• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.1-8
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• 2014

Cloud computing helps big data processing to make various information using IT resources. The government has to start the RPS(Renewable Portfolio Standard) and induce the production of electricity using renewable energy equipment. And the government manages system to gather big data that is distributed geographically. The companies can purchase the REC(Renewable Energy Certificate) to other electricity generation companies to fill shortage among their duty from the system. Because of the RPS use voluntary competitive market in REC trade and the prices have the large variation, RPS is necessary to predict the equitable REC price using RPS big data. This paper proposed REC price prediction method base on fuzzy logic using the price trend and trading condition infra in REC market, that is modeled in cloud computing environment. Cloud computing helps to analyze correlation and variables that act on REC price within RPS big data and the analysis can be predict REC price by simulation. Fuzzy logic presents balanced REC average trading prices using the trading quantity and price. The model presents REC average trading price using the trading quantity and price and the method helps induce well-converged price in the long run in cloud computing environment.

• Journal of Korean Home Economics Education Association
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• v.29 no.4
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• pp.149-159
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• 2017

The objective of this study was to investigate the effects of family meals on eating behavior, academic achievement, and quality of life among middle school students. A total of 302 participants were recruited from a middle school at Goyangsi. We asked participants about family meals, eating behavior, academic achievement, and quality of life, using structured questionnaires. Family meal questionnaires were classified according to frequency, rules, and awareness. The findings of this study were as follows. First, there were significant differences between rules(p<0.05) and awareness (p<0.05) of family meals and family type. Second, there were statistically significant differences between frequency and awareness of family meals and eating behavior, academic achievement, and quality of life. On the other hand, there was a significant difference between rules about family meals and eating behaviors and quality of life. Third, we found that factors of family meal were positively related to the eating behavior, academic achievement, and quality of life at the level of statistical significance. Finally, we found that participants with a higher frequency of family meals and more positive eating behavior were more likely to higher academic achievement and quality of life than those in lower frequency of family meals and less positive eating behavior among middle school students. The frequency of family meals has a strong effect on higher academic achievement and better quality of life. In conclusion, engagement in family meals was related to better eating behavior, academic achievement, and quality of life among middle school students. Our findings may warrant further studies to support the benefit of family meals in improving eating behavior, academic achievement, and quality of life among high school students as well as middle school students.

• Journal of the Korea Convergence Society
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• v.9 no.4
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• pp.99-110
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• 2018

The data were collected from 171 physiologic malodor patients diagnosed in KUMC halitosis control clinic between 2008 and 2016. We selected 11 independent variables and 3 dependent variables, then planned to extract some convergent common factors affecting their physiologic malodor. We thought that those extracted convergent common factors could be utilized when preparing the contents of oral malodor preventive program. We used multiple regression analysis and path analysis method, for the analysis of influence of 11 independent factors to three salivary dependent factors(resting salivary flow rate, salivary buffering capacity, salivary precipitation rate). We have presented the physiologic malodor patients' chracteristics by descriptive statistical analysis, and also statistically analysed convergent common factors influencing directly or indirectly to their three dependent factors. We could reason that the sex, the character, the intake habit of breakfast, and the regular food intake habit could affect resting salivary flow rate, salivary buffering capacity and salivary precipitation rate.

• Journal of Korean Ophthalmic Optics Society
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• v.17 no.2
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• pp.241-247
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• 2012

Purpose: This study was conducted to assess the association of metabolic syndrome and its components with glaucoma. Methods: We investigated the associations of metabolic syndrome and its components (abdominal obesity, impaired fasting glucose, high blood pressure, and dyslipidemia) with glaucoma using data from 19,162 adults aged 40 or above among the Korean National Health and Nutrition Examination Survey III (2005), IV (2007-9), V (2010). The logistic regression analysis was used for assessing those associations after adjusting demographic, lifestyle, and social economic status and for assessing the association of metabolic medication intakes with glaucoma risks. Results: After adjusting for age and sex, the risk of glaucoma was significantly higher in the subjects with impaired fasting glucose(Odds Ratio (95% confidence interval): 1.78 (1.25, 2.53)) and metabolic syndrome (1.45 (1.01, 2.08)) than subjects without those. These associations increased when additional adjusting for smoking, alcohol use, regular physical activity, income, education status(impaired fasting glucose: 1.89 (1.29, 2.77), metabolic syndrome: 1.52 (1.03, 2.25)). Glaucoma prevalence was borderline significantly increased according to the number of metabolic abnormalities(age and sex adjusted p for trend = 0.055). Use of antihypertensive medication was significantly associated with the risk of glaucoma. Conclusions: Metabolic syndrome and impaired fasting glucose were significantly associated with the increased glaucoma risk. Use of antihypertensive medication was also significantly associated with the increased glaucoma risk.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.7
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• pp.299-306
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• 2022

In recent, sensors embedded in robots, equipment, and circuits have become common, and research for diagnosing device failures by learning measured sensor data is being actively conducted. This failure diagnosis study is divided into a classification model for predicting failure situations or types and a regression model for numerically predicting failure conditions. In the case of a classification model, it simply checks the presence or absence of a failure or defect (Class), whereas a regression model has a higher learning difficulty because it has to predict one value among countless numbers. So, the reason that regression modeling is more difficult is that there are many irregular situations in which it is difficult to determine one output from a similar input when predicting by matching input and output. Therefore, in this paper, we focus on input and output data with periodicity, analyze the input/output relationship, and secure regularity between input and output data by performing sliding window-based input data patterning. In order to apply the proposed method, in this study, current and temperature data with periodicity were collected from MMC(Modular Multilevel Converter) circuit system and learning was carried out using ANN. As a result of the experiment, it was confirmed that when a window of 2% or more of one cycle was applied, performance of 97% or more of fit could be secured.

• Journal of KIISE:Software and Applications
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• v.35 no.3
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• pp.189-196
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• 2008

To build autonomous agents who can make a decision on behalf of humans in time-critical complex environments, the formulation of operational knowledge base could be essential. This paper proposes the methodology of how to formulate the knowledge base and evaluates it in a practical application domain. We analyze threat data received from the multiple sensors of Aircraft Survivability Equipment(ASE) for Korean helicopters, and integrate the threat data into the inductive model through compilation technique which extracts features of the threat data and relations among them. The compiled protocols of state-action rules can be implemented as the brain of the ASE. They can reduce the amounts of reasoning, and endow the autonomous agents with reactivity and flexibility. We report experimental results that demonstrate the distinctive and predictive patterns of threats in simulated battlefield settings, and show the potential of compilation methods for the successful detection of threat systems.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.4
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• pp.304-309
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• 2013

This paper proposes a intelligent approach on range decision of figure of merit. Unknown range of the underwater target and the non-fixed signal excess make the uncertainty for the tracking process. Using the input data of signal excess related to the range, we establish the rule of the fuzzy set and the original data acquired by sonar can be transformed to the fuzzified data set. To reduce the error arisen from the unexpected data, we use the new data transformed in fuzzy set. The piecewise relations of the min value, max one, and the mean one are calculated. The three values are used for the expected range of the underwater target. By analysing the fluctuation of the data, we can expect the target's position and the characteristics of the maneuvering. The examples are presented to show the performance and the effectiveness of the proposed method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.9
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• pp.2354-2360
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• 2009

Healthcare is a field where ubiquitous computing is most widely used. We propose a mining-based healthcare multi-agent system for ubiquitous computing environments. This proposed scheme select diagnosis patterns using mining in the real-time biosignal data obtained from a patient's body. In addition, we classify them into normal, emergency and be ready for an emergency. This proposed scheme can deal with the enormous quantity of real-time sensing data and performs analysis and comparison between the data of patient's history and the real-time sensory data. We separate Association rule exploration into two data groups: one is the existing enormous quantity of medical history data. The other group is real-time sensory data which is collected from sensors measuring body temperature, blood pressure, pulse. Proposed system has advantage that can handle urgent situation in the far away area from hospital through PDA and mobile device. In addition, by monitoring condition of patient in a real time base, it shortens time and expense and supports medical service efficiently.

• The KIPS Transactions:PartD
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• v.17D no.6
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• pp.453-462
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• 2010

The switch from analog to digital broadcast television is extended rapidly. The DTV can offer multiple programming choices, interactive capabilities and so on. Moreover, with the spread of Internet, the information exchange between the communities is increasing, too. These facts lead to the new TV service environment which can offer customized TV programs to personal/community users. This paper proposes a 'Customized Digital TV System for Individuals/Communities based on Data Stream Mining' which can analyze user's pattern of TV watching behavior. Due to the characteristics of TV program data stream and EPG(electronic program guide), the data stream mining methods are employed in the proposed system. When a user is watching DTV, the proposed system can control the surrounding circumstances as using the user behavior profiles. Furthermore, the channel recommendation system on the smart phone environment is proposed to utilize the profiles widely.

• Journal of Intelligence and Information Systems
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• v.13 no.2
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• pp.69-80
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• 2007

The preferences of customers change over time. However, existing collaborative filtering (CF) systems are static, since they only incorporate information regarding whether a customer buys a product during a certain period and do not make use of the purchase sequences of customers. Therefore, the quality of the recommendations of the typical CF could be improved through the use of information on such sequences. In this study, we propose a new methodology for enhancing the quality of CF recommendation that uses customer purchase sequences. The proposed methodology is applied to a large department store in Korea and compared to existing CF techniques. Various experiments using real-world data demonstrate that the proposed methodology provides higher quality recommendations than do typical CF techniques with better performance.