• Journal of Astronomy and Space Sciences
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• v.31 no.4
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• pp.317-323
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• 2014

Odyssey, one of the NASA's Mars exploration program and SELENE (Kaguya), a Japanese lunar orbiting spacecraft have a payload of Gamma-Ray Spectrometer (GRS) for analyzing radioactive chemical elements of the atmosphere and the surface. In these days, gamma-ray spectroscopy with a High-Purity Germanium (HPGe) detector has been widely used for the activity measurements of natural radionuclides contained in the soil of the Earth. The energy spectra obtained by the HPGe detectors have been generally analyzed by means of the Window Analysis (WA) method. In this method, activity concentrations are determined by using the net counts of energy window around individual peaks. Meanwhile, an alternative method, the so-called Full Spectrum Analysis (FSA) method uses count numbers not only from full-absorption peaks but from the contributions of Compton scattering due to gamma-rays. Consequently, while it takes a substantial time to obtain a statistically significant result in the WA method, the FSA method requires a much shorter time to reach the same level of the statistical significance. This study shows the validation results of FSA method. We have compared the concentration of radioactivity of $^{40}K$, $^{232}Th$ and $^{238}U$ in the soil measured by the WA method and the FSA method, respectively. The gamma-ray spectrum of reference materials (RGU and RGTh, KCl) and soil samples were measured by the 120% HPGe detector with cosmic muon veto detector. According to the comparison result of activity concentrations between the FSA and the WA, we could conclude that FSA method is validated against the WA method. This study implies that the FSA method can be used in a harsh measurement environment, such as the gamma-ray measurement in the Moon, in which the level of statistical significance is usually required in a much shorter data acquisition time than the WA method.

• Wind and Structures
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• v.21 no.6
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• pp.693-707
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• 2015

The efficiency of wind turbines (WT) is primarily reflected in their ability to generate electricity at any time. Downtimes of WTs due to "conventional" inspections are cost-intensive and undesirable for investors. For this reason, there is a need for structural health monitoring (SHM) systems, to enable service and maintenance on demand and to increase the inspection intervals. In general, monitoring increases the cost effectiveness of WTs. This publication concentrates on the application of two vibration-based SHM algorithms for stability and structural change monitoring of offshore WTs. Only data driven, output-only algorithms based on stochastic subspace identification (SSI) in time domain are considered. The centerpiece of this paper deals with the rough mathematical description of the dynamic behavior of offshore WTs and with the basic presentation of stochastic subspace-based algorithms and their application to these structures. Due to the early stage of the industrial application of SHM on offshore WT on the one side and the required confidentiality to the plant manufacturer and operator on the other side, up to now it is not possible to analyze different isolated structural damages resp. changes in a systematic manner, directly by means of in-situ measurement and to make these "acknowledgements" publicly available. For this reason, the sensitivity of the methods for monitoring purposes are demonstrated through their application on long time measurements from a 1:10 large scale test rig of an offshore WT under different conditions: undamaged, different levels of loosened bolt connections between tower parts, different levels of fouling, scouring and structure inclination. The limitation and further requirements for the approaches and their applicability on real foundations are discussed along the paper.

• Computers and Concrete
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• v.20 no.3
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• pp.319-327
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• 2017

Studying the structural behavior of prestressed segmented girders is quite important due to the large use this type of solution in viaducts and bridges. Thus, this work presents a nonlinear three-dimensional structural analysis of an externally prestressed segmented concrete girder through the Finite Element Method (FEM), using a customized ANSYS platform, version 14.5. Aiming the minimization of the computational effort by using the lowest number of finite elements, a new viscoelastoplastic material model has been implemented for the structural concrete with the UPF customization tool of ANSYS, adding new subroutines, written in FORTRAN programming language, to the main program. This model takes into consideration the cracking of concrete in its formulation, being based on fib Model Code 2010, which uses Ottosen rupture surface as the rupture criterion. By implementing this new material model, it was possible to use the three-dimensional 20-node quadratic element SOLID186 to model the concrete. Upon validation of the model, an externally prestressed segmented box concrete girder that was originally lab tested by Aparicio et al. (2002) has been computationally simulated. In the discretization of the structure, in addition to element SOLID186 for the concrete, unidimensional element LINK180 has been used to model the prestressing tendons, as well as contact elements CONTA174 and TARGE170 to simulate the dry joints along the segmented girder. Stresses in the concrete and in the prestressing tendons are assessed, as well as joint openings and load versus deflection diagrams. A comparison between numerical and experimental data is also presented, showing a good agreement.

• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.1
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• pp.13-24
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• 2014

This paper presents development of artificial neural network(ANN) based prediction method for section forces of TBM tunnel segment lining in an effort to develop an automatized design technique. A series of design cases were first developed and subsequently analyzed using the two-ring beam finite element model. The results were then used to form a database for use as training and validation data sets for ANN development. Using the database, optimized ANNs were developed that can readily be used to predict maximum sectional forces and their distributions. It is shown that the compute maximum section forces and their distributions by the developed ANNs are almost identical to the computed by the two-ring beam finite element model, implying that the developed ANNs can be used as design tools which expedite routine design calculation process. The results of this study indicate that the neural network model can be effectively used as a reliable and simple predictive tool for the prediction of segment sectional forces for design.

• Women's Health Nursing
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• v.3 no.1
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• pp.82-92
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• 1997

The purpose of this study was attempted to understand the substance and meaning of menopause experienced by women through informal interviews with oral consent. The informants were 6 perimenopausal women of 50-55 years old who are executing menopause. Colaizz's analytical method, a type of phenomenological analysis, was used to analyze data recorded by audiotape. One professor and a master's degree student who understand phenomenology, and the one who has a master of arts examined the validity between the meanings composed of the clusters of themes. Findings were turned out to be valid through validation process as the last step. The meaning of menopause implied both 'concept about menopause' and 'menopause as a time of change'. Menopause was mostly considered as cessation of menstruation as a physiological, natural, and normal process by aging. However, some people regarded menopause as a loss of youth and womanhood and lessening of every function of the body. Menopause as the time of changes means 'the period of' 'hormonal changes' such as change of menstruation, hot flushes, perspiration, and palpitation ; 'body function changes' of visual acuity, physical strength, sleeping, digestion, thoughts, bone and joints, skin sensibility, sexual pattern and intelligence ; 'emotional changes' such as anxiety, loneliness, gloominess, and nervousness. Menopause is a turning point on the women's life cycle accompanying various kind of changes and health problems. Therefore it is inevitable to develop strategy helping menopausal women pass through the critical successfully by adapting and coping with their critical period toward the healthy and better quality of life individually rather than putting them all into the standardized hormonal replacement protocol.

• Steel and Composite Structures
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• v.35 no.6
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• pp.789-798
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• 2020

The effect of Chitosan (CS), Carbon Nanotube (CNT) and hybrid (CS-CNT) fillers on the natural frequency of drilled composite plate is investigated by experimentally in this study. The numerical validation is also made with a program based on Finite Element Method (SolidWorks). Nine types filled and one neat composite plates are used in the study. The fillers ratios are 1% CS, 2% CS, 3% CS, 0.1% CNT, 0.2% CNT, 0.3% CNT, 1% CS+0.3% CNT, 2% CS+0.3% CNT, 3% CS+0.3% CNT. The specimens cut to certain sizes by water jet from the plates 400 mm × 400 mm in dimensions. Some of them are drilled in certain dimensions with drill. The natural frequency of each specimen is measured by the vibration test set up to determine the vibration characteristic. The vibration test set up includes an accelerometer, a current source power unit, a data acquisition card and a computer. A code is written in Matlab^® program for the signal processing. The study are investigated and discussed in four main points to understand the effect of the fillers on the natural frequency of the composite plate. These are the effect of fillers contents and amounts, orientation angles of fibers, holes numbers and holes sizes. As results, the natural frequency of the plate with 1% CS and 0.1% CNT hybrid filler is lower than those of the plates with other fillers ratios for 45° orientation angle. Besides, in the composite plate with 0° orientation angle, the natural frequency increases with increasing the filler ratio. Moreover, the natural frequency increases until a certain hole number and then it decreases. Furthermore, the natural frequency is not affected until a certain hole diameter but then it decreases.

• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.6
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• pp.111-119
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• 2011

The main objective of this study was to evaluate the effects of control-release fertilizer (CRF) on pollutant loadings from a small watershed. The Baran watershed, 386 ha in size, was selected as the study site, and the AGNPS (Agricultural Non-point Source Pollution) model was used to evaluate the effects of fertilizer types. Digital maps of digital elevation (DEM), slope distribution, channel, flow direction, landuse, soil, and curve number were extracted from the study watershed. Model parameters related to hydrology and water quality were calibrated and validated by comparing model predictions with the observed data collected for 2 years (1999 to 2000). Calibration and validation resulted in $R^2$ values of 0.75-0.91 for all the water quality parameters. All the paddy fields (21.2 %) of the study watershed were sprayed by either CRF or NPK (standard fertilizer). In CRF application, total nitrogen (TN) load was 4.9% less than NPK application, however total phosphorus (TP) load was 0.7 % more than NPK application. In CRF application, considering only paddy fields in the study area, TN load was 38.7 % less than NPK application. Using CRF in paddy fields could be one of the ways to reduce pollutant loadings from agricultural watersheds, however, in order to confirm it, more researches about effects of using CRF are necessary.

• Journal of the Korean Society of Systems Engineering
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• v.12 no.1
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• pp.59-71
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• 2016

The Nuclear and Fossil Steam Turbines record a considerable number of failures annually. Some of these failures reported are as result of blade failure. The failure of the L-0 blade in a Steam Turbine is one of the most reported blade failure in Nuclear and Fossil steam turbines. This paper seeks to identify the best Non Destructive Evaluation (NDE) method or methods to be used in the steam turbine L-0 blades inspection process. The development of systems engineering processes presents an opportunity to apply NDE inspection to the L-0 blades. This process apply computer modelling of the L-0 using ANSYS and by simulating the stresses experienced by the L-0 blade during operation it is possible to identify the most susceptible areas for crack formation and growth. The results from these models compared to industry data for validation. The analysis of these results used to predict the most probable failure location and failure modes. Therefore NDE inspection can be applied to these areas with greater degree of accuracy. This would be beneficial in the increasing the accuracy in the detection of cracks and hence save inspection time and the overall inspection cost. Furthermore, not only the location for crack formation and NDE inspection determined but also best the NDE inspection technique/techniques to be applied appropriately on the L-0 blade are prescribed.

• Journal of Distribution Science
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• v.17 no.11
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• pp.93-101
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• 2019

Purpose: Blockchain technology suggests ways to solve the problems in the existing industry. Among them, Cryptocurrency system, which is an element of Blockchain technology, is a very important factor for operating Blockchain. While Blockchain cryptocurrency has attracted attention, studies on cryptocurrency prices have been mainly conducted, however previous studies mainly conducted on Bitcoin prices. On the other hand, in the context of the creation and trading of various cryptocurrencies based on the Blockchain system, little research has been done on cryptocurrencies other than Bitcoin. Hence, this study attempts to find variables related to the prices of Dash, Litecoin, and Monero cryptocurrencies using machine learning techniques. We also attempt to find differences in the variables related to the prices for each cryptocurrencies and to examine machine learning techniques that can provide better performance. Research design, data, and methodology: This study performed Dash, Litecoin, and Monero price prediction analysis of cryptocurrency using Blockchain information and machine learning techniques. We employed number of transactions in Blockchain, amount of generated cryptocurrency, transaction fees, number of activity accounts in Blockchain, Block creation difficulty, block size, umber of created blocks as independent variables. This study tried to ensure the reliability of the analysis results through 10-fold cross validation. Blockchain information was hierarchically added for price prediction, and the analysis result was measured as RMSE and MAPE. Results: The analysis shows that the prices of Dash, Litecoin and Monero cryptocurrency are related to Blockchain information. Also, we found that different Blockchain information improves the analysis results for each cryptocurrency. In addition, this study found that the neural network machine learning technique provides better analysis results than support-vector machine in predicting cryptocurrency prices. Conclusion: This study concludes that the information of Blockchain should be considered for the prediction of the price of Dash, Litecoin, and Monero cryptocurrency. It also suggests that Blockchain information related to the price of cryptocurrency differs depending on the type of cryptocurrency. We suggest that future research on various types of cryptocurrencies is needed. The findings of this study can provide a theoretical basis for future cryptocurrency research in distribution management.

• The Journal of the Korea Contents Association
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• v.16 no.9
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• pp.160-169
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• 2016

Self-Compassion(SCS) Scale is developed by Neff(2003a) and translated by Kim, Lee, Cho, Chae, Lee(2008). But, there is the limitation with validation SCS and Korean version Self-Compassion(K-SCS) performed in college students and the incoherence for the results of the scale's factor analysis in other countries. Therefore, this study examined the validity of factor structure in SCS based on data in 435 adult aged from 18 to 79. For this, we conducted exploratory factor analysis(EFA) and confirmatory factor analysis(CFA), and we examined the each adequacy of two-factor, tree-factor and six-factor model. The result of EFA supported six-factor and the result of CFA was the six factor model best as well.