• 한국농공학회논문집
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• 제59권4호
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• pp.97-107
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• 2017

This study is to determine the coefficients of regression equations and to select the optimal regression equation in the LOADEST model after classifying the whole study period into 5 flow conditions for 16 watersheds located in the Nakdonggang waterbody. The optimized coefficients of regression equations were derived using the gradient descent method as a learning method in Tensorflow which is the engine of machine-learning method. In South Korea, the variability of streamflow is relatively high, and rainfall is concentrated in summer that can significantly affect the characteristic analysis of pollutant loads. Thus, unlike the previous application of the LOADEST model (adjusting whole study period), the study period was classified into 5 flow conditions to estimate the optimized coefficients and regression equations in the LOADEST model. As shown in the results, the equation #9 which has 7 coefficients related to flow and seasonal characteristics was selected for each flow condition in the study watersheds. When compared the simulated load (SS) to observed load, the simulation showed a similar pattern to the observation for the high flow condition due to the flow parameters related to precipitation directly. On the other hand, although the simulated load showed a similar pattern to observation in several watersheds, most of study watersheds showed large differences for the low flow conditions. This is because the pollutant load during low flow conditions might be significantly affected by baseflow or point-source pollutant load. Thus, based on the results of this study, it can be found that to estimate the continuous pollutant load properly the regression equations need to be determined with proper coefficients based on various flow conditions in watersheds. Furthermore, the machine-learning method can be useful to estimate the coefficients of regression equations in the LOADEST model.

• 제어로봇시스템학회논문지
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• 제16권12호
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• pp.1150-1158
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• 2010

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.

• Journal of Korean Neurosurgical Society
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• 제29권2호
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• pp.240-248
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• 2000

Objective : Spontaneous intracranial hypotension is a rarely reported syndrome of spontaneous postural headache associated with low CSF pressure and has rarely been demonstrated radiographically or surgically. But recently, it is being recognized with increasing frequency. The purpose of this study was to characterize clinical and imaging features, etiologic factors, and outcome in the spontaneous intracranial hypotension. Patients and Methods : We reviewed our experience with documented cases of spontaneous intracranial hypotension in 5 consecutive patients with orthostatic headaches from April 1998 to April 1999. Results : The mean age was 41 years(from 35 to 49 years). All patients had postural headaches, which were completely alleviated by recumbency position. Nausea, neck pain, horizontal diplopia, photophobia, and blurred vision were noted in some of the patients. Brain MRI showed diffuse pachymeningeal gadolinium enhancement, subdural collections of fluid, and descent of the brain. The opening pressure from lumbar puncture was $4cmH_2O$ or less in three of five patients whereas the opening pressure was within normal range in two patients. All patients underwent radioisotope cisternography and computerized tomographic myelography. On radioisotope cisternography, CSF leakage was suspected at the level of cervical area(1 patient), upper thoracic area(2 patients), mid-thoracic area(1 patient). Computed tomography myelography revealed extraarachnoid accumulation of contrast media(compatible finding with CSF leakage) at the level of cervical or thoracic area. In all patients, the symptoms resolved in response to supportive measures or epidural blood patch(1 patient). Conclusion : Spontaneous spinal CSF leakage is increasingly recognized as a cause of spinal postural headache. Most CSF leaks are located at the cervicothoracic junction or in the thoracic spine and can be demonstrated by variable diagnostic method. The condition is usually self-limiting and its prognosis is typically good.

• 한국터널지하공간학회 논문집
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• 제15권3호
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• pp.289-300
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• 2013

본 연구에서는 도로터널 화재해석에 적용하는 화재성장곡선을 적용하여 도로터널 화재시 열기류의 전파특성과 터널벽면에 미치는 열적특성을 수치 해석적으로 고찰하였다. 최대화재강도는 20, 100 MW로 하였으며, 터널내 풍속은 2.5 m/s로 유지하는 경우와 열부력에 의한 풍속으로 하는 경우에 대해서 터널연장 및 경사도에 따른 연기의 전파특성을 분석하고 열기류가 벽체에 미치는 영향을 평가하기 위해서 벽체표면온도와 벽체표면 열전달계수를 분석하였다. 터널내 열기류는 풍속을 2.5 m/s로 하는 경우에 벽체의 냉각효과에 의해서 일정거리 이후에는 급격하게 하강하여 하류까지 동일한 양상으로 흐르는 특성을 보이고 있으며, 벽체표면의 최대온도는 화재강도가 100 MW일 경우에 최대 $615^{\circ}C$까지 상승하고 있으며, 표면온도가 $380^{\circ}C$를 초과하는 면적은 아주 작은 것으로 나타나고 있다. 벽체의 표면열전달계수는 화재강도 및 터널내 풍속에 따라서 변하며 약 $13{\sim}23W/m^{\circ}C$의 범위에 있는 것으로 나타났다.

• Journal of Animal Science and Technology
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• 제54권2호
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• pp.71-75
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• 2012

본 연구는 한우 암소 초음파 측정자료 특징을 알아보고, 측정형질에 대한 유전모수를 추정하여 육질 또는 육량 개량을 위한 기초자료를 제공하기 위해 수행되었으며, 2001년부터 2009년까지 한국종축개량협회에 의해 측정된 한우 암소 36,893두를 이용하였고, 그 중 반복기록이 있는 개체는 7,913두였다. 유전모수 추정을 위하여 반복개체모형을 이용하였으며, 유전모수 추정에는 REMLF90 (Miztal, 2001)을 이용하였다. 유전모수 추정모형의 설정을 위하여 흉위, 영양도 및 초음파 측정치에 대한 출생년도, 출생계절, 측정년도, 측정계절, 측정지역, 측정연령 등의 환경효과를 추정하였다. 반복개체모형으로 추정된 배최장근단면적, 등지방두께 및 근내지방도에 대한 유전력이 각각 0.31, 0.38, 0.27로 나타났고, 다형질개체모형으로 추정한 유전력은 각각 0.02, 0.09, 0.07로 낮게 추정되었다. 반복개체모형을 이용한 반복력은 배최장근단면적, 등지방두께, 근내지방도가 각각 0.46, 0.57, 0.39로 나타났다. 분석모형 간의 추정치의 차이를 비교 할 때 반복모형에서의 유전력과 반복력이 높게 추정되었다. 따라서 반복형질 값을 가진 형질들의 측정치를 표준화하여 한우암소개량을 위한 기초자료로 활용된다면 유전능력평가와 개량사업 비용절감에 도움이 될 것으로 기대된다.