• Journal of Agricultural Extension & Community Development
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• v.25 no.1
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• pp.1-13
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• 2018

This study aims to identify factors affecting the acceptance intention of cultivating a new Italian ryegrass(IRG) variety using partial least square structural equation modeling(PLS-SEM) and find priority to maximize the acceptance intention of new IRG variety using importance-performance matrix analysis(IPMA). The data were collected on a seven-point Likert-type from 188 farm households located in Korea central region for two months. As a major result of PLS-SEM, expected effect significantly affected acceptance intention. The IPMA also showed expected effect should be considered as the most important factor to improve the acceptance intention. This study suggested the new technology distributors should scientifically prove and actively promote the effects such as increase in farm income, productivity improvement, labor saving and management efficiency caused by planting new IRG variety.

• Journal of KIISE:Software and Applications
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• v.31 no.11
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• pp.1519-1533
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• 2004

When Web-based special retrieval systems for scientific field extremely restrict the expression of user's information request, the process of the information content analysis and that of the information acquisition become inconsistent. In this paper, we apply the fuzzy retrieval model to solve the high time complexity of the retrieval system by constructing a reduced term set for the term's relatively importance degree. Furthermore, we perform a cluster retrieval to reflect the user's Query exactly through the similarity relation matrix satisfying the characteristics of the fuzzy compatibility relation. We have proven the performance of a proposed re-ranking model based on the similarity union of the fuzzy retrieval model and the document cluster retrieval model.

• Journal of Computing Science and Engineering
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• v.8 no.2
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• pp.94-106
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• 2014

Schema matching is widely used in many applications, such as data integration, ontology merging, data warehouse and dataspaces. In this paper, we propose a novel matching technique that is based on the order of attributes appearing in the schema structure of query results. The appearance order embodies the extent of the importance of an attribute for the user examining the query results. The core idea of our approach is to collect statistics about the appearance order of attributes from the query logs, to find correspondences between attributes in the schemas to be matched. As a first step, we employ a matrix to structure the statistics around the appearance order of attributes. Then, two scoring functions are considered to measure the similarity of the collected statistics. Finally, a traditional algorithm is employed to find the mapping with the highest score. Furthermore, our approach can be seen as a complementary member to the family of the existing matchers, and can also be combined with them to obtain more accurate results. We validate our approach with an experimental study, the results of which demonstrate that our approach is effective, and has good performance.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.2
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• pp.172-183
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• 2014

This paper measures and compares the efficiency of national health technology R&D programs focused on translational research program increasing importance using data envelopment analysis (DEA). Three input variables and three output variables are selected for DEA. Inputs are funds, researchers, and project period and outputs are SCI (E) papers, applied and granted patents, and impact factor. This study uses a three-stage approach. In the first stage, output-based DEA model is applied to evaluate the efficiency of decision making unit (DMU). In the second stage, based on efficiency scores of target diseases high-efficiency group and low-efficiency group are classified. And then strategic portfolio matrix of translational research program is composed of four dimensions combining research types. Mann-Whitney U test is then run to compare average efficiency scores among four groups. In the final stage, Tobit regression model is used to estimate factors likely to influence the efficiency. The results are expected to provide policy implications for effectively establishing investment strategy and managing performance of R&D program.

• Korean Journal of Materials Research
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• v.27 no.6
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• pp.307-311
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• 2017

Organometal halide perovskite materials, due to the tunability of their electronic and optical properties by control of composition and structure, have taken a position of significant importance in optoelectronic applications such as photovoltaic and lighting devices. Despite numerous studies on the structure - property relationship, however, practical application of these materials in electronic and optical devices is still limited by their processability during fabrication. Achieving nano-sized perovskite particles embedded in a polymer matrix with high loading density and outstanding photoluminescence performance is challenging. Here, we demonstrate that the careful control of nanoparticle formation and growth in the presence of poly(methyl methacrylate) results in perovskite nanoparticle - polymer nanocomposites with very good dispersion and photoluminescence. Furthermore, this approach is found to prevent further growth of perovskite nanoparticles, and thus results in a more uniform film, which enables fabrication using the perovskite nanoparticles.

• Journal of Microbiology
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• v.43 no.5
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• pp.411-416
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• 2005

The high-throughput sequencing of microbial genomes has resulted in the relatively rapid accumulation of an enormous amount of genomic sequence data. In this context, the problem posed by the detection of promoters in genomic DNA sequences via computational methods has attracted considerable research attention in recent years. This paper addresses the development of a predictive model, known as the dependence decomposition weight matrix model (DDWMM), which was designed to detect the core promoter region, including the -10 region and the transcription start sites (TSSs), in prokaryotic genomic DNA sequences. This is an issue of some importance with regard to genome annotation efforts. Our predictive model captures the most significant dependencies between positions (allowing for non­adjacent as well as adjacent dependencies) via the maximal dependence decomposition (MDD) procedure, which iteratively decomposes data sets into subsets, based on the significant dependence between positions in the promoter region to be modeled. Such dependencies may be intimately related to biological and structural concerns, since promoter elements are present in a variety of combinations, which are separated by various distances. In this respect, the DDWMM may prove to be appropriate with regard to the detection of core promoter regions and TSSs in long microbial genomic contigs. In order to demonstrate the effectiveness of our predictive model, we applied 10-fold cross-validation experiments on the 607 experimentally-verified promoter sequences, which evidenced good performance in terms of sensitivity.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.167-168
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• 2022

Recently, starting with the permanent suspension of Gori 1 in Korea, the importance of the disposal of concrete structures in nuclear power plants has emerged, and environmental and safety are required to be proved accordingly. Safe radioactive waste disposal technology that immobilizes harmful radioactive elements, which are by-products of nuclear power, inside a solid matrix and recycling measures are needed to secure an efficient waste disposal space. This study was conducted to confirm whether recycled cement generated in the process of radioactive concrete treatment can be used as a solidifying material for radioactive waste treatment. In order to simulate the concrete exposed to radiation, aqueous solutions of Di-water, CsCl 1M, and CoCl₂ 1M were used as blending water at W/B 0.5. Tricalcium phosphate and Prussian blue were substituted with 5 wt.% based on the weight of recycled cement as a binder to improve solidification performance, and their hydration characteristic was analyzed.

• Journal of Information Science Theory and Practice
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• v.4 no.1
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• pp.21-29
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• 2016

Given the difference in research performance in various scientific fields, this study aims to weight and valuate current indicators used for evaluation of scientific productions (publications), in order to adjust these indicators in comparison to each other and make possible a more precise evaluation of scientific productions. This is a scientometrics study using documentary, evaluative, and survey techniques. The statistical population consisted of 106 top Iranian researchers, scientists, and scientific and research managers. Then their research résumé information was gathered and analyzed based on research questions. In order to compare values, the data gathered from research production performance of the population was weighted using Shannon entropy method. Also, the weights of each scientific production importance according to expert opinions (extracted from other works) was analyzed and after adjustment the final weight of each scientific production was determined. A pairwise matrix was used in order to determine the ratios. According to the results, in the area of engineering sciences, patents (0.142) in the area of science, international articles (0.074) in the area of humanities and social sciences, books (0.174), and in the area of medical sciences, international articles (0.111) had the highest weight compared to other information formats. By dividing the weights for each type of publication, the value of each scientific production compared to other scientific productions in the same field and productions of other fields was calculated. Validation of the results in the studied population resulted in very high credibility for all investigated indicators in all four fields. By using these values and normalized ratios of publication indicators it is possible to achieve precise and adjusted results, making it possible to feasibly use these results in realistic policy making.

• Journal of Digital Convergence
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• v.15 no.8
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• pp.103-114
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• 2017

With the development of IT technology and the establishment of logistics infrastructure, online shopping market and online fresh food market are growing. The purpose of this study is to find a plan to activate Fresh Logistics through online shopping mall using BCG matrix and Importance Performance Analysis for online shopping companies. Firstly, the BCG matrix results Companies in 'cash cow', 'question mark', and 'dog' areas are in need of improvement. In order to improve this situation, it is necessary to activate the Fresh logistics market which is currently growing. Fresh logistics can be activated by investing in the maintenance and strengthening areas and key investment areas by minimizing the expenditure on the non-priority and excess areas according to the results of IPA. Therefore, it is expected that online shopping companies will be improved by activating online shopping market and online fresh logistics market.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.1-18
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• 2015

Collaborative filtering(CF) algorithm has been popularly used for recommender systems in both academic and practical applications. A general CF system compares users based on how similar they are, and creates recommendation results with the items favored by other people with similar tastes. Thus, it is very important for CF to measure the similarities between users because the recommendation quality depends on it. In most cases, users' explicit numeric ratings of items(i.e. quantitative information) have only been used to calculate the similarities between users in CF. However, several studies indicated that qualitative information such as user's reviews on the items may contribute to measure these similarities more accurately. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's reviews can be regarded as the informative source for identifying user's preference with accuracy. Under this background, this study proposes a new hybrid recommender system that combines with users' review mining. Our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and his/her text reviews on the items when calculating similarities between users. In specific, our system creates not only user-item rating matrix, but also user-item review term matrix. Then, it calculates rating similarity and review similarity from each matrix, and calculates the final user-to-user similarity based on these two similarities(i.e. rating and review similarities). As the methods for calculating review similarity between users, we proposed two alternatives - one is to use the frequency of the commonly used terms, and the other one is to use the sum of the importance weights of the commonly used terms in users' review. In the case of the importance weights of terms, we proposed the use of average TF-IDF(Term Frequency - Inverse Document Frequency) weights. To validate the applicability of the proposed system, we applied it to the implementation of a recommender system for smartphone applications (hereafter, app). At present, over a million apps are offered in each app stores operated by Google and Apple. Due to this information overload, users have difficulty in selecting proper apps that they really want. Furthermore, app store operators like Google and Apple have cumulated huge amount of users' reviews on apps until now. Thus, we chose smartphone app stores as the application domain of our system. In order to collect the experimental data set, we built and operated a Web-based data collection system for about two weeks. As a result, we could obtain 1,246 valid responses(ratings and reviews) from 78 users. The experimental system was implemented using Microsoft Visual Basic for Applications(VBA) and SAS Text Miner. And, to avoid distortion due to human intervention, we did not adopt any refining works by human during the user's review mining process. To examine the effectiveness of the proposed system, we compared its performance to the performance of conventional CF system. The performances of recommender systems were evaluated by using average MAE(mean absolute error). The experimental results showed that our proposed system(MAE = 0.7867 ~ 0.7881) slightly outperformed a conventional CF system(MAE = 0.7939). Also, they showed that the calculation of review similarity between users based on the TF-IDF weights(MAE = 0.7867) leaded to better recommendation accuracy than the calculation based on the frequency of the commonly used terms in reviews(MAE = 0.7881). The results from paired samples t-test presented that our proposed system with review similarity calculation using the frequency of the commonly used terms outperformed conventional CF system with 10% statistical significance level. Our study sheds a light on the application of users' review information for facilitating electronic commerce by recommending proper items to users.