• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5C
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• pp.343-350
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• 2006

The resistance bias factors for driven steel pipe piles are evaluated as a part of study to develop the LRFD(Load and Resistance Factor Design) for foundation structures in Korea. The 43 data sets of static load tests and soil property tests performed in the whole domestic area were collected and analyzed to determine the representative bearing capacities of the piles using various methods. Based on the statistical analysis of the data, the Davisson's criterion is proved to be the most reasonable method for estimation of pile bearing capacity among the methods used. The static bearing capacity formulas and the Meyerhof method using N values are applied to calculate the design bearing capacity of the piles. The resistance bias factors of the driven steel pipe piles are evaluated respectively as 0.98 and 1.46 by comparison of the bearing capacities for both of the static bearing capacity formulas and the Meyerhof method. It is also shown that uncertainty of the static bearing capacity formulas is relatively less than that of the Meyerhof method.

• Ecology and Resilient Infrastructure
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• v.9 no.1
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• pp.24-35
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• 2022

For water quality management, it is necessary to continuously improve the forecasting by analyzing the past water quality, and a Data-driven model is emerging as an alternative. Because the Data-driven model is built based on a wide range of data, it is essential to apply the correlation analysis method for the combination of input variables to obtain more reliable results. In this study, the Gamma Test was applied as a preceding step to build a faster and more accurate data-driven water quality prediction model. First, a physical-based model (HSPF, EFDC) was operated to produce daily water quality reflecting the complexity of the watershed according to various hydrological conditions for Paldang Dam. The Gamma Test was performed on the water quality at the water quality prediction site (Paldangdam2) and major rivers flowing into the Paldang Dam, and the method of selecting the optimal input data combination was presented through the analysis results (Gamma, Gradient, Standar Error, V-Ratio). As a result of the study, the selection criteria for a more efficient combination of input data that can save time by omitting trial and error when building a data-driven model are presented.

• Journal of Smart Tourism
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• v.4 no.2
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• pp.5-14
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• 2024

This study explores the transformative potential of machine learning (ML) and ML-driven data analytics in the hospitality industry. It provides a comprehensive overview of this emerging method, from explaining ML's origins to introducing the evolution of ML-driven data analytics in the hospitality industry. The present study emphasizes the shift embodied in ML, moving from explicit programming towards a self-learning, adaptive approach refined over time through big data. Meanwhile, social media analytics has progressed from simplistic metrics deriving nuanced qualitative insights into consumer behavior as an industry-specific example. Additionally, this study explores innovative applications of these innovative technologies in the hospitality sector, whether in demand forecasting, personalized marketing, predictive maintenance, etc. The study also emphasizes the integration of ML and social media analytics, discussing the implications like enhanced customer personalization, real-time decision-making capabilities, optimized marketing campaigns, and improved fraud detection. In conclusion, ML-driven hospitality data analytics have become indispensable in the strategic and operation machinery of contemporary hospitality businesses. It projects these technologies' continued significance in propelling data-centric advancements across the industry.

• Geomechanics and Engineering
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• v.33 no.3
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• pp.231-242
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• 2023

This paper represents a comparative study in which Gene Expression Programming (GEP), Group Method of Data Handling (GMDH), and multiple linear regressions (MLR) were utilized to derive new equations for the prediction of time-dependent bearing capacity of pile foundations driven in cohesive soil, technically called pile set-up. This term means that many piles which are installed in cohesive soil experience a noticeable increase in bearing capacity after a specific time. Results of researches indicate that side resistance encounters more increase than toe resistance. The main reason leading to pile setup in saturated soil has been found to be the dissipation of excess pore water pressure generated in the process of pile installation, while in unsaturated conditions aging is the major justification. In this study, a comprehensive dataset containing information about 169 test piles was obtained from literature reviews used to develop the models. to prepare the data for further developments using intelligent algorithms, Data mining techniques were performed as a fundamental stage of the study. To verify the models, the data were randomly divided into training and testing datasets. The most striking difference between this study and the previous researches is that the dataset used in this study includes different piles driven in soil with varied geotechnical characterization; therefore, the proposed equations are more generalizable. According to the evaluation criteria, GEP was found to be the most effective method to predict set-up among the other approaches developed earlier for the pertinent research.

• Steel and Composite Structures
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• v.52 no.4
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• pp.461-473
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• 2024

This study focuses on the reliability of a transmission line under wind excitation and evaluates the failure probability using explicit data resources. The data-driven framework for calculating the failure probability of a transmission line subjected to wind loading is presented, and a probabilistic method for estimating the yearly extreme wind speeds in each wind direction is provided to compensate for the incompleteness of meteorological data. Meteorological data from the Xuwen National Weather Station are used to analyze the distribution characteristics of wind speed and wind direction, fitted with the generalized extreme value distribution. Then, the most vulnerable tower is identified to obtain the fragility curves in all wind directions based on uncertainty analysis. Finally, the failure probabilities are calculated based on the presented method. The simulation results reveal that the failure probability of the employed tower increases over time and that the joint probability distribution of the wind speed and wind direction must be considered to avoid overestimating the failure probability. Additionally, the mixed wind climates (synoptic wind and typhoon) have great influence on the estimation of structural failure probability and should be considered.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.1
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• pp.44-51
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• 2016

Recently, artificial intelligence has reached the level of top information technologies that will have significant influence over many aspects of our future lifestyles. In particular, in the fields of machine learning technologies for classification and decision-making, there have been a lot of research efforts for solving estimation and control problems that appear in the various kinds of portfolio management problems via data-driven approaches. Note that these modern data-driven approaches, which try to find solutions to the problems based on relevant empirical data rather than mathematical analyses, are useful particularly in practical application domains. In this paper, we consider some applications of modern data-driven machine learning methods for portfolio management problems. More precisely, we apply a simplified version of the sparse Gaussian process (GP) classification method for classifying users' sensitivity with respect to financial risk, and then present two portfolio management issues in which the GP application results can be useful. Experimental results show that the GP applications work well in handling simulated data sets.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.6B
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• pp.867-874
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• 2010

Since high packet losses occur in multi-hop transmission of wireless sensor networks, reliable data transmission is required. Especially, in case of event-driven data, a loss recovery mechanism should be provided for lost packets. Because retransmission for lost packets is requested to a node that caches the packets, the caching node should maintains all of data for transmission in its buffer. However, nodes of wireless sensor networks have limited resources. Thus, both a loss recovery mechanism and a buffer management technique are provided for reliable data transmission in wireless sensor networks. In this paper, we propose a buffer management technique at a caching position determined by a loss recovery mechanism. The caching position of data is determined according to desirable reliability for the data. In addition, we validate the performance of the proposed method through computer simulations.

• Journal of Engineering Education Research
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• v.22 no.4
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• pp.61-70
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• 2019

As product development process becomes complex, it becomes more important for engineering students to experience collaborative product development. Especially the collaboration experience based on Product Data Management (PDM) systems is useful, since participants are likely to use the same environment for their professional product development. However, instructors have difficulties to evaluate contribution of each participant to their projects during the practices, since it is hard to trace personal activities for collaborative design processes. To solve this problem, this study suggests a data-driven objective method that analyses product data accumulated in PDM databases to evaluate numerically calculated contributions of participants to their class projects. As a result, the quantitative measures provided by the data-driven analysis with qualitative measures for project results can improve the fairness and quality of evaluation of contributions of participants to collaborative projects. This study implemented the proposed evaluation method with an information system and discussed the result of the application of the system to product development practices.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1095-1100
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• 2011

In this paper, PHM (Prognostics and Health Management) techniques are briefly outlined. Prognostics, being a central step within the PHM, is explained in more detail, stating that there are three approaches - experience based, data-driven and model based approaches. Representative articles in the field of prognostics are also given in terms of the type of faults. Model based method is illustrated by introducing a case study that was conducted to the crack growth of the gear plate in UH-60A helicopter. The paper also addresses the comparison of the OBM (Overall Bayesian Method), which was developed by the authors with the PF (Particle Filtering) method, which draws great attention recently in prognostics, through the study on a simple crack growth problem. Their performances are examined by evaluating the metrics introduced by PHM society.

• Journal of the Korean Data and Information Science Society
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• v.9 no.2
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• pp.179-188
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• 1998

Local polynomial regression estimation is the most popular one among kernel type regression estimator. In local polynomial regression function esimation bandwidth selection is crucial problem like the kernel estimation. When the regression curve has complicated structure variable bandwidth selection will be appropriate. In this paper, we propose a variable bandwidth selection method fully data driven. We will choose the bandwdith by selecting minimising estiamted MSE which is estimated by the pilot bandwidth study via croos-validation method. Monte carlo simulation was conducted in order to show the superiority of proposed bandwidth selection method.