• Korean Medical Education Review
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• v.13 no.1
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• pp.3-11
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• 2011

Every medical school aims to provide better education, and it sometimes requires changing the current education system. However, an attempt for a change may not always be successful. In many cases, it is so not because an intended change was not properly directed but because conflicts in the process of adopting the change were not properly handled. This paper suggests seven points for how to successfully bring a change in medical education. First, the medical education should not simply focus on the pass rate of the national medical examination but also on the cultivation of creative leaders. Second, the faculty of medical school should be creative, self-motivated, and passionate. Third, people in charge of an intended change should have a good understanding of complicated dynamics between the dean's office, medical education experts, professors, and students. Fourth, people who are leading the change should also grasp the possibility that a well-intended change might not be well-received by professors, students, and dean due to their tendency to be complacent with the current system. Fifth, a successful introduction of a change requires good teamwork of a thinker, an actor, and a coordinator. Sixth, a change takes time as it takes place through a step-by-step process. Seventh, an attempt for a change accompanies a negotiation with professors with different thoughts and views regarding education, and people who want a change need to be flexible in that negotiation. In addition to these seven points, people who are responsible for a change should be consistent and consider the renown of the school.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.153-166
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• 2003

This study presents the time-divided forecasting model to integrate evolutionary optimization algorithm and change point detection based on artificial neural networks (ANN) for the prediction of (Korea) stock price index. The genetic algorithm(GA) is introduced as an evolutionary optimization method in this study. The basic concept of the proposed model is to obtain intervals divided by change points, to identify them as optimal or near-optimal change point groups, and to use them in the forecasting of the stock price index. The proposed model consists of three phases. The first phase detects successive change points. The second phase detects the change-point groups with the GA. Finally, the third phase forecasts the output with ANN using the GA. This study examines the predictability of the proposed model for the prediction of stock price index.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2010.04a
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• pp.197-199
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• 2010

A gravity survey is a base of research earth gravity field determined, perception of the vertical motion, change of Geoid, sea-level changes, climate change etc. Recently, FG-5 was adopted in NGII. NGII has completed 4 points of absolute gravity survey and 1,400 points of relative gravity survey in 2009 to aim to observe 20 points of absolute gravity survey and 6,000 points of gravity control point by 2013. Using results of gravity survey, NGII will provide citizen with data for research about renewal of geoid model and geophysics. This study aims to go over examples of utilization of absolute gravimeter & method of utilization in korea.

• The Korean Journal of Food And Nutrition
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• v.32 no.5
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• pp.522-529
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• 2019

The purpose of this study was to develop a dietary education program, and to evaluate the effectiveness of the education program for children from low-income families. A total of 242 children (122 education groups and 120 control groups) were run six times a dietary education program from April to December 2018, and a questionnaire was administered before and after the education to evaluate effectiveness. Elementary school students were the most prevalent in the education and the control group. In the education group, the body height and weigh were 137.27 cm and 33.69 kg, respectively, and in the control group the body height and weight were 143.48 cm and 40.64 kg, respectively. The education group showed positive change in dietary self-efficacy and dietary knowledge compared to the control group. In particular, 'I can have meals regularly' (Education Group: 4.00 points from 3.71 points) and 'I can choose fruits instead of cookies candies as snacks (Education Group: 4.01 points from 3.70 points) The score increased after participation in the program. In the change of nutritional and hygiene knowledge of children, the education group scored 3.63 of 10 points before education, but the score significantly increased to 5.70 points after education(p<0.001).

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.83-92
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• 2020

A recent study to determine the fall is focused on analyzing fall motions using a recurrent neural network (RNN), and uses a deep learning approach to get good results for detecting human poses in 2D from a mono color image. In this paper, we investigated the improved detection method to estimate the position of the head and shoulder key points and the acceleration of position change using the skeletal key points information extracted using PoseNet from the image obtained from the 2D RGB low-cost camera, and to increase the accuracy of the fall judgment. In particular, we propose a fall detection method based on the characteristics of post-fall posture in the fall motion analysis method and on the velocity of human body skeleton key points change as well as the ratio change of body bounding box's width and height. The public data set was used to extract human skeletal features and to train deep learning, GRU, and as a result of an experiment to find a feature extraction method that can achieve high classification accuracy, the proposed method showed a 99.8% success rate in detecting falls more effectively than the conventional primitive skeletal data use method.

• 한국데이터정보과학회:학술대회논문집
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• 2004.10a
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• pp.61-61
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• 2004

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.687-695
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• 2009

The Bayesian multiple change-point estimation has been applied to the daily means of ozone and PM10 data in Seoul for the period 1999. We focus on the detection of multiple change-points in the ozone and PM10 bivariate vectors by evaluating the posterior probabilities and Bayesian information criterion(BIC) using the stochastic approximation Monte Carlo(SAMC) algorithm. The result gives 5 change-points of mean vectors of ozone and PM10, which are related with the seasonal characteristics.

• The Korean Journal of Applied Statistics
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• v.27 no.4
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• pp.589-603
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• 2014

When consecutive data follows different distributions(depending on the time interval) change-point detection infers where the changes occur first and then finds further inferences for each sub-interval. In this paper, we investigate the Bayesian detection of multiple change points. Utilizing the reversible jump MCMC, we can explore parameter spaces with unknown dimensions. In particular, we consider a model where the signal is a piecewise linear function. For the Bayesian inference, we propose a new Bayesian structure and build our own MCMC algorithm. Through the simulation study and the real data analysis, we verified the performance of our method.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.261-269
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• 2015

Nowadays the application of change point analysis has been indispensable in a wide range of areas such as quality control, finance, environmetrics, medicine, geographics, and engineering. Identification of times where process changes would help minimize the consequences that might happen afterwards. The main objective of this paper is to compare the change-point detection capabilities of Bayesian estimate and maximum likelihood estimate. We applied Bayesian and maximum likelihood techniques to formulate change points having a step change and multiple number of change points in a Poisson rate. After a signal from c-chart and Poisson cumulative sum control charts have been detected, Monte Carlo simulation has been applied to investigate the performance of Bayesian and maximum likelihood estimation. Change point detection capacities of Bayesian and maximum likelihood estimation techniques have been investigated through simulation. It has been found that the Bayesian estimates outperforms standard control charts well specially when there exists a small to medium size of step change. Moreover, it performs convincingly well in comparison with the maximum like-lihood estimator and remains good choice specially in confidence interval statistical inference.

• The Korean Journal of Applied Statistics
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• v.26 no.4
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• pp.569-579
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• 2013

In the field of single-molecule spectroscopy, it is essential to analyze luminescence Intensity changes that result from a single molecule. With the CdSe/ZnS core-shell structured quantum dot photon emission data Bayesian multiple change-point estimation is done with the gamma prior for Poisson parameters and truncated Poisson distribution for the number of change-points.