• Journal of the Computational Structural Engineering Institute of Korea
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• v.21 no.6
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• pp.535-541
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• 2008

As the concurrent engineering concept has emerged along with the support of optimization techniques, lots of endeavors have been made to apply optimization techniques to actual design problems for a holistic decision. Even if the range of design problems which the optimization is applicable to has been extended, most of ship designs still remain in an iterative approach due to the difficulties of seamless integration of all related design activities. In this approach, an entire design problem is divided into many sub-problems and carried out by many different disciplines through complicated internal interactions. This paper focuses on preliminary ship design process. This paper proposes a process centric integrated framework as the first step to establish a workflow based design process management system. The framework consists of two parts; a schedule management part to support a manager to monitor current progress status and adjust current schedule, and a process management part to assist a design to effectively perform a series of design activities by following a predefined procedure. Overall system are decomposed into modules according to the target to be managed in each module. Appropriate interactions between the decomposed modules are designed to achieve a consistency of the entire system. Design process model is also designed on a thorough analysis of actual ship design practice. The proposed framework will be embodied using a commercial workflow package.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1033-1042
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• 2023

This paper proposes a network intrusion detection system that identifies abnormal flows within the network. The majority of datasets commonly used in research lack time-series information, making it challenging to improve detection rates for attacks with fewer instances due to a scarcity of sample data. However, there is insufficient research regarding detection approaches. In this study, we build upon previous research by using the Artificial neural network(ANN) model and a stack ensemble technique in our approach. To address the aforementioned issues, we incorporate temporal information by leveraging adjacent flows and enhance the learning of samples from sparse attacks, thereby improving both the overall detection rate and the detection rate for sparse attacks.

• 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.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.672-682
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• 2020

The Korea Nowadays, which is research on digital twin technology for efficient operation in various industrial/manufacturing sites, is being actively conducted, and gradual depletion of fossil fuels and environmental pollution issues require new renewable/eco-friendly power generation methods, such as wave power plants. In wave power generation, however, which generates electricity from the energy of waves, it is very important to understand and predict the amount of power generation and operational efficiency factors, such as breakdown, because these are closely related by wave energy with high variability. Therefore, it is necessary to derive a meaningful correlation between highly volatile data, such as wave height data and sensor data in an oscillating water column (OWC) chamber. Secondly, the methodological study, which can predict the desired information, should be conducted by learning the prediction situation with the extracted data based on the derived correlation. This study designed a workflow-based training model using a machine learning framework to predict the pressure of the OWC. In addition, the validity of the pressure prediction analysis was verified through a verification and evaluation dataset using an IoT sensor data to enable smart operation and maintenance with the digital twin of the wave generation system.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.3
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• pp.301-315
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• 2010

Recently, competition between companies has intensified and so has the necessity of creating a new business value inventions has increased. A numbers of Business organizations are beginning to realize the importance of business process management. Processes however can often not go the way they were initially designed or non-efficient performance process model could be designed. This can be due to a lack of cooperation and understanding between business analysts and system developers. To solve this problem, business process mining which can be used as the basis of the business process re-engineering has been recognized to an important concept. Current process mining research has only focused their attention on extracting workflow-based process model from competed process logs. Thus there have a limitations in expressing various forms of business processes. The disadvantage in this method is process discovering time and log scanning time in itself take a considerable amount of time. This is due to the re-scanning of the process logs with each new update. In this paper, we will presents a modified FP-Tree algorithm for FP-Tree based business processes, which are used for association analysis in data mining. Our modified algorithm supports the discovery of the appropriate level of process model according to the user's need without re-scanning the entire process logs during updated.

• Journal of KIISE:Information Networking
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• v.32 no.3
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• pp.279-290
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• 2005

Recently, as the serious damage caused by DDoS attacks increases, the rapid detection and the proper response mechanisms are urgent. However, existing security mechanisms do not effectively defend against these attacks, or the defense capability of some mechanisms is only limited to specific DDoS attacks. In this paper, we propose a detection architecture against DDoS attack using data mining technology that can classify the latest types of DDoS attack, and can detect the modification of existing attacks as well as the novel attacks. This architecture consists of a Misuse Detection Module modeling to classify the existing attacks, and an Anomaly Detection Module modeling to detect the novel attacks. And it utilizes the off-line generated models in order to detect the DDoS attack using the real-time traffic. We gathered the NetFlow data generated at an access router of our network in order to model the real network traffic and test it. The NetFlow provides the useful flow-based statistical information without tremendous preprocessing. Also, we mounted the well-known DDoS attack tools to gather the attack traffic. And then, our experimental results show that our approach can provide the outstanding performance against existing attacks, and provide the possibility of detection against the novel attack.