Residual stresses play an important role in the performance of machined components. Component characteristics that are influenced by residual stress include fatigue life, corrosion resistance, and part distortion. The functional behavior of machined components can be enhanced or impaired by residual stresses. Because of this, understanding the residual stress imparted by machining is an important aspect of understanding machining and overall part quality. Machining-induced residual stress prediction has been a topic of research since the 1950s. Research efforts have been primarily composed of experimental findings, analytical modeling, finite element modeling, and various combinations of those efforts. Although there has been significant research in the area, there are still opportunities for advancing predictive residual stress methods. The objectives of the current research are as follows: (1) develop a method of predicting residual stress based on an analytical description of the machining process and (2) validate the model with experimental data. The research focuses on predicting residual stresses in machining based on first principles. Machining process output parameters such as cutting forces and cutting temperatures are predicted as part of the overall modeling effort. These output parameters serve as the basis for determining the loads which generate residual stresses due to machining. The modeling techniques are applied to a range of machining operations including orthogonal cutting, broaching, milling, and turning. The strengths and weaknesses of the model are discussed as well as opportunities for future work.

We propose a clustering algorithm which incorporates a similarity-based fuzzy clustering and principal component analysis. The proposed algorithm is capable of discovering clusters with hyper-spherical, hyperellipsoidal, or oblique hyper-ellipsoidal shapes. Besides, the number of the clusters need not be specified in advance by the user. For a given dataset, the orientation, locations, and the number of clusters obtained can truthfully reflect the characteristics of the dataset. Experimental results, obtained by running on datasets generated synthetically, show that our method performs better than other methods.

This study measured Ambient concentrations of air pollutants and Volatile Organic Compounds (VOCs) in industrial park in Kaohsiung City. The spatial distribution was investigated during different time periods and seasons. The ozone formation potential (OFP) of VOCs species were evaluated based on the maximum incremental reactivity (MIR). Also, this study using factor analysis to estimate the polluted source. The season distribution of air pollutants showed concentration in spring higher than summer, owing to air activities of summer are acute include wet precipitation, photochemical reaction, and convection. The time period distribution showed the results which NOx and O3 concentration occurred peaks at 7:00 − 8:00, 18:00 − 19:00 and 13:00 − 16:00, respectively. The reason is photochemical reaction, lead to concentration trend with time of NOx inversely to O3. The concentration trend with time of CO and PM10 similar to NOx. The polluted sources were estimated mobile. By the way, O3 is proportional to temperature, but it is Inversely proportional to humility. The seasons distribution of VOCs showed most abundant species included 2-butanone, toluene, and n-pentane in spring, while included toluene, acetone, m,p-xylene, and methyl methacrylate in summer. According to percent composition, most abundant categories in spring and summer were both aromatics, ketones, and alkanes. The TVOC concentration was spring (164.6 £gg/m3) higher than summer (116.4 £gg/m3). The time periods distribution of VOCs showed most abundant categories included aromatics and ketones in morning and evening, while included aromatics and alkanes in night. The TVOC concentration of evening (163.2 ¡Ó 62.7 £gg/m3) was highest, followed by night (159.9 ¡Ó 87.4 £gg/m3), Lowest was morning (98.4 ¡Ó 32.3 £gg/m3). Results showed alkanes and alkenes own higher concentration in night, ketones and esters in evening, and aromatics in evening and night. The reason is related with sunshine, inversion layer, and lower wind speed. By the way, TVOC is proportional to temperature. In spring, the OFP was 566.0 £gg-O3/m3, OFP/TVOC was 3.44. In summer the OFP was 629.3 £gg-O3/m3, OFP/TVOC was 5.41. It was worth mentioning highest OFP categories in spring and summer was both aromatics (332.2 £gg-O3/m3, 380.3 £gg-O3/m3), and highest OFP species was toluene (138.8 £gg-O3/m3) and methyl methacrylate (171.7 £gg-O3/m3) , respectively. The results from factor analyses showed the predominant source included mobile polluted source, petrol evaporation, related electronic industry, metallurgy industry, refinery, and architectural coatings escape in spring. The predominant source included mobile polluted source, petrol evaporation, plastic industry, steel industry, and related electronic industry in summer.

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Utility mining algorithms have recently been proposed to discover high utility itemsets from a quantitative database. Factors such as profits or prices are concerned in measuring the utility values of purchased items for revealing more useful knowledge to managers. Nearly all the existing algorithms are performed in a batch way to extract high utility itemsets. In real-world applications, transactions may, however, be inserted, deleted or modified in a database. The batch mining procedure requires more computational time for rescanning the whole updated database to maintain the up-to-date knowledge. In the first part of this thesis, two algorithms for data insertion and data deletion are respectively proposed for efficiently updating the discovered high utility itemsets based on pre-large concepts. The proposed algorithms firstly partition itemsets into three parts with nine cases according to whether they are large (high), pre-large or small transaction-weighted utilization in the original database. Each part is then performed by its own procedure to maintain and update the discovered high utility itemsets. Based on the pre-large concepts, the original database only need to be rescanned for much fewer itemsets in the maintenance process of high utility itemsets. Besides, the risk of privacy threats usually exists in the process of data collection and data dissemination. Sensitive or personal information are required to be kept as private information before they are shared or published. Privacy-preserving utility mining (PPUM) has thus become an important issue in recent years. In the second part of this thesis, two evolutionary privacy-preserving utility mining algorithms to hide sensitive high utility itemsets in data sanitization for inserting dummy transactions and deleting transactions are respectively proposed. The two evolutionary privacy-preserving utility mining algorithms find appropriate transactions for insertion and deletion in the data-sanitization process. They adopt a flexible evaluation function with three factors. Different weights are assigned to the three factors depending on users¡¦ preference. The maintenance algorithms proposed in the first part of this thesis are also used in the GA-based approach to reduce the cost of rescanning databases, thus speeding up the evaluation process of chromosomes. Experiments are conducted as well to evaluate the performance of the proposed algorithms.

The purpose of the current study was to assess the efficacy of mental models as a predictor of driving outcomes. In contrast to more traditional measures of knowledge, mental models capture the configural property of knowledge, that is, an individual's understanding of the interrelationships that exist among critical concepts within a particular knowledge domain. Given that research has consistently shown the usefulness of mental models for the prediction of performance in a number of settings, it was hypothesized that the development of accurate driving mental models would also play an important role in the prediction of driving outcomes, especially in comparison to traditional measures of driving knowledge—such as the multiple-choice type tests typically required to obtain a driver license. Mental models of 130 college students (52 percent females) between 17 and 21 years-old (M = 18.68, SD = 0.80) were analyzed and compared to a subject matter expert (SME) referent structure using Pathfinder. A statistically significant correlation was found for mental model accuracy and moving violations (r = –.18, p <.05), but not for at-fault crashes. Evidence of incremental validity of mental models over commonly used predictors of moving violations (but not for at-fault crashes) was also found. Exploratory analyses revealed that driving knowledge, general mental ability (GMA), and emotional stability were the best predictors of mental model accuracy. Issues related to the measurement of mental models were extensively addressed. First, statistically significant correlations between GMA and several mental model properties (i.e., accuracy scores, within participant similarity, and within participant correlation) suggest that challenges inherent to the task for eliciting mental models may influence mental model scores which, in turn, may lower mental model reliability estimates. Also, the selection of model components (i.e., terms) and the identification of the "best" reference structure for deriving mental model accuracy scores are undoubtedly critical aspects of mental model-related research. Along with illustrating the decisions made in the context of this particular study, some suggestions for conducting mental model-related research are provided.

Field Programmable Gate Arrays (FPGAs) are widely used in prototyping digital circuits. However commercial FPGAs are not very suitable for asynchronous design. Both the architecture of the FPGAs and the synthesis tools are mostly tailored to synchronous design. Therefore potential advantages of the asynchronous circuits could not be observed when they are implemented on commercial FPGAs. This is shown by designing an asynchronous arithmetic logic unit (ALU), implemented in the style of micropipelines, on the Xilinx Virtex XCV300 FPGA family. The hazard characteristics of the target FPGA have been analyzed and a methodology for selftimed asynchronous circuits has been proposed. The design methodology proposes first designing a hazard-free cell set, and then using relationally placed macros (RPMs) to keep the hazard-free behavior, and incremental design technique to combine modules in upper levels without disturbing their timing characteristics. The performance of the asynchronous ALU has been evaluated in terms of the logic slices occupied in the FPGA and data latencies, and a comparison is made with a synchronous ALU designed on the same FPGA.

This thesis considers the analysis of system performance and resource allocation for a coded OFDM system over frequency selective fading channels. Due to the inseparable role taken by channel coding in a coded OFDM system, an information theoretical analysis is carried out and taken as the basis for the system performance and throughput. Based on the results of the information theoretical analysis, the optimal system BER performance of a coded OFDM system is first shown to converge to the outage probability for large OFDM block lengths. Instead of evaluating the outage probability numerically, we provide in this thesis a simple analytical closed form approximation of the outage probability for a coded OFDM system over frequency selective quasi-static fading channels. Simulation results of the turbo-coded OFDM systems further confirm the approximation of the outage probability. By taking the instantaneous channel capacity as the analytical building block, system throughput of a coded OFDM system is then provided. With the aim to compare the performance difference between adaptive and uniform resource allocation strategies, the system throughput of different allocation schemes under various channel conditions is analyzed. First, it is demonstrated that adaptive power allocation over OFDM sub-carriers at the transmitter achieves very little gain in terms of throughput over a uniform power distribution scheme. Theoretical analysis is then provided of the throughput increase of adaptive-rate schemes compared with fixed-rate schemes under various situations. Two practical OFDM systems implementing rate-compatible-punctured-turbo-code-based (RCPT-based) hybrid automatic-repeat-request (Hybrid-ARQ) and redundancy incremental Hybrid-ARQ protocols are also provided to verify the analytical results.

The last few years have seen a resurgence of interest in the use of symbolic execution--program analysis technique developed more than three decades ago to analyze program execution paths. However, symbolic execution remains an expensive technique and scaling it remains a key technical challenge. There are two key factors that contribute to its cost: (1) the number of paths that need to be explored and (2) the cost of constraint solving, which is typically required for each path explored. Our insight is that the cost of symbolic execution can be reduced by an incremental approach, which uses static analysis and dynamic analysis to focus on relevant parts of code and reuse previous analysis results, thereby addressing both the key cost factors of symbolic execution. This dissertation presents Memoized Incremental Symbolic Execution, a novel approach that embodies our insight. Using symbolic execution in practice often requires several successive runs of the technique on largely similar underlying problems where successive problems differ due to some change, which may be to code, e.g., to fix a bug, to analysis parameters, e.g., to increase the path exploration depth, or to correctness properties, e.g., to check against stronger specifications that are written as assertions in code. Memoized Incremental Symbolic Execution, a three-fold approach, leverages the similarities in the successive problems to reduce the total cost of applying the technique. Our prototype tool-set is based on the Symbolic PathFinder. Experimental results show that Memoized Incremental Symbolic Execution enhances the efficacy of symbolic execution. / text

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