• Journal of Korean Society for Quality Management
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• v.49 no.2
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• pp.201-211
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• 2021

Purpose: This paper proposes a density adaptive grid algorithm for the k-NN regression model to reduce the computation time for large datasets without significant prediction accuracy loss. Methods: The proposed method utilizes the concept of the grid with centroid to reduce the number of reference data points so that the required computation time is much reduced. Since the grid generation process in this paper is based on quantiles of original variables, the proposed method can fully reflect the density information of the original reference data set. Results: Using five real-life datasets, the proposed k-NN regression model is compared with the original k-NN regression model. The results show that the proposed density adaptive grid-based k-NN regression model is superior to the original k-NN regression in terms of data reduction ratio and time efficiency ratio, and provides a similar prediction error if the appropriate number of grids is selected. Conclusion: The proposed density adaptive grid algorithm for the k-NN regression model is a simple and effective model which can help avoid a large loss of prediction accuracy with faster execution speed and fewer memory requirements during the testing phase.

• Journal of the Korean Data and Information Science Society
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• v.10 no.1
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• pp.147-154
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• 1999

Using a noninformative prior and an inverted gamma prior, the Bayesian predictive density and the prediction intervals for a future observation or the p - th order statistic of n' future observations from the censord Pareto model have been obtained. In additions, numerical examples are given in order to illustrate the proposed predictive procedure.

• Transactions of Materials Processing
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• v.6 no.6
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• pp.508-516
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• 1997

Forging process on sintered powder metals has been applied to produce automotive parts which require a high level of strength. In those parts, the measurement of relative density is very important because a low relative density density causes deterioration of strength. In the present study, an indentation force equation was proposed by which the result obtained from the hardness measurement is used to evaluate the relative density. This equation was applied to the prediction of the relative density in cylindrical specimens which were first sintered and then forged at the room temperature and at an elevated temperature. The experimental results were compared with predictions with and without consideration of the workhardening effect on the powder.

• Journal of Advanced Marine Engineering and Technology
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• v.23 no.4
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• pp.453-461
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• 1999

The estimation of fatigue life at the design stage is very important in order to arrive at feasible and cost effective solutions considering the total lifetime of the structure and machinery compo-nents. In this study the practical procedure of prediction of fatigue life by use of cumulative damage factors based on Miner-Palmgren hypothesis and probability density function is shown with a $135,000m^3$ LNG tank being used as an example. In particular the parameters of Weibull distribution taht determine the stress spectrum are dis-cussed. At the end some of uncertainties associated with fatigue life prediction are discussed. The main results obtained from this study are as follows: 1. The practical procedure of prediction of fatigue life by use of cumulative damage factors expressed in combination of probability density function and S-N data is proposed. 2. The calculated fatigue life is influenced by the shape parameter and stress block. The conser-vative fatigue design can be achieved when using higher value of shape parameter and the stress blocks divded into more stress blocks.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.16 no.11
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• pp.1035-1043
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• 2004

For a long road tunnel, a tunnel ventilation system may be used in order to reduce the pollution level below the required level. To control the tunnel pollution level, a closed loop control algorithm may be used. The feedforward prediction algorithm and the cascade control algorithm were developed to regulate the CO level in a tunnel. The feedforward prediction algorithm composed of the traffic estimation algorithm and the CO density prediction algorithm, and the cascade control algorithm composed of the jet fan control algorithm and the air velocity setpoint algorithm. The verification of control algorithms was carried out by dynamic models developed from the actual tunnel data. The simulation results showed that control algorithms developed for this study were effective for the control of the tunnel ventilation system.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.52 no.3
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• pp.137-143
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• 2003

The degree of contamination on outdoor insulator is ons of the most importance factor to determine the pollution level of outdoor insulation, and the sea salt is known as the most dangerous pollutant. As shown through the preceding study, the generation of salt pollutant and the pollution degree of outdoor insulator have a close relation with meteorological conditions, such as wind velocity, wind direction, precipitation and so fourth. So, in this paper, we made an investigation on the prediction method, a statistical estimation technique for equivalent salt deposit density of outdoor insulator with multiple linear regression analysis. From the results of the analysis, we proved the superiority of the prediction method in which the variables had a very close(about 0.9) correlation coefficient. And the results could be applied to establish the Pollution Prediction System for power utilities, and the system could provide an invaluable information for the design and maintenance of outdoor insulation system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.9
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• pp.1881-1888
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• 2011

Red tide is a natural phenomenon to bloom harmful algal, which fish and shellfish die en masse. Red tide damage with respect to sea farming has been occurred each year. Red tide damage can be minimized by means of prediction of red tide blooms. Red tide prediction using naive bayes classifier can be achieve good prediction results. The result of naive bayes method only determine red tide blooms, whereas the method can not know how increasing of red tide algae density. In this paper, we proposed the red tide blooms prediction method using fuzzy reasoning and naive bayes classifier. The proposed method can enhance the precision of red tide prediction and forecast the increasing density of red tide algae.

• Journal of the Korean GEO-environmental Society
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• v.17 no.6
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• pp.13-21
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• 2016

Since the landslide hazard areas prediction was analyzed by slope-angle and soil properties, regional characteristics is not taken. Therefore, in order to make more rational prediction, it is necessary to consider the characteristics of the region. Tree roots have been known to increase soil cohesion in landslide hazard areas and to vary the degrees depending on the tree type. In addition, a reasonable prediction of landslide hazard areas can be made by considering crown density based on crown distribution patterns of the area of interest. In this study, using the roots cohesion considering the crown density of the trees, which is in the landslides risk areas around Mt. Gwehwa in Sejong City, the landslides risk areas were predicted and compared with predicted results obtained by not considering root cohesion.

• Wind and Structures
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• v.9 no.5
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• pp.387-396
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• 2006

This paper presents the wind characteristics of Typhoon Dujuan as measured at a 50 m guyed mast in Hong Kong. The basic wind speed, wind direction and turbulent intensity are studied at two measurement levels of the structure. The power spectral density of the typhoon is compared with the von Karman prediction, and the coherence between wind speeds at the two measurement levels is found to This paper presents the wind characteristics of Typhoon Dujuan as measured at a 50 m guyed mast in Hong Kong. The basic wind speed, wind direction and turbulent intensity are studied at two measurement levels of the structure. The power spectral density of the typhoon is compared with the von Karman prediction, and the coherence between wind speeds at the two measurement levels is found to compare with Davenport's prediction. The effect of typhoon Dujuan on the response of the structure will be discussed in a companion paper (Law, et al. 2006).with Davenport's prediction. The effect of typhoon Dujuan on the response of the structure will be discussed in a companion paper (Law, et al. 2006).

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.3
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• pp.183-192
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• 2017

k-nearest neighbor (K-NN) is a well-known classification algorithm, being feature space-based on nearest-neighbor training examples in machine learning. However, K-NN, as we know, is a lazy learning method. Therefore, if a K-NN-based system very much depends on a huge amount of history data to achieve an accurate prediction result for a particular task, it gradually faces a processing-time performance-degradation problem. We have noticed that many researchers usually contemplate only classification accuracy. But estimation speed also plays an essential role in real-time prediction systems. To compensate for this weakness, this paper proposes correlation coefficient-based clustering (CCC) aimed at upgrading the performance of K-NN by leveraging processing-time speed and plurality rule-based density (PRD) to improve estimation accuracy. For experiments, we used real datasets (on breast cancer, breast tissue, heart, and the iris) from the University of California, Irvine (UCI) machine learning repository. Moreover, real traffic data collected from Ojana Junction, Route 58, Okinawa, Japan, was also utilized to lay bare the efficiency of this method. By using these datasets, we proved better processing-time performance with the new approach by comparing it with classical K-NN. Besides, via experiments on real-world datasets, we compared the prediction accuracy of our approach with density peaks clustering based on K-NN and principal component analysis (DPC-KNN-PCA).