• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.58.1-58.1
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• 2019

We have developed Ground Software (GSW) of BITSE. The ground software includes mission operation software, data visualization software and data processing software. Mission operation software is implemented using COSMOS. COSMOS is a command and control system providing commanding, scripting and data visualization capabilities for embedded systems. Mission operation software send commands to flight software and control coronagraph. It displays every telemetry packets and provides realtime graphing of telemetry data. Data visualization software is used to display and analyze science image data in real time. It is graphical user interface (GUI) and has various functions such as directory listing, image display, and intensity profile. The data visualization software shows also image information which is FITS header, pixel resolution, and histogram. It helps users to confirm alignment and exposure time during the mission. Data processing software creates 4-channel polarization data from raw data.

• Journal of the Korea Society of Computer and Information
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• v.23 no.12
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• pp.65-72
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• 2018

In this paper, we have studied how to reuse common data link software by applying software product line engineering. Existing common data link software performed different stages of design, implementation, and testing without sharing the accumulated knowledge of different developers. In this situation, developers agreed that sharing the assets of each project and reusing the previously developed software would save human and time costs. Even with the initial difficulties, the common Data Link is a continually proposed project in the defense industry, so we decided to build a product line. The common data link software can be divided into two domains. Among them, the initial feature model for the GUI software was constructed, and the following procedure was studied. Through this, we propose a plan to build a product line for core assets and reuse them in newly developed projects.

• Nuclear Engineering and Technology
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• v.39 no.2
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• pp.129-132
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• 2007

It is generally known that software reliability growth models such as the Jelinski-Moranda model and the Goel-Okumoto's non-homogeneous Poisson process (NHPP) model cannot be applied to safety-critical software due to a lack of software failure data. In this paper, by applying two of the most widely known software reliability growth models to sample software failure data, we demonstrate the possibility of using the software reliability growth models to prove the high reliability of safety-critical software. The high sensitivity of a piece of software's reliability to software failure data, as well as a lack of sufficient software failure data, is also identified as a possible limitation when applying the software reliability growth models to safety-critical software.

• Journal of Electrical Engineering and Technology
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• v.2 no.3
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• pp.386-390
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• 2007

At recent times, an essential issue in the replacement of the old analogue I&C to computer-based digital systems in nuclear power plants becomes the quantitative software reliability assessment. Software reliability models have been successfully applied to many industrial applications, but have the unfortunate drawback of requiring data from which one can formulate a model. Software that is developed for safety critical applications is frequently unable to produce such data for at least two reasons. First, the software is frequently one-of-a-kind, and second, it rarely fails. Safety critical software is normally expected to pass every unit test producing precious little failure data. The basic premise of the rare events approach is that well-tested software does not fail under normal routine and input signals, which means that failures must be triggered by unusual input data and computer states. The failure data found under the reasonable testing cases and testing time for these conditions should be considered for the quantitative reliability assessment. We presented the quantitative reliability assessment methodology of safety critical software for rare failure cases in this paper.

• Journal of the Korea Society of Computer and Information
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• v.20 no.8
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• pp.69-75
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• 2015

Nowadays, software plays various roles in applications in wide areas. However, the security problems caused by software vulnerabilities increase. So, it is necessary to improve software security and safety in software execution. In this paper, we propose an approach to improve the safety of software execution by managing information used in software through static data access analysis. The approach can detect the exposures of secure data in software execution by analyzing information property and flows through static data access analysis. In this paper, we implemented and experimented the proposed approach with a base language, and verify that the proposed approach can effectively detect the exposures of secure information. The proposed approach can be applied in several areas for improving software safety by analysing vulnerabilities from information flows in software execution.

• Korean Journal of Computational Design and Engineering
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• v.13 no.4
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• pp.314-322
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• 2008

This paper introduces an integration of Product Data Management (PDM) and Software Configuration Management (SCM). PDM and SCM have supported development of mechanical products and software products respectively. The importance of software components in the current products increases rapidly since the software enables the products to satisfy various customer requirements efficiently. Therefore the current product development needs enhanced product data management that can control both the hardware and software data seamlessly. This paper proposes an extended product data model for integrating SCM into PDM. The extension enables PDM document management to support the version control for software development. It also enables engineers to control both the software and hardware parts as integrated data objects during product configuration and engineering change management. The proposed model is implemented by using a commercial Product Lifecycle Management (PLM) system and a development of a network based robot system is tested by the implemented product development environment.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.292-299
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• 2021

A form of data mining software, based on manufacturing-based IIoT environment with the development of IT technologies are increasingly growing. However, it is difficult to evaluate the software quality in the same form as general software due to the characteristics of the software of a manufacturing company that has a large amount of data that needs to be carried out with big data and data mining. In addition, in a manufacturing-based environment where heterogeneous equipment and software are mixed, it is difficult to perform quality judgment on software used by applying existing quality characteristics. Therefore, in this paper, the characteristics of the manufacturing base are investigated, and a software quality evaluation model suitable for it is developed and evaluated.

• Journal of Information Technology Services
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• v.11 no.4
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• pp.171-180
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• 2012

Since software and hardware integration has became a strategic tool for companies to innovate their products, an information system that can comprehensively manage software and hardware integrated product development is critical for the current product development. This paper proposed a product data model that can support modules of related software and hardware parts in Product Data Management(PDM) integrated with Software Configuration Management(SCM). The model allows engineers to define software and hardware product structure independently, and support the integration module that can summon related software and hardware parts to build a comprehensive module for collaboration. Through the integration module, engineers can identify and examine the effectiveness of their design alternatives to other related parts form different disciplines. The product data model was implemented as a prototype PDM system and tested with an example robotics product.

• Journal of Integrative Natural Science
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• v.7 no.1
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• pp.67-73
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• 2014

We consider the mean value function for NHPP software reliability model and time series regression model in software failure data. We estimate parameters for the proposed models from two data sets. The values of SSE and MSE is presented from two data sets. We compare the predicted number of faults with the actual two data sets using the mean value function and regression curve.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.273-282
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• 2009

Missing data is one of the common problems in building analysis or prediction models using software project data. Missing imputation methods are known to be more effective missing data handling method than deleting methods in small software project data. While K nearest neighbor imputation is a proper missing imputation method in the software project data, it cannot use non-missing information of incomplete project instances. In this paper, we propose an approach to missing data imputation for numerical software project data by combining K nearest neighbor and maximum likelihood estimation; we also extend the average absolute error measure by normalization for accurate evaluation. Our approach overcomes the limitation of K nearest neighbor imputation and outperforms on our real data sets.