• Fire Science and Engineering
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• v.15 no.2
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• pp.46-52
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• 2001

To evaluate the usability of compartment fire models for predicting sprinkler response time, fire experiment was conducted and measured sprinkler response time. The experimental data was compared with zone model "FASTLite"and field model "FDS"and field Model "SMARTFIRE" A Compartment fire conducted in a 2.4 m by 3.6 m by 2.4 m ISO 9705 room and measured H.R.R was approximately 100.3 kW. In test, Sprinkler activation temperature used is $72^{\circ}c$ and responded at 198s. The output of FASTLite, SMARTFIRE and, FDS for this fire scenario were 209s, 183s, and 192s, respectively. As a results, prediction using FDS model approached to that of test very closely and other models showed good approximated results also.

• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.153-166
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• 2003

This study presents the time-divided forecasting model to integrate evolutionary optimization algorithm and change point detection based on artificial neural networks (ANN) for the prediction of (Korea) stock price index. The genetic algorithm(GA) is introduced as an evolutionary optimization method in this study. The basic concept of the proposed model is to obtain intervals divided by change points, to identify them as optimal or near-optimal change point groups, and to use them in the forecasting of the stock price index. The proposed model consists of three phases. The first phase detects successive change points. The second phase detects the change-point groups with the GA. Finally, the third phase forecasts the output with ANN using the GA. This study examines the predictability of the proposed model for the prediction of stock price index.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.04a
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• pp.545-550
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• 1997

It is well known that Statistical Energy Analysis(SEA) is one of very attractive analytical methods to solve shipboard noise problems. With reasonable successes, many applications of SEA to shipboard noise prediction have been reported. However when one wishes to obtain theoretical predictions by using SEA in practical systems, he will find difficulty in modeling of source systems, that is, foundations where to place main engine, generator, compressor, and so on. Also, he will find that it is hard to determine the amount of power flow from machinery to structures. In this paper, SEA of a simple foundation model was carried out using the estimated amount of power flow from source; the estimated mobility method. The comparison between the estimated and measured results is presented. That comparison shows a method to get structure-borne noise power from the combination of machinery and foundation. This prediction method gave a good results for a air-compressor mounted on a model foundation. The method is expected to give a reasonable power output in practical problems.

• Journal of the korean Society of Automotive Engineers
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• v.8 no.2
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• pp.31-42
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• 1986

The prediction of performance, exhaust emissions and EGR effect is made by the SI engine cycle simulation. In this simulation several models are employed - two zome, thermodynamic combustion, mass fraction burned, heat transfer, chemical equilibrium, chemical kinetics for NOx, laminar flame speed for ignition delay. The chemical species in burned gas considered are 13 species-CO$_{2}$, CO, $O_{2}$, H$_{2}$O, H$_{2}$,OH, H, O, N$_{2}$, NO$_{2}$, N, Ar - and the cylinder pressure, burned and unburned zone temperature and composition of gas are calculated at each crank angle through the compression, ignition delay, combustion and expansion process. To check the validity of the model, experimental study is done for measuring emissions, combustion pressure and engine output. The predicted values for pressure and emissions show qualitative agreement with the measured data and the EGR effect also shows similar tendency.

• Journal of Bio-Environment Control
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• v.1 no.2
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• pp.162-168
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• 1992

As the automation of nutrient solution management proceeds in the field of hydroponics, effective supporting systems to manage the nutrient solution by computer become needed. This study was attempt to predict the EC of nutrient solution using the neural networks. The multilayer perceptron consisting of 3 layers with the back propagation learning algorithm was selected for EC prediction, of which nine variables in the input layer were the concentrations of each ion and one variable in the output layer the EC of nutrient solution. The meq unit in ion concentration was selected fir input variable in the input layer. After the 10,000 learning sweeps with 108 sample data, the comparison of predicted and measured ECs for 72 test data showed good agreements with the correlation coefficient of 0.998. In addition, the predicted ECs by neural network showed relatively equal or closer to the measured ones than those by current complicated models.

• The Transactions of the Korean Institute of Power Electronics
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• v.21 no.6
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• pp.481-485
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• 2016

When analyzing economic feasibility for installing a PV generation plant at a certain location, the prediction of possible annual power production at the site using the target PV panels should be conducted on the basis of the local weather data provided by a local weather forecasting office. In addition, the prediction of PV generating power under certain weather conditions is useful for fault diagnosis and performance evaluation of PV generation plants during actual operation. This study analyzes PV cell characteristics according to a variety of weather conditions, including ambient temperature and irradiance level. From the analysis and simulation results, this work establishes a proper model that can predict the output characteristics of PV cells under changes in weather conditions.

• Proceedings of the Korea Concrete Institute Conference
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• 1998.10b
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• pp.656-661
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• 1998

This paper presents a method of statistical analysis and sensitivity analysis of creep and shrinkage effects in PSC box girder bridges. The statistical and sensitivity analyses are performed by using the numerical simulation of Latin Hypercube sampling. For each sample, the time-dependent structural analysis is performed to produce response data, which are then statistically analyzed. The probabilistic prediction of the confidence limits on long-term effects of creep and shrinkage is then expressed. Three measures are examined to quantify the sensitivity of the outputs to each of the input variables. These are rank correlation coefficient(RCC), partial rank correlation coefficient(PRCC) and standardized rank regression coefficient(SRRC) computed on the ranks of the observations. Probability band widens with time, which indicates an increase of prediction uncertainty with time. The creep model uncertainty factor and the relative humidity appear as the most dominant factors with regard to the model output uncertainty.

• Structural Engineering and Mechanics
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• v.26 no.3
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• pp.251-262
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• 2007

This paper examines the application of artificial neural networks (ANN) to the response prediction of geometrically nonlinear truss structures. Two types of analysis (deterministic and probabilistic analyses) are considered. A three-layer feed-forward backpropagation network with three input nodes, five hidden layer nodes and two output nodes is firstly developed for the deterministic response analysis. Then a back propagation training algorithm with Bayesian regularization is used to train the network. The trained network is then successfully combined with a direct Monte Carlo Simulation (MCS) to perform a probabilistic response analysis of geometrically nonlinear truss structures. Finally, the proposed ANN is applied to predict the response of a geometrically nonlinear truss structure. It is found that the proposed ANN is very efficient and reasonable in predicting the response of geometrically nonlinear truss structures.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.6
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• pp.681-689
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• 2009

Disk grinding was often applied to deburring process in order to enhance the final product quality. Inherent chamfering capability of the flexible disk grinding process in the early stage was analyzed with respect to various process parameters including workpiece length, wheel speed, depth of cut and feed. Initial chamfered edge defined as arc zone was characterized with local radius of curvature. Averaged radius and arc zone ratio was well evaluated using neural network system. Additional neural network analysis adding workpiece length showed enhance performance in predicting arc zone ratio and curvature radius with reduced error rate. A process condition design parameter was estimated using remaining input and output parameters with the prediction error rate lower than 2.0% depending on the relevant input parameter combination and neural network structure composition.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.445-450
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• 1998

In this paper, a robust nonlinear prediction-type controller (RNPC) is developed for the continuous time nonlinear system whose control objective is composed of system output and its desired value. The basic control law of RNPC is derived such that the future response of the system is first predicted by appropriate functional expansions and the control law minimizing the difference between the predicted and desired responses is then calculated. RNPC which involves two controls, i.e., the auxiliary and robust controls into the basic control, shows the stable closed loop dynamics of nonlinear system of any relative degree and provides the robustness to the nonlinear system with parameter/modeling uncertainty. Simulation tests for the position control of a two-link rigid body manipulator confirm the performance improvement and the robustness of RNPC.