• Proceedings of the Korean Information Science Society Conference
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• 2006.10a
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• pp.53-58
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• 2006

MicroRNA (miRNA)는 약 22 nt의 작은 RNA 조각으로 이루어져 있으며 stem-loop 구조의 precursor 형태에서 최종적으로 만들어 진다. miRNA는 mRNA의 3‘UTR에 상보적으로 결합하여 유전자의 발현을 억제하거나 mRNA의 분해를 촉진한다. miRNA를 동정하기 위한 실험적인 방법은 조직 특이적인 발현, 적은 발현양 때문에 방법상 한계를 가지고 있다. 이러한 한계는 컴퓨터를 이용한 방법으로 어느 정도 해결될 수 있다. 하지만 miRNA의 서열상의 낮은 보존성은 homology를 기반으로 한 예측을 어렵게 한다. 또한 기계학습 방법인 support vector machine (SVM) 이나 naive bayes가 적용되었지만, 생물학적인 의미를 해석할 수 있는 generative model을 제시해 주지 못했다. 본 연구에서는 우수한 miRNA 예측을 보일 뿐만 아니라 학습된 모델로부터 생물학적인 지식을 얻을 수 있는 Bayesian network을 적용한다. 이를 위해서는 생물학적으로 의미 있는 특질들의 선택이 중요하다. 여기서는 position weighted matrix (PWM)과 Markov chain probability (MCP), Loop 크기, Bulge 수, spectrum, free energy profile 등을 특질로서 선택한 후 Information gain의 특질 선택법을 통해 예측에 기여도가 높은 특질 25개 와 27개를 최종적으로 선택하였다. 이로부터 Bayesian network을 학습한 후 miRNA의 예측 성능을 10 fold cross-validation으로 확인하였다. 그 결과 pre-/mature miRNA 각 각에 대한 예측 accuracy가 99.99% 100.00%를 보여, SVM이나 naive bayes 방법보다 높은 결과를 보였으며, 학습된 Bayesian network으로부터 이전 연구 결과와 일치하는 pre-miRNA 상의 의존관계를 분석할 수 있었다.

• Korean Journal of Remote Sensing
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• v.17 no.2
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• pp.183-188
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• 2001

The NOAA AVHRR remotely sensed SST data, collected by the National Fisheries Research and Development Institute (NFRDI), are analyzed in order to understand the spatial and temporal distributions of SST in the sea near korea. Our study is based on 10-day SST images during last 7 years (1991-1997). For a time series analysis of multiple SST images, all of images must be consistent exactly at the same position by adjusting the scales and positions of each SST image. We devised an algorithm which automatically detects cloud pixels from multiple SST images. The cloud detection algorithm is based on a physical constraint that SST anomalies in the ocean do not exceed certain limits (we used $\pm$3$^{\circ}C$ as a criterion of SST anomalies). The remotely sensed SST data are tuned by comparing remotely sensed data with observed SST at coastal stations. Seasonal variations of SST are studied by harmonic fit of SST normals at each pixel and the SST anomalies are studied by statistical method. It was found that the SST anomalies are rather persistent for one or two months. Utilizing the persistency of SST anomalies, we devised an algorithm for a prediction of future SST. In the Markov lprocess model of SST anomalies, autoregression coefficients of SST anomalies during a time elapse of 10 days are between 0.5 and 0.7. The developed algorithm with automatic cloud pixel detection and rediction of future SST is expected to be incorporated to the operational real time service of SST around Korea.

• Journal of the Society of Naval Architects of Korea
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• v.57 no.1
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• pp.23-34
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• 2020

This paper covers the development of prediction techniques for ice load on ice-breakers operating in continuous ice-breaking under level ice conditions using particle-based continuum mechanics. Ice is assumed to be a linear elastic material until the fracture occurs. The maximum normal stress theory is used for the criterion of fracture. The location of the crack can be expressed using a local scalar function consisting of the gradient of the first principal stress and the corresponding eigen-vector. This expression is used to determine the relative position of particle pair to the new crack. The Hertz contact model is introduced to consider the collisions between ice fragments and the collisions between hull and ice fragments. In order to verify the developed technique, the simulation results for the three-point bending problems of ice-specimen and the continuous ice-breaking problem around a wedge-type model ship with bow angle of 20° are compared with the experimental results carrying out at Korea Research Institute of Ships and Ocean Engineering (KRISO).

• Journal of the Korean GEO-environmental Society
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• v.20 no.1
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• pp.19-25
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• 2019

In this present study, to evaluate the applicability of the monitoring system of the entire reservoir dam facility using the wireless sensor network system and a section representative of the domestic reservoir dam was selected as the test bed site and to operated a system that can evaluate the condition of the facility at the real time with monitoring. In order to set up a wireless sensor network system, the system assessment of present state was carried out for confirmation the risk factors and the limit values of the risk factors in limit state were calculated. The type and position of the sensor to be measured in the field were determined by setting the measurement items suitable for the hazardous area and the risk factor. In this paper, we evaluated the feasibility of the system by monitoring and constructing a wireless sensor network system in a field for a fill dam that can represent a domestic reservoir dam. Applicability evaluation was verified by comparing directly with the measurement of partial concentration method which is the measurement management technology of the dam.

• The korean journal of orthodontics
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• v.51 no.2
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• pp.77-85
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• 2021

Objective: To evaluate the accuracy of a multi-stage convolutional neural network (CNN) model-based automated identification system for posteroanterior (PA) cephalometric landmarks. Methods: The multi-stage CNN model was implemented with a personal computer. A total of 430 PA-cephalograms synthesized from cone-beam computed tomography scans (CBCT-PA) were selected as samples. Twenty-three landmarks used for Tweemac analysis were manually identified on all CBCT-PA images by a single examiner. Intra-examiner reproducibility was confirmed by repeating the identification on 85 randomly selected images, which were subsequently set as test data, with a two-week interval before training. For initial learning stage of the multi-stage CNN model, the data from 345 of 430 CBCT-PA images were used, after which the multi-stage CNN model was tested with previous 85 images. The first manual identification on these 85 images was set as a truth ground. The mean radial error (MRE) and successful detection rate (SDR) were calculated to evaluate the errors in manual identification and artificial intelligence (AI) prediction. Results: The AI showed an average MRE of 2.23 ± 2.02 mm with an SDR of 60.88% for errors of 2 mm or lower. However, in a comparison of the repetitive task, the AI predicted landmarks at the same position, while the MRE for the repeated manual identification was 1.31 ± 0.94 mm. Conclusions: Automated identification for CBCT-synthesized PA cephalometric landmarks did not sufficiently achieve the clinically favorable error range of less than 2 mm. However, AI landmark identification on PA cephalograms showed better consistency than manual identification.

• Journal of Convergence for Information Technology
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• v.12 no.1
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• pp.39-44
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• 2022

The purpose of this study is to extract the type, location, and absolute size of an object in an image using a deep learning algorithm, predict the relative distance between objects, and use this to detect contact between objects. To analyze the size ratio of objects, YOLO, a CNN-based object detection algorithm, is used. Through the YOLO algorithm, the absolute size and position of an object are extracted in the form of coordinates. The extraction result extracts the ratio between the size in the image and the actual size from the standard object-size list having the same object name and size stored in advance, and predicts the relative distance between the camera and the object in the image. Based on the predicted value, it detects whether the objects are in contact.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1312-1319
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• 2022

This paper defines an effectiveness index for optical continuous observation of unknown space objects and presents a range of design variables combinations that can satisfy the effectiveness index from a telescope/mount control system perspective using integrated simulation. The overall system-level simulation was implemented and the tracking performance was analyzed by considering design variables such as target position prediction and frame rate, image processing time and measurement error, target trajectory characteristics, and maneuver performance of mount gimbal. As a result of the analysis, it was confirmed that the continuous tracking performance of the optical observation system is dependent on the combination of frame rate and mount maneuver performance. In a situation where an optical observation system is designed or a similar system is implemented using COTS, an appropriate combination of parameters between design variables can be found through effectiveness analysis simulation as in this study.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.335-346
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• 2022

Buckling failure is a typical slope instability mode that should be paid more attention to. It is difficult to provide systematic guidance for the monitoring and management of such slopes due to unclear mechanism. Here we examine buckling failure as the potential instability mode for a slope above a railway tunnel in southwest China. A comprehensive model test system was developed that can be used to conduct buckling failure experiments. The displacement, stress, and strain of the slope were monitored to document the evolution of buckling failure during the experiment. Monitoring data reveal the deformation and stress characteristics of the slope with different slipping mass thicknesses and under different top loads. The test results show that the slipping mass is the main subject of the top load and is the key object of monitoring. Displacement and stress precede buckling failure, so maybe useful predictors of impending failure. However, the response of the stress variation is earlier than displacement variation during the failure process. It is also necessary to monitor the bedrock near the slip face because its stress evolution plays an important role in the early prediction of instability. The position near the slope foot is most prone to buckling failure, so it should be closely monitored.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.125-132
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• 2022

The Ministry of Environment recently analyzed the output data of 10 fine dust measuring stations and, as a result, announced that about 60% had an error that the existing atmospheric measurement concentration was higher. In order to accurately predict fine dust, the wind direction and measurement position must be corrected. In this paper, in order to solve these problems, fuzzy rules are used to solve these problems. In addition, in order to calculate the fine particulate sensation index actually felt by pedestrians on the street, a computer simulation experiment was conducted to calculate the fine particulate sensation index in consideration of weather conditions, temperature conditions, humidity conditions, and wind conditions.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.405-411
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• 2022

Korea's long-term care insurance for the elderly, which started in 2008, is a borrowed one from gaeho insurance, which started eight years earlier. Both countries have a policy of welfare equipment benefit systems to support the lives of the elderly who are intellectually and mentally weakened. In this study, we attempted to compare and examine the welfare equipment items in Korea with those in Japan and find out the characteristics of the items in Korea and Japan. In Korea, loitering-detection device, posture changing devices, and incontinence underwears were registered as welfare devices before Japan, and in Japan, automatic urine disposal systems, wheelchair electric assist device, position converters, and lifts for handicapped person were designated as welfare devices before Korea. In addition, the Japanese Ministry of Health, Labor and Welfare has announced the designation of the excretion prediction support device as a welfare device. If Korea and Japan cooperate to develop welfare equipment items together, it will be of great help in improving the quality of life of the elderly in both countries in a super-aged society.