• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.1
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• pp.77-85
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• 2008

The objective of the study is to construct a sensor fusion system for tool-condition monitoring (TCM) that will lead to a more efficient and economical drill usage. Drill-wear monitoring has an important attribute in the automatic machining processes as it can help preventing the damage of tools and workpieces, and optimizing the drill usage. In this study, we present the architectures of a multi-layer feed-forward neural network with Levenberg-Marquardt training algorithm based on sensor fusion for the monitoring of drill-wear condition. The input features to the neural networks were extracted from AE, vibration and current signals using the wavelet packet transform (WPT) analysis. Training and testing were performed at a moderate range of cutting conditions in the dry drilling of steel plates. The results show good performance in drill- wear monitoring by the proposed method of sensor fusion and neural network analysis.

• Korean Journal of Computational Design and Engineering
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• v.8 no.4
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• pp.222-230
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• 2003

Artificial intelligence has been used in the configuration design stage of high-speed train. The traction system of a high-speed train is composed of transformers, motor blocks, and traction motors of which locations and number in the trainset should be determined in the early stage of the train conceptual design. Components of the traction system are heavy parts in the train, so it gives strong influence to the top speeds and overall train configuration of high-speed trains. Top speeds have been predicted using the neural network with the associated data of the traction system. The neural networks have been learned with data sets of many commercially operated high-speed trains, and the predicted results have been compared with the actual values. The configuration design of the train set of a high-speed train determines the basic specification of the train and layout of the traction system. The neural networks is a useful design tool when there is not sufficient data for the configuration design and we need to use the existing data of other train for the prediction of trainset in development.

• 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.

• Proceedings of the KSR Conference
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• 2007.05a
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• pp.1948-1953
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• 2007

Intelligent agent is to decide what customers need on the internet and offer them accurate information. In this paper, the system which can recommend the tourism items in terms of customer's needs is proposed by appling the intelligent agent to railway tourism system. Most of previous agents are focused on price. But, this study proposes the Railway tourism system which offers each customer the best suitable information based on quality of information and reputation. The customer's needs are analyzed through intelligent agent and the information which is suitable for customer's needs is obtained the Artificial Neural Network Model.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.585-594
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• 2007

This paper presents the methods which estimate the influence of ground subsidence and the stability of buildings by tunnel excavation in urban area. First, we study the behaviour of ground subsidence using neural network and numerical method. And we analyze the characteristic of both methods. Using the both methods, we evaluate the stability of buildings by subway tunnel excavation and we compare the results of the neural network and numerical analysis.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.991-996
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• 2004

The international competition in refrigerator markets has continuously required the research for sound quality of a refrigerator to improve the quality of a life. In this paper, A new method for evaluation of the sound quality of a refrigerator is developed based on human sensibility engineering by using ANN(Artificial neural network). Finally it is applied to evaluate the sound qualify of refrigerator on the production line.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2004.05a
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• pp.410-415
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• 2004

The oscillation of the fluid caused by external forces is call ed sloshing, which occurs in moving vehicles with contained liquid masses, such as trucks, railroad cars, aircraft, and liquid rocket. This sloshing effect could be a severe problem in vehicle stability and control. In this study, the optimization design technique for reduction of the sloshing using evolutionary method is suggested. Two evolutionary methods are employed, respectively the artificial neural network(ANN) and genetic algorithm. An artificial neural network is used for the analysis of sloshing and genetic algorithm is adopted as optimization algorithm. As a result of optimization design, the optimized size and location of the baffle is presented

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.03b
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• pp.99-102
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• 1999

In this study, to investigate the effect of process variables such as reduction in area, semi-die angle and the rectangular ratio to the corner filling which influences the dimensional accuracy of the final product in the drawing of the cluadrangle rod from a round bar, it has been simulated by three dimensional rigid-plastic finite element method. In order to reduce the number of simulation artificial neural network has been introduced. Also, through the experimental investigation, the present results have been implemented on the industrial product. In results, the main process variable is the combination of the semi-die angle in case of the irregular shaped drawing process and reduction in area in the event of regular shaped drawing process, respectively.

• Speech Sciences
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• v.15 no.2
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• pp.89-97
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• 2008

There is a number of clinical conditions that affect directly or indirectly the physical properties of the vocal folds and thereby the pressure waveforms of elicited sounds. If the relationships between the clinical conditions and the voice quality are sufficiently reliable, it should be possible to detect these diseases or disorders. The focus of this paper is to determine the set of features and their values that would characterize the speaker's state of vocal folds. To the extent that these features can capture the anatomical, physiological, and neurological aspects of the speaker they can be potentially used to mediate an unobtrusive approach to diagnosis. We will show a new approach to this problem supported with results obtained from two disordered voice corpora.

• 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.