• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.51-62
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• 2022

AI-based speed prediction studies have been conducted quite actively. However, while the importance of explainable AI is emerging, the study of interpreting and reasoning the AI-based speed predictions has not been carried out much. Therefore, in this paper, 'Explainable Deep Graph Neural Network (GNN)' is devised to analyze the speed prediction and assess the nearby road influence for reasoning the critical contributions to a given road situation. The model's output was explained by comparing the differences in output before and after masking the input values of the GNN model. Using TOPIS traffic speed data, we applied our GNN models for the major congested roads in Seoul. We verified our approach through a traffic flow simulation by adjusting the most influential nearby roads' speed and observing the congestion's relief on the road of interest accordingly. This is meaningful in that our approach can be applied to the transportation network and traffic flow can be improved by controlling specific nearby roads based on the inference results.

• Food Science and Preservation
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• v.6 no.1
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• pp.29-36
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• 1999

Using Fuji apple fruits cultivated in Kyungpook prefecture, the calibration model for firmness evaluation of fruits by near infrared(NIR) reflectance spectroscopy was developed, and the various influence factors such as instrument variety, measuring method, sample group, apple peel and selection of firmness point were investigated. Spectra of sample were recorded in wavelength range of 1100∼2500nm using NIR spectrometer (InfraAlyzer 500), and data were analyzed by stepwise multiple linear regression of IDAS program. The accuracy of calibration model was the highest when using sample group with wide range, and the firmness mean values obtained in graph by texture analyser(TA) were used as standard data. Chemometrics models were developed using a calibration set of 324 samples and an independent validation set of 216 samples to evaluate the predictive ability of the models. The correlation coefficients and standard error of prediction were 0.84 and 0.094kg, respectively. Using developed calibration model, it was possible to monitor the firmness change of fruits during storage frequently. Time, which was reached to firmness high value in graph by TA, is possible to use as new parameter for freshness of fruit surface during storage.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.421-433
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• 2015

Recently, there have been fast-growing social network services based on the Internet environment and web technology development, the prevalence of smartphones, etc. Social networks also allow the users to convey the information and news so that they have a great influence on the public opinion formed by social interaction among users as well as the spread of information. On the other hand, these social networks also serve as perfect environments for rampant malware. Malware is rapidly being spread because relationships are formed on trust among the users. In this paper, an effective patch strategy is proposed to deal with malicious worms based on social networks. A graph is formed to analyze the structure of a social network, and subgroups are formed in the graph for the distributed patch strategy. The weighted directions and activities between the nodes are taken into account to select reliable key nodes from the generated subgroups, and the Incremental PageRanking algorithm reflecting dynamic social network features (addition/deletion of users and links) is used for deriving the high influential key nodes. With the patch based on the derived key nodes, the proposed method can prevent worms from spreading over social networks.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.39-44
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• 2005

Scheduling for digital system synthesis is assigning each operation in a control/data flow graph(CDFG) to a specific control step without violating precedence relation. It is one of the most important tasks due to its direct influence on the performance of the hardware synthesized. In this paper, we propose a resource-constrained scheduling algorithm. Our algorithm first analyzes the given CDFG to determine the number of functional units of each type, then assigns each operation to a control step while satisfying the constraints. It also tries to improve the solution iteratively by adjusting the number of functional units using the results collected from the previous scheduling. Experiments were performed to test the performance of the proposed algorithm, and results are presented

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.1-19
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• 2023

The purpose of this study was to find out what attributes are valid for the edge between students through longitudinal network analysis, and the results of TERGM (temporal exponential random graph model) and SAOM (stochastic actor-oriented model) statistical models were compared. The TERGM model interprets the research results based on the edge formation of the entire network, and the SAOM model interprets the research results on the surrounding networks formed by specific actors. The TERGM model expressed the influence of a previous time through a time term, and the SAOM model considered temporal dependence by implementing a network that evolves by an actor's opportunity as a ratio function.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.67-86
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• 2020

Recommender Systems have been huge influence users and business more and more. Recently the importance of E-commerce has been reached rapid growth greatly in world-wide COVID-19 pandemic. Recommender system is the center of E-commerce lively. Top ranked E-commerce managers mentioned that recommender systems have a major influence on customer's purchase such as about 50% of Netflix, Amazon sales from their recommender systems. Most algorithms have been focused on improving accuracy of recommender system regardless of novelty, diversity, serendipity etc. Recommender systems with only high accuracy cannot satisfy business long-term profit because of generating sales polarization. In addition, customers do not experience enjoyment of shopping from only focusing accuracy recommender system because customer's preference is changed constantly. Therefore, recommender systems with various values need to be developed for user's high satisfaction. Reranking is the most useful methodology to realize diversity of recommender system. In this paper, diversity of recommender system is represented through constructing high similarity with users who have different preference using each user's purchased item's category algorithm. It is distinguished from past research approach which is changing the algorithm of recommender system without user's diversity preference level. We tried to discover user's diversity preference level and observed the results how the effect was different according to user's diversity preference level. In addition, graph-based recommender system was used to show diversity through user's network, not collaborative filtering. In this paper, Amazon Grocery and Gourmet Food data was used because the low-involvement product, such as habitual product, foods, low-priced goods etc., had high probability to show customer's diversity. First, a bipartite graph with users and items simultaneously is constructed to make graph-based recommender system. However, each users and items unipartite graph also need to be established to show diversity of recommender system. The weight of each unipartite graph has played crucial role changing Jaccard Distance of item's category. We can observe two important results from the user's unipartite network. First, the user's diversity preference level is observed from the network and second, dissimilar users can be discovered in the user's network. Through the research process, diversity of recommender system is presented highly with small accuracy loss and optimalization for higher accuracy is possible controlling diversity ratio. This paper has three important theoretical points. First, this research expands recommender system research for user's satisfaction with various values. Second, the graph-based recommender system is developed newly. Third, the evaluation indicator of diversity is made for diversity. In addition, recommender systems are useful for corporate profit practically and this paper has contribution on business closely. Above all, business long-term profit can be improved using recommender system with diversity and the recommender system can provide right service according to user's diversity level. Lastly, the corporate selling low-involvement products have great effect based on the results.

• Journal of KIISE
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• v.44 no.2
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• pp.139-147
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• 2017

Most of the information prevailing in the Internet space consists of textual information. So one of the main topics regarding the huge document analyses that are required in the "big data" era is the development of an automated understanding system for textual data; accordingly, the automation of the keyword extraction for text summarization and abstraction is a typical research problem. But the simple listing of a few keywords is insufficient to reveal the complex semantic structures of the general texts. In this paper, a text-visualization method that constructs a graph by computing the related degrees from the selected keywords of the target text is developed; therefore, two construction models that provide the edge relation are proposed for the computing of the relation degree among keywords, as follows: influence-interval model and word- distance model. The finally visualized graph from the keyword-derived edge relation is more flexible and useful for the display of the meaning structure of the target text; furthermore, this abstract graph enables a fast and easy understanding of the target text. The authors' experiment showed that the proposed abstract-graph model is superior to the keyword list for the attainment of a semantic and comparitive understanding of text.

• Korean Journal of Applied Biomechanics
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• v.30 no.1
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• pp.27-35
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• 2020

Objective: The purpose of this study was to investigate the influence of clubhead velocity through regression analysis on the magnitude and time difference of the forward-backward, mediolateral, and vertical ground reaction peak forces generated by force plate during golf swing. Method: 16 subjects (age: 20.5±4.2 yrs, height: 176.0±5.4 cm, weight: 77.8±5.9 kg, handy: 2.4±1.7) who is elite golf player in high school and university, participated in this study. The study method adopted three-dimensional analysis with 8 cameras and ground reaction force measurement with two force plate. The analysis variables were clubhead velocity, and ground reaction analysis variables set four events in each graph based on the peak forces commonly generated in Fx, Fy, and Fz graphs of the ground reaction data during the golf swing. Results: As a result of analyzing the influence of ground reaction magnitude difference on clubhead velocity, the influence on clubhead velocity was ym4, zm1, xm4, zm2. The larger ym4, xm4, zm1, the fasterthe clubhead velocity, but the smallerthe zm2, the faster the clubhead velocity. And in time difference, the influence on the clubhead velocity was in the order of xt4, zt1, zt3. The shorter xt4, zt1, zt3 showed faster clubhead velocity. Conclusion: The leftfoot played a leading role in increasing the velocity of the clubhead. Although the result was caused by the interaction of the right foot and the left foot during the swing, the role of the left foot is relatively large.

• International Journal of Computer Science & Network Security
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• v.21 no.7
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• pp.8-16
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• 2021

The tourism industry is influenced by a large number of factors that affect the development scenarios of the tourism in different ways. At the same time, tourism is an important component of the national economy of any state, forms its image, investment attractiveness, is a source of income and a stimulus for business development. The aim of the article is to conduct an empirical study to identify the importance of cognitive determinants in the development of tourism. The study used general and special methods: systems analysis, synthesis, grouping, systematization, cognitive modeling, cognitive map, pulse method, predictive extrapolation. Target factors, indicators, and control factors influencing the development of tourism in Ukraine are determined and a cognitive model is built, which graphically reflects the nature of the influence of these factors. Four main scenarios of the Ukrainian tourism industry are established on the basis of creating a matrix of adjacency of an oriented graph and forecast modeling based on a scenario approach. The practical significance of the obtained results lies in the possibility of their use to forecast the prospects of tourism development in Ukraine, the definition of state policy to support the industry that will promote international and domestic tourism.

• The Journal of Information Systems
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• v.9 no.1
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• pp.27-44
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• 2000

The structure of neural networks is represented by a weighted directed graph with nodes representing units and links representing connections. Each link is assigned a numerical value representing the weight of the connection. In learning process, the values of weights are adjusted by errors. Following experiment results, the interval of adjusting weights, that is, epoch size influenced neural networks＇ performance. As epoch size is larger than a certain size, neural networks＇performance decreased drastically. And the number of hidden layer＇s node also influenced neural networks＇performance. The networks＇performance decreased as hidden layers have more nodes and then increased at some number of hidden layer＇s node. So, in implementing of neural networks the epoch size and the number of hidden layer＇s node should be decided by systematic methods, not empirical or heuristic methods.