• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.210-210
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• 2000

In this paper, we have investigated the properties of multi-layer perceptron (MLP) for odour patterns classification and concentration estimation simultaneously. When the MLP may be has a fast convergence speed with small error and excellent mapping ability for classification, it can be possible to use for classification and concentration prediction of volatile chemicals simultaneously. However, the conventional MLP, which is back-Propagation of error based on the steepest descent method, was difficult to use for odour classification and concentration estimation simultaneously, because it is slow to converge and may fall into the local minimum. We adapted the Levenberg-Marquardt(LM) algorithm [4,5] having advantages both the steepest descent method and Gauss-Newton method instead of the conventional steepest descent method for the simultaneous classification and concentration estimation of odours. And, We designed the artificial odour sensing system(Electronic Nose) and applied LM-BP algorithm for classification and concentration prediction of VOC gases.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.23 no.4
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• pp.42-48
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• 2015

This paper has its purpose on constructing a prediction model of the arrival sequencing strategy which reflects the actual sequencing patterns of air traffic controllers. As the first step, we analyzed a pair-wise sequencing of two aircraft entering TMA from different entering points. Based on the historical trajectory data, several traffic factors such as time, speed and traffic density were examined for the model. With statistically significant factors, we constructed a prediction model of arrival sequencing through a binary logistic regression analysis. With the estimated coefficients, the performance of the model was conducted through a cross validation.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.5
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• pp.123-130
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• 2008

In order to clarify detailed mechanism of the flexible disk grinding system, workpiece length was introduced and its performance was evaluated. Flat zone ratio increased as the workpiece length increased. Increasing wheel speed and depth of cut also enhanced process performance by producing larger flat zone ratio. Neural network system was successfully applied to predict minimum depth of engagement and flat zone ratio. An additional input parameter as workpiece length to the neural network system enhanced the prediction performance by reducing error rate. By rearranging the Input combinations to the network, the workpiece length was precisely predicted with the prediction error rate lower than 2.8% depending on the network structure.

• Proceedings of the KSR Conference
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• 2005.11a
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• pp.158-163
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• 2005

After the opening of Kyong-Bu High Speed Railway, tremendous change was occurred in the various realm in Korean Society. But in the Daegu-Busan section, KTX was still running on the conventional line where the railroad was improved to more straightly. For the noise prediction of KTX train which runs on the conventional line, components of noise source were analyzed using SEL(Sound Exposure Level) of meassured data. For the validation of suggested prediction equation, the predicted result was compared to the measured.

• Structural Engineering and Mechanics
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• v.45 no.2
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• pp.201-209
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• 2013

A new dynamic reliability analysis of structure under repeated random loads is proposed in this paper. The proposed method is developed based on the idea that the probability density of several times random loads can be derived from the probability density of single-time random load. The reliability prediction models of structure based on time responses under several times random loads with and without strength degradation are obtained by using the stress-strength interference theory and probability density evolution method. The resulting differential equations in the prediction models can be solved by using the forward finite difference method. Then, the probability density functions of strength redundancy of the structures can be obtained. Finally, the structural dynamic reliability can be calculated using integral method. The efficiency of the proposed method is demonstrated numerically through a speed reducer. The results have shown that the proposed method is practicable, feasible and gives reasonably accurate prediction.

• Current Optics and Photonics
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• v.5 no.6
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• pp.641-651
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• 2021

To handle the servo delay in a real-time adaptive optics system, a linear subspace system identification algorithm was employed to model the system, and the accuracy of the system identification was verified by numerical calculation. Experimental verification was conducted in a real test bed system. Through analysis and comparison of the experimental results, the convergence can be achieved only 200 times with prediction and 300 times without prediction. After the wavefront peak-to-valley value converges, its mean values are 0.27, 4.27, and 10.14 ㎛ when the communication distances are 1.2, 4.5, and 10.2 km, respectively. The prediction algorithm can effectively improve the convergence speed of the peak-to-valley value and improve the free-space optical communication performance.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.135-135
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• 2021

In water resources management, rainfall prediction with high accuracy is still one of controversial issues particularly in countries facing heavy rainfall during wet seasons in the monsoon climate. The aim of this study is to develop an artificial neural network (ANN) for predicting future six months of rainfall data (from April to September 2020) from daily meteorological data (from 1971 to 2019) such as rainfall, temperature, wind speed, and humidity at Seoul, Korea. After normalizing these data, they were trained by using a multilayer perceptron (MLP) as a class of the feedforward ANN with 15,000 neurons. The results show that the proposed method can analyze the relation between meteorological datasets properly and predict rainfall data for future six months in 2020, with an overall accuracy over almost 70% and a root mean square error of 0.0098. This study demonstrates the possibility and potential of MLP's applications to predict future daily rainfall patterns, essential for managing flood risks and protecting water resources.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.5
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• pp.1013-1019
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• 2016

This paper describes a novel Context-awareness Markov Chain Prediction (CMCP) algorithm based on movement prediction using Markov chain in Delay Tolerant Network (DTN). The existing prediction models require additional information such as a node's schedule and delivery predictability. However, network reliability is lowered when additional information is unknown. To solve this problem, we propose a CMCP model based on node behaviour movement that can predict the mobility without requiring additional information such as a node's schedule or connectivity between nodes in periodic interval node behavior. The main contribution of this paper is the definition of approximate speed and direction for prediction scheme. The prediction of node movement forwarding path is made by manipulating the transition probability matrix based on Markov chain models including buffer availability and given interval time. We present simulation results indicating that such a scheme can be beneficial effects that increased the delivery ratio and decreased the transmission delay time of predicting movement path of the node in DTN.

• Journal of Korea Spatial Information System Society
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• v.10 no.3
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• pp.31-43
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• 2008

Travel time prediction is an indispensable to many advanced traveler information systems(ATIS) and intelligent transportation systems(ITS). In this paper we propose a method to predict travel time using $Na{\ddot{i}}ve$ Bayesian classification method which has exhibited high accuracy and processing speed when applied to classily large amounts of data. Our proposed prediction algorithm is also scalable to road networks with arbitrary travel routes. For a given route, we consider time-varying average segment velocity to perform more accuracy of travel time prediction. We compare the proposed method with the existing prediction algorithms like link-based prediction algorithm [1] and Micro T* algorithm [2]. It is shown from the performance comparison that the proposed predictor can reduce MARE (mean absolute relative error) significantly, compared with the existing predictors.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.1-16
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• 2023

Accurate traffic information prediction is considered to be one of the most important aspects of intelligent transport systems(ITS), as it can be used to guide users of transportation facilities to avoid congested routes. Various deep learning models have been developed for accurate traffic prediction. Recently, ensemble techniques have been utilized to combine the strengths and weaknesses of various models in various ways to improve prediction accuracy and stability. Therefore, in this study, we developed and evaluated a traffic information prediction model using various deep learning models, and evaluated the performance of the developed deep learning models as a stacking ensemble. The individual models showed error rates within 10% for traffic volume prediction and 3% for speed prediction. The ensemble model showed higher accuracy compared to other models when no cross-validation was performed, and when cross-validation was performed, it showed a uniform error rate in long-term forecasting.