• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1151-1160
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• 2006

The similarity weight, the pearson's correlation coefficient, which is used in the recommender system has a weak point that it cannot predict all of the prediction value. The similarity weight, the vector similarity, has a weak point of the high MAE although the prediction coverage using the vector similarity is higher than that using the pearson's correlation coefficient. The purpose of this study is to suggest how to raise the prediction coverage. Also, the MAE using the suggested method in this study was compared both with the MAE using the pearson's correlation coefficient and with the MAE using the vector similarity, so was the prediction coverage. As a result, it was found that the low of the MAE in the case of using the suggested method was higher than that using the pearson's correlation coefficient. However, it was also shown that it was lower than that using the vector similarity. In terms of the prediction coverage, when the suggested method was compared with two similarity weights as I mentioned above, it was found that its prediction coverage was higher than that pearson's correlation coefficient as well as vector similarity.

• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.119-128
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• 2006

The similarity weight, the pearson's correlation coefficient, which is used in the recommender system has a weak point that it cannot predict all of the prediction value. The similarity weight, the vector similarity, has a weak point of the high MAE although the prediction coverage using the vector similarity is higher than that using the pearson's correlation coefficient. The purpose of this study is to suggest how to raise the prediction coverage. Also, the MAE using the suggested method in this study was compared both with the MAE using the pearson's correlation coefficient and with the MAE using the vector similarity, so was the prediction coverage. As a result, it was found that the low of the MAE in the case of using the suggested method was higher than that using the pearson's correlation coefficient. However, it was also shown that it was lower than that using the vector similarity In terms of the prediction coverage, when the suggested method was compared with two similarity weights as I mentioned above, it was found that its prediction coverage was higher than that pearson's correlation coefficient as well as vector similarity.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.22 no.52
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• pp.69-79
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• 1999

The main objective of this study are to propose two methods that would be a comprehensive measure of evaluation for non-normal process capability with Beta distributions. First method is introduced using process capability index $C_{psk}$ by the Pearson system and Johnson system. The Pearson system and the Johnson System selected for process capability index calculation have a equivalent result of this study that the ranking of the seven indices in terms of sensitivity to departure of the process median from the target value from the most sensitive one up to the least sensitive are $C^{*}_{pm}$ , $C_{psk}$ , $C_{s}$ , $C_{pmk}$ , $C_{pm}$ , $C_{pk}$ , $C_{p}$ . Second method show using the percentage nonconforming by the Pearson, Johnson and Burr functions. In thus study, we find that the Pearson system and the Burr system are a reasonable method to estimate percentage nonconforming. But, the exact procedure for deriving this estimate will be based on Beta distribution. Accordingly, if a process is not normally distributed , but normal-based techniques are used serious errors can result.

• Journal of the Korea Safety Management & Science
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• v.2 no.2
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• pp.41-55
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• 2000

The main objective of this paper is to propose a measure of evaluation for non-normal process capability. If a process is not normally distributed, but normal-based techniques are used, serious errors can result. Our approach to solve this problem is that the Pearson system, the Johnson system, and the Burr system are selected for estimating a measure of process capability using the percentage nonconforming. In this paper, we found that the Pearson system and the Johnson system were a conparatively reasonable methods to calculate out of specification by example.

• Journal of Korean Physical Therapy Science
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• v.4 no.4
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• pp.519-528
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• 1997

The purpose of this research were to evaluate the overall capacity of activity in hemiplegic patients caused by stroke, to learn the relationship of the overall capacity of activity with 8 out of 9 subtest of the Motor Assessment Scale (MAS) excluding general tonus subtest, and to use in creation of more efficient rehabilitation program by using Motor Assessment Scale (MAS). Twenty-four stroke patients (14 men and 10 women) were the subjects in this study. Their average age was 59.5 and they received average of 17.88 month of therapy. Collected data analysis was completed by using Statistic Analysis System (SAS). The results were as follows: 1) There was no difference in capacity of activity between right hemiplegia and left hemiplegia. 2) There was no difference in capacity of activity compared therapeutic period and age. 3) In comparing the relationship of the each subtest with the overall capacity of activity, upper arm function showed the highest relation (pearson's r = 0.914), and balance sitting (pearson's r= 0.812) and supine to sitting overside of bed (pearson'sr = 0.746) also showed large relationship. 4) Hand movement (pearson's r = -0.45) and advanced hand activity (pearson's r = -0.401) revealed relationship of general tonus with each subtest. 5) Supine to sitting over side of bed (pearson's r = 0.74), balanced sitting(pearson's r = 0.523), and sitting to standing (pearson's r = 0.723) showed large relationship with walking.

• Proceedings of the Korean Reliability Society Conference
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• 2000.11a
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• pp.375-384
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• 2000

The main objective of this paper to purpose a evaluating methods of process capability measures for exponential distributed quality characteristics. For correctly evaluating process capability , the first thing , exponential data is applied the Lilliefors test statistic to the null hypothesis of nornality. The next, exponential parameters is estimated in terms of MLE , ME , MME and then evaluated , respectively , process capability index based on exponential curved (Ιe) proposed by in this study and process capability indices based on Pearson system and Johnson system.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.2
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• pp.125-130
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• 2011

Since conventional optimization that is classified as a deterministic method does not consider the uncertainty involved in a modeling or manufacturing process, an optimum design is often determined to be on the boundaries of the feasible region of constraints. Reliability-based design optimization is a method for obtaining a solution by minimizing the objective function while satisfying the reliability constraints. This method includes an optimization process and a reliability analysis that facilitates the quantization of the uncertainties related to design variables. Moment-based reliability analysis is a method for calculating the reliability of a system on the basis of statistical moments. In general, on the basis of these statistical moments, the Pearson system estimates seven types of distributions and determines the reliability of the system. However, it is technically difficult to practically consider the Pearson Type IV distribution. In this study, we propose an enhanced Pearson Type IV distribution based on a kriging model and validate the accuracy of the enhanced Pearson Type IV distribution by comparing it with a Monte Carlo simulation. Finally, reliability-based design optimization is performed for a system with type IV distribution by using the proposed method.

• Journal of the Korea Society for Simulation
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• v.28 no.2
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• pp.89-95
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• 2019

This paper introduces the simulation concepts and technical approach of underwater weapon system performance analysis simulator, especially focused on probabilistic target detection concepts. We calculated the signal excess (SE) value using SONAR equation, then derived the probability density function(PDF) for target presence($H_1$) or absence($H_0$) cases, respectively. With the Neyman-Pearson detector criterion, we got the probability of detection($P_D$) while satisfying the given probability of false alarm($P_{FA}$). At every instance of simulation, target detection is decided in the probabilistic perspective. With the proposed detection implementation, we improved the model fidelity so that it could support the tactical decision during the operation.

• Convergence Security Journal
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• v.18 no.5_1
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• pp.59-66
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• 2018

The performance of a network intrusion detection system using the machine learning method depends heavily on the composition and the size of the feature set. The detection accuracy, such as the detection rate or the false positive rate, of the system relies on the feature composition. And the time it takes to train and detect depends on the size of the feature set. Therefore, in order to enable the system to detect intrusions in real-time, the feature set to beused should have a small size as well as an appropriate composition. In this paper, we show that the size of the feature set can be further reduced without decreasing the detection rate through using Pearson correlation coefficient between features along with the multi-objective genetic algorithm which was used to shorten the size of the feature set in previous work. For the evaluation of the proposed method, the experiments to classify 10 kinds of attacks and benign traffic are performed against NSL_KDD data set.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1129-1139
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• 2006

Pearson's correlation coefficient and vector similarity are generally applied to The users' similarity weight of user based recommender system. This study is needed to find that the correlation coefficient of similarity weight is effected by the number of pair response and significance probability. From the classified correlation coefficient by the significance probability test on the correlation coefficient and pair of response, the change of MAE is studied by comparing the predicted precision of the two. The results are experimentally related with the change of MAE from the significant correlation coefficient and the number of pair response.