• Journal of the Korean Society for information Management
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• v.34 no.1
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• pp.177-195
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• 2017

This study attempts to infer research fronts using factor graph model based on heterogeneous features. The model suggested by this study infers research fronts having documents with the potential to be cited multiple times in the future. To this end, the documents are represented by bibliographic, network, and content features. Bibliographic features contain bibliographic information such as the number of authors, the number of institutions to which the authors belong, proceedings, the number of keywords the authors provide, funds, the number of references, the number of pages, and the journal impact factor. Network features include degree centrality, betweenness, and closeness among the document network. Content features include keywords from the title and abstract using keyphrase extraction techniques. The model learns these features of a publication and infers whether the document would be an RF using sum-product algorithm and junction tree algorithm on a factor graph. We experimentally demonstrate that when predicting RFs, the FG predicted more densely connected documents than those predicted by RFs constructed using a traditional bibliometric approach. Our results also indicate that FG-predicted documents exhibit stronger degrees of centrality and betweenness among RFs.

• ETRI Journal
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• v.39 no.2
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• pp.255-263
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• 2017

This paper proposes a graph-theatrical approach to optimize spatial reuse by adopting a technique that quantizes the channel information into single bit sub-messages. First, we introduce an interference graph to model the network topology. Based on the interference graph, the computational requirements of the algorithm that computes the optimal spatial reuse factor of each user are reduced to quasilinear time complexity, ideal for practical implementation. We perform a resource allocation procedure that can maximize the efficiency of spatial reuse. The proposed spatial reuse scheme provides advantages in beamforming systems, where in the interference with neighbor nodes can be mitigated by using directional beams. Based on results of system level measurements performed to illustrate the physical interference from practical millimeter wave wireless links, we conclude that the potential of the proposed algorithm is both feasible and promising.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.419-424
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• 2012

This paper discusses the state-of-the-art techniques in real-time state estimation for the Smart Microgrids. The most popular method used in traditional power system state estimation is a Weighted Least Square(WLS) algorithm which is based on Maximum Likelihood(ML) estimation under the assumption of static system state being a set of deterministic variables. In this paper, we present a survey of dynamic state estimation techniques for Smart Microgrids based on Belief Propagation (BP) when the system state is a set of stochastic variables. The measurements are often too sparse to fulfill the system observability in the distribution network of microgrids. The BP algorithm calculates posterior distributions of the state variables for real-time sparse measurements. Smart Microgrids are modeled as a factor graph suitable for characterizing the linear correlations among the state variables. The state estimator performs the BP algorithm on the factor graph based the stochastic model. The factor graph model can integrate new models for solar and wind correlation. It provides the Smart Microgrids with a way of integrating the distributed renewable energy generation. Our study on Smart Microgrid state estimation can be extended to the estimation of unbalanced three phase distribution systems as well as the optimal placement of smart meters.

• The KIPS Transactions:PartD
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• v.10D no.7
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• pp.1207-1212
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• 2003

As development of the e-commerce, the distributing network for a logistics currency is more complicated. A management of the distributing network (DN) is important factor in distribution planning. The DN can be expressed to distribution center (DC) and the interaction of DC. An internal factor of DC and an external factor to occur by an interaction of DC have many influences on the DN. Therefore, for an efficient DN management plan, analysis of the DN that considered DC and an interaction of DC is required. Until now a study on a viewpoint of supply chain management as resources assignment was performed, but the study on analysis of the distribution network was not performed. This paper propose the distribution network analysis technique that considered DC and an interaction of DC. A proposed technique consists of two steps largely. First of all a DN is expressed with the graph that included an interaction of DC and DC. It uses a reachibility tree, and the following, a DN expressed with a graph is analyzed. Also we presented an example model, and show an usefulness of proposal technique with the analysis of this model.

• Journal of the Korea Society for Simulation
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• v.23 no.3
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• pp.33-40
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• 2014

Drifter is equipment which is hard to localize. Performance of prototype hasn't performed well compared to product of leading companies even though advanced foreign firm's product were dead copied. This study shows cases of approaching the factor which produces performance gap through drifter hydraulic analysis model which is core component of rock drill. Progression of procedure is following. 1) Securing reliability of the analysis model by comparing impact test result with analysis result. 2) Drawing a graph which indicates performance gap between prototype and drifter of advanced foreign firm by using analysis model. 3) Approaching the factor which produces performance gap with analysing variable of the analysis model. Software used for this analysis is SimulationX.

• The KIPS Transactions:PartC
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• v.12C no.7 s.103
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• pp.949-958
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• 2005

In the paper we study key agreement schemes when a party needs to establish a session key with each of several parties, thus having multiple session keys. This situation can be represented by a graph, tailed a key graph, where a vertex represents a party and an edge represents a relation between two parties sharing a session key. graphs to establish all session keys corresponding to all edges in a key graph simultaneously in a single session. A key agreement protocol of a key graph is a natural extension of a two-party key agreement protocol. We propose a new key exchange model for key graphs which is an extension of a two-party key exchange model. using the so-called randomness re-use technique which re-uses random values to make session keys for different sessions, we suggest two efficient key agreement protocols for key graphs based on the decisional Diffie-Hellman assumption, and prove their securities in the key exchange model of key graphs. Our first scheme requires only a single round and provides key independence. Our second scheme requires two rounds and provides forward secrecy. Both are proven secure In the standard model. The suggested protocols are the first pairwise key agreement protocols and more efficient than a simple scheme which uses a two-party key exchange for each necessary key. Suppose that a user makes a session key with n other users, respectively. The simple scheme's computational cost and the length of the transmitted messages are increased by a factor of n. The suggested protocols's computational cost also depends on n, but the length of the transmitted messages are constant.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.309-323
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• 2023

As the role of online reviews has become increasingly crucial, numerous studies have been conducted to utilize helpful reviews. Helpful reviews, perceived by customers, have been verified in various research studies to be influenced by factors such as ratings, review length, review content, and so on. The determination of a review's helpfulness is generally based on the number of 'helpful' votes from consumers, with more 'helpful' votes considered to have a more significant impact on consumers' purchasing decisions. However, recently written reviews that have not been exposed to many customers may have relatively few 'helpful' votes and may lack 'helpful' votes altogether due to a lack of participation. Therefore, rather than relying on the number of 'helpful' votes to assess the helpfulness of reviews, we aim to classify them based on review content. In addition, the text of the review emerges as the most influential factor in review helpfulness. This study employs text mining techniques, including topic modeling and sentiment analysis, to analyze the diverse impacts of content and emotions embedded in the review text. In this study, we propose a review helpfulness prediction model based on review content, utilizing movie reviews from IMDb, a global movie information site. We construct a review helpfulness prediction model by using an explainable Graph Neural Network (GNN), while addressing the interpretability limitations of the machine learning model. The explainable graph neural network is expected to provide more reliable information about helpful or non-helpful reviews as it can identify connections between reviews.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.3
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• pp.159-168
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• 2012

Relation in the new IT environment, such as the SNS, Cloud, Web3.0, has become an important factor. And these relations generate a transaction. However, existing relational database and graph database does not processe graph structure representing the relationships and transactions. This paper, we propose the technique that can be processed concurrently graph structures and transactions in a scalable complex network system. The proposed technique simultaneously save and navigate graph structures and transactions using the Topic Maps data model. Topic Maps is one of ontology language to implement the semantic web(Web 3.0). It has been used as the navigator of the information through the association of the information resources. In this paper, the architecture of the proposed technique was implemented and design using Cassandra - one of column type NoSQL. It is to ensure that can handle up to Big Data-level data using distributed processing. Finally, the experiments showed about the process of storage and query about typical RDBMS Oracle and the proposed technique to the same data source and the same questions. It can show that is expressed by the relationship without the 'join' enough alternative to the role of the RDBMS.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.7
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• pp.135-144
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• 2022

The relationship between applying knowledge management and accepting environmentally managed accounting is more complicated than previous studies suggested. Knowledge management is both an antecedent and a consequence of implementing environmentally managed accounting in the workplace. Nonetheless, none of the prior studies have systematically investigated this relationship. The current article attempted to scrutinize the reciprocated multifaceted tie between environmental managerial accounting and knowledge management by utilizing the methods of directed graph searches as well as directed acyclic graphs. The research data was gathered from 342 publicly-listed corporations in Vietnam's key stock markets. The empirical findings disclose that implementing knowledge management can lead to adopting environmental managerial accounting in business, which is, in turn, an antecedent of accepting knowledge management. More importantly, the current research found that the adoption of knowledge management is the first factor to affect the research model. Nonetheless, the usage of knowledge management in business can, in turn, have a positive effect back to the implementing extent of environmental managerial accounting. The findings are beneficial to scientists and particularly to executives by shedding new insight into this reciprocated bond, which can lead executives to make sound decisions regarding knowledge management and environmental managerial accounting for businesses to acquire competitive advantages.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.527-541
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• 2022

A company is a social group of many individuals that work together to obtain better results, and it is an organization that pursues common goals such as profit. As a result, forming networks among members, as well as individual communication abilities, is critical. The purpose of this research was to determine what factors influence the creation of employee advice relationships. Using the ERGM(Exponential Random Graph Model) approach, we looked at the network data of 44 individuals from consulting firms with offices in the United States and Europe. The significance of structural network factors like connectivity was first discovered. Second, the gender factor had the most significant main influence on the likelihood of adopting each other's advice. Third, geographical homogeneity resulted in higher link probabilities than major impacts of gender. This research looked at ways to make a company's network more efficient and active.