• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.229-243
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• 2022

The effects of monthly meteorological data measured at 11 stations in South Korea on pan coefficient were analyzed to develop the four types of multiple linear regression models for estimating pan coefficients. To evaluate the applicability of developed models, the models were compared with six previous models. Pan coefficients were most affected by air temperature for January, February, March, July, November and December, and by solar radiation for other months. On the whole, for 12 months of the year, the effects of wind speed and relative humidity on pan coefficient were less significant, compared with those of air temperature and solar radiation. For all meteorological stations and months, the model developed by applying 5 independent variables (wind speed, relative humidity, air temperature, ratio of sunshine duration and daylight duration, and solar radiation) for each station was the most effective for evaporation estimation. The model validation results indicate that the multiple linear regression models can be applied to some particular stations and months.

• The Plant Pathology Journal
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• v.26 no.1
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• pp.17-24
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• 2010

A logistic model for describing combined effects of both temperature and wetness period on appressorium formation was developed using laboratory data on percent appressorium formation of Colletotrichum acutatum. In addition, the possible use of the logistic model for forecasting infection risks was also evaluated as compared with a first-order linear model. A simplified equilibrium model for enzymatic reactions was applied to obtain a temperature function for asymptote parameter (A) of logistic model. For the position (B) and the rate (k) parameters, a reciprocal model was used to calculate the respective temperature functions. The nonlinear logistic model described successfully the response of appressorium formation to the combined effects of temperature and wetness period. Especially the temperature function for asymptote parameter A reflected the response of upper limit of appressorium formation to temperature, which showed the typical temperature response of enzymatic reactions in the cells. By having both temperature and wetness period as independent variables, the nonlinear logistic model can be used to determine the length of wetness periods required for certain levels of appressorium formation under different temperature conditions. The infection model derived from the nonlinear logistic model can be used to calculate infection risks using hourly temperature and wetness period data monitored by automated weather stations in the fields. Compared with the nonlinear infection model, the linear infection model always predicted a shorter wetness period for appressorium formation, and resulted in significantly under- and over-estimation of response at low and high temperatures, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.12
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• pp.1611-1617
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• 2018

The body temperature of livestock is directly related to the health of livestock such that it changes immediately when there exists health problem. Accordingly, the monitoring of livestock's temperature is one of most important tasks in farm management. However, the temperature of livestock is usually measured using skin-attached sensor which is significantly affected by the outside temperature and the condition of attachment which results in the inaccurate measurement of temperature. Herein we have proposed new scheme which estimates the body core temperature of cow based on measured data from neck-attached smart sensor. Especially, we have considered both schemes which estimate the exact temperature and which detect the unusually high temperature based on machine learning. We have found that the occurrence of high temperature can be detected accurately. The proposed scheme can be used in monitoring of health condition of cow and improving the efficiency of farm management.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.853-863
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• 2020

As the battery ages, the internal resistance of the battery increases, so the loss due to the internal resistance increases at the same charging current, causing the battery temperature to rise, which further accelerates battery aging. Therefore, it is necessary to optimize the charging conditions according to the aging of the battery or the current charge amount, and to accurately estimate this, estimation of the parameters of the equivalent circuit is most important. This paper proposes a new measurement technique that can measure the internal resistance of a battery by analyzing a specific high frequency voltage and current applied to the battery. In addition, in order to test the validity of the proposed measurement technique, the current charging amount was estimated based on the measured internal resistance, and the terminal voltage of the constant current charging mode was automatically set and operated. As a result, good results were obtained regardless of the battery voltage. If this equipment is installed in the charging device, it is believed that it will be of great help in the stability management of the aging reusable battery.

• Membrane and Water Treatment
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• v.9 no.6
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• pp.455-462
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• 2018

The measurement and monitoring of the biochemical oxygen demand (BOD) play an important role in the planning and operation of wastewater treatment plants. The most basic method for determining biochemical oxygen demand is direct measurement. However, this method is both expensive and takes a long time. A five-day period is required to determine the biochemical oxygen demand. This study has been carried out in a wastewater treatment plant in Turkey (Hurma WWTP) in order to estimate the biochemical oxygen demand a shorter time and with a lower cost. Estimation was performed using artificial neural network (ANN) method. There are three different methods in the training of artificial neural networks, respectively, multi-layered (ML-ANN), teaching learning based algorithm (TLBO-ANN) and artificial bee colony algorithm (ABC-ANN). The input flow (Q), wastewater temperature (t), pH, chemical oxygen demand (COD), suspended sediment (SS), total phosphorus (tP), total nitrogen (tN), and electrical conductivity of wastewater (EC) are used as the input parameters to estimate the BOD. The root mean squared error (RMSE) and the mean absolute error (MAE) values were used in evaluating performance criteria for each model. As a result of the general evaluation, the ML-ANN method provided the best estimation results both training and test series with 0.8924 and 0.8442 determination coefficient, respectively.

• The KSFM Journal of Fluid Machinery
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• v.14 no.1
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• pp.34-41
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• 2011

Fire characteristics can be analyzed more realistically by using more accurate properties related to the fire dynamics and one way to acquire these fire properties is to use one of the inverse property estimation techniques. In this study two optimization algorithms which are frequently applied for the inverse heat transfer problems are selected to demonstrate the procedure of obtaining pyrolysis properties of charring material with relatively simple thermal decomposition. Thermal decomposition is occurred at the surface of the charring material heated by receiving the radiative energy from external heat sources and in this process the heat transfer through the charring material is simplified by an unsteady 1-dimensional problem. The basic genetic algorithm(GA) and repulsive particle swarm optimization(RPSO) algorithm are used to find the eight properties of a charring material; thermal conductivity(virgin, char), specific heat(virgin, char), char density, heat of pyrolysis, pre-exponential factor and activation energy by using the surface temperature and mass loss rate history data which are obtained from the calculated experiments. Results show that the RPSO algorithm has better performance in estimating the eight pyrolysis properties than the basic GA for problems considered in this study.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.1
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• pp.160-167
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• 2010

This paper proposes an adaptive sliding mode observer (SMO), which adds the estimation function of the stator resistance to a new sliding mode observer for the robust sensorless control of permanent magnet synchronous motor (PMSM) with variable parameters. To reduce the chattering problem commonly found in the conventional sliding mode observer where the low-pass filter and additional position compensation of the rotor are used, the sigmoid function is used for the control of a switching function in this research. With the estimation of the stator resistance, the proposed observer can improve the control performance by reducing the estimation error of the motor's speed. Note that the stator resistance is varying with the ambient temperature and becomes an error source for the sensorless control of PMSM. The new sliding mode observer has better efficiency than the conventional adaptive sliding mode observer by reducing the time consuming integral calculations. The stability of the proposed adaptive sliding mode observer is verified by the Lyapunov function in determining the observer gains, and the effectiveness of the observer is demonstrated by simulations and experiments.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.3
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• pp.372-378
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• 2016

Clutch torque control is the key to the ride comfort improvement of a vehicle equipped with AMT (automated manual transmission). For such control, the torque transfer starting point, known as the "kiss point," should be indicated or at least estimated to compensate for the clutch torque. The kiss point changes due to wear, high temperature, and fatigue; as such, it should be estimated while the vehicle is being driven. In this study, the method of kiss point active estimation for an AMT vehicle with a dry-type clutch was devised. The kiss point is learned while the engine is in an idle state and while the transmission is at a neutral gear position. It is determined when the input shaft of the transmission starts to rotate by slowly engaging the clutch. The noise of the shaft speed signal during the slow engagement process is filtered for accurate control. The kiss point estimation at various clutch engagement speeds was analyzed via a vehicle test.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.51 no.8
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• pp.457-466
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• 2002

The speed and position control of SRM(Switched Reluctance Motor) needs the encoder or resolver to obtain the rotor position information. These position sensors can be affected by the EMI, dusty, and high temperature surroundings. Therefore the speed and position sensorless control has been studied widely In this paper, the binary observer of the SRM which has two feedback compensation loops to control the speed of SRM is proposed. One loop reduces the estimation error like the sliding mode observer, and the other removes the estimation error chattering occurred in the sliding mode observer. This observer is constructed on the basis of variable structure control theory and has the inertial term to exclude the chattering. This method has a good estimation performance in spite of nonlinear modeling of SRM. The advantages of the proposed method are verified experimentally.

• Journal of the Korean Society for Railway
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• v.9 no.2 s.33
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• pp.237-243
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• 2006

Deicing system is to melt frost or ice freezed in catenary line when the temperature is lower than $0^{\circ}C$ in winter. The principle of deicing system is to melt frost or ice by Joule heat of catenary impedance. The performance of deicing is dependant of deicing current determined by the length of deicing section, deicing impedance and current division ratio of catenary line and messenger line. So, we present technique for estimating deicing current and process for determination of deicing section in the conventional line. Deicing impedance is estimated using Carson-Pollaczek equation, and current division ratio of catenary line and messenger line is estimated using voltage drop, and deicing current is estimated using power system data of deicing system. For the determination of the final deicing section, we verified estimated value comparing with experiment value of deicing impedance and current division ratio of catenary line and messenger line using low voltage experiment. Finally, we verified the validity of estimation technique and process using a simulated test data of real deicing system operation in the Chungju Substation, Chungbuk line.