• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.1
• /
• pp.173-180
• /
• 2009

It is difficult to predict non-stationary or chaotic time series which includes the drift and/or the non-linearity as well as uncertainty. To solve it, we propose an effective prediction method which adopts data preprocessing and multiple model TS fuzzy predictors combined with model selection mechanism. In data preprocessing procedure, the candidates of the optimal difference interval are determined based on the correlation analysis, and corresponding difference data sets are generated in order to use them as predictor input instead of the original ones because the difference data can stabilize the statistical characteristics of those time series and better reveals their implicit properties. Then, TS fuzzy predictors are constructed for multiple model bank, where k-means clustering algorithm is used for fuzzy partition of input space, and the least squares method is applied to parameter identification of fuzzy rules. Among the predictors in the model bank, the one which best minimizes the performance index is selected, and it is used for prediction thereafter. Finally, the error compensation procedure based on correlation analysis is added to improve the prediction accuracy. Some computer simulations are performed to verify the effectiveness of the proposed method.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.11C
• /
• pp.1113-1121
• /
• 2009

In this paper, the channel estimation and pilot sequence design technique of multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems in multi-cell environments are studied for situations in which the inter cell interference (ICI) is the dominant channel impairment. We design pilot sequence aiming at minimizing mean square error and propose the channel estimation technique correspond to the designed pilot sequences. The proposed pilot sequences employ the sequences with good correlation properties such as Chu sequence and through simulations, it is shown that channel estimation algorithm using designed pilot sequence is effective for mitigating the ICI.

• The Korean Journal of Applied Statistics
• /
• v.26 no.6
• /
• pp.915-922
• /
• 2013

Quantile regression can estimate multiple conditional quantile functions of the response, and as a result, it provide comprehensive information of the relationship between the response and the predictors. However, when estimating several conditional quantile functions separately, two or more estimated quantile functions may cross or overlap and consequently violate the basic properties of quantiles. In this paper, we propose a new stepwise method to estimate multiple non-crossing quantile functions using constraints on the kernel coefficients. A simulation study are presented to demonstrate satisfactory performance of the proposed method.

• International Journal of Automotive Technology
• /
• v.6 no.5
• /
• pp.545-554
• /
• 2005

Passive velocity field control (PVFC) was previously developed for fully mechanical systems, in which the motion task was specified by behaviors in terms of a velocity field and the closed-loop was passive with respect to the supply rate given by the environment input. However, the PVFC was only applied to a single manipulator. The proposed control law was derived geometrically and the geometric and robustness properties of the closed-loop system were also analyzed. In this paper, we propose a virtual passivity-based algorithm to apply decentralized control to multiple 3­wheeled mobile robotic systems whose subsystems are under nonholonomic constraints and convey a common rigid object in a horizontal plain. Moreover, it is shown that multiple robot systems ensure stability and the velocities of augmented systems converge to a scaled multiple of each desired velocity field for cooperative mobile robot systems. Finally, the application of proposed virtual passivity-based decentralized algorithm via system augmentation is applied to trace a circle and the simulation results is presented in order to show effectiveness for the decentralized control algorithm proposed in this research.

• Proceedings of the Korean Information Science Society Conference
• /
• 2007.10b
• /
• pp.22-27
• /
• 2007

Phosphorylation is one of the most important post translational modifications which regulate the activity of proteins. The problem of predicting phosphorylation sites is the first step of understanding various biological processes that initiate the actual function of proteins in each signaling pathway. Although many prediction methods using single or multiple features extracted from protein sequences have been proposed, systematic data integration approach has not been applied in order to improve the accuracy of predicting general phosphorylation sites. In this paper, we propose an optimal way of integrating multiple features in the framework of multiple kernel learning. We optimally combine seven kernels extracted from sequence, physico-chemical properties, pairwise alignment, and structural information. Using the data set of Phospho. ELM, the accuracy evaluated by 5-fold cross-validation reaches 85% for serine, 85% for threonine, and 81% for tyrosine. Our computational experiments show significant improvement in the performance of prediction relative to a single feature, or to the combined feature with equal weights. Moreover, our systematic integration method significantly improves the prediction preformance compared with the previous well-known methods.

• Safety and Health at Work
• /
• v.9 no.4
• /
• pp.473-478
• /
• 2018

Background: Multiple Chemical Sensitivity (MCS) is an acquired disease which etiology remains unknown. It is characterized by the development of sensitivity to certain chemical products. Most of the hypotheses formulated to explain the syndrome associate it to a previous exposition to some kind of volatile chemical. University researchers in chemical laboratories suffer a phenomenon of multi-exposition to chemical agents at low concentration during long periods of time although in an irregular form. Many of these chemical agents have similar properties to those suspicious of causing MCS. This article studies the prevalence of MCS in laboratory researchers. Methods: The study group is university researchers in chemical laboratories. The control group was obtained from administrative personnel who work in the same universities and therefore, are not exposed to chemical products from the laboratories, but have the same exposition to the rest of environmental polluting agents from the area and from the buildings of the university. In this study, it is used the Quick Environmental Exposure and Sensitivity Inventory (QEESI) (sensitivity of 92%/specificity of 95%). Results: The results showed that the prevalence of MCS for the university researchers is not related to exposition by inhalation to multiple chemical agents, at low concentration. Conclusions: The results disagree with one of the main etiological hypotheses of MCS, which is based on the existence of hypersensitive people, who presents a response after prolonged expositions to very low concentrations during a long period of time.

• Nuclear Engineering and Technology
• /
• v.55 no.11
• /
• pp.4213-4227
• /
• 2023

Computer Automated Radioactive Particle Tracking (CARPT) technique has been successfully utilized to measure the velocity profiles and mixing parameters in different multiphase flow systems where a single radioactive tracer is used to track the tagged phase. However, many industrial processes use a wide range of particles with different physical properties where solid particles could vary in size, shape and density. For application in such systems, the capability of current single tracer CARPT can be advanced to track more than one particle simultaneously. Tracking multiple particles will thus enable to track the motion of particles of different size shape and density, determine segregation of particles and probing particle interactions. In this work, a newly developed Multiple Radioactive Particle Tracking technique (M-RPT) used to track two different radioactive tracers is demonstrated. The M-RPT electronics was developed that can differentiate between gamma counts obtained from the different radioactive tracers on the basis of their gamma energy peak. The M-RPT technique was validated by tracking two stationary and moving particles (Sc-46 and Co-60) simultaneously. Finally, M-RPT was successfully implemented to track two phases, solid and liquid, simultaneously in three phase slurry bubble column reactors.

• Proceedings of the KWS Conference
• /
• 2006.05a
• /
• pp.215-217
• /
• 2006

• Proceedings of the Korean Biophysical Society Conference
• /
• 2002.06b
• /
• pp.16-17
• /
• 2002

The major histocompatibility complex (MHC) is an important susceptibility locus for many human autoimmune diseases. The structural and functional properties of HLA-DR molecules that are associated with susceptibility to several autoimmune diseases, such as rheumatoid arthritis and multiple sclerosis, have been defined.(omitted)

• Proceedings of the KSR Conference
• /
• 2006.05b
• /
• pp.551-556
• /
• 2006