• Journal of the Korean Society for Railway
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• v.12 no.2
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• pp.267-275
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• 2009

Max-plus algebra is a nonlinear system comprised of two operations, maximization (max) and addition (Plus), which are corresponding to the addition and the multiplication in conventional algebra, respectively. This methodology is applicable to many discrete event systems containing the state transition with the maximization and addition operation. Timetable with connection is one of such systems. We present the method based on max-plus algebra, which can make up timetable considering transfer and analyse its stability and robustness. In this study, it will be shown how to make up the timetable of the urban train and analyse its stability using Max-Plus algebra.

• The Journal of the Acoustical Society of Korea
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• v.24 no.5
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• pp.294-300
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• 2005

Generally, traditional approaches to track the target position are to estimate ranges and bearings by 2-D MUSIC (MUltiple 519na1 Classification) method. and to associate estimates of 2-D MUSIC made at different time points with the right targets by JPDA (Joint Probabilistic Data Association) filter in the near field. However, the disadvantages of these approaches are that these have the data association Problem in tracking multiple targets. and that these require the heavy computational load in estimating a 2-D range/bearing spectrum. In case multiple targets are adjacent. the tracking performance degrades seriously because the estimate of each target's Position has a large error. In this paper, we proposed a new tracking algorithm using Position innovations extracted from the senor output covariance matrix in the near field. The proposed algorithm is demonstrated by the computer simulations dealing with the tracking of multiple closing and crossing targets.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1B
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• pp.41-51
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• 2012

Sometimes, there exist many alternatives for doing a SOC project. However, the limitation of the fund requires the determination of investment priority for the alternatives. This may be performed according to the degree of importance of individual alternatives. Especially, the river improvement project at the downstream of dams has complex and various values and this characteristics make it difficult decision-maker to do reasonable determination. This study aims to determine an investment priority of 33 alternatives in the river improvement project at the downstream of dams using PROMETHEE method which has advantages in determining the priority. In this study, we determined evaluation criteria and attributes by considering the functions and objectives of the river improvement project at the downstream of dams. The eigenvector method in AHP was used to estimate the relative importance of evaluation criterion. Based on the estimation, we determined investment priority of 33 alternatives by PROMETHEE method and the priority of alternatives was derived in the order of Juam regulation dam, Unmun dam, Yongdam dam and so on. The results of this study could provide a reasonable standard to the decision-maker for the determination of investment priority of alternatives.

• Journal of the Optical Society of Korea
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• v.18 no.4
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• pp.317-329
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• 2014

In vision measurement systems based on structured light, the key point of detection precision is to determine accurately the central position of the projected laser line in the image. The purpose of this research is to extract laser line centers based on a decision function generated to distinguish the real centers from candidate points with a high recognition rate. First, preprocessing of an image adopting a difference image method is conducted to realize image segmentation of the laser line. Second, the feature points in an integral pixel level are selected as the initiating light line centers by the eigenvalues of the Hessian matrix. Third, according to the light intensity distribution of a laser line obeying a Gaussian distribution in transverse section and a constant distribution in longitudinal section, a normalized model of Hessian matrix eigenvalues for the candidate centers of the laser line is presented to balance reasonably the two eigenvalues that indicate the variation tendencies of the second-order partial derivatives of the Gaussian function and constant function, respectively. The proposed model integrates a Gaussian recognition function and a sinusoidal recognition function. The Gaussian recognition function estimates the characteristic that one eigenvalue approaches zero, and enhances the sensitivity of the decision function to that characteristic, which corresponds to the longitudinal direction of the laser line. The sinusoidal recognition function evaluates the feature that the other eigenvalue is negative with a large absolute value, making the decision function more sensitive to that feature, which is related to the transverse direction of the laser line. In the proposed model the decision function is weighted for higher values to the real centers synthetically, considering the properties in the longitudinal and transverse directions of the laser line. Moreover, this method provides a decision value from 0 to 1 for arbitrary candidate centers, which yields a normalized measure for different laser lines in different images. The normalized results of pixels close to 1 are determined to be the real centers by progressive scanning of the image columns. Finally, the zero point of a second-order Taylor expansion in the eigenvector's direction is employed to refine further the extraction results of the central points at the subpixel level. The experimental results show that the method based on this normalization model accurately extracts the coordinates of laser line centers and obtains a higher recognition rate in two group experiments.

• 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.

• The Journal of the Korea Contents Association
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• v.20 no.9
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• pp.29-42
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• 2020

The purpose of this study is to present a qualitative research method for extracting and analyzing the comments written by YouTube video users. To do this, we used YouTube users' feedback to measure the hedonic, social, and utilitarian gratification of use and gratification theory(UGT) through by using analysis and topic modeling. The result of the measurement found that the first reason why users watch the trot singer, Cho Myung-sub's video in the KBS Korean broadcasting channel is to achieve hedonic gratification with high frequency. In word-document network analysis, the degree of centrality was high in words, such as 'cheering', 'thank you', 'fighting', and 'best'. Betweenness centrality is similar to the degree of centrality. Eigenvector centrality also shows that words such as 'love', 'heart', and 'thank you' are the most influential words of users' opinions. The results of the centrality analysis present that the majority of video users show their 'love', 'heart' and 'thank you' for the video. it indicates that the high words in centrality analysis is consistent with the high frequency words of hedonic and social gratification dimension of the UGT. The study has research methodological implication that shed light on the motivations for watching YouTube videos with UGT using text mining techniques that automate qualitative analysis, rather than following a survey-based structural equation model.

• Journal of the Korean Chemical Society
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• v.26 no.4
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• pp.195-204
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• 1982

Semi-empirical MO calculations, EHT, CNDO/2, MINDO/3, and MNDO met hods, were performed on various geometries of n-butane, n-alkyl radical and tetramethylene diracal (triplet) in order to compare eigenvalue and eigenvector properties with those obtained by STO-3G method. All methods predicted the same relative order of stabilities of various geometries for n-butane; geometrical preferences were found to be dominated by one-electron factor, ${\pi}$-orbital energy changes being more impotant in the semi-empirical methods. The hyperconjugative energy changes accompanying structural changes from $(n-{\sigma}{\ast})_{trans}$ to (n-{\sigma}{\ast})cis were underestimated in the EHT, CNDO/2 and MINDO/3, whereas those were overestimated in the MNDO. The net destabilizing effect of $(n-{\sigma}{\ast})_{trans}$ structure was mainly due to the large internuclear energy involved in the structure. Through-space interaction between $n_1$ and $n_2$ orbitals of diradical caused energy gap narrowing of ${\Delta}E_{sp}$ and ${\Delta}{\varepsilon}={\varepsilon}_0$-${\varepsilon}_{av}$; through-space interaction had opposing effect to that of through-bond interaction. Due to the less severe neglect of differential overlaps in the MNDO, this energy gap narrowing effect appeared amplified in the MNDO. In general orbital properties were found to be reproduced satisfactorily, but eigenvalue properties were not, in all the semi-empirical methods especially when ${\sigma}-{\sigma}{\ast}$ and n-$n-{\sigma}{\ast}$interactions were involved.

• The Journal of shipping and logistics
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• v.34 no.4
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• pp.527-544
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• 2018

In this study, SNA analysis was conducted to examine changes in passenger movements in domestic coastal shipping. The validity of derivation of centrality rankings was enhanced by using the connection centrality that reflected weights, which had not been applied in previous research. The results of the connection centrality analysis indicated that the network composition ratio of the South Sea region was high, and the results of analysis of betweenness centrality indicated that ports belonging to the South Sea region recorded high ranks. Jeju Island, which acts as a gateway to the West Sea and the South Sea, Mokpo, which acts as a gateway between the land and islands, those ports that are geographically close to the land, and those ports that are smoothly connected to small ports, were shown to have betweenness centrality. Meanwhile, in the results of analysis of eigenvector centrality, not only ports in the South Sea region but also many ports in the West Sea region were included in the high ranked ones. Using these results, the port authority can identify major ports in domestic coastal shipping, determine the priorities support, identify the current situation of the port connection relations, and establish strategies for management of key development areas. As future studies, studies in the aspect of economy that separate general passengers and island passengers and utilize data such as fares, distances, and time are necessary.

• 재활복지
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• v.20 no.4
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• pp.157-175
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• 2016

The purpose of this study was to analyze the perceptions of disabled sports that were reported the newspapers using semantic network analysis method. for this purpose, 745 news articles were selected from 21 source in Naver news searching engine. The main keyword for searching on newspapers was 'disabled sports'. Krkwic software was used for keyword cleansing and co-occurrence of text to text matrix in frequencies. Centrality indices that are degree, between and eigenvector, were used to analyze the perceptions of disabled sports from Netminer 4.0 for semantic network analysis. The conclusion of overall results from this study are follows; First, the core keyword of disabled sports in newspapers are 'impression', 'challenge', 'festival', 'dream' and hope. And there is different concepts of cognition among types of disability. Second, there are two elements on the perceptions of disabled sports from reported newspapers; sports performance and emotional. Specifically, main stream of keyword were 'Paralympics' and 'Special Olympics' on sports performance element and 'impressive' and 'challenge' in emotion element.