• Journal of the Institute of Electronics Engineers of Korea SC
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• v.37 no.6
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• pp.50-63
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• 2000

In this paper, a new EMG pattern classification method based on soft computing techniques is proposed to help the disabled and the elderly handle rehabilitation robotic arm systems. First, it is shown that EMG is more useful than existing input devices such as voice, a laser pointer and a keypad in view of naturality, extensibility, and applicability. Then, a new procedure is proposed to select the minimal feature set. As methods of classifying the pre-defined motions, a fuzzy pattern classification and fuzzy min-max neural networks (FMMNN) are designed using the selected features. As results, the motions are recognized with success rates of 83 percent and 90 Percent using fuzzy pattern classification and FMMNN, respectively.

• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.17-24
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• 2014

This paper proposes a fault detection method for low-speed rolling element bearings of an induction motor using acoustic emission signals and histogram modeling. The proposed method performs envelop modeling of the histogram of normalized fault signals. It then extracts and selects significant features of each fault using partial autocorrelation coefficients and distance evaluation technique, respectively. Finally, using the extracted features as inputs, the support vector regression (SVR) classifies bearing's inner, outer, and roller faults. To obtain optimal classification performance, we evaluate the proposed method with varying an adjustable parameter of the Gaussian radial basis function of SVR from 0.01 to 1.0 and the number of features from 2 to 150. Experimental results show that the proposed fault identification method using 0.64-0.65 of the adjustable parameter and 75 features achieves 91% in classification performance and outperforms conventional fault diagnosis methods as well.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.741-747
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• 2022

Fatigue lowers an individual's ability and makes it difficult to perform work. As fatigue accumulates, concentration decreases and thus the possibility of causing a safety accident increases. Awareness of fatigue is subjective, but it is necessary to quantitatively measure the level of fatigue in the actual field. In previous studies, it was proposed to measure the level of fatigue by expert judgment by adding objective indicators such as bio-signal analysis to subjective evaluations such as multidisciplinary fatigue scales. However this method is difficult to evaluate fatigue in real time in daily life. This paper is a study on the fatigue classification model that determines the fatigue level of workers in real time using speech data recorded in the field. Machine learning models such as logistic classification, support vector machine, and random forest are trained using speech data collected in the field. The performance evaluation showed good performance with accuracy of 0.677 to 0.758, of which logistic classification showed the best performance. From the experimental results, it can be seen that it is possible to classify the fatigue level using speech signals.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.4
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• pp.19-23
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• 2004

생체에서 발생되는 생체신호는 신호의 발생원에 따라서, 신호의 물리적 특성에 따라서, 또는 이를 측정하는 센서의 특성에 따라서 분류할 수 있으며, 그 중에서도 임상적 진료를 위한 의료의 범위를 포함하여 다른 분야에도 광범위하게 활용될 수 있는 생체 신호는 전기적인 형태로 측정되는 생체 전기 신호라고 할 수 있다. 여기에서는 생체에서 측정되는 전기적인 신호가 어떻게 활용되고 또 활용될 수 있는지 그 응용 범위에 대하여 살펴보고자 한다.(중략)

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2008.04a
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• pp.151-154
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• 2008

본 논문은 Bayesian Networks를 이용해서 EEG 신호를 분석해서 사람의 감정을 분석하는 방법을 제안하였다. 현제 연구자들은 Electroencephalogram(EEG) 신호를 기반으로 사람의 두뇌와 컴퓨터의 인터페이스에 관한 연구를 하고 있다. 기존에는 간질이나 발작 등을 의학 분야와 사람의 정서에 따라 뇌파분석을 하는 심리학의 영역에서 연구가 되어져 왔다. 최근에는 사람의 두뇌와 컴퓨터 간의 인터페이스를 통한 여러 가지 공학적인 접근이 이루어지고 있다. 본 논문에서는 사람의 감정에 따라 Brain-Computer Interface (BCI)를 통해서 EEG 신호를 분석하고 잡음을 제거해서 보다 정확한 신호를 추출한 다음 각각의 주파수 영역으로 분류를 하였다. 분류된 값들은 Bayesian Networks를 이용해서 피 실험자가 어떠한 감정을 나타내는지 확률 값으로 나타낸다. 확률 값에 의해서 피 실험자가 어떠한 감정인지를 인식하게 되는 것이다.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.209-220
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• 2014

In this paper, we analyze the discriminability of features for the classification of transient signals with an ambient noise in a shallow water. For the classification of the transient signals, robust features for the variance of a noise are required due to a low SNR under a marine environment. In the modelling the ambient noise in shallow water, theoretical noise model, Wenz's observation data from the shallow water, and Yule-walker filter are used. Discrimination of each feature of the transient signals with an additive ambient noise is analyzed by utilizing a Fisher score. As the analysis of a classification accuracy about the transient signals of 24 classes using the selected features with a high discriminability, the features selected in the environment without a noise relatively have a good classification accuracy. From the analyzed results, we finally select a total 16 features out of 28 features. The recognition using the selected features results in the classification accuracy of 92% in SNR 20dB using Multi-class SVM.

• The Journal of the Acoustical Society of Korea
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• v.39 no.6
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• pp.615-621
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• 2020

In this paper, we propose a multi-site based earthquake event classification method using graph convolution networks. In the traditional earthquake event classification methods using deep learning, they used single-site observation to estimate seismic event class. However, to achieve robust and accurate earthquake event classification on the seismic observation network, the method using the information from the multi-site observations is needed, instead of using only single-site data. Firstly, our proposed model employs convolution neural networks to extract informative embedding features from the single-site observation. Secondly, graph convolution networks are used to integrate the features from several stations. To evaluate our model, we explore the model structure and the number of stations for ablation study. Finally, our multi-site based model outperforms up to 10 % accuracy and event recall rate compared to single-site based model.

• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.48-57
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• 2003

This paper describes an approach for classifying myoelectric patterns using a multilayer perceptrons (MLP's) with genetic algorithm and hidden Markov models (HMM's) hybrid classifier. Genetic Algorithms play a role of selecting Multilayer Perceptron's optimized initial connection weights by its typical global search. The dynamic aspects of EMG are important for tasks such as continuous prosthetic control or various time length EMG signal recognition, which have not been successfully mastered by the most neural approaches. It is known that the hidden Markov model (HMM) is suitable for modeling temporal patterns. In contrast, the multilayer feedforward networks are suitable for static patterns. And, a lot of investigators have shown that the HMM's to be an excellent tool for handling the dynamical problems. Considering these facts, we suggest the combination of ANN and HMM algorithms that might lead to further improved EMG recognition systems.

• Journal of the Institute of Convergence Signal Processing
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• v.18 no.2
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• pp.43-48
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• 2017

The underwater transients signals contain the characteristics of complexity, time varying, nonlinear, and short duration. So it is very hard to model for these signals with reference patterns. In this paper we separate the whole length of signals into some short duration of constant length with overlapping frame by frame. The 20th LPC(Linear Predictive Coding) coefficients are extracted from the original signals using Durbin algorithm and applied to neural network. The 65% of whole signals were learned and 35% of the signals were tested in the neural network with two hidden layers. The types of the whales for sound classification are Blue whale, Dulsae whale, Gray whale, Humpback whale, Minke whale, and Northern Right whale. Finally, we could obtain more than 83% of classification rate from the test signals.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.05a
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• pp.195-198
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• 2009

본 연구는 사용자가 경험하는 감성 이미지를 추출하여 생리신호를 통해 자동으로 분류하는 시스템을 구현하고자 한다. 본 연구에서 개발한 시스템은 이미지의 감성평가를 위하여 착용과 휴대가 간편한 PPG생체신호를 활용하였고, 생활 속의 장면들에서 감성 별 이미지를 무선으로 PC에 저장 및 분류하는 시스템을 개발하였다. 또한 이미지의 라이프 로그 정보를 제공하는 기술을 개발하였다. 본 시스템은 사용자의 경험과 이미지를 연관시켜 생활 속 정보의 경험 피드백을 하는 데에 의의가 있다.