• International Journal of Aeronautical and Space Sciences
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• v.18 no.1
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• pp.48-55
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• 2017

Aeroacoustic computation of a fully-developed turbulent pipe flow at $Re_{\tau}=175$ and M = 0.1 is conducted by LES/LPCE hybrid method. The generation and propagation of acoustic waves are computed by solving the linearized perturbed compressible equations (LPCE), with acoustic source DP(x,t)/Dt attained by the incompressible large eddy simulation (LES). The computed acoustic power spectral density is closely compared with the wall shear-stress dipole source of a turbulent channel flow at $Re_{\tau}=175$. A constant decaying rate of the acoustic power spectrum, $f^{-8/5}$ is found to be related to the turbulent bursts of the correlated longitudinal structures such as hairpin vortex and their merged structures (or hairpin packets). The power spectra of the streamwise velocity fluctuations across the turbulent boundary layer indicate that the most intensive noise at ${\omega}^+$ < 0.1 is produced in the buffer layer with fluctuations of the longitudinal structures ($k_zR$ < 1.5).

• Proceedings of the KIEE Conference
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• 2000.11a
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• pp.125-128
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• 2000

This paper presents an energy function which provides the direct relationships between the system stability and parameters of the exciter system. The energy function is derived from the energy conservation law by using the first motion integral. he time derivative does not absolutely satisfy the seminegativeness. However dE/dt usually has big negative value just after the fault clearing so that the energy is rapidly decreased. In this situation, the system state can be obviously confined in a stable region if the intial energy is less than the UEP energy. With these observation, two theorems are developed regarding the state confinement and asymptotic stability. Based on two theorems a new approximated direct energy method is developed to analyze the transient stability with the consideration of the exciter control effects. The proposed method has been tested for a single-machine-infinite bus system.

• Journal of Korean society of Dental Hygiene
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• v.6 no.4
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• pp.361-374
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• 2006

The purpose of this study was to obtain basic data for development of oral health educational program on the control of handicapped children at social welfare facilities. For this research, it was investigated by a survey on the actual condition of dental health of handicapped children, and simultaneously by analyzing the relationships between the realities of child's dental condition and parents and guardian's acknowledgments and managements with regard to the dental health of children. This survey was conducted 135 children and their guardians being 4 social welfare facilities in Ulsan metropolitan city. 1. The average of DT, MT, FT and DMFT index were 1.82, 0.01, 0.98 and 2.84 respectively. 2. Rolling Toothbrushing method was the highest response(58.5%) and 3 times per a day(77.0%). A proxy of toothbrushing was parents(39.2%) and teacher(60.8%). 3. Recognition routes of toothbrushing method were family(13.3%), school(43.7%) and dental chinic(42.2%). 4. The numer of times electromotion tooth brushing was the highest response in more than 4 times per a day. Toothbrushing after eating between meals was higher negative response(50.4%). The number of times visiting dental clinic was the highest response in more than 5 times during a year(51.9%).

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.127-136
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• 2021

Real-time human recognition is a challenging task, as the images are captured in an unconstrained environment with different poses, makeups, and styles. This limitation is addressed by generating several facial images with poses, makeup, and styles with a single reference image of a person using Generative Adversarial Networks (GAN). In this paper, we propose deep learning-based human recognition using integration of GAN and Spatial Domain Techniques. A novel concept of human recognition based on face depiction approach by generating several dissimilar face images from single reference face image using Domain Transfer Generative Adversarial Networks (DT-GAN) combined with feature extraction techniques such as Local Binary Pattern (LBP) and Histogram is deliberated. The Euclidean Distance (ED) is used in the matching section for comparison of features to test the performance of the method. A database of millions of people with a single reference face image per person, instead of multiple reference face images, is created and saved on the centralized server, which helps to reduce memory load on the centralized server. It is noticed that the recognition accuracy is 100% for smaller size datasets and a little less accuracy for larger size datasets and also, results are compared with present methods to show the superiority of proposed method.

• Steel and Composite Structures
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• v.46 no.6
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• pp.731-744
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• 2023

Vehicle load information is an important role in operating and ensuring the structural health of cable-stayed bridges. In this regard, an efficient and economic method is proposed for vehicle load detection based on the observed cable tension and vehicle position using a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), a robust program for modeling and considering both geometric and material nonlinearities of bridge structures subjected to vehicle load with low computational costs. With the superiority of GNN, the proposed model is demonstrated to precisely capture complex nonlinear correlations between the input features and vehicle load in the output. Four popular machine learning methods including artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machines (SVM) are refereed in a comparison. A case study of a cable-stayed bridge with the typical truck is considered to evaluate the model's performance. The results demonstrate that the GNN-based model provides high accuracy and efficiency in prediction with satisfactory correlation coefficients, efficient determination values, and very small errors; and is a novel approach for vehicle load detection with the input data of the existing monitoring system.

• Geomechanics and Engineering
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• v.31 no.3
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• pp.265-279
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• 2022

Tunnel convergence prediction is essential for the safe construction and design of tunnels. This study proposes five machine learning models of deep neural network (DNN), K-nearest neighbors (KNN), Gaussian process regression (GPR), support vector regression (SVR), and decision trees (DT) to predict the convergence phenomenon during or shortly after the excavation of tunnels. In this respect, a database including 650 datasets (440 for training, 110 for validation, and 100 for test) was gathered from the previously constructed tunnels. In the database, 12 effective parameters on the tunnel convergence and a target of tunnel wall convergence were considered. Both 5-fold and hold-out cross validation methods were used to analyze the predicted outcomes in the ML models. Finally, the DNN method was proposed as the most robust model. Also, to assess each parameter's contribution to the prediction problem, the backward selection method was used. The results showed that the highest and lowest impact parameters for tunnel convergence are tunnel depth and tunnel width, respectively.

• Journal of the korean academy of Pediatric Dentistry
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• v.43 no.4
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• pp.382-390
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• 2016

Since it is favorable to include the incipient caries lesion in the diagnostic criteria in adolescence, this study had surveyed dental caries status of adolescents at ages of 13 and 16 by using WHO basic methods and ICDAS-II codes. In this study, mean DMFT index was 3.71, and mean DT index was 1.94. For both indices, the age 16 group showed higher values than the age 13 group. By groups of teeth, DMFT index and DT index exhibited highest to the lowest values in molar, premolar, and anterior teeth, respectively. 77.46% of total numbers of teeth were classified as code 0 in ICDAS-II. Compared to anterior teeth, numbers of decayed teeth were increased in posterior teeth. All caries lesions in anterior teeth and premolars were limited to enamel. ICDAS-II code is an useful method to detect the incipient caries lesion, allowing preventive control on caries management.

• Applied Chemistry for Engineering
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• v.9 no.2
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• pp.255-260
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• 1998

Metal hydrides, $LaNi_5$ and $LaNi_{4.5}Al_{0.5}$, were prepared using chemical synthetic method, and their physical properties were examined using various analytic techniques such as TGA, XRD, SEM and EDX. The activation of the chemically prepared $LaNi_5$ and $LaNi_{4.5}Al_{0.5}$ was achieved by two hydriding/dehydriding cycles only. The miasurements of P-C-T curves revealed that 6 and 5.5 hydrogen atoms were stored in LaNi5and $LaNi_{4.5}Al_{0.5}$, respectively. The hydriding reaction rated for $LaNi_{4.5}Al_{0.5}$ were measured by the method of initial rates. It was found that the shrinking unreacted core model could be applied for the analysis of hydriding kinetics of $LaNi_5$. The rate controlling step of this reaction was the dissociative chemisorption of hydrogen molecules on the surface of $LaNi_5$. The activation energy was $9.506kcal/mol-H_2$. The rates measured in the temperature range from 273 to 343K and in pressure difference ($P_o-P_{eq}$) range form 0.25 to 0.66atm could be expressed as the following equation ; $\frac{dX}{dt}=4.636(P_o-P_{eq})$ exp($\frac{-9506}{RT}$).

• KOREAN JOURNAL OF CROP SCIENCE
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• v.55 no.4
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• pp.284-291
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• 2010

A simulation modeling for predicting the harvesting date with high potato solids consists of development of mathematical models. The mathematical model on potato growth and its development should be obtained by using agricultural elements which analyze relations of solar radiation quantity, temperature, photon quantity, carbon dioxide exchange rate, water stress and loss, relative humidity, light intensity, and wind etc. But more reliable way to predict harvesting date against climatic change employs in vivo energy consumption for growth and induction shape in a slight environmental adaptation. Therefore, to calculate in vivo energy loss, we take a concept of estimate of the amount of basal metabolism in each tuber on the basis of $Wm={\int}^m_tf(x)dt$ and $Tp=\frac{Tm{\cdot}Wm^{Tp}}{Wm^{Tm}}$. In the validation experiments, results of measuring solid accumulation of potato harvested at simulated date agreed fairly well with the actual measured values in each regional field during the growth period of 2005-2009. The calculation method could be used to predict an appropriate harvesting date for a production of high potato solids according to weather conditions.

• Journal of Korea Multimedia Society
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• v.11 no.4
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• pp.423-433
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• 2008

The surface curvatures extracted from the face contain the most important personal facial information. In particular, the face shape using the depth information represents personal features in detail. In this paper, we develop a method for recognizing the range face images by combining the multiple face regions using fuzzy integral. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area and has to take into consideration of the orientated frontal posture to normalize. Multiple areas are extracted by the depth threshold values from reference point, nose tip. And then, we calculate the curvature features: principal curvature, gaussian curvature, and mean curvature for each region. The second step of approach concerns the application of eigenface and Linear Discriminant Analysis(LDA) method to reduce the dimension and classify. In the last step, the aggregation of the individual classifiers using the fuzzy integral is explained for each region. In the experimental results, using the depth threshold value 40 (DT40) show the highest recognition rate among the regions, and the maximum curvature achieves 98% recognition rate, incase of fuzzy integral.