• Analytical Science and Technology
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• v.25 no.4
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• pp.214-222
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• 2012

ASTM (American Society for Testing and Materials) D6751-10 suggests analytical methods as well as specifications for biodiesel quality. However, it is expensive and time-consuming to follow the ASTM testing methods to analyze biodiesel and various impurities. This paper develops a quantitative analysis system for biodiesel and impurities based on Infrared spectroscopy and a multivariate statistical method, PLS (partial least squares). In addition, four different pre-processing techniques were compared for spectrum correction and noise reduction. Savitzky-Golay pre-processing showed the best performance.

• Journal of Soil and Groundwater Environment
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• v.20 no.3
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• pp.51-64
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• 2015

In the present study, we develop two history matching techniques based on Markov chain Monte Carlo method where radial basis function and Gaussian distribution generated by unconditional geostatistical simulation are employed as the random walk transition kernels. The Bayesian inverse methods for aquifer characterization as the developed models can be effectively applied to the condition even when the targeted information such as hydraulic conductivity is absent and there are transient hydraulic head records due to imposed stress at observation wells. The model which uses unconditional simulation as random walk transition kernel has advantage in that spatial statistics can be directly associated with the predictions. The model using radial basis function network shares the same advantages as the model with unconditional simulation, yet the radial basis function network based the model does not require external geostatistical techniques. Also, by employing radial basis function as transition kernel, multi-scale nested structures can be rigorously addressed. In the validations of the developed models, the overall predictabilities of both models are sound by showing high correlation coefficient between the reference and the predicted. In terms of the model performance, the model with radial basis function network has higher error reduction rate and computational efficiency than with unconditional geostatistical simulation.

• Journal of Korean Academy of Nursing
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• v.47 no.3
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• pp.431-444
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• 2017

Purpose: This study was conducted to verify the effects of a memory and visual-motor integration program for older adults based on self-efficacy theory. Methods: A non-equivalent control group pretest-posttest design was implemented in this quasi-experimental study. The participants were 62 older adults from senior centers and older adult welfare facilities in D and G city (Experimental group=30, Control group=32). The experimental group took part in a 12-session memory and visual-motor integration program over 6 weeks. Data regarding memory self-efficacy, memory, visual-motor integration, and depression were collected from July to October of 2014 and analyzed with independent t-test and Mann-Whitney U test using PASW Statistics (SPSS) 18.0 to determine the effects of the interventions. Results: Memory self-efficacy (t=2.20, p=.031), memory (Z=-2.92, p=.004), and visual-motor integration (Z=-2.49, p=.013) increased significantly in the experimental group as compared to the control group. However, depression (Z=-0.90, p=.367) did not decrease significantly. Conclusion: This program is effective for increasing memory, visual-motor integration, and memory self-efficacy in older adults. Therefore, it can be used to improve cognition and prevent dementia in older adults.

• Computers and Concrete
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• v.21 no.6
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• pp.697-703
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• 2018

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

• Journal of the Earthquake Engineering Society of Korea
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• v.23 no.1
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• pp.55-70
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• 2019

The phase properties of ground acceleration records from Mw 5.5~6.5 earthquakes are analyzed. The interrelationships between phase properties and significant durations, as well as PGA, are clarified through both of theoretical and empirical approaches. The probabilistic characteristics of phase information is also discussed based on previous studies and it is shown that circular normal distribution is the most appropriate probability distribution for the phase angle and phase difference. Whereas those variates can be modeled by Gaussian random variables. From the survey results on the frequency dependency of the phase statistics, a simple model is introduced, which is possible to express the frequency dependency of phase information. It is also shown that the significant duration can be controlled by appropriately chosen standard deviation of phase difference for 4~8Hz frequency band and additional consideration of phase scattering in higher frequency band through a series of Monte Carlo simulations. The source of phase scattering effect is also pointed out and discussed.

• Journal of the Earthquake Engineering Society of Korea
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• v.25 no.6
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• pp.293-303
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• 2021

Seismic fragility functions for unreinforced masonry buildings were derived based on the incremental dynamic analysis of eight representative inelastic numerical models for application to Korea's earthquake damage estimation system. The effects of panel zones formed between piers and spandrels around openings were taken into account explicitly or implicitly regarding stiffness and inelastic deformation capacity. The site response of ground motion records measured at the rock site was used as input ground motion. Limit states were proposed based on the fraction of structural components that do not meet the required performance from the nonlinear static analysis of each model. In addition to the randomness of ground motion considered in the incremental dynamic analysis explicitly, supplementary standard deviation due to uncertainty that was not reflected in the fragility assessment procedure was added. The proposed seismic fragility functions were verified by applying them to the damage estimation of masonry buildings located around the epicenter of the 2017 Pohang earthquake and comparing the result with actual damage statistics.

• Structural Monitoring and Maintenance
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• v.9 no.2
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• pp.179-200
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• 2022

This paper proposes a data-driven methodology for online early damage identification under changing environmental conditions. The proposed method relies on two data analysis methods: feature-based method and hybrid principal component analysis (PCA) and kernel PCA to separate damage from environmental influences. First, spectral sub-band features, namely, spectral sub-band centroids (SSCs) and log spectral sub-band energies (LSSEs), are proposed as damage-sensitive features to extract damage information from measured structural responses. Second, hybrid modeling by integrating PCA and kernel PCA is performed on the spectral sub-band feature matrix for data normalization to extract both linear and nonlinear features for nonlinear procedure monitoring. After feature normalization, suppressing environmental effects, the control charts (Hotelling T² and SPE statistics) is implemented to novelty detection and distinguish damage in structures. The hybrid PCA-KPCA technique is compared to KPCA by applying support vector machine (SVM) to evaluate the effectiveness of its performance in detecting damage. The proposed method is verified through numerical and full-scale studies (a Bridge Health Monitoring (BHM) Benchmark Problem and a cable-stayed bridge in China). The results demonstrate that the proposed method can detect the structural damage accurately and reduce false alarms by suppressing the effects and interference of environmental variations.

• Journal of the Korea Society of Computer and Information
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• v.14 no.7
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• pp.9-15
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• 2009

The fuzzy weighted mean classifier is one of the most common classification models and could achieve high performance by adjusting the weights. However, the weights were generally decided based on the experience of experts, which made the resulting classifiers to suffer the lack of consistency and objectivity. To resolve this problem, in this paper, a weight deciding method based on the statistics of the data is introduced, which ensures the learned classifiers to be consistent and objective. To investigate the effectiveness of the proposed methods, Iris data set available from UCI machine learning repository is used and promising results are obtained.

• The Journal of Korean Academic Society of Nursing Education
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• v.29 no.4
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• pp.339-351
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• 2023

Purpose: The purpose of this study is to identify the educational needs of virtual reality simulations that can be applied to novice nurses during the waiting period before starting work in a hospital. Methods: A convergent mixed methods was used. The survey data were collected from 230 novice nurses, and a focus group interview was conducted with 6 new nurses. The data were collected from November 2022 to January 2023. Descriptive statistics, a frequency analysis, independent t-test, and an Importance-Performance Analysis were performed using SPSS 24.0. Results: Appropriate topics for virtual reality simulation education were indicated to be medications and intravenous injections, which are high priority topics in quantitative and qualitative research. The novice nurses wanted group activity training three to four times a week for two weeks before beginning work in a hospital. They also wanted an immersive virtual reality system based on a real hospital environment. Conclusion: Based on the above results, this study provides basic data for the development of a virtual reality simulation education that can improve the adaptation of novice nurses to clinical practice. A strategy was suggested to utilize the waiting time before beginning work in a hospital as educational time.

• Journal of Internet Computing and Services
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• v.17 no.4
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• pp.115-125
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• 2016

Recently, many attentions have been paid to conducting convergence research across diverse disciplines. Along with this convergence era, an IT-based multi-disciplinary convergence project, called EDISON (EDucation-research Integrated Simulation On the Net), has been launched to support the studies of researchers engaged in several computational science and engineering (CSE) fields and to boost learning motivations of CSE students. Since 2011, we have been successfully carrying out the EDISON project. EDISON as a cyber-learning platform enables CSE researchers to share their own high-performance computing (HPC) simulation softwares developed to solve their research problems accompanying large-scale computation and I/O and users to run the softwares with little constraints on the web. Also, the EDISON platform has been utilized as lecture material by many universities in Korea. This article introduces the construction and service statistics of this EDISON platform. Specifically, we explicate several distinctions between EDISON and existing other HPC service platforms and discuss a three-layered technical architecture of the EDISON platform. We then present the up-to-date service statistics of EDISON over the past four years. Finally, we conclude this article and describe future plans.