• 품질경영학회지
• /
• 제27권1호
• /
• pp.18-34
• /
• 1999

This paper is a brief review of the different procedures that are available for fitting theoretical distributions to data. The use of each technique Is illustrated by reference to a distribution system which including the Pearson, Johnson and Burr functions. These functions can be used to calculate percent out of specification. Therefore, in this paper a new method for estimating a measure of process capability for Gamma distributed variable data proposed using the percentage nonconforming. Process capability indices combines with the percentage nonconforming Information can be used to evaluate more accurately process capability.

• 유통과학연구
• /
• 제13권2호
• /
• pp.15-24
• /
• 2015

Purpose - Foreign exchange risk control is in an important component in the international supply chain management. This study shows the importance of the reference period in forecasting future exchange rates with a specific illustration of KIKO currency option contracts, and suggests feasible preventive measures. Research design, data, and methodology - Using monthly Won-Dollar exchange rate data for January 1995~July 2007, I evaluate the statistical characteristics of the exchange rate for two sub-periods; 1) a shorter period after the East Asian financial crisis and 2) a longer period including the financial crisis. The key instrument of analysis is the basic normal distribution theory. Results - The difference in the reference period could lead to an unexpected development in contract implementation and a consequent financial loss. We may avoid foreign exchange loss by using derivatives such as forwards or currency options. Conclusions - We should consider not only level values but also the volatilities of financial variables in making a binding financial contract. Appropriate measures may differ depending on the specific supply chain pattern. We may extend the study with surveys on actual risk measures.

• Journal of the Korean Statistical Society
• /
• 제36권4호
• /
• pp.489-500
• /
• 2007

In this paper, we consider Bayesian approach to the Fieller-Creasy problem using noninformative priors. Specifically we extend the results of Yin and Ghosh (2000) to the unbalanced case. We develop some noninformative priors such as the first and second order matching priors and reference priors. Also we prove the posterior propriety under the derived noninformative priors. We compare these priors in light of how accurately the coverage probabilities of Bayesian credible intervals match the corresponding frequentist coverage probabilities.

• 한국물환경학회지
• /
• 제24권6호
• /
• pp.676-681
• /
• 2008

The evaluation of the effluent water quality of sewage treatment plant is one of the most important factor in calculating total maximum daily loads (TMDLs). Current method to calculate 95% upper limit of effluent water quality of sewage treatment plant assuming normal distribution of data needs to be implemented in case of non-normal distribution. We have investigated the applicability of percentile ranking method as a non-parametric statistical analysis in case of non-normal distribution of data.

• 한국정보처리학회논문지
• /
• 제7권7호
• /
• pp.2050-2059
• /
• 2000

In a video-on-Demand(VOD) service, a server has to return to he normal playback quickly at a certain new frame position after interactive operations such as jump or last playback. In this paper, we propose an efficient scheme to write a transmission schedule for a playback restart of a video stream at a new frame position after interactive operations. The proposed scheme is based on convergence characteristics, that is transmission schedules with different playback startup frame position in a video stream meet each other at some frame position. The scheme applies a bandwidth smoothing from a new frame position to a convergence position without considering all remaining frames of a video stream. And then the scheme transmits video dta according to the new schedule from the new frame position to the convergence position, and then transmits the remaining video data according to the reference schedule from the convergence position, and then transmits the remaining video data according to the reference schedule from the convergence position to the last frame position. In this paper, we showed that there existed the convergence position corresponding to nay frame position in a video stream through many experiments based on MPEG-1 bit trace data. With the convergence we reduced the computational overhead of a bandwidth smoothing, which was applied to find a new transmission schedule after interactive operations. Also, storage overhead is greatly reduced by storing pre-calculated schedule information up to the convergence position for each I frame position of a video stream with video data off-line. By saving information on a transmission schedule off-line along with the video data and searching the schedule corresponding to the specified restarting frame position, we expect the possibility of normal playback of a video stream with small tolerable playback startup delay.

• Journal of Information Processing Systems
• /
• 제15권3호
• /
• pp.682-693
• /
• 2019

The traditional classification methods mostly assume that the data for class distribution is balanced, while imbalanced data is widely found in the real world. So it is important to solve the problem of classification with imbalanced data. In Mahalanobis-Taguchi system (MTS) algorithm, data classification model is constructed with the reference space and measurement reference scale which is come from a single normal group, and thus it is suitable to handle the imbalanced data problem. In this paper, an improved method of MTS-CBPSO is constructed by introducing the chaotic mapping and binary particle swarm optimization algorithm instead of orthogonal array and signal-to-noise ratio (SNR) to select the valid variables, in which G-means, F-measure, dimensionality reduction are regarded as the classification optimization target. This proposed method is also applied to the financial distress prediction of Chinese listed companies. Compared with the traditional MTS and the common classification methods such as SVM, C4.5, k-NN, it is showed that the MTS-CBPSO method has better result of prediction accuracy and dimensionality reduction.

• 대한의용생체공학회:의공학회지
• /
• 제32권2호
• /
• pp.134-143
• /
• 2011

The selection of meaningful clinical tests and its reference values from a high-dimensional clinical data with imbalanced class distribution, one class is represented by a large number of examples while the other is represented by only a few, is an important issue for differential diagnosis between similar diseases, but difficult. For this purpose, this study introduces methods based on the concepts of both discernibility matrix and function in rough set theory (RST) with two discretization approaches, equal width and frequency discretization. Here these discretization approaches are used to define the reference values for clinical tests, and the discernibility matrix and function are used to extract a subset of significant clinical tests from the translated nominal attribute values. To show its applicability in the differential diagnosis problem, we have applied it to extract the significant clinical tests and its reference values between normal (N = 351) and abnormal group (N = 101) with either cholecystitis or cholelithiasis disease. In addition, we investigated not only the selected significant clinical tests and the variations of its reference values, but also the average predictive accuracies on four evaluation criteria, i.e., accuracy, sensitivity, specificity, and geometric mean, during l0-fold cross validation. From the experimental results, we confirmed that two discretization approaches based rough set approximation methods with relative frequency give better results than those with absolute frequency, in the evaluation criteria (i.e., average geometric mean). Thus it shows that the prediction model using relative frequency can be used effectively in classification and prediction problems of the clinical data with imbalanced class distribution.

• 한국토양비료학회지
• /
• 제35권3호
• /
• pp.185-195
• /
• 2002

급변하는 농업환경을 신속히 파악하고 이에 대처한다는 것은 중요한 일이다. 특히 농경지의 이용형태가 다양화(多樣化)되고 고도화(高度化)됨에 따라 그 필요성이 한층 인정된다. 이에 부응하는 인공위성자료인 Landsat TM을 이용한 우리 나라에 가장 알맞은 지표면 변동탐지 방법에 대한 연구 결과를 요약하면 다음과 같다. 우리 나라에 알맞은 지표면의 변화탐지기법을 찾기 위해 화상간 차이법을 적용하였으며, 동시에 변화탐지도에 대한 평가를 위해 참조자료를 작성한 후 최적 임계값을 구하였다. 여기서 정상적인 참조자료(농경지가 인공물로 변한 경우는 양의 변화, 그 반대면 음의 변화)나 비정상적인 참조자료(인공물이 농경지로 변한 경우는 양의 변화, 그 반대면 음의 변화)로 평가하였다. 또한 최적 임계값은 '평균${\pm}$(표준편차 ${\times}$ T값)'로 하여 구하였다. 1987년부터 1993년까지 6년 동안 가장 결과가 좋은 화상간 차이법은 D1 화상(1.0)으로 정상적인 참조자료로 평가시 카파계수가 68.4%, 전체 정확도는 89.2%로 나타났다. 또한 전체면적 48,436 ha중 음으로 변화된 영역은 3,207 ha(6.6%), 양으로 변화된 영역은 5,117 ha(10.6%)로 밝혀졌다.

• 한국품질경영학회:학술대회논문집
• /
• 한국품질경영학회 1998년도 The 12th Asia Quality Management Symposium* Total Quality Management for Restoring Competitiveness
• /
• pp.133-142
• /
• 1998

This paper is a brief review of the different procedures that are available for fitting theoretical distributions to data. The use of each technique is illustrated by reference to a distribution system which including the Pearson, Poission approximation of Gamma distribution and Burr functions. These functions can be used to calculate percent out of specification. Therefore, in this paper a new methods for estimating a measure of non-normal process capability for Gamma distributed variable data proposed using the percentage nonconforming. Process capability indices combines with the percentage nonconforming information can be used to evaluate more accurately process capability.

• 한국농공학회논문집
• /
• 제61권1호
• /
• pp.107-120
• /
• 2019

Reservoir water level data identify the current water storage of the reservoir, and they are utilized as primary data for management and research of agricultural water. For the reservoir storage management, Korea Rural Community Corporation (KRC) installed water level stations at around 1,600 agricultural reservoirs and has been collecting the water level data every 10 minutes. However, various kinds of outliers due to noise and erroneous problems are frequently appearing because of environmental and physical causes. Therefore, it is necessary to detect outlier and improve the quality of reservoir water level data to utilize the water level data in purpose. This study was conducted to detect and classify outlier and normal data using two different models including the threshold model and the artificial neural network (ANN) model. The results were compared to evaluate the performance of the models. The threshold model identifies the outlier by setting the upper/lower bound of water level data and variation data and by setting bandwidth of water level data as a threshold of regarding erroneous water level. The ANN model was trained with prepared training dataset as normal data (T) and outlier (F), and the ANN model operated for identifying the outlier. The models are evaluated with reference data which were collected reservoir water level data in daily by KRC. The outlier detection performance of the threshold model was better than the ANN model, but ANN model showed better detection performance for not classifying normal data as outlier.