• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.3
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• pp.83-90
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• 2020

Prototype selection offers the advantage of ensuring low learning time and storage space by selecting the minimum data representative of in-class partitions from the training data. This paper designs a new training data generation method using hyper-rectangles that can be applied to general classification algorithms. Hyper-rectangular regions do not contain different class data and divide the same class space. The median value of the data within a hyper-rectangle is selected as a prototype to form new training data, and the size of the hyper-rectangle is adjusted to reflect the data distribution in the class area. A set cover optimization algorithm is proposed to select the minimum prototype set that represents the whole training data. The proposed method reduces the time complexity that requires the polynomial time of the set cover optimization algorithm by using the greedy algorithm and the distance equation without multiplication. In experimented comparison with hyper-sphere prototype selections, the proposed method is superior in terms of prototype rate and generalization performance.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.7
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• pp.75-83
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• 2007

IEC code based decision nile have been widely applied to detect incipient faults in power transformers. However, this method has a drawback to achieve the diagnosis with accuracy without experienced experts. In order to resolve this problem, we propose an artificial diagnosis algorithm to detect faults of power transformers using Self-Organizing Feature Map(SOM). The proposed method has two stages such as model construction and diagnostic procedure. First, faulty model is constructed by feature maps obtained by unsupervised learning for training data. And then, diagnosis is performed by compare feature map with it obtained for test data. Also the proposed method usぉms the possibility and degree of aging as well as the fault occurred in transformer by clustering and distance measure schemes. To demonstrate the validity of proposed method, various experiments are unformed and their results are presented.

• Journal of Korea Game Society
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• v.14 no.4
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• pp.105-120
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• 2014

Many of recent studies have suggested various positive outcomes of serious games. However, relatively little emphasis has been placed on the roles of user-centered factors from a psychological perspective. One of the main goals of serious games is the change of the user's perception and behavior towards a positive direction. To achieve this goal, psychological factors should be applied to the user's playing process in serious games. Inspired by construal-level theory(CLT), we propose a CLT applied model (CLT in process-outcome serious games model) considering psychological factors on the player's decision making. The model will be useful not only to game developers or designers but also to game researchers as a valuable tool in persuasion and learning for serious game users.

• Education of Primary School Mathematics
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• v.1 no.2
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• pp.85-95
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• 1997

Recently, by the popularization of computers and the development of many kinds of information transmission software, the living pattern in business offices and in home-life have changed rapidly. Because of the great progression of today's science technology, the influence of social education on the children is larger than that of the traditional school.. By a rapid change in the social atmosphere, there are some people who insist the traditional school education system is of no use any more. There have been many calls for reform of traditional schooling and in particular there has been major rethinking of school mathematics. The initial demand for change in elementary school mathematics is because of the poor achievement of students. There are even more compelling reasons for change. For example today's science technology society requires a different mathematical literacy for its citizens than that of the past. The importance of problem-solving based on interest and progress is more important than just paper-pencil computation in elementary schools. And also the increasing information wave of today's society demands that the school accept the long-distance education which could not be imagined in the past. Taking account of this variety, school education in the future should willingly introduce and apply the open education system to keep pace with today's society. To accept society demands actively, today's schools are going to accept and apply the idea of the open education. In this viewpoint, the purpose of the paper is to analyze the causes of under-achievement in mathematics teaming, the directions of school mathematics education, the system of textbooks and the problems of teaching-learning programs and paper-pencil test.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.37 no.3
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• pp.72-78
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• 2000

In this study, we propose a model verification algorithm based on DCT and neural network for ATM security system. We construct database about facial images after capturing thirty persons facial images in the same lumination and distance. To simulate model verification, we capture four learning images and test images per a man. After detecting edge in facial images, we detect a characteristic area of square shape using edge distribution in facial images. Characteristic area contains eye bows, eyes, nose, mouth and cheek. We extract characteristic vectors to calculate diagonally coefficients sum after obtaining DCT coefficients about characteristic area. Characteristic vectors is normalized between +1 and -1, and then used for input vectors of neural networks. Not considering passwords, simulations results showed 100% verification rate when facial images were learned and 92% verification rate when facial images weren't learned. But considering passwords, the proposed algorithm showed 100% verification rate in case of two simulations.

• Journal of Digital Convergence
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• v.18 no.10
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• pp.403-408
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• 2020

This paper is to evaluate the usability of two representative video meeting services currently used by university for research to improve the quality of university remote lecture. questionnaires based on Kano Model were designed and in-depth interviews were conducted to provide qualitative approaches. Screen-sharing functions, the one-dimensional functions was the most important function. and attractive functions had relatively diverse directions. For essential functions, there was a wide gap in quality due to user-specific equipment. The function in which other platforms exist or business-related was not important. Webex reacted negatively to the aging UI, while Zoom responded negatively to the unilateral mute function. In addition, the development direction was presented in five ways as a result of analysis of these results. under Corona-19 situation, I hope this study will lead to continuous research to make stepping stone for remoted educational development.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.6
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• pp.551-558
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• 2013

A support vector machine (SVM) is a very powerful classification algorithm when a set of training data, each marked as belonging to one of several categories, is given. Therefore, SVM techniques have been used as one of the diagnostic tools in machine learning as well as in pattern recognition. In this paper, we present the results of classifying ball bearing fault types and severities using SVM with an optimized feature set based on the minimum distance rule. A feature set as an input for SVM includes twelve time-domain and nine frequency-domain features that are extracted from the measured vibration signals and their decomposed details and approximations with discrete wavelet transform. The vibration signals were obtained from a test rig to simulate various bearing fault conditions.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.183-194
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• 2009

In this paper, we propose a new method for detecting objectionable images with an active shape model. Our method first learns the shape of breast lines through principle component analysis and alignment as well as the distribution of intensity values of corresponding landmarks, and then extracts breast lines with the learned shape and intensity distribution. To accurately select the initial position of active shape model, we obtain parameters on scale, rotation, and translation. After positioning the initial location of active shape model using scale and rotation information, iterative searches are performed. We can identify adult images by calculating the average of the distance between each landmark and a candidate breast line. The experiment results show that the proposed method can detect adult images effectively by comparing various results.

• Journal of Information Management
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• v.42 no.3
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• pp.27-54
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• 2011

This research has investigated the cyber university students' awareness of the portal service of the digital library. In order to identify characteristics of the cyber university students, the research examined demographic characteristics of the students and library usage status. Awareness on the portal service was also analyzed in accordance with the characteristics of the users. The analysis showed that the most needed service was concise/full search service, which was the most frequently used service. The students were most satisfied with the liaison service; service awareness of several libraries showed statistically significant results depending on age, occupation. Based on the analysis, the research proposed the following measures to increase the use of the digital library of cyber university students: active PR on library service, and intensifying the library user education.