• Korean Journal of Microbiology
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• v.49 no.1
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• pp.1-7
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• 2013

During meiosis, genetic recombinants are formed by homologous recombination accompanying with the programmed double-strand breaks (DSBs) and strand exchanges between homologous chromosomes. The mechanism is generated by recombination intermediates such as single-end invasions (SEIs) and double-Holliday junctions (dHJs), and followed by crossover (CO) or non-crossover (NCO) products. Our study was focused on the analysis of meiotic recombination intermediates (DSBs, SEIs, and dHJs) and final recombination products (CO and NCO). We identified these meiotic recombination intermediates using DNA physical analysis under HIS4LEU2 "hot spot" system in budding yeast, Saccharomyces cerevisiae. For DNA physical analysis, when the hot spot locus is recognized by restriction enzyme from synchronous meiotic cells, the fragmented DNA that are forming recombination intermediates can be detected and quantified through Southern hybridization analysis. Our study suggests that this system can analyze the structural change of recombination intermediates during DSB-SEI transition, double-Holiday junctions and crossover/non-crossover products in meiosis.

• The Journal of the Acoustical Society of Korea
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• v.36 no.1
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• pp.12-22
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• 2017

It is important to predict accurately reverberation level, which is a limiting factor in underwater target detection. Recently, the studies have been expanded from monostatic sonar to bistatic sonar in which source and receivers are separated. To simulate the bistatic reverberation level, the computation processes for propagation, scattering strength, and scattering cross section are different from those in monostatic case and more complex computation processes are required. Although there have been many researches for bistatic reverberation, few studies have assessed the bistatic scattering cross section which is a key factor in simulate reverberation level. In this paper, a new method to estimate the bistatic scattering cross section is suggested, which uses the area of intersection between two circles. Finally, the reverberation levels simulated with the scattering cross section estimated using the method suggested in this paper are compared with those estimated using the methods previously suggested and those measured from an acoustic measurements conducted in May 2013.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.353-361
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• 2020

Due to the rapid urbanization, various traffic problems such as traffic jams during commute and regular traffic jams are occurring. In order to solve these traffic problems, it is necessary to quickly and accurately estimate and analyze traffic volume. ITS (Intelligent Transportation System) is a system that performs optimal traffic management by utilizing the latest ICT (Information and Communications Technology) technologies, and research has been conducted to analyze fast and accurate traffic volume through various techniques. In this study, we proposed a deep learning-based vehicle detection method using UAV (Unmanned Aerial Vehicle) video for real-time traffic analysis with high accuracy. The UAV was used to photograph orthogonal videos necessary for training and verification at intersections where various vehicles pass and trained vehicles by classifying them into sedan, truck, and bus. The experiment on UAV dataset was carried out using YOLOv3 (You Only Look Once V3), a deep learning-based object detection technique, and the experiments achieved the overall object detection rate of 90.21%, precision of 95.10% and the recall of 85.79%.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1149-1161
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• 2021

With a trend of the utilization of computer vision for satellite images, cloud detection using deep learning also attracts attention recently. In this study, we conducted a U-Net cloud detection modeling using SPARCS (Spatial Procedures for Automated Removal of Cloud and Shadow) Cloud Dataset with the image data augmentation and carried out 10-fold cross-validation for an objective assessment of the model. Asthe result of the blind test for 1800 datasets with 512 by 512 pixels, relatively high performance with the accuracy of 0.821, the precision of 0.847, the recall of 0.821, the F1-score of 0.831, and the IoU (Intersection over Union) of 0.723. Although 14.5% of actual cloud shadows were misclassified as land, and 19.7% of actual clouds were misidentified as land, this can be overcome by increasing the quality and quantity of label datasets. Moreover, a state-of-the-art DeepLab V3+ model and the NAS (Neural Architecture Search) optimization technique can help the cloud detection for CAS500 (Compact Advanced Satellite 500) in South Korea.

• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.1-9
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• 2016

In accordance with the increased political and social utilization of social media, demands on online trend analysis and monitoring technologies based on social bigdata are also increasing rapidly. In this paper, we define 'risk' as issues which have probability of turn to negative public opinion among big social issues and classify their types in details. To define risk types, we conduct a complete survey on news documents and analyzed characteristics according to issue domains. We also investigate cross-medias analysis to find out how different public media and personalized social media. At the result, we define 58 risk types for 6 domains and developed automatic classification model based on machine learning algorithm. Based on empirical experiments, we prove the possibility of automatic detection for social issue risk in social media.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.1
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• pp.28-33
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• 2009

In the research of speech recognition, locating the beginning and end of a speech utterance in a background of noise is of great importance. The conventional methods for speech endpoint detection are based on two simple time-domain measurements-short-time energy, and short-time zero-crossing rate, which couldn't guarantee the precise results if in the low signal-to-noise ratio environments. This paper proposes a novel approach that finds the Lyapunov exponent of time-domain waveform. This proposed method has no use for obtaining the frequency-domain parameters for endpoint detection process, e.g. Mel-Scale Features, which have been introduced in other paper. Accordingly, this algorithm is low complexity and suitable for Digital Isolated Word Recognition System.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1211-1220
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• 2020

This research detects the features of the bright band (BB) through analysis of the vertical profile of range height indicator (RHI) and the slant range beam profile of plane position indicator (PPI) of the polarimetric radar measurements-horizontal reflectivity (ZH), differential reflectivity (ZDR), and cross-correlation coefficient (ρHV). As a result of the analysis, it is possible to clearly detect the bright band using the polarimetric radar measurements, and it is confirmed that the result is consistent by double searching for the BB using the RHI and PPI scan data at the same time. Based on these results, the accuracy of QPE (quantification of precipitation estimation) can be improved by applying the BB search method by the PPI slant range in this research to large rainfall radars that only scan PPI volumes in the field without RHI observations.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.3
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• pp.77-90
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• 2018

Software Defect Prediction (SDP) is a field of study that identifies defective modules. With insufficient local data, a company can exploit Cross-Project Defect Prediction (CPDP), a way to build a classifier using dataset collected from other companies. Most machine learning algorithms for SDP have used more than one parameter that significantly affects prediction performance depending on different values. The objective of this study is to propose a parameter selection technique to enhance the performance of CPDP. Using a Harmony Search algorithm (HS), our approach tunes parameters of cost-sensitive boosting, a method to tackle class imbalance causing the difficulty of prediction. According to distributional characteristics, parameter ranges and constraint rules between parameters are defined and applied to HS. The proposed approach is compared with three CPDP methods and a Within-Project Defect Prediction (WPDP) method over fifteen target projects. The experimental results indicate that the proposed model outperforms the other CPDP methods in the context of class imbalance. Unlike the previous researches showing high probability of false alarm or low probability of detection, our approach provides acceptable high PD and low PF while providing high overall performance. It also provides similar performance compared with WPDP.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.5
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• pp.613-621
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• 2013

This paper presents the direction finding proximity fuze sensor using the clutter rejection method and the adaptive target detection algorithm for anti-air missiles. To remove effects by clutter and detect a target accurately, the clutter rejection method of Legendre sequence with BPSK(Bi phase Shift Keying) modulation has been proposed and the Doppler signal which has cross correlation characteristics is obtained from reflected target signals. Considering the change of the Doppler signal, the adaptive target detection algorithm has been developed and the direction finding algorithm has been fulfilled by comparing received powers from adjacent three receiving antennas. The encounter simulation test apparatus was made to collect and analyze reflected signal and test results showed that the -10 dBsm target was detected over 10 meters and the target with mesh clutter was detected and direction was distinguished definitely.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.6
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• pp.531-538
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• 2016

In this paper, we design and fabricate a short range FMCW radar which detects and tracks a moving target in a two-dimensional domain using dual transceiver modules. For the short range radar, we propose a scheme for alternate extraction of the two-dimensional positions using one-dimensional range information from time division transceiver modules, and successfully apply the scheme to the two-dimensional short range radar. Measured results of the targets at 10 m and 30 m are presented as performance demonstration of each transceiver module. Also the performance of the two-dimensional radar is demonstrated using a two-dimensional target map, which uses the range bin corresponding to the frequency resolution, and the effectiveness of the proposed scheme is validated.