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.

Proton magnetic resonance spectroscopy has been widely applied to the diagnosis of brain diseases. In the meanwhile, the classification of brain abscesses plays an important role on the accurate prognosis in clinics. Recently, the interest in using proton MRS to classify pyogenic brain abscesses has been arising because of its non-invasive property and good accuracy in detecting metabolites. The brain abscess can be classified by means of the metabolites observed in the MR spectra, which may thus benefit the accuracy of the brain abscess diagnosis clinically. However, the interpretation of MR spectra by experienced radiologists can be also very subjective and therefore results in the variation of diagnosis. In this study, we investigate the potential possibility of using Principal Component Analysis (PCA) to classify the short TE MR spectra in more objective way.

The emission of volatile organic compounds (VOCs) and odors from petrochemical industrial complex, including China Petroleum company (CPC),Renwu and Dazher petrochemical industrial parks, causes poor air quality of northern Kaohsiung. The removal efficiencies of elevated stacks and flares might play important roles on ambient air quality in metro Kaohsiung. Consequently, this study applied a tethered balloon technology to measure the vertical profile of VOCs, and ascertained their three dimensional dispersion in the atmosphere. The vertical profile of VOCs in ambient atmosphere surrounding the petrochemical industrial complex was measured during the intensive sampling periods (September 17-18th and December 20-21st, 2009 and April 8-9th and July 7-8th, 2010). Moreover, this study was designed to sample and analyze VOCs emitted from elevated stacks and flares, and estimate their emission factors. Finally, the source identification and ozone formation were further determined by principal component analysis (PCA) and ozone formation potential (OFP). This study found that some regions had relatively poorer air quality than other regions surrounding the petrochemical industrial complex. Most sampling sites with poor air quality were located at the downwind region of the petrochemical industrial complex, particularly with the prevailing winds blown from the northwest. Moreover, stratification phenomena were frequently observed at most sampling sites, indicating that high-altitude VOCs pollution should be considered for ambient air quality. This study revealed that the indicators of VOCs in northern Kaohsiung were toluene, C2 (ethylene+acetylene+ethane), and acetone. Vertical sampling of VOCs showed that the species of VOCs at the ground and high altitude were different, suggesting that ambient air quality at high altitude might be affected by the emission of VOCs from elevated stacks and flares at the petrochemical industrial complex. Results obtained from PCA showed that the major sources of VOCs in the ambient atmosphere of the petrochemical industrial complex were similar to the characteristics of VOCs emitted from the petrochemical industrial complex. The characteristics of VOCs at high altitude had strong correlation with petrochemical industry, indicating that the ambient air quality of northern Kaohsiung was highly influenced by the emission of VOCs from high stacks and flares. In addition, major VOCs for O3 formation potential at northern Kaohsiung were aromatics and vinyls, with particular species of toluene and C2. Moreover, air pollution episodes resulting from high O3 concentration was usually observed in early winter. Flare sampling results indicated that major VOCs emitted from the ground flare of CPC were alkanes and vinyls. The average removal efficiency of TVOCs was 98.2%. The average emission factor of VOCs was 0.0186 kg NMHC/kg flare gas. In addition, stack sampling results indicated that the emission factors of crude oil distillation process (P105), mixing process (P060), and rubber manufacturing process (P408) were 0.105, 1.11, and 61.97 g/Kl, respectively. The emission factor of P105 was lower than AP-42, while that of P408 was higher than AP-42.

In order to understand the relative impact from natural and anthropogenic sources, Principal Component Analysis - Structural Equation Modeling (PCA-SEM) was used to analyze the phytoplankton dynamics in coastal waters of Taiwan. PCA was used to analyze the changes of the water quality, followed by constructing of conceptual model which incorporated with biological data, and finally verified by SEM. Three study sites were selected: Chang Hua coastal waters, Kaohsiung mud dumping waters and the adjacent waters of Kaohsiung Nansing project. These sites represent the ordinary coastal water ecosystem of western Taiwan, off-shore ocean with one defined pollution sources, and anthropogenic impacted water area, respectively. The results showed that in Chang Hua coastal waters, river input and seasonal change were the primary factors effecting phytoplankton change. Water temperature was the main reason of phytoplankton changes, followed by the influence of dissolved organic matter in Kaohsiung mud dumping site. Whereas waters near Kaohsiung Nansing project, cooling water from Daling power plant coupled with the change of nutrients and heavy metal concentrations, as well as oil pollution, were the major causes of phytoplankton variation. The goodness-of-fits were good for the three models in this study, revealing that PCA- SEM is suitable to analyze the environmental changes of the costal waters of Taiwan. Logistic methods used in this study are also good for the study of benthic or fish community, and are suitable to apply on environmental impact assessments.

The seasonal and diurnal concentrations of atmospheric carbonyls were measured by the LpDNPH-Cartridge and the microcomputer air sampling device at Nan-Chie and Hsiung-Kong sites in Kaohsiung area. Then, factor analysis and absolute principal component analysis were also used to determine the source apportionment in Kaohsiung area. Total concentrations of carbonyls were higher in Summer than in winter at Nan-Chie and Hsiung-Kong sites. Measurements showed that the highest carbonyls were formaldehyde and acetaldehyde, due to the fact that photochemical activities are stronger in summer than in winter. The concentrations of total carbonyls, formaldehyde, acetaldehyde were showed similar diurnal variations, that highest concentrations were found in the morning and noon, then drop down at afternoon and increased at night. Due to the fact that photochemical activities and vehicle exhausts. C1-C3 ratio indicated the local participation of anthropogenic hydrocarbons was important in the production of carbonyls in the Kaohsiung area. C1/C2 was highest in the summer than in the winter, that photochemical activities cause highest concentrations of formaldehyde, especially in the summer noon. The results of factor analysis and absolute principal component analysis showed that the primary pollution sources at Nan-Chie were traffic exhausts (diesel and gasoline vehicle) and stationary sources (petrochemical and food industry) and restaurant emissions, and the primary pollution sources at Hsiung-Kong were traffic exhausts (diesel and gasoline vehicle), stationary emissions (metal assembly and petrochemical industry) and restaurant emissions.

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.