• Journal of Korea Multimedia Society
• /
• v.7 no.7
• /
• pp.877-885
• /
• 2004

In this paper, we propose an enhancement shot change detection method using the neural net and the robust feature selection out of multiple features. The previous shot change detection methods usually used single feature and fixed threshold between consecutive frames. However, contents such as color, shape, background, and texture change simultaneously at shot change points in a video sequence. Therefore, in this paper, we detect the shot changes effectively using robust features, which are supplementary each other, rather than using single feature. In this paper, we use the typical CART (classification and regression tree) of data mining method to select the robust features, and the backpropagation neural net to determine the threshold of the each selected features. And to evaluation the performance of the robust feature selection, we compare the proposed method to the PCA(principal component analysis) method of the typical feature selection. According to the experimental result. it was revealed that the performance of our method had better that than the PCA method.

• Journal of the Korean Society for Nondestructive Testing
• /
• v.21 no.1
• /
• pp.80-90
• /
• 2001

The aim of this study is to develop the methodology which enables to identify the mechanical properties of element such as stress intensity factor by using the AE parameters. Considering the multivariate and nonlinear properties of AE parameters such as ringdown count, rise time, energy, event duration and peak amplitude from fatigue cracks of machine element the principal component regression(PCR) and artificial neural network(ANN) models for the estimation of stress intensity factor were developed and validated. The AE parameters were found to be very significant to estimate the stress intensity factor. Since the statistical values including correlation coefficients, standard mr of calibration, standard error of prediction and bias were stable, the PCR and ANN models for stress intensity factor were very robust. The performance of ANN model for unknown data of stress intensity factor was better than that of PCR model.

• Journal of the Korea Society for Simulation
• /
• v.18 no.3
• /
• pp.103-112
• /
• 2009

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this paper, principal component-based Auto-Associative support vector regression (PCSVR) was proposed for the sensor signal validation of the NPP. It utilizes the attractive merits of principal component analysis (PCA) for extracting predominant feature vectors and AASVR because it easily represents complicated processes that are difficult to model with analytical and mechanistic models. With the use of real plant startup data from the Kori Nuclear Power Plant Unit 3, SVR hyperparameters were optimized by the response surface methodology (RSM). Moreover the statistical techniques are integrated with PCSVR for the failure detection. The residuals between the estimated signals and the measured signals are tested by the Shewhart Control Chart, Exponentially Weighted Moving Average (EWMA), Cumulative Sum (CUSUM) and generalized likelihood ratio test (GLRT) to detect whether the sensors are failed or not. This study shows the GLRT can be a candidate for the detection of sensor drift.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.28 no.3
• /
• pp.361-367
• /
• 2010

We produced continuous vertical time series of 14 permanent GPS stations operated by National Geographic Information Institute by processing about five years of data. Then we computed the height velocities by using a linear regression fitting of those time series, and did principal component analysis to understand the overall characteristics of the series. The prominent signal obtained as the first mode of PCA results showed an average of 4.2 mm/yr vertical velocity. The values of the first mode eigenvectors were consistent at all sites. Thus, we concluded that all the 14 stations are uplifting nearly at the same velocity for the test period. Then changes of precision before and after removing the first mode signal from the 14 height time series were analyzed. As a result, the precision improved 34.8% on average.

• The Journal of the Korea Contents Association
• /
• v.12 no.7
• /
• pp.437-444
• /
• 2012

This study features an analysis of the socio-economic factors of ten cities on the Honam-line that affect the number of train passengers. The 3 main factors based on the principal component analysis were the population, the distance between two regions, and the area size of each region while the number of automobiles has been conventionally used instead of the area size of each region. A formula to predict the train passengers by the regression analysis was developed and showed a good agreement to the number of real passengers. When Honam highspeed railway is opened, the traveling time between two regions as well as the area size of each regions should be more precisely considered.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.25 no.5
• /
• pp.376-384
• /
• 2020

An energy storage system is composed of lithium-ion batteries in modern applications. Batteries are regarded as storage devices for renewable and residual energy. The failure of batteries can cause the performance reduction and explosion of battery systems. High maintenance cost is essential when dealing with the problem of battery safety. Therefore an accurate health diagnosis is required to ensure the high reliability of battery systems. A battery pack is a combination of single cells in series and parallel connections. A battery pack has to consider various factors to assess battery health. Battery health involves conventional factors and additional factors, such as cell-to-cell imbalance. For large applications, state-of-health (SOH) can be inaccurate because of the lack of factors that indicate the state of the battery pack. In this study, six characterization factors are proposed for improving the SOH estimation of battery packs. The six proposed characterization factors can be regarded as health indicators (HIs). The six HIs are applied to the principal component analysis (PCA) algorithm. To reflect information regarding capacity, voltage, and temperature, the PCA algorithm extracts new degradation factors by using the six HIs. The new degradation factors are applied to a multiple regression model. Results show the advancement and improvement of SOH estimation.

• Journal of Practical Agriculture & Fisheries Research
• /
• v.22 no.2
• /
• pp.59-67
• /
• 2020

Growth tests on the Wando and Baengnyeongdo cultivars of Saccharina japonica were performed at the Myeongcheon and Gyedo aquafarms, Goheung in Jeollanamdo, from February to July in 2003. Five environmental conditions and 2 traits were measured monthly. The data were used to analyze the growth patterns, relationships between traits and principal component. Box plots were used to display the growth patterns. Scatter plots and regression and correlation coefficients were used to determine the strength of relationships between the traits. A principal component analysis revealed that the first principal component explained more than 91.4% and 90.5% of the total sample variance in the Myeongcheon and Gyedo aquafarms. From the viewpoint of the economic traits (blade length, blade weight), the growth of populations from the Gyedo aquafarm was stronger than that of those from the Myeongcheon aquafarm, and the growth of the Baengnyeongdo cultivar was superior to that of the Wando one.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 1993.10a
• /
• pp.403-412
• /
• 1993

This paper presents the progress of the development of a nondestructive technique for the classification of normal, septicemic , and cadaver poultry carcasses by the Instrumentation and Sensing Laboratory at Beltsville, Maryland, U.S.A. The Sensing technique is based on the diffuse reflectance spectroscopy of poultry carcasses.

• 수도
• /
• s.2
• /
• pp.6-17
• /
• 1974

• Genomics & Informatics
• /
• v.14 no.4
• /
• pp.187-195
• /
• 2016

In genetic association studies with high-dimensional genomic data, multiple group testing procedures are often required in order to identify disease/trait-related genes or genetic regions, where multiple genetic sites or variants are located within the same gene or genetic region. However, statistical testing procedures based on an individual test suffer from multiple testing issues such as the control of family-wise error rate and dependent tests. Moreover, detecting only a few of genes associated with a phenotype outcome among tens of thousands of genes is of main interest in genetic association studies. In this reason regularization procedures, where a phenotype outcome regresses on all genomic markers and then regression coefficients are estimated based on a penalized likelihood, have been considered as a good alternative approach to analysis of high-dimensional genomic data. But, selection performance of regularization procedures has been rarely compared with that of statistical group testing procedures. In this article, we performed extensive simulation studies where commonly used group testing procedures such as principal component analysis, Hotelling's $T^2$ test, and permutation test are compared with group lasso (least absolute selection and shrinkage operator) in terms of true positive selection. Also, we applied all methods considered in simulation studies to identify genes associated with ovarian cancer from over 20,000 genetic sites generated from Illumina Infinium HumanMethylation27K Beadchip. We found a big discrepancy of selected genes between multiple group testing procedures and group lasso.