• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.139-147
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• 2023

The metal bush assembling process is a process of inserting and compressing a metal bush that serves to reduce the occurrence of noise and stable compression in the rotating section. In the metal bush assembly process, the head diameter defect and placement defect of the metal bush occur due to metal bush omission, non-pressing, and poor press-fitting. Among these causes of defects, it is intended to prevent defects due to omission of the metal bush by using signals from sensors attached to the facility. In particular, a metal bush omission is predicted through various data mining techniques using left load cell value, right load cell value, current, and voltage as independent variables. In the case of metal bush omission defect, it is difficult to get defect data, resulting in data imbalance. Data imbalance refers to a case where there is a large difference in the number of data belonging to each class, which can be a problem when performing classification prediction. In order to solve the problem caused by data imbalance, oversampling and composite sampling techniques were applied in this study. In addition, simulated annealing was applied for optimization of parameters related to sampling and hyper-parameters of data mining techniques used for bush omission prediction. In this study, the metal bush omission was predicted using the actual data of M manufacturing company, and the classification performance was examined. All applied techniques showed excellent results, and in particular, the proposed methods, the method of mixing Random Forest and SA, and the method of mixing MLP and SA, showed better results.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.10a
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• pp.535-538
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• 2001

Since 1990s, many literatures have shown that connectionist models, such as back propagation, recurrent network, and RBF (Radial Basis Function) outperform the traditional models, MA (Moving Average), AR (Auto Regressive), and ARIMA (Auto Regressive Integrated Moving Average) in time series prediction. Neural based approaches to time series prediction require the enough length of historical measurements to generate the enough number of training patterns. The more training patterns, the better the generalization of MLP is. The researches about the schemes of generating artificial training patterns and adding to the original ones have been progressed and gave me the motivation of developing VTG schemes in 1996. Virtual term is an estimated measurement, X(t+0.5) between X(t) and X(t+1), while the given measurements in the series are called actual terms. VTG (Virtual Tern Generation) is the process of estimating of X(t+0.5), and VTG schemes are the techniques for the estimation of virtual terms. In this paper, the alternative VTG schemes to the VTG schemes proposed in 1996 will be proposed and applied to multivariate time series prediction. The VTG schemes proposed in 1996 are called deterministic VTG schemes, while the alternative ones are called stochastic VTG schemes in this paper.

• Geomechanics and Engineering
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• v.19 no.3
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• pp.217-227
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• 2019

This study focuses on an improved prediction model to determine the limiting grouting pressure of compaction grouting considering the ground surface upheaval, which is caused by the three-dimensional conical shearing failure. The 2D-dimensional failure curve in Zou and Xia (2016) was improved to a three-dimensional conical shearing failure for compaction grouting through coordinate rotation. The process of compaction grouting was considered as the cavity expansion in infinite Mohr-Coulomb (M-C) soil mass. The prediction model of limiting grouting pressure of compaction grouting was proposed with limit equilibrium principle, which was validated by comparing the results in El-Kelesh et al. (2001) and numerical method. Furthermore, using the proposed prediction model, the vertical and horizontal grouting tube techniques were adopted to deal with the subgrade settlement in Shao-huai highway at Hunan Provence of China. The engineering applicability and effectiveness of the proposed model were verified by the field test. The research on the prediction model for the limiting grouting pressure of compaction grouting provides practical example to the rapid treatment technology of subgrade settlement.

• Communications for Statistical Applications and Methods
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• v.31 no.4
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• pp.441-457
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• 2024

Semicontinuous data are characterized by a mixture of a point probability mass at zero and a continuous distribution of positive values. This type of data is often modeled using a two-part model where the first part models the probability of dichotomous outcomes -zero or positive- and the second part models the distribution of positive values. Despite the two-part model's popularity, variable selection in this model has not been fully addressed, especially, in high dimensional data. The objective of this study is to investigate variable selection and prediction performance of penalized regression methods in two-part models. The performance of the selected techniques in the two-part model is evaluated via simulation studies. Our findings show that LASSO and ENET tend to select more predictors in the model than SCAD and MCP. Consequently, MCP and SCAD outperform LASSO and ENET for β-specificity, and LASSO and ENET perform better than MCP and SCAD with respect to the mean squared error. We find similar results when applying the penalized regression methods to the prediction of crime incidents using community-based data.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.11-16
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• 2023

Accurate hospital case modeling and prediction are crucial for efficient healthcare. In this study, we demonstrate the implementation of regression analysis methods in machine learning systems utilizing mathematical statics and machine learning techniques. The developed machine learning model includes Bayesian linear, artificial neural network, decision tree, decision forest, and linear regression analysis models. Through the application of these algorithms, corresponding regression models were constructed and analyzed. The results suggest the potential of leveraging machine learning systems for medical research. The experiment aimed to create an Azure Machine Learning Studio tool for the speedy evaluation of multiple regression models. The tool faciliates the comparision of 5 types of regression models in a unified experiment and presents assessment results with performance metrics. Evaluation of regression machine learning models highlighted the advantages of boosted decision tree regression, and decision forest regression in hospital case prediction. These findings could lay the groundwork for the deliberate development of new directions in medical data processing and decision making. Furthermore, potential avenues for future research may include exploring methods such as clustering, classification, and anomaly detection in healthcare systems.

• Journal of Korean Medical classics
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• v.32 no.1
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• pp.61-74
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• 2019

Objectives : The purpose of this study is to help select an appropriate word embedding method when analyzing East Asian traditional medicine texts as data. Methods : Based on prescription data that imply traditional methods in traditional East Asian medicine, we have examined 4 count-based word embedding and 2 prediction-based word embedding methods. In order to intuitively compare these word embedding methods, we proposed a "prescription generating game" and compared its results with those from the application of the 6 methods. Results : When the adjacent vectors are extracted, the count-based word embedding method derives the main herbs that are frequently used in conjunction with each other. On the other hand, in the prediction-based word embedding method, the synonyms of the herbs were derived. Conclusions : Counting based word embedding methods seems to be more effective than prediction-based word embedding methods in analyzing the use of domesticated herbs. Among count-based word embedding methods, the TF-vector method tends to exaggerate the frequency effect, and hence the TF-IDF vector or co-word vector may be a more reasonable choice. Also, the t-score vector may be recommended in search for unusual information that could not be found in frequency. On the other hand, prediction-based embedding seems to be effective when deriving the bases of similar meanings in context.

• Journal of Soil and Groundwater Environment
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• v.28 no.spc
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• pp.18-39
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• 2023

Rapid development of geophysical exploration and hydrogeologic monitoring techniques has yielded remarkable increase of datasets related to groundwater systems. Increased number of datasets contribute to understanding of general aquifer characteristics such as groundwater yield and flow, but understanding of complex heterogenous aquifers system is still a challenging task. Recently, applications of data science technique have become popular in the fields of geophysical explorations and monitoring, and such attempts are also extended in the groundwater field. This work reviewed current status and advancement in utilization of data science in groundwater field. The application of data science techniques facilitates effective and realistic analyses of aquifer system, and allows accurate prediction of aquifer system change in response to extreme climate events. Due to such benefits, data science techniques have become an effective tool to establish more sustainable groundwater management systems. It is expected that the techniques will further strengthen the theoretical framework in groundwater management to cope with upcoming challenges and limitations.

• Korean Journal of Remote Sensing
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• v.1 no.1
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• pp.53-61
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• 1985

The purpose of the study is to establish models of land use prediction system for development and management of land resources using remotely sensed data as well as ancillary data in the context of multi-disciplinary approach in the application to CheJoo Island. The model adopts multi-date processing techniques and is a spatial/temporal land-use projection strategy emerged as a synthesis of the probability transition model and the discriminant-annlysis model. A discriminant model is applied to all pixels in CheJoo landscape plane to predict the most likely change in land use. The probability transition model provides the number of these pixels that will convert to different land use in a gives future time increment. The synthetic model predicts the future change in land use and its volume of pixels in the landscape plane.

• International Journal of Railway
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• v.2 no.4
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• pp.175-179
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• 2009

The welding deformation and its prediction method at the HAZ (Heat-Affected Zone) are presented in this paper. The inherent strain method is well known as analytical method to predict welding deformation of large scale welded structure. Depend on the size of welding deformation in welding joints, the fatigue life, the stress concentration factor and the manufacturing quality of welded structure are decided. Many welded joints and its manufacturing control techniques are also required to railway rolling stock and its structural parts such as railway carbody and bogie frame. Proposed methods in this paper focus on the two different the inherent strain area at HAZ. This is main idea of proposed method and it makes more reliable result of welding deformation analysis at the HAZ.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.73-78
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• 2022

The aim of this paper is to investigate the prevalence of Deep Learning in the literature on Fire & Rescue Service. It is found that deep learning techniques are only beginning to benefit the firefighters. The popular areas where deep learning techniques are making an impact are situational awareness, decision making, mental stress, injuries, well-being of the firefighter such as his sudden fall, inability to move and breathlessness, path planning by the firefighters while getting to an fire scene, wayfinding, tracking firefighters, firefighter physical fitness, employment, prediction of firefighter intervention, firefighter operations such as object recognition in smoky areas, firefighter efficacy, smart firefighting using edge computing, firefighting in teams, and firefighter clothing and safety. The techniques that were found applied in firefighting were Deep learning, Traditional K-Means clustering with engineered time and frequency domain features, Convolutional autoencoders, Long Short-Term Memory (LSTM), Deep Neural Networks, Simulation, VR, ANN, Deep Q Learning, Deep learning based on conditional generative adversarial networks, Decision Trees, Kalman Filters, Computational models, Partial Least Squares, Logistic Regression, Random Forest, Edge computing, C5 Decision Tree, Restricted Boltzmann Machine, Reinforcement Learning, and Recurrent LSTM. The literature review is centered on Firefighters/firemen not involved in wildland fires. The focus was also not on the fire itself. It must also be noted that several deep learning techniques such as CNN were mostly used in fire behavior, fire imaging and identification as well. Those papers that deal with fire behavior were also not part of this literature review.