• Proceedings of the Safety Management and Science Conference
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• 2003.11a
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• pp.271-284
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• 2003

We developing for Gate-Container Yard model to execute the Input & Output process of containers which are waiting in container yard effectively and fast. The optimal model is developed to manage and trace the containers in yard. We developed the put-away strategies which considered the storage time for volume of transportation and the distance between blocks and gates using the MICRO-CRAFT (Computerized Relative Allocation Facilities Technique).

• Journal of the Korean Data and Information Science Society
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• v.15 no.2
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• pp.449-455
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• 2004

Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems for crisp data. This paper proposes a new method to evaluate interval regression models for crisp input-output data. The proposed method is based on quadratic loss SVM, which implements quadratic programming approach giving more diverse spread coefficients than a linear programming one. The proposed algorithm here is model-free method in the sense that we do not have to assume the underlying model function. Experimental result is then presented which indicate the performance of this algorithm.

• Korean Journal of Ecology and Environment
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• v.43 no.1
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• pp.161-167
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• 2010

In this study, we implemented an experimental approach of ecological model development in order to emphasize the importance of input variable selection with respect to time-delayed arrangement between input and output variables. Time-series modeling requires relevant input variable selection for the prediction of a specific output variable (e.g. density of a species). Inadequate variable utility for input often causes increase of model construction time and low efficiency of developed model when applied to real world representation. Therefore, for future prediction, researchers have to decide number of time-delay (e.g. months, weeks or days; t-n) to predict a certain phenomenon at current time t. We prepared a total of 3,900 equation models produced by Time-Series Optimized Genetic Programming (TSOGP) algorithm, for the prediction of monthly averaged density of a potamic phytoplankton species Stephanodiscus hantzschii, considering future prediction from 0- (no future prediction) to 12-months ahead (interval by 1 month; 300 equations per each month-delay). From the investigation of model structure, input variable selectivity was obviously affected by the time-delay arrangement, and the model predictability was related with the type of input variables. From the results, we can conclude that, although Machine Learning (ML) algorithms which have popularly been used in Ecological Informatics (EI) provide high performance in future prediction of ecological entities, the efficiency of models would be lowered unless relevant input variables are selectively used.

• Korean Management Science Review
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• v.22 no.1
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• pp.1-13
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• 2005

This paper proposed a quantitative evaluation method to measure the operational effectiveness of the local military manpower administrations. The proposed method compared the relative operational effectiveness level for 12 local military manpower administrations in Korea.. The method used the IDEA (imprecise Data Envelopment Analysis) model which Is able to measure relative operational effectiveness level, and also used two input variables (labor cost, operational cost) and three output variables (number of military applicants, number of civil application approved & processed, management accuracy level). Through the model output analysis, we presented the relative effectiveness scores, the reason for non-effectiveness, and the relationship between non-effective ness level and input/output variables for each local military manpower administration. We also presented a few recommendations how to improve the effectiveness level on particular local military manpower administration.

• ETRI Journal
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• v.31 no.2
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• pp.162-172
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• 2009

A linear-prediction-based blind equalization algorithm for single-input single-output (SISO) finite impulse response/infinite impulse response (FIR/IIR) channels is proposed. The new algorithm is based on second-order statistics, and it does not require channel order estimation. By oversampling the channel output, the SISO channel model is converted to a special single-input multiple-output (SIMO) model. Two forward linear predictors with consecutive prediction delays are applied to the subchannel outputs of the SIMO model. It is demonstrated that the partial parameters of the SIMO model can be estimated from the difference between the prediction errors when the length of the predictors is sufficiently large. The sufficient filter length for achieving the optimal prediction is also derived. Based on the estimated parameters, both batch and adaptive minimum-mean-square-error equalizers are developed. The performance of the proposed equalizers is evaluated by computer simulations and compared with existing algorithms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.7
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• pp.1007-1013
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• 2012

Modeling of photovoltaic power systems is significant to analytically predict its dynamics in practical applications. This paper presents a novel modeling algorithm of such system by using probability and statistic theories. We first establish a linear model basically composed of Fourier parameter sets for mapping the input/output variable of photovoltaic systems. The proposed model includes solar irradiation and ambient temperature of photovoltaic modules as an input vector and the inverter power output is estimated sequentially. We deal with these measurements as random variables and derive a parameter learning algorithm of the model in terms of statistics. Our learning algorithm requires computation of an expectation and joint expectation against solar irradiation and ambient temperature, which are analytically solved from the integral calculus. For testing the proposed modeling algorithm, we utilize realistic measurement data sets obtained from the Seokwang Solar power plant in Youngcheon, Korea. We demonstrate reliability and superiority of the proposed photovoltaic system model by observing error signals between a practical system output and its estimation.

• Proceedings of the KIPE Conference
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• 1996.06a
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• pp.85-88
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• 1996

New power factor correction(PFC) technique based on the averaged model of boost converter is proposed. Without measurement of input current, power factor correction scheme derived from the averaged model is presented. With the measurements of input voltage and output voltage, the control signal is generated to make the shape of the line current same as the input voltage. The characteristics of input line current distortion is analyzed by considering the generation of duty cycle.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.134-139
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• 1994

In this paper the comparison between the neural networks and traditional approaches as system identification method are considered. Two model structures of neural networks are the state space model and the input output model neural networks. The traditional methods are the AutoRegressive eXogeneous Input model and the Nonlinear AutoRegressive eXogeneous Input model. The examples considered do not represent any physical system, no a priori knowledge concerning their structure has been used in the identification process. Testing inputs for comparison are the sinusoidal, ramp and the noise ramp.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.786-789
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• 2002

A recurrent artificial neural network (ANN) model is developed to simulate hydrocarbon recovery process at petroleum-contaminated site. The groundwater extraction rate, vacuum pressure, and saturation hydraulic conductivity are selected as the input variables, while the cumulative hydrocarbon recovery volume is considered as the output variable. The experimental data fer establishing the ANN model are from implementation of a multiphase flow model for dual phase remediation process under different input variable conditions. The complex nonlinear and dynamic relationship between input and output data sets are then identified through the developed ANN model. Reasonable agreements between modeling results and experimental data are observed, which reveals high effectiveness and efficiency of the neural network approach in modeling complex hydrocarbon recovery behavior.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.948-950
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• 2003

In this literature, the CMOS current mode fout quadrant differentiator circuit is proposed. The implementation is base on an appropriate input stage that converts the input current into a compressed voltage at the input capacitor ($C_{gs}$) of the CMOS driver circuit. This input voltage use as the control output current which flow to the output node by passing through a MOS active load and use it as the feedback voltage to the input node. Simulation results with level 49 CMOS model of MOSIS are given to demonstrate the correct operation of the proposed configuration. But the gain of the circuit is too low so the output differentiate current also low. The proposed differentiator is expected to find several applications in analog signal processing system.