• Communications for Statistical Applications and Methods
• /
• 제23권5호
• /
• pp.385-397
• /
• 2016

The stress-strength models have been intensively investigated in the literature in regards of estimating the reliability ${\theta}$ = P(X > Y) using parametric and nonparametric approaches under different sampling schemes when X and Y are independent random variables. In this paper, we consider the problem of estimating ${\theta}$ when (X, Y) are dependent random variables with a bivariate underlying distribution. The empirical and kernel estimates of ${\theta}$ = P(X > Y), based on bivariate ranked set sampling (BVRSS) are considered, when (X, Y) are paired dependent continuous random variables. The estimators obtained are compared to their counterpart, bivariate simple random sampling (BVSRS), via the bias and mean square error (MSE). We demonstrate that the suggested estimators based on BVRSS are more efficient than those based on BVSRS. A simulation study is conducted to gain insight into the performance of the proposed estimators. A real data example is provided to illustrate the process.

• 자원ㆍ환경경제연구
• /
• 제10권2호
• /
• pp.217-234
• /
• 2001

The use of district heating is expanding very rapidly in Korea. High population densities and the relatively cold winters make district heating an economically attractive option. About 8 percent of Korean houses are already using district heating and the government is seeking to aggressively expand this number. It has set a target of 15 percent of the residential heat market to be met by district heating in the year 2001. The main purpose of this paper is to analyze the consumption behavior of households using district heating. By pooling time-series and cross-sectional data for 12 apartment complexes in Seoul area, a single demand function is estimated and used to forecast the amounts of heat demanded by the individual households. The results shows that the level of consumption varies among households, depending on the non-economic factors such as the installation of individual metering equipment and the volume of apartment building. When individual metering equipment is installed, the level of annual heat consumption per household declines, on average, about 22.1 Mcal per square meters, which is equivalent to 834 won per square meter in terms of heating expenditures. In case that the apartment building was built in more than 6 stories, annual consumption level reduces additionally about 17.3 Mcal per square meters and, thus, save the expenditures by 649 Won per square meters, compared to the opposite case.

• 대한토목학회논문집
• /
• 제31권5D호
• /
• pp.623-631
• /
• 2011

본 연구에서는 단속류 도로에서의 유고상황을 감지할 수 있는 새로운 유고감지기법을 제시하였다. 유고감지를 위하여 적용된 방법은 교통특성들을 이용한 라틴방격(Latin Square)분석법이다. 라틴방격분석법을 이용하여 기존 연구사례에서 시도했던 방법과는 다르게 차로별, 시간대별 교통특성의 변화를 분석하여 유고상황을 감지하였다. 사용된 교통특성자료는 맑은 기상상태에서 정상운영시 또는 유고발생시 관측된 교통량, 속도, 점유율 자료이며, 유고시 자료는 2차로에서 10분간 유고가 발생했었던 자료이다. 정상운영시 및 유고발생시에 대한 교통상황을 감지하기 위하여 교통량, 속도 및 점유율 등을 기준으로 각각 라틴방격분석을 시행했다. 분석결과, 라틴방격분석을 이용하여 교통상황을 감지하는 경우, 교통량을 기준으로 교통상황을 감지하는 것이 다른 교통특성을 기준으로 교통상황을 감지하는 것보다 감지능력이 우수한 것으로 나타났다.

• 한국정보시스템학회:학술대회논문집
• /
• 한국정보시스템학회 2000년도 추계학술대회
• /
• pp.36-44
• /
• 2000

The purpose of this study is to introduce a more efficient forecasting technique, which could help result the reduction of cost in removing the waste of airline in-flight meals. We will use a neural network approach known to many researchers as the “Outstanding Forecasting Technique”. We employed a multi-layer perceptron neural network using a backpropagation algorithm. We also suggested using other related information to improve the forecasting performances of neural networks. We divided the data into three sets, which are training data set, cross validation data set, and test data set. Time lag variables are still employed in our model according to the general view of time series forecasting. We measured the accuracy of our model by “Mean Square Error”(MSE). The suggested model proved most excellent in serving economy class in-flight meals. Forecasting the exact amount of meals needed for each airline could reduce the waste of meals and therefore, lead to the reduction of cost. Better yet, it could enhance the cost competition of each airline, keep the schedules on time, and lead to better service.

• Journal of Biosystems Engineering
• /
• 제22권2호
• /
• pp.256-265
• /
• 1997

The objectives of this study were to examine the ability to predict soluble solid and firmness in intact apples based on the visible/near-infrared spectroscopic technique. Two cultivars of apples, Delicious and Gala, were handled, tested and analyzed separately. Reflectance spectra, Magness-Tayor (MT) firmness, and soluble solids in apples were measured sequentially. Maximum and minimum diameters, height, and weight of apples were recorded before the MT firmness tests. A spectrophotometer was used to collect reflectance spectra of intact apples over a wavelength range of 400 to 2, 498 nm. The W firmness tests were conducted using a standard 11.1mm (7/16 in.) MT probe mounted in an Instron universal testing machine. A digital refractormeter was used to measure soluble solid contents in the apples. Apple samples were divided into a calibration set and a prediction set. The calibration set was used during model development, and the prediction set was used to predict soluble solids and firmness from unknown spectra. The method of partial least square (PLS) analysis was used. An unique set of PLS loading vectors (factors) was developed for soluble solid content and firmness. The PLS model showed good correlations between predicted and measured soluble solids of intact apples in 860～1078 nm of the wavelengths. However, the PLS analysis was not good enough to predict the apple firmness.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제5권4호
• /
• pp.327-332
• /
• 2005

We investigatea new fuzzy-neural networks-Hybrid Fuzzy set based polynomial Neural Networks (HFSPNN). These networks consist of genetically optimized multi-layer with two kinds of heterogeneous neurons thatare fuzzy set based polynomial neurons (FSPNs) and polynomial neurons (PNs). We have developed a comprehensive design methodology to determine the optimal structure of networks dynamically. The augmented genetically optimized HFSPNN (namely gHFSPNN) results in a structurally optimized structure and comes with a higher level of flexibility in comparison to the one we encounter in the conventional HFPNN. The GA-based design procedure being applied at each layer of gHFSPNN leads to the selection leads to the selection of preferred nodes (FSPNs or PNs) available within the HFSPNN. In the sequel, the structural optimization is realized via GAs, whereas the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gHFSPNN is quantified through experimentation where we use a number of modeling benchmarks synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.

• 전자공학회논문지B
• /
• 제33B권7호
• /
• pp.99-106
• /
• 1996

Estimation and removal procedures for baseline drift have been developed using linear, cubic spline, and bilineared transformed high pass filter. Linear and cubic spline interpolation with the PQ and TP segmens, which are considered to be isoelectric, as fiducial points ahve been estimated respectively. For a quantitative validation of the estimation procedure, 4 ECGs with arfificial baseline drift were constructed and analyzed by mean square error calculations and amplitude histograms. Also real ECGs were analyzed in a test set of the CSE data set 3 and set 4. Baseline drift detecton rule were designed and new method for the decision of fiducial point were constructed to avoid distorting as the case of premature ventricular or atrial contraction. From these comparison, proposed cubic spline method with PQ and TP segment (CS_PQ & TP) emerged as the most efficient method.

• 정보보호학회논문지
• /
• 제29권4호
• /
• pp.795-805
• /
• 2019

최근 알려지지 않은 공격에 대처하기 위한 네트워크 비정상(anomaly) 탐지 기술에 대한 관심이 한층 높아지고 있다. 이러한 기술 개발을 위해 데이터 마이닝(data mining), 기계학습(machine learning), 그리고 딥러닝(deep learning)등을 활용한 다양한 연구가 진행되고 있다. 본 논문에서는 분류(classification) 문제를 다루는 데이터 마이닝 기술 중 가장 전통적인 방법 중 하나인 의사결정나무(decision tree)를 이용하여 NSL-KDD 데이터 셋을 대상으로 네트워크 비정상 탐지 가능성을 보여준다. 의사결정나무의 과대적합(over-fitting) 단점을 해소하기 위해 카이-제곱(chi-square) 테스트를 통해 최적의 속성 선택(feature selection)을 수행하고, 선택된 13개의 속성을 사용한 의사결정나무 모델 환경에서 NSL-KDD 시험 데이터 셋 KDDTest+에 대해 84% 그리고 KDDTest-21에 대해 70%의 네트워크 비정상 검출 정확도를 보였다. 제시된 정확도는 기존 의사결정나무 모델 적용 시 이들 시험 데이터 셋을 대상으로 알려진 정확도 81% 그리고 64% 수준과 비교해 약 3% 그리고 6% 각각 향상된 결과다.

• 한국농공학회지
• /
• 제11권3호
• /
• pp.1707-1718
• /
• 1969

The purpose of this experiment is to determine the relations between numbers, arrangements and pumping rates of wells. In this experiment, well pipes were vertically set up in an artificial water-bearing sand layer of homogeneous quality. Wells were arranged in different ways and their number was varied in order to observe the variation of pumping rates. Sands were filled in a square tank, $183cm{\times}91.5cm{\times}91.5cm$ so as to secure a water-bearing layer. Water was constantly supplied from a supply tank located at an end of the tank. The number of well pipes was varied from one to four. Well pipes were connected by a horizontal header pipes were connected by a horizontal header pipe located above them and one pump was used. Pumping rates were measured, when they were arranged in longitudinal and lateral directions, They were also arranged in a square and triangle. The main results thus obtained are presented as follows: (1) When well pipes are laid out in a longitudinal line, i. e., in a flow direction, the ratios of pumping rates of one-pipe well and wells 2-, 3- and 4- pipe t, are 1.903, 2.506 and 2.66, respectively. (2) When well pipes are laid out in a lateral line, i.e., in a perpendicular direction to flow, the same ratios as (1) are 1.912, 2.527 and 2.88. (3) When four pipes are laid out in a square and three pipes are laid out in a triangle, pumping rates are deereased, Comparing with the cases when pipes are laid out in a straight line.

• 전기학회논문지
• /
• 제65권4호
• /
• pp.622-627
• /
• 2016

This paper describes the development of a portable potentiostat which can perform square wave voltammetry on electrochemical sensors and wireless transmission of the measured data to a smartphone using Bluetooth. The potentiostat consists of a square wave potential pulse generation circuit for applying the potential pulse to the electrochemical sensor, a reduction/oxidation (or redox) current measurement circuit, and Bluetooth for wireless data transmission to an Android-based smartphone. The measured data are then processed to show the output graph on the smart phone screen in real time. This data transformation into a graph is carried out by developing and installing a simple transformation application software in the Android-based smartphone. This application software also enables the user to set and change the measurement parameters such as the applied voltage range and measured current range at user's convenience. The square voltammetry output data measured with the developed portable potentiostat were almost same as the data of the commercial potentiostat. The measured oxidation peak current with the commercial potentiostat was $11.35{\mu}A$ at 0.26 V and the measured oxidation peak current with the developed system was $12.38{\mu}A$ at 0.25 V. This proves that performance of the developed portable measurement system is comparable to the commercial one.