• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.927-938
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• 2021

A similar magnitude of non-gravitational perturbations are act on the formation flying low earth orbit satellites with a certain time difference. Using this temporal correlation, the non-gravity acceleration of the low earth orbiting satellites can be transferred for the othersatellites. There is a period in which the accelerometer data of one satellite is unavailable for GRACE and GRACE-FO satellites. In this case, the accelerometer data transplant method described above is officially used to recover the accelerometer data at the Jet Propulsion Laboratory (JPL). In this paper, we proposed a model for predicting accelerometer data of formation flying low earth orbit satellites using a neural network (NN) model to improve the estimation accuracy of the transplant method. Although the transplant method cannot reflect the satellite's position and space environmental factors, the NN model can use them as model inputs to increase the prediction accuracy. A prediction test of an accelerometer data using NN model was performed for one month, and the prediction accuracy was compared with the transplant method. The NN model outperformsthe transplant method with 55.0% and 40.1% error reduction in the along-track and radial directions, respectively.

• Journal of the Korea Convergence Society
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• v.4 no.3
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• pp.43-50
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• 2013

This paper presents a model predictive control (MPC) based on a neural network (NN) model for air/fuel ration (AFR) control of automotive engines. The novelty of the paper is that the severe nonlinearity of the engine dynamics are modelled by a NN to a high precision, and adaptation of the NN model can cope with system uncertainty and time varying effects. A single dimensional optimization algorithm is used in the paper to speed up the optimization so that it can be implemented to the engine fast dynamics. Simulations on a widely used mean value engine model (MVEM) demonstrate effectiveness of the developed method.

• The Journal of the Acoustical Society of Korea
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• v.15 no.6
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• pp.5-11
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• 1996

This paper is the study for recognizing single vowels of speaker-independent, and we suppose a method of speech recognition using VQ(Vector Quantization)/NN(Neural Network). This method makes a VQ codebook, which is used for obtaining the observation sequence, and then claculates the probability value by comparing each codeword with the data, finally uses these probability values for the input value of the neural network. Korean signle vowels are selected for our recognition experiment, and ten male speakers pronounced eight single vowels ten times. We compare the performance of our method with those of fuzzy VQ/HMM and conventional VQ/NN According to the experiment result, the recognition rate by VQ/NN is 92.3%, by VQ/HMM using fuzzy is 93.8% and by VQ/NN using fuzzy is 95.7%. Therefore, it is shown that recognition rate of speech recognition by fuzzy VQ/NN is better than those of fuzzy VQ/HMM and conventional VQ/HMM because of its excellent learning ability.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.603-612
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• 2022

The study aims were to evaluate a machine-learning, algorithm-based, forest biomass-estimation model to estimate subnational forest biomass and to comparatively analyze REDD+ forest reference emission levels. Time-series Landsat satellite imagery and ESA Biomass Climate Change Initiative information were used to build a machine-learning-based biomass estimation model. The k-nearest neighbors algorithm (kNN), which is a non-parametric learning model, and the tree-based random forest (RF) model were applied to the machine-learning algorithm, and the estimated biomasses were compared with the forest reference emission levels (FREL) data, which was provided by the Paraguayan government. The root mean square error (RMSE), which was the optimum parameter of the kNN model, was 35.9, and the RMSE of the RF model was lower at 34.41, showing that the RF model was superior. As a result of separately using the FREL, kNN, and RF methods to set the reference emission levels, the gradient was set to approximately -33,000 tons, -253,000 tons, and -92,000 tons, respectively. These results showed that the machine learning-based estimation model was more suitable than the existing methods for setting reference emission levels.

• International Journal of Highway Engineering
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• v.10 no.3
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• pp.129-138
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• 2008

The finite element method(FEM) was one of tools used to solve problem of previous Concrete Pavement and was applied to Korea Pavement Research Program Study. This study used the ABAQUS and the fortran analysis program to calculate the critical stress on jointed concrete pavement and compared and analyzed the results by using neural networks and linear regression model. In that case, which are not enough analysises by using FEM programs though many input variables, when the results of FEM with NN and linear regression models are compared, there are some differences. The other cases, which are reduced input variables and a lot of analysises each of them, results of Neural Networks(NN) and linear regression models are simulated to them of FEM. But, the result of NN is more exact than them of linear regression at the (0,0), (1,1). On the results of this study, it is suggested that the calculation of stress using NN is more compatible to Korea Pavement Research Program Study.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.6
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• pp.1010-1016
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• 2004

In this paper, a scheme for estimating the parameters of solar cells and a NN-based emulator for predicting the maximum power point are presented. The diode model with series and shunt resistors is used to estimate parameters highly affecting its V-I characteristic curve and both a real-coded genetic algorithm and the model adjustment technique are employed. For implementing the emulator, a multi-layered neural network incorporating with the BP algorithm is used. A set of simulation works using both field data and generated data are carried out to demonstrate the effectiveness of the proposed method.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.1
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• pp.111-124
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• 1999

This evapaper is toluate the forecasting performance of three neural network(NN) approaches against ARIMA model using the famous time series analysis competition data. The first NN approach is to analyze the second Makridakis (M2) Competition Data using Multilayer Perceptron (MLP) that has been the most popular NN model in time series analysis. Since it is recently known that MLP suffers from bias/variance dilemma, two approaches are suggested in this study. The second approach adopts Cascade Correlation Network (CCN) that was suggested by Fahlman & Lebiere as an alternative to MLP. In the third approach, a time series is separated into two series using Noise Filtering Network (NFN) that utilizes autoassociative memory function of neural network. The forecasts in the decomposition analysis are the sum of two prediction values obtained from modeling each decomposed series, respectively. Among the three NN approaches, Decomposition Analysis shows the best forecasting performance on the M2 Competition Data, and is expected to be a promising tool in analyzing socio-economic time series data because it reduces the effect of noise or outliers that is an impediment to modeling the time series generating process.

• Structural Engineering and Mechanics
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• v.46 no.5
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• pp.673-693
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• 2013

This paper presents a systematic design and training procedure for the feed-forward back-propagation neural network (NN) modeling of both forward and inverse behavior of a rotary magnetorheological (MR) damper based on experimental data. For the forward damper model, with damper force as output, an optimization procedure demonstrates accurate training of the NN architecture with only current and velocity as input states. For the inverse damper model, with current as output, the absolute value of velocity and force are used as input states to avoid negative current spikes when tracking a desired damper force. The forward and inverse damper models are trained and validated experimentally, combining a limited number of harmonic displacement records, and constant and half-sinusoidal current records. In general the validation shows accurate results for both forward and inverse damper models, where the observed modeling errors for the inverse model can be related to knocking effects in the measured force due to the bearing plays between hydraulic piston and MR damper rod. Finally, the validated models are used to emulate pure viscous damping. Comparison of numerical and experimental results demonstrates good agreement in the post-yield region of the MR damper, while the main error of the inverse NN occurs in the pre-yield region where the inverse NN overestimates the current to track the desired viscous force.

• Journal of the Korea Computer Industry Society
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• v.2 no.9
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• pp.1191-1198
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• 2001

An increasingly important facet of software development is the ability to estimate the associate cost and effort of development early in the development life cycle. Most of the proposed models are based upon a combination of intuition, expert judgement, and regression analysis of empirical data. Overall, the work has failed to produce any single model that can be applied with a reasonable degree of success to a variety of environments. This paper presents a neural network (NN) model that related software development effort to software size measured in function element types. The heuristic approach is applied to decide the number of hidden neurons in NN from the relationship between input-output pairs. The research describes appropriate NN modeling in the context of a case study for 24 software development projects. Also, this paper compared the NN model with a regression analysis model and found the NN model has better accuracy.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2000.11b
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• pp.124-131
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• 2000

In this study, the NEURAL NETWORK (hereinafter referred to as NN) was applied to control of the nonlinear factors for turning movement of the crawler vehicle and experiment was carried out using a small model of crawler vehicle in order to inspect an application of NN. Furthermore, CHAOS NEURAL NETWORK (hereinafter referred to as CNN) was also applied to this control so as to compare with conventional NN. CNN is especially effective for plane in many variables with local minimum which conventional NN is apt to fall into, and it is relatively useful to nonlinear factors. Experiment of turning on the slope of crawler vehicle was performed in order to estimate an adaptability of nonlinear problems by NN and CNN. The inclination angles of the road surface which the vehicles travel on, were respectively 4deg, 8deg, 12deg. These field conditions were selected by the object for changing nonlinear magnitude in turning phenomenon of vehicle. Learning of NN and CNN was carried out by referring to positioning data obtained from measurement at every 15deg in turning. After learning, the sampling data at every 15deg were interpolated based on the constructed learning system of NN and CNN. Learning and simulation programs of NN and CNN were made by C language ("Association of research for algorithm of calculating machine (1992)"). As a result, conventional NN and CNN were available for interpolation of sampling data. Moreover, when nonlinear intensity is not so large under the field condition of small slope, interpolation performance of CNN was a little not so better than NN. However, when nonlinear intensity is large under the field condition of large slope, interpolation performance of CNN was relatively better than NN.