• Applied Chemistry for Engineering
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• v.9 no.3
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• pp.430-434
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• 1998

Preparative HPLC (High-Performance Liquid Chromatography) is mainly used for separating useful component from biological samples. By reversed-phase HPLC packed with preparative packings ($15{\mu}m$), the adsorption characteristics with sample size were investigated. Sample was 5'-GMP, a flavor enhancer, and the composition of mobile phase was 20mM $KH_2PO_4$ solution:methanol (97:3 vol.%). From the experimental results, the effect of sample size on retention factor was negligible, but the peak was asymmetrical above $1{\mu}g$ of sample. In addition, the increase in sample size deteriorated the number of theoretical plates, and at small concentration, the number of theoretical plates was less because of large peak width. In the experimental condition, the adsorption isotherm of 5'-GMP was relatively well represented by Freundlich equation.

• Fashion & Textile Research Journal
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• v.6 no.4
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• pp.497-502
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• 2004

The mechanical properties of jean slacks to the type of cutting lines in knee region and the bending time, after doing bending 0 times, 500 times, and 1500 times, the effects of whether or not cutting line, the fabric direction of the cutting area, and the number of cutting lines were investigated for tensile, shearing, compression, and mixing value of mechanical properties. The results are as follows: EM to bending times were larger in order of weft

• The Journal of the Korean dental association
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• v.52 no.9
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• pp.558-569
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• 2014

Sample size determination is critical, but not easy to do. Sample size defined as the number of observations in a sample to be studied should be big enough to have a high likelihood of detecting a true difference between groups. Practical procedure for determining sample size, using $G^*$power and previous dental articles, was shown in this study. Examples involving independent t-test, paired t-test, one-way analysis of variance(ANOVA), and one-way repeated-measures(RM) ANOVA were used. The purpose of this study is to enable researchers with non-statistical backgrounds to use in practice freely available statistical software G*power to determine sample size and power.

• Journal of Veterinary Clinics
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• v.29 no.1
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• pp.68-77
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• 2012

Whenever planning a study design or preparing a research proposal it is highly recommended that investigators decide the optimum sample size that is required to yield an outcome of interest with a predetermined level of precision. This is because that, all else being equal, if a study with less than the optimum sample size would not detect the significance of differences in reality, and similarly, if a study with more than the optimum sample size will be costly. For these reasons, the majority of peer reviewed biomedical journals assess the adequacy of sample size requirements. The calculated sample size is used as a target number of samples to be collected to provide an estimate of the parameter with the desired and predetermined level of accuracy, and the sample size is a major determinant of the probability of detecting diseased animals from the population. There is no single method of calculating sample size for any given study design. In this context, the purpose of this article is to provide a collection of formulas and examples for some typical situations likely to be encountered in veterinary clinical practice and to highlight the importance of performing prospective sample size calculations when planning a research. Specifically, this paper is concerned with the basic principle of sample size calculation, and considerations for methodological applications were illustrated for a given data set. Also included in this paper is factors influencing sample size calculations using a statistically valid techniques. Appropriate methods to consider these factors are presented.

• The Korean Journal of Food & Health Convergence
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• v.5 no.3
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• pp.35-40
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• 2019

In this study, general bacterial counts and coliform counts, which are hygienic indicator microorganisms, were tested for candy, chocolate, and jelly which are easily available and enjoyed around. After dropping each sample on the desk, indoors, and outdoors, it is immediately collected, or washed and collected to confirm the myth of the 3-second rule. Immediately after removing the wrapping paper, each sample was dropped on the desk, indoors, and outdoors, and after 3 seconds from the moment of contact with the surface, and then collected in a sample bag using sterilized sanitary gloves. After the same operation, each sample was rinsed for 5 seconds using sterilized sanitary gloves and sterilized distilled water, and then collected in a sample bag. The number of bacteria detected in non-washing candies was 41 CFU/g at outdoor and the number of bacteria detected in non-washing chocolate was 76 CFU/g at outdoor. The number of bacteria detected in non-washing jellies was 79 CFU/g at outdoor. Coliform group was not detected in all samples. This showed good results at the level of m = 10,000 or less, which is an allowable value suggested in the Food Code. Also, effect of washing on contaminated food was confirmed. This result is remarkably low compared with the microorganism specimens shown in Food Code, and it is confirmed that contamination occurs but not high value. Therefore, the myth of the 3-second rule is true compared to the figures based on Food Code. However, it showed the characteristics of bacteria that could survive and cross-contaminate on dry food surfaces and emphasized the importance of hygiene through food contact to unsanitary surfaces to minimize the risk of food poisoning.

• The Journal of Korean Medicine
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• v.17 no.2 s.32
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• pp.178-190
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• 1996

This experiment was performed in order to study the effects of Haepyoyijintang on the pulmonary injury caused by $SO_2$. Healthy adult male rats weighting about 250g were divided into 4 groups- the Normal group, the Control group, the group of Haepyoyijintang administration for 5 days after $SO_2$ gas exposure (Sample I ), and the group of Haepyoyijintang administration for 10 days before and for 5 days after $SO_2$ gas exposure (SampleⅡ). The results were obtained as follows; 1. In the total number of leucocyte, Sample I and Sample II decreased significantly compared with Control group. 2. In the erythrocyte sedimentation rate, Sample I and Sample Ⅱ decreased significantly compared with Control group. 3. In the lung weight, Sample I and Sample II decreased significantly compared with Control group. 4. In the lung TBA level, only Sample II decreased significantly compared with Control group. 5. In the tracheal glycoprotein level, Sample I and Sample II decreased significantly compared with Control group. According to the above results, Haepyoyijintang has significant effects on the pulmonary injury caused by $SO_2$ in rats.

• The Journal of Korean Medicine
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• v.18 no.1
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• pp.344-356
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• 1997

This experiment was performed in order to study the clinical effect of Sojadodamgangkitang on the pulmonary injury caused by $SO_2$. Healthy adult male rats weighing about 250g were divided into 4 groups-the Normal group, the Control group, the group of Sojadodamgangkitang administration for 5 days after $SO_2$ gas exposure(Sample I), and the group of Sojadodamgangkitang administration for 10 days before and for 5days after $SO_2$ gas exposure(Sample II). The results were obtained as. follows; 1. In comparison with Control group, Sample I and Sample II were revealed significant effect on the total number of leucocyte. 2. In comparison with Control group, Sample I and Sample II were revealed significant effect on. the erythrocyte sedimentation rate. 3. In the effect on the lung weight, Sample I and Sample II were revealed significantly. 4. In the effect on the lung TBA level, only Sample II was revealed significantly. 5. In the effect on the tracheal glycoprotein level, Sample I and Sample II were revealed significantly. According to above results, Sojadodamgangkitang had significant effect on the pulmonary injury caused by $SO_2$ in rats.

• Journal of Industrial Technology
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• v.20 no.A
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• pp.159-167
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• 2000

In the study rainfall frequency analysis attemped the many specific property data record duration it is differance from occur to error-term and probability ditribution of concern manifest. error-term analysis of method are fact sample data using method in other hand it is not appear to be fault that sample data of number to be small random variates. Therefore, day-rainfall data: to randomicity consider of this study sample data to the Monte Carlo method by randomize after data recode duration of form was choice method which compared an assumed maternal distribution from splitting frequency analysis consequence. In the conclusion, frequency analysis of chuncheon region rainfall appeared samll RMSE to the Gamma II distribution. In the rainfall frequency analysis estimate RMSE using random variates great transform, RMSE is appear that return period increasing little by little RMSE incresed and data number incresing to RMSE decreseing.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.4
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• pp.798-804
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• 2003

In this paper, We have designed an advanced circuits for controlling the Ink Nozzle of Printer Head We can fully increase the number of nozzle by reducing the number of Input/Output PADs using the proposed new circuit. The proposed circuit is tested with only 20 nozzles to evaluate functional test using FPGA sample chip. The new circuit architecture can be estimated. Full circuit for controlling 320 nozzles was designed and simulated from ASIC full custom methodology, then the circuit was fabricated by applying 3${\mu}{\textrm}{m}$ CMOS process design rule.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2538-2541
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• 2005

This paper deals one of the methods of system identification, especially on-line system identification in time-domain. The algorithm in this study needs all states of the system as well input to it for system identification. In this reason, Kalman filter is used for state estimation. But in order to implement a state estimator, the fact that a system model must be known is logical contradiction. To overcome this, state estimation and system parameter estimation are performed simultaneously in one sample. And the result of the system parameter estimation is used as basis to state estimation in next sample. On-line system identification comes, in every sample by performing both processes of state estimation and parameter estimation that are related mutually and recursively. This paper demonstrates the validity of proposed algorithm through an example of an unstable inverted pendulum system. This algorithm can be useful for on-line system identification of a system that has fewer number of measurable output than system order or number of states.