• Korean Journal of Food Science and Technology
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• v.30 no.1
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• pp.118-124
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• 1998

Dehulled cowpea was microparticulated and coarse fractions and fine fractions were collected by air classification at air classifying wheel speed (ACWS) of 15,000 rpm, 12,000 rpm and 9,000 rpm, respectively. Protein content in fine fraction after air classification was 2 times higher than that of microparticulated cowpea, emulsion capacity was about 3 times than coarse fraction. The coarse fraction of the highest viscosity on the gelatinization properties were detected by amylograph, was C-3 (9,000 rpm coarse)fraction. The majority of microparticulated cowpea particles were oval shaped starch and the rest of them were indeterminate minute particles which had some sharp corners. As an application test, microparticulated cowpea and coarse fraction (C-3) were used for mook (Korea traditional starch jelly) preparation and the wet milled cowpea starch was compared as a control. Some impurities induced discoloring was detected by sensory evaluation but after washing, it made no difference in sensory scores between washed starch and the control cowpea mook. And also syneresis of washed cowpea was less than control. At the above result, it can be to recovery about 85% of cowpea starch using microparticulation/air classification technology.

• The Journal of Engineering Research
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• v.5 no.1
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• pp.57-62
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• 2004

Korean natural zeolite with feldspar and illite as impurities was purified by an air classification method. X-ray powder diffraction analyses showed that the air classification effectively separated zeolite and impurities, and reduce the amount of impurity of the natural zeolite. The zeolite with air classification was treated with 1N NaOH solutions at temperatures at 100, 150, $200^{\circ}C$ for 17hours. The obtained hydrothermal treatment of phase change to phillilsite and analcime from mordenite and clinoptilolite.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.420-422
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• 1998

All Fault Detection and Diagnosis(FDD) methods utilize classification techniques. The objective of this study was to demonstrate the application of classification techniques to the problem of diagnosing faults in data generated by a variable-air-volume(VAV) air-handling unit(AHU) simulation model and to describe the characteristics of the techniques considered. Artificial neural network classifier and fuzzy clustering classifier were considered for fault diagnostics.

• Journal of the Korean Wood Science and Technology
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• v.49 no.4
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• pp.328-335
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• 2021

Considerable differences exist in the characteristics of size reduction and classification because of biomass species. Miscanthus sacchariflorus (M. sacchariflorus) Goedae-Uksae 1 is not used efficiently because of the imperfections of the processing technology for this biomass. Therefore, for the best use of specific biomass, improvement in the feedstock preparation of the biomass for processing, such as pellet manufacturing, is necessary. In this study, a laboratory-scale cone grinder and air classifier were designed and combined to investigate the performance of the combination system for M. sacchariflorus. The average equivalent spherical diameter of particles showed a close relationship with air velocity for air classification. The air velocity range to classify proper particles for pelletization was determined to be 6.0-6.8 m/s. The mass ratios of the collected particles to feed mass for four lengths of chopped M. sacchariflorus were 45.1%:46.1%, 39.1%:46.6%, and 44.1%:52.8% at the first, second, and third steps in simulating the multistep combination system, respectively.

• Proceedings of the KSEEG Conference
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• 1999.04a
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• pp.279-281
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• 1999

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.49 no.1
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• pp.19-23
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• 2000

Partial discharges(PD)in air insulated electric power apparatus often lead to deterioration of solid insulation by electron bombardments and electrochemical reaction. The PD caused to reduce the life time of power apparatus and to increase power losses. Thus understanding and classification of PD patterns in air are very important to discern sources of PD. In this paper, PD in air by using statistical methods was investigated. We classified air discharges, corona, surface discharges and cavity discharges by Kohonen network. For classification of PD patterns, we used statistical operators and parameters such as skewness$(S^+,\; S^-),\; kurtosis(K^+, K^-),\; mean phase(AP^+, AP^-)$, cross-correlation factor(CC) and asymmetry derived from the mean pulse-height phase distribution$(H_{avg}(\phi))$, the max pulse-height phase distribution $(H_{qmax}(\phi))$, the pulse count phase distribution $(H_n(\phi))$ and the pulse height vs. Repetition rate $(H_q(n))$.

• Resources Recycling
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• v.14 no.2
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• pp.3-9
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• 2005

A study on the air and gravity separation has been performed for the removal of chlorine containing materials from ASR of end-of-life vehicles. The gravity separation was also conducted on waste plastics collected from ASR. In this work, ASR were previously shredded to pass through 8 mm sieve prior to separation tests and the gravity separation of waste plastics was conducted for three different particle sizes. The two-stage air classification was conducted with the range of air flow rate of 9~20 M$^3$/hr at first stage and 25~34 M$^3$/hr at second stage, respectively. The fraction of overflow product was remarkably increased in the 2nd stage air classification because of high air flow rate while that of underflow product obtained from 1st stage air classification was found to be 62~66%. From the results of gravity separation on waste plastics, it was also found that the amount of the float product was much greater than sink product. It is believed that the gravity separation may be used very efficiently for the removal of calorine bearing materials from waste plastics.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
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• v.1 no.1
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• pp.41-46
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• 1972

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1999.05a
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• pp.704-707
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• 1999

Partial discharge(PD) in air insulated electric power systems is responsible for considerable power lossesfrom high voltage transmission lines. PD in air often leads to deterioration of insulation by the combined action of the discharge ions bombarding the surface and the action of chemical compounds that are formed by the discharge and may give rise to interference in ommunication systems. PD can indicate incipient failure. Thus understanding and classification of PD in air is very important to discern source of PD. In this paper, we investigated PD in air by using statical method. We classified air discharge with corona, surface discharge and cavity discharge by source of discharge. we used the mean pulse-height phase distribution $H_{qmean}(\psi)$, the max pulse-height phase distribution $H_{qmax}(\psi)$ , the pulse count phase distribution $H_n(\psi)$ and the max pulse height vs. repetition rate $H_{q}(n)$ for analysis PD pattern. We used statistical operators, such as skewness(S+. S-1, kurtosis(K+, K-), mean phase(AP+. AP-), cross-correlation factor(CC) and asymmetry from the distribution.

• Journal of the Ergonomics Society of Korea
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• v.35 no.5
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• pp.371-381
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• 2016

Objective:This study aims to evaluate the features of heart rate variability (HRV) and respiratory signals as indices for a driver's drowsiness and waking status in order to develop the classification model for a driver's drowsiness and waking status using those features. Background: Driver's drowsiness is one of the major causal factors for traffic accidents. This study hypothesized that the application of combined bio-signals to monitor the alertness level of drivers would improve the effectiveness of the classification techniques of driver's drowsiness. Method: The features of three heart rate variability (HRV) measurements including low frequency (LF), high frequency (HF), and LF/HF ratio and two respiratory measurements including peak and rate were acquired by the monotonous car driving simulation experiments using the photoplethysmogram (PPG) and respiration sensors. The experiments were repeated a total of 50 times on five healthy male participants in their 20s to 50s. The classification model was developed by selecting the optimal measurements, applying a binary logistic regression method and performing 3-fold cross validation. Results: The power of LF, HF, and LF/HF ratio, and the respiration peak of drowsiness status were reduced by 38%, 22%, 31%, and 7%, compared to those of waking status, while respiration rate was increased by 3%. The classification sensitivity of the model using both HRV and respiratory features (91.4%) was improved, compared to that of the model using only HRV feature (89.8%) and that using only respiratory feature (83.6%). Conclusion: This study suggests that the classification of driver's drowsiness and waking status may be improved by utilizing a combination of HRV and respiratory features. Application: The results of this study can be applied to the development of driver's drowsiness prevention systems.