• Nutrition Research and Practice
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• v.11 no.3
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• pp.247-256
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• 2017

BACKGROUND/OBJECTIVES: Easy access to intervention and support for certain behaviors is important for obesity prevention and management. The available technology such as smartphone applications can be used for intervention regarding healthy food choices for obesity prevention and management in elementary-school students. The transtheoretical model (TTM) is comprised of stages and processes of change and can be adopted to tailored education for behavioral change. This study aims to develop TTM-based nutrition contents for mobile applications intended to change eating behaviors related to weight gain in young children. SUBJECTS/METHODS: A synthesized algorithm for tailored nutrition messages was developed according to the intake status of six food groups (vegetables, fruits, sugar-sweetened beverages, fast food and instant food, snacks, and late-night snacks), decision to make dietary behavioral changes, and self-confidence in dietary behavioral changes. The messages in this study were developed from December 2014 to April 2015. After the validity evaluation of the contents through expert consultation, tailored nutrition information messages and educational contents were developed based on the TTM. RESULTS: Based on the TTM, stages of subjects are determined by their current intake status, decision to make dietary behavioral changes, and self-confidence in dietary behavioral changes. Three versions of tailored nutrition messages at each TTM stage were developed so as to not send the same messages for three weeks at most, and visual materials such as figures and tables were developed to provide additional nutritional information. Finally, 3,276 tailored nutrition messages and 60 nutrition contents for applications were developed. CONCLUSIONS: Smartphone applications may be an innovative medium to deliver interventions for eating behavior changes directly to individuals with favorable cost-effectiveness. In addition, using the TTM for tailored nutrition education for healthy eating is an effective approach.

• The Journal of the Acoustical Society of Korea
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• v.22 no.2
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• pp.121-128
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• 2003

Acoustic Echo Cancellation(m) is very active research topic having many applications like teleconference and hands-free communication and it employs Double Talk Detector(DTD) to indicate whether the near-end speaker is active or not. However. the DTD is very sensitive to the variation of acoustical environment and it sometimes provides wrong information about the near-end speaker. In this paper, we are focusing on the development of robust DTD algorithm which is a basic building block for reliable AEC system. The proposed AEC system consists of delayless subband AEC and narrow-band DTD. Delayless subband AEC has proven to have excellent performance of echo cancellation with a low complexity and high convergence speed. In addition, it solves the signal delay problem in the existing subband AEC. On the other hand, the proposed narrowband DTD is operating on low frequency subband. It can take most advantages from the narrow subband such as a low computational complexity due to the down-sampling and the reliable DTD decision making procedure because of the low-frequency nature of the subband signal. From the simulation results of the proposed narrowband DTD and wideband DTD, we confirm that the proposed DTD outperforms the wideband DTD in a sense of removing possible false decision making about the near-end speaker activity.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.143-156
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• 2010

Faced with an increase of malicious threats from the Internet as well as local area networks, many companies are considering deploying a security system. To help a decision maker select a suitable security tool, this paper proposed a three-step integrated framework using linear fuzzy regression (LFR) and physical programming (PP). First, based on the experts' estimations on security criteria, analytic hierarchy process (AHP) and quality function deployment (QFD) are employed to specify an intermediate score for each criterion and the relationship among these criteria. Next, evaluation value of each criterion is computed by using LFR. Finally, a goal programming (GP) method is customized to obtain the most appropriate security tool for an organization, considering a tradeoff among the multi-objectives associated with quality, credibility and costs, utilizing the relative weights calculated by the physical programming weights (PPW) algorithm. A numerical example provided illustrates the advantages and contributions of this approach. Proposed approach is anticipated to help a decision maker select a suitable security tool by taking advantage of experts' experience, with noises eliminated, as well as the accuracy of mathematical optimization methods.

• Journal of the Korea Society of Computer and Information
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• v.17 no.7
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• pp.167-174
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• 2012

Data mining technique can be adapted to analysing Employment information in order to discover valuable information out of large data. As the issue employment such as jobless of college graduate, recruitment for women, recruitment for elders etc. became social problem, there are many efforts of various public employment services and studies. The factors that affects the college graduate's employment type (regular, temporary, daily) can be used to guide employment and to prepare employment for college students. In analyzing large number of attributes and the huge amount of data elements, regular statistical methods faces their limitation; therefore, data-mining technique is more suitable for the dataset of about 170 attributes and 20,000 elements. We divide the factors that may affect the employment type into personal factor, school factor, company factor, and experience factor; decision tree algorithm is used to find out the interesting relationship between the attributes of the factors and employment type. Personal factors such as the income of parents and marital status were the most affective factors to the employment type. The learned decision tree was able to classify the employment type with 87% of accuracy. We also assume the level of the school affects the employment type of the graduates.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.698-708
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• 2020

VVC (Versatile Video Coding), which has been developing as a next generation video coding standard. Compared to HEVC (High Efficiency Video Coding), VVC is improved by about 34% in RA (Random Access) configuration and about 30% in LDB (Low-Delay B) configuration by adopting various techniques such as recursive block partitioning structure and GPM (Geometric Partitioning Mode). But the encoding complexity is increased by about 10x and 7x, respectively. In this paper, we propose a fast decision method of GPM mode and block partitioning using directionality of block to reduce encoding complexity of VVC. The proposed method is to apply the Hough transform to the current block to identify the directionality of the block, thereby determining the GPM mode and the specific block partitioning method to be skipped in the rate-distortion cost search process. As a result, compared to VTM8.0, the proposed method reduces about 31.01% and 29.84% encoding complexity for RA and LDB configuration with 2.48% and 2.69% BD-rate loss, respectively.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.5
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• pp.927-940
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• 2017

Landslide has been a major disaster in Indonesia, and recent climate change and indiscriminate urban development around the mountains have increased landslide risks. Java Island, Indonesia, where more than half of Indonesia's population lives, is experiencing a great deal of damage due to frequent landslides. However, even in such a dangerous situation, the number of inhabitants residing in the landslide-prone area increases year by year, and it is necessary to develop a technique for analyzing landslide-hazardous and vulnerable areas. In this regard, this study aims to evaluate landslide susceptibility of Java, an island of Indonesia, by using GIS-based spatial prediction models. We constructed the geospatial database such as landslide locations, topography, hydrology, soil type, and land cover over the study area and created spatial prediction models by applying Weight of Evidence (WoE), decision trees algorithm and artificial neural network. The three models showed prediction accuracy of 66.95%, 67.04%, and 69.67%, respectively. The results of the study are expected to be useful for prevention of landslide damage for the future and landslide disaster management policies in Indonesia.

• Journal of the military operations research society of Korea
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• v.33 no.2
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• pp.101-113
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• 2007

The period of military personnel service will be phased down by 2014 according to 'The law of National Defense Reformation' issued by the Ministry of National Defense. For this reason, the ROK army provides discrimination education to 'newly recruited privates' for more effective individual performance in the on-the-job training. For the training to be more effective, it would be essential to predict the degree of achievements by new privates in the training. Thus, we used data mining techniques to develop a classification model which classifies the new privates into one of two achievements groups, so that different skills of education are applied to each group. The target variable for this model is a binary variable, whose value can be either 'a group of general control' or 'a group of special control'. We developed four pure classification models using Neural Network, Decision Tree, Support Vector Machine and Naive Bayesian. We also built four hybrid models, each of which combines k-means clustering algorithm with one of these four mining technique. Experimental results demonstrated that the highest performance model was the hybrid model of k-means and Neural Network. We expect that various military education programs could be supported by these classification models for better educational performance.

• Journal of Elementary Mathematics Education in Korea
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• v.17 no.1
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• pp.67-86
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• 2013

The purpose of this study is to analyze selection of factors of Schoenfeld's problem solving behavior shown in problem solving process of mathematically gifted students based on brain preference of the students and to present suggestions related to hemispheric lateralization that should be considered in teaching such students. The conclusions based on the research questions are as follows. First, as for problem solving methods of the students in the Gifted Education Center based on brain preference, the students of left brain preference showed more characteristics of the left brain such as preferring general, logical decision, while the students of right brain preference showed more characteristics of the right brain such as preferring subjective, intuitive decision, indicating that there were differences based on brain preference. Second, in the factors of Schoenfeld's problem solving behavior, the students of left brain preference mainly showed factors including standardized procedures such as algorithm, logical and systematical process, and deliberation, while the students of right brain preference mainly showed factors including informal and intuitive knowledge, drawing for understanding problem situation, and overall examination of problem-solving process. Thus, the two types of students were different in selecting the factors of Schoenfeld's problem solving behavior based on the characteristics of their brain preference. Finally, based on the results showing that the factors of Schoenfeld's problem solving behavior were differently selected by brain preference, it may be suggested that teaching problem solving and feedback can be improved when presenting the factors of Schoenfeld's problem solving behavior selected more by students of left brain preference to students of right brain preference and vice versa.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.1A
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• pp.26-34
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• 2002

Mobile telecommunication systems such as IS-95 and IMT-2000 employ frame based communication using frames up to 20 msec in length and the receiving end has to store the whole frome before it is being processed. The size of the frame buffer ofter dominates those of the processing unit such as soft decision Viterbi decoder. The frame buffer for IMT-2000, for example, has to be increased 80 times as large as that of IS-95. One of the parameters deciding the number of bits in a frame will be obviously the number of bits in soft quantization. Start after striking space key 2 times. This paper has studied a new aspect of offset and quantization step size on BER performance and proposes a new 3-bit soft quantization algorithm which shows similar performance as that of 4-bit soft decision Viterbi receiver. The optimal offset values and step sizes for the other practical quantization levels ---16, 8, 4, 2--- have also been found. In addition, a new optimal symbol metric table has been devised which takes the accumulation value of various repeated signals and produces a rescaled 3-bit valu.tart after striking space key 2 times.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.6
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• pp.28-37
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• 2009

In this paper, we propose an obstacle localization method using a road feature based on a V-disparity map binarized by a maximum frequency value. In a conventional method, the detection performance is severely affected by the size, number and type of obstacles. It's especially difficult to extract a large obstacle or a continuous obstacle like a median strip. So we use a road feature as a new decision standard to localize obstacles irrespective of external environments. A road feature is proper to be a new decision standard because it keeps its rough feature very well in V-disparity under environments where many obstacles exist. And first of all, we create a binary V-disparity map using a maximum frequency value to extract a road feature easily. And then we compare the binary V-disparity map with a median value to remove noises. Finally, we use a linear interpolation for rows which have no value. Comparing this road feature with each column value in disparity map, we can localize obstacles robustly. We also propose a post-processing technique to remove noises made in obstacle localization stage. The results in real road tests show that the proposed algorithm has a better performance than a conventional method.