• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.7
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• pp.71-78
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• 2017

There are several algorithms to classify emotion, such as Support-vector-machine (SVM), Bayesian decision rule, etc. However, many researchers have insisted that these methods have minor problems. Therefore, in this paper, we propose a novel method for emotion recognition based on Electroencephalogram (EEG) signal using the Gradient method which was proposed by Han. We also utilize a database for emotion analysis using physiological signals (DEAP) to obtain objective data. And we acquire four channel brainwaves, including Fz (${\alpha}$), Fp2 (${\beta}$), F3 (${\alpha}$), F4 (${\alpha}$) which are selected in previous study. We use 4 features which are power spectral density (PSD) of the above channels. According to performance evaluation (4-fold cross validation), we could get 85% accuracy in valence axis and 87.5% in arousal. It is 5-7% higher than existing method's.

• The Journal of the Acoustical Society of Korea
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• v.41 no.5
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• pp.518-524
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• 2022

In this paper, we propose a method for combining convolutional neural networks and attention mechanism to improve the recognition performance of emotional stress from Electroencephalogram (EGG) signals. In the proposed method, EEG signals are decomposed into five frequency domains, and spatial information of EEG features is obtained by applying a convolutional neural network layer to each frequency domain. As a next step, salient frequency information is learned in each frequency band using a gate transformer-based attention mechanism, and complementary frequency information is further learned through inter-frequency mapping to reflect it in the final attention representation. Through an EEG stress recognition experiment involving a DEAP dataset and six subjects, we show that the proposed method is effective in improving EEG-based stress recognition performance compared to the existing methods.

• Journal of IKEEE
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• v.22 no.3
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• pp.816-821
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• 2018

Recently, machine learning algorithms based on artificial neural networks started to be used widely as classifiers in the field of EEG research for emotion analysis and disease diagnosis. When a machine learning model is used to classify EEG data, if training data is composed of only data having similar characteristics, classification performance may be deteriorated when applied to data of another group. In this paper, we propose a method to construct training data set by selecting several groups of data using semi-supervised learning algorithm to improve these problems. We then compared the performance of the two models by training the model with a training data set consisting of data with similar characteristics to the training data set constructed using the proposed method.

• Fire Science and Engineering
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• v.22 no.1
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• pp.93-98
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• 2008

Full-scale fire extinguishment tests were conducted to develop loaded stream fire extinguishing systems for protecting underground transmission cables. The dimension of test mock-up was 5.5m height${\times}3m$ width ${\times}6m$ length, and six 154kV OF cables were piled up. Gasoline was used to ignite cates. Linear heat detection cables were installed on top of 154 kV OF cable and discharge nozzles were installed on the top and sidewalls, respectively. As a result, both surface fires and deap-seated fires were extinguished successfully within the specified 3 minutes by discharging loaded stream agent.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.1
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• pp.1-8
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• 2014

This paper proposes an emotion classifier from EEG signals based on Bayes' theorem and a machine learning using a perceptron convergence algorithm. The emotions are represented on the valence and arousal dimensions. The fast Fourier transform spectrum analysis is used to extract features from the EEG signals. To verify the proposed method, we use an open database for emotion analysis using physiological signal (DEAP) and compare it with C-SVC which is one of the support vector machines. An emotion is defined as two-level class and three-level class in both valence and arousal dimensions. For the two-level class case, the accuracy of the valence and arousal estimation is 67% and 66%, respectively. For the three-level class case, the accuracy is 53% and 51%, respectively. Compared with the best case of the C-SVC, the proposed classifier gave 4% and 8% more accurate estimations of valence and arousal for the two-level class. In estimation of three-level class, the proposed method showed a similar performance to the best case of the C-SVC.

• Fire Science and Engineering
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• v.29 no.2
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• pp.13-19
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• 2015

Real scale fire tests was carried out for extinguishing performance evaluation of the wetting agent. The experiment was conducted in accordance with a Class A fire extinguishing test methods specified in the 'Type Approval of the Manual System Fire Extinguisher and Technical Standards of Test'. In addition, the subjects of this experiment were the wood flour and rice husk. Fire-fighting water, the three kinds of wetting agents used in the country and this study was used, was undertaken to determine a clear discrimination of the water and wetting agent. In the experimental results, it was confirmed that the internal temperature is maintained long time in the case of water. The internal temperature were rapidly lowered in the experiment of wetting agents. Therefore, the discrimination of extinguishing ability was confirmed by the temperature distribution in accordance with time. Based on the results of this experiment, this study is expected to be used as a underlying material on presenting a method of optimized performance evaluation of wetting extinguishing agent.