Master of Science in Engineering Mechanics, Cleveland State University, 2013, Fenn College of Engineering

The use of failure assessment diagrams (FAD) for evaluating the integrity of components containing crack-like flaws has developed a well-defined methodology over the years that includes a correction factor to account for combined loading effects that are a result of primary and secondary stresses. The load combination factor, Ψ, is based on the Option 1 FAD currently in use in the Central Electricity Generating Board's (CEGB) report No. R/H/R6 (R6) and the API-579-1/ASME FFS-1 fitness-for-service standard. The Ψ factors for the Option 2 FAD based on ASME B&PV Code Section VIII, Division 2 material stress-strain curves are developed and tabulated here for a wide range materials used for the construction of pressure vessels. The Ψ factors based on the Option 1 FAD are recalculated here and compared to current published data. The approach utilizing Option 1 FAD methods is evaluated here with regard to its conservatism and applicability to material models other than the Ramberg-Osgood model. In addition, a sensitivity analysis is performed to estimate Ψ factor errors due to uncertainty in material property parameters. A critical review of the tabulated data in API-579 is performed and errors are identified along with suggested solutions to correct the data.

Committee: Stephen Duffy PhD (Committee Chair); Paul Lin PhD (Committee Member); Norbert Delatte PhD (Committee Member) Subjects: Engineering; Materials Science; Mechanical Engineering; Metallurgy; Nuclear Engineering; Petroleum Engineering

MS, University of Cincinnati, 2012, Engineering and Applied Science: Computer Engineering

Recently, XML (eXtensible Markup Language) files have become of great importance in business enterprises. Information in the XML files can be easily shared across the web. Thus, extracting data from XML documents and creating XML documents become important topics of discussion. There are many APIs (Application Program Interfaces) available which can perform these operations. For beginners in XML processing, selecting an API for a specific project is a difficult task. In this thesis we compare various APIs that are capable of extracting data and / or creating XML files. The comparison is done based on the performance time for different types of inputs which form different cases. The codes for all the different cases are implemented. Two different systems, one with Windows 7 OS and another with Mac OS are used to perform all the experiments. Using the results found we propose a suitable API for a given condition. In addition to the performance, programming ease for these APIs is taken into consideration as another aspect for comparison. To compare the programming ease, aspects such as number of lines of code, complexity of the code and complexity of understanding the coding for the particular API are considered. Thus, we are also able to suggest an appropriate API based on programming ease.

Committee: Carla Purdy PhD (Committee Chair); Raj Bhatnagar PhD (Committee Member); George Purdy PhD (Committee Member) Subjects: Computer Engineering

Master of Science in Engineering, Youngstown State University, 2025, Rayen School of Engineering

Internet of Things (IoT) devices, such as thermostats, lighting systems, and fitness trackers, have revolutionized both residential and industrial environments, enabling users to remotely control and manage them. Although many IoT devices are often manageable through Original Equipment Manufacturer (OEM) software applications, overseeing devices from various OEM origins simultaneously, or even customized hardware, can be complex and tedious. To address this challenge, an IoT gateway serves as a centralized hub that supports wireless connectivity across various protocols. Bluetooth Low Energy (BLE), a widely adopted wireless communication protocol in low-power-consuming devices such as sensors, is therefore employed in many IoT gateways. Large-scale IoT networks significantly benefit from an IoT gateway, as it provides a unified management point for all connected devices. OEMs of IoT gateways may offer a software development kit (SDK) to facilitate application customization, enabling the attainment of specific design requirements. This thesis presents the design, development, and implementation of two software applications to acquire real-time sensor data from custom BLE-enabled printed circuit boards (PCBs). Leveraging an IoT gateway, its compatible SDK, and a library of sample programs, two applications are developed to monitor sensor data: a serial terminal interface and a dynamic web-based dashboard.

Committee: Vamsi Borra PhD (Advisor); Frank Li PhD (Committee Member); Ghassan Salim MS (Committee Member) Subjects: Computer Engineering; Computer Science; Electrical Engineering; Engineering

MS, University of Cincinnati, 2023, Engineering and Applied Science: Aerospace Engineering

A growing interest in finding new ways to solve Computational Fluid Dynamics (CFD) problems has motivated researchers to employ a data science approach for numerical analysis. To optimize time and memory-related issues with data science, one of the approaches can be the use of machine learning (ML) algorithms. As Python is a go through language for ML developments, there arises a need for data visualization. The in-line data visualization capability within the same environment can simplify envisioning the data before and after modeling. To accelerate and simplify this data processing, the Gas Turbine Simulation Laboratory (GTSL) at the University of Cincinnati (UC) initiated this project to support data interactions and manipulation needs within the Python environment. A variety of algorithms for reading, writing, converting file formats, data extraction, splitting blocks, in-line visualization, and computing variables from CFD data were rewritten to be more efficient. The object-oriented nature of the code reduces the complexity of the Advanced Programming Interface (API) and eases the addition of classes and modules without compromising the performance. One of the other advantages of using Python is that many of the modules are compatible with most operating systems used, making the API easily portable and user-friendly. Another way to optimize time and memory is by parallel computing. Utilizing the Curriculum Practical Training (CPT) with an internship at Altair Engineering Inc., the power of the Message Passing Interface (MPI) was explored. One of the strategies used by the Altair Flux solver to divide the global domain for parallel solving was Domain Decomposition (DDM). DDM splits the domain into homogeneous regions based on their physics, and Flux assigns each of these regions to a processor for parallel solving. However, the performance of parallel computing is limited by the slowest processor, and the regions formed will not necessarily be of (open full item for complete abstract)

Committee: Paul Orkwis Ph.D. (Committee Chair); Daniel Cuppoletti Ph.D. (Committee Member); Shaaban Abdallah Ph.D. (Committee Member) Subjects: Aerospace Engineering

MS, University of Cincinnati, 2021, Engineering and Applied Science: Computer Science

The current progress in computer technology is matched by the increase in malware and cyber-attacks, resulting in a nearly constant battle between establishing a complete malware detection technique and newly evolving smart malicious code. Malicious code gain access into the system through network or any connection with external device. In this digital world they are spread easily through internet and when executed causes severe impacts. Machine learning methods are proven to be efficient than signature based methods in detecting new malware.The analysis of malware is made difficult by the fact that, to a large extent, malware and benign code use the same instructions.This suggests that the difference in behavior might be due not to the instructions used, but in how they are used. In particular, the context in which instructions are used seems to play an important role in deciding between malicious and benign code.We have progressed towards defining and extracting the context of API from Portable Execution files of the Windows operating system. It is suggested that the context can be used as a feature in a machine learning algorithm towards identifying attempts to corrupt the system and to elude the antivirus scanners by code obfuscation.

Committee: Anca Ralescu Ph.D. (Committee Chair); Kenneth Berman Ph.D. (Committee Member); Chia Han Ph.D. (Committee Member); Dan Ralescu Ph.D. (Committee Member) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 2021, Computer Science and Engineering

The past decade has witnessed the success of big data processing frameworks, which provide simple interfaces to parallelize and scale applications efficiently. Comparing the design of MapReduce, Spark, and Reduction Object paradigm, we identified that the design of pattern-based Application Programming Interfaces (APIs) can significantly impact the captured application types as well as middleware performance. Therefore, by concluding common patterns in popular big-data and machine learning applications, we want to build new frameworks with both expressive interfaces and efficient middleware, to achieve better parallelism, locality, programmability, fault tolerance, and coverage of applications. To approach this, Chapter 2 studies the impact of API design on programmability and middleware performance of MapReduce(-like) frameworks. Specifically, we introduce two different variations of the original MapReduce API and efficient implementations of all three APIs. Through performance comparison and modeling, we identify that though MapReduce and similar frameworks have demonstrated high programmability, they fall short in terms of performance. We show that Reduction-Object-based APIs, which only require small additional effort from programmers, can provide high performance. Following this work, in Chapter 3, we built a high-throughput stream processing framework that offers a high-level API to the users (similar to Reduction Object), is fault-tolerant, and is also more efficient and scalable than current solutions. Particularly, a cost-efficient MPI/OpenMP-based fault-tolerant scheme is incorporated so that the system can survive node failures with only a modest degradation of performance. A comparison against state-of-the-art streaming frameworks shows our system boosts the throughput of test cases by up to 10X and achieves desirable parallelism when scaled out. In the fast-evolving Internet of Things (IoT) scenario, we envision the potential of leveraging esta (open full item for complete abstract)

Committee: Gagan Agrawal Dr (Advisor); Radu Teodorescu Dr (Advisor); Feng Qin Dr (Committee Member); Christopher Stewart Dr (Committee Member) Subjects: Computer Engineering; Computer Science

Master of Science (MS), Bowling Green State University, 2021, Computer Science

Along with the immense popularity of Android applications, the Android ecosystem is under constant threat of malware attacks. This issue warrants developing efficient tools to detect malware apps. There is a large body of work in the literature that has applied static analysis for malware detection. For instance, one popular idea has been to extract API-calls from the app code and then to use those API-calls as artifacts to train machine learning models to classify malware and benign apps. However, most of this line of work does not incorporate the true execution sequence of the API-calls, and thus misses out to capture a potentially rich signature. Furthermore, while evaluating the vetting accuracy, many of the prior work report their primary results on a randomly selected test set that are not spatially consistent (malware percentage in the test set approximating real-world scenario) and/or temporally consistent (having correct time split of train and test data) which artificially inflates the performance of the model. In this thesis, we explore if tracking the true sequence of the API-calls improves the effectiveness of the vetting process and present results ranging from testing on a random test set to a spatially and temporally consistent test set. We perform deep learning-based malware classification using a graph that we name API sequence graph which preserves the true sequence of API calls. The experiments show that our best performing model achieves AuPRC ranging from 0.977 to 0.86 and an F1-score of 0.955 to 0.83 depending on the consistency of the test set. The results show that our best-performing model, based on the true sequence of API calls, outperforms a quasi-sequence-based model.

Committee: Sankardas Roy Ph.D. (Advisor); Jong Kwan Lee Ph.D. (Committee Member); Qing Tian Ph.D. (Committee Member) Subjects: Computer Science

Master of Science in Computer Engineering (MSCE), Wright State University, 2019, Computer Engineering

Navigating in a Virtual Environment with traditional input devices such as mouse, joysticks and keyboards provide limited maneuverability and is also time consuming. While working in a virtual environment, changing parameters to obtain the desired visualization requires time to achieve by manually entering parameter values in an algorithm to test outcomes. The following thesis presents an alternate user interface to reduce user efforts, while navigating within the Virtual Environment. The user interface is an Android application which is designed to accommodate spoken commands. This Speech Enabled User Interface termed as the Speech Navigation Application (SNA), provides the user with an option to voice out the commands which they wish to see enacted/reciprocated in a virtual environment. The user can change the parameters to meet their needs. The idea behind this project was to minimize the effort needed to change parameters of any visualization, so as to obtain the desired view as per the requirement. This paper explains in detail the design, implementation and evaluation of the working project. This system is analyzed by simulating the working prototype in the DIVE.

Committee: Thomas Wischgoll Ph.D. (Advisor); Yong Pei Ph.D. (Committee Member); John Gallagher Ph.D. (Committee Member) Subjects: Computer Engineering; Computer Science

MS, University of Cincinnati, 2016, Engineering and Applied Science: Mechanical Engineering

Additive Manufacturing (AM) is a process where an initially conceptualized 3D CAD model is fabricated by adding successive layers of material on top of each other while eliminating the need for any process planning. Metal powder based AM processes are gaining popularity in several industries such as aerospace, health care, architecture, industrial design, automotive and consumer products due to the ease with which complex and intricate parts can be manufactured. However, achieving part quality and meeting the design tolerances is one of the most crucial challenges faced by AM among various others such as minimizing support structures, build time, build cost, energy expenditure and support structures removal. The primary cause for not achieving the design tolerances can be assigned to the staircase effect, which is inevitable in AM processes. The leading factor that affects the staircase error and in turn the part quality is the part build orientation. Apart from part quality, build orientation also influences build time, which is another vital aspect since it directly affects the manufacturing cost of the part. The objective of this thesis is to provide an approach to identify an optimal part build orientation which will satisfy all the Geometric Dimensioning and Tolerancing (GD&T) of the part while minimizing its build time. Siemens PLM NX API is used to extract the GD&T callouts and associated geometric information of the CAD model. This is used later to verify if the design tolerances are met. Next, geometric correlation between build orientation, slice thickness and GD&T errors are established. A non-linear constrained weighted optimization model is also developed to identify the best build orientation for meeting the design tolerances and minimizing part errors along with build time.

Committee: Sundararaman Anand Ph.D. (Committee Chair); Thomas Richard Huston Ph.D. (Committee Member); David Thompson Ph.D. (Committee Member) Subjects: Mechanical Engineering

Doctor of Philosophy (PhD), Ohio University, 2016, Electrical Engineering & Computer Science (Engineering and Technology)

In software development, developers usually receive bug reports that describe the abnormal behaviors of the software products. When a new bug report is received, developers usually need to reproduce the bug and perform code reviews to find the cause, a process that can be tedious and time-consuming. To alleviate developers' manual efforts of finding the bug, this dissertation presents a learning-to-rank approach that ranks all the source code files for a given bug report automatically. To improve the ranking performance, this dissertation also introduces using word-embedding-based text similarities to bridge the lexical gap between natural languages in bug reports and code in source files. First, a tool for ranking all the source files with respect to how likely they are to contain the cause of the bug would enable developers to narrow down their search and improve productivity. This dissertation introduces an adaptive ranking approach that leverages project knowledge through functional decomposition of source code, API descriptions of library components, bug-fixing history, code change history, and the file dependency graph. Given a bug report, the ranking score of each source file is computed as a weighted combination of an array of features, where the weights are trained automatically on previously solved bug reports using a learning-to-rank technique. We evaluate the ranking system on six large-scale open source Java projects, using the before-fix version of the project for every bug report. The experimental results show that the learning-to-rank approach outperforms three recent state-of-the-art methods. In particular, our method makes correct recommendations within the top 10 ranked source files for over 70% of the bug reports in the Eclipse Platform and Tomcat projects. Second, we propose bridging the lexical gap by projecting natural language statements and code snippets as meaning vectors in a shared representation space. In the proposed architecture, (open full item for complete abstract)

Committee: Chang Liu (Advisor); Razvan Bunescu (Advisor) Subjects: Computer Science

MS, University of Cincinnati, 2014, Engineering and Applied Science: Computer Science

Wild?re is one of the most signi?cant disturbances responsible for reshaping the terrain and changing the ecosystem as well as causing massive loss of human lives and properties. The growing trend in terms of frequency and intensity over the last decade have necessitated the development of more portable and ef?cient Wildfire Management Systems. In this thesis, different from the traditional desktop application of similar systems, we proposed and implemented a web-based Wildfire Management System----“ForestFireCloud”. Taking full advantage of the powerful functionality of Data Visualization and GIS data processing of the new Google Maps API v3, along with other modern web developing framework, we construct a portable wildfire monitoring, modeling and management platform, including several subsystems: a near real-time wildfire monitoring system based on WMS and RSS; a new fire danger assessment model targeting both meteorological and anthropogenic factors; a fire propagation simulation system based on cellular automata(CA). To monitor current wildfire status in real-time, we mainly use Google Map API to visualize fire observation data in KML and JSON format gathered from data feeds provided by several national wildfire management agencies. In our fire danger assessment system, a modified Keetch-Byram Drought Index (KBDI) is introduced as a diagnostic and forecasting measure to assess the potential of wildfire; a web-based KBDI calculator and visualization system is also implemented. Besides, we introduce a new mechanism to visualize fire record data and intense traffic spot; based on that we conduct an experiment to observe the correlation between traffic hotspot and wildfire occurrence, which yields a strong evidence shows a causality relation between the two objects. At last, to target the difficulty of precise prediction of wild?re propagation behavior caused by uncertainties in weather conditions as well as imperfect knowledge about exact vegetation and topogra (open full item for complete abstract)

Committee: Chia Han Ph.D. (Committee Chair); Susanna Tong Ph.D. (Committee Member); Anca Ralescu Ph.D. (Committee Member) Subjects: Computer Science

Master of Science (MS), Bowling Green State University, 2014, Geology

Planning of shale gas infrastructure and drilling sites for hydraulic fracturing has important spatial implications. The evaluation of conflicting and competing objectives requires an explicit consideration of multiple criteria as they have important environmental and economic implications. This study presents a web-based multi-criteria spatial decision support system (SDSS) prototype with a flexible and user-friendly interface that could provide educational or decision-making capabilities with respect to hydraulic fracturing site-selection in eastern Ohio. One of the main features of this spatial decision support system is to emphasize potential trade-offs between important factors of environmental and economic implications from hydraulic fracturing activities using a weighted linear combination (WLC) method. WLC is a simple approach that integrates users' preferences into an overall assessment and offers a rationale for trade-offs between decision criteria and objectives. In the prototype, the GIS-enabled analytical components allow spontaneous visualization of available alternatives on maps which provide value-added features for decision support processes and derivation of final decision maps. The SDSS prototype exhibits a straightforward decision-making procedure with easy-to-use web interface and facilitates non-expert participation capabilities. It comprises of a mapping module, decision-making tool, group decision data statistics, and social media sharing tools. The system architecture combines a variety of closely related components using Silverlight, ArcGIS API for Silverlight, ArcGIS Server, and ArcSDE for SQL Server software. During the decision-making process, users are guided through a logical flow of successively presented forms and standardized criteria maps to generate visualization of trade-off scenarios and alternative solutions tailored to their personal preferences. Finally, the results and the preferences from all users are graphed for visualiz (open full item for complete abstract)

Committee: Peter Gorsevski (Advisor); Charles Onasch (Committee Member); Margaret Yacobucci (Committee Member) Subjects: Geographic Information Science

Master of Science, The Ohio State University, 2008, Computer and Information Science

We live in an era of rapidly developing information technologies. Computers and the internet make finding information easier than ever before. Unfortunately not everyone can fully benefit from this: a vast amount of information is available in only textual form, making it difficult or even impossible for visually impaired and dyslexic persons to access it. Special tools for vocalizing textual information can be of great assistance to them, and a valuable tool for sighted people as well.The goal of this project is to design and implement speech APIs that are easy to extend and use with a variety of speech engines on different operating systems. JSAPI (Java Speech API) serves as a base for the project. Proposed by Sun Microsystems, JSAPI consists of a set of well defined interfaces for Java classes and the speech markup language JSML. No implementation of JSAPI is provided by Sun. The few available implementations of JSAPI, firstly, do not have the full set of features that may be needed for speech enabled applications and, secondly, are limited to a specific speech engine and operating system. This project takes significant steps towards solving these problems.

Committee: Eitan Gurari PhD (Advisor); Rajiv Ramnath PhD (Committee Member) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 1972, Mechanical Engineering

Committee: Helmuth Engelman (Advisor) Subjects:

Master of Science (MS), Ohio University, 2012, Computer Science (Engineering and Technology)

Stochastic Differential Equations are important in many models of various physical or artificial phenomena. To get meaningful results it is desirable to solve the initial value numerical integration problem for a sufficiently large ensemble of realizations. Each element of the ensemble has the same form, thus exposing inherent data-parallelism. We implemented a cross-platform library written in C++ and CUDA that exploits data-parallelism by integrating all realizations in parallel using CUDA, and then computing the properties of the solution using CUDA again. This offers a great speed up over a sequential approach while keeping the overall algorithm for arriving at results essentially the same.

Committee: Frank Drews (Advisor); David Chelberg (Committee Member); David Juedes (Committee Member); Martin Mohlenkamp (Committee Member) Subjects: Computer Science

Master of Science, Miami University, 2009, Computer Science and Systems Analysis

Realizing the advantages of simulation-based methodologies requires the use of a software environment that is conducive for modeling, simulation, and analysis. Furthermore, parallel simulation methods must be employed to reduce the time for simulation, particularly for large problems, to enable analysis in reasonable timeframes. Accordingly, this thesis covers the development of a general purpose agent-based, parallel simulation environment called MUSE (Miami University Simulation Environment). MUSE, provides an Application Program Interface (API) for agent-based modeling and a framework for parallel simulation. The API was developed in C++ using its object oriented features. The core parallel simulation capabilities of MUSE were realized using the Time Warp synchronization methodology and the Message Passing Interface (MPI). Experiments show MUSE to be a scalable and efficient simulation environment.

Committee: Dhanajai Rao PhD (Advisor); Mufit Ozden PhD (Committee Member); Lukasz Opyrchal PhD (Committee Member) Subjects: Computer Science

Master of Science, University of Akron, 2005, Mechanical Engineering

In today's petroleum refining industry, aging infrastructure is a primary concern when considering replacement costs and safe operation. As vessels, piping, and tankage age in service, they are subjected to various forms of degradation or damage that may eventually comprise structural integrity. An engineering or Fitness-For-Service (FFS) assessment is required to evaluate structural integrity and safely extend the life of damaged equipment. Guidelines for performing a FFS assessment have been documented in API RP 579. The goal of API 579 is to ensure the safety of plant personnel and the public while aging equipment continues to operate, provide technically sound Fitness-For-Service assessment procedures for various forms of damage, and help optimize maintenance and operation of existing facilities while enhancing long-term economic viability. The procedures in API 579 (2000 release) provide computational methods to assess flaws that are found in in-service equipment caused by various damage mechanisms. The focus of this study is to review the technical basis for the Fitness-For-Service assessment procedures for general and local metal loss. Extensive validation of these procedures along with additional development is presented. The conclusions of the study are recommended as the best practices to be included in future versions of API 579.

Committee: Paul Lam (Advisor) Subjects: