• The Korean Journal of Ecology
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• v.24 no.4
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• pp.213-217
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• 2001

The purpose of this study is to investigate the effects of hardwood competitions in stand structure and dynamics by applying prediction models for unthinned loblolly pine (Pinus taeda L.) plantations. A parameter recovery procedure for the Weibull distribution function based on four percentile equations was applied to develop diameter distribution prediction models. Four percentiles of the cumulative diameter distribution prediction equations were predicted as a function of quadratic mean diameter plus competin hardwood trees perhectare varibales. According to the results of this study. it was found that as the amount of competing hardwood trees increased, diameter distributions in terms of stand structure dynamics tended to be more skewed to the right. Therefore, the influence of non-planted hardwood trees interspecific competitoin on planted loblolly pines showed negative effects on the stand structure and dynamics.

• Smart Structures and Systems
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• v.24 no.4
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• pp.507-524
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• 2019

A Bayesian dynamic linear model (BDLM) is presented for a data-driven analysis for response prediction and load effect separation of a revolving auditorium structure, where the main loads are self-weight and dead loads, temperature load, and audience load. Analyses are carried out based on the long-term monitoring data for static strains on several key members of the structure. Three improvements are introduced to the ordinary regression BDLM, which are a classificatory regression term to address the temporary audience load effect, improved inference for the variance of observation noise to be updated continuously, and component discount factors for effective load effect separation. The effects of those improvements are evaluated regarding the root mean square errors, standard deviations, and 95% confidence intervals of the predictions. Bayes factors are used for evaluating the probability distributions of the predictions, which are essential to structural condition assessments, such as outlier identification and reliability analysis. The performance of the present BDLM has been successfully verified based on the simulated data and the real data obtained from the structural health monitoring system installed on the revolving structure.

• Design & Manufacturing
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• v.16 no.3
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• pp.1-8
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• 2022

In this study, an artificial neural network(ANN) was constructed to establish the relationship between process condition prameters and the qualities of the injection-molded product in the injection molding process. Six process parmeters were set as input parameter for ANN: melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time. As output parameters, the mass, nominal diameter, and height of the injection-molded product were set. Two learning structures were applied to the ANN. The single-task learning, in which all output parameters are learned in correlation with each other, and the multi-task learning structure in which each output parameters is individually learned according to the characteristics, were constructed. As a result of constructing an artificial neural network with two learning structures and evaluating the prediction performance, it was confirmed that the predicted value of the ANN to which the multi-task learning structure was applied had a low RMSE compared with the single-task learning structure. In addition, when comparing the quality specifications of injection molded products with the prediction values of the ANN, it was confirmed that the ANN of the multi-task learning structure satisfies the quality specifications for all of the mass, diameter, and height.

• Proceedings of the Korea Concrete Institute Conference
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• 1999.04a
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• pp.281-286
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• 1999

Nonlinear temperature distribution induced by the hydration heat generates thermal stress in mass concrete. At early ages, such thermal stress may induce thermal cracks in the structure which can affect on the durability and safety of the structure. Up to now, a lot of works have focused on the prediction of temperature distribution and thermal stress in the structure. In most of such works, however, the inside of structure was considered as adiabatic state to predict temperature distribution and the thermal stress. And due to the lacks of appropriate analysis models after crack, there was little research on the crack occurrence. This paper deals with the prediction of the temperature distribution in the structure using the rate of hydration heat generation and also estimates the behavior of structure before and after cracking due to hydration heat using crack band model.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2000.11a
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• pp.28-31
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• 2000

We describe a hidden Markov model, HMMTIR, for general protein sequence based on the I-sites library of sequence-structure motifs. Unlike the linear HMMs used to model individual protein families, HMMSTR has a highly branched topology and captures recurrent local features of protein sequences and structures that transcend protein family boundaries. The model extends the I-sites library by describing the adjacencies of different sequence-structure motifs as observed in the database, and achieves a great reduction in parameters by representing overlapping motifs in a much more compact form. The HMM attributes a considerably higher probability to coding sequence than does an equivalent dipeptide model, predicts secondary structure with an accuracy of 74.6% and backbone torsion angles better than any previously reported method, and predicts the structural context of beta strands and turns with an accuracy that should be useful for tertiary structure prediction. HMMSTR has been incorporated into a public, fully-automated protein structure prediction server.

• Transactions of Materials Processing
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• v.23 no.6
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• pp.380-385
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• 2014

An analysis procedure is proposed to improve the prediction accuracy of springback as well as to evaluate the structural stability of the tooling used for fabricating a side sill part from UHSS. The analysis couples the stamping analysis and the subsequent analysis of the tool structural. The deformation and stress results for the tool structure are obtained from the proposed analysis procedure. The results show that the amount of deformation and stresses are so high that the tool structure must be reinforced and the tooling design must consider structural stability. Springback is predicted with CAE in order to compare the prediction accuracy between the given tool geometry and the geometry from the structural analysis. The simulation results with the deformed tool can predict the experimental springback tendency accurately.

• Journal of Korean Society of Transportation
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• v.5 no.1
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• pp.59-74
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• 1987

A prediction is an indispensable element to research of Social Science, especially in Regional planning, City planning, and Transportation planning. Since 1930s, varieties of prediction methods have been developed. In the 1980s, numerical models have been used by high-developed computers. even though the numerical models can be figured mathematically, it could not be applied practically due to it's expertness and complicateness. And even professional planners often can not use their ideas which are valuable experiences in prediction process, because they are not knowledgable for numerical models. The YSIM developed by author, is available as follows. i)Numerical modeling of professional experiences ii)Providing a foundation of large-scale model iii) Understanding of research object structure The YSIM make use of matrix to identify the system structure which is similar to the Cross Impact Method. To evaluated the YSIM availabilities, it is compared with the early developed methodologies such as KSIM, QSIM, and SPIN. As the result, it was confirmed that YSIM was more accurate in the prediction. The algorithms in YSIM is programmed for use of PCs.

• 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.

• Journal of Broadcast Engineering
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• v.12 no.6
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• pp.624-629
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• 2007

While single view video coding uses the temporal prediction scheme, multi-view video coding (MVC) applies both temporal and inter-view prediction schemes. Thus, the key problem of MVC is how to reduce the inter-view redundancy efficiently, because various existing video coding schemes have already provided solutions to reduce the temporal correlation. In this paper, we propose a global disparity compensation scheme which increases the inter-view correlation and a new inter-view prediction structure based on the global disparity compensation. By experiment, we demonstrate that the proposed global disparity compensation scheme is less sensitive to change of the search range. In addition, the new Inter-view prediction structure achieved about $0.1{\sim}0.3dB$ quality improvement compared to the reference software.

• Journal of Internet Computing and Services
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• v.11 no.4
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• pp.85-94
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• 2010

In this paper, we propose a location-prediction structure that can provide user service in advance. It consists of seven steps and supplies intelligent services which can forecast user's location. Context information collected from physical sensors and a history database is so difficult that it can't present importance of data and abstraction of data because of heterogeneous data type. Hence, we offer the location-prediction that change data type from heterogeneous data to homogeneous data. Extracted data is clustered by SOFM, then it gets user's location information by ARIMA and realizes the services by a reasoning engine. In order to validate the proposed location-prediction, we built a test-bed and test it by the scenario.