addition, the coefficient of thermal expansion (CTE) of HS concrete is lower than that of NS concrete. In this study, the effects of introducing a region of crumb rubber concrete into a steel-reinforced concrete column were analyzed. The inclusion of crumb rubber concrete into a column will greatly increase the thermal resistivity of the overall column, leading to a reduction in core temperature as well as the rate at which the column is heated. Different cases were analyzed while varying the positioning of the crumb-rubber region to characterize the effect of position on the improvement of fire resistance. Computer simulated finite element analysis was used to calculate the temperature and strain distribution with time across the column's cross-sectional area with specific interest in the steel - concrete region. Of the several cases which were investigated, it was found that the improvement of time before failure ranged between 32 to 45 minutes.
ContributorsZiadeh, Bassam Mohammed (Author) / Phelan, Patrick (Thesis advisor) / Kaloush, Kamil (Thesis advisor) / Jiang, Hanqing (Committee member) / Arizona State University (Publisher)
Created2011
Description
Advancements in computer vision and machine learning have added a new dimension to remote sensing applications with the aid of imagery analysis techniques. Applications such as autonomous navigation and terrain classification which make use of image classification techniques are challenging problems and research is still being carried out to find better solutions. In this thesis, a novel method is proposed which uses image registration techniques to provide better image classification. This method reduces the error rate of classification by performing image registration of the images with the previously obtained images before performing classification. The motivation behind this is the fact that images that are obtained in the same region which need to be classified will not differ significantly in characteristics. Hence, registration will provide an image that matches closer to the previously obtained image, thus providing better classification. To illustrate that the proposed method works, naïve Bayes and iterative closest point (ICP) algorithms are used for the image classification and registration stages respectively. This implementation was tested extensively in simulation using synthetic images and using a real life data set called the Defense Advanced Research Project Agency (DARPA) Learning Applied to Ground Robots (LAGR) dataset. The results show that the ICP algorithm does help in better classification with Naïve Bayes by reducing the error rate by an average of about 10% in the synthetic data and by about 7% on the actual datasets used.
ContributorsMuralidhar, Ashwini (Author) / Saripalli, Srikanth (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2011
Description
This study presents the results of one of the first attempts to characterize the pore water pressure response of soils subjected to traffic loading under saturated and unsaturated conditions. It is widely known that pore water pressure develops within the soil pores as a response to external stimulus. Also, it has been recognized that the development of pores water pressure contributes to the degradation of the resilient modulus of unbound materials. In the last decades several efforts have been directed to model the effect of air and water pore pressures upon resilient modulus. However, none of them consider dynamic variations in pressures but rather are based on equilibrium values corresponding to initial conditions. The measurement of this response is challenging especially in soils under unsaturated conditions. Models are needed not only to overcome testing limitations but also to understand the dynamic behavior of internal pore pressures that under critical conditions may even lead to failure. A testing program was conducted to characterize the pore water pressure response of a low plasticity fine clayey sand subjected to dynamic loading. The bulk stress, initial matric suction and dwelling time parameters were controlled and their effects were analyzed. The results were used to attempt models capable of predicting the accumulated excess pore pressure at any given time during the traffic loading and unloading phases. Important findings regarding the influence of the controlled variables challenge common beliefs. The accumulated excess pore water pressure was found to be higher for unsaturated soil specimens than for saturated soil specimens. The maximum pore water pressure always increased when the high bulk stress level was applied. Higher dwelling time was found to decelerate the accumulation of pore water pressure. In addition, it was found that the higher the dwelling time, the lower the maximum pore water pressure. It was concluded that upon further research, the proposed models may become a powerful tool not only to overcome testing limitations but also to enhance current design practices and to prevent soil failure due to excessive development of pore water pressure.
ContributorsCary, Carlos (Author) / Zapata, Claudia E (Thesis advisor) / Wiczak, Matthew W (Thesis advisor) / Kaloush, Kamil (Committee member) / Sandra, Houston (Committee member) / Arizona State University (Publisher)
Created2011
Description
A recent joint study by Arizona State University and the Arizona Department of Transportation (ADOT) was conducted to evaluate certain Warm Mix Asphalt (WMA) properties in the laboratory. WMA material was taken from an actual ADOT project that involved two WMA sections. The first section used a foamed-based WMA admixture, and the second section used a chemical-based WMA admixture. The rest of the project included control hot mix asphalt (HMA) mixture. The evaluation included testing of field-core specimens and laboratory compacted specimens. The laboratory specimens were compacted at two different temperatures; 270 °F (132 °C) and 310 °F (154 °C). The experimental plan included four laboratory tests: the dynamic modulus (E*), indirect tensile strength (IDT), moisture damage evaluation using AASHTO T-283 test, and the Hamburg Wheel-track Test. The dynamic modulus E* results of the field cores at 70 °F showed similar E* values for control HMA and foaming-based WMA mixtures; the E* values of the chemical-based WMA mixture were relatively higher. IDT test results of the field cores had comparable finding as the E* results. For the laboratory compacted specimens, both E* and IDT results indicated that decreasing the compaction temperatures from 310 °F to 270 °F did not have any negative effect on the material strength for both WMA mixtures; while the control HMA strength was affected to some extent. It was noticed that E* and IDT results of the chemical-based WMA field cores were high; however, the laboratory compacted specimens results didn't show the same tendency. The moisture sensitivity findings from TSR test disagreed with those of Hamburg test; while TSR results indicated relatively low values of about 60% for all three mixtures, Hamburg test results were quite excellent. In general, the results of this study indicated that both WMA mixes can be best evaluated through field compacted mixes/cores; the results of the laboratory compacted specimens were helpful to a certain extent. The dynamic moduli for the field-core specimens were higher than for those compacted in the laboratory. The moisture damage findings indicated that more investigations are needed to evaluate moisture damage susceptibility in field.
ContributorsAlossta, Abdulaziz (Author) / Kaloush, Kamil (Thesis advisor) / Witczak, Matthew W. (Committee member) / Mamlouk, Michael S. (Committee member) / Arizona State University (Publisher)
Created2011
Description
The theme for this work is the development of fast numerical algorithms for sparse optimization as well as their applications in medical imaging and source localization using sensor array processing. Due to the recently proposed theory of Compressive Sensing (CS), the $\ell_1$ minimization problem attracts more attention for its ability to exploit sparsity. Traditional interior point methods encounter difficulties in computation for solving the CS applications. In the first part of this work, a fast algorithm based on the augmented Lagrangian method for solving the large-scale TV-$\ell_1$ regularized inverse problem is proposed. Specifically, by taking advantage of the separable structure, the original problem can be approximated via the sum of a series of simple functions with closed form solutions. A preconditioner for solving the block Toeplitz with Toeplitz block (BTTB) linear system is proposed to accelerate the computation. An in-depth discussion on the rate of convergence and the optimal parameter selection criteria is given. Numerical experiments are used to test the performance and the robustness of the proposed algorithm to a wide range of parameter values. Applications of the algorithm in magnetic resonance (MR) imaging and a comparison with other existing methods are included. The second part of this work is the application of the TV-$\ell_1$ model in source localization using sensor arrays. The array output is reformulated into a sparse waveform via an over-complete basis and study the $\ell_p$-norm properties in detecting the sparsity. An algorithm is proposed for minimizing a non-convex problem. According to the results of numerical experiments, the proposed algorithm with the aid of the $\ell_p$-norm can resolve closely distributed sources with higher accuracy than other existing methods.
ContributorsShen, Wei (Author) / Mittlemann, Hans D (Thesis advisor) / Renaut, Rosemary A. (Committee member) / Jackiewicz, Zdzislaw (Committee member) / Gelb, Anne (Committee member) / Ringhofer, Christian (Committee member) / Arizona State University (Publisher)
Created2011
Description
The structural design of pavements in both highways and airfields becomes complex when one considers environmental effects and ground water table variation. Environmental effects have been incorporated on the new Mechanistic-Empirical Pavement Design Guide (MEPDG) but little has been done to incorporate environmental effects on airfield design. This work presents a developed code produced from this research study called ZAPRAM, which is a mechanistically based pavement model based upon Limiting Strain Criteria in airfield HMA pavement design procedures. ZAPRAM is capable of pavement and airfield design analyses considering environmental effects. The program has been coded in Visual Basic and implemented in an event-driven, user-friendly educational computer program, which runs in Excel environment. Several studies were conducted in order to insure the validity of the analysis as well as the efficiency of the software. The first study yielded the minimum threshold number of computational points the user should use at a specific depth within the pavement system. The second study was completed to verify the correction factor for the Odemark's transformed thickness equation. Default correction factors were included in the code base on a large comparative study between Odemark's and MLET. A third study was conducted to provide a comparison of flexible airfield pavement design thicknesses derived from three widely accepted design procedures used in practice today: the Asphalt Institute, Shell Oil, and the revised Corps of Engineering rutting failure criteria to calculate the thickness requirements necessary for a range of design input variables. The results of the comparative study showed that there is a significant difference between the pavement thicknesses obtained from the three design procedures, with the greatest deviation found between the Shell Oil approach and the other two criteria. Finally, a comprehensive sensitivity study of environmental site factors and the groundwater table depth upon flexible airfield pavement design and performance was completed. The study used the newly revised USACE failure criteria for subgrade shear deformation. The methodology utilized the same analytical methodology to achieve real time environmental effects upon unbound layer modulus, as that used in the new AASHTO MEPDG. The results of this effort showed, for the first time, the quantitative impact of the significant effects of the climatic conditions at the design site, coupled with the importance of the depth of the groundwater table, on the predicted design thicknesses. Significant cost savings appear to be quite reasonable by utilizing principles of unsaturated soil mechanics into the new airfield pavement design procedure found in program ZAPRAM.
ContributorsSalim, Ramadan A (Author) / Zapata, Claudia (Thesis advisor) / Witczak, Matthew (Thesis advisor) / Kaloush, Kamil (Committee member) / Arizona State University (Publisher)
Created2011
Description
Advancements in mobile technologies have significantly enhanced the capabilities of mobile devices to serve as powerful platforms for sensing, processing, and visualization. Surges in the sensing technology and the abundance of data have enabled the use of these portable devices for real-time data analysis and decision-making in digital signal processing (DSP) applications. Most of the current efforts in DSP education focus on building tools to facilitate understanding of the mathematical principles. However, there is a disconnect between real-world data processing problems and the material presented in a DSP course. Sophisticated mobile interfaces and apps can potentially play a crucial role in providing a hands-on-experience with modern DSP applications to students. In this work, a new paradigm of DSP learning is explored by building an interactive easy-to-use health monitoring application for use in DSP courses. This is motivated by the increasing commercial interest in employing mobile phones for real-time health monitoring tasks. The idea is to exploit the computational abilities of the Android platform to build m-Health modules with sensor interfaces. In particular, appropriate sensing modalities have been identified, and a suite of software functionalities have been developed. Within the existing framework of the AJDSP app, a graphical programming environment, interfaces to on-board and external sensor hardware have also been developed to acquire and process physiological data. The set of sensor signals that can be monitored include electrocardiogram (ECG), photoplethysmogram (PPG), accelerometer signal, and galvanic skin response (GSR). The proposed m-Health modules can be used to estimate parameters such as heart rate, oxygen saturation, step count, and heart rate variability. A set of laboratory exercises have been designed to demonstrate the use of these modules in DSP courses. The app was evaluated through several workshops involving graduate and undergraduate students in signal processing majors at Arizona State University. The usefulness of the software modules in enhancing student understanding of signals, sensors and DSP systems were analyzed. Student opinions about the app and the proposed m-health modules evidenced the merits of integrating tools for mobile sensing and processing in a DSP curriculum, and familiarizing students with challenges in modern data-driven applications.
ContributorsRajan, Deepta (Author) / Spanias, Andreas (Thesis advisor) / Frakes, David (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
With robots being used extensively in various areas, a certain degree of robot autonomy has always been found desirable. In applications like planetary exploration, autonomous path planning and navigation are considered essential. But every now and then, a need to modify the robot's operation arises, a need for a human to provide it some supervisory parameters that modify the degree of autonomy or allocate extra tasks to the robot. In this regard, this thesis presents an approach to include a provision to accept and incorporate such human inputs and modify the navigation functions of the robot accordingly. Concepts such as applying kinematical constraints while planning paths, traversing of unknown areas with an intent of maximizing field of view, performing complex tasks on command etc. have been examined and implemented. The approaches have been tested in Robot Operating System (ROS), using robots such as the iRobot Create, Personal Robotics (PR2) etc. Simulations and experimental demonstrations have proved that this approach is feasible for solving some of the existing problems and that it certainly can pave way to further research for enhancing functionality.
ContributorsVemprala, Sai Hemachandra (Author) / Saripalli, Srikanth (Thesis advisor) / Fainekos, Georgios (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2013
Description
In contemporary society, sustainability and public well-being have been pressing challenges. Some of the important questions are:how can sustainable practices, such as reducing carbon emission, be encouraged? , How can a healthy lifestyle be maintained?Even though individuals are interested, they are unable to adopt these behaviors due to resource constraints. Developing a framework to enable cooperative behavior adoption and to sustain it for a long period of time is a major challenge. As a part of developing this framework, I am focusing on methods to understand behavior diffusion over time. Facilitating behavior diffusion with resource constraints in a large population is qualitatively different from promoting cooperation in small groups. Previous work in social sciences has derived conditions for sustainable cooperative behavior in small homogeneous groups. However, how groups of individuals having resource constraint co-operate over extended periods of time is not well understood, and is the focus of my thesis. I develop models to analyze behavior diffusion over time through the lens of epidemic models with the condition that individuals have resource constraint. I introduce an epidemic model SVRS ( Susceptible-Volatile-Recovered-Susceptible) to accommodate multiple behavior adoption. I investigate the longitudinal effects of behavior diffusion by varying different properties of an individual such as resources,threshold and cost of behavior adoption. I also consider how behavior adoption of an individual varies with her knowledge of global adoption. I evaluate my models on several synthetic topologies like complete regular graph, preferential attachment and small-world and make some interesting observations. Periodic injection of early adopters can help in boosting the spread of behaviors and sustain it for a longer period of time. Also, behavior propagation for the classical epidemic model SIRS (Susceptible-Infected-Recovered-Susceptible) does not continue for an infinite period of time as per conventional wisdom. One interesting future direction is to investigate how behavior adoption is affected when number of individuals in a network changes. The affects on behavior adoption when availability of behavior changes with time can also be examined.
ContributorsDey, Anindita (Author) / Sundaram, Hari (Thesis advisor) / Turaga, Pavan (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2013