• Journal of Institute of Control, Robotics and Systems
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• v.8 no.11
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• pp.941-947
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• 2002

Two methods of constructing T-S fuzzy model which is equivalent to a given nonlinear system are presented. The first method is to obtain an equivalent T-S fuzzy model by using the sum of linearly independent scalar functions with constant real matrix coefficients. The sum of products of linearly independent scalar functions is used in the second method. The former method is to formulate the procedures of T-S fuzzy modeling dealt in many examples of previous publications; the latter is a new method. By comparing the number of linearly independent functions used in the two methods, we can easily find out which method makes fewer rules than the other. The nonlinear dynamics of an inverted Pendulum on a cart is used as an equivalent T-5 fuzzy modeling example.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.15 no.8
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• pp.652-660
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• 2003

T-history method, measuring heat-of-fusion of phase change material (PCM) in sealed tubes, has the advantages of a simple experimental device and no requirements in sampling process. However, a degree of supercooling used in selecting the range of latent heat release and neglecting sensible heat during the phase change process can cause significant errors in determining the heat of fusion in the original method, which has been improved in order to predict better results by us. In the present study, the modified method was applied to a variety of PCM such as paraffin and lauric acid having very small or no supercooling with a satisfactory precision. Also the selection of inflection point and temperature measurement position was fumed out not to affect the accuracy of heat-of-fusion significantly. As a result, the method can provide an appropriate means to assess a new developed PCM by cycle test even if a very accurate value cannot be obtained.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.1
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• pp.25-35
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• 2013

Automatically deriving only individual don't-care minterms that can effectively reduce a Boolean logic expressions are being investigated. Don't-care conditions play an important role in optimizing logic design. The type of unknown don't-care minterms that can always reduce the number of product terms in Boolean expression are referred as single hypothetical don't-care (S-HDC) minterms. This paper describes the Quasi Quine-McCluskey method that systematically derives S-HDC minterms. For the most part, this method is similar to the original Quine-McCluskey method in deriving the prime implicants. However, the Quasi Quine-McCluskey method further derives S-HDC minterms by applying so-called a combinatorial comparison operation. Upon completion of the procedure, the designer can review generated S-HDC minterms to test its appropriateness for a particular application.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.214-218
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• 1999

The aim of our study is to develop new algorism for sea fog detection by using Geostational Meteorological Satellite-5(GMS-5) and suggest the techniques of its continuous detection. So as to detect daytime sea fog/stratus(00UTC, May 10, 1999), visible accumulated histogram method and surface albedo method are used. The characteristic value during daytime showed A(min) > 20％ and DA < 10％ when visble accumulated histogram method was applied. And the sea fog region which detected is of similarity in composite image and surface albedo method. In case of nighttime sea fog(18UTC, May 10, 1999), infrared accumulated histogram method and maximum brightness temperature method are used, respectively. Maximum brightness temperature method(T_max method) detected sea fog better than IR accumulated histogram method. In case of T_max method, when infrared value is larger than T_max, fog is detected, where T_max is an unique value, maximum infrared value in each pixel during one month. Then T_max is beneath 700hpa temperature of GDAPS(Global Data Assimilation and Prediction System). Sea fog region which detected by T_max method was similar to the result of National Oceanic and Atmosheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) DCD(Dual Channel Difference). But inland visibility and relative humidity didn't always agreed well.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.12
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• pp.1223-1229
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• 2001

Though conventional calorimetry methods such as differential scanning calorimetry (DSC) and differential thermal analysis (DTA) are used generally in measuring heat of fusion, T-history method has the advantages of a simple experimental apparatus and no requirements of sampling process, which is particularly useful for measuring thermal properties of inhomogeneous phase change materials (PCMs) in sealed tubes. However, random criteria (a degree of supercooling) used in selecting the range of latent heat release and neglecting sensible heat during the phase change process can cause significant errors in determining the heat of fusion. In the present study, it was shown that a 40% discrepancy exists between the original T-history and the present methods when analyzing the same experimental data. As a result, a reasonable modification to the original T-history method is proposed.

• International journal of advanced smart convergence
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• v.12 no.1
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• pp.64-69
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• 2023

We devise a collaboration construction method based on the SPRT (Sequential Probability Ratio Test) for malware detection in IoT. In our method, high-end IoT nodes having capable of detecting malware and generating malware signatures harness the SPRT to give a reward of malware signatures to low-end IoT nodes providing useful data for malware detection in IoT. We evaluate our proposed method through simulation. Our simulation results indicate that the number of malware signatures provided for collaboration is varied in accordance with the threshold for fraction of useful data.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.9
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• pp.657-662
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• 2022

In this paper, the calculation using simulation and two measurement methods for G/T of the telemetry are analyzed. Antenna gain and noise temperature are calculated by using ICARA and Antenna Noise Temperature Calculator. System G/T were calculated by using Antenna gain/noise temperature, LNA gain/noise temperature, cable loss. The first G/T measurement method is Y-factor measurement method, which is to calculate G/T by comparing LNA noise temperature and a signal level difference when an antenna and a 50ohm termination are respectively connected to an LNA input terminal Second method is Solar calibration measurement method that is to calculate G/T by comparing noise level difference when looking at the sun and lowest level point. Finally, the accuracy was reviewed by comparing the G/T calculation results with the two measurement methods, and the optimal measurement method according to antenna performance and operating environment was presented.

• Investigative Magnetic Resonance Imaging
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• v.4 no.1
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• pp.42-51
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• 2000

Purpose: Projection-type Fast Spin Echo (PFSE) imaging is robust to patient motion or flow related artifact compared to conventional Fast Spin Echo (FSE) imaging, however, it has difficulty in controlling $T_2$ contrast. In this paper, Tz contrast in the PFSE method is analyzed and compared with those of the FSE method with various effective echo times by computer simulation. The contrasts in the FSE and PFSE methods are also compared by experiments with volunteers. From the analysis and simulation, it is shown that ${T_2}-weighted$ images can well be obtained by the PFSE method proposed. Materials and methods: Pulse sequence for the PFSE method is implemented at a 1.0 Tesla whole body MRI system and $T_2$ contrasts in the PFSE and FSE methods are analyzed by computer simulation and experiment with volunteers. For the simulation, a mathematical phantom composed of various $T_2$ values is devised and $T_2$ contrast in the reconstructed image by the PFSE is compared to those by the FSE method with various effective echo times. Multi-slice ${T_2}-weighted$ head images of the volunteers obtained by the PFSE method are also shown in comparison with those by the FSE method at a 1.0 Tesla whole body MRI system. Results: From the analysis, $T_2$ contrast by the PFSE method appears similar to those by the FSE method with the effective echo time in a range of SO-lOOms. Using a mathematical phantom, contrast in the PFSE image appears close to that by the FSE method with the effective echo time of 96ms. From experiment with volunteers, multi-slice $T_2-weighted$ images are obtained by the PFSE method having contrast similar to that of the FSE method with the effective echo time of 96ms. Reconstructed images by the PFSE method show less motion related artifact compared to those by the FSE method. Conclusion: The projection-type FSE imaging acquires multiple radial lines with different angles in polar coordinate in k space using multiple spin echoes. The PFSE method is robust to patient motion or flow, however, it has difficulty in controlling $T_2$ contrast compared to the FSE method. In this paper, it is shown that the PFSE method provides good $T_2$ contrast (${T_2}-weighted$ images) similar to the FSE method by both computer simulation and experiments with volunteers.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.8 no.2
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• pp.47-60
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• 1975

For the calculation of population parameter and estimation of recruitment of a fish population, an application of multiple regression method was used with some statistical inferences. Then, the differences between the calculated values and the true parameters were discussed. In addition, this method criticized by applying it to the statistical data of a population of bigeye tuna, Thunnus obesus of the Indian Ocean. The method was also applied to the available data of a population of Pacific saury, Cololabis saira, to estimate its recuitments. A stock at t year and t+1 year is, $N_{0,\;t+1}=N_{0,\;t}(1-m_t)-C_t+R_{t+1}$ where $N_0$ is the initial number of fish in a given year; C, number o: fish caught; R, number of recruitment; and M, rate of natural mortality. The foregoing equation is $$\phi_{t+1}=\frac{(1-\varrho^{-z}{t+1})Z_t}{(1-\varrho^{-z}t)Z_{t+1}}-\frac{1-\varrho^{-z}t+1}{Z_{t+1}}\phi_t-a'\frac{1-\varrho^{-z}t+1}{Z_{t+1}}C_t+a'\frac{1-\varrho^{-z}t+1}{Z_{t+1}}R_{t+1}......(1)$$ where $\phi$ is CPUE; a', CPUE $(\phi)$ to average stock $(\bar{N})$ in number; Z, total mortality coefficient; and M, natural mortality coefficient. In the equation (1) , the term $(1-\varrho^{-z}t+1)/Z_{t+1}$s almost constant to the variation of effort (X) there fore coefficients $\phi$ and $C_t$, can be calculated, when R is a constant, by applying the method of multiple regression, where $\phi_{t+1}$ is a dependent variable; $\phi_t$ and $C_t$ are independent variables. The values of Mand a' are calculated from the coefficients of $\phi_t$ and $C_t$; and total mortality coefficient (Z), where Z is a'X+M. By substituting M, a', $Z_t$, and $Z_{t+1}$ to the equation (1) recruitment $(R_{t+1})$ can be calculated. In this precess $\phi$ can be substituted by index of stock in number (N'). This operational procedures of the method of multiple regression can be applicable to the data which satisfy the above assumptions, even though the data were collected from any chosen year with similar recruitments, though it were not collected from the consecutive years. Under the condition of varying effort the data with such variation can be treated effectively by this method. The calculated values of M and a' include some deviation from the population parameters. Therefore, the estimated recruitment (R) is a relative value instead of all absolute one. This method of multiple regression is also applicable to the stock density and yield in weight instead of in number. For the data of the bigeye tuna of the Indian Ocean, the values of estimated recruitment (R) calculated from the parameter which is obtained by the present multiple regression method is proportional with an identical fluctuation pattern to the values of those derived from the parameters M and a', which were calculated by Suda (1970) for the same data. Estimated recruitments of Pacific saury of the eastern coast of Korea were calculated by the present multiple regression method. Not only spring recruitment $(1965\~1974)$ but also fall recruitment $(1964\~1973)$ was found to fluctuate in accordance with the fluctuations of stock densities (CPUE) of the same spring and fall, respectively.

• Korean Journal of Forensic Psychology
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• v.11 no.1
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• pp.21-36
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• 2020

In P300-based concealed information test (P300 CIT), it evaluates whether the P300 amplitude for the probe is significantly greater than that of the irrelevant to determine if the suspect is telling a lie. An independent sample t-test or a bootstrap method can be used as a statistical test to make that decision. Rosenfeld et al. (2004) used the bootstrap method, claiming that "t tests on single sweeps are too insensitive to use to compare mean probe and irrelevant P300s within individuals" and their method has been accepted to date. The purpose of the study is to evaluate whether the power of t-test is lower than that of the bootstrap method in the P300 CIT. The Monte Carlo study was conducted by using EEG collected from 39 participants. The results showed that the type I error rates of the t-test and the percentile bootstrap method were similar and the power of the percentile bootstrap method was slightly higher than that of the t-test. The type I error rates of the t-test and the percentile bootstrap method were slightly lower than the significance level and the powers of the two tests were also slightly lower than that of the theoretical t-test. On the other hand, the type I error rate and power of the standard error Bootstrap method were the same as those of the theoretical t-test and its power was .012 ~ .081 higher than that of t-test depending on experimental conditions.