• Transactions of the Korean Society of Mechanical Engineers
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• v.19 no.5
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• pp.1182-1189
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• 1995

Several methods have been developed to predict the creep rupture time of the steam pipes in thermal power plant. However, existing creep life prediction methods give very conservative value at operating stress of power plant and creep rupture strain cannot be well estimated. Therefore, in this study, creep rupture time and strain prediction method accounting for material damage and grain boundary sliding is newly proposed and compared with the existing experimental data. The creep damage evolves by continuous cavity nucleation and constrained cavity growth. The results showed good correlation between the theoretically predicted creep rupture time and the experimental data. And creep rupture strain may be well estimated by using the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.524-524
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• 2000

We have been developing a method to build one-step-ahead prediction models for time series using genetic programming (GP). Our model building method consists of two stages. In the first stage, functional forms of the models are inherited from their parent models through crossover operation of GP. In the second stage, the parameters of the newborn model arc optimized based on an iterative method just like the back propagation. The proposed method has been applied to various kinds of time series problems. An application to the seismic ground motion was presented in the KACC'99, and since then the method has been improved in many aspects, for example, additions of new node functions, improvements of the node functions, and new exploitations of many kinds of mutation operators. The new ideas and trials enhance the ability to generate effective and complicated models and reduce CPU time. Today, we will present a couple of financial applications, espc:cially focusing on gold price prediction in Tokyo market.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2299-2301
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• 1998

In this paper, a modified RBF (Radial Basis Function) neural network structure is suggested for the prediction of time series with non-linear, non-stationary characteristics. Conventional RBF neural network predicting time series by using past outputs is for sensing the trajectory of the time series and for reacting when there exists strong relation between input and hidden neuron's RBF center. But this response is highly sensitive to level and trend of time serieses. In order to overcome such dependencies, hidden neurons are modified to react to the increments of input variable and multiplied by increments(or decrements) of out puts for prediction. When the suggested structure is applied to prediction of Lorenz equation, and Rossler equation, improved performances are obtainable.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.225-227
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• 2009

International standard specification, H.264/SVC improved from H.264/AVC, is set up so as to promote free use of huge multimedia data in various channel environments.;H.264/AVC is a international standard speicification for video compression, adopted and commercialized as standard for DMB broadcasting by JVT of ISO/IEC MPEG and ITU-T VCEG. SVC standard uses 'intra/inter prediction' in AVC as well as 'inter-layer intra prediction', 'inter-layer motion prediction' and 'inter-layer residual prediction' to improve efficiency of encoding. Among prediction technologies, 'inter-layer intra prediction' is to use co-located block of up sampled sublevels as a prediction signal. At this time, application of interpolation is one of the most important factors to determine encoding efficiency. SVC's currently using poly-phase FIR filter of 4-tap and 2-tap respectively to luma components. This paper is written for the purpose of analyzing encoding performance according to the interpolation. For this purpose, we applied poly-phase FIR filter of '2-tap', '4-tap' and '6-tap' respectively to luma components and then measured bit-rate, PNSR and running time of interpolation filter. We're expecting that the analysis results of this paper will be utilized for effective application of interpolation filter. SVC standard uses 'intra/inter prediction' in AVC as well as 'inter-layer intra prediction', 'inter-layer motion prediction' and 'inter-layer residual prediction' to improve efficiency of encoding.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.3
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• pp.29-36
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• 2005

With the rapid growth of the Internet, the Internet-based robot has been realized by connecting off-line robot to the Internet. However, because the Internet is often irregular and unreliable, the varying time delay in data transmission is a significant problem for the construction of the Internet-based robot system. Thus, this paper is concerned with the development of an Internet-based robot system, which is insensitive to the Internet time delay. For this purpose, the PPS (Position Prediction Simulator) is suggested and implemented on the system. The PPS consists of two parts : the robot position prediction part and the projective virtual scene part. In the robot position prediction part, the robot position is predicted for more accurate operation of the mobile robot, based on the time at which the user's command reaches the robot system. The projective virtual scene part shows the 3D visual information of a remote site, which is obtained through image processing and position prediction. For the verification of this proposed PPS, the robot was moved to follow the planned path under the various network traffic conditions. The simulation and experimental results showed that the path error of the robot motion could be reduced using the developed PPS.

• Geomechanics and Engineering
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• v.13 no.5
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• pp.845-860
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• 2017

It is very important to study displacement prediction in geotechnical engineering. Nowadays, the grey system method, time series analysis method and artificial neural network method are three main methods. Based on the brief introduction, the three methods are analyzed comprehensively. Their merits and demerits, applied ranges are revealed. To solve the shortcomings of the artificial neural network method, a new prediction method based on new evolutionary neural network is proposed. Finally, through two real engineering applications, the analysis of three main methods and the new evolutionary neural network method all have been verified. The results show that, the grey system method is a kind of exponential approximation to displacement sequence, and time series analysis is linear autoregression approximation, while artificial neural network is nonlinear autoregression approximation. Thus, the grey system method can suitably analyze the sequence, which has the exponential law, the time series method can suitably analyze the random sequence and the neural network method almostly can be applied in any sequences. Moreover, the prediction results of new evolutionary neural network method is the best, and its approximation sequence and the generalization prediction sequence are all coincided with the real displacement sequence well. Thus, the new evolutionary neural network method is an acceptable method to predict the measurement displacements of geotechnical engineering.

• Korean Journal of Ecology and Environment
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• v.43 no.1
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• pp.161-167
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• 2010

In this study, we implemented an experimental approach of ecological model development in order to emphasize the importance of input variable selection with respect to time-delayed arrangement between input and output variables. Time-series modeling requires relevant input variable selection for the prediction of a specific output variable (e.g. density of a species). Inadequate variable utility for input often causes increase of model construction time and low efficiency of developed model when applied to real world representation. Therefore, for future prediction, researchers have to decide number of time-delay (e.g. months, weeks or days; t-n) to predict a certain phenomenon at current time t. We prepared a total of 3,900 equation models produced by Time-Series Optimized Genetic Programming (TSOGP) algorithm, for the prediction of monthly averaged density of a potamic phytoplankton species Stephanodiscus hantzschii, considering future prediction from 0- (no future prediction) to 12-months ahead (interval by 1 month; 300 equations per each month-delay). From the investigation of model structure, input variable selectivity was obviously affected by the time-delay arrangement, and the model predictability was related with the type of input variables. From the results, we can conclude that, although Machine Learning (ML) algorithms which have popularly been used in Ecological Informatics (EI) provide high performance in future prediction of ecological entities, the efficiency of models would be lowered unless relevant input variables are selectively used.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.1 s.4
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• pp.31-44
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• 2004

The study of Estimation model for the short-term travel time prediction. There is a different solution which has predicted the link travel time to solve this problem. By using this solution, the link travel time is predicted based on link conditions from time to time. The predicated link travel time is used to search the shortest path. Before providing a dynamic shortest path finding, the prediction model should be verified. To verify the prediction model, three models such as Kalman filtering, Stochastic Process, ARIMA. The ARIMA model should adjust optimal parameters according to the traffic conditions. It requires a frequent adjustment process of finding optimal parameters. As a result of these characteristics, It is difficult to use the ARIMA model as a prediction. Kalman Filtering model has a distinguished prediction capability. It is due to the modification of travel time predictive errors in the gaining matrix. As a result of these characteristics, the Kalman Filtering model is likely to have a non-accumulative errors in prediction. Stochastic Process model uses the historical patterns of travel time conditions on links. It if favorably comparable with the other models in the sense of the recurrent travel time condition prediction. As a result, for the travel time estimation, Kalman filtering model is the better estimation model for the short-term estimation, stochastic process is the better for the long-term estimation.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.5
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• pp.281-293
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• 2021

Oil and steel prices, which are major pricescosts in the shipbuilding industry, were predicted. Firstly, the error of the moving average line (N=3-5) was examined, and in all three error analyses, the moving average line (N=3) was small. Secondly, in the linear prediction of data through existing theory, oil prices rise slightly, and steel prices rise sharply, but in reality, linear prediction using existing data was not satisfactory. Thirdly, we identified the limitations of linear prediction methods and confirmed that oil and steel price prediction was somewhat similar to actual moving average line prediction methods. Due to the high volatility of major price flows, large errors were inevitable in the forecast section. Through the time series analysis method at the end of this paper, we were able to achieve not bad results in all analysis items relative to artificial intelligence (Prophet). Predictive data through predictive analysis using eight predictive models are expected to serve as a good research foundation for developing unique tools or establishing evaluation systems in the future. This study compares the basic settings of artificial intelligence programs with the results of core price prediction in the shipbuilding industry through time series prediction theory, and further studies the various hyper-parameters and event effects of Prophet in the future, leaving room for improvement of predictability.

• Journal of Ocean Engineering and Technology
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• v.15 no.3
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• pp.43-48
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• 2001

In this paper, the real-time prediction of high temperature creep strength and creep for nickel-based superalloy Udimet 720 (high-temperature and high-pressure gas turbine engine materials) was performed on round-bar type specimens under pure load at the temperatures of 538, 649 and 704$^{\circ}C$. The predictive equation of ISM creep has better reliability than that of LMP and LMP-ISM, and its reliability is getting better for long time creep prediction ($10^3~10^5$h).