Steel industry is a highly polluted industry and one of the most important stationary sources in Kaohsiung City. The steel manufacturing process could emit a huge amount of particles, such as the sintering process, the blast furnace operation, and the raw material handling process. Suspended particles emitted from steel industry could deteriorate ambient air quality and cause adverse effects on human health. In order to understand the impact of steel industry on ambient air quality in Siaogang District and to identify potential pollution sources, this study selected a integrated steel manufacturing plant located at Siaogang District to conduct a sampling protocol of suspended particulate matter (PM) at ambient sites (A1~A5) and sensitive sites (S1~S5) from July 2011 to March 2012. The size distribution of suspended particles in four seasons was measured with PM10 high-volume samplers, dichotomous samplers, and MOUDI for 3 days (24 hours for single sampling), and dustfall samplers for one month, to investigate the spatial distribution and temporal variation of PM concentration. After sampling, the physicochemical properties of PM, including mass concentration, particle size distribution, dustfall concentration, water-soluble ionic species, metallic elements, and carbonaceous contents, were further analyzed. Field measurement of ambient PM showed that the averaged ambient PM10 concentration (53.54 - 203.56 £gg/m3) were higher than sensitive sites (55.06 - 140.07 £gg/m3) and the averaged ambient PM2.5 concentration of ambient (23.10 - 120.21£gg/m3) were higher than sensitive sites (12.52 - 65.62 £gg/m3). No matter ambient or sensitive sites, it showed a tendency of lower concentration in summer, indicating that concentration variation of PM10 and PM2.5 were highly affected by meteorological factors (such as wind direction, wind speed, and relative humidity) in Siaogang District. Furthermore, a t-test result showed that ambient and sensitive sites have similar pollution sources since the p-values were in significantly different. Chemical analysis of PM results showed that the most abundant water-soluble ionic species of PM at the ambient and sensitive sites were secondary inorganic aerosols (SO42-, NO3-, and NH4+) and [NO3-]/[SO42-] showed that ionic species were mainly emitted from stationary sources. Fe, Al, K and Ca were the major metallic elements of this study, and the major pollution sources contain industries, traffics, and road dusts. Additionally, the raw material handling process was the major pollution source of PM. Correlation analysis of OC and EC showed that PM at ambient and sensitive sites were originated from primary sources, such as vehicles, industries, road dusts, and human activities. Results obtained from PCA and CMB receptor modeling showed that both PM2.5 and PM10 highly correlated with wind direction in different season and the major pollution sources were industry pollution (including petroleum refineries, power plants, waste incinerators, consistent operating steel mills and electric arc furnace steel mills, etc.), followed by local traffics and derivative. Furthermore, marine aerosols were one of the important pollution sources at sensitive sites (S1, S4, and S5) where close to the ocean.

Magnetic Resonance Imaging (MRI) can obtain the tissues of in vivo non-invasively. Proton MR Spectroscopy uses the resonance principle to collect the signals of proton and transforms them to spectrums. It provides information of metabolites in patient¡¦s brain for doctors to observe the change of pathology. Observing the metabolites of brain abscess patients is most important process in clinical diagnosis and treatment. Then, doctors use different spectrums of echo time (TE) to enhance the accuracy in the diagnosis. In our study, we use independent component analysis (ICA) to analyze MR spectroscopy. After analyzing, the independent components represent the elements which compose the input data. Then, we use the projection which is mentioned by Ssu-Ying Lu¡¦s Thesis to help us observe the relationship between independent components and spectrums of patients. We also discuss the result of spectrums with using ICA and PCA and discover some questions (whether it need to do scale normalization before inputting data or not, the result of scale normalization doesn¡¦t expect, and the peak in some independent components confuse us by locating in indistinct place) to discuss and to find possible reason after experiments.

For many undersea research application scenarios, instruments need to be deployed for more than one month which is the basic time interval for many phenomena. With limited power supply and memory, management strategies are crucial for the success of data collection. For acoustic recording of undersea activities, in general,either preprogrammed duty cycle is configured to log partial time series,or spectrogram of signal is derived and stored,to utilize the available memory storage efficiently.To overcome this limitation, we come up with an algorithm to classify different and store only the sound data of interest. Features like characteristic frequencies, large amplitude of selected frequencies or intensity threshold are used to identify or classify different patterns. On main limitation for this type of approaches is that the algorithm is generally range-dependent, as a result, also sound-level-dependent. This type of algorithms will be less robust to the change of the environment.One the other hand, one interesting observation is that when human beings look at the spectrogram, they will immediately tell the difference between two patterns. Even though no knowledge about the nature of the source, human beings still can discern the tiny dissimilarity and group them accordingly. This suggests that the recognition and classification can be done in spectrogram as a recognition problem. In this work, we propose to modify Principal Component Analysis by generating feature points from moment invariant and sound Level variance, to classify sounds of interest in the ocean. Among all different sound sources in the ocean, we focus on three categories of our interest, i.e., rain, ship and whale and dolphin. The sound data were recorded with the Passive Acoustic Listener developed by Nystuen, Applied Physics Lab, University of Washington. Among all the data, we manually identify twenty frames for each cases, and use them as the base training set. Feed several unknown clips for classification experiments, we suggest that both point-based feature extraction are effective ways to describe whistle vocalizations and believe that this algorithm would be useful for extracting features from noisy recordings of the callings of a wide variety of species.

This thesis investigates the determinants of financial development in Turkey. Principle Component Analysis (PCA) is employed in order to examine the main determinants of financial sector development and shed light on the structure of the financial system in Turkey. The empirical studies on financial development suffer from the measurement problem. This study aims to remedy the measurement problem by providing proxies that explain different aspects of financial development more accurately than other proxies used in the extant literature. Hence, the present study constitutes a strong basis for studies that rely on measuring financial development in Turkey.

High-dimensional data take place from digital image processing, gene expression micro arrays, neuronal population activities to financial time series. Dimensionality Reduction - extracting low dimensional structure from high dimension - is a key problem in many areas like information processing, machine learning, data mining, information retrieval and pattern recognition, where we find some data reduction techniques. In this thesis we will give a survey about modern data reduction techniques, representing the state-of-the-art of theory, methods and application, by introducing the language of mathematics there. This needs a special care concerning the questions of, e.g., how to understand discrete structures as manifolds, to identify their structure, preparing the dimension reduction, and to face complexity in the algorithmically methods. A special emphasis will be paid to Principal Component Analysis, Locally Linear Embedding and Isomap Algorithms. These algorithms are studied by a research group from Vilnius, Lithuania and Zeev Volkovich, from Software Engineering Department, ORT Braude College of Engineering, Karmiel, and others. The main purpose of this study is to compare the results of the three of the algorithms. While the comparison is beeing made we will focus the results and duration.

Functional data refer to data which consist of observed functions or curves evaluated at a finite subset of some interval. In this dissertation, we discuss statistical analysis, especially classification and regression when data are available in function forms. Due to the nature of functional data, one considers function spaces in presenting such type of data, and each functional observation is viewed as a realization generated by a random mechanism in the spaces. The classification procedure in this dissertation is based on dimension reduction techniques of the spaces. One commonly used method is Functional Principal Component Analysis (Functional PCA) in which eigen decomposition of the covariance function is employed to find the highest variability along which the data have in the function space. The reduced space of functions spanned by a few eigenfunctions are thought of as a space where most of the features of the functional data are contained. We also propose a functional regression model for scalar responses. Infinite dimensionality of the spaces for a predictor causes many problems, and one such problem is that there are infinitely many solutions. The space of the parameter function is restricted to Sobolev-Hilbert spaces and the loss function, so called, e-insensitive loss function is utilized. As a robust technique of function estimation, we present a way to find a function that has at most e deviation from the observed values and at the same time is as smooth as possible.

The enzymatic hydrolysis of cellulose to glucose by cellulases is one of the major steps in the conversion of lignocellulosic biomass to biofuel. This hydrolysis by cellulases, a heterogeneous reaction, currently suffers from some major limitations, most importantly a dramatic rate slowdown at high degrees of conversion in the case of crystalline cellulose. Various rate-limiting factors were investigated employing experimental as well as computational studies. Cellulose accessibility and the hydrolysable fraction of accessible substrate (a previously undefined and unreported quantity) were shown to decrease steadily with conversion, while cellulose reactivity, defined in terms of hydrolytic activity per amount of actively adsorbed cellulase, remained constant. Faster restart rates were observed on partially converted cellulose as compared to uninterrupted hydrolysis rates, supporting the presence of an enzyme clogging phenomenon. Cellulose crystallinity is a major substrate property affecting the rates, but its quantification has suffered from lack of consistency and accuracy. Using multivariate statistical analysis of X-ray data from cellulose, a new method to determine the degree of crystallinity was developed. Cel7A CBD is a promising target for protein engineering as cellulose pretreated with Cel7A CBDs exhibits enhanced hydrolysis rates resulting from a reduction in crystallinity. However, for Cel7A CBD, a high throughput assay is unlikely to be developed. In the absence of a high throughput assay (required for directed evolution) and extensive knowledge of the role of specific protein residues (required for rational protein design), the mutations need to be picked wisely, to avoid the generation of inactive variants. To tackle this issue, a method utilizing the underlying patterns in the sequences of a protein family has been developed.

<p>Arbetet som presenteras i den här rapporten handlar om ansiktsigenkänning från webbkameror med hjälp av principal component analysis samt artificiella neurala nätverk av typen feedforward. Arbetet förbättrar tekniken med hjälp av filterbaserade metoder som bland annat används inom ansiktsdetektering. Dessa filter bygger på att skicka med redundant data av delregioner av ansiktet.</p>

Brain decoding involves the determination of a subject’s cognitive state or an associated stimulus from functional neuroimaging data measuring brain activity. In this setting the cognitive state is typically characterized by an element of a finite set, and the neuroimaging data comprise voluminous amounts of spatiotemporal data measuring some aspect of the neural signal. The associated statistical problem is one of classification from high-dimensional data. We explore the use of functional principal component analysis, mutual information networks, and persistent homology for examining the data through exploratory analysis and for constructing features characterizing the neural signal for brain decoding. We review each approach from this perspective, and we incorporate the features into a classifier based on symmetric multinomial logistic regression with elastic net regularization. The approaches are illustrated in an application where the task is to infer from brain activity measured with magnetoencephalography (MEG) the type of video stimulus shown to a subject. / Graduate

The research project, Optimization of an array of Peptidic Indicator Displacement Assays for the Discrimination of Cabernet Sauvignon Wines, describes the multiple step lab trials conducted to optimize an array of ensembles composed of synthesized peptides and PCV:Cu+2 complexes for the differentiation of seven Cabernet Sauvignon wines with different tannin levels. This report also includes the methods and analysis used. The analysis interpreted by principal component analysis. / text