• Journal of Environmental Science International
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• v.14 no.1
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• pp.33-40
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• 2005

The size of minimum order stream has a very sensitive effect on runoff analysis model using the divergence characteristic of stream. Therefore, in this study, the threshold area of minimum order stream has been examined the change characteristic of topographical parameters. The subject basin of the research was the upper basin of the Kumho water gage station which is located in the middle of the Kumho river. The 1:25,000 numerical geography which was constructed $10{\times}10m$ mesh was used. The range of investigation of topographical parameters are number of stream order, length, area, slope, basin relief, sinuosity ratio, drainage density and total stream length etc. It was found from the result of analysis that the threshold value of minimum order stream has a very big effect on topographical parameters of basin. It was found that the threshold area of minimum ord er stream revealed under $0.10km^{2}.$ Furthermore, the parameters showed a serious change except for over $0.10km^{2}.$

• Physical Therapy Korea
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• v.9 no.3
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• pp.1-9
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• 2002

The purpose of this study was to compare the experimental pain threshold when used in luminous lamp radiation and nonluminous lamp radiation with healthy person. Thirty normal subjects were randomly assigned two groups: a luminous lamp radiation group, and a nonluminous lamp radiation group. The infrared lamps were applied on L3 for thirty minutes. Each group was measured for experimental pain threshold and local temperature before, 15 and 30 minute radiation. For statistical differences in change of the experimental pain threshold and local temperature due to differences in lamp ray was compared using the independent t-test. And, General linear model for profile plots test was used. The results were as: 1. Local temperature was significantly increased in the nonluminous lamp group (p<.01). 2. Experimental pain thershold was significantly increased in the luminous lamp group (p<.05),(p<.01). This study indicate that luminous lamp radiation was more effects of increase experimental pain thershold than nonluminous lamp radiation. Further study is needed to compare the effects of after period radiation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.11C
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• pp.1161-1168
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• 2003

In this paper, we propose an improved statistical model-based voice activity detection algorithm and threshold update method. We first improve signal-to-noise ratio by using speech enhancement preprocessing algorithm combined power subtraction method and matched filter, then apply it to LLR test optimum decision rule for improving the performance even in low SNR conditions. And we propose an adaptive threshold update method that was not concerned in any papers. We also perform extensive computer simulations to demonstrate the performance improvement of the proposed VAD algorithm employing the proposed speech enhancement preprocessing algorithm and adaptive threshold update method under various background noise environments. Finally we verify our results by comparing ITU-T G.729 Annex B.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.44 no.7 s.361
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• pp.16-23
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• 2007

For a fully depleted SOI type symmetric double gate MOSFET, a simple expression for the threshold voltage has been derived in a closed-form To solve analytically the 2D Poisson's equation in a silicon body, the two-dimensional potential distribution is assumed approximately as a polynomial of fourth-order of x, vertical coordinate perpendicular to the silicon channel. From the derived expression for the surface potential, the threshold voltage can be obtained as a simple closed-form. Simulation result shows that the threshold voltage is exponentially dependent on channel length for the range of channel length up to $0.01\;[{\mu}m]$.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.125-140
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• 2013

We call a data set in which the number of records belonging to a certain class far outnumbers the number of records belonging to the other class, 'imbalanced data set'. Most of the classification techniques perform poorly on imbalanced data sets. When we evaluate the performance of a certain classification technique, we need to measure not only 'accuracy' but also 'sensitivity' and 'specificity'. In a customer churn prediction problem, 'retention' records account for the majority class, and 'churn' records account for the minority class. Sensitivity measures the proportion of actual retentions which are correctly identified as such. Specificity measures the proportion of churns which are correctly identified as such. The poor performance of the classification techniques on imbalanced data sets is due to the low value of specificity. Many previous researches on imbalanced data sets employed 'oversampling' technique where members of the minority class are sampled more than those of the majority class in order to make a relatively balanced data set. When a classification model is constructed using this oversampled balanced data set, specificity can be improved but sensitivity will be decreased. In this research, we developed a hybrid model of support vector machine (SVM), artificial neural network (ANN) and decision tree, that improves specificity while maintaining sensitivity. We named this hybrid model 'hybrid SVM model.' The process of construction and prediction of our hybrid SVM model is as follows. By oversampling from the original imbalanced data set, a balanced data set is prepared. SVM_I model and ANN_I model are constructed using the imbalanced data set, and SVM_B model is constructed using the balanced data set. SVM_I model is superior in sensitivity and SVM_B model is superior in specificity. For a record on which both SVM_I model and SVM_B model make the same prediction, that prediction becomes the final solution. If they make different prediction, the final solution is determined by the discrimination rules obtained by ANN and decision tree. For a record on which SVM_I model and SVM_B model make different predictions, a decision tree model is constructed using ANN_I output value as input and actual retention or churn as target. We obtained the following two discrimination rules: 'IF ANN_I output value <0.285, THEN Final Solution = Retention' and 'IF ANN_I output value ${\geq}0.285$, THEN Final Solution = Churn.' The threshold 0.285 is the value optimized for the data used in this research. The result we present in this research is the structure or framework of our hybrid SVM model, not a specific threshold value such as 0.285. Therefore, the threshold value in the above discrimination rules can be changed to any value depending on the data. In order to evaluate the performance of our hybrid SVM model, we used the 'churn data set' in UCI Machine Learning Repository, that consists of 85% retention customers and 15% churn customers. Accuracy of the hybrid SVM model is 91.08% that is better than that of SVM_I model or SVM_B model. The points worth noticing here are its sensitivity, 95.02%, and specificity, 69.24%. The sensitivity of SVM_I model is 94.65%, and the specificity of SVM_B model is 67.00%. Therefore the hybrid SVM model developed in this research improves the specificity of SVM_B model while maintaining the sensitivity of SVM_I model.

• Journal of Animal Science and Technology
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• v.44 no.4
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• pp.383-390
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• 2002

The data were consisted of 1,262 records for carcass traits observed at Hanwoo steers from 1998 to 2001 at Namwon and Deakwanryung branch of National Livestock Research Institute, Rural Development Administration. Pedigrees of young bulls were traced back to search for magnifying inbreeding. Genetic parameters for carcass traits with Gibbs sampling in a threshold animal model were compared to estimates with REML algorithm in linear model. As the results, most of bulls were not inbred and sire pedigree group was non-inbred population. However, most of the bulls fell in some relationship with each other. Heritability estimates as fully posterior means by Gibbs samplers in threshold model were higher than those by REML in linear model. Furthermore, these estimates in threshold model using GS showed higher estimates than estimates using tested young bulls in previous study and same model. Heritability estimate by GS for marbling score was 0.74 and genetic correlation estimate between marbling score and body weight at slaughter was –0.44. Further study for correlation of breeding values between REML algorithm in linear model and Gibbs sampling algorithm in threshold model was needed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.12
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• pp.4991-4996
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• 2010

Clustering method in wireless sensor network is the technique that forms the cluster to aggregate the data and transmit them at the same time that they can use the energy efficiently. Even though cluster group model is based on clustering, it differs from previous method that reducing the total energy consumption by separating energy overload to cluster group head and cluster head. In this thesis, I calculate the optimal cluster group number and cluster number in this kind of cluster group model according to threshold of energy consumption model. By using that I can minimize the total energy consumption in sensor network and maximize the network lifetime. I also show that proposed cluster group model is better than previous clustering method at the point of network energy efficiency.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.179-184
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• 2021

In our study we performed a sentiments analysis from the images. For this purpose, we used 153 images that contain: people, animals, buildings, landscapes, cakes and objects that we divided into two categories: images that suggesting a positive or a negative emotion. In order to classify the images using the two categories, we created two models. The SAI-G model was created with Google's AutoML Vision service. The SAI-C model was created on the Clarifai platform. The data were labeled in a preprocessing stage, and for the SAI-C model we created the concepts POSITIVE (POZITIV) AND NEGATIVE (NEGATIV). In order to evaluate the performances of the two models, we used a series of evaluation metrics such as: Precision, Recall, ROC (Receiver Operating Characteristic) curve, Precision-Recall curve, Confusion Matrix, Accuracy Score and Average precision. Precision and Recall for the SAI-G model is 0.875, at a confidence threshold of 0.5, while for the SAI-C model we obtained much lower scores, respectively Precision = 0.727 and Recall = 0.571 for the same confidence threshold. The results indicate a lower classification performance of the SAI-C model compared to the SAI-G model. The exception is the value of Precision for the POSITIVE concept, which is 1,000.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.1
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• pp.11-17
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• 2001

We consider the M/G/1 with Bernoulli feedback, where the served customers wait in the feedback queue for rework with probability p. It is important to decide the moment of dispatching in feedback systems because of the dispatching cost for rework. Up to date, researches have analyzed for the instantaneous-dispatching model or the case that dispatching epoch is determined by the state of feedback queue. In this paper we deal with a dispatching model whose dispatching epoch depends on main queue. We adopt supplementary variable method for our model and a numerical example is given for clarity.

• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.85-89
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• 1997

In this paper, we consider a random replacement model with minimal repair, which is a generalization of the random replacement model introduced Lee and Lee(1994). It is assumed that a system is minimally repaired when it fails and replaced only when the accumulated operating time of the system exceeds a threshold time by a supervisor who arrives at the system for inspection according to Poisson process. Assigning the corresponding cost to the system, we obtain the expected long-run average cost per unit time and find the optimum values of the threshold time and the supervisor's inspection rate which minimize the average cost.