• 한국환경과학회지
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• 제24권11호
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• pp.1443-1450
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• 2015

Wastewater treatment plant(WWTP) has been recognized as a high energy consuming plant. Usually many WWTPs has been operated in the excessive operation conditions in order to maintain stable wastewater treatment. The energy required at WWTPs consists of various subparts such as pumping, aeration, and office maintenance. For management of energy comes from process operation, it can be useful to operators to provide some information about energy variations according to the adjustment of operational variables. In this study, multiple regression analysis was used to establish an energy estimation model. The independent variables for estimation energy were selected among operational variables. The $R^2$ value in the regression analysis appeared 0.68, and performance of the electric power prediction model had less than ${\pm}5%$ error.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권6호
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• pp.351-358
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• 2003

This paper presents a new method for finding the Block Pulse series coefficients, deriving the Block Pulse integration operational matrices and generalizing the integration operational matrices which are necessary for the control fields using the Block Pulse functions. In order to apply the Block Pulse function technique to the problems of state estimation or parameter identification more efficiently, it is necessary to find the more exact value of the Block Pulse series coefficients and integral operational matrices. This paper presents the method for improving the accuracy of the Block Pulse series coefficients and derives the related integration operational matrices and generalized integration operational matrix by using the Lagrange second order interpolation polynomial.

• International Journal of Naval Architecture and Ocean Engineering
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• 제13권1호
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• pp.367-378
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• 2021

This paper develops a dynamic regression model to quantify the contribution of key external factors to operational energy efficiency of ships. On this basis, kernel density estimation is applied to explore distribution patterns of fluctuations in operational performance. An empirical analysis based on these methods show that distribution of fluctuations in Energy Efficiency Operational Indicator (EEOI) is leptokurtic and fat tailed, rather than a normal one. Around 85% of fluctuations in EEOI can be jointly explained by capacity utilization and sailing speed, while the rest depend on other external factors largely beyond control. The variations in capacity utilization and sailing speed cannot be fully passed on to the energy efficiency performance of ships, due to complex interactions between various external factors. The application of the methods is demonstrated, showing a potential approach to develop a rating mechanism for use in the legally binding framework on operational energy efficiency of ships.

• 한국ITS학회 논문지
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• 제17권5호
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• pp.29-38
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• 2018

본 연구에서는 버스사고 발생 시 교통카드자료를 이용한 운행지연비용을 추정하는 방법을 제안하였다. 12개 버스회사를 대상으로 설문조사를 통하여 사고 시 평균 운행지연시간을 조사하였고, 실제 교통사고자료와 교통카드자료를 기반으로 운행지연비용을 추정하였다. 조사결과 버스사고시 운행지연으로 평균 45분의 손실시간이 발생하는 것으로 파악되었다. 교통카드자료 분석결과 사고가 발생한 노선의 재차인원의 총 합은 659명으로 추정되었고, 이에 따른 손실 시간은 총 494.25시간으로 계산되었다. 본 방법론으로 추정된 운행지연 비용은 약 186.9천원/건이며, 이는 사회적기관비용의 6.37%에 해당하는 값으로 나타나 운행지연비용이 도로교통사고 비용에 유의한 영항을 미치는 것으로 평가되었다.

• 한국국방경영분석학회지
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• 제35권3호
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• pp.17-30
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• 2009

본 연구에서는 무기체계 소요기획 단계에서 목표운용가용도를 산정할 때 활용할 수 있는 운용가용도 계산 모델을 제안한다. 운용가용도 계산 모델은 체계의 운용가용도 계산에 필요한 시간요소 정의와 시간 값 추정 방법으로 구성된다. 운용가용도 계산 시간요소는 총시간과 총비가동시간 요소로 구성된다. 시간요소는 운용형태종합 및 임무유형(OMS/MP : Operational Mode Summary/Mission Profile) 요소를 기초로 정의하였다. 총시간은 전시 작전기간 혹은 평시 365일이며, 총비가동시간은 총예방정비시간, 총고장정비시간, 총행정/군수지연시간으로 구성된다. 그리고 이러한 시간요소에 대하여 무기체계 유형별 시간 값 추정 방법을 제시하였다.

• 전기학회논문지P
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• 제53권4호
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• pp.175-182
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• 2004

This paper presents a new method for finding the Block Pulse series coefficients, deriving the Block Pulse integration operational matrices and generalizing the integration operational matrices which are necessary for the control fields using the Block Pulse functions. In order to apply the Block Pulse function technique to the problems of state estimation or parameter identification more efficiently, it is necessary to find the more exact value of the Block Pulse series coefficients and integral operational matrices. This paper presents the method for improving the accuracy of the Block Pulse series coefficients and derives generalized integration operational matrix and applied the matrix to the analysis of linear time-invariant system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2277-2279
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• 2003

This paper presents a new method for finding the Block Pulse series coefficients and deriving the Block Pulse integration operational matrices which are necessary for the control fields using the Block Pulse functions. This paper presents the method for improving the accuracy of the Block Pulse series coefficients and derives the related integration operational matrices by using the Lagrange second order interpolation polynomial and expands that matrix to general form.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.23-25
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• 2007

The partial distortion elimination (PDE) scheme is used to decrease the sum of absolute difference (SAD) computational complexity, since the SAD calculation has been taken much potion of the video compression. In motion estimation (ME) based on PDE, it is ideal that the initial value of SAD in summing performance has large value. The traditional scan order methods have many operation time and high operational complexity because these adopted the division or multiplication. In this paper, we introduce the new scan order and search order by using only adder. We define the average value which is called to rough average value (RAVR). Which is to reduce the computational complexity and increase the operational speed and then we can obtain the improvement of SAD performance. And also this RAVR is used to decide the search order sequence, since the difference RAVR between the current block and candidate block is small then this candidate block has high probability to suitable candidate. Thus, our proposed algorithm combines above two main concepts and suffers the improving SAD performance and the easy hardware implementation methods.

• 한국과학교육학회지
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• 제21권1호
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• pp.76-88
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• 2001

본 연구는 길이, 질량, 시간, 온도와 같은 일부 기본 물리량에 대한 사람들의 어림의 정확성과 방법에 대해 탐색하였다. 대학 물리교육과의 구성원(학부 1학년부터 교수까지) 중 임의로 선정된 총 40명이 2종류의 대상물 세트에 대해 직관적 어림과 조작적 어림 활동을 수행하였다. 직관적 어림에서는 참여자들이 각 대상물을 직면하자마자 해당 물리량의 값을 어림하였으며, 조작적 어림에서는 별도의 대상물에 대해서 충분한 시간을 두고 다양한 가용한 방법들을 (예컨대, 길이를 어림하기 위해 연필을 활용하거나 시간을 어림하기 위해 맥박을 세거나) 동원하여 어림하였다. 연구의 결과는 다음과 같이 요약될 수 있겠다: (1) 길이, 질량, 온도에 대해서는 직관적 어림이 더 정확하였으나 시간에 대해서는 조작적 어림이 더 정확하였다. (2) 물리학에 대한 경험의 정도와 어림의 정확성 사이에는 정적인 상관관계가 존재하지 않았다. (3)일반적으로 길이에 대한 어림이 가장 정확하였으며 질량에 대한 어림은 가장 부정확하였다. (4) 사람들은 조작적 어림을 위해서 스스로의 다양한 방법을 사용하였는데, 예컨대 대상물 주변의 각종 사물들을 활용하거나 특정한 값을 갖는 마음 속의 측정 단위(즉, 30cm, 50cm, 1kg, 1근, 1초 등)들을 적용하였다.

• 환경영향평가
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• 제14권4호
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• pp.157-164
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• 2005

Existing models that predict of Daily water supply include statistical models and neural network model. The neural network model was more effective than the statistical models. Only neural network model, which predict of Daily water supply, is focused on estimation of the operational control. Neural network model takes long learning time and gets into local minimum. This study proposes Neuro Genetic hybrid model which a combination of genetic algorithm and neural network. Hybrid model makes up for neural network's shortcomings. In this study, the amount of supply, the mean temperature and the population of the area supplied with water are use for neural network's learning patterns for prediction. RMSE(Root Mean Square Error) is used for a MOE(Measure Of Effectiveness). The comparison of the two models showed that the predicting capability of Hybrid model is more effective than that of neural network model. The proposed hybrid model is able to predict of Daily water, thus it can apply real time estimation of operational control of water works and water drain pipes. Proposed models include accidental cases such as a suspension of water supply. The maximum error rate between the estimation of the model and the actual measurement was 11.81% and the average error was lower than 1.76%. The model is expected to be a real-time estimation of the operational control of water works and water/drain pipes.