• Science of Emotion and Sensibility
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• v.11 no.4
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• pp.553-564
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• 2008

Generalizability theory was employed to examine how the reliability of polygraph test is affected by the number of relevant questions, the number of repeated tests (the number of of charts), and the number of raters(scorers). The data consisted of the results of the polygraph tests administered to 31 crime suspects. The sample was drawn from the real polygraph tests based on Backster ZCT and archived by the Prosecutor's Office of the Republic of Korea. The numerical scores assigned by thirteen raters to the test charts were analyzed to determine the generalizability of the scores. The largest variance component was accounted for by the examinee factor(43.97%) and the residual variance component was 16.84% of the total variance. The variance component due to the interaction between the examinee and the chart factors was 12.17% and the variance component due to the three way interaction of the examinee, the repeated test, and the relevant question factors was 10.31%. The generalizability coefficient for the current measurement procedure as practiced by the Korean Prosecutor's Office was 0.74 which suggests that the current procedure is acceptable. However, measurement procedures with the combination of more than two relevant questions, more than three repeated tests, and more than two raters were generally found to yield generalizability coefficients larger than 0.80. Therefore, such procedures need to be considered seriously in order to significantly improve the reliability of polygraph test.

• The Korean Journal of Applied Statistics
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• v.29 no.4
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• pp.595-611
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• 2016

We use nonlinear regression models (such as the Hill Model) when we analyze data in toxicology and/or pharmacology. In nonlinear regression models an estimator of parameters and estimation of measurement about uncertainty of the estimator are influenced by the variance structure of the error. Thus, estimation methods should be different depending on whether the data are homoscedastic or heteroscedastic. However, we do not know the variance structure of the error until we actually analyze the data. Therefore, developing estimation methods robust to the variance structure of the error is an important problem. In this paper we propose a method to estimate parameters in nonlinear regression models based on a preliminary test. We define an estimator which uses either the ordinary least square estimation method or the iterative weighted least square estimation method according to the results of a simple preliminary test for the equality of the error variance. The performance of the proposed estimator is compared to those of existing estimators by simulation studies. We also compare estimation methods using real data obtained from the National Toxicology program of the United States.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.155-160
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• 2014

There are many image processing based algorithms and applications that fail when illumination change occurs. Therefore, the illumination change has to be detected then the illumination change occurred images need to be enhanced in order to keep the appropriate algorithm processing in a reality. In this paper, a new method for detecting illumination changes efficiently in a real time by using local region information and fuzzy logic is introduced. The effective way for detecting illumination changes in lighting area and the edge of the area was selected to analyze the mean and variance of the histogram of each area and to reflect the changing trends on previous frame's mean and variance for each area of the histogram. The ways are used as an input. The changes of mean and variance make different patterns w hen illumination change occurs. Fuzzy rules were defined based on the patterns of the input for detecting illumination changes. Proposed method was tested with different dataset through the evaluation metrics; in particular, the specificity, recall and precision showed high rates. An automatic parameter selection method was proposed for contrast limited adaptive histogram equalization method by using entropy of image through adaptive neural fuzzy inference system. The results showed that the contrast of images could be enhanced. The proposed algorithm is robust to detect global illumination change, and it is also computationally efficient in real applications.

• Korean Journal of Construction Engineering and Management
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• v.16 no.6
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• pp.144-155
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• 2015

This paper is to understand investment's efficiency and performance of commercial real estate assets diversified by use and district. To do so, this paper divides two different commercial real estate markets(office build market and retail real estate market) in Seoul city by district into "GBD(Gangnam Business District), YBD(Yeouido Business District), and CBD(Central Business District)" and "GBD(Gangnam Business District), SBD(Shinchon Business District), and CBD(Central Business District)" respectively, configures these districts each other to structure portfolios as its portion varies based on Markowitz's Mean-Variance principle, and looks at risk-return relationship of portfolios to find out efficiency, performance, and optimal investment chosen based upon Sharpe's Performance Index. As a result, the portfolio configured by "10 to 30% of office building asset at CBD" and "70 to 90% of retail real estate asset at CBD" is shown to be the most optimal, suggesting the highest quarterly Sharpe's performance index of 2.7118~2.7776 with quarterly rate of return of 1.826%~1.838% and quarterly standard deviation of 0.573~0.589. Furthermore, it is obvious that diversified portfolio configured by use(office-retail) shows better investment performance than that by district with same type of asset(office-office or retail-retail). Finally, results driven from this research will play an important role to stimulate real estate and construction markets through enlarging ideas as to diversified investment by use and district on real estate indirect investment products.

• Convergence Security Journal
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• v.16 no.7
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• pp.147-158
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• 2016

The purpose of this study was to investigate the influence of watching 'Real Man' program on university student s' military image and security awareness. For the purpose of the study, 392 university students in Seoul, Chungcheong and Jeolla were selected. With the collected data, factorial analysis, t-test, frequency analysis, one-way analysis of variance, and multiple regression analysis were performed through SPSS 21.0. First, according to personal characteristic and watching degree, there were differences in military image and security awareness. There were significant differences in rationality, coherence, familiarity, violence, authority among sub factors of military image and in perspective about policy towards North Korea, persepectives about North Korea, military threats of North Korea, security will among sub factors of security awareness. Second, military image of university students who watched 'Real Man' had an influence on security awareness. Military image had an meaningful influence on perspective about North Korea, military threats of North Korea, security will among sub factors of security awareness.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.6
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• pp.812-819
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• 2012

Interference and jamming are becoming increasing concern to a radar system nowdays. AESA(Active Electronically Steered Array) antennas and adaptive beamforming(ABF), in which antenna beam patterns can be modified to reject the interference, offer a potential solution to overcome the problems encountered. In this paper, we've developed a planar active phased array radar system, in which ABF, target detection and tracking algorithm operate in real-time. For the high output power and the low noise figure of the antenna, we've designed the S-band TRMs based on GaN HEMT. For real-time processing, we've used wavelenth division multiplexing technique on fiber optic communication which enables rapid data communication between the antenna and the signal processor. Also, we've implemented the HW and SW architecture of Real-time Signal Processor(RSP) for adaptive beamforming that uses SMI(Sample Matrix Inversion) technique based on MVDR(Minimum Variance Distortionless Response). The performance of this radar system has been verified by near-field and far-field tests.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.57-67
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• 2024

This paper explores the potential of electromyography (EMG) as a means of gesture recognition for user input in gesture-based interaction. EMG utilizes small electrodes within muscles to detect and interpret user movements, presenting a viable input method. To classify user gestures based on EMG data, machine learning techniques are employed, necessitating the preprocessing of raw EMG data to extract relevant features. EMG characteristics can be expressed through formulas such as Integrated EMG (IEMG), Mean Absolute Value (MAV), Simple Square Integral (SSI), Variance (VAR), and Root Mean Square (RMS). Additionally, determining the suitable time for gesture classification is crucial, considering the perceptual, cognitive, and response times required for user input. To address this, segment sizes ranging from a minimum of 100ms to a maximum of 1,000ms are varied, and feature extraction is performed to identify the optimal segment size for gesture classification. Notably, data learning employs overlapped segmentation to reduce the interval between data points, thereby increasing the quantity of training data. Using this approach, the paper employs four machine learning models (KNN, SVC, RF, XGBoost) to train and evaluate the system, achieving accuracy rates exceeding 96% for all models in real-time gesture input scenarios with a maximum segment size of 200ms.

• The Journal of Industrial Distribution & Business
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• v.9 no.5
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• pp.63-72
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• 2018

Purpose - This work analyzes, in detail, the specification of vector error correction model (VECM) and thus examines the relationships and impact among seven economic variables for USA - balance on current account (BCA), index of stock (STOCK), gross domestic product (GDP), housing price indices (HOUSING), a measure of the money supply that includes total currency as well as large time deposits, institutional money market funds, short-term repurchase agreements and other larger liquid assets (M3), real rate of interest (IR_REAL) and household credits (LOAN). In particular, we search for the main explanatory variables that have an effect on stock and real estate market, respectively and investigate the causal and dynamic associations between them. Research design, data, and methodology - We perform the time series vector error correction model to infer the dynamic relationships among seven variables above. This work employs the conventional augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root techniques to test for stationarity among seven variables under consideration, and Johansen cointegration test to specify the order or the number of cointegration relationship. Granger causality test is exploited to inspect for causal relationship and, at the same time, impulse response function and variance decomposition analysis are checked for both short-run and long-run association among the seven variables by EViews 9.0. The underlying model was analyzed by using 108 realizations from Q1 1990 to Q4 2016 for USA. Results - The results show that all the seven variables for USA have one unit root and they are cointegrated with at most five and three cointegrating equation for USA. The vector error correction model expresses a long-run relationship among variables. Both IR_REAL and M3 may influence real estate market, and GDP does stock market in USA. On the other hand, GDP, IR_REAL, M3, STOCK and LOAN may be considered as causal factors to affect real estate market. Conclusions - The findings indicate that both stock market and real estate market can be modelled as vector error correction specification for USA. In addition, we can detect causal relationships among variables and compare dynamic differences between countries in terms of stock market and real estate market.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.431-445
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• 2006

In this paper we review some methods for analyzing functional data and illustrate real application of functional data analysis. Representing methods for functional data by using basis function, analyzing functional variation by functional principal component analysis and functional linear models are reviewed. For a real application, we use temperature and precipitation data measured in Korea from the January of 1970 to the May of 2004. We apply functional principal component analysis for each data and test the significance of regional division done by using shining hours. We also estimate functional regression model for temperature and precipitation.

• Korean Journal of Human Ecology
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• v.23 no.2
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• pp.251-268
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• 2014

Applying the data from 64 single(26 men and 38 women) and 71 childless married men and women(37 men and 34 women) aged between 30 and 45, this study is to understand their future fertility intention. For this purpose, ideal and real number of children that participants plan to have were compared using paired t-test. Second, demographic variables(sex, age, marital status), child care related variables(thoughts about caring children, child care value), individual characteristics(gender role attitude, relation orientation) and social context variables(perceived economic condition, recognition of low fertility policies) were included in a stepwise regression model to explain expected number of children participants plan to have in the future. Results showed that ideal number of children participants wish to have was significantly higher than real number of children they expect to have in the Korean society. The stepwise regression model explained 35% of the variance of the dependent variable. Among four types of variables, child care related variables most powerfully explained expected number of children study participants plan to have in the future. Finally, age, child care value, gender role attitude, and relation orientation significantly explained expected number of children in the future.