• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.2
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• pp.179-187
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• 2010

The use of high frequency band, broad band and MIMO antenna is expected in the next generation mobile communication system. By the rapid increase of demand for wireless communications and the explosive increase of the mobile communication services, researches for optimization of next-generation mobile communication system are required. In the existing MIMO channel models, propagation-environments are commonly classified into urban, suburban, rural area, etc. However such approaches can have drawbacks in that many different morphologies may exist even in the urban area, for example. In this paper, we introduced path morphology concept, and proposed the method of morphology classification considering the building height, density, etc. Delay spread(DS), angular spread(AS) of AoD and AoA analyzed for each environment using the ray tracing technique. Based on the analysis, a MIMO channel model appropriate in domestic environment was suggested.

• Journal of the Korea Institute of Building Construction
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• v.21 no.5
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• pp.483-493
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• 2021

Urban overcrowding has created an explosive supply and demand for high-rise buildings. High-rise buildings are contributing to enhancing the image of the city by serving as focal points, but due to the stack effect, malfunction of elevator doors, difficulties in opening and closing the doors and windows of the outer wall, smoke and odors spreading to the upper floors, noise, energy loss, fire and pollutants have been causing various unexpected problems such as rapid spread of fire. This study classified high-rise buildings according to their vertical zoning, analyzed the causes of and solutions to the stack effect, and derived design and construction methods. Through the initial plan to block the outside air and securing airtightness through precise construction, we sought ways to secure the airtightness inside and outside the building by actively blocking the airflow from the lower floors. In addition, the facility solution can be a measure to reduce the specific phenomena caused by the stack effect, but it should only be applied to the minimum extent because the potential for secondary damage is high. This study emphasized the need for systematic stack effect management by suggesting design and construction measures for each vertical zoning of the causes and countermeasures of the stack effect. It is expected that this study will be helpful not only for design and construction, but also for building maintenance.

• Journal of the Society of Disaster Information
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• v.20 no.1
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• pp.47-59
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• 2024

Purpose: This study aims to propose measures for the prevention of fire and explosion accidents within manufacturing facilities by improving the existing classification criteria for hazardous locations based on the leakage patterns of flammable liquids. The objective is to suggest ways to safely manage ignition sources and combustible materials. Method: The hazardous locations were calculated using "KS C IEC 60079-10-1," and the calculated explosion hazard distances were visualized in 3D. Additionally, the formula for the atmospheric dispersion of flammable vapors, as outlined in "P-91-2023," was utilized to calculate the dispersion rates within the hazardous locations represented in 3D. Result: Visualization of hazardous locations in 3D enabled the identification of blind spots in the floor plan, facilitating immediate recognition of ignition sources within these areas. Furthermore, when calculating the time taken for the Lower Explosive Limit (LEL) to reach within the volumetric space of the hazardous locations represented in 3D, it was found that the risk level did not correspond identically with the explosion hazard distances. Conclusion: Considering the atmospheric dispersion of flammable liquids, it was concluded that safety management should be conducted. Therefore, a method for calculating the concentration values requiring detection and alert based on realistically achievable ventilation rates within the facility is proposed.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.179-196
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• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.