• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Journal of the Korea Society for Simulation
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• v.17 no.2
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• pp.91-103
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• 2008

In order to simulate the behavior of automated manufacturing systems, the performance of material handling systems should be measured dynamically. Multi-Agent technology could be well adapted for the development of simulator for distributed and intelligent manufacture systems. A multi-agent system is composed of one coordination agent and multiple application agents. Issues in AGVS simulator can be classified by the set-up and operating problems. Decisions on the number of vehicles, bi- or uni-directional guide-path, etc. are fallen into the set-up problem category, while deadlock tree algorithm and conflict resolution are in operating problem. In this paper, a multi-agent based deadlock-free simulator for automated guided vehicle system(AGVS) are proposed through the use of multi-agent technologies and the development of deadlock-free algorithm. In this AGVS simulator proposed, well-known Floyd algorithm is used to create AGVS Guide path, through which AGVS move. Also, AGVs avoid vehicle conflict and deadlock using check path algorithm. And Moving vehicle agents are operated in real-time control by coordination agent. AGV position is dynamically calculated based on the concept of rolling time horizon. Simulator receives and presents operating information of vehicle in AGVS Gaunt chart. The performance of the proposed algorithm and developed simulator based on multi-agent are validated through set of experiments.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.10
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• pp.1161-1170
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• 2011

This paper presents a solution to the inverse problem for the service boundary conditions of thermal-flow and structure analysis in a catalyst substrate. The exhaust-gas purification efficiency of a catalyst substrate is influenced by the shape parameter, catalyst ingredients and so on and is estimated by the thermal flow uniformity. The formulations of the inverse problem of obtaining the thermal-flow parameters (inlet temperature, velocity, heat of reaction, convective heat-transfer coefficient) and the direct problem of estimating from a given outlet temperature distribution are described. An experiment was designed and the response-surface optimization technique was used to solve the proposed inverse problem. The temperature distribution of the catalyst substrate was obtained by thermal-flow analysis for the predicted thermal-flow parameters. The thermal stress and durability assessments for the catalyst substrate were performed on the basis of this temperature distribution. The efficiency and accuracy of the inverse approach have been demonstrated through the achievement of good agreement between the thermal-flow response surface model and the results of experimental vehicle tests.

• Journal of Internet Computing and Services
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• v.19 no.5
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• pp.89-96
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• 2018

In online communities, a large number of participants can exchange their opinion using replies without time and space restrictions. While the online space provides quick and free communication, it also easily triggers unnecessary quarrels and conflicts. The network established on the discussion participants is an important cue to analyze the confrontation and predict serious disputes. In this paper, we present a quantitative measure for polarity observed on the discussion network built from reply exchanges in online communities. The proposed method uses the comment exchange information to establish the user interaction network graph, computes its maximum spanning tree, and then performs vertex coloring to assign two colors to each node in order to divide the discussion participants into two subsets. Using the proportion of the comment exchanges across the partitioned user subsets, we compute the polarity measure, and quantify how discussion participants are bipolarized. Using experimental results, we demonstrate the effectiveness of our method for detecting polarization and show participants of a specific discussion subject tend to be divided into two camps when they debate.

• Journal of Technology Innovation
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• v.21 no.1
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• pp.279-301
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• 2013

This study demonstrates how social network analysis can be used for identifying potential buyers in technology marketing; in such, the methodology and empirical results are proposed. First of all, we derived the three most important 'seed' keywords from 'technology description' sections. The technologies are generated by various types of R&D activities organized by South Korea's public research institutes in the fundamental science fields. Second, some 3, 000 words were collected from websites related to the three 'seed' keywords. Next, three network matrices (i.e., one matrix per seed keyword) were constructed. To explore the technology network structure, each network is analyzed by degree centrality and Euclidean distance. The network analysis suggests 100 potentially demanding companies and identifies seven common companies after comparing results derived from each network. The usefulness of the result is verified by investigating the business area of the firm's homepages. Finally, five out of seven firms were proven to have strong relevance to the target technology. In terms of social network analysis, this study expands its application scope of methodology by combining semantic network analysis and the technology marketing method. From a practical perspective, the empirical study suggests the illustrative framework for exploiting prospective demanding companies on the web, raising possibilities of technology commercialization in the basic research fields. Future research is planned to examine how the efficiency of process and accuracy of result is increased.

• The Journal of the Korea Contents Association
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• v.13 no.1
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• pp.115-126
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• 2013

The purpose of this study is to understand the utility of election campaign advertising by applying Q methodology that draw upon schematic model in subjectivity study. A survey was carried out among college students to classify the 16 selected Q-statements into a normal distribution using a 7 point scale. The collected data was analyzed using QUANL program, and principal component analysis using varimax rotation was used to identify the types of perceived utility of election campaign advertising. Type I can be categorized by a strong concern for the advertising technique improvement, Type II can be categorized by advocating for interest induction and Type III can be categorized by the truth inducement. The use of Q methodology provides insights into audience perceptions on the utility of election campaign advertising that would not be available through traditional methodologies and offers a foundation for audience involvement to address and overcome concerns about the utility of advertising for election campaign.

• The KIPS Transactions:PartD
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• v.17D no.2
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• pp.87-102
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• 2010

A vast amount of traffic data is produced every day from detection devices but this data includes a considerable amount of errors and missing values. Moreover, this information is periodically deleted before it could be used as important analysis information. Therefore, this paper discusses the implementation of an integrated traffic history database system that continuously stores the traffic data as a multidimensional model and increases the validity and completeness of the data via a flow of processing steps, and provides a what-if analysis function. The implemented system provides various techniques to correct errors and missing data patterns, and a what-if analysis function that enables the analysis of results under various conditions by allowing the flexible definition of various process related environment variables and combinations of the processing flows. Such what-if analysis functions dramatically increase the usability of traffic data but are not provided by other traffic data systems. Experimantal results for cleaning the traffic history data showed that it provides superior performance in terms of validity and completeness.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2003.11a
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• pp.31-35
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• 2003

폴리카보네이트는 내열성과 투명성이 우수한데 비해 내후성이 좋지 않아 황변 및 물성이 저하되고, 내찰상성이 약하여 긁히기 쉬운데다 이물질에 의해 오염되기 쉬워 투명성이 저하되는 문제점이 있다. 이러한 단점을 극복하고, 사용하는 용도에 따라 요구되는 다양한 기능성을 부여하기 위하여 폴리카보네이트 표면에 기능성층을 형성시킴으로써 그 목적을 달성하고자 한다. 본 논문에서는 이온 주입기술을 이용하여, 폴리카보네이트 표면의 전기전도도 특성을 향상시키고, 피부암 및 백내장 등을 유발하는 유해한 자외선 (UV-A, UV-B)을 차단하려 한다. 표면전기전도도의 향상은 이물질로부터 오염되는 정도를 낮추며, 정전기를 방지할 수 있다. PC(Polycarbonate) 표면에 $N^+,\;Ar^+,\;Kr^+,\;Xe^+$ 이온을 에너지 20keV에서 50keV을 사용하여, 주입량 $5{\times}10^{15}\;{\sim}\7{\times}10^{16}\cm^2$ 로 조사하였다. 이온 주입된 PC의 표면을 두 접점 방법의 표면 저항 측정으로 표면전기전도도 특성을 알아보았고, 자외선차단 특성은 UV-Vis 로 분석하였다. 이들 전기적 광학적 특성간의 상관관계를 관찰하고, 이러한 특성을 나타내는 화학적 기능그룹들의 변화를 보기 위해 FTIR 분석법으로 관찰하였다. 이온조사량의 증가에 따라 표면저항은 $10^7{\Omega}/sq$까지 감소하여 표면전기특성을 증가시키며, 자외선 차단 특성은 UV-A를 95%까지 차단하여 인체에 유해한 자외선 차단에 유용함을 확인하였다. 이러한 특성은 PC 표면에 카본 네트워크 형성과 $\pi$전자들의 운동량을 증가시키는 구조로 고분자 사슬들의 결합구조 변형에 의한 것으로 생각된다.블을 가지고 파서를 설계하였다. 파서의 출력으로 AST가 생성되면 번역기는 AST를 탐색하면서 의미적으로 동등한 MSIL 코드를 생성하도록 시스템을 컴파일러 기법을 이용하여 모듈별로 구성하였다.적용하였다.n rate compared with conventional face recognition algorithms. 아니라 실내에서도 발생하고 있었다. 정량한 8개 화합물 각각과 총 휘발성 유기화합물의 스피어만 상관계수는 벤젠을 제외하고는 모두 유의하였다. 이중 톨루엔과 크실렌은 총 휘발성 유기화합물과 좋은 상관성 (톨루엔 0.76, 크실렌, 0.87)을 나타내었다. 이 연구는 톨루엔과 크실렌이 총 휘발성 유기화합물의 좋은 지표를 사용될 있고, 톨루엔, 에틸벤젠, 크실렌 등 많은 휘발성 유기화합물의 발생원은 실외뿐 아니라 실내에도 있음을 나타내고 있다.>10)의 $[^{18}F]F_2$를 얻었다. 결론: $^{18}O(p,n)^{18}F$ 핵반응을 이용하여 친전자성 방사성동위원소 $[^{18}F]F_2$를 생산하였다. 표적 챔버는 알루미늄으로 제작하였으며 본 연구에서 연구된 $[^{18}F]F_2$가스는 친핵성 치환반응으로 방사성동위원소를 도입하기 어려운 다양한 방사성의 약품개발에 유용하게 이용될 수 있을 것이다.었으나 움직임 보정 후 영상을 이용하여 비교한 경우, 결합능 변화가 선조체 영역에서 국한되어 나타나며 그 유의성이 움직임 보정 전에 비하여 낮음을 알 수 있었다. 결론: 뇌활성화 과제 수행시에 동반되는 피험자의 머리 움직임에 의하여 도파민 유리가 과대평가되었으며 이는 이 연구에서 제안한 영상정합을 이용한 움직임 보정기법에 의해서 개선되었다. 답이 없는 문제, 문제 만들기, 일반화가 가능한 문제 등으

• Journal of Korea Water Resources Association
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• v.45 no.12
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• pp.1321-1330
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• 2012

In this study, the daily inflow at the basin of Chungju dam is predicted using wavelet-artificial neural network for nonlinear model. Time series generally consists of a linear combination of trend, periodicity and stochastic component. However, when framing time series model through these data, trend and periodicity component have to be removed. Wavelet transform which is denoising technique is applied to remove nonlinear dynamic noise such as trend and periodicity included in hydrometeorological data and simple noise that arises in the measurement process. The wavelet-artificial neural network (WANN) using data applied wavelet transform as input variable and the artificial neural network (ANN) using only raw data are compared. As a results, coefficient of determination and the slope through linear regression show that WANN is higher than ANN by 0.031 and 0.0115 respectively. And RMSE and RRMSE of WANN are smaller than those of ANN by 37.388 and 0.099 respectively. Therefore, WANN model applied in this study shows more accurate results than ANN and application of denoising technique through wavelet transforms is expected that more accurate predictions than the use of raw data with noise.

• Journal of agriculture & life science
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• v.53 no.5
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• pp.153-165
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• 2019

This paper analyzes the priorities of introducing agricultural environmental conservation practices for the successful introduction of agricultural environmental conservation programs and promotes customized agricultural environment conservation practices packages suitable for the local environment. Agricultural environmental conservation practice consists of three fields: soil, water, and air, nine sub-fields, and 30 practice skills. Using the advantages of AHP and BWS appropriately for priority analysis, three areas and nine sub-fields are measured using AHP techniques, and the practical activities of each fields are measured by priorities using BWS techniques to enhance the differentiation and completeness of research. In addition, the criteria for evaluating priorities of practical activities used 'Environmental effectiveness' and 'Technical feasibility'. As a result of the priority evaluation, the 'Soil testing and reduction of fertilizer and livestock manure application' activities were evaluated as having the highest priority. Based on the results of the priorities for these practical activities, examples of customized practical activity packages by farming type and environmental conditions were presented.