• Journal of Institute of Control, Robotics and Systems
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• v.6 no.3
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• pp.273-283
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• 2000

In this paper we suggest an optimal design method of Fuzzy-Neural Networks(FNN) model for complex and nonlinear systems. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM(Hard C-Means) Clustering Algorithm to find initial parameters of the membership function. The parameters such as parameters of membership functions learning rates and momentum weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. According to selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity (distribution of I/O data we show that it is available and effective to design and optimal FNN model structure with a mutual balance and dependency between approximation and generalization abilities. This methodology sheds light on the role and impact of different parameters of the model on its performance (especially the mapping and predicting capabilities of the rule based computing). To evaluate the performance of the proposed model we use the time series data for gas furnace the data of sewage treatment process and traffic route choice process.

• Journal of Digital Contents Society
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• v.3 no.2
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• pp.255-264
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• 2002

Multimedia is now applied to various real worlds. In particular, the focus of CSCW(Computer Supported Cooperated Work) for multimedia education system has increased. DOORAE is a framework for supporting development of applications running on distributed multimedia environment and multimedia distance education system. EDA is a system is able to detect automatically a software error based on distributed multimedia. It has been designed and implemented for construction and experiment of effective DOORAE environment. It detects an error by polling periodically the process with relation to session. Conventional method detects an error by polling periodically all the process. This papaer explains a performance analysis of an error detection system running on distributed multimedia environment using the rule-based SES and DEVS modeling and simulation techniques. In DEVS, a system has a time base, inputs, states, outputs, and functions.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.246-251
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• 1990

A fuzzy control scheme has been proposed for a supercritical extraction process which has attracted much attention recently as a new separation technology. Based on the manual operation experience, three control pairs between manipulated and output variables are selected first and then seven membership functions are defined for control error and time rate of the error, respectively for each control pair, resulting in forty nine Fuzzy control rules. In addition to these, the membership functions are defined in two steps (coarse and fine) to enhance control performance. Fuzzy inference is performed using MAX-MTN composition rule and defuzzified control output is calculated based on center of gravity method. The prosed Fuzzy control scheme has been assessed through numerical simulation. As a result, the proposed scheme shows good control performance comparable with that by INA(inverse nyquist array) which usually requires complicated design procedure.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.191-196
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• 1998

A new Fuzzy Logic Controller(FLC) called a Gradient-Descent Based Self-Organizing Controller is presented. The Self-Organizing Controller(SOC) has two inputs such as error and change of error, and updates control rules with monitoring a performance measure. There are many works in the SOC which concentrate on the self-organizing ability in control rule base, but have a few research on the performance measure which is akin to sliding mode control. With this procedure, we can get a robust performance measure on the SOC. To verify the perfomance of proposed controller, we have performed for the cart-pole system which is one of the well-known benchmark problem in the control literature.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1205-1207
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• 1996

The computed-torque method (CTM) shows good trajectory tracking performance in controlling robot manipulator if there is no disturbance or modelling errors. But with the increase of a payload or the disturbance of a manipulator, the tracking errors become large. So there have been many researchs to reduce the tracking error. In this paper, we propose a new control algorithm based on the CTM that decreases a tracking error by generating new reference trajectory to the controller. In this algorithm we used a fuzzy system based on the rule bases. For the numerical simulation, we used a 2-link robot manipulator. To simulate the disturbance due to a modelling uncertainty, we added errors to each elements of the inertia matrix and the nonlinear terms and assumed a payload to the end-effector. In the simulations of several cases, our method showed better trajectory tracking performance compared with the CTM.

• The Journal of the Acoustical Society of Korea
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• v.27 no.5
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• pp.252-255
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• 2008

In this paper, we apply a discriminative weight training to a support vector machine (SVM) based gender identification. In our approach, the gender decision rule is expressed as the SVM of optimally weighted mel-frequency cepstral coefficients (MFCC) based on a minimum classification error (MCE) method which is different from the previous works in that different weights are assigned to each MFCC filter bank which is considered more realistic. According to the experimental results, the proposed approach is found to be effective for gender identification using SVM.

• The Journal of the Acoustical Society of Korea
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• v.28 no.5
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• pp.471-476
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• 2009

In this paper, we apply a discriminative weight training to a support vector machine (SVM) based speech/music classification for the selectable mode vocoder (SMV) of 3GPP2. In our approach, the speech/music decision rule is expressed as the SVM discriminant function by incorporating optimally weighted features of the SMV based on a minimum classification error (MCE) method which is different from the previous work in that different weights are assigned to each the feature of SMV. The performance of the proposed approach is evaluated under various conditions and yields better results compared with the conventional scheme in the SVM.

• ETRI Journal
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• v.37 no.3
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• pp.541-550
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• 2015

In this paper, we propose a classification-based approach for hybridizing statistical machine translation and rulebased machine translation. Both the training dataset used in the learning of our proposed classifier and our feature extraction method affect the hybridization quality. To create one such training dataset, a previous approach used auto-evaluation metrics to determine from a set of component machine translation (MT) systems which gave the more accurate translation (by a comparative method). Once this had been determined, the most accurate translation was then labelled in such a way so as to indicate the MT system from which it came. In this previous approach, when the metric evaluation scores were low, there existed a high level of uncertainty as to which of the component MT systems was actually producing the better translation. To relax such uncertainty or error in classification, we propose an alternative approach to such labeling; that is, a cut-off method. In our experiments, using the aforementioned cut-off method in our proposed classifier, we managed to achieve a translation accuracy of 81.5% - a 5.0% improvement over existing methods.

• Journal of KIISE
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• v.41 no.12
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• pp.1081-1089
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• 2014

Context-sensitive spelling-error correction methods are largely classified into rule-based methods and statistical data-based methods, the latter of which is often preferred in research. Statistical error correction methods consider context-sensitive spelling error problems as word-sense disambiguation problems. The method divides a vocabulary pair, for correction, which consists of a correction target vocabulary and a replacement candidate vocabulary, according to the context. The present paper proposes a method that integrates a word-phrase n-gram model into a conventional model in order to improve the performance of the probability model by using a correction vocabulary pair, which was a result of a previous study performed by this research team. The integrated model suggested in this paper includes a method used to interpolate the probability of a sentence calculated through each model and a method used to apply the models, when both methods are sequentially applied. Both aforementioned types of integrated models exhibit relatively high accuracy and reproducibility when compared to conventional models or to a model that uses only an n-gram.

• ETRI Journal
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• v.34 no.6
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• pp.900-910
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• 2012

In this paper, a novel trellis-coded spatial modulation (TCSM) design method is presented and analyzed. Inspired by the key idea of trellis-coded modulation (TCM), the detailed analysis is firstly provided on the unequal error protection performance of spatial modulation constellation. Subsequently, the Ungerboeck set partitioning rule is proposed and applied to develop a general method to design the novel TCSM schemes. Different from the conventional TCSM approaches, the novel one based on the Ungerboeck set partitioning rule has similar properties as the classic TCM, which has simple but effective code design criteria. Moreover, the novel designed schemes are robust and adaptive to the generalized Rician fading channels, which outperform the traditional TCSM ones. For examples, the novel 4-, 8-, and 16-state TCSM schemes are constructed by employing different transmit antennas and different modulation schemes in different channel conditions. Simulation results clearly demonstrate the advantages of the novel TCSM schemes over the conventional ones.