• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.45-55
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• 2022

The size of the cryptocurrency market is growing. For example, market capitalization of bitcoin exceeded 500 trillion won. Accordingly, many studies have been conducted to predict the price of cryptocurrency, and most of them have similar methodology of predicting stock prices. However, unlike stock price predictions, machine learning become best model in cryptocurrency price predictions, conceptually cryptocurrency has no passive income from ownership, and statistically, cryptocurrency has at least three times higher liquidity than stocks. Thats why we argue that a methodology different from stock price prediction should be applied to cryptocurrency price prediction studies. We propose Reverse Walk-forward Validation (RWFV), which modifies Walk-forward Validation (WFV). Unlike WFV, RWFV measures accuracy for Validation by pinning the Validation dataset directly in front of the Test dataset in time series, and gradually increasing the size of the Training dataset in front of it in time series. Train data were cut according to the size of the Train dataset with the highest accuracy among all measured Validation accuracy, and then combined with Validation data to measure the accuracy of the Test data. Logistic regression analysis and Support Vector Machine (SVM) were used as the analysis model, and various algorithms and parameters such as L1, L2, rbf, and poly were applied for the reliability of our proposed RWFV. As a result, it was confirmed that all analysis models showed improved accuracy compared to existing studies, and on average, the accuracy increased by 1.23%p. This is a significant improvement in accuracy, given that most of the accuracy of cryptocurrency price prediction remains between 50% and 60% through previous studies.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1447-1448
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• 2011

The CSPE cables are used for three years in nuclear power plant. The accelerated thermal aging of chloro sulfonate polyethylene(CSPE) jacket of test cables were carried out for the period equal to 10, 20 and 30 years in air at 90 and $100^{\circ}C$, respectively. The electrical volume resistivity, density, XPS, FE-SEM, EDS and XRF of the accelerated thermal aging of CSPE were measured. The validation of condition monitering method of accelerated thermal aging CSPE was estimated by them. The best validation of condition monitoring method of accelerated aging CSPE is electrical volume resistivity because change thermal of the specimen showed distinction.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.65 no.4
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• pp.242-249
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• 2016

The railway system is being converted to the computer system from the existing mechanical device, and the dependency on software is being increased rapidly. Though the size and degree of complexity of software for railway system are slower than the development speed of hardware, it is expected that the size will be grown bigger gradually and the degree of complexity will be increased also. Accordingly, the validation of reliability and safety of embedded software for railway system was started to become influential as the important issue. Accordingly, various software test and validation activities are highly recommended in the international standards related railway software. In this paper, we presented a software coding analysis tool using symbolic execution for railway system, and presented its result of implementation.

• Asia pacific journal of information systems
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• v.16 no.4
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• pp.217-238
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• 2006

In this study, we propose a virtual community recommendation model based on user behavioral models. It is designed to recommend optimal virtual communities for an active user by applying case-based reasoning (CBR) using behavioral factors suggested in the technology acceptance model (TAM) and its extensions. Also, it is designed to filter its case-base by considering the user's needs type before applying CBR. To test the usefulness of our model, we conduct two-step validation - experimental validation for the collected data, and survey validation for investigating the actual satisfaction level. Experimental results show that our model presents effective recommendation results in an efficient way. In addition, they also show that the information on the user's needs type may generate opportunities for cross-selling other commercial items.

• Hwankyungkyoyuk
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• v.22 no.4
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• pp.40-52
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• 2009

The purpose of this study was to validate New Ecological Paradigm(NEP) Scale for use with elementary school students in Korea, and to obtain evidences of validation for interpreting the scores on the instrument. Three pilot tests were taken to modify and 1,003 fifth and sixth grade students in elementary schools participated in this survey. To examine the reliability and validity of the NEP Scale for elementary school students in Korea, item analysis(mena, skewness, kurtosis, item-total correlation), internal consistency estimate, exploratory factor analysis, confirmatory factor analysis, and t-test were used. After analyzing their results, the authors suggest that a 2-dimensional(pro-NEP, anti-NEP) modified NEP Scale, with 15 items and revised wording, is appropriate for use with elementary school students in Korea.

• Progress in Medical Physics
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• v.27 no.4
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• pp.267-271
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• 2016

In this study, we present the commissioning and validation results of a dedicated scanning nozzle. The dedicated scanning nozzle is installed in one of the two gantry treatment rooms at Samsung Proton Therapy Center. Following a successful completion of the acceptance test, the commissioning process including the beam data measurement for treatment planning system has been conducted. Extended measurements have been conducted as a validation of the clinical performance of the nozzle and various quality assurance protocols have been prepared.

• Structural Monitoring and Maintenance
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• v.2 no.4
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• pp.383-397
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• 2015

This article identifies and addresses current limitations on the use of numerical models for validation and/or calibration of damage detection methodologies that are based on the analysis of the high frequency response of the structure to identify the occurrence of abrupt anomalies. Distributed-plasticity non-linear fiber-based models in combination with experimental data from a full-scale reinforced concrete column test are used to point out current modeling techniques limitations. It was found that the numerical model was capable of reproducing the global and local response of the structure at a wide range of inelastic demands, including the occurrences of rebar ruptures. However, when abrupt sudden damage occurs, like rebar fracture, a high frequency pulse is detected in the accelerations recorded in the structure that the numerical model is incapable of reproducing. Since the occurrence of such pulse is fundamental on the detection of damage, it is proposed to add this effect to the simulated response before it is used for validation purposes.

• Computers and Concrete
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• v.24 no.4
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• pp.295-302
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• 2019

Evolutionary algorithms based on conventional statistical methods such as regression and classification have been widely used in data mining applications. This work involves application of gene expression programming (GEP) for predicting compressive strength of fly ash geopolymer concrete, which is gaining increasing interest as an environmentally friendly alternative of Portland cement concrete. Based on 56 test results from the existing literature, a model was obtained relating the compressive strength of fly ash geopolymer concrete with the significantly influencing mix design parameters. The predictions of the model in training and validation were evaluated. The coefficient of determination ($R^2$), mean (${\mu}$) and standard deviation (${\sigma}$) were 0.89, 1.0 and 0.12 respectively, for the training set, and 0.89, 0.99 and 0.13 respectively, for the validation set. The error of prediction by the model was also evaluated and found to be very low. This indicates that the predictions of GEP model are in close agreement with the experimental results suggesting this as a promising method for compressive strength prediction of fly ash geopolymer concrete.

• Journal of Fisheries and Marine Sciences Education
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• v.23 no.1
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• pp.13-22
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• 2011

The purpose of this study was to development and validation of the 'course evaluation' scale for learner-centered in university. The research collected preliminary data from 1,567 university students's responses for item and scale quality analyses, and collected 2,539 university students's for item and scale quality analyses, and 300 university professors's responses for validation. Data were analyzed to obtain item quality, reliability, and validity analysis. The results of the study were as follows; The 'course evaluation' scale for learner-centered in university was defined by 5 factors. The 5 factors were structure and sincerity of lecture, suitability of report and test, level of consulting for student, application of educational media, communication. The results of the confirmatory factor analysis confirmed five sub-scales in the 'course evaluation' scale for learner-centered in university scale. Criterion-related validity evidence was obtained from the correlation analysis as the criterion measures. Cross validity evidence was obtained from the confirmatory factor analysis in university professors.

• Bulletin of the Korean Chemical Society
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• v.32 no.7
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• pp.2397-2404
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• 2011

Variable selection k nearest neighbor QSAR modeling approach was applied to a data set of 80 3-arylisoquinolines exhibiting cytotoxicity against human lung tumor cell line (A-549). All compounds were characterized with molecular topology descriptors calculated with the MolconnZ program. Seven compounds were randomly selected from the original dataset and used as an external validation set. The remaining subset of 73 compounds was divided into multiple training (56 to 61 compounds) and test (17 to 12 compounds) sets using a chemical diversity sampling method developed in this group. Highly predictive models characterized by the leave-one out cross-validated $R^2$ ($q^2$) values greater than 0.8 for the training sets and $R^2$ values greater than 0.7 for the test sets have been obtained. The robustness of models was confirmed by the Y-randomization test: all models built using training sets with randomly shuffled activities were characterized by low $q^2{\leq}0.26$ and $R^2{\leq}0.22$ for training and test sets, respectively. Twelve best models (with the highest values of both $q^2$ and $R^2$) predicted the activities of the external validation set of seven compounds with $R^2$ ranging from 0.71 to 0.93.