• 대한한방내과학회지
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• 제39권4호
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• pp.699-707
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• 2018

Objectives: This study was designed to investigate characteristics of digital infrared thermal imaging (DITI) and quantitative evaluations in patients with burning mouth syndrome (BMS). Methods: We reviewed the clinical records of 38 patients with BMS who visited the Oral Diseases Clinic of Kyung Hee University Korean Medicine Hospital from March 1st, 2018 to June 30th, 2018. The subjects were evaluated with digital infrared thermal imaging (DITI) and for heart rate variability (HRV), unstimulated salivary flow rate (USFR), and the proportion of coated tongue. Results: Most patients showed higher temperatures on the central part of the tongue (T2) than on the middle of the forehead (T1). The patients tended to have a high Low frequency/High frequency (LF/HF) ratio. Statistically significant negative correlations were noted between the age of patients and the temperature of T1 and T2. Statistically significant negative correlations were also observed between the LF/HF ratio and 'T1-T2' values. Conclusions: This study suggests that DITI and HRV are useful for the validation of patients with BMS. Correlations between the result values suggest that sympathetic function acceleration is related to temperature distribution and, ultimately, to symptoms.

• 응용통계연구
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• 제19권2호
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• pp.319-330
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• 2006

현실세계에서 관찰되는 시그널(signal)은 다양한 주파수(frequency)들의 시그널로 혼합되어 있는 경우가 많다. 예를 들어 태양 흑점 자료의 경우 약 11년 주기와 85년 주기로 변동한다는 사실은 널리 알려져 있다. 또한 경제 시계열 자료의 경우는 통상적으로 계절요인(seasonal component), 순환요인(cyclic component) 그리고 장기적인 추세요인(long-term trend)으로 분해하여 분석한다. 이러한 시계열 자료를 구성요소별로 분해하는 것은 오래된 주제중 하나이다. 전통적인 시계열자료 분석기법으로 스펙트럴 분석기법 등이 널리 사용되고 있으나 시계열 자료들이 비정상(nonstationary)일 경우에는 적용하기 어렵다. Huang et. al(1998)은 경험적 모드분해법(empirical mode decomposition)이라고 하는 자료적응적인(data-adaptive) 방법을 제안하였는데, 비정상성(nonstationarity)에 대한 강건성(robustness)으로 여러 분야에 널리 응용되고 있다. 그러나 Huang et. at(1998)은 잡음(error)에 의해 오염된 자료에 대한 구체적인 처리방법은 제시하지 못하고 있다. 본 논문을 통하여 효율적인 잡음제거 방법을 제안하고자 한다.

• 한국수자원학회논문집
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• 제36권6호
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• pp.1025-1033
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• 2003

수공구조물의 설계를 비롯하여, 수자원 분야의 기술적 설계의 기초는 수문자료의 처리와 분석에 중심을 두고 있다고 할 수 있다. 수문 자료의 분석방법 중 가장 보편적이면서도 중요한 방법은 자료들의 관계를 도식적으로 규명하는 회귀분석이다. 수위-유량 관계곡선과 같은 수문 자료에 대한 기존의 매개변수적 회귀모형이 갖는 단점은 자료의 특성에 따라, 복수의 회귀식이 산정되거나 동일자료에 대해서도 서로 다른 회귀식이 산정됨으로써 신뢰할 수 있는 회귀곡선을 만들기가 어렵다는 것이다. 이에 비해 주어진 자료에 의해 도출되는 kernel 회귀모형은 자료의 특성과 경향성을 적절히 표현해 줄 수 있는 방법이다. 본 논문에서는 비매개변수적 방법인 kernel 회귀모형을 분석하고, kernel 회귀모형의 중요 인자인 bandwidth의 선택 방법에 따른 kernel 회귀모형의 특성에 대해 비교 분석하였다.

• Environmental Engineering Research
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• 제21권4호
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• pp.333-340
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• 2016

An Artificial Neural Network including a Radial Basis Function (RBF) and a Time Delay Neural Network (TDNN) was used to predict total dissolved solid (TDS) in the river Zayanderud. Water quality parameters in the river for ten years, 2001-2010, were prepared from data monitored by the Isfahan Regional Water Authority. A factor analysis was applied to select the inputs of water quality parameters, which obtained total hardness, bicarbonate, chloride and calcium. Input data to the neural networks were pH, $Na^+$, $Mg^{2+}$, Carbonate ($CO{_3}^{-2}$), $HCO{_3}^{-1}$, $Cl^-$, $Ca^{2+}$ and Total hardness. For learning process 5-fold cross validation were applied. In the best situation, the TDNN contained 2 hidden layers of 15 neurons in each of the layers and the RBF had one hidden layer with 100 neurons. The Mean Squared Error and the Mean Bias Error for the TDNN during the training process were 0.0006 and 0.0603 and for the RBF neural network the mentioned errors were 0.0001 and 0.0006, respectively. In the RBF, the coefficient of determination ($R^2$) and the index of agreement (IA) between the observed data and predicted data were 0.997 and 0.999, respectively. In the TDNN, the $R^2$ and the IA between the actual and predicted data were 0.957 and 0.985, respectively. The results of sensitivity illustrated that $Ca^{2+}$ and $SO{_4}^{2-}$ parameters had the highest effect on the TDS prediction.

• 대한의용생체공학회:의공학회지
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• 제36권4호
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• pp.103-108
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• 2015

Discrimination of Parkinson's disease (PD) from Essential tremor (ET) is often misdiagnosed in clinical practice. Since tremor is time-varying signal, and dominant and harmonic frequencies are shown in tremor only with moderate or severe symptom, there are some limitations to use frequency related features. Moreover, patients with PD or ET can suffer from both resting tremor and postural tremor. In this study, 28 patients with PD and 17 patients with ET were enrolled. Tremor was measured with accelerations on the more affected hand during resting and postural conditions. The ratio of root mean square (RMS) of resting tremor to RMS of postural tremor, the mean coefficients of autocorrelation function (ACF), and the mean of differences of two adjacent coefficients of ACF at resting and postural were calculated and compared between PD and ET. The performance showed 98% accuracy with support vector machine and leave-one-out cross validation. In addition, the method accurately differentiated the patients with tremor-dominant PD from patients with ET, with 100% accuracy. Therefore, the developed algorithm can assist clinicians in diagnosing and categorizing patients with tremor, especially, patients with mild symptom or the early stage of a disease, for proper treatment.

• Journal of the Korean Data and Information Science Society
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• 제28권2호
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• pp.349-359
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• 2017

본 논문에서는 미국 국립암연구소의 SEER 프로그램에서 제공하는 우측 대장암 3기 자료에 Cox 비례위험모형을 적합하여 생존분석을 하였다. 우측 대장암 3기 환자의 사망률에 유의한 영향을 미치는 공변량들을 파악하고, 관심있는 공변량들을 가진 환자의 생존율을 추정하였다. Schoenfeld 잔차를 기반한 검정과 Schoenfeld 잔차 도표, $log[-log\{{\hat{S}}(t)\}]$ 도표를 이용하여 분석에 사용된 공변량들이 비례위험 가정을 만족함을 확인하였다. 적합된 Cox 비례위험모형의 타당성을 검증하기 위해 10-fold 교차 검증을 이용하여 calibration 도표와 시간에 의존하는 ROC 곡선 아래 면적을 계산하였다. 이를 통해 적합된 Cox 비례위험모형의 타당성을 확인하였다.

• Advances in environmental research
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• 제5권3호
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• pp.153-167
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• 2016

We applied multilayer perceptron (MLP) and radial basis function (RBF) neural network in upstream and downstream water quality stations of the Karaj Reservoir in Iran. For both neural networks, inputs were pH, turbidity, temperature, chlorophyll-a, biochemical oxygen demand (BOD) and nitrate, and the output was dissolved oxygen (DO). We used an MLP neural network with two hidden layers, for upstream station 15 and 33 neurons in the first and second layers respectively, and for the downstream station, 16 and 21 neurons in the first and second hidden layer were used which had minimum amount of errors. For learning process 6-fold cross validation were applied to avoid over fitting. The best results acquired from RBF model, in which the mean bias error (MBE) and root mean squared error (RMSE) were 0.063 and 0.10 for the upstream station. The MBE and RSME were 0.0126 and 0.099 for the downstream station. The coefficient of determination ($R^2$) between the observed data and the predicted data for upstream and downstream stations in the MLP was 0.801 and 0.904, respectively, and in the RBF network were 0.962 and 0.97, respectively. The MLP neural network had acceptable results; however, the results of RBF network were more accurate. A sensitivity analysis for the MLP neural network indicated that temperature was the first parameter, pH the second and nitrate was the last factor affecting the prediction of DO concentrations. The results proved the workability and accuracy of the RBF model in the prediction of the DO.

• Journal of Biosystems Engineering
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• 제27권2호
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• pp.133-142
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• 2002

This is a fundamental study to develop a sensor to detect weeds in paddy field using machine vision adopted spectralphotometric technique in order to use the sensor to spread herbicide selectively. A set of spectral reflectance data was collected from dry and wet soil and leaves of rice and 6 kinds of weed to select desirable wavelengths to classify soil, rice and weeds. Stepwise variable selection method of discriminant analysis was applied to the data set and wavelengths of 680 and 802 m were selected to distinguish plants (including rice and weeds) from dry and wet soil, respectively. And wavelengths of 580 and 680 nm were selected to classify rice and weeds by the same method. Validity of the wavelengths to distinguish the plants from soil was tested by cross-validation test with built discriminant function to prove that all of soil and plants were classified correctly without any failure. Validity of the wavelengths for classification of rice and weeds was tested by the same method and the test resulted that 98% of rice and 83% of weeds were classified correctly. Feasibility of CCD color camera to detect weeds in paddy field was tested with the spectral reflectance data by the same statistical method as above. Central wavelengths of RGB frame of color camera were tried as tile effective wavelengths to distingush plants from soil and weeds from plants. The trial resulted that 100% and 94% of plants in dry soil and wet soil, respectively, were classified correctly by the central wavelength or R frame only, and 95% of rice and 85% of weeds were classified correctly by the central wavelengths of RGB frames. As a result, it was concluded that CCD color camera has good potential to be used to detect weeds in paddy field.

• 천문학회보
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• 제46권2호
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• pp.75.2-76
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• 2021

MESSIER is a science satellite project to observe the Low Surface Brightness (LSB) sky at UV and optical wavelengths. The wide-field, optical system of MESSIER is optimized minimizing optical aberrations through the use of a Linear Astigmatism Free - Three Mirror System (LAF-TMS) combined with freeform mirrors. One of the key factors in observations of the LSB is the shape and spatial variability of the Point Spread Function (PSF) produced by scatterings and diffraction effects within the optical system and beyond (baffle). To assess the various factors affecting the PSF in this design, we use PhoSim, the Photon simulator, which is a fast photon Monte Carlo code designed to include all these effects, and also atmospheric effects (for ground-based telescopes) and phenomena occurring inside of the sensor. PhoSim provides very realistic simulations results and is suitable for simulations of very weak signals. Before the application to the MESSIER optics system, PhoSim had not been validated for confocal off-axis reflective optics (LAF-TMS). As a verification study for the LAF-TMS design, we apply Phosim sequentially. First, we use a single parabolic mirror system and compare the PSF results of the central field with the results from Zemax, CODE V, and the theoretical Airy pattern. We then test a confocal off-axis Cassegrain system and check PhoSim through cross-validation with CODE V. At the same time, we describe the shapes of the freeform mirrors with XY and Zernike polynomials. Finally, we will analyze the LAF-TMS design for the MESSIER optical system.

• 기술혁신학회지
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• 제10권2호
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• pp.183-205
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• 2007

본 연구는 기술력평가에 근거해서 중소기업 부실예측 가능성을 사전에 예측할 수 있는 최적 판별 모형을 개발 제안하였다. 판별모형에 포함될 설명변수는 요인분석과 판별모형의 단계별 선택방법에 의하여 선정되었다. 분석결과 선형판별모형이 로지스틱판별모형보다 임계확률 관점에서 적절한 것으로 나타났다. 최적 선형판별모형의 분류 정분류율은 70.4%, 분류 예측력은 67.5%로 나타났다. 최적 선형판별모형의 활용도를 높이기 위해서 확실 범주와 유보범주를 구분할 수 있는 경계값을 설정하였다. 분석결과를 활용하면 기술금융 취급기관은 부실위험 평가와 더불어 기술금융 신청기업의 순위를 부여할 때 유용하게 사용할 수 있을 것으로 기대된다.