• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.5
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• pp.37-42
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• 2015

In order to provide fundamental data for the creation of environmental design criteria for horticultural facilities, we developed a method to easily calculate the seasonal heating load applying heating degree-hour while taking into account heating load reductions due to solar radiation in the daytime, and reviewed through greenhouse heating experiments. Heating experiments and measuring meteorological environments were carried out in three greenhouses located at Buyeo, Cheonan, and Buan, and we derived reduction factors of seasonal heating load according to hours of sunshine. Daily mean hours of sunshine during the experiment period in each of the greenhouse was 4.0 to 8.3 hours, and the reduction factor of seasonal heating load was 0.64 to 0.85, has been shown to decrease linearly with the increase in hours of sunshine. A method to estimate the seasonal heating load for greenhouses was developed using the reduction factor of seasonal heating load derived from the greenhouse heating experiment, including the adjustment factor of seasonal heating load according to hours of sunshine. The developed method was validated through heating experiments in a greenhouse located at Cheonan. Greenhouse seasonal heating loads calculated by the method developed in this study were analyzed to show the estimate error of 1.2 to 5.0%. It showed that the accuracy increased 2.3 times more than when using the heating load reduction factor of 0.75 applied uniformly in previous studies. Thus, the calculation method of seasonal heating load for greenhouses considering hours of sunshine developed in this study could be utilized for energy estimation, management planning, and economic evaluation in greenhouse design.

• Journal of Korean Institute of Industrial Engineers
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• v.35 no.3
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• pp.203-212
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• 2009

The bullwhip effect refers to the phenomenon where demand variability is amplified when one moves upward a supply chain. In this paper, we exactly quantify the bullwhip effect for cases of seasonal demand processes in a two-echelon supply chain with a single retailer and a single supplier. In most of the previous research, some measures of performance for the bullwhip effect are developed for cases of non-seasonal demand processes. The retailer performs demand forecast with a multiplicative seasonal mixed model by using the minimum mean square error forecasting technique and employs a base stock policy. With the developed bullwhip effect measure, we investigate the impact of seasonal factor on the bullwhip effect. Then, we prove that seasonal factor plays an important role on the occurrence of the bullwhip effect.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.10 no.2
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• pp.173-179
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• 1998

An experimental study was conducted on the effect of compressor capacity control range of heat pump on the seasonal energy efficiency ratio with variation of the maximum and minimum compressor input frequencies. To obtain seasonal energy efficiency ratio, steady state test at the maximum, minimum and intermediate compressor speed and cyclic test at the minimum compressor speed should be conducted. Maximum input frequency was varied to 95Hz, 105Hz, and 115Hz, and the minimum input frequency was varied to 35Hz, 45Hz, and 55Hz. The seasonal energy efficiency ratio increased as the input frequency of the compressor decreased. The maximum input frequency had only slight effects on the SEER.

• 한국태양에너지학회:학술대회논문집
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• 2009.11a
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• pp.26-31
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• 2009

This study was analysed influence of urban higher temperature in Busan about time series analysis of AWS data. The results are as follows. (1) The temperature of Busan show min $13.2^{\circ}C$ ~max $15.8^{\circ}C$ by 50 years, it is on the rise. (2) The seasonal adjustment series, summer appeared min $17.5^{\circ}C$ ~max $28.9^{\circ}C$ with primitive series similarly. The winter was min $-11.4^{\circ}C$ ~max $17.9^{\circ}C$, the minimum temperature was more lowly than primitive series and maximum temperature was more higher than primitive series. The results, seasonal adjustment series is guessed with influence difference urban structural element beside seasonal factor. (3) Regional analytical result, January appeared with range of min 28% ~max 196% of the seasonal factor and August appeared min 90% ~ max 106%. One of the case which is of 100% or more of the seasonal factor January 12nd~17th, August appears at the 15~17th.

• Journal of Korean Elementary Science Education
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• v.30 no.2
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• pp.204-212
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• 2011

This research is about the six graders' preconceptions in elementary school about a cause of the seasonal change before learning. The result of this research is that the patterns of six graders's preconceptions in elementary school are earth's rotation, earth's revolution, the distance between the earth and the sun and the location factor between the sun and our country, which are mixed up with two more things. Especially many patterns of students explain the seasonal change of our country using the change of location of our country by earth's rotation, the location factor between the earth and the sun and the distance between the earth and the sun by earth's revolution.

• Journal of Korean Society on Water Environment
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• v.23 no.1
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• pp.161-168
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• 2007

The main aim of this work is focus on the Geum river water quality evaluation of pollution data obtained by monitoring measurement during the period 2001-2005. The complex data matrix 19 (entire monitoring stations)*13 (parameters), 60 (month)*13 (parameters) and 20 (season)*13 (parameters) were treated with different multivariate techniques such as factor analysis/principal component analysis (FA/PCA). FA/PCA identified two factor (19*13) classified pollutant Loading factor (BOD, COD, pH, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P, Chl-a), seasonal factor (water temp, SS) and three Factor (60*13, 20*13) classified pollutant Loading factor (BOD, COD, Cond, T-N, T-P, $NH_3$-N, $NO_3$-N, $PO_4$-P), seasonal factor (water temp, SS) and metabolic factor (Chl-a, pH). Loadings of pollutant factor is potent influence main factor in the Geum river which is explained by loadings of pollutant factor at whole sampling stations (71.16%), month (52.75%) and season (56.57%) of main water quality stations. Result of this study is that pollutant loading factor is affected at Gongju 1, 2, Buyeo 1, 2, Gangkyeong, Yeongi stations by entire stations and entire month (Gongju 1, Cheongwon stations), April, May, July and August (buyeo 1) by month. Also the pollutant Loading factor is season gives an influence in winter (Gongju 1, buyeo 1) from main sampling stations, but Cheongwon characteristic is non-seasonal influenced. This study presents necessity and usefulness of multivariate statistic techniques for evaluation and interpretation of large complex data set with a view to get better information data effective management of water sources.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.450-453
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• 2003

This paper presents the estimation on Load Characteristic Factor(k) which is considered to load pattern and seasonal characteristic of consumer. We can calculate the loss of distribution networks through the equation composing of Load Factor(LF), Loss Load Factor(LLF) and load characteristic factor(k). This equation is similar to the method of Regulator-General Victoria, Australia. Generally, the conventional method for calculating the distribution losses uses k with a constant value from 0.1 to 0.3. However, the k which is a relationship between LF and LLF can be varied by load pattern and seasonal characteristics. It is necessary to estimate the k according to load characteristics. This paper shows the result for recalculating k using the KEPCO's SOMAS data measured in distribution networks.

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.1
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• pp.68-75
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• 1991

The purpose of this thesis is to develop the new technique for the analysis of seasonal time series by extending the vector sample auto-correlation function(VSACF), which was developed for ARMA modelling procedure. After the problems of VSACF for modelling seasonal time series are investigated, the adjacent variance is defined and used for decomposing the seasonal factor from the seasonal time series. The seasonal indices are calculated and the VSACF is applied to the transformed series. The automatic procedure for modelling seasonal time series is suggested and applied to the real data, the international airline passenger travel.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.3
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• pp.111-118
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• 2017

Performance factors such as the EER(Energy Efficiency Ratio) and the COP (Coefficient of Performance) are being replaced by seasonal energy efficiency factors, like the SEER (Seasonal EER) and the SCOP (Seasonal COP) to evaluate the performance of a heat pump by the time of the year. Seasonal performance factors, such as the CSPF (Cooling Seasonal Performance Factor) and the HSPF (Heating Seasonal Performance Factor) are used to describe the heat pump's performance during the cool and hot seasons. In this study, the optimization of all heat pump's operating parameters was experimentally conducted to enhance the SEER based on the EU standard (EN 14825). Moreover, the SEER was improved by the compressor frequency, as well as indoor and outdoor fan speeds. In addition, the performance characteristics of the heat pump were studied under partial load conditions. As a result, the SEER was enhanced by 17% when the compressor frequency was optimized. An additional 2% improvement was achievable with the optimization of indoor and outdoor fan speeds.

• Korean Journal of Adult Nursing
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• v.21 no.6
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• pp.593-602
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• 2009

Purpose: This study was to provide baseline data about nurses' Influenza A (H1N1) knowledge, awareness, and practice of infection control and to identify the significant factor affecting the level of practice. Methods: The subjects of this study were 144 nurses who worked at Influenza A (H1N1) regional base Hospital in D city. Data were collected by self-reported questionnaires during September 2009. The collected data were analyzed using SPSS/WIN 12.0 program. Results: The knowledge of Influenza A (H1N1) was statistically different according to age, unit, career and experience of seasonal influenza vaccination during the last year. The awareness of infection control was statistically different according to age, career, experience of seasonal influenza vaccination for last year and intention to get seasonal influenza vaccination for this year. The practice of infection control was statistically different according to unit, experience of seasonal influenza vaccination for last year, intention to get seasonal influenza vaccination for this year and intention to get Influenza A (H1N1) vaccination for this year. There was positive correlation among knowledge, awareness and practice (p < .05). Awareness was the significant factor affecting the level of practice. Conclusion: An educational program focusing on strategy to change nurse's awareness can be effective for infection control of Influenza A (H1N1) in regional base hospitals.