• Journal of Fisheries and Marine Sciences Education
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• v.28 no.4
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• pp.1076-1088
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• 2016

This study was to validate the WSBS_PECT (Workplace Spirituality Belief Scale for Prospective Early Childhood Teacher) using discriminant analysis on prospective early childhood teachers' happiness and career maturity. The data from 523 prospective early childhood teachers were analyzed statistically through t-test and binary logistic regression model. The results indicated that 1) the higher group in workplace spirituality belief significantly gets more scores of happiness and career maturity than the lower group, 2) 1 factors of the WSBS_PECT has discriminant power on prospective early childhood teachers' happiness, and 3) 2 factors ('meaning for life' and 'belief on calling for ECE teacher job') of the WSBS_PECT are effective to discriminate prospective early childhood teachers' career maturity. Further statistical works are supplementary needed to validate the WSBS_PECT and to increase its' feasibility.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.342-344
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• 2018

This paper shows the possibility to substitute statistical methods such as mean imputation, correlation coefficient analysis, graph correlation analysis for the proposed algorithm, and replace statistician for processing various abnormal data measured in the water treatment process with it. In addition, this study aims to model a data-filtering system based on a recent fractile pattern and a deep learning-based LSTM algorithm in order to improve the reliability and validation of the algorithm, using the open-sourced libraries such as KERAS, THEANO, TENSORFLOW, etc.

• Journal of the Korean Chemical Society
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• v.57 no.6
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• pp.761-768
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• 2013

Removal of COD and TSS from rice mill wastewater was investigated using continuous electrocoagulation method (CEC). The electrical energy consumption (EEC) of the process was also examined in order to evaluate the economic viability. The Box-Behnken statistical experiment design (BBD) and response surface methodology (RSM) were used to investigate the effects of major operating variables. Initial pH, current density, electrode distance and flow rate were selected as independent variables in BBD while COD removal, TSS removal and EEC were considered as the response functions. The predicted values of responses obtained using the response function was in good agreement with the experimental data. Optimum operating conditions were found to be pH of 7, current density of 15 mA $cm^{-2}$, electrode distance of 5 cm and flow rate of 70 ml/min. Under these conditions, greater than 89% removal of COD and TSS were obtained with EEC value of 7 KWh.

• Journal of KIISE
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• v.43 no.7
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• pp.812-819
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• 2016

This paper introduces and analyzes the theoretical basis and method of the conventional initial-value assignment problem and feasibility of image segmentation. The paper presents topological evidence and a method of appropriate initial-value assignment based on topology theory. Subsequently, the paper shows minimum conditions for feasibility of image segmentation based on separation axiom theory of topology and a validation method of effectiveness for image modeling. As a summary, this paper shows image segmentation with its mathematical validity based on topological analysis rather than statistical analysis. Finally, the paper applies the theory and methods to conventional Gaussian random field model and examines effectiveness of GRF modeling.

• Journal of the Society of Naval Architects of Korea
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• v.58 no.4
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• pp.234-242
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• 2021

In this study, we introduce the prediction of brake power for low-speed full ships and container carriers using the linear regression and a machine learning approach. The residual resistance coefficient, wake fraction coefficient, and thrust deduction factor are predicted by regression models using the main dimensions of ship and propeller. The brake power of a ship can be calculated by these coefficients according to the 1978 ITTC performance prediction method. The mean absolute error of the predicted power was under 7%. As a result of several validation cases, it was confirmed that the machine learning model showed slightly better results than linear regression.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.166-166
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• 2020

Recently, As the frequency of localized heavy rains increases, the use of high-resolution radar data is increasing. The produced radar rainfall has still gaps of spatial and temporal compared to gauge observation rainfall, and in many studies, various statistical techniques are performed for correct rainfall. In this study, the precipitation correction of the S-band Dual Polarization radar in use in the flood forecast was performed using the ConvAE algorithm, one of the Convolutional Neural Network. The ConvAE model was trained based on radar data sets having a 10-min temporal resolution: radar rainfall data, gauge rainfall data for 790minutes(July 2017 in Cheongju flood event). As a result of the validation of corrected radar rainfall were reduced gaps compared to gauge rainfall and the spatial correction was also performed. Therefore, it is judged that the corrected radar rainfall using ConvAE will increase the reliability of the gridded rainfall data used in various physically-based distributed hydrodynamic models.

• Journal of Information Technology Applications and Management
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• v.30 no.6
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• pp.53-68
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• 2023

Through the COVID-19 pandemic, the demand for non-face-to-face communication systems has surged, leading to an increased prevalence of virtual interactions across various domains, such as tasks, meetings, orders and deliveries, and even student education. Against this backdrop, interest in the metaverse platform has been on the rise, with metaverse services like Zepeto, Roblox, and Minecraft expanding beyond gaming to encompass educational fields as well. This study aims to identify authenticity factors influencing metaverse platform-based educational services and examine their impact. The authenticity components are defined as integrity, empathy, interactivity, presence, and uniqueness. The study investigates the effects of these authenticity components on both service value and service satisfaction. To achieve this, a survey involving 320 metaverse users was conducted, and the model was subjected to statistical validation. The findings of this research underscore that perceiving metaverse education services, still in the early stages of introduction, as authentic educational methods for learners positively influences satisfaction with the educational service.This study holds significance as it lays the theoretical groundwork for enhancing the authenticity of educational services in virtual space. It defines and proposes authenticity elements for customer satisfaction in metaverse educational services, which are still in their nascent stages. Moving forward, it is anticipated that various studies will be conducted to enhance the value of metaverse education services and achieve higher customer satisfaction as customer experiences evolve and deepen.

• Geomechanics and Engineering
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• v.39 no.1
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• pp.27-41
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• 2024

Accurately estimating the performance of tunnel boring machines (TBMs) is crucial for mitigating the substantial financial risks and complexities associated with tunnel construction. Machine learning (ML) techniques have emerged as powerful tools for predicting non-linear time series data. In this research, six advanced meta-heuristic optimization algorithms based on long short-term memory (LSTM) networks were developed to predict TBM penetration rate (TBM-PR). The study utilized 1125 datasets, partitioned into 20% for testing, 70% for training, and 10% for validation, incorporating six key input parameters influencing TBM-PR. The performances of these LSTM-based models were rigorously compared using a suite of statistical evaluation metrics. The results underscored the profound impact of optimization algorithms on prediction accuracy. Among the models tested, the LSTM optimized by the particle swarm optimization (PSO) algorithm emerged as the most robust predictor of TBM-PR. Sensitivity analysis further revealed that the orientation of discontinuities, specifically the alpha angle (α), exerted the greatest influence on the model's predictions. This research is significant in that it addresses critical concerns of TBM manufacturers and operators, offering a reliable predictive tool adaptable to varying geological conditions.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.53 no.5
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• pp.699-706
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• 2020

Predictive models in food microbiology are used for predicting microbial growth or death rates using mathematical and statistical tools considering the intrinsic and extrinsic factors of food. This study developed predictive growth models for Bacillus cereus on dried laver Pyropia pseudolinearis stored at different temperatures (5, 10, 15, 20, and 25℃). Primary models developed for specific growth rate (SGR), lag time (LT), and maximum population density (MPD) indicated a good fit (R²≥0.98) with the Gompertz equation. The SGR values were 0.03, 0.08, and 0.12, and the LT values were 12.64, 4.01, and 2.17 h, at the storage temperatures of 15, 20, and 25℃, respectively. Secondary models for the same parameters were determined via nonlinear regression as follows: SGR=0.0228-0.0069*T₁+0.0005*T₁²; LT=113.0685-9.6256*T₁+0.2079*T₁²; MPD=1.6630+0.4284*T₁-0.0080*T₁² (where T₁ is the storage temperature). The appropriateness of the secondary models was validated using statistical indices, such as mean squared error (MSE<0.01), bias factor (0.99≤B_f≤1.07), and accuracy factor (1.01≤A_f≤1.14). External validation was performed at three random temperatures, and the results were consistent with each other. Thus, these models may be useful for predicting the growth of B. cereus on dried laver.

• Journal of Information Processing Systems
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• v.14 no.3
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• pp.751-770
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• 2018

Web applications are indispensable in the software industry and continuously evolve either meeting a newer criteria and/or including new functionalities. However, despite assuring quality via testing, what hinders a straightforward development is the presence of defects. Several factors contribute to defects and are often minimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases of software development is important. Therefore, a fault prediction model for identifying fault-prone classes in a web application is highly desired. In this work, we compare 14 machine learning techniques to analyse the relationship between object oriented metrics and fault prediction in web applications. The study is carried out using various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, the input basis set for each release is first optimized using filter based correlation feature selection (CFS) method. It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statistical analysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of these metrics in the defect prediction of web applications. The overall predictive ability of different fault prediction models is first ranked using Friedman technique and then statistically compared using Nemenyi post-hoc analysis. The results not only upholds the predictive capability of machine learning models for faulty classes using web applications, but also finds that ensemble algorithms are most appropriate for defect prediction in Apache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique and the statistical analysis of the datasets.