• Journal of ILASS-Korea
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• v.21 no.2
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• pp.104-110
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• 2016

Main purpose of the current paper is to show results of time lag analysis using phase information of flame transfer function in order to predict combustion instabilities in a gas turbine combustor. The flame transfer function (FTF) is modeled using a commercial Computational Fluid Dynamics (CFD) code (Fluent). Comparisons of the modeled flame shapes with the measured ones were made using the optimized heat transfer conditions and combustion models. The FTF modeling results show a quite good agreement with the measurement data in predicting the phase delay (i.e. time lag). Time lag analysis results using the phase of FTF shows better combustion instability prediction accuracy than using time lag calculated from the steady state flame length.

• Journal of Technology Innovation
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• v.7 no.1
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• pp.176-186
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• 1999

This paper starts out by reviewing the literature that in different ways utilizes patent data as an output of Research ＆ Development (R&D) investment. The main focus, however, is an analysis of time-lag between industrial R&D input and its output. To achieve this research's purpose, the basic data associated with the industrial R&D input (expenditure, researchers) and output (applied patent and utilities) for the past 15 years, from 1980 to 1994, in the areas of electrical-electronic, mechanical and chemical industries have been collected. And the raw input data were altered into real flow data (but stock data) using Laspeyres approach and analyzed using multiple regression analysis, especially stepwise regression analysis. The result of this study can be summarized as follows: a) The time-lag; between industrial R&D input and its output is within 1 to 3 years. b) The time-lag: of patents was longer than that of utility models. c) The time-lag: in electrical-electronic, chemical industry was longer than that of the mechanical industry.

• Journal of Information Technology Applications and Management
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• v.16 no.4
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• pp.205-222
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• 2009

Considering time-lag in the performance evaluation of information system (IS) investment is important because its effect reveals after certain period of time passed. Particularly it is more in the systems of e-government informatization projects which the amount of investment and the scale of business are huge. Many methods to solve this issue have been proposed such as system dynamics methods, simulations, structural equations etc. However, it is still difficult and unsolved problem because collecting practical data for time-lag analysis is very hard. In this paper, we analyze IS time-lag effect through factor analysis using the accumulated practical operational DB data. For the performance evaluation of the G4C system, the representative e-government web portal, we selected eleven factors reflecting time passing in G4C DB data. With these factors this paper conduct time-lag analysis in four view points. First, we conducted 'Stabilizing of G4C system' and got a result that IS is needed about three years for the stabilization. Second, we conducted 'Utilization of G4C system' and got a result that the utilization reaches appropriate level after in three years later after the introduction of G4C system. Third, we conducted 'Cost reduction effect' and got a result that cost reduction is stable in the third year after the introduction of G4C system. Lastly, we conducted 'System maturity effect' and got a result that the system reaches to the quality level that users expect after third to fourth years. According to the results of this research, we found that performance of IS improv continuously not immediately, and it needs three or four years of time-lag.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.8-14
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• 2019

Most of the data envelopment analysis (DEA) models evaluate the relative efficiency of a decision making unit (DMU) based on the assumption that inputs in a specific period are consumed to produce the output in the same period of time. However, there may be some time lag between the consumption of input resources and the production of outputs. A few models to handle the concept of the time lag effect have been proposed. This paper suggests a new multi-period input DEA model considering the consistent time lag effects. Consistency of time lag effect means that the time delay for the same input factor or output factor are consistent throughout the periods. It is more realistic than the time lag effect for the same output or input factor can vary over the periods. The suggested model is an output-oriented model in order to adopt the consistent time lag effect. We analyze the results of the suggested model and the existing multi period input model with a sample data set from a long-term national research and development program in Korea. We show that the suggested model may have the better discrimination power than existing model while the ranking of DMUs is not different by two nonparametric tests.

• Journal of Technology Innovation
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• v.22 no.1
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• pp.1-22
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• 2014

R&D investment also has a gestation period similar to other investments in economics. The gestation period originates from time lag effect of input and output. Thus it is necessary to consider time lag effects when analyzing the relationship between firms' R&D investment and R&D performance. The main objective of this research is to estimate the length of time lag effect of R&D investment. The Almon distribution lag model was applied to estimate the time lag effect. The firm level panel dataset was established from 2002 to 2009. The net value of R&D investment and the number of patent applications were used to measure R&D input and output, respectively. This method found the estimated time lag to be 1~2 years across all datasets. The same analyses were applied to chemical, metal, electronic, exact science, and machinery industries' data. And we found there were differences among sectors in regard to the time lag effect.

• Proceedings of the Technology Innovation Conference
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• 1997.07a
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• pp.160-171
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• 1997

This paper starts out by reviewing the literature that in different ways utilizes patent data as a output of R&D investment. The main focus, however, is an analysis of time-lag between R&D input and output. To achieve this research objective, the basic data associated with the R&D input(expenditure, researchers) and output(patent, utilities) for the past 15 years, from 1980 to 1994, in the areas of electrical-electronic, mechanical and chemical industries have been collected. And the raw output data were altered it to objective data using Laspeyres approach and analyzed using multiple regression analysis, especially stepwise regression analysis. The result of this study can be summarized as follows: a) The time-lag between R&D input and output is from 1 to 4 years. This result is equal to the research conclusion of the existing foreign studies. b) It was found that the time-lag of patents was longer than of utility models. c) It was showed that the time-lag of electrical-electronic, mechanical industry was longer than the chemical one.

• Water Engineering Research
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• v.2 no.4
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• pp.219-229
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• 2001

Synthetic unit hydrograph equations for rainfall run-off characteristics analysis and estimation of design flood have long and quite frequently been presented, the Snyder and SCS synthetic unit hydrograph. The major inputs to the Snyder and SCS synthetic unit hydrograph are lag time and peak coefficient. In this study, the methods for estimating lag time and peak coefficient for small watersheds proposed by Zhao and McEnroe(1999) were applied to the Kum river basin in Korea. We investigated lag times of relatively small watersheds in the Kum river basin in Korea. For this investigation the recent rainfall and stream flow data for 10 relatively small watersheds with drainage areas ranging from 134 to 902 square kilometers were gathered and used. 250 flood flow events were identified along the way, and the lag time for the flood events was determined by using the rainfall and stream flow data. Lag time is closely related with the basin characteristics of a given drainage area such as channel length, channel slope, and drainage area. A regression analysis was conducted to relate lag time to the watershed characteristics. The resulting regression model is as shown below: ※ see full text (equations) In the model, Tlag is the lag time in hours, Lc is the length of the main river in kilometers and Se is the equivalent channel slope of the main channel. The coefficient of determinations (r$^2$)expressed in the regression equation is 0.846. The peak coefficient is not correlated significantly with any of the watershed characteristics. We recommend a peak coefficient of 0.60 as input to the Snyder unit-hydrograph model for the ungauged Kum river watersheds

• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.378-384
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• 2017

In this paper, an efficient short-time Fourier analysis method for the quasi-periodic signals is proposed via an optimal fixed-lag finite impulse response (FIR) smoother approach using a receding horizon scheme. In order to deal with time-varying Fourier coefficients (FCs) of quasi-periodic signals, a state space model including FCs as state variables is augmented with the variants of FCs. Through an optimal fixed-lag FIR smoother, FCs and their increments are estimated simultaneously and combined to produce final estimates. A lag size of the optimal fixed-lag FIR smoother is chosen to minimize the estimation error. Since the proposed estimation scheme carries out the correction process with the estimated variants of FCs, it is highly probable that the smaller estimation error is achieved compared with existing approaches not making use of such a process. It is shown through numerical simulation that the proposed scheme has better tracking ability for estimating time-varying FCs compared with existing ones.

• Journal of Korea Technology Innovation Society
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• v.19 no.4
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• pp.687-710
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• 2016

This study aims to empirically estimate distributed lag effects of expenditure by type of R&D on scientific publication in the national R&D program. To analyze the lag structure between them, we used a dataset comprised of panel data from 104 technologies categorized by 6T (IT, BT, NT, ST, ET, CT) from 2007 to 2014, and employed multiple regression analysis based on the polynomial distributed lag model. This is because it is highly likely to emerge multicollinearity, if a distributed lag model without special restrictions is applied to multiple regression analysis. The main results are as follows. In the case of basic research, its lag effects are relatively evenly distributed during four years. On the other hand, the applied research and experimental development have distributed lag effects for three years and two years respectively. Therefore, when it comes to analyzing performance of scientific publication, it is necessary to be performed with characteristics of the time lag by type of R&D.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.720-725
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• 1991

In this thesis, the lead-lag compensator is designed to improve output characteristics of flyback zero voltage switching quasi-resonant converters. The switch and the diode are assumed ideally. And the SMPS is modelled by state equations with four operation modes. And the model for controller design is also achived by using a state space averaging method, which is continuous time average of state variables every period. The lag, the lead and the lead-lag compensator is designed the SMPS respectively. The time domain analysis and the frequency domain analysis are done for each compensated circuit. It is possible increasing the phase margin and improving the transient response by the compensators. The phase lag compensator has small overshoot comparatively. But the bandwidth is narrower than the others, so it has longest settling time. For the phase lead compensator, the response come to steady-state within short period. But the overshoot is the largest due to its large peak gain. Finally, the phase lead-lag compensator has medium characteristics in the overshoot and the settling time.