• Title/Summary/Keyword: Future Prediction

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A Study on the Syllable Recognition Using Neural Network Predictive HMM

  • Kim, Soo-Hoon;Kim, Sang-Berm;Koh, Si-Young;Hur, Kang-In
    • 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.

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Development of A Permanent Deformation Model based on Shear Stress Ratio for Reinforced-Roadbed Materials (전단응력비 개념에 기초한 강화노반의 영구변형 모델 수립)

  • Lim, Yu-Jin;Lee, Seong-Hyeok;Kim, Dae-Seong;Park, Mi-Yun
    • Proceedings of the KSR Conference
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    • 2011.10a
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    • pp.2049-2056
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    • 2011
  • The reinforced-roadbed materials composed of crushed stones are used for preventing vertical deformation and reducing impact load caused by highspeed train. Repeated load application can induce deformation in the reinforced-roadbed layer so that it causes irregularity of track. Thus it is important to understand characteristics of permanent deformation in the reinforced-subbase materials. The characteristics of permanent deformation can be simulated by prediction model that can be obtained by performing repetitive triaxial test. The prediction model of permanent deformation is a key-role in construction of design method of track. The prediction model of permanent deformation is represented in usual as the hyperbolic function with increase of number of load repetition. The prediction model is sensitive to many factors including stress level etc. so that it is important to define parameters of the model as clearly as possible. Various data obtained from repetitive triaxial test and resonant column test using the reinforced-roadbed of crushed stone are utilized to develop a new prediction model based on concept of shear-stress ratio and elastic modulus. The new prediction model of permanent deformation can be adapted for developing design method of track in the future.

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Real-Time Building Load Prediction by the On-Line Weighted Recursive Least Square Method (실시간 가중 회기최소자승법을 사용한 익일 부하예측)

  • 한도영;이재무
    • Korean Journal of Air-Conditioning and Refrigeration Engineering
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    • v.12 no.6
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    • pp.609-615
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    • 2000
  • The energy conservation is one of the most important issues in recent years. Especially, the energy conservation through improved control strategies is one of the most highly possible area to be implemented in the near future. The energy conservation of the ice storage system can be accomplished through the improved control strategies. A real time building load prediction algorithm was developed. The expected highest and the lowest outdoor temperature of the next day were used to estimate the next day outdoor temperature profile. The measured dry bulb temperature and the measured building load were used to estimate system parameters by using the on-line weighted recursive least square method. The estimated hourly outdoor temperatures and the estimated hourly system parameters were used to predict the next day hourly building loads. In order to see the effectiveness of the building load prediction algorithm, two different types of building models were selected and analysed. The simulation results show less than 1% in error for the prediction of the next day building loads. Therefore, this algorithm may successfully be used for the development of improved control algorithms of the ice storage system.

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Validation of OpenDrift-Based Drifter Trajectory Prediction Technique for Maritime Search and Rescue

  • Ji-Chang Kim;Dae, Hun, Yu;Jung-eun Sim;Young-Tae Son;Ki-Young Bang;Sungwon Shin
    • Journal of Ocean Engineering and Technology
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    • v.37 no.4
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    • pp.145-157
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    • 2023
  • Due to a recent increase in maritime activities in South Korea, the frequency of maritime distress is escalating and poses a significant threat to lives and property. The aim of this study was to validate a drift trajectory prediction technique to help mitigate the damages caused by maritime distress incidents. In this study, OpenDrift was verified using satellite drifter data from the Korea Hydrographic and Oceanographic Agency. OpenDrift is a Monte-Carlo-based Lagrangian trajectory modeling framework that allows for considering leeway, an important factor in predicting the movement of floating marine objects. The simulation results showed no significant differences in the performance of drift trajectory prediction when considering leeway using four evaluation methods (normalized cumulative Lagrangian separation, root mean squared error, mean absolute error, and Euclidean distance). However, leeway improved the performance in an analysis of location prediction conformance for maritime search and rescue operations. Therefore, the findings of this study suggest that it is important to consider leeway in drift trajectory prediction for effective maritime search and rescue operations. The results could help with future research on drift trajectory prediction of various floating objects, including marine debris, satellite drifters, and sea ice.

A Study on the Development of Steel Corrosion Prediction System (철근 부식 예측 시스템의 개발에 관한 연구)

  • 김도겸;박승범;이택우;이종석;이장화
    • Proceedings of the Korea Concrete Institute Conference
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    • 1999.10a
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    • pp.743-746
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    • 1999
  • One of the main deteriorating factors that affect the service life of concrete structures is the corrosion of reinforcement. The chlorides penetrate the concrete, destroy the passive layer surrounding the steel, and help initiate the steel corrosion. A Corrosion Prediction System(CPS) has been developed to assist the engineer in analyzing the service life of existing sea-shore structures and future concrete repairs by calculate the chloride diffusion in concrete. The CPS calculates mixing design, physical properties or recent chloride profiles. The CPS can be used to evaluate changes in concrete cover, chloride loads, and environmental conditions in different structural designs.

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Flow Prediction by Analytical Response Function (해석적 해법에 의한 흐름의 예측)

  • 윤태훈
    • Water for future
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    • v.8 no.2
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    • pp.93-99
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    • 1975
  • A linear and optimum linear systems have been reviewed in some detail. The procedure of the solution of the Wiener-Hopf equation analytically in time domain is given and the prediction of downstream outflow for given upstream inflow are made. The predicted results are fairly satisfaotory. The intended physical interpretation of the analytical solution could be descriptable but it was found that the evaluation of the parameters of the response function is rather difficult due to complicacy and a great deal of works.

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A Note on the Strong Mixing Property for a Random Coefficient Autoregressive Process

  • Lee, Sang-Yeol
    • Journal of the Korean Statistical Society
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    • v.24 no.1
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    • pp.243-248
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    • 1995
  • In this article we show that a class of random coefficient autoregressive processes including the NEAR (New exponential autoregressive) process has the strong mixing property in the sense of Rosenblatt with mixing order decaying to zero. The result can be used to construct model free prediction interval for the future observation in the NEAR processes.

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