• Communications for Statistical Applications and Methods
• /
• v.14 no.1
• /
• pp.133-140
• /
• 2007

The support vector machine has been well developed as a powerful tool for solving classification problems. In many real world applications, each training point has a different effect on constructing classification rule. Lin and Wang (2002) proposed fuzzy support vector machines for this kind of classification problems, which assign fuzzy memberships to the input data and reformulate the support vector classification. In this paper another intuitive approach is proposed by using the fuzzy ${\alpha}-cut$ set. It will show us the trend of classification functions as ${\alpha}$ changes.

• Proceedings of the Korea Concrete Institute Conference
• /
• 1989.10a
• /
• pp.53-58
• /
• 1989

Thicknesses of the base concrete blocks for large machines were estimated by analyzing the resonance modes of mechanical vibrations. The vibration was produced by the mechanical impact and detected by a wideband conical transcuder. There signals were analyzed by FET and thicknesses were obtained by the peaks of frequency spectrum. The estimated thickness upto 100cm are in good agreement with the real ones. For the layered concrete block, the estimated thickness is dependent on the acoustic reflective index at the boundary of the two layers.

• Proceedings of the KIEE Conference
• /
• 2006.07a
• /
• pp.262-263
• /
• 2006

This paper presents a scheme to solve the congestion problem with phase-shifting transformer(PST) and power generation using linear programming method. A good design of PST and power generation control can improve total transfer capability(TTC) in interconnected systems. This paper deals with an application of optimization technique for TTC calculation. linear programming method is used to maximize power flow of tie line subject to security constraints such as voltage magnitude and real power flow. The proposed method is applied to 10 machines 39 buses model systems to show its effectiveness.

• Journal of the Korean Data and Information Science Society
• /
• v.19 no.1
• /
• pp.25-36
• /
• 2008

Error Correcting Output Codes (ECOC) can improve generalization performance when applied to multicategory classification problem. In this study we propose a new criterion to select hyperparameters included in ECOC scheme. Instead of margins of a data we propose to use the probability of misclassification error since it makes the criterion simple. Using this we obtain an upper bound of leave-one-out error of OVA(one vs all) method. Our experiments from real and synthetic data indicate that the bound leads to good estimates of parameters.

• Journal of the Korean Data and Information Science Society
• /
• v.20 no.5
• /
• pp.949-954
• /
• 2009

The autoregressive process is applied in this paper to kernel regression in order to infer nonlinear models for predicting responses. We propose a kernel method for the autoregressive data which estimates the mean function by kernel machines. We also present the model selection method which employs the cross validation techniques for choosing the hyper-parameters which affect the performance of kernel regression. Artificial and real examples are provided to indicate the usefulness of the proposed method for the estimation of mean function in the presence of autocorrelation between data.

• Journal of the Korea Society for Simulation
• /
• v.8 no.1
• /
• pp.35-49
• /
• 1999

The CC-NUMA based, distributed shared memory is an emerging architecture for multiprocessor computer systems because of its scalability and easy of programming. In this paper, we analyzed performance of a ring-based, CC-NUMA multiprocessor computer system using a commercial workload targeted for popular OLTP applications. Based on the traces collected from real machines, the characteristics of the commercial workload could be obtained. The simulation results showed that the bottleneck on the ring could be effectively removed by using a dual ring structure. We believe our simulation methodology and results will help us to design better multiprocessor computer systems for commercial application domains.

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.6
• /
• pp.1539-1547
• /
• 2014

In this paper we apply the autoregressive process to the nonlinear quantile regression in order to infer nonlinear quantile regression models for the autocorrelated data. We propose a kernel method for the autoregressive data which estimates the nonlinear quantile regression function by kernel machines. Artificial and real examples are provided to indicate the usefulness of the proposed method for the estimation of quantile regression function in the presence of autocorrelation between data.

• Journal of the Korean Physical Society
• /
• v.81
• /
• pp.258-266
• /
• 2022

Noisy intermediate scale quantum (NISQ) computers are a promising platform for studying many-body quantum states, such as interacting topological states. Here we prepare a one-dimensional bosonic symmetry-protected topological (SPT) phases using variational quantum eigensolver (VQE) algorithms, and demonstrate the randomized measurement of the corresponding many-body topological invariant, on a trapped-ion quantum computer. We show that the randomized measurement protocol is applicable in real machines, with the dominant error arising from the imperfect preparation of the quantum states.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.21 no.8
• /
• pp.1937-1947
• /
• 1996

In the case of developing the real-time system using object-oriented method, k the problem of the timing constraints is certainly considered. we propose the method of modeling the object-oriented real-time system using the OMT methodology and the SDL. And we also present the predictable time table that reflects the constraints of real-time system into dynamic model of OMTs and the predicatable time formula of the sequence, repeat, and parallel routine. The proposed method is applied to the estimate of the maximum process time of the ATMs(Automatic teller machines) and is used to specifying the functional specification for the user interface of the ATMs using the SDL syntax and the object interaction graph.

• Fourth Industrial Review
• /
• v.3 no.1
• /
• pp.13-20
• /
• 2023

Purpose - By designing a PEF(Personalized Education Feedback) system for real-time prediction of learning achievement and motivation through real-time EEG analysis of learners, this system provides some modules of a personalized adaptive learning system. By applying these modules to e-learning and offline learning, they motivate learners and improve the quality of learning progress and effective learning outcomes can be achieved for immersive self-directed learning Research design, data, and methodology - EEG data were collected simultaneously as the English test was given to the experimenters, and the correlation between the correct answer result and the EEG data was learned with a machine learning algorithm and the predictive model was evaluated.. Result - In model performance evaluation, both artificial neural networks(ANNs) and support vector machines(SVMs) showed high accuracy of more than 91%. Conclusion - This research provides some modules of personalized adaptive learning systems that can more efficiently complete by designing a PEF system for real-time learning achievement prediction and learning motivation through an adaptive learning system based on real-time EEG analysis of learners. The implication of this initial research is to verify hypothetical situations for the development of an adaptive learning system through EEG analysis-based learning achievement prediction.