• Title/Summary/Keyword: Explosive modeling

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MIMO Channel Modeling Using Concept of Path Morphology (Path Morphology 개념을 이용한 MIMO 채널 모델링)

  • Jeong, Won-Jeong;Yoo, Ji-Ho;Kim, Tae-Hong;Kim, Myung-Don;Chung, Hyun-Kyu;Bae, Seok-Hee;Pack, Jeong-Ki
    • 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.

Proposal for Ignition Source and Flammable Material Safety Management through 3D Modeling of Hazardous Area: Focus on Indoor Mixing Processes (폭발위험장소 구분도의 3D Modeling을 통한 점화원 및 가연물 안전관리 방안 제안: 실내 혼합공정을 중심으로)

  • Hak-Jae Kim;Duk-Han Kim;Young-Woo Chon
    • 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.

3-Dimensional Terrain Model of Ruins Distribution Using GIS (GIS를 이용한 유적분포 3차원 지형모델)

  • Kwak, Young-Joo;Kang, In-Joon;Jang, Yong-Gu;Kang, Young-Shin;Kim, Sang-Seok
    • Journal of Korean Society for Geospatial Information Science
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    • v.12 no.4 s.31
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    • pp.27-33
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    • 2004
  • Recently, as a part of NGIS project, cultural-assets digital map is produced by cultural properties administration and local autonomous entities. Cultural-assets unified GIS(Geographic Information System) is essential to cultural properties managers md other organizations which are executing land related business for appling it at planning stage. With explosive national land developments, it has been obligated to implement surface survey by the cultural properties protection Art. In this paper, the authors used the district of Sachon city and made 3D terrain model using digital map which is made of 1/5000 stale. Moreover, the authors improved to RDBMS(Relational Database Management System) structure and remodeled the existing DB design in detail, and also tried recovery of past sea level, based on researches about the water level of southern area. By recover past sea level. the shell mound, apart from lever is actually near lever at past, and it make sure its nature. The authors suggested to realize shape and kind of remains which have 3D information of accuracy and actualization from surface-surveying to excavation.

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A Study on the Buyer's Decision Making Models for Introducing Intelligent Online Handmade Services (지능형 온라인 핸드메이드 서비스 도입을 위한 구매자 의사결정모형에 관한 연구)

  • Park, Jong-Won;Yang, Sung-Byung
    • Journal of Intelligence and Information Systems
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    • v.22 no.1
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    • pp.119-138
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    • 2016
  • Since the Industrial Revolution, which made the mass production and mass distribution of standardized goods possible, machine-made (manufactured) products have accounted for the majority of the market. However, in recent years, the phenomenon of purchasing even more expensive handmade products has become a noticeable trend as consumers have started to acknowledge the value of handmade products, such as the craftsman's commitment, belief in their quality and scarcity, and the sense of self-esteem from having them,. Consumer interest in these handmade products has shown explosive growth and has been coupled with the recent development of three-dimensional (3D) printing technologies. Etsy.com is the world's largest online handmade platform. It is no different from any other online platform; it provides an online market where buyers and sellers virtually meet to share information and transact business. However, Etsy.com is different in that shops within this platform only deal with handmade products in a variety of categories, ranging from jewelry to toys. Since its establishment in 2005, despite being limited to handmade products, Etsy.com has enjoyed rapid growth in membership, transaction volume, and revenue. Most recently in April 2015, it raised funds through an initial public offering (IPO) of more than 1.8 billion USD, which demonstrates the huge potential of online handmade platforms. After the success of Etsy.com, various types of online handmade platforms such as Handmade at Amazon, ArtFire, DaWanda, and Craft is ART have emerged and are now competing with each other, at the same time, which has increased the size of the market. According to Deloitte's 2015 holiday survey on which types of gifts the respondents plan to buy during the holiday season, about 16% of U.S. consumers chose "homemade or craft items (e.g., Etsy purchase)," which was the same rate as those for the computer game and shoes categories. This indicates that consumer interests in online handmade platforms will continue to rise in the future. However, this high interest in the market for handmade products and their platforms has not yet led to academic research. Most extant studies have only focused on machine-made products and intelligent services for them. This indicates a lack of studies on handmade products and their intelligent services on virtual platforms. Therefore, this study used signaling theory and prior research on the effects of sellers' characteristics on their performance (e.g., total sales and price premiums) in the buyer-seller relationship to identify the key influencing e-Image factors (e.g., reputation, size, information sharing, and length of relationship). Then, their impacts on the performance of shops within the online handmade platform were empirically examined; the dataset was collected from Etsy.com through the application of web harvesting technology. The results from the structural equation modeling revealed that the reputation, size, and information sharing have significant effects on the total sales, while the reputation and length of relationship influence price premiums. This study extended the online platform research into online handmade platform research by identifying key influencing e-Image factors on within-platform shop's total sales and price premiums based on signaling theory and then performed a statistical investigation. These findings are expected to be a stepping stone for future studies on intelligent online handmade services as well as handmade products themselves. Furthermore, the findings of the study provide online handmade platform operators with practical guidelines on how to implement intelligent online handmade services. They should also help shop managers build their marketing strategies in a more specific and effective manner by suggesting key influencing e-Image factors. The results of this study should contribute to the vitalization of intelligent online handmade services by providing clues on how to maximize within-platform shops' total sales and price premiums.

A Study on the Black Box Design using Collective Intelligence Analysis (집단지성 분석법을 활용한 블랙박스 디자인 개발 연구)

  • Lee, Hee young;Hong, Jeong Pyo;Cho, Kwang Soo
    • Science of Emotion and Sensibility
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    • v.21 no.2
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    • pp.101-112
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    • 2018
  • This study was carried out to enhance the competitiveness of blackbox design for domestic and international companies, based on the explosive growth of the blackbox market due to development of blackbox design for vehicle accident prevention and post-treatment. In the past, the blackbox market has produced products indiscriminately to meet the ever-increasing demand of consumers. Therefore, we thought a new design method was necessary to effectively investigate the needs of rapidly changing consumers. In this study, we aimed to identify the best-selling blackbox to understand the design flow, and the optimum area for a blackbox, considering the uniqueness of associated vehicle. Based on discussion with blackbox design experts, we studied the direction of design and the problems with blackbox use, which were reflected in blackbox development. Through this research, two types of design - leading blackbox (A type) and mass production blackbox (B type) - were proposed for compatibility of the blackbox with the car. The leading type of blackbox was positioned so that it was wrapped with the room mirror hinge before the screw was fastened, in order to achieve an integrated design. Therefore, we designed an integrated form and resolved the placement problem of an adhesive blackbox. To blend, the mass production blackbox implemented material and surface processing in the same way with the car, and adopted the slide structure to automatically turn off the main body power when removing the SDcard, reflecting consumer needs. This study considers evolving consumer needs through a case study and collective intelligence and deals with implementation of the whole design process during mass production. In this study, we aimed to strengthen the competitiveness of the blackbox design based on design method and its realization.

The Impact of Perceived Risks Upon Consumer Trust and Purchase Intentions (인지된 위험의 유형이 소비자 신뢰 및 온라인 구매의도에 미치는 영향)

  • Hong, Il-Yoo B.;Kim, Woo-Sung;Lim, Byung-Ha
    • Asia pacific journal of information systems
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    • v.21 no.4
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    • pp.1-25
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    • 2011
  • Internet-based commerce has undergone an explosive growth over the past decade as consumers today find it more economical as well as more convenient to shop online. Nevertheless, the shift in the common mode of shopping from offline to online commerce has caused consumers to have worries over such issues as private information leakage, online fraud, discrepancy in product quality and grade, unsuccessful delivery, and so forth, Numerous studies have been undertaken to examine the role of perceived risk as a chief barrier to online purchases and to understand the theoretical relationships among perceived risk, trust and purchase intentions, However, most studies focus on empirically investigating the effects of trust on perceived risk, with little attention devoted to the effects of perceived risk on trust, While the influence trust has on perceived risk is worth studying, the influence in the opposite direction is equally important, enabling insights into the potential of perceived risk as a prohibitor of trust, According to Pavlou (2003), the primary source of the perceived risk is either the technological uncertainty of the Internet environment or the behavioral uncertainty of the transaction partner. Due to such types of uncertainty, an increase in the worries over the perceived risk may negatively affect trust, For example, if a consumer who sends sensitive transaction data over Internet is concerned that his or her private information may leak out because of the lack of security, trust may decrease (Olivero and Lunt, 2004), By the same token, if the consumer feels that the online merchant has the potential to profit by behaving in an opportunistic manner taking advantage of the remote, impersonal nature of online commerce, then it is unlikely that the merchant will be trusted, That is, the more the probable danger is likely to occur, the less trust and the greater need to control the transaction (Olivero and Lunt, 2004), In summary, a review of the related studies indicates that while some researchers looked at the influence of overall perceived risk on trust level, not much attention has been given to the effects of different types of perceived risk, In this context the present research aims at addressing the need to study how trust is affected by different types of perceived risk, We classified perceived risk into six different types based on the literature, and empirically analyzed the impact of each type of perceived risk upon consumer trust in an online merchant and further its impact upon purchase intentions. To meet our research objectives, we developed a conceptual model depicting the nomological structure of the relationships among our research variables, and also formulated a total of seven hypotheses. The model and hypotheses were tested using an empirical analysis based on a questionnaire survey of 206 college students. The reliability was evaluated via Cronbach's alphas, the minimum of which was found to be 0.73, and therefore the questionnaire items are all deemed reliable. In addition, the results of confirmatory factor analysis (CFA) designed to check the validity of the measurement model indicate that the convergent, discriminate, and nomological validities of the model are all acceptable. The structural equation modeling analysis to test the hypotheses yielded the following results. Of the first six hypotheses (H1-1 through H1-6) designed to examine the relationships between each risk type and trust, three hypotheses including H1-1 (performance risk ${\rightarrow}$ trust), H1-2 (psychological risk ${\rightarrow}$ trust) and H1-5 (online payment risk ${\rightarrow}$ trust) were supported with path coefficients of -0.30, -0.27 and -0.16 respectively. Finally, H2 (trust ${\rightarrow}$ purchase intentions) was supported with relatively high path coefficients of 0.73. Results of the empirical study offer the following findings and implications. First. it was found that it was performance risk, psychological risk and online payment risk that have a statistically significant influence upon consumer trust in an online merchant. It implies that a consumer may find an online merchant untrustworthy if either the product quality or the product grade does not match his or her expectations. For that reason, online merchants including digital storefronts and e-marketplaces are suggested to pursue a strategy focusing on identifying the target customers and offering products that they feel best meet performance and psychological needs of those customers. Thus, they should do their best to make it widely known that their products are of as good quality and grade as those purchased from offline department stores. In addition, it may be inferred that today's online consumers remain concerned about the security of the online commerce environment due to the repeated occurrences of hacking or private information leakage. Online merchants should take steps to remove potential vulnerabilities and provide online notices to emphasize that their website is secure. Second, consumer's overall trust was found to have a statistically significant influence on purchase intentions. This finding, which is consistent with the results of numerous prior studies, suggests that increased sales will become a reality only with enhanced consumer trust.

Product Recommender Systems using Multi-Model Ensemble Techniques (다중모형조합기법을 이용한 상품추천시스템)

  • Lee, Yeonjeong;Kim, Kyoung-Jae
    • 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.