Power system protection is important for service reliability and quality assurance. Various faults may occur due to natural and artificial calamity. Dispatchers are use the changed statuses of protection devices from the Supervisory Control and Data Acquisition (SCADA) system to identify the fault. To reduce the outage duration and promptly restore power services, fault section detection has to be done effectively and accurately with fault alarms. In this thesis, artificial neural networks (ANN) and Grey Relational Analysis (GRA) are used to develop the restoration schemes for emergency operation in a power system including fault section detection (FSD), restoration strategy(RS), and voltage correction(VC). The optimal power flow (OPF) is responsible for verifying the proposed schemes by off-line analysis. With a IEEE 30-Bus power system, computer simulations were conducted to show the effectiveness of the proposed restoration schemes.

Factors of very rapidly rising oil prices, the running out time limits on continued use of fossil fuels, as well as elements of the Kyoto Protocol, have greatly arouses the increasing emphasis on natural and renewable energy sources. 40 minutes of total solar radiation on earth could provide enough power to meet the energy needs of all human beings for approximately one year. The potential of solar energy is virtually limitedless. Moreover, by means of solar powered batteries, solar energy can be directly converted to electric power. Since it neither pollutes the environment or ecology, solar is an extremely clean source of energy. The life-span of solar cell is very long, possibly 20 years or more. The capability of solar batteries to provide energy is approximately proportional to the intensity of the sunlight. This thesis proposes the use of Artificial intelligence for "Sun Track Solar Energy System". This system employs Fuzzy Logic Control Theory, combined with Grey Relational Analysis, for tracking the angle of the sun, and further control the motor to adjust the angle for tracking, so direct sunlight could be acquired to increase power output. As a result of the experiment, comparing the electricity generated from the fix angle solar battery with the AI-based Sun Track Solar Energy System, the latter one has an efficiency increase up to 23% for the same amount of sunlight.

In recent years, the Asian dust storms occurred frequently. It was estimated that approximately eight hundred million metric tons of Asian dusts transported to the atmosphere yearly. During the dust storm period, Asian dusts not only induce poor air quality, but also reduce atmospheric visibility and influence human health. In order to investigate the physicochemical characteristics and source allocation of Asian dusts, this study collect the Asian dusts in the Pescadores Islands during the years of 2002~2006. In addition, this study collected top soils in three regions of Inner Mongolia and resuspended the soil samples in a resuspension chamber to analyze their chemical composition. Moreover, this study applied enrichment factor analysis (EF) and grey relational analysis to allocate the potential sources of Asian dusts and compare them with the transportation routes obtained from backward trajectory. During Asian dust storm periods, the concentration of atmospheric particulate matter (PM10) in the Pescadores Islands increased significantly, probably is 3~6 times of PM10 during non-dust storm periods. Among them, coarse particles (PM2.5-10) particularly rose from 10~30 £gg/m3 to 80~130 £gg/m3 and the size distribution changed from bi-modal distribution to single modal distribution of coarse particles during Asian dust storm periods. This study further analyzed the chemical composition of Asian dusts, including water-soluble ionic species, carbon contents, and metallic contents. For fine particles (PM2.5), the order of water-soluble ionic species was SO42- > NH4+ > NO3- > Cl- > Ca2+ > Na+ > K+ > Mg2+ > F-. For coarse particles (PM2.5-10), the order of water-soluble ionic species was Cl- > SO42- > NO3- > Na+ > Ca2+ > NH4+ > Mg2+ > K+ > F-. The carbon contents distributes mainly in fine particles. The major contents of both fine and coarse particles were crustal elements (i.e. Al, Fe, Na, Mg, K, Ca, and Sr). In addition to the analysis of physicochemical characteristics of Asian dusts, this study applied HYSPLIT MODEL to figure out their transportation routes by backward trajectory. According to the backward trajectories, this study compartmentalized Asian dusts storm transportation routes into three categories: Eastward Transportation and Retraced (ETR), Southeasterly Transportation and Circumrotated (STC), and Straight Southeasterly Transportation (SST). Analyzing the spatial and temporary background variables to investigate the influence of transportation routes on Asian dusts¡¦ physicochemical characteristic. This study revealed that the physicochemical characteristics were very similar for same category of Asian dust storms, which can be used to allocate the source regions of Asian dust storms. This study resuspended the soil samples collected in Inner Mongolia inside a resuspension chamber and collected the suspended particles (PM2.5, PM2.5-10) for chemical analysis. Chemical analysis results indicated that the fingerprints of chemical composition for different regions were similar but still distinguishable, which can be used to identify the source areas of Asian dusts. This study further compare and correlate the Asian dusts collected at the Pescadores Islands during Asian dust storm periods with the soils collected in Inner Mongolia chemically by enrichment factor and grey relational analysis. This study further compared the source allocation of Asian dust storms obtained from enrichment factor, grey relational analysis, and backward trajectory and found the results of these three methods were quite similar. For enrichment factor analysis, 88% of similarity was obtained by using two separate reference elements (Al and Fe). The similarity of backward trajectory and grey relational analysis reached as high as 83%. Moreover, the backward trajectory and enrichment factor were similar up to 75%, while the grey relational analysis and enrichment factor were similar up to 69%. Overall, two out of three aforementioned methods can effectively allocate the source regions of Asian dusts by 94%, while all three methods can successfully allocate the source regions of Asian dusts by 56%. Comparison of three aforementioned methods showed that they can be applied to allocate the source regions of Asian dusts.

One component of wireless communications of increasing necessity in both civilian and military applications is the process of automatic modulation classification. Modulation of a detected signal of unknown origin requiring interpretation must first be determined before the signal can be demodulated. This thesis presents a novel architecture for a modulation classifier that determines the most likely modulation using Grey Relational Analysis with the extraction and combination of multiple signal features. An evaluation of data preprocessing methods is conducted and performance of the classifier is investigated with the addition of each new signal feature used for classification. / Master of Science

Software effort estimation by analogy is a viable alternative method to other estimation techniques, and in many cases, researchers found it outperformed other estimation methods in terms of accuracy and practitioners' acceptance. However, the overall performance of analogy based estimation depends on two major factors: similarity measure and attribute selection & weighting. Current similarity measures such as nearest neighborhood techniques have been criticized that have some inadequacies related to attributes relevancy, noise and uncertainty in addition to the problem of using categorical attributes. This research focuses on improving the efficiency and flexibility of analogy-based estimation to overcome the abovementioned inadequacies. Particularly, this thesis proposes two new approaches to model and handle uncertainty in similarity measurement method and most importantly to reflect the structure of dataset on similarity measurement using Fuzzy modeling based Fuzzy C-means algorithm. The first proposed approach called Fuzzy Grey Relational Analysis method employs combined techniques of Fuzzy set theory and Grey Relational Analysis to improve local and global similarity measure and tolerate imprecision associated with using different data types (Continuous and Categorical). The second proposed approach presents the use of Fuzzy numbers and its concepts to develop a practical yet efficient approach to support analogy-based systems especially at early phase of software development. Specifically, we propose a new similarity measure and adaptation technique based on Fuzzy numbers. We also propose a new attribute subset selection algorithm and attribute weighting technique based on the hypothesis of analogy-based estimation that assumes projects that are similar in terms of attribute value are also similar in terms of effort values, using row-wise Kendall rank correlation between similarity matrix based project effort values and similarity matrix based project attribute values. A literature review of related software engineering studies revealed that the existing attribute selection techniques (such as brute-force, heuristic algorithms) are restricted to the choice of performance indicators such as (Mean of Magnitude Relative Error and Prediction Performance Indicator) and computationally far more intensive. The proposed algorithms provide sound statistical basis and justification for their procedures. The performance figures of the proposed approaches have been evaluated using real industrial datasets. Results and conclusions from a series of comparative studies with conventional estimation by analogy approach using the available datasets are presented. The studies were also carried out to statistically investigate the significant differences between predictions generated by our approaches and those generated by the most popular techniques such as: conventional analogy estimation, neural network and stepwise regression. The results and conclusions indicate that the two proposed approaches have potential to deliver comparable, if not better, accuracy than the compared techniques. The results also found that Grey Relational Analysis tolerates the uncertainty associated with using different data types. As well as the original contributions within the thesis, a number of directions for further research are presented. Most chapters in this thesis have been disseminated in international journals and highly refereed conference proceedings.

The purpose of this study is to find the relationship between financial health in an ice hockey club and its sport success. The study answers the research question: How can financial health of Swedish ice hockey clubs be able to explain the sport success in the Swedish Hockey League? Based on the research question, the study uses the theory Benchmarking and a more specific benchmarking terminology called Financial benchmarking. The study selects eight financial variables in order to benchmark the icehockey clubs in the Swedish Hockey League (SHL). A particular methodology within financial benchmarking, called Grey Relational Analysis (GRA), is used in order to determine the financial health of the clubs in relation to each other and therefore be able to rank the clubs based on each individual variable. The same financial variables, with the addition of four non-financial variables and exclusion of two financial variables, are used in a selected Logistic Regression model to explain how the variables contribute to the sport success of the clubs. The main conclusions which can be drawn from the study are as follows: The variables Net sales and Net profit are the two only variables which are statistically significant and are able to contribute to sport success. Secondly, the club HV71 is overall the club with the most optimal financial health in SHL, among the 12 clubs investigated. Lastly, accounting trends within this industry affects the financial outcome and further how it explained sport success. Trends such as a minimal or no amount of long-term liabilities is common among the clubs, where instead the total amount of liabilities mainly consists of current liabilities. It can be further concluded that profitability, revenue and equity are financial corner stones in a hockey club which participates in SHL.

Power quality is a main subject to considerable attentions from utilities and customers owing to the popular uses of many non-linear electronic equipment in recent years. Harmonics, voltage swell, voltage sag, and, power interruption could downgrade the service quality. To ensure the power quality, detecting harmonic and voltage disturbances becomes an important issue. In other words, a detection method with classification capability will be helpful for detecting disturbances. The thesis proposed two models of power quality detection for power system disturbances using voltage-current(V-I) characteristics in the time domain with hybrid wavelets grey relational analysis (WGRA), and self-organizing feature map network (WSOM). Morlet wavelets are responsible for extracting features from voltages and currents. GRA and SOM were employed to identify the types of various disturbance patterns. Computer simulations have demonstrated the computational efficiency and accurate recognition capability for power quality detection and discrimination with an IEEE 14-Bus power system.

With the deregulation of power industry and the market competition, low cost, reliable power supply, and secured system operations are major concerns of the advanced deregulation markets. Power system protection is important for service reliability and quality assurance. To reduce the outage duration and promptly restore power services, fault section estimate has to be done effectively and accurately with fault alarms. First, an operational strategy for secondary power system restoration using Modified Grey Relational Analysis (MGRA) is proposed. The Restoration Scheme (RS) can be divided into three steps involving fault section determination, recovering process, and voltage correction process. Three GRAs are incorporated to design the overall restoration scheme. The first GRA uses network switching status to identify the fault. The second GRA combines switching states and load levels for network recovery. The third GRA uses capacitor bank control to support bus voltages. For security operation of restoration scheme, an Equivalent Current Injection (ECI) based hybrid current-power Optimal Power Flow (OPF) model with Predictor-Corrector Interior Point Algorithm (PCIPA) is used to verify the proposed scheme by off-line analysis to confirm a secure overall network operation including load-power balance, power generation limits, voltage limits, and power flow limits. The proposed method can further decompose into two sub-problems. Computer simulations were conducted with an IEEE 30-bus power system to show the effectiveness of the proposed restoration scheme and the PCIPA technique is very accurate, robust, and efficient for the modified OPF solution.

Software effort estimation by analogy is a viable alternative method to other estimation techniques, and in many cases, researchers found it outperformed other estimation methods in terms of accuracy and practitioners¿ acceptance. However, the overall performance of analogy based estimation depends on two major factors: similarity measure and attribute selection & weighting. Current similarity measures such as nearest neighborhood techniques have been criticized that have some inadequacies related to attributes relevancy, noise and uncertainty in addition to the problem of using categorical attributes. This research focuses on improving the efficiency and flexibility of analogy-based estimation to overcome the abovementioned inadequacies. Particularly, this thesis proposes two new approaches to model and handle uncertainty in similarity measurement method and most importantly to reflect the structure of dataset on similarity measurement using Fuzzy modeling based Fuzzy C-means algorithm. The first proposed approach called Fuzzy Grey Relational Analysis method employs combined techniques of Fuzzy set theory and Grey Relational Analysis to improve local and global similarity measure and tolerate imprecision associated with using different data types (Continuous and Categorical). The second proposed approach presents the use of Fuzzy numbers and its concepts to develop a practical yet efficient approach to support analogy-based systems especially at early phase of software development. Specifically, we propose a new similarity measure and adaptation technique based on Fuzzy numbers. We also propose a new attribute subset selection algorithm and attribute weighting technique based on the hypothesis of analogy-based estimation that assumes projects that are similar in terms of attribute value are also similar in terms of effort values, using row-wise Kendall rank correlation between similarity matrix based project effort values and similarity matrix based project attribute values. A literature review of related software engineering studies revealed that the existing attribute selection techniques (such as brute-force, heuristic algorithms) are restricted to the choice of performance indicators such as (Mean of Magnitude Relative Error and Prediction Performance Indicator) and computationally far more intensive. The proposed algorithms provide sound statistical basis and justification for their procedures. The performance figures of the proposed approaches have been evaluated using real industrial datasets. Results and conclusions from a series of comparative studies with conventional estimation by analogy approach using the available datasets are presented. The studies were also carried out to statistically investigate the significant differences between predictions generated by our approaches and those generated by the most popular techniques such as: conventional analogy estimation, neural network and stepwise regression. The results and conclusions indicate that the two proposed approaches have potential to deliver comparable, if not better, accuracy than the compared techniques. The results also found that Grey Relational Analysis tolerates the uncertainty associated with using different data types. As well as the original contributions within the thesis, a number of directions for further research are presented. Most chapters in this thesis have been disseminated in international journals and highly refereed conference proceedings. / Applied Science University, Jordan.

不同股市的報酬關聯隨時間動態改變，本研究欲了解近期美國、台灣與亞太地區的中國大陸、香港、日本及韓國的報酬連動關係，並進一步觀察次級房貸風暴期間美股對這些地區的關聯改變趨勢。本論文採用灰色理論與時間序列兩種方法，實證發現次級房貸風暴發生期間，台股及亞太地區主要指數不論在報酬率或是報酬率波動性受美股影響的程度大多增強。 實證結果顯示，在風暴期間的報酬率傳導關係，亞太以韓國影響台股最顯著，美股則全面影響亞太指數；在報酬率波動性溢傳上，亞太以日本、美股以道瓊工業影響台股最強，台股則是電子類股被美股影響最重，但營建類股在與美股或是亞太指數的關聯趨勢變化卻最明顯。另外，灰關聯分析對時間序列檢定的關聯組合可以提供互補的關聯強弱關係說明，且具有相當的正確性。 / Connections between stock markets are dynamically changing, and it affects investor's transnational investment portfolio. We focus on the relationships of stock markets among the United States, Taiwan, Japan, Korea, China and Hong Kong, and eager to understand the connection tendency between Untied States and Asian-Pacific area during the subprime mortgage crisis period. The identified research methods are time series and grey theory, including Granger causality test, GARCH model and grey relational analysis. We find out the returns and volatility in Asian-Pacific stock markets were all affected increasly by U.S. market during the subprime mortgage crisis. The main empirical results are as follows: In the relationships of returns, Korea affects Taiwan mostly in the Asian-Pacific area, and U.S. market affects all the others entirely during the subprime crisis. In the relationships of volatility, Japan and Dow Jones index affects Taiwan deeply during the period; within all the Taiwan indexs, Electronic Sector Index was affected by the U.S. market mostly than others during the same period, but the connection tendency in the Construction Sector Index with other markets changes more obviously. Otherwise, grey relational analysis can provide complementary explainations as compared to time sereies in the strength of relationships, and the explainations are with plenty credibility.