• Journal of Power Electronics
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• v.19 no.1
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• pp.288-295
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• 2019

Rectifiers are widely used in industrial applications. Although detailed models of rectifiers are usually used to evaluate their performance, they are complex and time-consuming. Therefore, the Average Value Model (AVM) has been introduced to meet the demand for a simple and accurate model. This type of rectifier modeling can be used to simplify the simulations of large systems. The AVM of diode rectifiers has been an area of interest for many electrical engineers. However, healthy diode rectifiers are only considered for average value modeling. By contrast, faults occur frequently on diodes, which eventually cause the diodes to open-circuit. Therefore, it is essential to model bridge rectifiers under this faulty condition. Indeed, conventional AVMs are not appropriate or accurate for faulty rectifiers. In addition, they are significantly different in modeling. In this paper, a novel application of the parametric average value of a three-phase line-commutated rectifier is proposed in which one diode of the rectifier is considered open-circuited. In order to evaluate the proposed AVM, it is compared with experimental and simulation results for the application of a brushless synchronous generator field. The results clearly demonstrate the accuracy of the proposed model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.7
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• pp.1557-1564
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• 2010

In this paper, we propose a maximum entropy-based emotion recognition model using the individual average difference of emotional signal, because an emotional signal pattern depends on each individual. In order to accurately recognize a user's emotion, the proposed model utilizes the difference between the average of the input emotional signals and the average of each emotional state's signals(such as positive emotional signals and negative emotional signals), rather than only the given input signal. With the aim of easily constructing the emotion recognition model without the professional knowledge of the emotion recognition, it utilizes a maximum entropy model, one of the best-performed and well-known machine learning techniques. Considering that it is difficult to obtain enough training data based on the numerical value of emotional signal for machine learning, the proposed model substitutes two simple symbols such as +(positive number)/-(negative number) for every average difference value, and calculates the average of emotional signals per second rather than the total emotion response time(10 seconds).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.11
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• pp.1497-1502
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• 2014

Electric load forecasting is essential for stable electric power supply, efficient operation and management of power systems, and safe operation of power generation systems. The results are utilized in generator preventive maintenance planning and the systemization of power reserve management. Development and improvement of electric load forecasting model is necessary for power system maintenance and operation. This paper proposes daily maximum electric load forecasting methods for the next 4 weeks with a seasonal autoregressive integrated moving average model and an exponential smoothing model. According to the results of forecasting of daily maximum electric load forecasting for the next 4 weeks of March, April, November 2010~2012 using the constructed forecasting models, the seasonal autoregressive integrated moving average model showed an average error rate of 6,66%, 5.26%, 3.61% respectively and the exponential smoothing model showed an average error rate of 3.82%, 4.07%, 3.59% respectively.

• Journal of Electrical Engineering and Technology
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• v.9 no.5
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• pp.1562-1568
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• 2014

Based on the average method and the geometrical technique to calculate the average value, the average model of the open-loop step-up converter in CCM operation is established. The DC equilibrium point and corresponding small signal model is derived. The control-to-output transfer function is presented and analyzed. The theoretical analysis and PSIM simulations shows that the control-to-output transfer function includes not only the DC input voltage and the DC duty cycle, but also the two inductors, the two energy-transferring capacitors, the switching frequency and the load. Finally, the hardware circuit is designed, and the circuit experimental results are given to confirm the effectiveness of theoretical derivations and analysis.

• Journal of Power Electronics
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• v.11 no.1
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• pp.59-63
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• 2011

This paper presents the modeling of the sampling effect in the p-type average current mode control. The prediction of the high frequency components near half of the switching frequency in the current loop gain is given for the p-type average current mode control. By the proposed model, the prediction accuracy is improved when compared to that of conventional models. The proposed method is applied to a buck converter, and then the measurement results are analyzed.

• Journal of Korean Society of Transportation
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• v.20 no.3
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• pp.159-168
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• 2002

The Highway Capacity Manual(HCM) suggests estimating the average green time for the performance evaluation of the traffic actuated operation and Provides the average green time estimation model. However, the model provides with much room for improvements. This document proposes a new analytical model that overcomes the shortage of the HCM model. The average green times estimated by the HCM model and the proposed model were compared. A computer program using the proposed model was coded for the study, while the ACT348 program was used for the implementation of the HCM model Through the comparison study based on the 1,196 hypothetical simulation data surrogating field data, it was found that the average green times estimated by the proposed model yields much nicer one-to-one linear relationship to the simulation results than the ones from the HCM model in both exclusive-only and shared-permitted cases. The R2 values of the proposed and the HCM models with those cases are 0.90 and 0.56, and 0.86 and 0.57, respectively.

• Journal of Auto-vehicle Safety Association
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• v.14 no.1
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• pp.62-67
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• 2022

This study is to construct the simple estimating model for the HIC15 of the driver dummy using the frontal impact test results. Test results of 9 vehicles of Hyundai Sonata from the MY2002~MY2020 USNCAP are utilized for constructing the linear regression model. The average accelerations extracted from the vehicle crash pulses are handled as the main factors. The average accelerations of 10 ms interval within 0~100 ms are calculated from the crash pulse data of 9 vehicles. The present estimating model of the HIC15 using the average accelerations of 10 ms interval in the 0~80 ms range shows good agreement with the tested value within 2.4% maximum error.

• Journal of Ocean Engineering and Technology
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• v.23 no.2
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• pp.47-52
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• 2009

A Lagrangian particle tracking model coupled with the Princeton Ocean Model were used to estimate the average residence time of coastal water in Masan Bay, Korea. Our interest in quantifying the transport time scales in Masan Bay was stimulated by the search for a mechanistic understanding of this spatial variability, which is consistent with the concept of spatially variable transport time scales. Tidal simulation was calibrated through a comparison with the results of semi-diurnal current and water elevation measured at the tidal stations of Masan, Gadeokdo. In the model simulations, particles were released in eight cases, including slack before ebb, peak ebb, slack before flood, and peak flood, during both spring and neap tides. The averaged values obtained from the particle release simulations were used for the average residence times of the coastal water in Masan Bay. The average residence times for the southeastern parts of Somodo and the Samho River, Masan Bay were estimated to be about 20~50days and 70~80days, respectively. The spatial difference for the average residence time was controlled by the tidal currents and distance from the mouth of the bay. Our results might provide useful for understanding the transport and behavior of coastal water in a bay and might be used to estimate the dissimilative capacity for environmental assessment.

• Magazine of the Korean Society of Agricultural Engineers
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• v.35 no.3
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• pp.36-46
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• 1993

The purposes of this study are to develop three-layered Green-Ampt infiltration model considering temporal variation of physical properties of soil and to evaluate the model with field experiment on bare-tilled and soybean-growing soil plots under natural rainfalls. Infiltration tests were conducted on a sandy loam soil. The model has three-layered soil profile including a surface crust, a tilled layer, a subsoil and considers temporal variation of porosity, hydraulic conductivity, capillary pressure head on a tilled layer by natural rainfalls and canopy density variation of crop. Field measurement of porosity, average hydraulic conductivity and average capillary presure head on a tilled layer were conducted by soil sampler and air-entry permeameter at regular intervals-after tillage. It was found that temporal variation of porosity and average hydraulic conductivity might be expressed as a function of cumulative rainfall energy and average capillary pressure head might be expressed as a function of porosity of a tilled soil. The model was calibrated by an optimization technique, Hooke and Jeeves method using hourly surface runoff data. With the calibrated parameters, the model was verified satisfactorily.

• Journal of Korean Society of Transportation
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• v.30 no.2
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• pp.117-126
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• 2012

The transportation sector accounts for 33% of the total $CO_2$ emissions. Effective control measures for reducing $CO_2$ emissions are urgently needed to address global warming. Objective and reliable methods to estimate $CO_2$ emissions are a prerequisite for the implementation of such effective control measures. However, existing models have not been successful. Even though the average-speed model is one of the most convenient and useful methods for estimating $CO_2$ emissions, it cannot distinguish between a variety of roads as well as traffic conditions in the model. The results of this study found that there may be significant discrepancies between emissions estimated by the current average-speed model and those measured in real driving conditions. This paper proposed the subdivision of emission factors in the average-speed model depending on both traffic and road conditions.