• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1883-1887
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• 1991

Identification theory for linear discrete system has been presented by a great many reference, but research works for identification of continuous-time system are less than preceding identification. In fact, a great man), systems for engineering are continuous-time systems, hence, research for identification of continuous-time system has important meaning. This paper offers the following results: 1. Corresponding relations for the parameters of continuous-time model and discrete model may be shown, when single input-output system has general characteristic roots. 2. To do identification of single variable continuity linear system with stability constraints from samples of input-output data, it is necessary to use optimization with stability constraints. 3. Main results of this paper may be explained by a simple example.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1994.04a
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• pp.25-32
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• 1994

The pre-processor for finite to element structural analysis considering the user-friendly device is developed by using GUI. This can be used on WINDOWS' environment which is realized the multi-tasking and the concurrency by object-oriented paradigm. Data input can be done easily through menu, dialog box, automatic stepwise input and concurrent representation with the structural geometry on multiple windows. It in designed to control integratedly the pre-processing, execution and the post-processing of the finite element structural analysis program on multiple windows, and input data can be seen with result outputs at the same time. In addition, the object-oriented programming environment makes convenient revision and addition of the program components for expanding the scope of analysis and making better user environment.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.5
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• pp.86-93
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• 1997

With recent development of speech recognition technology and multimedia computer systems, more potential applications of voice will become a reality. In this paper, we implement speech interface on the windows95 environment for practical use fo multimedia computers with voice. Speech interface is made up of three modules, that is, speech input and detection module, speech recognition module, and application module. The speech input and etection module handles th elow-level audio service of win32 API to input speech data on real time. The recognition module processes the incoming speech data, and then recognizes the spoken command. DTW pattern matching method is used for speech recognition. The application module executes the voice command properly on PC. Each module of the speech interface is designed and examined on windows95 environments. Implemented speech interface and experimental results are explained and discussed.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.70-78
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• 1995

The identification of a fuzzy model using input-output data consists of two parts :Structure identification and parameter identification. In this paper an algorithm to identify those parameters and structures is suggested to solve the problems of the conventional methods. Given a set of input-output data, the consequent parameters are identified by the Hough transform and clustering method, each of which considers the linearity and continuity respectively. The gradient descent algorithm is used to fine-tune parameters of a fuzzy model. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation, where we only consider a single input and single output system.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.6
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• pp.59-67
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• 2013

This paper presents a neural net type process model for enhancing learning compensation function in hot strip finishing rolling mill. Adequate input and output variables of process model are chosen, the proposed model was designed as single layer neural net. Equivalent carbon content, strip thickness and rolling speed are suggested as input variables, and looper's manipulation variable is proposed as output variable. According to simulation result using process data to show the validity of the proposed process model, neural net type process model's outputs give almost similar data to process output under same input conditions.

• Journal of the Korean Data and Information Science Society
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• v.27 no.3
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• pp.827-833
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• 2016

In this paper, we propose a deep least squares support vector machine (LS-SVM) for regression problems, which consists of the input layer and the hidden layer. In the hidden layer, LS-SVMs are trained with the original input variables and the perturbed responses. For the final output, the main LS-SVM is trained with the outputs from LS-SVMs of the hidden layer as input variables and the original responses. In contrast to the multilayer neural network (MNN), LS-SVMs in the deep LS-SVM are trained to minimize the penalized objective function. Thus, the learning dynamics of the deep LS-SVM are entirely different from MNN in which all weights and biases are trained to minimize one final error function. When compared to MNN approaches, the deep LS-SVM does not make use of any combination weights, but trains all LS-SVMs in the architecture. Experimental results from real datasets illustrate that the deep LS-SVM significantly outperforms state of the art machine learning methods on regression problems.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.125-130
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• 2003

This Paper describes a Nonlinear adoptive noise canceller using Neural Network for Machine Tools Controller System. Back-Propagation Learning Algorithm based MLP (Multi Layer Perceptron)is used an adaptive filters. In this Paper. it assume that the noise of primary input in the adaptive noise canceller is not the same characteristic as that of the reference input. Experimental results show that the neural network base noise canceller outperforms the linear noise canceller. Especially to make noise cancel close to realtime, Primary Input is divided by Unit and each divided pan is processed for very short time than all the processed data are unified to whole data.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.24 no.3
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• pp.354-362
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• 2000

This paper identifies a discrete-time linear combustion model of Refuse Incineration Plant(RIP) which characterizes steam generation quantity, where the RIP is considered as a MIMO system with thirteen-inputs and one-output. The structure of RIP model is described as an ARX model which are analytically obtained from the combustion dynamics. Furthermore, using the Instrumental Variable(IV) identification algorithm, model structure and unknown parameters are identified from experimental input-output data sets, In result, it is shown that the identified ARX model well approximates the input-output combustion characteristics given by experimental data sets.

• Journal of Power System Engineering
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• v.3 no.2
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• pp.34-41
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• 1999

This paper identifies a linear combustion model of Refuse Incineration Plant(RIP) which characterizes its combustion dynamics, where the proposed model has thirteen-inputs and one-output. The structure of the RIP model is given as an ARX model which obtained from the theoretical analysis. And then, some unknown model parameters are decided from experimental input-output data sets, using system identification algorithm based on Instrumental Variables(IV) method. In result, it is shown that the proposed model well approximates the input-output combustion characteristics riven by experimental data sets.

• Journal of the Korean Society for Nondestructive Testing
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• v.25 no.6
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• pp.475-482
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• 2005

The iterative calculation by least square method was used to determine the source location of acoustic emission in rock, as so called "traditional method". The results were compared with source coordinates infered from the application of neural network system for new input data, as so called "new method". Input data of the neural network were based on the time differences of longitudinal waves arrived from acoustic emission events at each transducer, the variation of longitudinal velocities at each stress level, and the coordinates of transducer as in the traditional method. The momentum back propagation neural network system adopted to determine source location, which consists of three layers, and has twenty-seven input processing elements. Applicability of the new method were identified, since the results of source location by the application of two methods were similarly concordant.