• International Journal of Highway Engineering
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• v.16 no.1
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• pp.57-62
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• 2014

PURPOSES : Capacity is a main factor of determining the number of lane in highway design or the level of service in road on operation. Previous studies showed that breakdown may occur before capacity is reached, and then it was concluded that capacity is a stochastic value rather than a deterministic one. In general, estimating capacity is based on average over maximum traffic volume observed for capacity state. This method includes the empirical distribution method(EDM) and would underestimate capacity. This study estimated existing empirical methods of estimating stochastic highway capacity. Among the studied methods are the product limit method(PLM) and the selected method(SM). METHODS : Speed and volume data were collected at three freeway bottleneck sites in Cheonan-Nonsan and West Sea Freeway. The data were grouped into a free-flow state or capacity state with speeds observed in the bottlenecks and the upstream. The data were applied to the empirical methods. RESULTS : The results show that the PLM and SM estimated capacity higher than EDM. The reason is that while the EDM is based on capacity observations only, the PLM and SM are based on free-flow high volumes and capacity observations. CONCLUSIONS : The PLM and SM using both free-flow and capacity observations would be improved to enhance the reliability of the capacity estimation.

• Environmental and Resource Economics Review
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• v.9 no.5
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• pp.903-924
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• 2000

This paper examines the alternative method to measure the state of overall economic activity. The macroeconomic variables, used for business cycle, take more than a month after a period for collection and aggregation. The electricity generation data is compiled in mechanical ways just after the period. Based on this fact, we develop the two stage estimation method for coincident economic indicators in order to detect the business cycle in an earlier period, using Stock-Watson's Dynamic Factor Model. Using monthly data from 1970 to 1999, it is found that the experimental coincidence economic indicators are well-fitted to data and also that the estimates of two stage estimation method have good explanatory power, equivalent to the experimental coincidence economic indicators. While the RMSE of coincidence economic indicators is found to be 1.27%, that of the experimental coincidence economic indicators is found to be 1.31% and that of the two stage estimation method is around 1.44%. If we take consideration into the fact that it measures the business cycle in one month earlier, we come to the conclusion that the two stage estimation is of great use.

• Journal of the Korean Data and Information Science Society
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• v.14 no.4
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• pp.1091-1102
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• 2003

The Weibull variate is commonly used as a lifetime distribution in reliability applications. Estimation of parameters is revisited in the two-parameter Weibull distribution. The method of product spacings, the method of quantile estimates and the method of least squares are applied to this distribution. A comparative study between a simple minded estimate, the maximum likelihood estimate, the product spacings estimate, the quantile estimate, the least squares estimate, and the adjusted least squares estimate is presented.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.1
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• pp.1-8
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• 2021

For the safe and reliable operation of lithium-ion batteries in electric vehicles or energy storage systems, having accurate information of the battery, such as the state of charge (SOC), is essential. Many different techniques of battery SOC estimation have been developed, such as the Kalman filter. However, when this filter is applied to multiple batteries, it has difficulty maintaining the accuracy of the estimation over all cells owing to the difference in parameter values of each cell. The difference in the parameter of each cell may increase as the operation time accumulates due to aging. In this paper, a novel deep neural network (DNN)-based SOC estimation method for multi-cell application is proposed. In the proposed method, DNN is implemented to determine the nonlinear relationships of the voltage and current at different SOCs and temperatures. In the training, the voltage and current data obtained at different temperatures during charge/discharge cycles are used. After the comprehensive training with the data obtained from the cycle test with a cell, the resulting algorithm is applied to estimate the SOC of other cells. Experimental results show that the mean absolute error of the estimation is 1.213% at 25℃ with the proposed DNN-based SOC estimation method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11a
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• pp.320.1-320
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• 2002

In a hard disk drive (HDD) control system, a state-space controller/observer design is popularly adopted fur its advantages such as effective filtering of position and velocity, use of estimation error to handle servo defects, etc. In this report, a systematic method is proposed to accommodate the transport delay in the plant dynamics into the state estimator. (omitted)

• International Journal of Naval Architecture and Ocean Engineering
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• v.5 no.2
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• pp.246-262
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• 2013

With increases in international oil prices, the proportion of fuel cost to the operational costs of a ship is currently increasing. To reduce fuel cost, a method for determining an economical route for a ship based on the acquisition of the sea state and the estimation of fuel consumption is proposed. The proposed method consists of three items. The first item is to acquire the sea state information in real time. The second item is to estimate the fuel consumption of a ship according to the sea state. The last item is to find an economical route for minimal fuel consumption based on the previous two items. To evaluate the applicability of the proposed method, it was applied to routing problems in various ocean areas. The result shows that the proposed method can yield economical ship routes that minimize fuel consumption. The results of this study can contribute to energy savings for environmentally friendly ships.

• ETRI Journal
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• v.39 no.5
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• pp.643-651
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• 2017

Human age estimation is one of the key factors in the field of Human-Robot Interaction/Human-Computer Interaction (HRI/HCI). Owing to the development of deep-learning technologies, age recognition has recently been attempted. In general, however, deep learning techniques require a large-scale database, and for age learning with variations, a conventional database is insufficient. For this reason, we propose an age estimation method using artificially generated data. Image data are artificially generated through 3D information, thus solving the problem of shortage of training data, and helping with the training of the deep-learning technique. Augmentation using 3D has advantages over 2D because it creates new images with more information. We use a deep architecture as a pre-trained model, and improve the estimation capacity using artificially augmented training images. The deep architecture can outperform traditional estimation methods, and the improved method showed increased reliability. We have achieved state-of-the-art performance using the proposed method in the Morph-II dataset and have proven that the proposed method can be used effectively using the Adience dataset.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1927-1934
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• 2018

In this paper, we studied the state of charge (SOC) estimation algorithm of a high-capacity lithium secondary battery for electric vehicles (EVs) considering temperature characteristics. Nonlinear characteristics of high-capacity lithium secondary batteries are represented by differential equations in the mathematical form and expressed by the state space equation through battery modeling to extract the characteristic parameters of the lithium secondary battery. Charging and discharging equipment were used to perform characteristic tests for the extraction of parameters of lithium secondary batteries at various temperatures. An extended Kalman filter (EKF) algorithm, a state observer, was used to estimate the state of the battery. The battery capacity and internal resistance of the high-capacity lithium secondary battery were investigated through battery modeling. The proposed modeling was applied to the battery pack for EVs to estimate the state of the battery. We confirmed the feasibility of the proposed study by comparing the estimated SOC values and the SOC values from the experiment. The proposed method using the EKF is expected to be highly applicable in estimating the state of the high-capacity rechargeable lithium battery pack for electric vehicles.

• Journal of Photoscience
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• v.12 no.2
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• pp.57-61
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• 2005

The ground and excited state dipole moments of Coumarin 450 (C 450) laser dye were measured at room temperature in several solvents of varying dipole moments. The ground state dipole moment (${\mu}_g$) is estimated by using the modified Onsagar model and the excited state dipole moments (${\mu}_e$) were estimated by the method of solvatochromism as well as by utilizing the microscopic solvent polarity parameter ($E^N_T$). Further, the deviation of some of the points from the linearity of the $E^N_T$ versus Stokes shift indicates the existence of specific type of solute-solvent interaction. The excited state dipole moment of C 450 were found to be higher than those of the ground state and is interpreted in terms of the resonance structure of the molecule. A reasonable agreement has been observed between the values obtained by the method of solvatochromism and modified Onsagar model. It is observed that, corresponding to cyclohexane solution, the fluorescence maxima shift towards the red region with increasing the polarity of the solvents, hence the transition involved are of ${\pi}-{\pi}^*$ type.

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.1
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• pp.138-145
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• 2000

This paper presents a robust fault diagnosis and fault tolerant control method for the control systems in closed-loop affected by unknown inputs or disturbances. The fault diagnostic scheme is based on the disturbance-decoupled state estimation using a 2-stage state observer for state, actuator bias and sensor bias. The estimated bias show the occurrence time, location and type of the faults directly. The estimated state is used for state feedback to achieve fault tolerant control against the faults. Simulation results show that the method has definite fault tolerant ability against actuator and sensor faults, moreover, the faults can be detected on-line, isolated and estimated simultaneously.