• East Asian mathematical journal
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• v.24 no.2
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• pp.187-190
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• 2008

As a generalization of BCI-algebras, the notion of pseudo-BCI algebras is introduced, and some of their properties are investigated. Characterizations of pseudo-BCI algebras are established. Some conditions for a pseudo-BCI algebra to be a pseudo-BCK algebra are given.

• Communications of the Korean Mathematical Society
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• v.9 no.2
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• pp.275-278
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• 1994

In 1966, Iseki [4] introduced the notion of BCI-algebras which is a generalization of BCK-algebras. The ideal theory plays an important role in studying BCK/BCI-algebras. In this paper we study decompositions of ideals in BCI-algebras, and give a characterization of closed ideals. Also we define ignorable ideals in BCI-algebras, and investigates its properties.(omitted)

• Communications of the Korean Mathematical Society
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• v.9 no.2
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• pp.265-270
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• 1994

In 1966, Iseki [2] introduced the notion of BCI-algebras which is a generalization of BCK-algebras. Lei and Xi [3] discussed a new class of BCI-algebra, which is called a p-semisimple BCI-algebra. For p-semisimple BCI-algebras, a subalgebra is an ideal. But a subalgebra of an arbitrary BCI/BCK-algebra is not necessarily an ideal. In this note, a BCI/BCK-algebra that every subalgebra is an ideal is called a normal BCI/BCK-algebra, and we give characterizations of normal BCI/BCK-algebras. Moreover we give a positive answer to the problem which is posed in [4].(omitted)

• Journal of applied mathematics & informatics
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• v.40 no.5_6
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• pp.1105-1115
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• 2022

The main objective of this paper is to introduce the notion of AMR-algebra and its generalization, and to compare them with other algebras such as BCK, BCI, BCH, · · ·, etc. We show moreover that the K-part of AMR-algebra is an abelian group, and the weak AMR-algebra is also an abelian group and generalizes many known algebras like BCI, BCH, and G.

• Journal of Korea Game Society
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• v.10 no.6
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• pp.115-124
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• 2010

In the beginning brain waves were used for monkeys to control robot arm with neural activity. In recent years there are research that measured brain waves are used for the control of programs which monitor the progression of dementia or enhance of attention in children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD). Moreover, low-price devices that can be used as a game control interface have become available. One of the problems associated with control using brain waves is that the mean amplitude, mean wavelength, and mean vibrational frequency of the brain waves differ from individual to individual. This paper attempts to propose a method to normalize measured brain waves using normal distribution and calculate the waveforms that can be used in controlling games. For this, a framework in which brain waves are converted in seven stages has been suggested. In addition, the estimation process in each stage has been described. In an experiment the waveforms of two subjects have been compared using the proposed method in the BCI English word learning program. The level of similarity between two subjects' waveforms has been compared with correlation coefficient. When the proposed method was applied, both meditation and concentration increased by 13% and 8%, respectively. Because the proposed regularization method is converted into a waveform fit for control functions by reducing personal characteristics reflected in the brain waves, it is fitting for application programs such as games.

• Journal of KIISE
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• v.41 no.12
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• pp.1050-1057
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• 2014

Electroencephalogram (EEG)-based brain-computer interfaces (BCI) can be used for a number of purposes in a variety of industries, such as to replace body parts like hands and feet or to improve user convenience. In this paper, we propose a method to decompose and extract motor imagery EEG signal using Empirical Mode Decomposition (EMD) and Fast Fourier Transforms (FFT). The EEG signal classification consists of the following three steps. First, during signal decomposition, the EMD is used to generate Intrinsic Mode Functions (IMFs) from the EEG signal. Then during feature extraction, the power spectral density (PSD) is used to identify the frequency band of the IMFs generated. The FFT is used to extract the features for motor imagery from an IMF that includes mu rhythm. Finally, during classification, the Support Vector Machine (SVM) is used to classify the features of the motor imagery EEG signal. 10-fold cross-validation was then used to estimate the generalization capability of the given classifier., and the results show that the proposed method has an accuracy of 84.50% which is higher than that of other methods.