• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.6
• /
• pp.838-842
• /
• 2007

In this paper, we define that a fuzzy convergence approach limit and a fuzzy approach Chuchy structure on X. And we investigate the relations between the category CAP and the category FCAP. And we show that the categories $FCAP_{RC}$ and $uFACHY_{cpl}$ are isomorphic.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.95-100
• /
• 1998

A notion of 'fuzzy' convergence of filters on a set is introduced. We show that the collection of fuzzy L-limit spaces forms a cartesian closed topological category and obtain an interesting relationship between the notions of 'fuzzy' convergence structure and convergence approach spaces.

• Journal of Korea Multimedia Society
• /
• v.17 no.11
• /
• pp.1354-1362
• /
• 2014

In order to solve the complicated problems faced by the spread of diversified information devices and changes in the user environment in recent years, the importance of training creative talent as well as the need for interdisciplinary convergent approach have emerged. As a future technology development project, convergence education with the design sector for training creative talent in the IT field is also urgent. In this study, we recognized the importance and problems of the convergent approach between IT and design sector by examining the convergence theory and cases with a focus on effective creativity development and sought a new direction for convergence education in the future by investigating the awareness on the convergent approach of college students and analyzing the results. This will be able to have significance as a basic study for discovering creative and innovative convergent talent in the IT and design sector in the future and strengthening basic competence.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.16 no.6
• /
• pp.307-312
• /
• 2021

This paper proposes an adaptive time-delayed control approach with the integral sliding-mode surface for the fast convergence rate of robot manipulators. Adaptive switching gain aims to guarantee the system stability in such a way as to suppress time-delayed estimation error in the proposed control approach. Moreover, it makes an effort to increase the convergence ability in reaching the phase. An integral sliding-mode surface is employed to achieve a fast convergence rate in the sliding phase. The stability of the proposed one is proved to be asymptotically stable in the Lyapunov stability. The efficiency of the proposed control approach is illustrated with a tutorial example in robot manipulator, which is compared to that of the existing control approach.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2021.11a
• /
• pp.1001-1004
• /
• 2021

Pain is an unpleasant experience for the patient. The recognition and assessment of pain help tailor the treatment to the patient, and they are also challenging in the medical. In this paper, we propose an approach for pain recognition through a deep neural network applied to pre-processed physiological. The proposed approach applies the idea of shortcut connections to concatenate the spatial information of a convolutional neural network and the temporal information of a recurrent neural network. In addition, our proposed approach applies the attention mechanism and achieves competitive performance on the BioVid Heat Pain dataset.

• Proceedings of the KIEE Conference
• /
• 2004.11c
• /
• pp.565-567
• /
• 2004

In active noise control (ANC) systems, the convergence behavior of the conventional Filtered-X Least Mean Square (FXLMS) algorithm may be affected by nonlinear distortions in the secondary path (e.g., in the power amplifiers, loudspeakers, transducers, etc.), which may lead to degradation of the error-reduction performance of the ANC systems. In this paper, a stable FXLMS algorithm with fast convergence is proposed to compensate for undesirable nonlinear distortions in the secondary-path of ANC systems by employing the Volterra filtering approach. In particular, the proposed approach is based on the utilization of the conventional P-th order inverse approach to nonlinearity compensation in the secondary path of ANC systems. Finally, the simulation results showed that the proposed approach yields a better convergence behavior In the nonlinear ANC systems than the conventional FXLMS.

• Journal of electromagnetic engineering and science
• /
• v.16 no.1
• /
• pp.7-12
• /
• 2016

This paper presents the design of a negative group delay circuit (NGDC) using the filter synthesis approach. The proposed design method is based on a frequency transformation from a low-pass filter (LPF) to a bandstop filter (BSF). The predefined negative group delay (NGD) can be obtained by inserting resistors into resonators. To implement a circuit with a distributed transmission line, a circuit conversion technique is employed. Both theoretical and experimental results are provided for validating of the proposed approach. For NGD bandwidth and magnitude flatness enhancements, two second-order NGDCs with slightly different center frequencies are cascaded. In the experiment, group delay of $5.9{\pm}0.5ns$ and insertion loss of $39.95{\pm}0.5dB$ are obtained in the frequency range of 1.935-2.001 GHz.

• The Journal of Asian Finance, Economics and Business
• /
• v.7 no.12
• /
• pp.593-604
• /
• 2020

This study analyzes human development convergence and the impact of funds transfer to the regions using σ and β-convergence analysis method. Observations were made in all Indonesia's provinces in the period 2010-2019. The coefficient of variation calculation shows a dispersion in the inequality of human development, which means that convergence occurred. This is also documented by the clustering analysis results developed in the study. The results are in line with the hypothesis of neoclassical theory, which shows the tendency for provinces with lower human development levels to grow relatively faster. The dynamic panel data approach with the GMM model shows that a model built with explanatory variables for transfer of funds to regions may lead to the process of convergence of human development - 2.21% per year or 31 years to cover the half-life of convergence. This is a consequence of the Special Allocation Fund and the Village Fund, which positively impact the convergence process, and the General Allocation Fund and the Revenue Sharing Fund with negative signs slowing the convergence process. This evidence opens opportunities to review the justification of the weighting component in determining the amount of funds transferred to the region to accelerate the convergence process of human development.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.31B no.7
• /
• pp.49-57
• /
• 1994

An efficient approach for the computationtion of the optimum convergence factor is proposed for the LMS algorithm applied to a transversal FIR structure in this study. The approach automatically leads to an optimum step size algorithm at each weight in every iteration that results in a dramatic reduction in terms of convergence time. The algorithm is evaluated in system identification application where two alternative computer simulations are considered for time-invariant and time-varying system cases. The results show that the proposed algorithm needs not appropriate convergence factor and has better performance than AGC(Automatic Gain Control) algorithm and Karni algorithm, which require the convergence factors controlled arbitrarily in computer simulation for time-invariant system and time-varying systems. Also, itis shown that the proposed algorithm has the excellent adaptability campared with NLMS(Normalized LMS) algorithm and RLS (Recursive least Square) algorithm for time-varying circumstances.

• Journal of Korea Multimedia Society
• /
• v.24 no.4
• /
• pp.528-537
• /
• 2021

Face recognition has gained significant notice because of its application in many businesses: security, healthcare, and marketing. In this paper, we will present the recognition method using the combination of correlation filters (CF) and Support Vector Machine (SVM). Firstly, we evaluate the performance and compared four different correlation filters: minimum average correlation energy (MACE), maximum average correlation height (MACH), unconstrained minimum average correlation energy (UMACE), and optimal-tradeoff (OT). Secondly, we propose the machine learning approach by using the OT correlation filter for features extraction and SVM for classification. The numerical results on National Cheng Kung University (NCKU) and Pointing'04 face database show that the proposed method OT-SVM gets higher accuracy in face recognition compared to other machine learning methods. Our approach doesn't require graphics card to train the image. As a result, it could run well on a low hardware system like an embedded system.