• Journal of Institute of Control, Robotics and Systems
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• v.22 no.3
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• pp.241-246
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• 2016

Nowadays many people have an interest in facial expression and the behavior of people. These are human-robot interaction (HRI) researchers utilize digital image processing, pattern recognition and machine learning for their studies. Facial feature point detector algorithms are very important for face recognition, gaze tracking, expression, and emotion recognition. In this paper, a cascade facial feature point detector is used for finding facial feature points such as the eyes, nose and mouth. However, the detector has difficulty extracting the feature points from several images, because images have different conditions such as size, color, brightness, etc. Therefore, in this paper, we propose an algorithm using a modified cascade facial feature point detector using a convolutional neural network. The structure of the convolution neural network is based on LeNet-5 of Yann LeCun. For input data of the convolutional neural network, outputs from a cascade facial feature point detector that have color and gray images were used. The images were resized to $32{\times}32$. In addition, the gray images were made into the YUV format. The gray and color images are the basis for the convolution neural network. Then, we classified about 1,200 testing images that show subjects. This research found that the proposed method is more accurate than a cascade facial feature point detector, because the algorithm provides modified results from the cascade facial feature point detector.

• The Journal of Industrial Distribution & Business
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• v.11 no.8
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• pp.31-38
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• 2020

Purpose: This study uses 'Autoregressive Integrated Moving Average Model' to predict the impact of a sharp drop in the base rate due to COVID-19 at the present time when government policies for stabilizing house prices are in progress. The purpose of this study is to predict implications for the direction of the government's house policy by predicting changes in house transaction prices and house rental prices after a sharp cut in the base rate. Research design, data, and methodology: The ARIMA intervention model can build a model without additional information with just one time series. Therefore, it is a time-series analysis method frequently used for short-term prediction. After the subprime mortgage, which had shocked since the global financial crisis in April 2007, the bank's interest rate in 2020 is set at a time point close to zero at 0.75%. After that, the model was estimated using the interest rate fluctuations for the Bank of Korea base interest rate, the house transaction price index, and the house rental price index as event variables. Results: In predicting the change in house transaction price due to interest rate intervention, the house transaction price index due to the fall in interest rates was predicted to change after 3 months. As a result, it was 102.47 in April 2020, 102.87 in May 2020, and 103.21 in June 2020. It was expected to rise in the short term. In forecasting the change in house rental price due to interest rate intervention, the house rental price index due to the drop in interest rate was predicted to change after 3 months. As a result, it was 97.76 in April 2020, 97.85 in May 2020, and 97.97 in June 2020. It was expected to rise in the short term. Conclusions: If low interest rates continue to stimulate the contracted economy caused by COVID-19, it seems that there is ample room for house transaction and rental prices to rise amid low growth. Therefore, In order to stabilize the house price due to the low interest rate situation, it is considered that additional measures are needed to suppress speculative demand.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.253-253
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• 2015

This research evaluated the effect of two main sources on the results of the ocean regional circulation model (ORCMs) during downscaling and nesting the results from the coarse data. The two sources should be the domain size, and temporal and spatial resolution different between driving and driven data. The Big-Brother Experiment is applied to examine the impact of them on the results of the ORCMs separately. Within resolution of 3km grid point ORCMs applying in the Big-Brother Experiment framework, it showed that the simulation results of the ORCMs depend on the domain size and specially the spatial and temporal resolution of lateral boundary conditions (LBCs). The domain size can be selected at 9.5 times larger than the interest area, and the spatial resolution between driving data and driven model can be up to 3 of ratio resolution and updating frequency of the LBCs can be up to every 6 hours per day.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.769-771
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• 1999

In general, the most of GIS is only deal with the material and geometric data which are position, radius, length etc except a corrosion data. In present, the owner of metallic structures having an interest in that my structures do corrode or not and how many life time is there? So, we need the development of GIS interconnected corrosion prediction system on the view point of the efficiency of operation and the protection for big accident. The results of development of its system are described in this paper. It can do life prediction and interference analysis and also newest corrosion data should be updated regularly.

• 한국기계연구소 소보
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• s.13
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• pp.3-13
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• 1984

Fatigue failure of axle housing could cause many injuries and much financial loss. This challenges the engineer to improve design decisions involving fatigue. Endurance evaluation of axle housing is great interest to auto-mobile manufacturers for the sake of safety and reliability. Axle housing is subjected to gross vehicle weight(G.V.W)as mean load and alternating load. Theoretical design diagram involving mean and alternating stresses is used for the evaluation of axle housing fatigue endurance with the equivalent stress of fatigue critical area on the axle housing. Four point bending fatigue tests on axle housing with constant amplitude loading at approximately R=0 were performed with 50 ton servohydraulic strucural fatigue testing machine developed at KIMM. Specimens were made with the same material STKM 13B as the axle housing and tested to obtain S_N data. Five specimens of STKM 13B were tested at 253.61 MPa and weibull distribution was obtained at the same stress level. Material data and structural data were compares and fatigue stress property factor and fatigue life property factor were obtained.

• Journal of the Korea Safety Management & Science
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• v.5 no.3
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• pp.107-119
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• 2003

Expectation and interest about e-CRM are rising for more efficient customer management in on-line including electronic commerce. The decision-making tree can be used usefully as the data mining technology for e-CRM. In this paper, the representative decision making techniques, CART, C4.5, CHAID analyzed the differences in personalization point of view with actuality customer data through an experiment. With these analysis data, it is proposed a new decision-making tree system that has big advantage in personalization techniques. Through new system, it can get following advantage. First, it can form superior model more qualitatively in personalization by adding individual's weight value. Second it can supply information personalized more to customer. Third, it can have high position about customer's loyalty than other site of similar types of business. Fourth, it can reduce expense that cost marketing and decision-making. Fifth, it becomes possible that know that customer through smooth communication with customer who use personalized service wants and make from goods or service's quality to more worth thing.

• The Korean Journal of Applied Statistics
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• v.26 no.1
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• pp.93-109
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• 2013

Geostatistical data or point-referenced data have the information on the monitoring stations of interest where the observations are measured. Practical geostatistical data are obtained from a wide variety of observational monitoring networks that are mainly operated by the Korean government. When we analyze geostatistical data and predict the expectations at unobservable locations, we can improve the reliability of the prediction by utilizing some relevant spatial data obtained from different observational monitoring networks and blend them with the measurements of our main interest. In this paper, we consider the hierarchical spatial linear model that enables us to link spatial variables from different resources but with similar patterns and guarantee the precision of the prediction. We compare the proposed model to a classical linear regression model and simple kriging in terms of some information criteria and one-leave-out cross-validation. Real application deals with Sulfur Dioxide($SO_2$) measurements from the urban air pollution monitoring network and wind speed data from the surface observation network.

• Korean Journal of Computational Design and Engineering
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• v.10 no.2
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• pp.114-120
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• 2005

This paper presents a novel method for radius measurement of fillet regions of polygonal models by using optimum onhogonal planes. The objective function for finding an optimum onhogonal plane is designed based on the orthogonality between the normal vectors of the faces in a filet region and the plane that is to be found. Direct search methods are employed to solve the defined optimization problem since no explicit derivatives of the object function can be calculated. Once an optimum orthogonal plane is obtained, the intersection between the onhogonal plane and the faces of interest is calculated, and necessary point data in the fillet region for measuring radii are extracted by some manipulation. Then, the radius of the fillet region in question is measured by least squares fitting of a circle to the extracted point data. The proposed radius measuring method could eliminate the burden of defining a plane for radius measurement, and automatically find a necessary optimum orthogonal plane. It has an advantage in that it can measure fillet radii without prior complicated segmentation of fillet regions and explicit information of neighboring surfaces. The proposed method is demonstrated trough some mea-surement examples.

• The Journal of the Korean dental association
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• v.56 no.9
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• pp.479-490
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• 2018

3D printing technology supporting the specific patient medical services is actively being implemented in dentistry. The purpose of this study is to introduce the legal and institutional considerations to the medical practitioners in dentistry who must observe when they manufacture medical devices using 3D printers, and to provide a ways to activate and enhance their utilization in the domestic approval point of view for medical devices. Through the public data of government agencies and related organizations, the statutory system and compliance matters related to the manufacture of 3D printing medical devices have been examined and reviewed for the government's improvement efforts. Through the study, the government has been actively improving the system and making policy, but the active interest and participation of medical professionals and related workers are continually required to solve the problems which are scattered. 3D printing technology is expected to be more frequently utilized in the field of dentistry in near future. Therefore, it is essential to establish measures to improve the regulation through continuous cooperation with the related ministries with the long-term point of view enhancing smooth entry to the market for the medical devices by taking data from the continued research.

• The Korean Journal of Applied Statistics
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• v.29 no.2
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• pp.355-368
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• 2016

Outliers and influential data points distort many data analysis measures. Jang and Anderson-Cook (2014) proposed a graphical method called a rework plot for exploratory analysis purpose so that there could be a possible visualization of the trace of the impact of the possible outlying and/or influential data points on the univariate/bivariate data analysis and regression. They developed 3-D plot as well as pairwise plot for the appropriate measures of interest. This paper further extends their approach to identify its strength. We can use rework plots as a graphical exploratory data analysis tool to evaluate the impact of outliers in skewness and kurtosis of univariate data.