• 한국해양공학회지
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• 제21권1호
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• pp.64-68
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• 2007

Recently, there has been considerable research in the field of application of Laser-Arc hybrid welding for superstructures, such as ship-structures, transport vehicles etc. However, the study on heat distribution and welding residual stress of hybrid weld by numerical simulation leaves much to be desired. Therefore, in this study, an optimized welding condition and numerical simulation for hybrid welding, using previous numerical analysis to calculate the heat source for hybrid welding, has been analyzed. For this purpose, fundamental welding phenomena of the hybrid process, using Laser and, is investigated. In order to calculate temperature and residual stress distribution in hybrid welds, a finite element heat source model is developed on the basis of experimental results and characteristics of temperature. Residual stress distribution in hybrid welds are understood from the result of simulation, and compared with the experimental values.

• 한국자동차공학회논문집
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• 제12권1호
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• pp.145-152
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• 2004

This paper compares seat vibrations of a small passenger car and a SUV. The results also include the comparison of the human body accelerations and the ride values, such as the component ride values, and SEAT values of 12 axis accelerations obtained at the human body and seat track. The ride comfort evaluation is usually carried out by experiments of real cars which are expensive and sometimes may contain errors by passenger's postures. Simulations by computer, on the other hand, enable to solve these problems when the accuracy is proven. This paper, thus, also shows the correlation of human body vibration between experiments and computer simulations. For the computer simulation, korean dummy models are developed from the Hybrid III models by scaling the body data of Hybrid III to those of Korean men and women. From the comparison between the test data and simulation data, a nice correlation in trends was shown.

• Journal of Information Processing Systems
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• 제20권2호
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• pp.159-172
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• 2024

The number of healthcare products available for pets has increased in recent times, which has prompted active research into wearable devices for pets. However, the data collected through such devices are limited by outliers and missing values owing to the anomalous and irregular characteristics of pets. Hence, we propose pet behavior recognition based on a hybrid one-dimensional convolutional neural network (CNN) and long short- term memory (LSTM) model using pet wearable devices. An Arduino-based pet wearable device was first fabricated to collect data for behavior recognition, where gyroscope and accelerometer values were collected using the device. Then, data augmentation was performed after replacing any missing values and outliers via preprocessing. At this time, the behaviors were classified into five types. To prevent bias from specific actions in the data augmentation, the number of datasets was compared and balanced, and CNN-LSTM-based deep learning was performed. The five subdivided behaviors and overall performance were then evaluated, and the overall accuracy of behavior recognition was found to be about 88.76%.

• Geomechanics and Engineering
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• 제20권3호
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• pp.191-205
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• 2020

Soil shear strength parameters play a remarkable role in designing geotechnical structures such as retaining wall and dam. This study puts an effort to propose two accurate and practical predictive models of soil shear strength parameters via hybrid artificial neural network (ANN)-based models namely genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. To reach the aim of this study, a series of consolidated undrained Triaxial tests were conducted to survey inherent strength increase due to addition of polypropylene fibers to sandy soil. Fiber material with different lengths and percentages were considered to be mixed with sandy soil to evaluate cohesion (as one of shear strength parameter) values. The obtained results from laboratory tests showed that fiber percentage, fiber length, deviator stress and pore water pressure have a significant impact on cohesion values and due to that, these parameters were selected as model inputs. Many GA-ANN and PSO-ANN models were constructed based on the most effective parameters of these models. Based on the simulation results and the computed indices' values, it is observed that the developed GA-ANN model with training and testing coefficient of determination values of 0.957 and 0.950, respectively, performs better than the proposed PSO-ANN model giving coefficient of determination values of 0.938 and 0.943 for training and testing sets, respectively. Therefore, GA-ANN can provide a new applicable model to effectively predict cohesion of fiber-reinforced sandy soil.

• Restorative Dentistry and Endodontics
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• 제45권2호
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• pp.13.1-13.10
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• 2020

Objective: The aim of this study was to evaluate the influence of different modeling agents on the surface microhardness (Vickers hardness number; VHN), roughness (Ra), and color change (ΔE) of a nano-hybrid composite with or without exposure to discoloration by coffee. Materials and Methods: Sixty-four cylinder-shaped nano-hybrid composite specimens were prepared using a Teflon mold. The specimens' surfaces were prepared according to the following groups: group 1, no modeling agent; group 2, Modeling Liquid; group 3, a universal adhesive (G-Premio Bond); and group 4, the first step of a 2-step self-adhesive system (OptiBond XTR). Specimens were randomly allocated into 2 groups (n = 8) according to the storage medium (distilled water or coffee). VHN, Ra, and ΔE were measured at 24 hours, 1 week, and 6 weeks. The Kruskal-Wallis test followed by the Bonferroni correction for pairwise comparisons was used for statistical analysis (α = 0.05). Results: Storage time did not influence the VHN of the nano-hybrid composite in any group (p > 0.05). OptiBond XTR Primer application affected the VHN negatively in all investigated storage medium and time conditions (p < 0.05). Modeling Liquid application yielded improved Ra values for the specimens stored in coffee at each time point (p < 0.05). Modeling Liquid application was associated with the lowest ΔE values in all investigated storage medium and time conditions (p < 0.05). Conclusion: Different types of modeling agents could affect the surface properties and discoloration of nano-hybrid composites.

• Smart Structures and Systems
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• 제14권6호
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• pp.1105-1129
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• 2014

The unscented Kalman filter (UKF) has been developed for nonlinear model parametric identification, and it assumes that the model parameters are symmetrically distributed about their mean values without any constrains. However, the parameters in many applications are confined within certain ranges to make sense physically. In this paper, a constrained unscented Kalman filter (CUKF) algorithm is proposed to improve accuracy of numerical substructure modeling in hybrid testing. During hybrid testing, the numerical models of numerical substructures which are assumed identical to the physical substructures are updated online with the CUKF approach based on the measurement data from physical substructures. The CUKF method adopts sigma points (i.e., sample points) projecting strategy, with which the positions and weights of sigma points violating constraints are modified. The effectiveness of the proposed hybrid testing method is verified by pure numerical simulation and real-time as well as slower hybrid tests with nonlinear specimens. The results show that the new method has better accuracy compared to conventional hybrid testing with fixed numerical model and hybrid testing based on model updating with UKF.

• 한국해양공학회:학술대회논문집
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• 한국해양공학회 2006년 창립20주년기념 정기학술대회 및 국제워크샵
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• pp.193-196
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• 2006

Recently many research are going on in the field of application of Laser and Laser-Arc hybrid welding for superstructures such as ship-structures, transport vehicles etc. Therefore in this study an optimized welding condition and numerical simulation for hybrid welding by using previous numerical analysis which is used to calculate the heat source for Laser and Laser-Arc hybrid welding has been analyzed. For this purpose, fundamental welding phenomena of hybrid process(Laser+MIG) are determined based on the experiments. In order to calculate temperature and residual stress distribution in Laser and Laser-Arc hybrid welds, finite element heat source model is developed on the basis of experiment results and characteristics of temperature and residual stress distribution in Laser and Laser-Arc hybrid welds are understood from the result of simulation and found comparable to the experimental values.

• 대한기계학회논문집A
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• 제28권6호
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• pp.807-815
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• 2004

In order to evaluate the static stability of hybrid carbody structures of Korean Tilting Train eXpress(TTX) caused by degradation of composites under ground environments, T300/AD6005 graphite/epoxy composite specimens were exposed to accelerated environmental conditions including ultraviolet radiation, temperature and moisture fer 2000 hours. It was found that the stiffness and strength of composites after aging were lower than those of unexposed specimens, and decreased as the aging time increases. The values of the degraded properties were used in the static analysis to check the static stability of hybrid carbody structures caused by environmental degradation of composites. The results shown that the structural stability of hybrid carbody structures was affected by the degradation of composites after exposure to accelerated aging environments.

• Journal of applied mathematics & informatics
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• 제4권1호
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• pp.1-16
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• 1997

In this paper we study efficient parallel implementation for hybrid iterative methods BICGSTAB and BICGSTAB $(\ell)$ with ${Well}=2$ on the CRAY C90 and the efficiency of their parallel performance is evaluated. numerical experiments suggest that on the CRAY C90 a parallel inner product algorithm called PDOTB be used for the par-allelization of hybrid iterative methods containing sensitive values of inner products. Lastly it is shown that the number of iterations in which parallel hybrid iterative methods satisfy a certain convergence criterion depends on the number of processors to be used.

• 전자공학회논문지B
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• 제32B권12호
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• pp.1652-1662
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• 1995

In this paper, a hybrid neural network is proposed to improve the learning ability of a neural network. The union of the characteristics of a Self-Organizing Neural Network model and of multi-layer perceptron model using the backpropagation learning method gives us the advantage of reduction of the learning error and the learning time. In learning process, the proposed hybrid neural network reduces the number of nodes in hidden layers to reduce the calculation time. And this proposed neural network uses the fuzzy feedback values, when it updates the responding region of each node in the hidden layer. To show the effectiveness of this proposed hybrid neural network, the boolean function(XOR, 3Bit Parity) and the solution of inverse kinematics are used. Finally, this proposed hybrid neural network is applied to the visual tracking control of a PUMA560 robot, and the result data is presented.