• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.2
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• pp.141-146
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• 2016

This paper presents a method to improve time interval error for asynchronous communication systems. The proposed method is designed and simulated with multi-phase VCO, interpolator, phase selector, up-down counter, comparator and adder. The simulation results for CAN communication system show that the maximum time interval error can be tightly managed for satisfying the required specification. The proposed frequency modulation method can be properly used for asynchronous communication systems requiring high reliability.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.7
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• pp.875-882
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• 2014

In this paper, we propose a new parameter estimation method that can deal with the data of multiple time intervals simultaneously. If there are common modes in the multiple time intervals, it is possible to create a new polynomial by summing the coefficients of the prediction error polynomials of each time interval. By calculating the roots of the new polynomial, it is possible to estimate the common modes that exist in each time interval. The accuracy of the proposed parameter estimation method has been proven by using appropriate test signals.

• Journal of Korean Society of Transportation
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• v.35 no.1
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• pp.63-78
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• 2017

The purpose of this study is to determine the optimal aggregation interval to increase the reliability when estimating representative value of individual vehicle travel time collected by DSRC equipment in interrupted traffic flow section in National Highway. For this, we use the bimodal asymmetric distribution data, which is the distribution of the most representative individual vehicle travel time collected in the interrupted traffic flow section, and estimate the MSE(Mean Square Error) according to the variation of the aggregation interval of individual vehicle travel time, and determine the optimal aggregation interval. The estimation equation for the MSE estimation utilizes the maximum estimation error equation of t-distribution that can be used in asymmetric distribution. For the analysis of optimal aggregation interval size, the aggregation interval size of individual vehicle travel time was only 3 minutes or more apart from the aggregation interval size of 1-2 minutes in which the collection of data was normally lost due to the signal stop in the interrupted traffic flow section. The aggregation interval that causes the missing part in the data collection causes another error in the missing data correction process and is excluded. As a result, the optimal aggregation interval for the minimum MSE was 3~5 minutes. Considering both the efficiency of the system operation and the improvement of the reliability of calculation of the travel time, it is effective to operate the basic aggregation interval as 5 minutes as usual and to reduce the aggregation interval to 3 minutes in case of congestion.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.6 no.1
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• pp.21-29
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• 1998

The purpose of the present paper is to determine the time required and error tolerance limits for flight data computation. The results of statistical analysis showed that the calculator side computation required about 50 seconds for each question and wind side computation needed about 115 seconds for each question. In case of error tolerance limits, it was found that the error tolerance limit for altitude computation war 90 feet and two knots of interval was recommanded for the speed computation in calculator side, and one degree of interval for heading computation and five knots interval for speed computation in wind side.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.520-521
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• 2017

The converted error is occurred by the time difference between the time interval signal and the clock in a Time-to-Digital Converter of counter-type. If the clock period is $T_{CLOCK}$ the converted error is a maximum $T_{CLOCK}$ by the time difference between the start signal and the clock. And the converted error is a maximum $-T_{CLOCK}$ by the time difference between the stop signal and the clock. However, when the clock is synchronized with the start signal and the colck is generated during the time interval signal the range of converted digital error is from 0 to $(1/2)T_{CLOCK}$.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.1
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• pp.36-40
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• 2010

In this paper, the well-known LOB-based emitter localization using linear LSE algorithm is numerically implemented and the heuristic guidelines for the parameter values to achieve 1% RMS error are presented. In the simulation, we changed the total observation durations for LOB measurements, time interval between successive LOB measurements and sensor trajectories. The effects of the time interval of LOB measurements, the time duration of the LOB measurements and the angle of flight path arc on the performance are illustrated. The dependence of the performance on the various parameters is investigated and rule-of-thumbs for the parameter values corresponding to 1% RMS error are presented for each simulation condition.

• Journal of IKEEE
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• v.17 no.4
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• pp.537-543
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• 2013

This paper proposed a low EMI spread spectrum clock generator (SSCG) using discontinuous frequency modulation technique. The proposed SSCG is designed for triangular frequency modulation with high modulation depth. When the maximum time interval error (MTIE) of the SSCG is higher than given limit, the output frequency of SSCG is divided by two and used for reducing the time interval error (TIE). This discontinuous frequency modulation technique can effectively reduce the EMI within given limit. The simulated EMI of proposed SSCG was reduced by 18.5dB than that of conventional methods.

• Communications for Statistical Applications and Methods
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• v.3 no.2
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• pp.29-36
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• 1996

In regression model with nested error structure interval estimations about variability on different stages are proposed. This article derives an approximate confidence interval on the variance in the first stage and an exact confidence interval on the variance in the second stage in two stage regression model. The approximate confidence interval is vased on Ting et al. (1990) method. Computer simulation is procided to show that the approximate confidence interval maintains the stated confidence coeffient.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.13 no.5
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• pp.422-428
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• 1988

An iterative learning control system is a control system which makes system outputs follow desired outputs by iterating its trials over a finite time interval. In a discrete time system, we proposed one method in which present control inputs can be obtained by a linear combination of the input sequence and time-shifted error sequence at previous trial. In contrast with a continous time learning control system which needs differential opreration of an error signal, the time shift operation of the error sequence is simpler in a computer control system and its effectiveness is shown by a simulation.

• Journal of the Korean Chemical Society
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• v.15 no.1
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• pp.11-14
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• 1971

A modified form of the integrated Michaelis-Menten equation would provide a useful means of evaluating enzyme kinetic parameters in nonlinear progress reaction with time. A slight modification of the Lineweaver-Burk form (and other variants) using for the velocity, the change in substrate concentration divided by time ($\={v}$), and for the velocity, the change in substrate for the time interval ($\={S}$), allows this linear reciprocal form to be used with negligible error even when as much as half of the substrate is utilized during the time interval.