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国立情報学研究所で電子化したコンテンツを使用している。

Inspection of solder joint interconnection has been a crucial process in the electronics manufacturing industry to reduce manufacturing cost, improve yield, and ensure product quality and reliability. New inspection techniques are urgently needed to fill in the gap between available inspection capabilities and industry requirement of low-cost, fast-speed, and highly reliable inspection systems. The laser ultrasound inspection system under development aims to provide a solution that can overcome some of the limitations of current inspection techniques. Specifically, the fully developed system will be an automated system that is capable of inspecting hidden solder joints with multiple defect types. This research work includes the following aspects: 1) Inspection system integration and automation to improve system throughput and capability, system performance characterization by stability study and gage repeatability and reproducibility study , 2) Development and implementation of signal processing methods, including time-domain correlation coefficient analysis, auto-comparison method, and frequency-domain spectral estimation, to allow for fast and accurate interpretation of vibration signals, 3) Development of a finite element modal model followed by experimental validation. The modal analysis results indicate there are unique mode frequencies and mode shapes associated with certain solder joint defects, and 4) Study of the systems unique capability in detecting solder joint fatigue cracks.

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Seismic hazard analyses require an estimate of earthquake ground motions from future events. These predictions are achieved through Ground Motion Prediction Equations, which include a prediction of the median and the standard deviation of ground motion parameters. The differences between observed and predicted ground motions, when normalized by the standard deviation, are referred to as epsilon (𝜖). For spectral accelerations, the correlation structure of normalized residuals across oscillator periods is important for guiding ground motion selection. Correlation structures for large global datasets have been studied extensively. These correlation structures reflect effects that are averaged over the entire dataset underlying the analyses. This paper considers the effects of site response, at given sites, on the correlation structure of normalized residuals. This is achieved by performing site response analyses for two hypothetical soil profiles using a set of 85 rock input motions. Results show that there is no significant difference between correlation coefficients for rock ground motions and correlation coefficients after considering the effects of site response for the chosen sites. / Master of Science

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The focus for this research is to develop and apply statistical methods to analyze and interpret high-throughput biological data. We developed a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. This computational approach is not only applicable to DNA microarray analysis but is also applicable to proteomics data or any other high-throughput analysis methodology. The suppression of APY1 and APY2 in mutants expressing an inducible RNAi system resulted in plants with a dwarf phenotype and disrupted auxin distribution, and we used these mutants to discover what genes changed expression during growth suppression. We evaluated the gene expression changes of apyrase-suppressed RNAi mutants that had been grown in the light and in the darkness, using the NimbleGen Arabidopsis thaliana 4-Plex microarray, respectively. We compared the two sets of large-scale expression data and identified genes whose expression significantly changed after apyrase suppression in light and darkness, respectively. Our results allowed us to highlight some of the genes likely to play major roles in mediating the growth changes that happen when plants drastically reduce their production of APY1 and APY2, some more associated with growth promotion and others, such as stress-induced genes, more associated with growth inhibition. There is a strong rationale for ranking all these genes as prime candidates for mediating the inhibitory growth effects of suppressing apyrase expression, thus the NimbleGen data will serve as a catalyst and valuable guide to the subsequent physiological and molecular experiments that will be needed to clarify the network of gene expression changes that accompany growth inhibition. / text

When a cyclist is cycling on a suburban road, it’s a problem to notice fast rear approaching vehicles in some cases. Looking back frequently is not a good idea. Finding some technical way to help cyclist perceiving rear approaching vehicles is quite necessary. This project aims to find some proper sensor to detect rear approaching vehicles. It’s separated into three steps. First, choose the suitable sensor and capture data. Then, find proper analyzing tool to analyze the capture data. Last but not least, draw a conclusion after analyzing contrast. Microphone is chosen as the sensor to recording the sounds form rear approaching vehicles. ”iRig Recorder FREE” is the program to transfer audio format. And the analyzing tool is to be Matlab. Matlab audio analysis makes good frequency spectrum for each piece of audio data. According to the frequency spectrum, the unique amplitude change around 1000 Hz is found when there is a rear approaching vehicle. This change is always distinct with or without noise. After getting the spectrum of different audio sources, the cross-correlation coefficient between 800 Hz and 1200 Hz is computed to see the correlation level. Then according to cross-correlation coefficient between new captured data and knowledge data, we can determine if there is a rear approaching vehicle in the new data or not. So, this project proves that the cross-correlation coefficient of frequency spectrum can determine if there is rear approaching vehicles or not. The future work would be automatic computer detect depending on this method.

This paper studies the dependence structure between the real estate and financial markets in the United States from roughly 1975 to 2010, including the stock, bond and foreign exchange markets. This analysis uses dynamic copulas, including the Gaussian, Gumbel and Clayton copula. The Gumbel and Clayton copulas are used to separately capture the tail dependence of data. The dependence between the property indices (HPI and NCREIF) and the three financial markets is analyzed using the parameters of the copula. The property indices are divided in two different ways: by different regions and by different types of real estate. Although we study the dependence between the real estate and the financial markets in the U.S., the main objective of this paper is to analyze the change in the dependence structure when financial disasters occur. This study indicates that the real estate and the stock markets were positively related during this time period, and this dependence drove extreme movement when financial crises occurred. This dependence differed depending on the type of financial crisis, such as the Internet bubble crisis or the financial crisis in 2008. The dependence between the real estate and bond markets was also positively related, and extreme movement also occurred during financial crises. As for the dependence between the real estate and foreign exchange markets, although the results shows that dependence decreased when financial crises occurred, this is because the value of U.S. dollars are opposite to those of the index, and the left tail dependence exists as previous result. When looking at different regions or types of property, the differences in dependence structure were not obvious, although they were positively related. Both right and left tail dependences existed for most regions and property types, although some regions or types showed either right or left tail dependences alone. Therefore, investors should focus on the relationship between different markets, not on the region or type of real estate.

With gradually increasing interdependence of international political and economic environments, part of Asian countries' financial markets reform adopted progressive policies towards liberalization and internationalization. Therefore, the integration of international financial markets has attracted a bunch of scholars to investigate related topics of international stock market. Granger and (1993) documented that most of the economic variables have nonlinear characters. Chelley-Steeley (2004) uses smooth transition regression model to explore the financial market integration of regional and global markets among emerging and developed countries. Smooth transition regression model considered the possibility of nonlinear changes in regression parameters. This paper applies the smooth transition regression model to reinvestigate Chelley-Steeley¡¦s (2004) study of nonlinear relationship of stock markets among some East Asian countries and the United States, Japan and Germany. The main difference of our model and Chelley-Steeley¡¦ model is that we relax his constant market index correlation between two countries by allowing the autoregressive process on market index correlation. Empirical evidences of linear model, original non-linear model and our non-linear extension model show that our non-linear extension model outperformedthe other two models in terms of goodness of fit.