• 한국식품과학회지
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• 제30권1호
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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.

• 공학논문집
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• 제5권1호
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• pp.57-62
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• 2004

국내 천연 제올라이트에 포함된 feldspar와 illite의 불순광물을 공기 분급 조작 의하여 정제하였다. 공기 분급된 제올라이트를 XRD로 분석한 결과 공기분급에 의하여 제올라이트와 불순광물을 분리할 수 있었고, 공기 분급을 함으로서 불순광물이 감소된 것을 알 수 있었다. 또한, 공기 분급된 천연 제올라이트를 1N NaOH용액으로 100, 150, $200^{\circ}C$에서 17시간동안 수열처리한 결과 mordenite와 clinoptiolite에서 phillilsite와 analcime의 상변화를 얻을 수 있었다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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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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• 제49권4호
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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.

• 대한자원환경지질학회:학술대회논문집
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• 대한자원환경지질학회 1999년도 춘계 공동학술발표회
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• pp.279-281
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• 1999

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제49권1호
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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 $(H_q(n))$.

• 자원리싸이클링
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• 제14권2호
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• pp.3-9
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• 2005

폐자동차 ASR으로부터 염소성분을 제거하기 위하여 풍력선별 및 비중선별 실험을 수행하였다. 또한, ASR플라스틱만을 분리하여 물을 매체로 한 비중선별을 실시하였다. ASR 시료는 모두 8 mm이하로 재분쇄하여 선별실험에 사용하였으며, 플라스틱은 3가지 입도로 나누어 비중선별 실험을 하였다. 풍력선별은 공기유량 9~20 M$^3$/hr 범위의 1단계와 공기유량 25~34 M$^3$/hr 범위의 2단계로 나누어 실시하고 각 산물의 비율과 재질분포를 조사하였다. 1단계 풍력선별후 underflow 산물의 비율은 62~66%인 것으로 나타났으며, 공기유량이 큰 2단계 풍력선별에서는 overflow 산물의 비율이 크게 증가하였다. 폐플라스틱만을 대상으로 한 비중선별 실험결과 부유물질이 침강물질에 비해 다소 많게 나타났으며, 염소함량에 있어서는 최대 수백개의 염소함량 차이를 보여 순수 플라스틱의 경우 매우 우수한 염소함유 재질의 분리효과를 얻을 수 있었다.

• 대한설비공학회지:설비저널
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• 제1권1호
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• pp.41-46
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• 1972

• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 1999년도 춘계학술대회 논문집
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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.

• 대한인간공학회지
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• 제35권5호
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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.