• Journal of the Korean Solar Energy Society
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• v.39 no.1
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• pp.11-19
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• 2019

Spectroscopic ellipsometry is a powerful tool for analyzing optical properties of material. Ellipsometry measurement results is usually given by change of polarization state of probe light, so the measured result should be properly treated and transformed to meaningful parameters by transformation and modeling of the measurement result. In case of hydrogenated amorphous silicon, Tauc-Lorentz dispersion is usually used to model the measured ellipsometry spectrum. In this paper, modeling of spectroscopic ellipsometry result of hydrogenated amorphous silicon using Tauc-Lorentz dispersion is discussed.

• Journal of the Optical Society of Korea
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• v.17 no.5
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• pp.392-398
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• 2013

As the essential techniques for the CD (Critical Dimension) measurement of the LCD pattern, there are various modules such as an optics design, auto-focus [1-4], and precise edge detection. Since the operation of image enhancement to improve the CD measurement repeatability, a ring type of the reflected lighting optics is devised. It has a simpler structure than the transmission light optics, but it delivers the same output. The edge detection is the most essential function of the CD measurements. The CD measurement is a vital inspection for LCDs [5-6] and semiconductors [7-8] to improve the production yield rate, there are numbers of techniques to measure the CD. So in this study, a new subpixel algorithm is developed through facet modeling, which complements the previous sub-pixel edge detection algorithm. Currently this CD measurement system is being used in LCD manufacturing systems for repeatability of less than 30 nm.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1041-1045
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• 2010

Clusters can provide scalable and flexible architectures for parallel computing servers and data centers. Their performance prediction has been a very challenging issue. Existing performance measurement methodologies are able to measure the performance of servers already constructed. Thus they cannot provide a way to predict the overall system performance in advance when designing the system at the initial phase or adding more nodes for more capacity. Therefore, the performance modeling and prediction methodology for large-scale clusters is highly required. In this paper, we suggest a methodology to predict the performance of large-scale clusters, which consists of measurement, modeling and prediction steps. We apply the methodology to a real cluster server and show its usefulness.

• Asia pacific journal of information systems
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• v.17 no.4
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• pp.207-228
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• 2007

Measuring Information Technology(IT) organizations' activities have been limited to mainly measure financial indicators for a long time. However, according to the multifarious functions of Information System, a number of researches have been done for the new trends on measurement methodologies that come with financial measurement as well as new measurement methods. Especially, the researches on IT Balanced Scorecard(BSC), concept from BSC measuring IT activities have been done as well in recent years. BSC provides more advantages than only integration of non-financial measures in a performance measurement system. The core of BSC rests on the cause-and-effect relationships between measures to allow prediction of value chain performance measures to allow prediction of value chain performance measures, communication, and realization of the corporate strategy and incentive controlled actions. More recently, BSC proponents have focused on the need to tie measures together into a causal chain of performance, and to test the validity of these hypothesized effects to guide the development of strategy. Kaplan and Norton[2001] argue that one of the primary benefits of the balanced scorecard is its use in gauging the success of strategy. Norreklit[2000] insist that the cause-and-effect chain is central to the balanced scorecard. The cause-and-effect chain is also central to the IT BSC. However, prior researches on relationship between information system and enterprise strategies as well as connection between various IT performance measurement indicators are not so much studied. Ittner et al.[2003] report that 77% of all surveyed companies with an implemented BSC place no or only little interest on soundly modeled cause-and-effect relationships despite of the importance of cause-and-effect chains as an integral part of BSC. This shortcoming can be explained with one theoretical and one practical reason[Blumenberg and Hinz, 2006]. From a theoretical point of view, causalities within the BSC method and their application are only vaguely described by Kaplan and Norton. From a practical consideration, modeling corporate causalities is a complex task due to tedious data acquisition and following reliability maintenance. However, cause-and effect relationships are an essential part of BSCs because they differentiate performance measurement systems like BSCs from simple key performance indicator(KPI) lists. KPI lists present an ad-hoc collection of measures to managers but do not allow for a comprehensive view on corporate performance. Instead, performance measurement system like BSCs tries to model the relationships of the underlying value chain in cause-and-effect relationships. Therefore, to overcome the deficiencies of causal modeling in IT BSC, sound and robust causal modeling approaches are required in theory as well as in practice for offering a solution. The propose of this study is to suggest critical success factors(CSFs) and KPIs for measuring performance for IT organizations and empirically validate the casual relationships between those CSFs. For this purpose, we define four perspectives of BSC for IT organizations according to Van Grembergen's study[2000] as follows. The Future Orientation perspective represents the human and technology resources needed by IT to deliver its services. The Operational Excellence perspective represents the IT processes employed to develop and deliver the applications. The User Orientation perspective represents the user evaluation of IT. The Business Contribution perspective captures the business value of the IT investments. Each of these perspectives has to be translated into corresponding metrics and measures that assess the current situations. This study suggests 12 CSFs for IT BSC based on the previous IT BSC's studies and COBIT 4.1. These CSFs consist of 51 KPIs. We defines the cause-and-effect relationships among BSC CSFs for IT Organizations as follows. The Future Orientation perspective will have positive effects on the Operational Excellence perspective. Then the Operational Excellence perspective will have positive effects on the User Orientation perspective. Finally, the User Orientation perspective will have positive effects on the Business Contribution perspective. This research tests the validity of these hypothesized casual effects and the sub-hypothesized causal relationships. For the purpose, we used the Partial Least Squares approach to Structural Equation Modeling(or PLS Path Modeling) for analyzing multiple IT BSC CSFs. The PLS path modeling has special abilities that make it more appropriate than other techniques, such as multiple regression and LISREL, when analyzing small sample sizes. Recently the use of PLS path modeling has been gaining interests and use among IS researchers in recent years because of its ability to model latent constructs under conditions of nonormality and with small to medium sample sizes(Chin et al., 2003). The empirical results of our study using PLS path modeling show that the casual effects in IT BSC significantly exist partially in our hypotheses.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.06a
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• pp.297-302
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• 2007

Recently, electrical resistivity survey is used in the various fields and applied to urban area with many electrical noises. Therefor it's necessary to observe the electrical noise effect of the geological structure. The physical scale modeling was conducted for measuring the electric noise effect of the two geological models at various distances, depths and diameters of the electric noise objects. The results are as following. 1. When conductive noise object was vertical to the strike of geological structure and moved to the strike direction, the effect of conductive noise object at various separated distances to the measurement line was disappeared at a half distance measurement line length regardless of electrode arrays. 2. When conductive noise object was vertical to the strike of geological structure and moved to the strike direction, the effect of conductive noise object at various depths was disappeared at 4unit apart from the measurement line regardless of electrode arrays. 3. When conductive noise object was vertical to the strike of geological structure and moved to the strike direction, the effect of conductive noise object at various diameters was disappeared at 4unit apart from the measurement line regardless of electrode arrays.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.10a
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• pp.18-23
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• 2000

The radar method is becoming one of the major nondestructive testing (NDT) techniques for concrete structures. Numerical modeling of electromagnetic wave is needed to analyze radar measurement results and to study the influence of measurement parameters on the radar measurements. Finite difference-time domain (FD-TD) method is used to simulate electromagnetic wave propagation through concrete specimens. Three concrete specimens with a 19.1 mm rebar embedded at 40 mm, 60 mm, and 80 mm depth are modeled in 3-dimension. As results, 2-D image processing scheme of modeling data has been developed and applied to the imaging of steel bars inside concrete.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1685-1688
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• 2004

I propose a modeling of a mechanism for weight fire uniformity measurement of a tire and a way I interpret a Sampling signal by Loadcell through an analysis, and to measure fire uniformity in this study. Correct a weight fire uniformity measurement was possible through the production of conversion and influence factor of a signal with a basis with the model who was an oscillation mechanics enemy.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.2
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• pp.45-52
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• 2002

Load modeling is classified into two methods according to approaching method, so called the measurement and component-based method. The measurement method is to model the load characteristics measured directly at substations and feeders. But it is difficult to measure continuously load characteristics from naturally occurring. system variation. The component-based method consists of the fellowing process; component load modeling, composition rate estimation and aggregation of component loads, etc. In this paper, the characteristic experiment of component loads was performed to obtain data for the component load modeling as the component-based method. At first, representative component loads were selected by the proposed method considering the accuracy of load modeling and the performance possibility of component load experiment in the laboratory. Also an algorithm was Proposed to identify the reliability of data obtained from the component load characteristic experiments. In addition, the results were presented as the case studies.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.261-264
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• 2004

In this paper, we identify the memory effect of high power(125W) laterally diffused metal oxide-semiconductor(LDMOS) RF Power Amplifier(PA) by two tone IMD measurement. We measure two tone IMD by changing the tone spacing and the power level. Different asymmetric IMD is founded at different center frequency measurements. We propose the Tapped Delay Line-Neural Network(TDNN) technique as the modeling method of LDMOS PA based on two tone IMD data. TDNN's modeling accuracy is highly reasonable compared to the memoryless adaptive modeling method.

• Journal of Electrical Engineering and Technology
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• v.4 no.2
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• pp.149-158
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• 2009

It is important to derive reliable parameter values in the measurement-based load model development of electric power systems. However parameter estimation tasks, in practice, often face the parameter identifiability issue; whether or not the model parameters can be estimated with a given input-output data set in reliable manner. This paper introduces concepts and practical definitions of the local identifiability of model parameters. A posteriori local identifiability is defined in the sense of nonlinear least squares. As numerical examples, local identifiability of third-order induction motor (IM) model and a Z-induction motor (Z-IM) model is studied. It is shown that parameter ill-conditioning can significantly affect on reliable parameter estimation task. Numerical studies show that local identifiability can be quite sensitive to input data and a given local solution. Finally, several countermeasures are proposed to overcome ill-conditioning problem in measurement-based load modeling.