• Transactions of the Korean Society of Mechanical Engineers B
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• v.26 no.12
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• pp.1729-1737
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• 2002

A mathematical model for the prediction of carbon-dioxide absorption rate during the transient state of rotary type absorber is developed. The rotary type absorber operates using a fast rotating porous structure and clean water. The model for the transient state rotary type absorbers is based on the steady state model of packed tower absorber. The paper manipulates the operating data of an arbitrary quasi-steady state condition of rotary type absorber for the determination of the coefficients involved in the model developed. The prediction accuracy is evaluated from the measured data of rotary type absorber operated under fast transient state. The measured data include the mole fraction of carbon dioxide in mixed gas and the pressure of absorber. The relative error in carbon dioxide prediction is estimated to be 20% at maximum. The model is successfully applied for the prediction of the behavior of a closed cycle diesel engine.

• ETRI Journal
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• v.46 no.3
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• pp.461-472
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• 2024

We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.

• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.147-156
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• 2013

Each field of modern society, industrialization and the development of science and technology are rapidly changing. However, as a side effect of rapid social change has caused various problems. Crime of the side effects of rapid social change is a big problem. In this paper, a model for predicting crime and Markov chains applied to the crime, predictive modeling is proposed. Markov chain modeling of the existing one with the overall status of the case determined the probability of predicting the future, but this paper predict the events to increase the probability of occurrence probability of the prediction and the recent state of the entire state was divided by the probability of the prediction. And the whole state and the probability of the prediction and the recent state by applying the average of the prediction probability and the probability of the prediction model were implemented. Data was applied to the incidence of crime. As a result, the entire state applies only when the probability of the prediction than the entire state and the last state is calculated by dividing the probability value. And that means when applied to predict the probability, close to the crime was concluded that prediction.

• The Journal of the Acoustical Society of Korea
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• v.17 no.2E
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• pp.26-30
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• 1998

In this paper, we compose neural network predictive HMM(NNPHMM) to provide the dynamic feature of the speech pattern for the HMM. The NNPHMM is the hybrid network of neura network and the HMM. The NNPHMM trained to predict the future vector, varies each time. It is used instead of the mean vector in the HMM. In the experiment, we compared the recognition abilities of the one hundred Korean syllables according to the variation of hidden layer, state number and prediction orders of the NNPHMM. The hidden layer of NNPHMM increased from 10 dimensions to 30 dimensions, the state number increased from 4 to 6 and the prediction orders increased from 10 dimensions to 30 dimension, the state number increased from 4 to 6 and the prediction orders increased from the second oder to the fourth order. The NNPHMM in the experiment is composed of multi-layer perceptron with one hidden layer and CMHMM. As a result of the experiment, the case of prediction order is the second, the average recognition rate increased 3.5% when the state number is changed from 4 to 5. The case of prediction order is the third, the recognition rate increased 4.0%, and the case of prediction order is fourth, the recognition rate increased 3.2%. But the recognition rate decreased when the state number is changed from 5 to 6.

• Smart Structures and Systems
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• v.14 no.6
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• pp.1269-1289
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• 2014

With the sub-stepping technique, the numerical analysis in real-time dynamic hybrid testing is split into the response analysis and signal generation tasks. Two target computers that operate in real-time may be assigned to implement these two tasks, respectively, for fully extending the simulation scale of the numerical substructure. In this case, the integration time-step of solving the dynamic response of the numerical substructure can be dozens of times bigger than the sampling time-step of the controller. The time delay between the real and desired feedback forces becomes more striking, which challenges the well-developed delay compensation methods in real-time dynamic hybrid testing. This paper focuses on displacement prediction and force correction for delay compensation in the real-time dynamic hybrid testing with a large integration time-step. A new displacement prediction scheme is proposed based on recently-developed explicit integration algorithms and compared with several commonly-used prediction procedures. The evaluation of its prediction accuracy is carried out theoretically, numerically and experimentally. Results indicate that the accuracy and effectiveness of the proposed prediction method are of significance.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.8-14
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• 2008

There are two a approaches for the Cognitive Radio(CR) development. One is 'Full CR', which Joseph Mitola III proposed, and another is 'Spectrum CR', which is currently being standardized. The target approach of this paper is the latter and we develop a Cognitive Engine(CE) and simulated a channel set management(CSM), which is a core function of CE. The Channel set management evaluates channel quality and Incumbent User(IU) vacancy possibility and classifies the channel set, which is performed by using channel state history. Especially, a very important function for the channel set management is a channel state prediction and this paper proposed a Hidden Markov Model(HMM) based channel state prediction and a method for increasing performance. Also, we applied the proposed method into our simulator and simulated channel state prediction. Through the simulation, we verified as we applied our proposed scheme, the performance of channel state prediction gets better and through comparing with RS and SS, we verified the HMM based Channel state prediction is better.

• Agricultural and Biosystems Engineering
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• v.3 no.1
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• pp.18-28
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• 2002

Neural network based models were developed and evaluated for predicting corn yield from aerial images based on 1998 and 1994 image data. The model used images in multi-spectral bands such as R, G, B, and IR (Red, Green, Blue and Infrared). The inputs to the neural network consisted of mean and standard deviation of multispectral bands of the aerial images. Performances of several neural network architectures using back-propagation with momentum were compared. The maximum yield prediction accuracy obtained was 97.81%. The BPNN model prediction accuracy could be enhanced by using more number of observations to the model, other data transformation techniques, or by performing optical calibration of the aerial image.

• Proceedings of the Korean Geotechical Society Conference
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• 2000.11a
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• pp.201-208
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• 2000

In this study, the behavior of saturated sandy soils under dynamic loads - pore water pressure and effective stress - was investigated using Disturbed State Concept(DSC) model. The model parameters are evaluated from laboratory test data. During the process of loading and reverse loading, DSC model is utilized to trace strain-hardening and cyclic softening behavior. The procedure of back prediction proposed in this study are verified by comparing with laboratory test results. From the back prediction of pore water pressure and effective mean pressure under cyclic loading, excess pore water pressure increases up to initial effective confining pressure and effective mean pressure decrease close to zero in good greement with laboratory test results. Those results represent the liquefaction of saturated sandy soils under dynamic loads. The number of cycles at initial liquefaction using the model prediction is in good agreement with laboratory test results. Therefore, the results of this study state that the liquefaction of saturated sandy soils can be explained by the effective tress analysis.

• Journal of Aerospace System Engineering
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• v.18 no.3
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• pp.48-55
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• 2024

In recent years, as the commercialization of unmanned aerial vehicles (UAVs) has been actively promoted, much attention has been focused on developing a technology to ensure the safety of UAVs. In general, the UAV has the potential to enter an uncontrollable state caused by sudden maneuvers, disturbances, and pilot error. To prevent entering an uncontrolled situation, it is essential to predict the flight state of the UAV. In this paper, we propose a flight state prediction technique based on an improved CNN-LSTM hybrid mode to enhance the flight state prediction performance. Simulation results show that the proposed prediction technique offers better state prediction performance than the existing prediction technique, and can be operated in real-time in an on-board environment.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.1
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• pp.39-42
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• 2002

For the purpose of modeling EEG signal which has nonstationary and nonlinear dynamic characteristics, this paper propose a state feedback real time recurrent neural network model. The state feedback real time recurrent neural network is structured to have memory structure in the state of hidden layers so that it has arbitrary dynamics and ability to deal with time-varying input through its own temporal operation. For the model test, Mackey-Glass time series is used as a nonlinear dynamic system and the model is applied to the prediction of three types of EEG, alpha wave, beta wave and epileptic EEG. Experimental results show that the performance of the proposed model is better than that of other neural network models which are compared in this paper in some view points of the converging speed in learning stage and normalized mean square error for the test data set.