• Journal of the Korea institute for structural maintenance and inspection
• /
• v.23 no.7
• /
• pp.66-71
• /
• 2019

In this paper, a study was conducted on the method of using GPR data to predict rebar thickness inside a facility. As shown in the cases of poor construction, such as the use of rebars below the domestic standard and the construction of reinforcement, information on rebar thickness can be found to be essential for precision safety diagnosis of structures. For this purpose, the B-scan data of GPR was obtained by gradually increasing the diameter of rebars by making specimen. Because the B-scan data of GPR is less visible, the data was converted into the heatmap image data through migration to increase the intuition of the data. In order to compare the results of application of commonly used B-scan data and heatmap data to CNN, this study extracted areas for rebars from B-scan and heatmap data respectively to build training and validation data, and applied CNN to the deployed data. As a result, better results were obtained for the heatmap data when compared with the B-scan data. This confirms that if GPR heatmap data are used, rebar thickness can be predicted with higher accuracy than when B-scan data is used, and the possibility of predicting rebar thickness inside a facility is verified.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.3
• /
• pp.33-42
• /
• 2020

There has been difficulties in identifying objects by relying on the naked eye in various surveillance systems. There is a growing need for automated surveillance systems to replace soldiers in the field of military surveillance operations. Even though the object detection technology is developing rapidly in the civilian domain, but the research applied to the military is insufficient due to a lack of data and interest. Thus, in this paper, we applied one of deep learning algorithms, Convolutional Neural Network-based binary classification to develop an autonomous identification model of both friend and foe helicopters (AH-64, Mi-17) among the military weapon systems, and evaluated the model performance by considering accuracy, precision, recall and F-measure. As the result, the identification model demonstrates 97.8%, 97.3%, 98.5%, and 97.8 for accuracy, precision, recall and F-measure, respectively. In addition, we analyzed the feature map on convolution layers of the identification model in order to check which area of imagery is highly weighted. In general, rotary shaft of rotating wing, wheels, and air-intake on both of ally and foe helicopters played a major role in the performance of the identification model. This is the first study to attempt to classify images of helicopters among military weapons systems using CNN, and the model proposed in this study shows higher accuracy than the existing classification model for other weapons systems.

• Journal of the Korea Society for Simulation
• /
• v.29 no.1
• /
• pp.11-21
• /
• 2020

Cardio-cerebrovascular diseases (CCD) account for 24% of the causes of death to Koreans and its proportion is the highest except cancer. Currently, the risk of the cardiovascular disease for domestic patients is based on the Framingham risk score (FRS), but accuracy tends to decrease because it is a foreign guideline. Also, it can't score the risk of cerebrovascular disease. CCD is hard to predict, because it is difficult to analyze the features of early symptoms for prevention. Therefore, proper prediction method for Koreans is needed. The purpose of this paper is validating IG-MLP (Information Gain - Multilayer Perceptron) evaluation based feature selection method using CCD data with simulation. The proposed method uses the raw data of the 4^th ~ 7^th of The Korea National Health and Nutrition Examination Survey (KNHANES). To select the important feature of CCD, analysis on the attributes using IG-MLP are processed, finally CCD prediction ANN model using optimize feature set is provided. Proposed method can find important features of CCD prediction of Koreans, and ANN model could predict more accurate CCD for Koreans.

• Korean Journal of Remote Sensing
• /
• v.33 no.5_2
• /
• pp.631-640
• /
• 2017

The estimation of rice yield affects the income of farmers as well as the fields related to agriculture. Moreover, it has an important effect on the government's policy making including the control of supply demand and the price estimation. Thus, it is necessary to build the crop yield estimation model and from the past, many studies utilizing empirical statistical models or artificial neural network algorithms have been conducted through climatic and satellite data. Presently, scientists have achieved successful results with deep learning algorithms in the field of pattern recognition, computer vision, speech recognition, etc. Among deep learning algorithms, the SSAE (Stacked Sparse AutoEncoder) algorithm has been confirmed to be applicable in the field of forecasting through time series data and in this study, SSAE was utilized to estimate the rice yield in South Korea. The climatic and satellite data were used as the input variables and different types of input data were constructed according to the period of rice growth in South Korea. As a result, the combination of the satellite data from May to September and the climatic data using the 16 day average value showed the best performance with showing average annual %RMSE (percent Root Mean Square Error) and region %RMSE of 7.43% and 7.16% that the applicability of the SSAE algorithm could be proved in the field of rice yield estimation.

• Journal of IKEEE
• /
• v.22 no.1
• /
• pp.162-167
• /
• 2018

In this paper, we propose a method to classify radar signals according to the jamming technique by applying the machine learning to parameter data extracted from received radar signals. In the present army, the radar signal is classified according to the type of threat based on the library of the radar signal parameters mostly built by the preliminary investigation. However, since radar technology is continuously evolving and diversifying, it can not properly classify signals when applying this method to new threats or threat types that do not exist in existing libraries, thus limiting the choice of appropriate jamming techniques. Therefore, it is necessary to classify the signals so that the optimal jamming technique can be selected using only the parameter data of the radar signal that is different from the method using the existing threat library. In this study, we propose a method based on machine learning to cope with new threat signal form. The method classifies the signal corresponding the new jamming method for the new threat signal by learning the classifier composed of the hidden Markov model and the neural network using the existing library data.

• Journal of Korean Society of Transportation
• /
• v.20 no.1
• /
• pp.7-18
• /
• 2002

To activate public transportation service, Bucheon City built Bus Information System based on Beacon type, and operates it for no.22 line. This research analyzes an effect of BIS operations, and mainly it analyzes far reliability evaluation of bus arrival time information and passenger satisfaction about BIS. As results of reliability evaluation of arrival time information service, it is proven to be practically inappropriate to use as arrival time data because it is not only travel time between each bus stop but also previous travel time history data. In order to improve this matter, neural network model was evaluated as the most outstanding one as result of experiment in applying current arrival time Prediction model. This research cannot help limiting for evaluation of operation effect in Bucheon City because there is no Bus Information System based on GPS type in Korea. For the future ITS model city, in the case of building ITS model city based on GPS type, it is possible to compare two systems relatively. In addition to that, fur the consideration of reliability of bus arrival time information, it is required to develop Predictable model and research factors that affect to bus operation.

• Journal of Digital Convergence
• /
• v.13 no.12
• /
• pp.245-257
• /
• 2015

This study was to develop the predictive model for severity-adjusted mortality of inpatients with multiple chronic conditions and analyse the factors on the variation of hospital standardized mortality ratio(HSMR) to propose the plan to reduce the variation. We collect the data "Korean National Hospital Discharge In-depth Injury Survey" from 2008 to 2010 and select the final 110,700 objects of study who have chronic diseases for principal diagnosis and who are over the age of 30 with more than 2 chronic diseases including principal diagnosis. We designed a severity-adjusted mortality predictive model with using data-mining methods (logistic regression analysis, decision tree and neural network method). In this study, we used the predictive model for severity-adjusted mortality ratio by the decision tree using Elixhauser comorbidity index. As the result of the hospital standardized mortality ratio(HSMR) of inpatients with multiple chronic conditions, there were statistically significant differences in HSMR by the insurance type, bed number of hospital, and the location of hospital. We should find the method based on the result of this study to manage mortality ratio of inpatients with multiple chronic conditions efficiently as the national level. So we should make an effort to increase the quality of medical treatment for inpatients with multiple chronic diseases and to reduce growing medical expenses.

• BMB Reports
• /
• v.53 no.2
• /
• pp.100-105
• /
• 2020

While liver histopathology is heterogeneous in diabetes, the underlying mechanisms remain unclear. We investigated whether glycemic variation resulting from differential diets can induce heterogeneity in diabetic liver and the underlying molecular mechanisms. We generated end-stage non-obese diabetic model rats by subtotal-pancreatectomy in male Sprague-Dawley rats and ad libitum diet for 7 weeks (n = 33). The rats were then divided into three groups, and fed a standard- or a low-protein diet (18 or 6 kcal%, respectively), for another 7 weeks: to maintain hyperglycemia, 11 rats were fed ad libitum (18AL group); to achieve euglycemia, 11 were calorie-restricted (18R group), and 11 were both calorie- and protein-restricted with the low-protein diet (6R group). Overnight-fasted liver samples were collected after the differential diets together with sham-control (18S group), and histology and molecular changes were compared. Hyperglycemic-18AL showed glycogenic hepatopathy (GH) without steatosis, with the highest GSK-3β inactivation because of Akt activation during hyperglycemia; mitochondrial function was not impaired, compared to the 18S group. Euglycemic-18R showed neither GH nor steatosis, with intermediate GSK-3β activation and mitochondrial dysfunction. However, euglycemic-6R showed both GH and steatosis despite the highest GSK-3β activity and no molecular evidence of increased lipogenesis or decreased ApoB expression, where mitochondrial dysfunction was highest among the groups. In conclusion, heterogeneous liver histopathology developed in end-stage non-obese diabetic rats as the glycemic levels varied with differential diets, in which protein content in the diets as well as glycemic levels differentially influenced GSK-3β activity and mitochondrial function in insulin-deficient state.

• Analytical Science and Technology
• /
• v.24 no.6
• /
• pp.544-555
• /
• 2011

The acute toxicity in the rainbow trout (Oncorhynchus mykiss) was analyzed and predicted using quantitative structure-activity relationships (QSAR). The aquatic toxicity, 96h $LC_{50}$ (median lethal concentration) of 275 organic pesticides, was obtained from EU-funded project DEMETRA. Prediction models were derived from 558 2D molecular descriptors, calculated in PreADMET. The linear (multiple linear regression) and nonlinear (support vector machine and artificial neural network) learning methods were optimized by taking into account the statistical parameters between the experimental and predicted p$LC_{50}$. After preprocessing, population based forward selection were used to select the best subsets of descriptors in the learning methods including 5-fold cross-validation procedure. The support vector machine model was used as the best model ($R^2_{CV}$=0.677, RMSECV=0.887, MSECV=0.674) and also correctly classified 87% for the training set according to EU regulation criteria. The MLR model could describe the structural characteristics of toxic chemicals and interaction with lipid membrane of fish. All the developed models were validated by 5 fold cross-validation and Y-scrambling test.

• Journal of Biosystems Engineering
• /
• v.21 no.4
• /
• pp.414-421
• /
• 1996

한국과 일본의 경우 건표고를 외관의 품질상태 에 따라 12등급에서 16등급으로 구분하고 있다. 그리고 등급판정 작업은 임의로 추출한 샘플을 대상으로 전문 감정가에 의해 수작업으로 수행되고 있다. 건표고의 품질을 결정짓는 외관의 품질인자들은 갓과 내피에 고루 분포하고 있다. 본 논문에서는 컴퓨터 영상처리 시스템에 의거하여 개발한 건표고 자동 등급판정 및 선별 시작시스템의 구조와 기능 그리고 성능에 대하여 설명하였다. 개발한 시작시스템은 표고의 이송과 취급자동화를 위한 진동이송기, 반전장치, 컨베이어 이송장치와 두 세트의 컴퓨터 영상처리 시스템, 그리고 시스템 통괄제어를 위한 IBM PC AT호환 컴퓨터, 디지털 입출력 보드, 전공압실린더 구동제어를 위한 PLC등으로 구성하였다. 등급판정의 효율성 및 실시간 작업시스템을 고려하여 건표고의 등급판정은 두 세트의 컴퓨터 영상처리 시스템을 이용하여 이송되는 건표고의 갓 또는 내피 중 어디가 위를 향하는 지에 따라 두 단계에 걸쳐 독립적으로 판정을 수행하도록 하였다. 첫 번째 영상처리부에서는 갓표면 영상으로부터 4등급의 고품질 표고를 분류하며 두 번째 영상처리부에서는 내피표면 영상으로부터 중간 및 저품질 표고를 8개의 등급으로 분류한다. 실시간 영상정보처리를 목적으로 기존에 개발한 신경회로망을 이용한 등급판정 알고리즘을 시작시스템에 적용하였다. 개발한 시작기는 88% 이상의 등급판정 정확도를 보여 주었으며, 전공압시스템의 구동제약으로 인하여 표고 1개당 약0.7초의 선별시간이 소요되었다. 일조 선별라인의 경우 본 연구에서 제안한 시작기의 선별능력은 표고가 일차 처리부로 갓이 위로 올라와 있는 상태로 계속 공급된다면 시간당 대략 5,000여 개의 표고를 처리할 수 있을 것으로 기대된다.보강하여 가능하면 B-Pillar의 Middle이 Bending type collapse을 방지하여 Pelvis와 Door가 먼저 접촉하는 방법 등이 적용가능하다. 제작하기 이전에 설계된 부품에 대한 스프링 상수 및 내구특성을 체계적으로 규명하여 제품 시험의 횟수를 줄이고, 보다 정밀한 제품을 제작할 수 있도록 하기 위한 것이다.세포수는 초기 배반포기배에서 팽윤 배반포기배로 진행됨에 따라 두배에서 세배 정도 증가되었음을 알 수 있었다. 또한, differential labelling과 bisbenzimide기법에서 얻어진 각각의 총세포수를 비교하였을 때 총세포수는 발달의 진행 정도에 따라 증가되며 그와 동시에 동일한 군 간의 세포수도 거의 유사함을 알 수 있었다. 따라서, ICM과 TE를 differential labelling하는 기법은 수정란의 quality를 평가하는데 매우 유용한 기법으로서 착상전 embryo 발달을 연구하는데 효과적으로 이용될 수 있다는 것을 시사한다. 고도의 유의차를 나타낸 반면 비수구, 초생수로구 및 Bromegrass 목초구 간에는 아무런 유의차가 인정되지 않았다. 7. 농지보전 처리구인 배수구와 초생수로구는 비처리구에 비해 낮은 침두 유출량과 낮은 토양유실량을 나타내었다.구보다 14% 절감되는 것으로 나타났다.작용하는 것으로 사료된다.된다.정량 분석한 결과이다. 시편의 조성은 33.6 at% U, 66.4 at% O의 결과를 얻었다. 산화물 핵연료의 표면 관찰 및 정량 분석 시험시 시편 표면을 전도성 물질로 증착시키지 않고, Silver Paint 에 시편을 접착하는 방법으로도 만족한 시험 결과를 얻을 수 있었다.째, 회복기 중에 일어나는 입자들의 유입은 자기폭풍의 지속시간을 연장시키는 경향을 보이며 큰