• Smart Structures and Systems
• /
• v.15 no.3
• /
• pp.699-715
• /
• 2015

A modal parameter based damage sensitive feature (DSF) is defined to mimic the relative change in any diagonal element of the stiffness matrix of a model of a structure. The damage assessment is performed in a statistical pattern recognition framework using empirical complementary cumulative distribution functions (ECCDFs) of the DSFs extracted from measured operational vibration response data. Methods are discussed to perform probabilistic structural health assessment with respect to the following questions: (a) "Is there a change in the current state of the structure compared to the baseline state?", (b) "Does the change indicate a localized stiffness reduction or increase?", with the latter representing a situation of retrofitting operations, and (c) "What is the severity of the change in a probabilistic sense?". To identify a range of normal structural variations due to environmental and operational conditions, lower and upper bound ECCDFs are used to define the baseline structural state. Such an approach attempts to decouple "non-damage" related variations from damage induced changes, and account for the unknown environmental/operational conditions of the current state. The damage assessment procedure is discussed using numerical simulations of ambient vibration testing of a bridge deck system, as well as shake table experimental data from a 4-story steel frame.

• Journal of radiological science and technology
• /
• v.44 no.4
• /
• pp.315-325
• /
• 2021

The purpose of this study is to investigate the changes in lipid proton (LP) composition according to the induced obese fatty liver and to use it as basic data for treatment and diagnosis of fatty liver in the future. The phantom study was conducted to identify differences between STEAM and PRESS Pulse sequences in LP concentration. A high-fat diet (60%) was administered to eight Sprague-Dawley rats to induce obesity and fatty liver disease. Baseline magnetic resonance imaging /spectroscopy data were obtained prior to the introduction of high-fat diet, and data acquisition experiments were performed after eight weeks using procedures identical to those used for baseline studies. The six lipid proton metabolites were calculated using LCModel software. The correlation between the fat percentage and each LP, revealed that the methylene protons at 1.3 ppm showed the highest positive correlation. The α-methylene protons to carboxyl and diallylic protons showed negative correlation with fat percentage. The methylene proton showed the highest increase in the LP; however, it constituted only 71.86% of the total LP concentration. The methylene proton plays a leading role in fat accumulation in liver parenchyma.

• Wind and Structures
• /
• v.32 no.3
• /
• pp.205-217
• /
• 2021

Cold regions with high air density and wind speed attract wind energy producers across the globe exhibiting its potential for wind exploitation. However, exposure of wind turbine blades to such cold conditions bring about devastating impacts like aerodynamic degradation, production loss and blade failures etc. A series of wind tunnel tests were performed to investigate the effect of icing on the aerodynamic properties of wind turbine blades. A baseline clean wing configuration along with four different ice accretion geometries were considered in this study. Aerodynamic force coefficients were obtained from the surface pressure measurements made over the test model using MPS4264 Simultaneous pressure scanner. 3D printed Ice templates featuring different ice geometries based on Icing Research Tunnel data is utilized. Aerodynamic characteristics of both the clean wing configuration and Ice accreted geometries were analysed over a wide range of angles of attack (α) ranging from 0° to 24° with an increment of 3° for three different Reynolds number in the order of 105. Results show a decrease in aerodynamic characteristics of the iced aerofoil when compared against the baseline clean wing configuration. The key flow field features such as point of separation, reattachment and formation of Laminar Separation Bubble (LSB) for different icing geometries and its influence on the aerodynamic characteristics are addressed. Additionally, attempts were made to understand the influence of Reynolds number on the iced-aerofoil aerodynamics.

• Wind and Structures
• /
• v.32 no.3
• /
• pp.267-281
• /
• 2021

Bending-twisting coupling induced in big composite wind turbine blades is one of the passive control mechanisms which is exploited to mitigate loads incurred due to deformation of the blades. In the present study, flutter characteristics of bend-twist coupled blades, designed for load alleviation in wind turbine systems, are investigated by time-domain analysis. For this purpose, a baseline full GFRP blade, a bend-twist coupled full GFRP blade, and a hybrid GFRP and CFRP bend-twist coupled blade is designed for load reduction purpose for a 5 MW wind turbine model that is set up in the wind turbine multi-body dynamic code PHATAS. For the study of flutter characteristics of the blades, an over-speed analysis of the wind turbine system is performed without using any blade control and applying slowly increasing wind velocity. A detailed procedure of obtaining the flutter wind and rotational speeds from the time responses of the rotational speed of the rotor, flapwise and torsional deformation of the blade tip, and angle of attack and lift coefficient of the tip section of the blade is explained. Results show that flutter wind and rotational speeds of bend-twist coupled blades are lower than the flutter wind and rotational speeds of the baseline blade mainly due to the kinematic coupling between the bending and torsional deformation in bend-twist coupled blades.

• Journal of Korean Academy of Nursing
• /
• v.52 no.2
• /
• pp.214-227
• /
• 2022

Purpose: This study was aimed at investigating the incidence and risk factors of dyslipidemia in menopausal women using a Korean community-based longitudinal study. Methods: The subjects were 245 postmenopausal women without dyslipidemia who had participated in the Ansan-Ansung cohort study from 2001~2002 (baseline) to 2015~2016 (seventh follow-up visit). The dyslipidemia incidence was measured as incidence proportion (%) and incidence rate per 100 person-years. The predictors of developing dyslipidemia were analyzed with Cox's proportional hazard model. Results: The incidence of new dyslipidemia during the follow-up period was 78.4% (192 patients), and 11.9 per 100 person-years. Mean duration from menopause to developing dyslipidemia was 5.3 years in new dyslipidemia cases. The triglyceride/high density lipoprotein (TG/HDL-C) ratio at baseline (hazard ratio = 2.20; 95% confidence interval = 1.39~3.48) was independently associated with developing dyslipidemia. Conclusion: Dyslipidemia occurs frequently in postmenopausal women, principally within five years after menopause. Therefore, steps must be taken to prevent dyslipidemia immediately after menopause, particularly in women with a high TG/HDL-C ratio at the start of menopause.

• Culinary science and hospitality research
• /
• v.14 no.2
• /
• pp.170-180
• /
• 2008

The purpose of this study was to measure the causal relationships between past consumption, health belief, subjective, attitude, intention and purchase of organic foods. Total 326 copies of questionnaire were completed. The structural equation model was used to measure the causal effect among constructs. The results demonstrated that the confirmatory factor analysis model provided a good model fit. The proposed model yielded a significantly better fit to the data than the baseline model and the extended model. The effects of past consumption, health belief and subjective norm on attitude and intention were statistically significant. The effects of attitude on intention and behavior to purchase organic food were statistically significant. As expected, health belief and subjective had significant effects on behavior to purchase organic foods. Moreover, past consumption, health belief and subjective norm had indirect influences on intention through mediated variables. Based on the empirical results and findings, some suggestions are provided to the institutions concerned so as to facilitate this organic sector's on-going expansion in the food industry.

• International Journal of Computer Science & Network Security
• /
• v.23 no.9
• /
• pp.47-54
• /
• 2023

For Bengali music emotion classification, deep learning models, particularly CNN and RNN are frequently used. But previous researches had the flaws of low accuracy and overfitting problem. In this research, attention-based Conv1D and BiGRU model is designed for music emotion classification and comparative experimentation shows that the proposed model is classifying emotions more accurate. We have proposed a Conv1D and Bi-GRU with the attention-based model for emotion classification of our Bengali music dataset. The model integrates attention-based. Wav preprocessing makes use of MFCCs. To reduce the dimensionality of the feature space, contextual features were extracted from two Conv1D layers. In order to solve the overfitting problems, dropouts are utilized. Two bidirectional GRUs networks are used to update previous and future emotion representation of the output from the Conv1D layers. Two BiGRU layers are conntected to an attention mechanism to give various MFCC feature vectors more attention. Moreover, the attention mechanism has increased the accuracy of the proposed classification model. The vector is finally classified into four emotion classes: Angry, Happy, Relax, Sad; using a dense, fully connected layer with softmax activation. The proposed Conv1D+BiGRU+Attention model is efficient at classifying emotions in the Bengali music dataset than baseline methods. For our Bengali music dataset, the performance of our proposed model is 95%.

• Korean Journal of Radiology
• /
• v.23 no.8
• /
• pp.811-820
• /
• 2022

Objective: To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes. Materials and Methods: Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses. Results: Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825-0.910) in the training cohort and 0.890 (0.844-0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness. Conclusion: The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.40 no.2
• /
• pp.69-77
• /
• 2022

The 3D spatial model is an analysis framework for solving urban problems and is used in various fields such as urban planning, environment, land and housing management, and disaster simulation. The utilization of drones that can capture 3D images in a short time at a low cost is increasing for the construction of 3D spatial model. In terms of building a virtual city and utilizing simulation modules, high location accuracy of aerial survey and precision of 3D spatial model function as important factors, so a method to increase the accuracy has been proposed. This study analyzed location accuracy of aerial survey and precision of 3D spatial model by each condition of aerial survey for urban areas where buildings are densely located. We selected Daerim 2-dong, Yeongdeungpo-gu, Seoul as a target area and applied shooting angle, shooting altitude, and overlap rate as conditions for the aerial survey. In this study, we calculated the location accuracy of aerial survey by analyzing the difference between an actual survey value of CPs and a predicted value of 3D spatial Model. Also, We calculated the precision of 3D spatial Model by analyzing the difference between the position of Point cloud and the 3D spatial Model (3D Mesh). As a result of this study, the location accuracy tended to be high at a relatively high rate of overlap, but the higher the rate of overlap, the lower the precision of 3D spatial model and the higher the shooting angle, the higher precision. Also, there was no significant relationship with precision. In terms of baseline-height ratio, the precision tended to be improved as the baseline-height ratio increased.

• Smart Structures and Systems
• /
• v.15 no.4
• /
• pp.1139-1158
• /
• 2015

This paper proposes an efficient system identification method for modeling nonlinear behavior of civil structures. This method is developed by integrating three different methodologies: principal component analysis (PCA), artificial neural networks, and fuzzy logic theory, hence named PANFIS (PCA-based adaptive neuro-fuzzy inference system). To evaluate this model, a 3-story building equipped with a magnetorheological (MR) damper subjected to a variety of earthquakes is investigated. To train the input-output function of the PANFIS model, an artificial earthquake is generated that contains a variety of characteristics of recorded earthquakes. The trained model is also validated using the1940 El-Centro, Kobe, Northridge, and Hachinohe earthquakes. The adaptive neuro-fuzzy inference system (ANFIS) is used as a baseline. It is demonstrated from the training and validation processes that the proposed PANFIS model is effective in modeling complex behavior of the smart building. It is also shown that the proposed PANFIS produces similar performance with the benchmark ANFIS model with significant reduction of computational loads.