• Journal of the Korean Society of Systems Engineering
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• v.1 no.2
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• pp.69-76
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• 2005

Defense analysis & evaluation includes menace analysis, validation analysis, problem analysis, scientific technical analysis, technical trade-off analysis, alternative analysis, cost analysis, etc. Reliable related data is required to perform these analysis activities efficiently. but in case of these defense analysis & evaluation data acquisition system, the data is insufficient and scattered about each organization. The data of database system is also not utilized sufficiently. Abroad technical data is also low level data such as catalog or military officer's collection. Therefore, this paper propose defense analysis & evaluation data acquisition system by systems engineering process. we also propose construction method of data acquisition system.

• Journal of the Korea Safety Management & Science
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• v.14 no.4
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• pp.107-116
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• 2012

The issues raised so far in the development of safety-critical systems have centered on how effectively the safety requirements are met in systems design. The systems are becoming more complex due to the increasing demand on the functionality and performance. As such, the integration of both the systems design and systems safety processes becomes more important and at the same time quite difficult to carry out. In this paper, an approach to solving the problem is presented, which is based on an integrated data model. To do so, the data generated from the inputs and outputs of the systems design and systems safety processes are analyzed first. The results of analysis are used to extract common attributes among the data, thereby making it possible to define classes. The classes then become the cores of the interface data model through which the interaction between the two processes under study can be modeled and interpreted. The approach taken has also been applied in a design case to demonstrate its value. It is expected that the results of the study could play a role of the stepping stone in extending to the architecture development of the integrated process.

• IE interfaces
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• v.17 no.4
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• pp.407-413
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• 2004

Recently, many enterprises are adopting EAI (Enterprise Application Integration) technologies for integrating heterogeneous enterprise information systems. Among EAI levels, data-level integration is relatively straightforward and most popular. However, most commercial solutions provide complex functionalities and are expensive for implementing the data integration tasks at the small & medium enterprises. Also, they have their own proprietary architectures and have a restricted interoperability. Proposed in this paper is the development of a data integration middleware for facilitating data exchanges between the heterogeneous information systems. The main feature of this middleware is a explicit mapping of meta data about the relationships between source and target data. Based on this explicit mapping, users who do not have expertise in information technology at the small & medium enterprises can easily execute data exchange tasks among various information systems.

• Korean Management Science Review
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• v.20 no.2
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• pp.1-16
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• 2003

Previous research on data extraction and integration for data warehousing has concentrated mainly on the relational DBMS or partly on the object-oriented DBMS. Mostly, it describes issues related with the change data (deltas) capture and the incremental update by using the triggering technique of active database systems. But, little attention has been paid to data extraction approaches from other types of source systems like hierarchical DBMS, etc. and from source systems without triggering capability. This paper argues, from the practical point of view, that we need to consider not only the types of information sources and capabilities of ETT tools but also other factors of source systems such as operational characteristics (i.e., whether they support DBMS log, user log or no log, timestamp), and DBMS characteristics (i.e., whether they have the triggering capability or not, etc), in order to find out appropriate data extraction techniques that could be applied to different source systems. Having applied several different data extraction techniques (e.g., DBMS log, user log, triggering, timestamp-based extraction, file comparison) to S bank's source systems (e.g., IMS, DB2, ORACLE, and SAM file), we discovered that data extraction techniques available in a commercial ETT tool do not completely support data extraction from the DBMS log of IMS system. For such IMS systems, a new date extraction technique is proposed which first creates Index database and then updates the data warehouse using the Index database. We illustrates this technique using an example application.

• Korean Journal of Computational Design and Engineering
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• v.5 no.3
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• pp.215-223
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• 2000

The product data exchange between heterogeneous CAB and PDM systems is a crucial issue for the integration of product development systems. STEP offers an efficient mechanism of product data exchange between heterogeneous systems. This paper introduces a UML-based mapping methodology for the product data models. The suggested mapping method has been applied to exchange the product structure data between CAD and PDM systems. Based on the STEP methods, we developed an interface module between a CAD system and a PDM system.

• IE interfaces
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• v.15 no.2
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• pp.159-171
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• 2002

Data Warehouses integrate data from multiple heterogeneous information sources and transform them into a multidimensional representation for decision support applications. Data warehousing has emerged as one of the most powerful tools in delivering information to users. Most previous researches have focused on marketing, customer service, financing, and insurance industry. Further, relatively less research has been done on data warehouse systems in the complex manufacturing industry such as ship production, which is characterized complex product structures and production processes. In the ship production, data warehouse systems is a requisite for effective cost analysis because collecting and analysis of diverse and large of cost-related(material/production cost, productivity) data in its operational systems, was becoming increasingly cumbersome and time consuming. This paper proposes architecture of the data warehouse systems to support cost analysis in the ship production. Also, in order to illustrate the usefulness of the proposed architecture, the prototype system is designed and implemented with the object of the enterprise of producing a large-scale ship.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.391-394
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• 1996

The effects of time-sampling on nonlinear output regulation problem is investigated. Output regulatedness is preserved under time sampling as in linear systems, however output regulatability is not robust with respect to time-sampling, and thus one needs to seek an approximate nonlinear sampled-data output regulator.

• Asia pacific journal of information systems
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• v.31 no.2
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• pp.185-196
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• 2021

The paper aims to analyze and predict sales of liquor in the state of Iowa by applying machine learning algorithms to models built for prediction. We have taken recourse of Azure ML and Spark ML for our predictive analysis, which is legacy machine learning (ML) systems and Big Data ML, respectively. We have worked on the Iowa liquor sales dataset comprising of records from 2012 to 2019 in 24 columns and approximately 1.8 million rows. We have concluded by comparing the models with different algorithms applied and their accuracy in predicting the sales using both Azure ML and Spark ML. We find that the Linear Regression model has the highest precision and Decision Forest Regression has the fastest computing time with the sample data set using the legacy Azure ML systems. Decision Tree Regression model in Spark ML has the highest accuracy with the quickest computing time for the entire data set using the Big Data Spark systems.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.896-908
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• 2022

As a part of probabilistic risk (or safety) assessment (PRA or PSA) of nuclear power plants (NPPs), the primary role of human reliability analysis (HRA) is to provide credible estimations of the human error probabilities (HEPs) of safety-critical tasks. In this regard, it is vital to provide credible HEPs based on firm technical underpinnings including (but not limited to): (1) how to collect HRA data from available sources of information, and (2) how to inform HRA practitioners with the collected HRA data. Because of these necessities, the U.S. Nuclear Regulatory Commission and the Korea Atomic Energy Research Institute independently developed two dedicated HRA data collection systems, SACADA (Scenario Authoring, Characterization, And Debriefing Application) and HuREX (Human Reliability data EXtraction), respectively. These systems provide unique frameworks that can be used to secure HRA data from full-scope training simulators of NPPs (i.e., simulator data). In order to investigate the applicability of these two systems, two papers have been prepared with distinct purposes. The first paper, entitled "SACADA and HuREX: Part 1. The Use of SACADA and HuREX Systems to Collect Human Reliability Data", deals with technical issues pertaining to the collection of HRA data. This second paper explains how the two systems are able to inform HRA practitioners. To this end, the process of estimating HEPs is demonstrated based on feed-and-bleed operations using HRA data from the two systems.

• Journal of the Korean Society of Systems Engineering
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• v.13 no.2
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• pp.9-17
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• 2017

Concept development of a real-time Field Programmable Gate Array (FPGA)-based switched Ethernet data communication network for the Man-Machine Interface System (MMIS) is presented in this paper. The proposed design discussed in this research is based on the systems engineering (SE) approach. The design methodology is effectively developed by defining the concept development stage of the life-cycle model consisting of three successive phases, which are developed and discussed: needs analysis; concept exploration; and concept definition. This life-cycle model is used to develop an FPGA-based time-triggered Ethernet (TTE) switched data communication network for the non-safety division of MMIS system to provide real-time data transfer from the safety control systems to the non-safety division of MMIS and between the non-safety systems including control, monitoring, and information display systems. The original IEEE standard 802.3 Ethernet networks were not typically designed or implemented for providing real-time data transmission, however implementing a network that provides both real-time and on-demand data transmission is achievable using the real-time Ethernet technology. To develop the design effectively, context diagrams are implied. Conformance to the stakeholders needs, system requirements, and relevant codes and standards together with utilizing the TTE technology are used to analyze, synthesize, and develop the MMIS non-safety data communication network of the APR1400 nuclear power plant.