• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.125-132
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• 2011

Fire monitoring system detects a fire based on the values of various sensors, such as smoke, CO, temperature, or change of temperature. It detects a fire by comparing sensed values with predefined threshold values for each sensor. However, to prevent a fire it is required to predict a situation which has a possibility of fire occurrence. In this work, we propose a fire recognition system using a fuzzy inference method. The rule base is constructed as a combination of fuzzy variables derived from various sensed values. In addition, in order to solve generalization and formalization problems of rule base construction from expert knowledge, we analyze features of fire patterns. The constructed rule base results in an improvement of the recognition accuracy. A fire possibility is predicted as one of 3 levels(normal, caution, danger). The training data of each level is converted to fuzzy rules by FCM(fuzzy C-means clustering) and those rules are used in the inference engine. The performance of the proposed approach is evaluated by using forest fire data from the UCI repository.

• Atmosphere
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• v.33 no.5
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• pp.531-548
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• 2023

We developed the Aviation Convective Index (ACI) for predicting deep convective area using the operational global Numerical Weather Prediction model of the Korea Meteorological Administration. Seasonally optimized ACI (ACI_SnOpt) was developed to consider seasonal variabilities on deep convections in Korea. Yearly optimized ACI (ACI_YrOpt) in Part 1 showed that seasonally averaged values of Area Under the ROC Curve (AUC) and True Skill Statistics (TSS) were decreased by 0.420% and 5.797%, respectively, due to the significant degradation in winter season. In Part 2, we developed new membership function (MF) and weight combination of input variables in the ACI algorithm, which were optimized in each season. Finally, the seasonally optimized ACI (ACI_SnOpt) showed better performance skills with the significant improvements in AUC and TSS by 0.983% and 25.641% respectively, compared with those from the ACI_YrOpt. To confirm the improvements in new algorithm, we also conducted two case studies in winter and spring with observed Convectively-Induced Turbulence (CIT) events from the aircraft data. In these cases, the ACI_SnOpt predicted a better spatial distribution and intensity of deep convection. Enhancements in the forecast fields from the ACI_YrOpt to ACI_SnOpt in the selected cases explained well the changes in overall performance skills of the probability of detection for both "yes" and "no" occurrences of deep convection during 1-yr period of the data. These results imply that the ACI forecast should be optimized seasonally to take into account the variabilities in the background conditions for deep convections in Korea.

• Atmosphere
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• v.33 no.5
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• pp.519-530
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• 2023

Deep convection can make adverse effects on safe and efficient aviation operations by causing various weather hazards such as convectively-induced turbulence, icing, lightning, and downburst. To prevent such damage, it is necessary to accurately predict spatiotemporal distribution of deep convective area near the airport and airspace. This study developed a new index, the Aviation Convective Index (ACI), for deep convection, using the operational global Unified Model of the Korea Meteorological Administration. The ACI was computed from combination of three different variables: 3-hour maximum of Convective Available Potential Energy, averaged Outgoing Longwave Radiation, and accumulative precipitation using the fuzzy logic algorithm. In this algorithm, the individual membership function was newly developed following the cumulative distribution function for each variable in Korean Peninsula. This index was validated and optimized by using the 1-yr period of radar mosaic data. According to the Receiver Operating Characteristics curve (AUC) and True Skill Score (TSS), the yearly optimized ACI (ACI_YrOpt) based on the optimal weighting coefficients for 1-yr period shows a better skill than the no optimized one (ACI_NoOpt) with the uniform weights. In all forecast time from 6-hour to 48-hour, the AUC and TSS value of ACI_YrOpt were higher than those of ACI_NoOpt, showing the improvement of averaged value of AUC and TSS by 1.67% and 4.20%, respectively.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.109-130
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• 2011

One of the major problems in the area of data mining is the size of the data, as most data set has huge volume these days. Streams of data are normally accumulated into data storages or databases. Transactions in internet, mobile devices and ubiquitous environment produce streams of data continuously. Some data set are just buried un-used inside huge data storage due to its huge size. Some data set is quickly lost as soon as it is created as it is not saved due to many reasons. How to use this large size data and to use data on stream efficiently are challenging questions in the study of data mining. Stream data is a data set that is accumulated to the data storage from a data source continuously. The size of this data set, in many cases, becomes increasingly large over time. To mine information from this massive data, it takes too many resources such as storage, money and time. These unique characteristics of the stream data make it difficult and expensive to store all the stream data sets accumulated over time. Otherwise, if one uses only recent or partial of data to mine information or pattern, there can be losses of valuable information, which can be useful. To avoid these problems, this study suggests a method efficiently accumulates information or patterns in the form of rule set over time. A rule set is mined from a data set in stream and this rule set is accumulated into a master rule set storage, which is also a model for real-time decision making. One of the main advantages of this method is that it takes much smaller storage space compared to the traditional method, which saves the whole data set. Another advantage of using this method is that the accumulated rule set is used as a prediction model. Prompt response to the request from users is possible anytime as the rule set is ready anytime to be used to make decisions. This makes real-time decision making possible, which is the greatest advantage of this method. Based on theories of ensemble approaches, combination of many different models can produce better prediction model in performance. The consolidated rule set actually covers all the data set while the traditional sampling approach only covers part of the whole data set. This study uses a stock market data that has a heterogeneous data set as the characteristic of data varies over time. The indexes in stock market data can fluctuate in different situations whenever there is an event influencing the stock market index. Therefore the variance of the values in each variable is large compared to that of the homogeneous data set. Prediction with heterogeneous data set is naturally much more difficult, compared to that of homogeneous data set as it is more difficult to predict in unpredictable situation. This study tests two general mining approaches and compare prediction performances of these two suggested methods with the method we suggest in this study. The first approach is inducing a rule set from the recent data set to predict new data set. The seocnd one is inducing a rule set from all the data which have been accumulated from the beginning every time one has to predict new data set. We found neither of these two is as good as the method of accumulated rule set in its performance. Furthermore, the study shows experiments with different prediction models. The first approach is building a prediction model only with more important rule sets and the second approach is the method using all the rule sets by assigning weights on the rules based on their performance. The second approach shows better performance compared to the first one. The experiments also show that the suggested method in this study can be an efficient approach for mining information and pattern with stream data. This method has a limitation of bounding its application to stock market data. More dynamic real-time steam data set is desirable for the application of this method. There is also another problem in this study. When the number of rules is increasing over time, it has to manage special rules such as redundant rules or conflicting rules efficiently.

• The Korean Journal of Pesticide Science
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• v.9 no.1
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• pp.26-34
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• 2005

3D-QSAR studies for the fungicidal activities against resistance phytophthora blight (RPC; 95CC7303) and sensitive phytophthora blight (Phytopthora capsici) (SPC; 95CC7105) by a series of new 2-alkoxyphenyl-3-phenylthioisoindoline-1-one derivatives (A & B) were studieded using comparative molecular similarity indices analyses (CoMSIA) methodology. From the based on the results, the two CoMSIA models, R5 and S1: as the best models were derivated. The statistical results of the models showed the best predictability and fitness for the fungicidal activities based on the cross- validated value ($q^2=0.714{\sim}0.823$) and non cross-validated, value ($r^2_{ncv.}=0.918{\sim}0.954$), respectively. The model R5 for fungicidal activity of RPC generated from the field fit alignment and combination of electrostatic field, H-bond acceptor field and LUMO molecular orbital field. The model S1 (or S5) for fungicidal activity of SPC generated from the atom based fit alignment and combination of steric field and HOMO molecular orbital field. The models also shows that inclusion of H-bond acceptor field (A) improved the statistical significance of the models. From the based graphical analyses of CoMSIA contribution maps, it was revealed that the novel selective character for fungicidal activities between the two fungi by modify of X-sub-stituent on the N-phenyl group and R-substituent on the S-phenyl group will be able to achivement.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.6
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• pp.1619-1637
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• 2016

With wide distribution of smart phones and development of mobile network, social network service (SNS) is displaying remarkable growth rates. Users build new social relations by sharing their interests, which brings surging growth to the SNS based on the combination between the strength of expanding the place for communication and distribution of smart phones featured with easy portability. This study is designed to understand impact factors of SNS on users in Korea and to conduct empirical research on casual relationship between the factors above and the factors affecting personal information behavior through the privacy protection and self-efficacy. In order to accomplish the objective above, the study presented a research model applied with key variables of the Health Belief Model (HBM) predicting behaviors capable of recognizing and preventing individual diseases in the field of health communication. To perform empirical verification on the research model of this study, a survey was conducted upon college students at N university located in Chungcheongnam-do and K university in rural area, who have experiences using the SNS. Through this survey, a total of 186 samples were collected, and path analysis was performed in order to analyze the relationship between the factors. Based on the findings from the survey, first, variables Perceived probability, Perceived severity, Perceived impairment of the HBM, key factors of personal information protection behavior on the SNS, were found to exhibit negative relationship with self-efficacy, and Perceived probability, Perceived benefit, Perceived impairment were found to exhibit negative relationship with privacy protection. But the above, Perceived severity showed positive relationship with privacy protection, and Perceived benefit and self-efficacy also displayed positive relationship. Second, although self-efficacy, a parameter, showed positive relationship with privacy protection, it demonstrated negative relationship with personal information protection behavior. Lastly, privacy protection exhibited positive relationship with personal information protection behavior. By presenting theoretical model reflected with characteristics of prevention based on these findings above unlike previous studies on personal information protection using technologies threatening personal information, this study is to provide theoretical and operational foundation capable of offering explanations how to predict personal information protection behavior on the SNS in the future.

• Journal of KIISE:Software and Applications
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• v.36 no.9
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• pp.691-696
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• 2009

Introduction: There are two types of asthma according to aspirin hypersensitivity: aspirin intolerant asthma (AIA) and aspirin tolerant asthma (ATA). The genetic risk factors that are related with asthma have been investigated intensively and extensively. However the combinatory effects of single nucleotide polymorphisms (SNPs) have hardly been evaluated. In this paper we searched the best set of SNPs that are useful to diagnose the two types of asthma. Methods: We examined 246 asthmatic patients (94 having aspirin intolerant asthma and 152 having aspirin tolerant asthma) and analyzed 25 SNPs typed in them, which are suspected to be associated with asthma. Normalized mutual information values of combinations of typed SNPs are calculated, and those with high normalized mutual information values are selected. We use support vector machines to evaluate the prediction accuracy of the selected combinations. Results: The best combination model turns out four-locus and consists of ALOX5_p1_1708, B2ADR_q1_46, CCR3_p1_520, CysLTR1_p1_634. Its normalized mutual information value is 0.053 and the accuracy in predicting ATA disease risk among asthmatic patients is 71.14%.

• Composites Research
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• v.36 no.3
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• pp.199-204
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• 2023

A combination of a metallic mesh and an adhesive layer of metallic particle/epoxy composite was introduced as an intermediate layer to enhance the adhesion between cold-sprayed particles and fiber-reinforced composites (FRCs). Aluminum was considered for both the metallic particles in the adhesive and the metallic mesh. To predict the mechanical characteristics of the intermediate bond layer under a high strain rate, the properties of the adhesive layer needed to be calculated or measured. Therefore, in this study, the Al particle/epoxy adhesive was homogenized by using a rule of mixture. To verify the homogenization, the penetration depth, and the thickness decrease after the cold spray deposition from the undeformed surface, was monitored with FE analysis and compared with experimental observation. The comparison displayed that the penetration depth was comparable to the diameters of one cold spray particle, and thus the homogenization approach can be reasonable for the prediction of the stress level of particulate polymer composite interlayer under a high strain rate for cold spray processing.

• Journal of Korean Association for Spatial Structures
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• v.11 no.3
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• pp.95-103
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• 2011

Among structural systems for complex-shaped tall buildings, diagrid system is widely used because of its structural efficiency and beauty of form. Recently, mega frame is favorably employed as a suitable structural system for skyscrapers because this structural system has sufficient stiffness against the lateral forces by combination of mega members which consist of many columns and girders. Diagrid mega frame system is expected to be promising structural system for future super tall buildings. However, it takes tremendous analysis times and engineer's efforts to predict the structural behavior of tall buildings applied with diagrid mega frame system because the diagrid mega frame structure has significant numbers of elements and nodes. Therefore, efficient analytical method for all buildings applied with diagrid mega frame system has been proposed in this study to reduce the efforts and time required for the analysis and design of diagrid mega frame structure. To this end, an efficient modelling technique using the characteristics of diagrid mega frame structures and an efficient analytical model using minimal DOFs by the matrix condensation method were proposed in this study. Based on the analysis of an example structure, the effectiveness and accuracy of the proposed method have been verified by the comparison between the results of the proposed method and the conventional method.

• Information Systems Review
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• v.7 no.2
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• pp.41-59
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• 2005

Mobile games have emerged as the most innovative entertainment technology adding new revenue streams, taking advantage of the potential of wireless consumer applications and service offerings. Mobile games, like any other types of computer game, offer a unique value for users in providing an exciting digital experience in virtual worlds. Players can become empowered through the development of new characters and strategies within games to achieve rewarding successes against the computers and other players. In this paper, we attempt to investigate the factors influencing the usage and acceptance of the mobile games in Korea, based on the extended version of the Technology Acceptance Model(TAM). Based on data collected from survey, we show that perceived usefulness is the major determinant for users to play mobile games. Two factors, including perceived enjoyment and self-expressiveness, are empirically shown to determine perceived usefulness. In addition, perceived ease of use, rewards, operational quality of device, and design/story have been showed to significantly and directly affect perceived enjoyment. It was also confirmed that self-efficacy and operational quality of device are the antecedents of perceived ease of use. Based upon the statistical results, some useful guidelines for game development and market penetration strategies are also provided.