• Journal of Biosystems Engineering
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• v.33 no.5
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• pp.309-316
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• 2008

The goal of this study was to develop prediction models to estimate the storage days of tomato. The transmittance spectral data measured on tomato were preprocessed through normalization, SNV, Savitzky-Golay, and Norris Gap and then were used to build the prediction models using partial least square (PLS) method. For the experiments, the tomato samples of different varieties were collected at different harvest time. The samples were taken right after harvest from the field and then were stored in a low-temperature storage room in which room temperature was maintained at $10^{\circ}C$. The transmittance spectral data of the tomato samples were measured at three-day intervals for 16 days. The performance of the prediction models was affected by the preprocessing techniques as well as the varieties and harvest time of the tomato. The best model was found when SNV was applied. The accuracy of the best model was 90.2%. It can be concluded that the transmittance spectra are useful information for predicting the period of storage of tomato.

• Journal of Biosystems Engineering
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• v.42 no.1
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• pp.62-68
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• 2017

Purpose: The storage life of pearl millet-based, deep fried, ready-to-eat snacks, packaged in aluminum-laminated polyethylene having a thickness of $50{\mu}m$ (with and without nitrogen) was evaluated under storage conditions of $38{\pm}2^{\circ}C$ and 90% RH. Methods: The moisture content, free fatty acids (FFA), peroxide value, and crispness of the snack were evaluated throughout the storage period. The moisture content, FFA, and peroxide value increased with an increase in storage period, but the increase was less in packages flushed with nitrogen gas. The crispness decreased with an increase in the storage period, for snacks both with and without nitrogen packages. However, the decrease was less in nitrogen-flushed packages. FFA and peroxide values were strongly correlated with the moisture content of the snack. The storage life of the snack was found to be 60 and 45 days in packages with and without nitrogen respectively. Conclusions: The snack's predicted storage life, for snacks with and without nitrogen packages, was determined as 294 and 254 days respectively.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.2
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• pp.173-180
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• 2005

In this study, quantity and quality of collected rainwater by the ground collection system were investigated and the Rainwater Collection Prediction Model was developed to predict the amount of collected rainwater. The quantity of collected rainwater in the collection system was 9516 L(38.2%) and the quantity of infiltrated rainwater in the collection system was 9946 L(40.2%) through $25m^2$ area for the study period, respectively. Average turbidity of collected rainwater in collection system was 2.2 NTU, and average turbidity of infiltrated rainwater in collection system was 2.3 NTU for study period, respectively. The predicted amount by the model and the actual collected amount were 9842.4 L and 9516 L, which were very close showing that prediction was excellent. The optimal rainwater storage tank volume was simulated with a certain consumption condition for various cities with different rainfall patterns.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.307-307
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• 2022

• Journal of Korean Society of Water and Wastewater
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• v.18 no.1
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• pp.66-72
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• 2004

In this study, quantity and quality of collected rainwater by sand, gravel, soil, lawn and concrete surface, as collection materials were investigated and Rainwater Collection Prediction Model was developed to predict the amount of collected rainwater. The quantity of collected rainwater in concrete surface, gravel, sand, soil and lawn collection system was 1,067L(93.2%), 1,006L(87.8%), 902L(78.8%), 800L(69.9%), 788.5L(68.8%) for 8 months period, respectively. The average turbidity of collected rainwater in concrete surface, gravel, sand, soil and lawn collection system was 3.2NTU, 2.2NTU, 1.9NTU, 1.7NTU, 1.5NTU for 8 months period, respectively. For sand collection material, predicted amount by the Model and actual collected amount were 931.5L and 902L, which were very closed. For gravel collection material, predicted amount by Model and actual collected amount were 1,028.21. and 1,006L, which were very closed. To simulate the optimal rainwater storage volume, the rainfall and evaporation data in Dae-jeon city were used. For sand collection system with 30m2 area, the maximum storage volume was $17m^3$ and 62% of the year was secured for use of 240L/day.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.12 no.11
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• pp.982-993
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• 2000

This paper describes an optimal control scheduling of an encapsulated ice storage system with a chiller of nominal chiller 34RT(103,200kcal/hr) and an ice storage tank of 170RT-hrs(514,080 kcal). The optimization technique used in the study is dynamic programing. The objective function is summed cost during a day including charge and discharge periods. Control strategies being used commercially are chiller priority and storage priority control. In chiller priority control, the chiller is allowed to run at full capacity during the day, subject to limitations of the building load, and the ice is only melted when and if the load exceeds the chillers full capacity. In contrast to chiller priority control, the aim in storage priority control is to melt as much as ice as possible during the day time period. The system simulation calculates the operation costs for the three control strategies in the condition of the same cooling load and the same ice storage system. The simulation period is a day, assuming that initially the tank is stored fully and the cooling load is perfectly predicted for the scheduling. Also Final state of the tank is to be charged fully.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.611-612
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• 2015

Prediction algorithm of the energy storage system in accordance with the load pattern can cause economic loss in case of a failure prediction. In this paper, we compare the electricity charge between industrial power system with ESS - this case's operation is based on Non-prediction operation method. - and without ESS. In addition, we derive the payback period.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.4
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• pp.3-12
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• 2007

The purpose of this paper is to analyze the relationship between meteorological factors and agricultural reservoir storage, and predict the reservoir storage by multiple regression equation selected by high correlated meteorological factors. Two agricultural reservoirs (Geumgwang and Gosam) located in the upsteam of Gongdo water level gauging station of Anseong-cheon watershed were selected. Monthly reservoir storage data and meteorological data in Suwon weather station of 21 years (1985-2005) were collected. Three cases of correlation (case 1: yearly mean, case 2: seasonal mean dividing a year into 3 periods, and case 3: lagging the reservoir storage from 1 month to 3 months under the condition of case 2) were examined using 8 meteorological factors (precipitation, mean/maximum/minimum temperature, relative humidity, sunshine hour, wind velocity and evaporation). From the correlation analysis, 4 high correlated meteorological factors were selected, and multiple regression was executed for each case. The determination coefficient ($R^{2}$) of predicted reservoir storage for case 1 showed 0.45 and 0.49 for Geumgwang and Gosam reservoir respectively. The predicted reservoir storage for case 2 showed the highest $R^{2}$ of 0.46 and 0.56 respectively in the period of April to June. The predicted reservoir storage for 1 month lag of case 3 showed the $R^{2}$ of 0.68 and 0.85 respectively for the period of April to June. The results showed that the status of agricultural reservoir storage could be expressed with couple of meteorological factors. The prediction enhanced when the storage data are divided into periods rather than yearly mean and especially from the beginning time of paddy irrigation (April) to high decrease of reservoir storage (June) before Jangma.

• Korean journal of food and cookery science
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• v.15 no.2
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• pp.127-132
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• 1999

Dried noodles (somyon) were stored for 7 months at 25, 35 and 45$^{\circ}C$, and changes of water activity, amylograms and color of dried noodle at 4 week intervals were comparatively analyzed. The water activities during storage period were 0.43∼0.56 at all storage temperature. The breakdown of dried noodle by RVA(rapid visco analyser) increased as storage period increased. Color difference ($\Delta$E) was chosen for quality index due to the highest correlation coefficient between sensory score and color difference. The shelf-life of dried noodle was estimated from change of color, which was linearly increased as the storage period increased. The activation energy and Q$\sub$10/ value for color difference were 75.21 kJ/mol and 2.76 at 25$^{\circ}C$, respectively. Shelf-life of dried noodle at 25 were 27.9 months, respectively.

• Food Engineering Progress
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• v.21 no.4
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• pp.312-317
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• 2017

Weight loss that influences quality and farmer incomes is affected by the storage environment of agricultural products. The interior of storage should be maintained at high humidity to prevent the weight loss of products which contain a lot of moisture. The research had constantly proceeded with change in the heat exchanger surface areas, humidity systems, and weight loss forecast to maintain high humidity within storage. Relative humidity that exerts an effect weight loss of crop is influenced by storage temperature, leak state, and volume of product. When weight loss is predicted, different conditions of these factors are derived. In case of CA storage, ways of forecasting the weight loss become easier compared to cold storage due to sealed storage with external environment during storage period. In this study, apples were stored in purge-type CA storage and weight loss has been predicted by using operating characteristics and environmental conditions. As a result, humidity variation in the storage fluctuates with the operation of the unit-cooler. Furthermore, unit-cooler operation factor is influenced by outside temperature and respiration heat. Prediction value of weight loss according to temperature and humidity has been most accurately predicted. Prediction value through defrosting water measured shows unit-cooler work quality. K-value needs verification to calculate the VPD method.