• Korean Journal of Remote Sensing
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• v.29 no.4
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• pp.389-397
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• 2013

Recently high spatial resolution space-borne Synthetic Aperture Radar (SAR) systems have launched and have been operated successfully. Interferometric SAR (InSAR) processing with the space-based high resolution observations acquired by these systems can provide more detail information for various geodetic applications. Coherence is regarded as a critical parameter in the evaluating the quality of an InSAR pair. In this study, we evaluate the coherence characteristics of high-resolution data acquired by TerraSAR-X (X-band) and ALOS PALSAR (L-band) and intermediate-resolution data acquired by Envisat ASAR (C-band) over western Texas, U.S.A. Our coherence analysis reveals that the high-resolution X-band TSX (3.1 cm) data has a high coherence level (0.3-0.6), similar to that of the L-band ALOS PALSAR data (23.5 cm) in short temporal baselines. Further more, the TSX coherence values are significantly higher than those of the C-band (5.6 cm) Envisat ASAR data. The higher coherence of the TSX dataset is a surprising result, because common scattering theories suggest that the longer wavelength SAR data maintain better coherence. In vegetated areas the shorter wavelength radar pulse interacts mostly with upper sections of the vegetation and, hence, does not provide good correlation over time in InSAR pairs. Thus, we suggest that the higher coherence values of the TSX data reflect the data's high-resolution, in which stable and coherent scatters are better maintained. Although, however, the TSX data show a very good coherence with short temporal baseline (11-33 days), the coherences are significantly degraded as the temporal baselines are increased. This result confirms previous studies showing that the coherence has a strong dependency on the temporal baseline.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.6
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• pp.63-70
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• 2011

A bicycle is different from vehicles in the structure that a rider is fully exposed to the surrounding environment. Therefore, it needs to make use of prior information about local weather, air quality, trail road condition. Moreover, since it depends on human power for moving, it should acquire route property such as hill slope, winding, and road surface to improve its efficiency in everyday use. Recent mobile applications which are to be used during bicycle riding let us aware of the necessity of development of intelligent bicycles. This study aims to develop a riding state (up-hill, down-hill, accelerating, braking) recognition algorithm using a low-power wrist watch type embedded system which has 3-axis accelerometer and wireless communication capability. The developed algorithm was applied to 19 experimental riding data and showed more than 95% of correct recognition over 83.3% of the total dataset. The altitude and temperature sensor also in the embedded system mounted on the bicycle is being used to improve the accuracy of the algorithm. The developed riding state recognition algorithm is expected to be a platform technology for intelligent bicycle interface system.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.10
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• pp.1670-1676
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• 2018

Objective: This study was conducted to enable on-line prediction of primal and commercial cut weights in Korean slaughter pigs by AutoFom III, which non-invasively scans pig carcasses early after slaughter using ultrasonic sensors. Methods: A total of 162 Landrace, Yorkshire, and Duroc (LYD) pigs and 154 LYD pigs representing the yearly Korean slaughter distribution were included in the calibration and validation dataset, respectively. Partial least squares (PLS) models were developed for prediction of the weight of deboned shoulder blade, shoulder picnic, belly, loin, and ham. In addition, AutoFom III's ability to predict the weight of the commercial cuts of spare rib, jowl, false lean, back rib, diaphragm, and tenderloin was investigated. Each cut was manually prepared by local butchers and then recorded. Results: The cross-validated prediction accuracy ($R^2cv$) of the calibration models for deboned shoulder blade, shoulder picnic, loin, belly, and ham ranged from 0.77 to 0.86. The $R^2cv$ for tenderloin, spare rib, diaphragm, false lean, jowl, and back rib ranged from 0.34 to 0.62. Because the $R^2cv$ of the latter commercial cuts were less than 0.65, AutoFom III was less accurate for the prediction of those cuts. The root mean squares error of cross validation calibration (RMSECV) model was comparable to the root mean squares error of prediction (RMSEP), although the RMSECV was numerically higher than RMSEP for the deboned shoulder blade and belly. Conclusion: AutoFom III predicts the weight of deboned shoulder blade, shoulder picnic, loin, belly, and ham with high accuracy, and is a suitable process analytical tool for sorting pork primals in Korea. However, AutoFom III's prediction of smaller commercial Korean cuts is less accurate, which may be attributed to the lack of anatomical reference points and the lack of a good correlation between the scanned area of the carcass and those traits.

• Korean Journal of Ecology and Environment
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• v.41 no.2
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• pp.228-236
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• 2008

The purpose of this study was to evaluate fish metric attributes using a model of Lentic Ecosystem Health Assessment (LEHA) and apply the model to the dataset sampled from six sites of Namyang Reservoir during October 2005$\sim$May 2006. The model was composed of 11 metries and the metric attributes were made of physical, chemical and biological parameters. Trophic composition's metrics showed that tolerant species ($M_3$, 80%) and omnivore species ($M_4$, 92%) dominated the fish fauna, indicating a biological degradation in the aquatic ecosystem. The metric of $M_7$, relative proportions of exotic species, also showed greater than 8% of the total, indicating a ecological disturbance. The average value of LEHA model was 24.3 (n= 12) in the reservoir, indicating a "poor condition" by the criteria of An and Han (2007). Spatial variation based on the model values was low (range: $21{\sim}26$), and temporal variation occurred due to a monsoon rainfall. Electrical conductivity (EC) and tropic state index of chlorophyll-$\alpha$ [TSI(CHL)] was greater in the premonsoon than the postmonsoon.

• Horticultural Science & Technology
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• v.29 no.2
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• pp.116-122
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• 2011

In this study, we report the generation and analysis of 1,392 expressed sequence tags (ESTs) from Korean Stewartia (Stewartia koreana Nakai). A cDNA library was generated from the young leaf tissue and a total of 1,392 cDNA were partially sequenced. EST and unigene sequence quality were determined by computational filtering, manual review, and BLAST analyses. Finally, 1,301 ESTs were acquired after the removal of the vector sequence and filtering over a minimum length 100 nucleotides. A total of 893 unigene, consisting of 150 contigs and 743 singletons, was identified after assembling. Also, we identified 95 new microsatellite-containing sequences from the unigenes and classified the structure according to their repeat unit. According to homology search with BLASTX against the NCBI database, 65% of ESTs were homologous with known function and 11.6% of ESTs were matched with putative or unknown function. The remaining 23.2% of ESTs showed no significant similarity to any protein sequences found in the public database. Annotation based searches against multiple databases including wine grape and populus sequences helped to identify putative functions of ESTs and unigenes. Gene ontology (GO) classification showed that the most abundant GO terms were transport, nucleotide binding, plastid, in terms biological process, molecular function and cellular component, respectively. The sequence data will be used to characterize potential roles of new genes in Stewartia and provided for the useful tools as a genetic resource.

• Nuclear Engineering and Technology
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• v.52 no.11
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• pp.2452-2459
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• 2020

Neutron spectrum is essential to the safe operation of reactors. Traditional online neutron spectrum measurement methods still have room to improve accuracy for the application cases of wide energy range. From the application of artificial neural network (ANN) algorithm in spectrum unfolding, its accuracy is difficult to be improved for lacking of enough effective training data. In this paper, an adaptive deviation-resistant neutron spectrum unfolding method based on transfer learning was developed. The model of ANN was trained with thousands of neutron spectra generated with Monte Carlo transport calculation to construct a coarse-grained unfolded spectrum. In order to improve the accuracy of the unfolded spectrum, results of the previous ANN model combined with some specific eigenvalues of the current system were put into the dataset for training the deeper ANN model, and fine-grained unfolded spectrum could be achieved through the deeper ANN model. The method could realize accurate spectrum unfolding while maintaining universality, combined with detectors covering wide energy range, it could improve the accuracy of spectrum measurement methods for wide energy range. This method was verified with a fast neutron reactor BN-600. The mean square error (MSE), average relative deviation (ARD) and spectrum quality (Qs) were selected to evaluate the final results and they all demonstrated that the developed method was much more precise than traditional spectrum unfolding methods.

• Bulletin of the Korean Chemical Society
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• v.30 no.5
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• pp.1121-1126
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• 2009

This paper describes a pattern recognition method of Magnoliae flos based on a gas chromatographic/mass spectrometric (GC/MS) analysis of the essential oil components. The botanical drug is mainly comprised of the four magnolia species (M. denudata, M. biondii, M. kobus, and M. liliflora) in Korea, although some other species are also being dealt with the drug. The GC/MS separation of the volatile components, which was extracted by the simultaneous distillation and extraction (SDE), was performed on a carbowax column (supelcowax 10; 30 m{\time}0.25 mm{\time}0.25{\mu}m$) using temperature programming. Variance in the retention times for all peaks of interests was within RSD 2% for repeated analyses (n = 9). Of the 74 essential oil components identified from the magnolia species, approximately 10 major components, which is $\alpha$-pinene, $\beta$-pinene, sabinene, myrcene, d-limonene, eucarlyptol (1,8-cineol), $\gamma$-terpinene, p-cymene, linalool, $\alpha$-terpineol, were commonly present in the four species. For statistical analysis, the original dataset was reduced to the 13 variables by Fisher criterion and factor analysis (FA). The essential oil patterns were processed by means of the multivariate statistical analysis including hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA). All samples were divided into four groups with three principal components by PCA and according to the plant origins by HCA. Thirty-three samples (23 training sets and 10 test samples to be assessed) were correctly classified into the four groups predicted by PCA. This method would provide a practical strategy for assessing the authenticity or quality of the well-known herbal drug, Magnoliae flos.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.10
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• pp.465-472
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• 2015

We propose a novel plane detection in disparity space and evaluate its performance. Our method simplifies and makes scenes in disparity space easily dealt with by approximating various surfaces as planes. Moreover, the approximated planes can be represented in the same size as in the real world, and can be employed for obstacle detection and camera pose estimation. Using a stereo matching technique, our method first creates a disparity image which consists of binocular disparity values at xy-coordinates in the image. Slants of disparity values are estimated by exploiting a line simplification algorithm which allows our method to reflect global changes against x or y axis. According to pairs of x and y slants, we label the disparity image. 4-connected disparities with the same label are grouped, on which least squared model estimates plane parameters. N plane models with the largest group of disparity values which satisfy their plane parameters are chosen. We quantitatively and qualitatively evaluate our plane detection. The result shows 97.9%와 86.6% of quality in our experiment respectively on cones and cylinders. Proposed method excellently extracts planes from Middlebury and KITTI dataset which are typically used for evaluation of stereo matching algorithms.

• Korean Journal of Social Welfare Studies
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• v.49 no.2
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• pp.5-38
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• 2018

The purpose of this study is to examine work-life balance by analyzing time schedules of married couple. The 2014 Korea Time Use Survey dataset was used for the analysis. Finally, 6,262 time diaries of 3,131 households were included in the analysis. The study used sequence analysis in particular, by applying the Lesnard(2014)'s dynamic hamming matching (DHM) method, which is useful for the time diary analysis where timing is a key factor. This study explored daily schedules of each man and woman according to 9 types of couple-combined work-schedules, which had been already derived by cluster analysis. The daily schedules were identified according to the activities divided as labor, housework, sleep, self-management, active leisure, passive leisure, and others. Here, time allocation was analyzed through various graphs showing average time amount and modal states by time period. Based on the analysis, it summarized that "long working hours as a main factor of work-life imbalance", "gender inequality of time use", "non-standard hours work impairing quality of life and "poverty of leisure time"as characteristics of work-life imbalance. Finally this study discussed the social policy implications to support work-life balance.

• Journal of Broadcast Engineering
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• v.26 no.2
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• pp.155-166
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• 2021

Most object detection algorithms are studied based on RGB images. Because the RGB cameras are capturing images based on light, however, the object detection performance is poor when the light condition is not good, e.g., at night or foggy days. On the other hand, high-quality infrared(IR) images regardless of weather condition and light can be acquired because IR images are captured by an IR sensor that makes images with heat information. In this paper, we performed the object detection algorithm based on the compression ratio in RGB and IR images to show the detection capabilities. We selected RGB and IR images that were taken at night from the Free FLIR Thermal dataset for the ADAS(Advanced Driver Assistance Systems) research. We used the pre-trained object detection network for RGB images and a fine-tuned network that is tuned based on night RGB and IR images. Experimental results show that higher object detection performance can be acquired using IR images than using RGB images in both networks.