On-line Model Parameter Estimations for Time-delay Systems

This paper concerns a problem of on-line model parameter estimations for multiple time-delay systems. In order to estimate unknown model parameters from measured state variables, we propose two schemes using Lyapunov's direct method, called parallel and series-parallel model estimators. It is shown through a numerical example that the proposed parallel and series-parallel model estimators can be effective when sufficiently rich inputs are applied.open1122sciescopu

Whitening Technique Based on Gram–Schmidt Orthogonalization for Motor Imagery Classification of Brain–Computer Interface Applications

A novel whitening technique for motor imagery (MI) classification is proposed to reduce the accuracy variance of brain–computer interfaces (BCIs). This method is intended to improve the electroencephalogram eigenface analysis performance for the MI classification of BCIs. In BCI classification, the variance of the accuracy among subjects is sensitive to the accuracy itself for superior classification results. Hence, with the help of Gram–Schmidt orthogonalization, we propose a BCI channel whitening (BCICW) scheme to minimize the variance among subjects. The newly proposed BCICW method improved the variance of the MI classification in real data. To validate and verify the proposed scheme, we performed an experiment on the BCI competition 3 dataset IIIa (D3D3a) and the BCI competition 4 dataset IIa (D4D2a) using the MATLAB simulation tool. The variance data when using the proposed BCICW method based on Gram–Schmidt orthogonalization was much lower (11.21) than that when using the EFA method (58.33) for D3D3a and decreased from (17.48) to (9.38) for D4D2a. Therefore, the proposed method could be effective for MI classification of BCI applications

A Novel Quick-Response Eigenface Analysis Scheme for Brain–Computer Interfaces

The brain–computer interface (BCI) is used to understand brain activities and external bodies with the help of the motor imagery (MI). As of today, the classification results for EEG 4 class BCI competition dataset have been improved to provide better classification accuracy of the brain computer interface systems (BCIs). Based on this observation, a novel quick-response eigenface analysis (QR-EFA) scheme for motor imagery is proposed to improve the classification accuracy for BCIs. Thus, we considered BCI signals in standardized and sharable quick response (QR) image domain; then, we systematically combined EFA and a convolution neural network (CNN) to classify the neuro images. To overcome a non-stationary BCI dataset available and non-ergodic characteristics, we utilized an effective neuro data augmentation in the training phase. For the ultimate improvements in classification performance, QR-EFA maximizes the similarities existing in the domain-, trial-, and subject-wise directions. To validate and verify the proposed scheme, we performed an experiment on the BCI dataset. Specifically, the scheme is intended to provide a higher classification output in classification accuracy performance for the BCI competition 4 dataset 2a (C4D2a_4C) and BCI competition 3 dataset 3a (C3D3a_4C). The experimental results confirm that the newly proposed QR-EFA method outperforms the previous the published results, specifically from 85.4% to 97.87% ± 0.75 for C4D2a_4C and 88.21% ± 6.02 for C3D3a_4C. Therefore, the proposed QR-EFA could be a highly reliable and constructive framework for one of the MI classification solutions for BCI applications

Simple Pretreatment Method Development for Iron and Calcium Carbonate Samples

From the 20th International Radiocarbon Conference held in Kona, Hawaii, USA, May 31-June 3, 2009.Since iron artifacts generally contain trace amounts of carbon, an iron sample needs to be relatively large, as compared to other materials, and a specially designed combustion system is required. An elemental analyzer (EA) was used for the combustion of iron without any special chemical treatment. CO2 gas with 1 mg of carbon was obtained from the combustion of an iron artifact by using an EA and reduced to graphite for accelerator mass spectrometry (AMS) measurement. In this work, AMS dating results done at the Korea Institute of Geoscience and Mineral Resources (KIGAM) for several ancient iron artifacts are presented and compared with independently estimated ages. This method was found to be useful for the pretreatment of iron artifacts that contained 0.1% carbon. A simple pretreatment method using an EA was also applied to calcium carbonate (CaCO3) samples. Samples were preheated overnight at 100-300 C, without any special chemical treatment. This removed modern CO2 contamination and the background level decreased to a comparable value measured in samples treated with phosphoric acid under vacuum.The Radiocarbon archives are made available by Radiocarbon and the University of Arizona Libraries. Contact [email protected] for further information.Migrated from OJS platform February 202

Simple Pretreatment Method Development for Iron and Calcium Carbonate Samples

From the 20th International Radiocarbon Conference held in Kona, Hawaii, USA, May 31-June 3, 2009.Since iron artifacts generally contain trace amounts of carbon, an iron sample needs to be relatively large, as compared to other materials, and a specially designed combustion system is required. An elemental analyzer (EA) was used for the combustion of iron without any special chemical treatment. CO2 gas with 1 mg of carbon was obtained from the combustion of an iron artifact by using an EA and reduced to graphite for accelerator mass spectrometry (AMS) measurement. In this work, AMS dating results done at the Korea Institute of Geoscience and Mineral Resources (KIGAM) for several ancient iron artifacts are presented and compared with independently estimated ages. This method was found to be useful for the pretreatment of iron artifacts that contained 0.1% carbon. A simple pretreatment method using an EA was also applied to calcium carbonate (CaCO3) samples. Samples were preheated overnight at 100-300 C, without any special chemical treatment. This removed modern CO2 contamination and the background level decreased to a comparable value measured in samples treated with phosphoric acid under vacuum.The Radiocarbon archives are made available by Radiocarbon and the University of Arizona Libraries. Contact [email protected] for further information.Migrated from OJS platform February 202