• Journal of Digital Convergence
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• v.14 no.11
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• pp.233-240
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• 2016

Information system development projects, have a mechanism for many of the costs generated by a variety of risk factors. In general, the probability that the software project of the information system is carried out successfully in the delivery time is very low. This prediction of a formal cost is needed as the most important factor since it can prevent the project from being failed. However, objectivity of most of the project scale calculation during the calculation criteria is insufficient. Further, it is the actual situation that the management of the base line is not properly made during the project. Therefore, in this paper, we propose a model to calculate the number of steps it takes to develop on the basis of a methodology in an attempt to overcome the limitation of being unpractical in the early stage of the information system development project. It is expected to be a tool to estimate the effort and cost required by the information system development business through these convergence proposals model.

• The KIPS Transactions:PartD
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• v.9D no.1
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• pp.75-80
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• 2002

Putnam develops SLIM (Software LIfecycle Management) model based upon the assumption that the manpower utilization during software project development is followed by a Rayleigh distribution. To obtain the manpower distribution, we have to be estimate the total development effort and difficulty ratio parameter. We need a way to accurately estimate these parameters early in the requirements and specification phase before investment decisions have to be made. Statistical tests show that system attributes are highly correlation (redundant) so that Putnam discards one and get a parameter estimator from the other attributes. But, different statistical method has different system attributes and presents different performance. To select the principle system attributes, this paper uses the principle component analysis (PCA) instead of Putnam's method. The PCA's results improve a 9.85 percent performance more than the Putnam's result. Also, this model seems to be simple and easily realize.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.4
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• pp.61-74
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• 2007

In Common Criteria evaluation scheme, sponsor and evaluator should estimate evaluation cost and duration of IT security system evaluation in contracting the evaluation project. In this paper, We analyzed study result that achieve at 2003 and 2005, and utilized part of study result. And we empirically estimate relative evaluation effort ratios among evaluation assurance levels($EAL1{\sim}EAL7$) in CC v2.3 and CC v3.1. Also, we estimate the ratios from 'developer action elements', adjusted 'content and presentation of evidence elements', and 'evaluator action elements 'for each assurance component. We, especially, use ratio of amount of effort for each 'evaluator action elements', that was obtained from real evaluators in KISA in 2003. Our result will useful for TOE sponsor as well as evaluation project manager who should estimate evaluation cost and duration for a specific EAL and type of TOE, in a new CC v3.1 based evaluation schem.

• Journal of KIISE:Software and Applications
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• v.37 no.7
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• pp.536-547
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• 2010

Relative importance and complexity of recent software is getting increased because the software is needed to provide considerable amount of functions and high performance. Therefore, developing reliable software is importantly issued. In order to develop reliable software, it is necessary to manage software reliability at the early phases, but most reliability estimation models are used at system or operational test phases. In order to develop highly reliable software, it is necessary to manage software reliability at the early test phases based on characteristic of the phases that is developers and testers are not separated and developers perform test and debug activities together. Therefore, a new reliability estimation model considering test and debug time together is necessarily needed. In this paper, we propose a new reliability estimation model to manage reliability of individual units from the early test phases and in order to show how to fit the model to actual data and usefulness, we collected industrial data and used it for the experiment.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.541-546
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• 2005

Tracking control of an automatic pipe cutting robot (APCROMB) is studied. Using magnetic force APCROMB, which is designed and developed in Kyung Hee University, binds itself to the pipe and executes unmanned cutting process. The gravity effect on the movement of APCROMB varies as it rotates around the cylindrical pipe laid in the gravitational field. To maintain a constant velocity and consistent cutting performance against the varying gravitational effect, the authors adopt a multi-rate repetitive learning controller (MRLC), which learns the required effort to cancel the repetitive tracking errors caused by nonlinear effect. In addition to the varying gravity effect other types of nonlinear disturbances including backlash in the driving system and the slip between the wheels of APCROMB and the pipe also cause degradation in the cutting process. In order to identify those nonlinear disturbances the position estimation based on the encoder attached at the motor is not good enough. To identify the absolute angular position of APCROMB the authors propose the angular position estimation based on the signals from a MEMS-type two-axis accelerometer mounted on APCROMB. The tracking performances of APCROMB with a MRLC using the encoder-based position estimation is experimentally measured and results are shown. Also the difference between the encoder-based angular displacement measurement and the accelerometerbased angular displacement measurement is included.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.6
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• pp.143-151
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• 2009

Extreme hydrologic events cause serious disaster, such as flood and drought. Many researchers have an effort to estimate design rainfalls or discharges. This study evaluated parameter estimation methods to estimate probability rainfalls with low uncertainty which will be used in design rainfalls. This study collected rainfall data from Incheon, Gangnueng, Gwangju, Busan, and Chupungryong gage station, and generated synthetic rainfall data using ARMA model. This study employed the maximum likelihood method and the Bayesian inference method for estimating parameters of the Gumbel and GEV distribution. Using a bootstrap resampling method, this study estimated the confidence intervals of estimated probability rainfalls. Based on the comparison of the confidence intervals, this study recommended a proper parameter estimation method for estimating probability rainfalls which have a low uncertainty.

• Geomechanics and Engineering
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• v.36 no.2
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• pp.111-119
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• 2024

In the realm of rock excavation projects, precise estimation of the drilling rate index stands as a pivotal factor in strategic planning and cost assessment. This study introduces and evaluates two pioneering computational intelligence models designed for the prognostication of the drilling rate index, a pivotal parameter with direct implications for cost estimation in rock excavation projects. These models, denoted as the Relevance Vector Regression (RVR) optimized with the Invasive Weed Optimization algorithm (IWO) (RVR-IWO model) and the RVR integrated with the Shuffled Frog Leaping algorithm (SFL) (RVR-SFL model), represent a groundbreaking approach to forecasting drilling rate index. The RVR-IWO and RVR-SFL models were meticulously devised to harness the capabilities of computational intelligence and optimization techniques for drilling rate index estimation. This research pioneers the integration of IWO and SFL with RVR, constituting an unprecedented effort in forecasting drilling rate index. The primary objective of this study was to gauge the precision and dependability of these models in forecasting the drilling rate index, revealing significant distinctions between the two. In terms of predictive precision, the RVR-IWO model emerged as the superior choice when compared to the RVR-SFL model, underscoring the remarkable efficacy of the Invasive Weed Optimization algorithm. The RVR-IWO model delivered noteworthy results, boasting a Variance Account for (VAF) of 0.8406, a Mean Squared Error (MSE) of 0.0114, and a Squared Correlation Coefficient (R²) of 0.9315. On the contrary, the RVR-SFL model exhibited slightly lower precision, yielding an MSE of 0.0160, a VAF of 0.8205, and an R2 of 0.9120. These findings serve to highlight the potential of the RVR-IWO model as a formidable instrument for drilling rate index prediction, particularly within the framework of rock excavation projects. This research not only makes a significant contribution to the realm of drilling engineering but also underscores the broader adaptability of the RVR-IWO model in tackling an array of challenges within the domain of rock engineering. Ultimately, this study advances the comprehension of drilling rate index estimation and imparts valuable insights into the practical implementation of computational intelligence methodologies within the realm of engineering projects.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.9 no.1
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• pp.100-111
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• 2006

This study was carried out to increase the efficiency of site investigation through the evaluation of the applicable methods of investigation for cutting slope design. In an effort to find out the applicability of the method employed during the slope investigation, some tests were carried out on 6 subjects i.e location of weak zones, characteristics of discontinuities, distribution of strata, in situ tests for geo-technical properties, laboratory test and estimation of weathering. The method was highly applicable and produced expected results during the test of weak zones, discontinuities and distribution of strata. In order to apply the method to in- situ test for geo-technical properties and laboratory test on soil and rock slope, a statistic analysis of the existing data were required in advance. However its design applicability on rock slope was good although actual cases of application were not many due to limitation of the cases for investigation. The method was frequently referenced but not actually applied for anti seismic design test and estimation of weathering.

• Journal of the Korea Society of Computer and Information
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• v.9 no.3
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• pp.27-34
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• 2004

As software developing paradigm has been changing to complicate Web environment, study of complexity becomes vigorous. Yet still it seems that general agreement has not to be reached to architecture or complexity measure of Web application. And so traditional complexity metrics - program size(LOC) and Cyclomatic Complexity can be derived from the source code after implementation. it is not helpful to the early phase of software development life cycle - analysis and design phase. In this study 6 Web projects has been used for deriving applications with possible errors suited by Complexity Indicator. Using 61 programs derived, linear correlation between complexity, number of classes and number of methods has been proposed. As Web application complexity could be estimated before implementation, effort and cost management will be processed more effectively.

• The KIPS Transactions:PartD
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• v.11D no.7 s.96
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• pp.1491-1498
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• 2004

Successful project planning relics on a good estimation of the manpower required to complete a project, together with the schedule options that may be available. Despite the extensive research done developing new and better models, existing software manpower estimation models are present only the total manpower or instantaneous manpower distribution according to the testing time for the software life-cycle. This paper suggests the manpower estimating models for software testing phase as well as testing process and debugging process in accordance with de-tected faults. This paper presents the polynomial model for effort based on testing and debugging faults. These models are verified by 5 different software project data sets with coefficient of determination and mean magnitude of relative error for adaptability of model.