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홍대승,이장훈,강선홍,임화영,Hong, Dae-Seung,Lee, Jang-Hun,Kang, Sun-Hong,Yim, Hwa-Young 한국전자통신학회 2010 한국전자통신학회 논문지 Vol.5 No.5
본 연구에서는 분뇨수집운반차량의 분뇨수거량을 정확하게 계측하고, 계측된 결과를 이용하여 청소요금을 산출할 수 있는 시스템을 개발하였다. 탱크내부의 높이를 계측할 수 있는 수위센서 센서회로 그리고 분뇨수거 작업자가 편리하게 사용할 수 있도록 휴대형 중앙처리연산장치를 개발하였다. 청소요금을 수거현장에서 징수할 수 있도록 영수증 출력기능도 내장되었다. 또한 경사진 현장에서 작업을 진행하여도 각도센서를 이용하여 탱크 내부에 분뇨가 수거되는 량을 정확하게 측정할 수 있도록 하였다. 본 연구결과를 통하여 분뇨수거 시 발생되는 민원을 감소시킬 수 있으며, 특화된 시스템의 수출로 외화획득이 기대된다. In this study, our system that the volume of collected human waste in the septic tank truck is measured accurately and the fee of disposing human waste can be calculated by using measured results has been developed. The level sensor and its circuits which can measure the height of the tank, the hand-held system that can be used by workers easily and simply with micro-controller have been developed. Also, this system has been built in the receipt printing function to charge for disposal fee. Even when working on a sloping field, this system can measure the accurate collected volume of human waste in the tank using the X-Y axis angle sensor. The results of this study expect that the popular complaints that generated from human waste can be reduced, the exportation possibility of our specialized systems can acquire foreign currency.
퍼지클러스터링 기법과 신경회로망을 이용한 고장표시기의 고장검출 능력 개선에 관한 연구
홍대승(Dae-Seung Hong),임화영(Hwa-Young Yim) 한국지능시스템학회 2007 한국지능시스템학회논문지 Vol.17 No.3
본 논문은 전력계통의 배전계통시스템에서 FRTU(Feeder remote terminal unit)의 고장검출 알고리즘의 개선에 관한 연구이다. FRTU는 상과 지락에 관한 고장검출을 할 수 있다. 특히 고장픽업 기능과 돌입억제기능은 일반적인 부하전류로부터 고장전류를 구별할 수 있다. FRTU는 돌입전류 또는 설정값을 초과한 고장잔류가 발생하면 고장표시기(FI)로 고장을 발생한다. 짧은 시간 푸리에 변환(STFT) 분석은 주파수와 시간에 관한 정보를 제공하고, 퍼지 중심 평균 클러스터링(FCM) 알고리즘은 고조파의 특성을 추출한다. 고장 검출기의 신경회로망 시스템은 최급강하법을 이용하여 고장상태로부터 돌입전류를 구별하도록 학습된다. 본 논문에서는 FCM과 신경회로망을 이용하여 고장검출기법을 개선하였다. 검증에 사용된 데이터는 22.9KV 배전계통 시스템애서 실제 측정된 데이터이다. This paper focuses on the improvement of fault detection algorithm in FRTU(feeder remote terminal unit) on the feeder of distribution power system. FRTU is applied to fault detection schemes for phase fault and ground fault. Especially, cold load pickup and inrush restraint functions distinguish the fault current from the normal load current. FRTU shows FI(Fault Indicator) when the fault current is over pickup value or inrush current. STFT(Short Time Fourier Transform) analysis provides the frequency and time information. FCM(Fuzzy C-Mean clustering) algorithm extracts characteristics of harmonics. The neural network system as a fault detector was trained to distinguish the inrush current from the fault status by a gradient descent method. In this paper, fault detection is improved by using FCM and neural network. The result data were measured in actual 22.9㎸ distribution power system.
이산 웨이블릿 변환과 신경회로망을 이용한 FRTU의 고장판단 능력 개선에 관한 연구
洪大昇(Dae-Seung Hong),高鈗錫(Yoon-Seok Ko),姜泰求(Tae-Ku Kang),朴學烈(Hak-Yeol Park),任化永(Hwa-Young Yim) 대한전기학회 2007 전기학회논문지 Vol.56 No.7
This paper proposes the improved fault decision algorithm using DWT(Discrete Wavelet Transform) and ANNs for the FRTU(Feeder Remote Terminal Unit) on the feeder in the power distribution system. Generally, the FRTU has the fault decision scheme detecting the phase fault, the ground fault. Especially FRTU has the function for 2000㎳. This function doesn't operate FI(Fault Indicator) for the Inrush current generated in switching time. But it has a defect making it impossible for the FI to be operated from the real fault current in inrush restraint time. In such a case, we can not find the fault zone from FI information. Accordingly, the improved fault recognition algorithm is needed to solve this problem. The DWT analysis gives the frequency and time-scale information. The new-a! network system as a fault recognition was trained to distinguish the inrush current from the fault status by a gradient descent method. In this paper, fault recognition algorithm is improved by using voltage monitoring system, DWT and neural network. All of the data were measured in actual 22.9㎸ power distribution system.
신경회로망과 DWT를 이용한 고장표시기의 고장검출 개선에 관한 연구
홍대승(Dae-Seung Hong),임화영(Hwa-Young Yim) 대한전기학회 2007 대한전기학회 학술대회 논문집 Vol.2007 No.4
This paper presents research about improvement of fault detection algorithm in FRTU on the feeder of distribution system. FRTU(Feeder Remote Terminal Unit) is applied to fault detection schemes for phase fault, ground fault, and cold load pickup and Inrush restraint functions distinguish the fault current and the normal load current. FRTU is occurred FI(Fault Indicator) when current is over pick-up value also inrush current is occurred FRTU indicate FI. Discrete wavelet transform(DWT) analysis gives the frequency and time-scale information. The neural network system as a fault detector was trained to discriminate inrush current from the fault status by a gradient descent method. In this paper, fault detection is improved using voltage monitoring system with DWT and neural network. These data were measured in actual 22.9㎸ distribution system.