• Journal of the Korean Society for Railway
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• v.6 no.2
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• pp.81-86
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• 2003

This study proposes the neural network simulator for the integrity evaluation of weld zone in railway rails. For these purposes, the ultrasonic signals for defects(crack) of weld zone in frames are acquired in the type of time series data and echo strength. The detection of the natural defects in railway truck is performed using the characteristics of echodynamic pattern in ultrasonic signal. And then their applications evaluated feature extraction based on the time-frequency-attractor domain(peak to peak, rise time, rise slope, fall time, fall slope, pulse duration, power spectrum, and bandwidth) and attractor characteristics (fractal dimension and attractor quadrant) etc. The constructed neural network simulator agrees fairly well with the measured results of test block(defect location, beam propagation distance, echo strength, etc). The Proposed neural network simulator in this study can be used for the integrity evaluation of weld zone in railway rails.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.03a
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• pp.107-111
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• 1998

This paper presents a now approach to the design of intelligent contorl system for track vehicle system using fuzzy logic based on neural network. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. Moreover, We develop a Windows 95 version dynamic simulator which can simulate a track vehicle model in 3D graphics space. It is proposed a learning controller consisting of two neural network-fuzzy based of independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The dynamic simulator for track vehicle is developed by Microsoft Visual C++. Graphic libraries, OpenGL, by Silicon Graphics, Inc. were utilized for 3D Graphics. The performance of the proposed controller is illustrated by simulation for trajectory tracking of track vehicle speed.

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.2
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• pp.132-140
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• 2002

A momentum backpropagation neural network is prepared to carry out real-time dynamics simulations of a passenger car. A full-car model of fifteen degrees of freedom was constructed for vehicle dynamics analysis. Human body dynamics analysis was performed for a male driver(50 percentile Korean adult) restrained by a three point seatbelt system. The trained data using the neural network were obtained using a dynamic solver, ADAMS . The neural network were formed based on the dynamics of the simulator. The optimized hidden layer was obtained by selecting the optimal number of hidden layers. The driving scenario including bump passing and lane changing has been used for the estimation of the proposed neural network. A comparison between the trained data and neural network outputs is found to be satisfactory to show the applicability of the suggested approach.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.5
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• pp.261-268
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• 2020

Genetic algorithms find the optimal solution by mimicking the evolution of natural organisms. In this study, the genetic algorithm was used to enable Pac-Man's reinforcement learning, and a simulator to observe the evolutionary process was implemented. The purpose of this paper is to reinforce the learning of the Pacman AI of the simulator, and utilize genetic algorithm and artificial neural network as the method. In particular, by building a low-power artificial neural network and applying it to a genetic algorithm, it was intended to increase the possibility of implementation in a low-power embedded system.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.5
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• pp.969-975
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• 2002

Visual effects are important cues for providing occupants with virtual reality in a vehicle simulator which imitates real driving. The viewpoint of an occupant is sensitively dependent upon the occupant's posture, therefore, the total human body motion must be considered in a graphic simulator. A real-time simulation is required for the dynamic analysis of complex human body motion. This study attempts to apply a neural network to the motion analysis in various driving situations. A full car of medium-sized vehicles was selected and modeled, and then analyzed using ADAMS in such driving conditions as bump-pass and lane-change for acquiring the accelerations of chassis of the vehicle model. A hybrid III 50%ile adult male dummy model was selected and modeled in an ellipsoid model. Multibody system analysis software, MADYMO, was used in the motion analysis of an occupant model in the seated position under the acceleration field of the vehicle model. Acceleration data of the head were collected as inputs to the viewpoint movement. Based on these data, a back-propagation neural network was composed to perform the real-time analysis of occupant motions under specified driving conditions and validated output of the composed neural network with MADYMO result in arbitrary driving scenario.

• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.349-352
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• 2005

The energy management of an HEV using optimal control and global optimization is thought to be closest to the best operation of the system. However, there are some controversies on the ways of defining the optimization problems and constituting the optimal control simulators. Here, we presented a simulator which adopts the concept of equivalent fuel economy and leads the vehicle to run in a more efficient way. In order to realize the optimal operation of the HEV and check the validity of the control logics, we also developed a forward-facing simulator. The simulator was developed with the Cruise and MATLAB co-simulation interface. Especially, neural network controller was used for the hybrid control module in the simulator. With the simulator, the optimal operation could be converted into hybrid control rules and the validity of the operation was verified.

• Journal of the Korea Society for Simulation
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• v.14 no.4
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• pp.55-68
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• 2005

The advancement in semiconductor technology is leading toward smaller critical dimension designs and larger wafer manufactures. Due to such phenomena, semiconductor industry is in need of an accurate control of the process. Photolithography is one of the key processes where the pattern of each layer is formed. In this process, precise superposition of the current layer to the previous layer is critical. Therefore overlay parameters of the semiconductor photolithography process is targeted for this research. The complex relationship among the input parameters and the output metrologies is difficult to understand and harder yet to model. Because of the superiority in modeling multi-nonlinear relationships, neural networks is used for the simulator modeling. For training the neural networks, conjugate gradient method is employed. An experiment is performed to evaluate the performance among the proposed neural network simulator, stepwise regression model, and the currently practiced prediction model from the test site.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.213-216
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• 2006

During the last decade several different methods have been proposed for the estimation of thermal deformations in the line heating process. These are mainly based on the assumption of residual strains in the heat-affected zone or simulated relations between heating conditions and residual deformations. However these results were restricted in the application from the too simplified heating conditions or the shortage of the data. The purpose of this paper is to develop a simulator of thermal deformation in the line heating using the artificial neural network. Two neural network predicting the maximum temperature and deformations at the heating line are studied. Deformation data from the line heating experiments are used for learning data for the network. It was observed that thermal deformation predicted by the neural network correlate well with the experimental result.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.370-375
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• 2006

This paper suggests the method for estimating a partial discharge (PD) location using divide of the inside transformer as a grid. The PD location is found swiftly and economically compared with the typical method detecting a PD. The reason is that the location of PD is detected in the section. The estimation of PD location is trained using the Neural Network. JavaNNS(Java Neural Network Simulator) and SNNS(Stuttgart Neural Network Simulator) are used for searching the location of PD. The simulation procedure is following, The transformer is assumed that the case is a regular hexahedron. The sensor is installed in a proper location. A section of PD location is set as a target, and training set is studied with several PD locations in the inside of the transformer. As a result of training process, the learning capability of neural network is excellent. The PD location is detected by division of internal structure of transformer and application of neural network.

• Proceedings of the KIPE Conference
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• 2007.07a
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• pp.394-396
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• 2007

In this paper proposed to neural network PI self-tuning direct controller using Error back propagation algorithm. Proposed controller applies to speed controller and current controller. Also, this built up the interface environment to drive it simply and exactly in any kind of reference, environment fluent and parameter transaction of PMSM. Neural network PI self-tuning simulator using Visual C++ and Matlab Simulation is organized to construct this environment, Built up interface has it's own purpose that even the user who don't have the accurate knowledge of Neural network can embody operation characteristic rapidly and easily.