• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.480-483
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• 2008

OMG 가 시작한 MDA(Model Driven Architecture) 는 소프트웨어 개발자들사이에 빠르게 전파되고 있다. UML 은 OMG 에 의해 MDA 를 위한 언어로 선택되었는데, UML 은 PIM(Platform Independent Model)에서 PSM(Platform Specific Model)을 생성하기에는 충분하지 않다. 하지만, 이러한 PIM-PSM 변환을 가능한한 자동화할수 있는데 이 논문은 자동 코드 생성을 위해 UML 의 확장 방법인 Stereotype 과 Tagged-Value 에 대해 논하게 된다. 또한, Aspect-Oriented 접근을 위해서 AOP 로 확장된 UML 에서 비 AOP 언어로 코드 생성이 가능하게 되는 새로운 방법도 제안을 한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.730-733
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• 2008

원자력 발전소 정보 관리 시스템인 GPM 시스템에서 원자력 발전소 유지보수 프로세스에서 발생하는 도면, 문서 등 데이터와 비즈니스 프로세스를 모델링한 데이터 모델을 범용 제품 모델 (GPM, Generic Product Model) 클래스 라이브러리에 통합하였다. 이를 통해 제시하는 표준 데이터 모델은 지능적 P&ID 시스템의 기본 데이터 모델로 사용될 수 있으며, 이를 기반으로 향후에는 지식화된 정보 서비스 제공이 가능하게 된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.210-213
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• 2024

이 연구는 거대한 언어 모델 (Large Language Model, LLM)과 도구를 결합한 시스템의 보안 문제를 다룬다. 프롬프트 주입과 같은 보안 취약점을 분석하고 이를 극복하기 위한 프롬프트 권한 분리 기법을 제안한다. 이를 통해 LLM-도구 결합 시스템에서의 사용자 데이터의 기밀성과 무결성을 보장한다.

• The Journal of Information Systems
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• v.29 no.4
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• pp.57-76
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• 2020

Purpose Although examining the antecedents of employees' extra-role behavior (i.e. information security participation behavior) in the information security context is significant for researchers and practitioners, most behavioral security studies have focused on employees' in-role behavior (i.e. information security policy compliance). Thus, this research addresses this gap by investigating how organizational information security climate influences information security participation behavior based on social information processing theory and Griffin and Neal's safety model. Design/methodology/approach We developed a research model by applying Griffin and Neal's safety model to the information security context and then tested our research model by conducting an online survey for employees of organizations with information security policies. Structural equation modeling (SEM) with SmartPLS 3.3.2 is used to test the corresponding hypothesis. Findings Our results show that organizational information security climate, information security knowledge, information security motivation are effective in motivating information security participation behavior. Also, we find that organizational information security climate positively influences both information security knowledge and information security motivation. Our findings emphasize the importance of organizational information security climate because it is capable of affecting employees on information security participation behavior. Our study contributes to the literature on information security by exploring the role of organizational information security climate in enhancing employees' information security participation behavior.

• The Transactions of the Korea Information Processing Society
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• v.6 no.11S
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• pp.3399-3409
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• 1999

Information filtering for internet search, in which new information retrieval environment is given, is different from traditional methods such as bibliography information filtering, news-group and E-mail filtering. Therefore, we cannot expect high performance from the traditional information filtering models when they are applied to the new environment. To solve this problem, we inspect the characteristics of the new filtering environment, and propose a semantic-based filtering model which includes a new filtering method using WordNet. For extracting keywords from documents, this model uses the SDCC(Semantic Distance for Common Category) algorithm instead of the TF/IDF method usually used by traditional methods. The world sense ambiguation problem, which is one of causes dropping efficiency of internet search, is solved by this method. The semantic-based filtering model can filter web pages selectively with considering a user level and we show in this paper that it is more convenient for users to search information in internet by the proposed method than by traditional filtering methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.45-48
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• 2006

The system that can generate biological brain information processing mechanism more precisely may have several abilities such as exact cognition, situation decision, learning and inference, and output decision. In this paper, to implement high level information processing and thinking ability in a complex system, the information processing layer based on the biological brain is introduced. The biological brain information processing mechanism, which is analyzed in this paper, provides fundamental information about intelligent engineering system, and the design of the layer that can mimic the functions of a brain through engineering definitions can efficiently introduce an intelligent information processing method having a consistent flow in various engineering systems. The applications proposed in this paper are expected to take several roles as a unified model that generates information process in various areas, such as engineering and medical field, with a dream of implementing humanoid artificial intelligent system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2632-2649
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• 2012

Most approaches to human action recognition is limited due to the use of simple action datasets under controlled environments or focus on excessively localized features without sufficiently exploring the spatio-temporal information. This paper proposed a framework for recognizing realistic human actions. Specifically, a new action representation is proposed based on computing a rich set of descriptors from keypoint trajectories. To obtain efficient and compact representations for actions, we develop a feature fusion method to combine spatial-temporal local motion descriptors by the movement of the camera which is detected by the distribution of spatio-temporal interest points in the clips. A new topic model called Markov Semantic Model is proposed for semantic feature selection which relies on the different kinds of dependencies between words produced by "syntactic " and "semantic" constraints. The informative features are selected collaboratively based on the different types of dependencies between words produced by short range and long range constraints. Building on the nonlinear SVMs, we validate this proposed hierarchical framework on several realistic action datasets.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11c
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• pp.1685-1688
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• 2003

객체지향 환경에서 복잡한 소프트웨어 시스템을 개발하기 위해서는, 그것의 복잡성과 대규모성 때문에 추상화에 의한 다계층적인 use case model 의 사용이 불가피하다. 이러한 경우 모델의 consistency 유지가 매우 주요하고 어려운 이슈가 된다. 본 논문에서는 각 추상화 단계에 따른 use case model 들 사이에서 자동적으로 형식적인 consistency 를 체킹할 수 있는 방법을 제안한다. 이 접근 방법은 rule 을 기반으로 하여 actor tree, use cose composition diagram를 use case description을 활용한다. 본 접근법을 검증하기 위하여, ITS 아키텍처 (Intelligent Transportation System architecture)의 한 파트를 예로 들어 적용하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.479-481
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• 2023

Generative Adversarial Networks (GANs) have facilitated substantial improvement in single-image super-resolution (SR) by enabling the generation of photo-realistic images. However, the high memory requirements of GAN-based SRs (mainly generators) lead to reduced performance and increased energy consumption, making it difficult to implement them onto resource-constricted devices. In this study, we propose an efficient and compressed architecture for the SR-GAN (generator) model using the model compression technique Knowledge Distillation. Our approach involves the transmission of knowledge from a heavy network to a lightweight one, which reduces the storage requirement of the model by 58% with also an increase in their performance. Experimental results on various benchmarks indicate that our proposed compressed model enhances performance with an increase in PSNR, SSIM, and image quality respectively for x4 super-resolution tasks.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.599-614
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• 2021

This paper proposes a digital signage system based on an intelligent recommendation model. The proposed system consists of a server and an edge. The server manages the data, learns the advertisement recommendation model, and uses the trained advertisement recommendation model to determine the advertisements to be promoted in real time. The advertisement recommendation model provides predictions for various products and probabilities. The purchase index between the product and weather data was extracted and reflected using correlation analysis to improve the accuracy of predicting the probability of purchasing a product. First, the user information and product information are input to a deep neural network as a vector through an embedding process. With this information, the product candidate group generation model reduces the product candidates that can be purchased by a certain user. The advertisement recommendation model uses a wide and deep recommendation model to derive the recommendation list by predicting the probability of purchase for the selected products. Finally, the most suitable advertisements are selected using the predicted probability of purchase for all the users within the advertisement range. The proposed system does not communicate with the server. Therefore, it determines the advertisements using a model trained at the edge. It can also be applied to digital signage that requires immediate response from several users.