• Journal of Environmental Science International
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• v.24 no.11
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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.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.6
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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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• v.13 no.1
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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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.5
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• pp.29-38
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• 2018

This study aims to propose a method for the estimation of operational delay cost using transportation card data in bus accidents. Average operational delay time from bus accidents was surveyed among 12 bus companies through an interview method. Then, the operational delay cost was estimated using actual traffic accident data and transportation card data. Results showed that average loss time per bus accident was found to be 45 minutes. In addition, total occupancy of 659 was estimated for the accidents investigated using transportation card data, resulting a total loss time of 494.25 hours. An estimated operational delay cost was 186.9 thousand won per accident, which was 6.37% of social agency cost. The magnitude of this number implied that operational delay cost may have a significant impact on traffic accident cost if included.

• Journal of the military operations research society of Korea
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• v.35 no.3
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• pp.17-30
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• 2009

In this study, we propose the operational availability computation model that can be used on the weapon system's requirement planning phase. The proposed model consists of the time parameters of Ao(Operational Availability) for a system and each time parameter's estimation method. The time parameters for Ao computation are TT(Total Time) and TDT(Total Down Time). The time parameters are defined by considering OMS/MP(Operational Mode Summary/Mission Profile) elements. TT is a calendar time as a specific mission time at wartime or one year at peacetime. TDT consists of TPM(Total Preventive Maintenance time), TCM (Total Corrective maintenance time), TALDT(Total Administrative and Logistics Down Time). Then the estimation method for these time parameters are presented by the weapon systems types.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.53 no.4
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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.

• Proceedings of the KIEE Conference
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• 2003.07d
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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.

• Proceedings of the KIEE Conference
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• 2007.04a
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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.

• Journal of The Korean Association For Science Education
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• v.21 no.1
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• pp.76-88
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• 2001

This study explored people's accuracy and methods of estimating some base physical quantities, i.e. length, mass, time and temperature. A total of 40 members, ranging from freshmen to professors, of a physics education department of a local university were asked to make two different kinds of estimations, intuitive and operational, on two sets of objects. For intuitive estimation, they were asked to make estimations on four given objects (length - wood chopsticks, mass - rubber eraser, time electric fan, temperature - water in a cup) as soon as they faced with the objects, usually within a few seconds of seeing. For operational estimation, they were allowed to make estimations on a different set of objects (length - plastic rod, mass - lock, time - simple pendulum, temperature - water in a cup) with enough time and they could apply various available methods (e.g. using pencil to estimate the object's length, counting their own pulse rate to estimate time) for the estimation. The findings of this study can be summarized as follows: (1) for length, mass and temperature the intuitive estimations were better performed while for the time estimation the result was the reverse; (2) there was no positive relationship between the amount of physics experience and the accuracy of the estimation; (3) in general, people's accuracy of the length estimation was best performed while their mass estimation was worst performed; (4) people used their own various methods for estimation, esp. using nearby objects around them and applying mental units which have convenient values (e.g. 30cm, 50cm, 1kg, 1 Keun, 1 second).

• Journal of Environmental Impact Assessment
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• v.14 no.4
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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.