• Journal of Information Technology Services
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• v.18 no.4
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• pp.67-82
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• 2019

The purpose of this study is to improve the calculation method of the software operation pricing proposed by the software business price calculation guide from 'input manpower method' to 'rate calculation method'. The software operation pricing of the input manpower method is not objectively calculated in the domestic IT outsourcing situation where the statistical data based on the activity based estimating is insufficient and it is decided by agreement between the owner and the client. In addition, there was no standard for adjusting the productivity according to the characteristics of the operation service. In order to improve this, an operational correction factor item that can affect the software operation productivity was selected based on foreign and domestic standards, and it was confirmed through the first questionnaire to IT operation managers. In order to determine the level of difficulty of the fixed operational correction factors, the operational correction factor using AHP technique was confirmed through a second questionnaire for pairwise comparison. The operational difficulty calculation table was developed with reference to COCOMO and ITIL standards. Finally, we propose a new pricing scheme that reflects the operating rate. Regression analysis was carried out by collecting the data of the domestic public institutions on the estimated cost and the actual cost calculated from the new rate method software operation pricing. The results of the regression analysis show that the estimated cost and the actual cost are related to each other. Mean magnitude of relative error(MMRE) and PRED[25] analysis were added for accuracy analysis. MMRE and PRED also showed satisfactory results, confirming the possibility of replacing the rate method software operation pricing.

• Geomechanics and Engineering
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• v.28 no.4
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• pp.385-396
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• 2022

The accurate determination of the subgrade reaction modulus (K_s) of soil is an important factor for geotechnical engineers. This study estimated the K_s of soft soil improved with floating deep cement mixing (DCM) columns. A novel prediction model was developed that emphasizes the accuracy of identifying the most significant parameters of K_s. Several multi-layer perceptron (MLP) models that were trained using the Levenberg Marquardt (LM) backpropagation method were developed to estimate K_s. The models were trained using a reliable database containing the results of 36 physical modelling tests. The input parameters were the undrained shear strength of the DCM columns, undrained shear strength of soft soil, area improvement ratio and length-to-diameter ratio of the DCM columns. Grey wolf optimization (GWO) was coupled with the MLPs to improve the performance indices of the MLPs. Sensitivity tests were carried out to determine the importance of the input parameters for prediction of K_s. The results showed that both the MLP-LM and MLP-GWO methods showed high ability to predict K_s. However, it was shown that MLP-GWO (R = 0.9917, MSE = 0.28 (MN/m²/m)) performed better than MLP-LM (R =0.9126, MSE =6.1916 (MN/m²/m)). This proves the greater reliability of the proposed hybrid model of MLP-GWO in approximating the subgrade reaction modulus of soft soil improved with floating DCM columns. The results revealed that the undrained shear strength of the soil was the most effective factor for estimation of K_s.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.95-107
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• 1999

The design models of a new product in general are created using clay models or wooden mock-ups. The reverse engineering(RE) technology enables us to quickly create the CAD model of the new product by capturing the surface of the model using laser digitizers or coordinate measuring machines. Rapid prototyping (RP) is another technology that can reduce the product development time by fabricating the physical prototype of a part using a layered manufacturing technique. In reverse engineering process, however, the digitizer generates an enormous amount of point data, and it is time consuming and also inefficient to create surfaces out of these data. In addition, the surfacing operation takes a great deal of time and skill and becomes a bottleneck. In rapid prototyping, a faceted model called STL file has been the industry standard for providing the CAD input to RP machines. It approximates the CAD model of a part using many planar triangular patches and has drawbacks. A novel procedure that overcomes these problems and integrates RE with RP is proposed. Algorithms that drastically reduce the point clouds data have been developed. These methods will facilitate the use of reverse engineered geometric data for rapid prototyping, and thereby will contribute in reducing the product development time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1373-1392
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• 2017

WiFi Direct (WD) is a state of the art technology for a Device-to-Device (D2D) communication in 802.11 networks. The performance of the WD system can be significantly affected by some key factors such as the type of application, specifications of MAC and PHY layer parameters, and surrounding environment etc. It is, therefore, important to develop a system model that takes these factors into account. In this paper, we focus on investigating the design parameters of the PHY layer that could maximize the efficiency of the WD 802.11 system. For this purpose, a basic theoretical model is formulated for a WD network under a 2x2 Multiple In Multiple Out (MIMO) TGn channel B model. The design level parameters such as input symbol rate and antenna spacing, as well as the effects of the environment, are thoroughly examined in terms of path gain, spectral density, outage probability and Packet Error Rate (PER). Thereafter, a novel adaptive algorithm is proposed to choose optimal parameters in accordance with the Quality of Experience (QoE) for a targeted application. The simulation results show that the proposed method outperforms the standard method thereby achieving an optimal performance in an adaptive manner.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.09a
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• pp.254-257
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• 2003

Statistical analysis is performed to estimate the correlations between geological or geographical factor and groundwater inflow rates in the Seoul subway system. Correlation analysis shows that among several geological and geographical factors fractures and streams have most strong effects on inflow rate into tunnels. In particular, subway line 5∼8 are affected more by these factors than subway line 1∼4. Time series analysis is carried out to forecast groundwater inflow rate. Time series analysis is a useful empirical method for simulation and forecasts in case that physical model can not be applied to. The time series of groundwater inflow rates is calculated using the observation data. Transfer function-noise model is applied with the precipitation data as input variables. For time series analysis, statistical methods are performed to identify proper model and autoregressive-moving average models are applied to evaluation of inflow rate. Each model is identified to satisfy the lowest value of information criteria. Results show that the values by result equations are well fitted with the actual inflow rate values. The selected models could give a good explanation of inflow rates variation into subway tunnels.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.1
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• pp.33.2-33.2
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• 2014

Halo merger trees are essential backbones of semi-analytic models for galaxy formation and evolution. Recent studies have pointed out that extracting merger trees from numerical simulations of structure formation is non-trivial; different algorithm can give differing merger histories. Thus they should be carefully understood before being used as input for models of galaxy formation. As one of the projects proposed in the SUSSING MERGER TREES Workshop, we investigate the impact of different halo merger trees on a semi-analytic model. We find that the z = 0 global galaxy properties in our model show differences between trees when using a common parameter set, but that these differences are not very significant. However, the star formation history of the Universe and the properties of satellite galaxies can show marked differences between trees with different methods for constructing a tree. Calibrating the SAM for each tree individually to the empirical data can reduce the discrepancies between the z = 0 global galaxy properties, however this is at cost of increasing the differences in evolutionary histories of galaxies. Furthermore, the underlying physics implied can vary, resulting in key quantities such as the supernova feedback efficiency differing by factors of 2. Such a change alters the regimes where star formation is primarily suppressed by supernovae. Therefore, halo merger trees extracted from a common halo catalogue using different, but reliable, algorithms can result in a difference in the semi-analytic model, however, given the enormous uncertainties in galaxy formation physics, these are not necessarily significant.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.314-317
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• 2002

This paper present a methods for performance evaluation of digital governor and we treat the way to reduce the trial and error when the developed digital governing system is applied to station. The parameters of the model are estimated using input-output data, the model adjustment technique and a genetic algorithm(GA). To verify validity of the completed model, the comparision model and real output signals have been carried out. The developed digital governing system is applied to Sumjingang hydro-power plant, Korea Hydro Nuclear Power Corporation, it has replaced mechanical governing system. Test was performed in order to demonstrate the performance evaluation of the digital governor parameters.

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1729-1738
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• 2014

The offline verification of state of charge estimation, power estimation, fault diagnosis and emergency control of battery management system (BMS) is one of the key technologies in the field of electric vehicle battery system. It is difficult to test and verify the battery management system software in the early stage, especially for algorithms such as system state estimation, emergency control and so on. This article carried out the virtual environment modeling for verification of battery management system. According to the input/output parameters of battery management system, virtual environment is determined to run the battery management system. With the integration of the developed BMS model and the external model, the virtual environment model has been established for battery management system in the vehicle's working environment. Through the virtual environment model, the effectiveness of software algorithm of BMS was verified, such as battery state parameters estimation, power estimation, fault diagnosis, charge and discharge management, etc.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.10
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• pp.1469-1478
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• 2015

In this paper, The methodology of Type-2 fuzzy set-based Radial Basis Function Neural Network(T2RBFNN) is proposed for Sewage Treatment Process and the simulator is developed for application to the real-world sewage treatment plant by using the proposed model. The proposed model has robust characteristic than conventional RBFNN. architecture of network consist of three layers such as input layer, hidden layer and output layer of RBFNN, and Type-2 fuzzy set is applied to receptive field in contrast with conventional radial basis function. In addition, the connection weights of the proposed model are defined as linear polynomial function, and then are learned through Back-Propagation(BP). Type reduction is carried out by using Karnik and Mendel(KM) algorithm between hidden layer and output layer. Sewage treatment data obtained from real-world sewage treatment plant is employed to evaluate performance of the proposed model, and their results are analyzed as well as compared with those of conventional RBFNN.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.1
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• pp.135-142
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• 2012

In this paper, we proposed a new architecture called radial basis function-based polynomial neural networks classifier that consists of heterogeneous neural networks such as radial basis function neural networks and polynomial neural networks. The underlying architecture of the proposed model equals to polynomial neural networks(PNNs) while polynomial neurons in PNNs are composed of Fuzzy-c means-based radial basis function neural networks(FCM-based RBFNNs) instead of the conventional polynomial function. We consider PNNs to find the optimal local models and use RBFNNs to cover the high dimensionality problems. Also, in the hidden layer of RBFNNs, FCM algorithm is used to produce some clusters based on the similarity of given dataset. The proposed model depends on some parameters such as the number of input variables in PNNs, the number of clusters and fuzzification coefficient in FCM and polynomial type in RBFNNs. A multiobjective particle swarm optimization using crowding distance (MoPSO-CD) is exploited in order to carry out both structural and parametric optimization of the proposed networks. MoPSO is introduced for not only the performance of model but also complexity and interpretability. The usefulness of the proposed model as a classifier is evaluated with the aid of some benchmark datasets such as iris and liver.