A methodology which calculates a point estimate and confidence intervals for system reliability directly from component failure data is proposed and evaluated. This is a nonparametric approach which does not require the component time to failures to follow a known reliability distribution. The proposed methods have similar accuracy to the traditional parametric approaches, can be used when the distribution of component reliability is unknown or there is a limited amount of sample component data, are simpler to compute, and use less computer resources. Depuy et al. (1982) studied several parametric approaches to calculating confidence intervals on system reliability. The test systems employed by them are utilized for comparison with published results. Four systems with sample sizes per component of 10, 50, and 100 were studied. The test systems were complex systems made up of I components, each component has n observed (or estimated) times to failure. An efficient method for calculating a point estimate of system reliability is developed based on counting minimum cut sets that cause system failures. Five nonparametric approaches to calculate the confidence intervals on system reliability from one test sample of components were proposed and evaluated. Four of these were based on the binomial theory and the Kolomogorov empirical cumulative distribution theory. 600 Monte Carlo simulations generated 600 new sets of component failure data from the population with corresponding point estimates of system reliability and confidence intervals. Accuracy of these confidence intervals was determined by determining the fraction that included the true system reliability. The bootstrap method was also studied to calculate confidence interval from one sample. The bootstrap method is computer intensive and involves generating many sets of component samples using only the failure data from the initial sample. The empirical cumulative distribution function of 600 bootstrapped point estimates were examined to calculate the confidence intervals for 68, 80, 90 95 and 99 percent confidence levels. The accuracy of the bootstrap confidence intervals was determined by comparison with the distribution of 600 point estimates of system reliability generated from the Monte Carlo simulations. The confidence intervals calculated from the Kolomogorov empirical distribution function and the bootstrap method were very accurate. Sample sizes of 10 were not always sufficient for systems with reliabilities close to one.

Semen is one of the most common biological fluids encountered by a forensic serologist on varying substrate types. Seminal fluid contains many enzymes, proteins, and cellular material such as acid phosphatase (AP), prostate-specific antigen (PSA), semenogelin (Sg), and spermatozoa; detection of these components can aid in the forensic identification of body fluids. Forensic laboratories usually follow a prescribed testing workflow (from visual examination including an alternate light source (ALS) and AP testing to PSA or Sg testing followed by a microscopic examination for sperm cells) to ensure laboratory resources are being used in a proper manner with minimal waste of both time and resources. However, this approach can be problematic when degradation of semen stains results in the inability to detect the presence of certain seminal components. When a stain yields negative results for an AP reaction, no further analyses for semen may be performed and analysis comes to an end. In common practice, evidentiary items containing biological fluids may not be immediately recovered following an incident and/or may not be stored properly, causing contamination or exposure of these biological fluids to harsh environments, potentially degrading the sample. This study investigates how exposure to different environmental conditions and packaging types affects the degradation of the four most common semen components targeted in forensic testing: AP, PSA, Sg, and spermatozoa. Semen stains were prepared and exposed to ten different storage and/or environmental conditions to compare their effects on the detectability of seminal components (fluorescence, AP, PSA, Sg, and spermatozoa as well as deoxyribonucleic acid (DNA) testing) for a period of approximately four months. Samples from each condition were tested on select days throughout the study. Minimal changes in detection of the seminal components were observed under the five conditions in which the stains remained dry: packaged in paper stored at room temperature, packaged in paper stored at high temperatures, exposed to sunlight, exposed to ultraviolet light, and stored in high humidity. Two of the conditions involved exposure to outdoor environments. The stains openly exposed to the elements or buried in soil exhibited the most notable degradation of all components when compared to other conditions. Negative results were obtained for nearly all seminal components (AP, PSA, or Sg) on Day 8 for stains openly exposed outdoors and Day 32 for buried samples. The remaining three conditions exposed the stains to damp or wet conditions and gave variable results throughout the study. DNA quantification was performed for select samples from each condition to assess DNA degradation. Most samples did not exhibit DNA degradation on quantification results up to Day 112; however, two samples exposed to outdoor environments exhibited DNA degradation as early as Day 8 (earliest day quantified). More notably, two samples from Day 112 demonstrated the presence of non-degraded DNA in sufficient quantity for profiling, while the presumptive semen analyses (AP, PSA, and Sg) for the same samples exhibited negative results when using an AP reaction cut-off time of 2 minutes. These results suggest that an allotted time of 2 minutes for AP detection may not be sufficient in some samples, and that valuable DNA evidence may go undiscovered, especially when other presumptive tests show negative results. Overall, the results revealed variation in the sequence and rate of degradation for seminal components in semen stains exposed to different environmental conditions. It was not possible to predict which of the remaining components of semen would be detectable based on the outcome of any one of the tests. Therefore, it is recommended that comprehensive testing of possible semen stains is performed, even after negative presumptive results are obtained, when the case scenario suggests exposure to damp/wet or otherwise less than ideal conditions.

In this diploma thesis, we propose a prototype of a dental sandblasting machine. This machine’s drawing is composed using a CAD program, which helped with checking the assembly ability. Later in this work, we discuss the design of each component and chosen technologies for production. We chose to describe in detail and prototype using the CNC machine the “exit jet” component, which combines an abrasive and air with water. The main outputs of this work are the prototype of this component and its technical rating and the technical-economical rating of the whole proposed design of the machine.

Gait initiation (GI) is an important locomotor transition task that includes anticipatory postural adjustments and the joint propulsion necessary for the first step of walking. Metrics associated with this task are known to change across the lifespan and may provide valuable information for fall risk indication, as falls often occur during transitional tasks. Assessments of discrete variables between fallers and non-fallers at GI have provided insight into differences between groups. However, more complex approaches such as time-series principal component analysis (PCA) may allow the examination of changes in magnitude, pattern, and timing not detectable using discrete comparisons alone. Therefore, this thesis aims to characterize differences between fallers and non-fallers by examining the kinematics and kinetics of gait initiation using time-series PCA. A sample of 56 community-dwelling older adults was recruited for this study and completed five walking trials where GI was measured by two force platforms. PCA of centre of pressure kinematics and kinetics time-series data were used to identify the critical features of the signal, and multivariate analysis of covariance was used to compare the individual loading scores of each principal component for each phase between groups. It was revealed that fallers demonstrated differences in the range of mediolateral movement during weight transfer and forward progression, a greater range of anteroposterior movement in forward progression, and a more gradual rise in vertical forces in the first step, associated with a shorter first step length. These findings point to a tendency for fallers to prioritize stability over forward progression performance, and differences in postural control strategies, compared to non-fallers. Further, the use of time-series PCA helped to highlight differences not detectable using discrete analysis alone. / Graduate

A principal component least mean square (PC-LMS) adaptive algorithm is described that has considerable benefits for large control systems used to implement feedforward control of single frequency disturbances. The algorithm is a transform domain version of the multichannel filtered-x LMS algorithm. The transformation corresponds to the principal components of the transfer function matrix between the sensors and actuators in a control system at a single frequency. The method is similar to other transform domain LMS algorithms because the transformation can be used to accelerate convergence when the control system is ill-conditioned. This ill-conditioning is due to actuator and sensor placement on a continuous structure. The principal component transformation rotates the control filter coefficient axes to a more convenient coordinate system where (1) independent convergence factors can be used on each coordinate to accelerate convergence, (2) insignificant control coordinates can be eliminated from the controller, and (3) coordinates that require excessive control effort can be eliminated from the controller. The resulting transform domain algorithm has lower computational requirements than the filtered-x LMS algorithm. The formulation of the algorithm given here applies only to single frequency control problems, and computation of the decoupling transforms requires an estimate of the transfer function matrix between control actuators and error sensors at the frequency of interest. The feasibility of the method was demonstrated in real-time noise control experiments involving 48 microphones and 12 control actuators mounted on a closed cylindrical shell. Convergence of the PC-LMS algorithm was more stable than the filtered-x LMS algorithm. In addition, the PC-LMS controller produced more noise reduction with less control effort than the filtered-x LMS controller in several tests. / Ph. D.

京都大学 / 0048 / 新制・論文博士 / 博士(農学) / 乙第13034号 / 論農博第2835号 / 新制||農||1044(附属図書館) / 学位論文||H28||N5007(農学部図書室) / 32992 / 京都大学大学院農学研究科農学専攻 / (主査)教授 坪山 直生, 教授 黒木 裕士, 教授 妻木 範行 / 学位規則第4条第2項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第20072号 / 医博第4165号 / 新制||医||1018(附属図書館) / 33188 / 京都大学大学院医学研究科医学専攻 / (主査)教授 前川 平, 教授 小川 誠司, 教授 小柳 義夫 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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<p>The capability of continuously producing good quality products with high productivity and low cost is critical for manufacturers. Generally, products are made up of components, which enable the product to perform its purpose. A complex product may be assembled from many components through multiple assembly stages. Any quality defects in a component may build up in the product. A good understanding of how the quality of components impacts the quality of products in a complex manufacturing system is essential for keeping the competitiveness of a manufacturer. </p> <p>In this research, a series of quality management models are proposed based on studying the relationship between component quality and product quality. Optimal quality control leads to increased competitiveness of a manufacturer, since it helps reduce cost, increase production, and limit environmental impact. The research starts from studying the tolerance allocation problem, which is fundamental of managing the tradeoff between quality, productivity, cost, and waste. First, a tolerance allocation method that minimizes cost is proposed. This model jointly considers process variation and tolerance specifications. The relation between manufacturer, user, design, and processing are embedded in the cost model. To solve the tolerance allocation problem from the root cause, i.e., the variations in production processes, a second tolerance allocation model is then provided. This model considers both product design (tolerance selection) and operation planning (or production rate selection). Relations among production rate, production cost, processing precision, and waste are considered. Furthermore, a new process control model that extends traditional statistical process control techniques is proposed. Data acquired from a manufacturing system are usually in the forms of time series, and anomalies in the time series are generally related to quality defects. A new method that can detect anomalies in time series data with long length and high dimensionality is developed. This model is based on recurrent neural networks, and the parameters of the neural networks can be trained using data acquired during routine operation of a manufacturing system. This is very beneficial because often, there are few data labeled as anomalies, since anomalies are hopefully rare events in a well-managed system. Last, quality control of remanufacturing is studied. A component-oriented reassembly model is proposed to manage the varied quality of returned component and varied needs of customers. In this model, returned components are inspected and assigned scores according to their quality/function, and categorized in a reassembly inventory. Based on the reassembly inventory, components are paired under the control of a reassembly strategy. A reassembly-score iteration algorithm is developed to identify the optimal reassembly strategy. The proposed model can reassemble products to meet a larger variety of customer needs, while simultaneously producing better remanufactured products.</p> In summary, this dissertation presents a series of novel quality management models to keep manufacturers’ competitiveness. These models are based on studying factors that impact component and product quality at multiple stages of a product life cycle. It was found that analyzing the relationship between component and product quality is a very effective way of improving product quality, saving cost, and reducing environmental impact of manufacturing.

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / There are currently several types of databases that have different ways of manipulating data that affects the performance of transactions when dealing with the information stored. And it is very important for companies to manage information fast, so they do not lose any operation because of a bad performance of a database, in the same way, they need to operate fast while keeping the integrity of the information. Likewise, every database category's purpose is to serve a specific or specifics use cases to perform fast to manage the information when needed, so in this paper, we study and analyze the SQL, NoSQL and In Memory databases to understand their fit uses cases and make performance tests to build a decision tree that can help to take the decision to choose what database category to use to maintain a good performance. The precision of the tests of relational databases was 96.26% in NoSQL databases was 91.83% and finally in IMDBS was 93.87%.