• 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.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.173-178
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• 2023

As Korean literature spreads around the world, its position in the overseas publishing market has become important. As demand in the overseas publishing market continues to grow, it is essential to predict future book sales and analyze the characteristics of books that have been highly favored by overseas readers in the past. In this study, we proposed ensemble learning based prediction model and analyzed characteristics of the cumulative sales of more than 5,000 copies classified as good sellers published overseas over the past 5 years. We applied the five ensemble learning models, i.e., XGBoost, Gradient Boosting, Adaboost, LightGBM, and Random Forest, and compared them with other machine learning algorithms, i.e., Support Vector Machine, Logistic Regression, and Deep Learning. Our experimental results showed that the ensemble algorithm outperforms other approaches in troubleshooting imbalanced data. In particular, the LightGBM model obtained an AUC value of 99.86% which is the best prediction performance. Among the features used for prediction, the most important feature is the author's number of overseas publications, and the second important feature is publication in countries with the largest publication market size. The number of evaluation participants is also an important feature. In addition, text mining was performed on the four book reviews that sold the most among good-selling books. Many reviews were interested in stories, characters, and writers and it seems that support for translation is needed as many of the keywords of "translation" appear in low-rated reviews.

• Journal of Korean Society of Forest Science
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• v.83 no.2
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• pp.175-190
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• 1994

In order to get fundamental information for prediction of landslide hazard, both forest and site factors affecting slope stability were investigated in many areas of active landslides. Twelve descriptors were identified and quantified to develop the prediction model by multivariate statistical analysis. The main results obtained could be summarized as follows : The main factors influencing a large scale of landslide were shown in order of precipitation, age group of forest trees, altitude, soil texture, slope gradient, position of slope, vegetation, stream order, vertical slope, bed rock, soil depth and aspect. According to partial correlation coefficient, it was shown in order of age group of forest trees, precipitation, soil texture, bed rock, slope gradient, position of slope, altitude, vertical slope, stream order, vegetation, soil depth and aspect. The main factors influencing a landslide occurrence were shown in order of age group of forest trees, altitude, soil texture, slope gradient, precipitation, vertical slope, stream order, bed rock and soil depth. Two prediction models were developed by magnitude and frequency of landslide. Particularly, a prediction method by magnitude of landslide was changed the score for the convenience of use. If the total store of the various factors mark over 9.1636, it is evaluated as a very dangerous area. The mean score of landslide and non-landslide group was 0.1977 and -0.1977, and variance was 0.1100 and 0.1250, respectively. The boundary value between the two groups related to slope stability was -0.02, and its predicted rate of discrimination was 73%. In the score range of the degree of landslide hazard based on the boundary value of discrimination, class A was 0.3132 over, class B was 0.3132 to -0.1050, class C was -0.1050 to -0.4196, class D was -0.4195 below. The rank of landslide hazard could be divided into classes A, B, C and D by the boundary value. In the number of slope, class A was 68, class B was 115, class C was 65, and class D was 52. The rate of landslide occurrence in class A and class B was shown at the hige prediction of 83%. Therefore, dangerous areas selected by the prediction method of landslide could be mapped for land-use planning and criterion of disaster district. And also, it could be applied to an administration index for disaster prevention.

• Molecules and Cells
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• v.27 no.5
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• pp.547-556
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• 2009

Parathyroid hormone is the most important endocrine regulator of calcium concentration. Its N-terminal fragment (1-34) has sufficient activity for biological function. Recently, site-directed mutagenesis studies demonstrated that substitutions at several positions within shorter analogues (1-14) can enhance the bioactivity to greater than that of PTH (1-34). However, designing the optimal sequence combination is not simple due to complex combinatorial problems. In this study, support vector machines were introduced to predict the biological activity of modified PTH (1-14) analogues using mono-substituted experimental data and to analyze the key physicochemical properties at each position that correlated with bioactivity. This systematic approach can reduce the time and effort needed to obtain desirable molecules by bench experiments and provide useful information in the design of simpler activating molecules.

• The KSFM Journal of Fluid Machinery
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• v.11 no.5
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• pp.30-36
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• 2008

Axial fan without static blades requires the duct as a guidance for unskewed inflows. This work examined the geometric effects of a duct guided the in and out flows through an impeller. The present methodologies are computational predictions with parallel work by experimental validation. Several different positions of a rotor in a duct are proposed for plausible models of a rotor inside a duct. The optimum axial position of an impeller in a duct is found at the #4 model where the impeller lies on the inlet edge of a circular duct. The model shows a wider inlet area. The result of computational prediction is in good agreement with experiment measurement.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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• pp.680-687
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• 2012

One of the frequent problems in the stabilized gimbal system is the rejection of disturbances associated with moving components. Very often such disturbances have non-linear characteristics. In a typical gimbal system, each gimbal and platform are connected by a mutual bearing which induces inevitable friction. Particularly, the non-linear Coulomb friction causes position errors as well as slow responses that lead to unfavorable performance. In this paper, a modified PID controller that is augmented by Coulomb friction estimator is presented. Through constantly estimating the Coulomb friction torque, it is applied to the output of the existing PID controller. The effectiveness of the proposed controller is evaluated through a series of experiments.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.69-74
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• 2011

In the curving detection method by using an accelerometer, the ride comfort in the first car is worse than one in the others due to spend the time to calculate the tilting command and drive the tilting mechanism after entering in the curve. In order to enhance the ride comfort in the first car, the preditive curve detection method which predicts the distance from a train to the starting point of curve by using the GPS, Tachometer, Ground balise and position DB for track. In this study, we predicted and evaluated the ride comfort for predictive curve detection method in transient curves according to the shape and dimension of transient curve and the various driving speed. Also, we predicted the improvement of the ride comfort for predictive curve detection method by comparing with the result of the ride comfort for predictive curve detection method and for curve detection method using an accelerometer in the short transient curve.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.1-10
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• 2003

This paper presents a high-precision magnetically levitated (maglev) stage to meet demanding motion specifications in the next-generation precision manufacturing and nanotechnology. Characterization of dynamic behaviors of such a motion stage is a crucial task. In this paper, we address the issues related to the stochastic modeling of the stage including transfer function identification, and noise/disturbance analysis and prediction. Provided are test results on precision dynamics, such as fine settling, effect of optical table oscillation, and position ripple. To deal with the dynamic coupling in the platen, we designed and implemented a multivariable linear quadratic regulator, and performed time-optimal control. We demonstrated how the performance of the current maglev stage can be improved with these analyses and experimental results. The maglev stage operates with positioning noise of 5 nm rms in $\chi$ and y, acceleration capabilities in excess of 2g(20 $m/s^2$), and closed-loop crossover frequency of 100 Hz.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.3
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• pp.273-279
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• 2004

This research is concerned with the development of an Internet-based robot system, which is insensitive to the unpredictable internet time delay. For this purpose, a simple mobile robot system that moves in response to the user s direct control on the internet has been developed. The time delay in data transmission is an important problem for the construction of this kind of system. Therefore, the PPS (Position Prediction Simulator) is suggested and implemented to compensate for the time delay problem of the internet. The simulation and experimental results show that the distance error can be reduced using the developed PPS.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.2
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• pp.18-26
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• 1992

MR(Model Reduction) is presented in order to estimate the frequency of multiple sinusoids from the finite noisy data with the white or colored noises. MR, using the reduced rank models, is designed, appling the approximation of linear system to LP(Linear Prediction). The MR method is analyzed. Monte-carlo simulations are conducted for MR and Lp. The results are compared with in terms of mean, root-mean square and relative bias. MR eliminates effectevely the extremeous and exceptional poles appearing in LP and improves the accuracy of LP. Especially, MR gives promising results in short noisy measurements, low SNR's and colored noises. Power spectral density and angular frequency position are showed by figures, for examples. Finally, the new method is utilized to the communication and biomedical systems estimating the characteristics of the signal and the system identification modelling the dynamic systems from experimental data.