• Korean Journal of Food Science and Technology
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• v.25 no.5
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• pp.558-564
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• 1993

The drying characteristics of fluidized bed drying with different drying conditions using naked barley were carried out. This fluidized drying mechanism of naked barley was consisted of consecutive two falling rate parts, first falling rate period and second falling rate period without showing constant rate period. The drying rate constant was increased with decreasing charged amount and relative humidity and increasing air temperature and air velocity. Since the drying rate constant expressed by Arrhenius type equation in the falling rate period showed good linearity, the falling rate period was considered as the controlling step. The activation energy of first falling step was 1,900 cal/gmol, while for second falling step the values showed 2,500 cal/gmol.

• Korean Journal of Food Science and Technology
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• v.33 no.6
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• pp.706-713
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• 2001

The influencing factor in drying characteristics of fluidized bed drying with different drying conditions for husked barley were carried out. This fluidized drying mechanism of husked barley was consisted of consecutive two falling rate parts, first falling rate period and second falling rate period without showing constant rate period. The drying rate constant was increased with decreasing charged amount and relative humidity and increasing air temperature and air velocity. Since the drying rate constant expressed by Arrhenius type equation in the falling rate period showed good linearity, the falling rate period was condsidered as the controlling step. The activation energy of first falling step was 1,100 cal/gmol, while for second falling step the values showed 1,600 cal/gmol.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.9
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• pp.827-832
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• 2010

Many researchers have tried to detect the falling and to reduce the injury associated with falling. Normally the method of detection of a loss of balance is more efficient than that of a compensatory motion in order to predict the falling. The detection algorithm of the loss of balance was composed of three main parts: parts of processing of measured data, construction of an internal model and detection of the loss of balance. The internal model represented a simple dynamic motion balancing with two rear legs of a four-legged chair and was a simplified model of a central nervous system of a person. The internal model was defined by the experimental data obtained within a fixed time interval, and was applied to the detecting algorithm to the end of the experiment without being changed. The balancing motion controlled by the human brain was improved in process of time because of the experience accruing to the brain from controlling sensory organs. In this study a reconstruction method of the internal model was used in order to improve the success rate and the detecting time of the algorithm and was changed with time the same as the brain did. When using the reconstruction method, the success rate and the detecting time were 95 % and 0.729 sec, respectively and those results were improved by about 7.6 % and 0.25 sec in comparison to the results of the paper of Ahmed and Ashton-Miller. The results showed that the proposed reconstruction method of the internal model was efficient to improve the detecting performance of the algorithm.

• The Korean Journal of Ecology
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• v.12 no.1
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• pp.9-20
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• 1989

Annual production and decomposition of litter were studied in piuns rigida platation in Mt. Kwan-ak. The peak time of litter production was October-November. The litterfalll at that time was 54% of annual litter production. But the peak time of litterfall of reproductive parts was Junejuly. The peak times of litter production in Alnus hirsuta plantation were June-July, and November. It is thought that difference of seasonal distribution of letterfall between two forest types were assigned to litter falling factors such as temperature and insects. Total amount of letter loss increased according to time. Particulary, the peak time of loss rate was July-August. It was shown that the protein and phosphorus were accumulated in the letter for an experimental period. The amount of crude protein increased to 150% of initial amount. The amount of potassium was increased by August and then decreased rapidly. The contents of crude fat, holocellulose, lignin, and Na were decreased slowly. The loss of holocellulose was the largiest among them. Calcium was in steady state. It was thought that this variation pattern was assigned to leaching of soluble parts by high temperature, rainfall and growth rate of microorganisms.

• Journal of Korean Society on Water Environment
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• v.27 no.4
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• pp.499-508
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• 2011

We analyzed the variations of water quality with flow regime alterations to determine the characteristics of the stream where the stream management is considerably difficult due to the high variability of the flow rates. In this study, both flow rates and water qualities were monitored at the tributaries, 34 in count, of both Geum River and Sabgyo Lake Basins. The variation of water qualities were divided into 2 types, based on their stream flow rates, known as Type I and Type II. If the water quality of a stream increases during low flow rate periods compared with high flow rate periods, it is classified as Type I; if the water quality of the stream increases during high flow rate periods compared with low flow rate periods, it falls under Type II. The analysis for the variations of water qualities, of all 43 basins, resulted to 24 basins under Type I and Nineteen 19 basins under Type II. The variations of water qualities were analyzed first by using Regression Analysis followed by Statistical Analysis. The average slope of the variations of water qualities and the slope of the standard deviations were 0.00135 and 0.00477, respectively. The Probability Distributions of both Type I and Type II basins were 61.1% and 38.9%, respectively. The basin having a probability distribution of 61.1% and is also known as Type I, increases during periods of low flow rates, due to the presence of point sources. Therefore, the basin should be enforced with stream management. Before the stream management can be implemented in all streams falling under Type II, the sources of contaminants should first be estimated. These contaminants can be classified into two parts, the first is Point source pollution and the second is Non-point source pollution, where the Non-Point source pollution can be sub-divided into two types, with storm runoff and without storm runoff.