• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.73-76
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• 2001

다중등급을 갖고 있는 대용량 데이터베이스 환경에서 각 보안등급을 갖고 있는 사용자가 데이터베이스에 접근할 때 확장된 강제적 접근제어(MAC:Mandatory Access Control) 방식과 역한 그래프(Role Graph)를 이용해 하위등급의 사용자가 상위등급의 데이터를 추론하거나 인지하는 데이터 유출을 방지하여 데이터의 무결성(integrity)과 데이터베이스 관리시스템(DBMS:Database Management System) 전체의 보안을 유지하며 각 보안등급의 데이터와 사용자를 효율적으로 관리하고 제어한 수 있는 역할관리 보안모델을 제안한다.

• Communications for Statistical Applications and Methods
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• v.20 no.2
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• pp.129-136
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• 2013

Linear regression is the most basic statistical model for exploring the relationship between a numerical response variable and several explanatory variables. Logistic regression secures the role of linear regression for the dichotomous response variable. In this paper, we propose a biplot-type display of the multivariate data guided by the linear regression and/or the logistic regression. The figures show the directional flow of the response variable as well as the interrelationship of explanatory variables.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.637-639
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• 2003

컴퓨터 시스템의 발달로 인해 여러 사용자가 여러 자원을 동시에 사용할 수 있는 환경으로 발전하면서 기존의 사용자 기반의 접근제어가 아닌 역할을 중심으로 하는 접근제어 모델이 제안되었다. 이러한 역할기반 접근제어 기법을 위한 참고 모델로서 역할 그래프 모델이 소개되었지만, 엄격한 충돌 처리 방식 때문에 실제 응용시스템에 적용하는 것은 한계가 있었다. 본 논문에서는 이러한 한계를 극복하기 위해서 충돌되는 권한을 세분화하고 이를 이용하여 좀 더 유연한 권한 삽입 연산을 할 수 있도록 하였다. 이러한 유동적 권한 삽입 방식을 통해 역할 그래프 모델을 좀 더 다양하게 적용한 수 있을 것이다.

• Journal of Astronomy and Space Sciences
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• v.40 no.4
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• pp.259-266
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• 2023

The Standard Model (SM) does not provide an information for 26% of dark matter of the universe. In the dark sector, dark matter is supposed to be linked with the hypothetical particles called dark photons that have similar role to photons in electromagnetic interaction in the SM. Besides astronomical observation, there are studies to find dark matter candidates using accelerators. In this paper, we searched for dark photons using future electron-positron colliders, including Circular Electron Positron Collider (CEPC)/CEPC, Future Circular Collider (FCC-ee)/Innovative Detector for Electron-positron Accelerator (IDEA), and International Linear Collider (ILC)/International Large Detector (ILD). Using the parameterized response of the detector simulation of Delphes, we studied the sensitivity of a double dark photon mode at each accelerator/detector. The signal mode is double dark photon decay channel, e⁺e^- → A'A', where A' (dark photon with spin 1) decaying into a muon pair. We used MadGraph5 to generate Monte Carlo (MC) events by means of a Simplified Model. We found the dark photon mass at which the cross-sections were the highest for each accelerator to obtain the maximum number of events. In this paper we show the expected number of dark photon signal events and the detector efficiency of each accelerator. The results of this study can facilitate in the dark photon search by future electron-positron accelerators.

• 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.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.176-189
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• 2024

Vertical takeoff and landing (VTOL) is a core feature of unmanned aerial vehicles (UAVs), which are commonly referred to as drones. In emerging smart logistics, drones are expected to play an increasingly important role as mobile platforms. Therefore, research on last-mile delivery using drones is on the rise. There is a growing trend toward providing drone delivery services, particularly among retailers that handle small and lightweight items. However, there is still a lack of research on a structural definition of the VTOL drone flight model for multi-point delivery service. This paper describes a VTOL drone flight route structure for a multi-drone delivery service using rotary-wing type VTOL drones. First, we briefly explore the factors to be considered when providing drone delivery services. Second, a VTOL drone flight route model is introduced using the idea of the nested graph. Based on the proposed model, we describe various time-related attributes for delivery services using drones and present corresponding calculation methods. Additionally, as an application of the drone route model and the time attributes, we comprehensively describe a simple example of the multi-drone delivery for first-come-first-served (FCFS) services.

• Journal of Internet Computing and Services
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• v.21 no.3
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• pp.123-131
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• 2020

Along with the rapid development of artificial intelligence technology, natural language processing, which deals with human language, is also actively studied. In particular, BERT, a language model recently proposed by Google, has been performing well in many areas of natural language processing by providing pre-trained model using a large number of corpus. Although BERT supports multilingual model, we should use the pre-trained model using large amounts of Korean corpus because there are limitations when we apply the original pre-trained BERT model directly to Korean. Also, text contains not only vocabulary, grammar, but contextual meanings such as the relation between the front and the rear, and situation. In the existing natural language processing field, research has been conducted mainly on vocabulary or grammatical meaning. Accurate identification of contextual information embedded in text plays an important role in understanding context. Knowledge graphs, which are linked using the relationship of words, have the advantage of being able to learn context easily from computer. In this paper, we propose a system to extract Korean contextual information using pre-trained BERT model with Korean language corpus and knowledge graph. We build models that can extract person, relationship, emotion, space, and time information that is important in the text and validate the proposed system through experiments.

• ETRI Journal
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• v.33 no.2
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• pp.219-229
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• 2011

In this paper, we propose a transformation scheme used to analyze online gaming traffic properties and develop a traffic model. We analyze the packet size and the inter departure time distributions of a popular first-person shooter game (Left 4 Dead) and a massively multiplayer online role-playing game (World of Warcraft) in order to compare them to the existing scheme. Recent online gaming traffic is erratically distributed, so it is very difficult to analyze. Therefore, our research focuses on a transformation scheme to obtain new smooth patterns from a messy dataset. It extracts relatively heavy-weighted density data and then transforms them into a corresponding dataset domain to obtain a simplified graph. We compare the analytical model histogram, the chi-square statistic, and the quantile-quantile plot of the proposed scheme to an existing scheme. The results show that the proposed scheme demonstrates a good fit in all parts. The chi-square statistic of our scheme for the Left 4 Dead packet size distribution is less than one ninth of the existing one when dealing with erratic traffic.

• The Korean Journal of Applied Statistics
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• v.30 no.3
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• pp.403-415
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• 2017

In this paper, we investigate cosponsorship networks found in the 17th National Assembly of Republic of Korea. New legislation should be sponsored by at least 10 legislators including one main sponsor. Cosponsorship networks can be constructed, using directional links from cosponsors of legislation to its main sponsor; subsequently, these networks indicate the social relationships among the legislators. We apply Exponential Random Graph Model (ERGM) for valued networks to capture structural properties and the covariate effects of networks. We find the effect of the same party has the greatest influence on the composition of the network. Mutuality also plays an important role in the cosponsorship network; in addition, the effect of the number of elections won by a legislator has a small but significant influence.