• Korean Journal of Remote Sensing
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• v.25 no.4
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• pp.321-338
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• 2009

The objective of this paper is to compare spatial prediction capabilities of univariate kriging algorithms for generating GIS-based thematic maps from ground survey data with asymmetric distributions. Four univariate kriging algorithms including traditional ordinary kriging, three non-linear transform-based kriging algorithms such as log-normal kriging, multi-Gaussian kriging and indicator kriging are applied for spatial interpolation of geochemical As and Pb elements. Cross validation based on a leave-one-out approach is applied and then prediction errors are computed. The impact of the sampling density of the ground survey data on the prediction errors are also investigated. Through the case study, indicator kriging showed the smallest prediction errors and superior prediction capabilities of very low and very high values. Other non-linear transform based kriging algorithms yielded better prediction capabilities than traditional ordinary kriging. Log-normal kriging which has been widely applied, however, produced biased estimation results (overall, overestimation). It is expected that such quantitative comparison results would be effectively used for the selection of an optimal kriging algorithm for spatial interpolation of ground survey data with asymmetric distributions.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2000.04a
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• pp.347-356
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• 2000

This paper compares four models of artificial neural networks (ANN) supported by genetic algorithms the prediction of stock price index. Previous research proposed many hybrid models of ANN and genetic algorithms(GA) in order to train the network, to select the feature subsets, and to optimize the network topologies. Most these studies, however, only used GA to improve a part of architectural factors of ANN. In this paper, GA simultaneously optimized multiple factors of ANN. Experimental results show that GA approach to simultaneous optimization for ANN (SOGANN3) outperforms the other approaches.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.357-366
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• 2010

Many tree algorithms have been developed for regression problems. Although they are regarded as good algorithms, most of them suffer from loss of prediction accuracy when there are many noise variables. To handle this problem, we propose the multi-step GUIDE, which is a regression tree algorithm with a variable selection process. The multi-step GUIDE performs better than some of the well-known algorithms such as Random Forest and MARS. The results based on simulation study shows that the multi-step GUIDE outperforms other algorithms in terms of variable selection and prediction accuracy. It generally selects the important variables correctly with relatively few noise variables and eventually gives good prediction accuracy.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.1
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• pp.184-191
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• 2010

Many researchers have been considering genetic algorithms to system optimization problems. Especially, real-coded genetic algorithms are very effective techniques because they are simpler in coding procedures than binary-coded genetic algorithms and can reduce extra works that increase the length of chromosome for wide search space. Thus, this paper presents a fuzzy system design technique to improve the performance of the fuzzy system. The proposed system consists of two procedures. The primary tuning procedure coarsely tunes fuzzy sets of the system using the k-means clustering algorithm of which the structure is very simple, and then the secondary tuning procedure finely tunes the fuzzy sets using enhanced real-coded genetic algorithms based on the primary procedure. In addition, this paper constructs multiple fuzzy systems using a data preprocessing procedure which is contrived for reflecting various characteristics of nonlinear data. Finally, the proposed fuzzy system is applied to the field of time series prediction and the effectiveness of the proposed techniques are verified by simulations of typical time series examples.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.11B
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• pp.744-749
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• 2005

This paper proposes the prediction based QoS routing algorithm, PSS(Prediction Safety-Shortest) algorithm that minimizes network state information overhead and presumes more accurate knowledge of the present state of all the links within the network. We apply time series model to the available bandwidth prediction to overcome inaccurate information of the existing QoS routing algorithms. We have evaluated the performance of the proposed model and the existing algorithms on MCI networks, it thus appears that we have verified the performance of this algorithm.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1116-1120
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• 2010

In this paper we present two refined neighbor selection algorithms for recommender systems and also show how the attributes of the items can be used for higher prediction quality. The refined neighbor selection algorithms adopt the transitivity-based neighbor selection method using virtual neighbors and alternate neighbors, respectively. The experimental results show that the recommender systems with the proposed algorithms outperform other systems and they can overcome the large scale dataset problem as well as the first rater problem without deteriorating prediction quality.

• Proceedings of the Acoustical Society of Korea Conference
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• 1993.06a
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• pp.280-287
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• 1993

Code Excited Linear Prediction(CELP) speech coders exhibit good performance at data rates as low as 4800bps. The major drawback to CELP type paper, a comparative performance study of three pitch searching algorithms for the CELP vocoder was conducted. For each of the algorithms, a standard pitch searching algorithm was used by the sequential pitch searching algorithm that was implimented in the QCELP vocoder. The algorithms used in this study were 1) using the skip table(TABLE), 2) using the symmetrical property of the autocorrelation(SYMMT), and 3) using the preprocessing autocorrelation(PREPC). Performance scores are presented for each of the three pitch searching algorithms based on computation speed and on pitch prediction error.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06c
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• pp.188-194
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• 1994

Code excited linear prediction speech coders exhibit good performance at data rates as low as 4800bps. But the major drawback to CELP type coders is their large computational requirements. Therefore, in this paper a comparative performance study of three pitch searching algorithms for the CELP vocoder was conducted. For each of the algorithms, a standard pitch searching algorithm was used by the full pitch searching algorithm that was implimented in the QCELP vocoder. The algorithms used in this study is to reduce the pitch searching time 1) using the skip table, 2) using the symmetrical property of the autocorrelation , and 3) using the preprocessing autocorrelation, 4) using the positive autocorrelation, 5) using the preliminary pitch. Performance scores are presented for each of the five pitch searching algorithms based on computation speed and on pitch prediction error.

• Computers and Concrete
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• v.25 no.6
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• pp.565-574
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• 2020

Over the years, several machine learning approaches have been proposed and utilized to create a prediction model for the high-performance concrete (HPC) slump flow. Despite HPC is a highly complex material, predicting its pattern is a rather ambitious process. Hence, choosing and applying the correct method remain a crucial task. Like some other problems, prediction of HPC slump flow suffers from abnormal attributes which might both have an influence on prediction accuracy and increases variance. In recent years, different studies are proposed to optimize the prediction accuracy for HPC slump flow. However, more state-of-the-art regression algorithms can be implemented to create a better model. This study focuses on several methods with different mathematical backgrounds to get the best possible results. Four well-known algorithms Support Vector Regression, M5P Trees, Random Forest, and MLPReg are implemented with optimum parameters as base learners. Also, redundant features are examined to better understand both how ingredients influence on prediction models and whether possible to achieve acceptable results with a few components. Based on the findings, the MLPReg algorithm with optimum parameters gives better results than others in terms of commonly used statistical error evaluation metrics. Besides, chosen algorithms can give rather accurate results using just a few attributes of a slump flow dataset.

• Geomechanics and Engineering
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• v.38 no.1
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• pp.69-77
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• 2024

The development of shield-driven cross-river tunnels in China is witnessing a notable shift towards larger diameters, longer distances, and higher water pressures due to the more complex excavation environment. Complex geological formations, such as fault and karst cavities, pose significant construction risks. Real-time adjustment of shield tunneling parameters based on parameter prediction is the key to ensuring the safety and efficiency of shield tunneling. In this study, prediction models for the torque and thrust of the cutter plate of ultra-large diameter slurry shield TBMs is established based on integrated learning algorithms, by analyzing the real data of Heyan Road cross-river tunnel. The influence of geological complexities at the excavation face, substantial burial depth, and high water level on the slurry shield tunneling parameters are considered in the models. The results reveal that the predictive models established by applying Random Forest and AdaBoost algorithms exhibit strong agreement with actual data, which indicates that the good adaptability and predictive accuracy of these two models. The models proposed in this study can be applied in the real-time prediction and adaptive adjustment of the tunneling parameters for shield tunneling under complex geological conditions.