• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.785-788
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• 2012

최근 B2C 서비스산업에 있어 기업 간의 경쟁이 심화되고 새로운 비즈니스 가치 창출을 위한 필요 성이 증대되고 있는 상황에서, 기업들은 비즈니스 프로세스 관리 기술에 많은 관심을 기울이고 있다. 프로세스의 최적화를 통해 지속적으로 서비스 품질을 개선하기 위해 비즈니스 프로세스 재설계의 근거로 사용될 수 있는 비즈니스 프로세스 마이닝이 중요한 개념으로 인식되고 있다. 하지만 기존의 프로세스 마이닝에 관한 연구에서는 완성되어 있는 프로세스 로그를 기반으로 워크플로우 기반의 프 로세스 모델을 추출하는 단조로운 형태였기 때문에 다양한 형태의 비즈니스 프로세스를 표현하는데 한계가 있었다. 본 논문에서는 컨벤션, 대학,병원등 광범위한 지식서비스 분야에서 적합한 Prototype 기관을 Test bed로 다양한 프로세스 마이닝 기법으로 분석하여 해당 조직의 문제 프로세스를 발견하 고 개선점을 제안하다. 또한 B2C 서비스 산업에서 적절한 Test bed를 선정하여, 실제 프로세스를 기 존의 legacy system의 event log file에서 분석하여 bottle neck process를 찾아내고, 문제 프로세스를 개선하는 과정을 자동화된 모델링 및 분석 툴을 사용하여 실증적으로 보여준다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.8A
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• pp.1169-1176
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• 2000

In this paper, label assignment schemes considering the IP flow model for the efficient MPLS traffic engineering are proposed and evaluated. Based on the IP flow model, the IP flows are classified into transient flows and base flows. Base flows, which last for a long time, transmit data in high bit rate, and be composed of many packets, have good implications for the MPLS traffic engineering, because they usually cause network congestion. To make use of base flows for the MPLS traffic engineering, we propose two base flow classifiers and label assignment schemes where transient flows are allocated to the default LSPs and base flows to explicit LSPs. Proposed schemes are based on the traffic-driven label triggering method combined with a routing tabel. The first base flow classifier uses both flow size in packet counts and routing entries, and the other one, extending the dynamic X/Y flow classifier, is based on a cut-through ratio. Proposed schemes are shown to minimize the number of labels, not degrading the total cut-through ratio.

• Annual Conference of KIPS
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• 2024.05a
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• pp.298-301
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• 2024

클라우드 환경에서 컨테이너 사용이 증가하면서 컨테이너 환경을 대상으로 하는 여러 보안 위협이 증가하고 있다. 대표적인 악성 컨테이너는 크립토재킹 컨테이너로, 인스턴스 소유자의 승인 없이 리소스를 탈취하여 암호화폐를 채굴하는 공격이다. 이러한 공격은 리소스 낭비를 초래할 뿐 아니라 자원을 공유하는 정상 컨테이너나 호스트 인프라에까지도 영향을 미칠 수 있다. 따라서 본 논문에서는 크립토재킹 컨테이너를 탐지하기 위한 앙상블 기반의 크립토재킹 컨테이너 탐지 프레임워크 설계를 제안한다. 또한, 앙상블 모델 학습을 위한 데이터 수집에 있어 크립토재킹 컨테이너의 동적 특징을 나타내는 시스템 콜 및 네트워크 플로우 기반의 특성 활용 가능성을 사례 연구를 통해 분석하였다.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.3
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• pp.125-138
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• 2016

The service identification in service-oriented developments has been conducted by based on workflow, goals, scenarios, usecases, components, features, and patterns. However, the identification of service by semantic approach at the business value view was not detailed yet. In order to enhance accuracy of identifying business service, this paper proposes a method for identifying business service by analyzing syntax and semantics in XL-BPMN model. The business processes based on business scenario are identified, and they are designed in a XL-BPMN business process model. In this business process model, an unit business service is identified through binding closely related activities by the integrated analysis result of syntax patterns and properties-based semantic similarities between activities. The method through XL-BPMN model at upper business levels can identify the reusable unit business service with high accuracy and modularity. It also can accelerate more service-oriented developments by reusing identified services.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.259-264
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• 2023

In this study, we devised a parametric analysis workflow for efficiently analyzing the material properties of 3D woven materials. The parametric model uses wire spacing in the woven materials as a design parameter; we generated 2,500 numerical models with various combinations of these design parameters. Using MATLAB and ANSYS software, we obtained various material properties, such as bulk modulus, thermal conductivity, and fluid permeability of the woven materials, through a parametric batch analysis. We then used this large dataset of material properties to perform a regression analysis to validate the relationship between design variables and material properties, as well as the accuracy of numerical analysis. Furthermore, we constructed an artificial neural network capable of predicting the material properties of 3D woven materials on the basis of the obtained material database. The trained network can accurately estimate the material properties of the woven materials with arbitrary design parameters, without the need for numerical analyses.

• The Journal of the Korea Contents Association
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• v.9 no.5
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• pp.61-75
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• 2009

Generally, the development of the database application includes the requirement analysis phase of creating ERD (Entity Relationship Diagram) and process models, coding, and testing. From the above phases, the analysis phase is not most formalized. It is usually hard task because (1) customers don't know the details of the desired system; (2) developers can't with ease understand the business logic of the customers; (3) the outcomes of the analysis, which are ERD and process models, are not easy to understand to the customers. This paper propose that the executional forms, which are better to understand the systems, should be presented to the customers instead of the ERD. These forms should accept the data input so that customers can review the various aspects of the outcome models. The developers should be able to instantly implement the business logic and also should be able to visually demonstrate the logic in order to get the details of it. For this goal, the customer supplied business logic should be able to be implemented by the references between forms, actions, constraints from the perspective of the data flow. The customers try to execute the forms implementing the business logic and review their supplied logic find new necessary business logic of their own. Iterating these processes for the requirement analysis would result in the success of the analysis which is sufficiently detailed without conflicts.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.1
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• pp.53-59
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• 2008

In spite of increasing complexity of wireless sensor network applications, most of the sensor node platforms still have severe resource constraints. Especially a small amount of memory and absence of a memory management unit (MMU) cause many problems in managing application thread stacks. Hence, a shared-stack was proposed, which allows several threads to share one single stack for minimizing the amount of memory wasted by fixed-size stacks. In this paper, we present the memory usage models for thread stacks by deriving the overflow probability of the fixed-size stack and the shared-stack and also show that the shared-stack is more reliable than the fixed-size stack.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.287-308
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• 2023

After launching its service in November 2022, ChatGPT has rapidly increased the number of users and is having a significant impact on all aspects of society, bringing a major turning point in the history of artificial intelligence. In particular, the inference ability of large language models such as ChatGPT is improving at a rapid pace through prompt engineering techniques. This reasoning ability can be considered as an important factor for companies that want to adopt artificial intelligence into their workflows or for individuals looking to utilize it. In this paper, we begin with an understanding of in-context learning that enables inference in large language models, explain the concept of prompt engineering, inference with in-context learning, and benchmark data. Moreover, we investigate the prompt engineering techniques that have rapidly improved the inference performance of large language models, and the relationship between the techniques.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.127-135
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• 2023

This research paper explores the application of FinBERT, a variational BERT-based model pre-trained on financial domain, for sentiment analysis in the financial domain while focusing on the process of identifying suitable training data and hyperparameters. Our goal is to offer a comprehensive guide on effectively utilizing the FinBERT model for accurate sentiment analysis by employing various datasets and fine-tuning hyperparameters. We outline the architecture and workflow of the proposed approach for fine-tuning the FinBERT model in this study, emphasizing the performance of various datasets and hyperparameters for sentiment analysis tasks. Additionally, we verify the reliability of GPT-3 as a suitable annotator by using it for sentiment labeling tasks. Our results show that the fine-tuned FinBERT model excels across a range of datasets and that the optimal combination is a learning rate of 5e-5 and a batch size of 64, which perform consistently well across all datasets. Furthermore, based on the significant performance improvement of the FinBERT model with our Twitter data in general domain compared to our news data in general domain, we also express uncertainty about the model being further pre-trained only on financial news data. We simplify the complex process of determining the optimal approach to the FinBERT model and provide guidelines for selecting additional training datasets and hyperparameters within the fine-tuning process of financial sentiment analysis models.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.641-643
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• 2021

The Internet of Things (IoT) connects all markets and industries, enabling new business models for a variety of services and service providers. The Internet of Medical Things (IoMT) not only accelerates medical advances, but also enables treatment with a more human approach. In addition, it improves treatment methods and quality of precision medical care through data, enables timely treatment, and improves operational productivity of medical institutions through a simplified workflow. However, since the medical field directly affects human health and life, securing security has become an issue above all else, and is a target for hackers trying to exploit it. Therefore, in this study, IoMT technology and security threats and countermeasures in the medical field are analyzed.