• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.212-217
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• 1993

In this paper, the hardening depth in Laser surface hardening process is estimated using a multilayered neural network. Input data of the neural network are surface temperature of five points, power and travelling speed of Laser beam. A FDM(finite difference method) is used for modeling the Laser surface hardening process. This model is used to obtain the network's training data sample and to evaluate the performance of the neural network estimator. The simulational results showed that the proposed scheme can be used to estimate the hardening depth on real time.

• The Journal of the Korea Contents Association
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• v.9 no.1
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• pp.348-358
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• 2009

Diffuse axonal injury(DAI) is a common form of traumatic brain injury and thought to be a major contributor to cognitive dysfunction. Physical activity has been shown to beneficial effects on physical health and influences in hippocampus which is an important location for memory and learning. The purpose of this study was to investigate the effect of motor training on motor performance and axonal regeneration in hippocampus through the immunoreactivity of GAP-43 after diffuse axonal injury in the rats. The experimental groups were applied motor training(beam-walking, rotarod, and Morris water maze) but control groups were not. The time performing the motor tasks and GAP-43 immunohistochemistry were used for the result of axonal recovery. There were meaningful differences between experimental groups and control groups on motor performance and GAP-43 immunohistochemistry. The control groups showed increasing tendency with the lapse of time, but experimental groups showed higher. Therefore, Motor training after DAI improve motor outcomes which are associated with dynamically altered immunoreactivity of GAP-43 in axonal injury regions, particularly hippocampus, and that is related with axonal regeneration.

• Journal of Korea Multimedia Society
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• v.13 no.10
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• pp.1547-1556
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• 2010

Until now, many Computer Aided Education(CAE) contents are developed for kids and children with disabilities The contents cover various types of training, including visual perception training, intelligence development training, and literature education areas. Major problems on those contents are those contents requires long training time on desktop machine, which deteriorates human activities. These problems also cause inaction syndrome for young kids and children with disabilities. Solving this problem, we require a human motion sensing contents on touch screen or touch board, which interacts with a trainees and enhancing activity, collaboration and immersiveness. We implement and develope an education contents interacts with a trainee using beam projector or screen and IR(Infra-Red) pens using wiimote controller sensing technology.

• Advances in aircraft and spacecraft science
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• v.7 no.4
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• pp.353-369
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• 2020

This paper presents the flutter analysis and optimum design of axially functionally graded box beam cantilever wing section by considering various geometric and material parameters. The coupled dynamic equations of the continuous model of wing system in terms of material and cross-sectional properties are formulated based on extended Hamilton's principle. By expressing the lift and pitching moment in terms of plunge and pitch displacements, the resultant two continuous equations are simplified using Galerkin's reduced order model. The flutter velocity is predicted from the solution of resultant damped eigenvalue problem. Parametric studies are conducted to know the effects of geometric factors such as taper ratio, thickness, sweep angle as well as material volume fractions and functional grading index on the flutter velocity. A generalized surrogate model is constructed by training the radial basis function network with the parametric data. The optimized material and geometric parameters of the section are predicted by solving the constrained optimal problem using firefly metaheuristics algorithm that employs the developed surrogate model for the function evaluations. The trapezoidal hollow box beam section design with axial functional grading concept is illustrated with combination of aluminium alloy and aluminium with silicon carbide particulates. A good improvement in flutter velocity is noticed by the optimization.

• International Journal of Concrete Structures and Materials
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• v.1 no.1
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• pp.63-73
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• 2007

Optimum multi-layered feed-forward neural network (NN) models using a resilient back-propagation algorithm and early stopping technique are built to predict the shear capacity of reinforced concrete deep and slender beams. The input layer neurons represent geometrical and material properties of reinforced concrete beams and the output layer produces the beam shear capacity. Training, validation and testing of the developed neural network have been achieved using 50%, 25%, and 25%, respectively, of a comprehensive database compiled from 631 deep and 549 slender beam specimens. The predictions obtained from the developed neural network models are in much better agreement with test results than those determined from shear provisions of different codes, such as KBCS, ACI 318-05, and EC2. The mean and standard deviation of the ratio between predicted using the neural network models and measured shear capacities are 1.02 and 0.18, respectively, for deep beams, and 1.04 and 0.17, respectively, for slender beams. In addition, the influence of different parameters on the shear capacity of reinforced concrete beams predicted by the developed neural network shows consistent agreement with those experimentally observed.

• Speech Sciences
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• v.8 no.4
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• pp.17-33
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• 2001

In this paper, we implement the large variable vocabulary speech recognition system, which is characterized by no additional pre-training process and no limitation of recognized word list. We have designed the system in order to achieve the high recognition rate using the decision tree based state tying algorithm and in order to reduce the processing time using the gaussian selection based variable flooring algorithm, the limitation algorithm of the number of nodes and ENNS algorithm. The gaussian selection based variable flooring algorithm shows that it can reduce the total processing time by more than half of the recognition time, but it brings about the reduction of recognition rate. In other words, there is a trade off between the recognition rate and the processing time. The limitation algorithm of the number of nodes shows the best performance when the number of gaussian mixtures is a three. Both of the off-line and on-line experiments show the same performance. In our experiments, there are some differences of the recognition rate and the average recognition time according to the distinction of genders, speakers, and the number of vocabulary.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.320-324
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• 2013

A crack identification method using an equivalent bending stiffness for cracked beam and committee of neural networks is presented. The equivalent bending stiffness is constructed based on an energy method for a straight thin-walled pipe, which has a through-the-thickness crack, subjected to bending. Several numerical analysis for a steel cantilever pipe using the equivalent bending stiffness are carried out to extract the natural frequencies and mode shapes of the cracked beam. The extracted modal properties are used in constructing a training patterns of a neural network. The input to the neural network consists of the modal properties and the output is composed of the crack location and size. Multiple neural networks are constructed and each individual network is trained independently with different initial synaptic weights. Then, the estimated crack locations and sizes from different neural networks are averaged. Experimental crack detection is carried out for 3 damage cases using the proposed method, and the identified crack locations and sizes agree reasonably well with the exact values.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.6
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• pp.190-194
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• 2006

This research investigates vehicle structure acceleration and vehicle deformation with RCAR crash test. To investigate vehicle damage characteristics in an individual case, it is possible to RCAR low speed crash test. In this study, two tests were conducted to evaluate difference between RCAR new condition and RCAR old condition. A two large vehicles were subjected to a frontal crash test at a speed of 15km/h with an offset of 40% $10^{\circ}$ angle barrier and flat barrier. The results of the 15km/h with an offset of 40% $10^{\circ}$ angle barrier revealed high acceleration value on the vehicle structure and high repair cost compared to the RCAR 15km/h with an offset of 40% flat barrier. So in order to improve damage characteristics in low speed crash of vehicle structure and body component of the monocoque type passenger vehicles, the end of front side member and front back beam should be designed with optimum level and to supply the end of front side member as a partial condition approx 300mm.

• Transactions of the Korean Society of Automotive Engineers
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• v.13 no.2
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• pp.108-113
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• 2005

The damageability and repairability of similar platform type vehicles could be very concerned with design optimization. In all the vehicles crash tested, small size passenger vehicles were weakness in aspect of damageability and repairability. The most critical area appears to be repair cost considering that parts cost is the largest portion of total repair cost segments. Besides repair cost, attaching method of front sidemember and subframe are placed special importance for impact energy absorption and damageability and repairability. So in order to improve damageability and repairability of vehicle structure and body component of the monocoque type passenger vehicles, the end of front side member and front back beam should be designed with optimum level and to supply the end of front side member as a partial condition approx 300mm. The effectiveness of design concept on the 40% offset frontal impact characteristics of the passenger vehicle structure is investigated and summarized.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.7
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• pp.711-719
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• 2010

Heliostat, as a concentrator reflecting the incident solar energy to the receiver located at the tower, is the most important system in the tower-type solar thermal power plant, since it determines the efficiency and performance of solar thermal plower plant. Thus, a good sun tracking ability as well as its good optical property are required. In this paper, we propose a method to compensate the heliostat sun tracking error. We first model the sun tracking error, which could be measured using BCS (Beam Characterization System), by multilayered neural network. Then the extended Kalman filter was employed to train the neural network. Finally the model is used to compensate the sun tracking errors. Simulated result shows that the method proposed in this paper improve the heliostat sun tracking performance dramatically. It also shows that the training of neural network by the extended Kalman filter provides faster convergence property, more accurate estimation and higher measurement noise rejection ability compared with the other training methods like gradient descent method.