• Journal of Electrical Engineering and Technology
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• 제9권3호
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• pp.1060-1071
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• 2014

Signals of the Electroencephalogram (EEG) can reflect the electrical background activity of the brain generated by the cerebral cortex nerve cells. This has been the mostly utilized signal, which helps in effective analysis of brain functions by supervised learning methods. In this paper, an approach for improving the accuracy of EEG signal classification is presented to detect epileptic seizures. Moreover, Independent Component Analysis (ICA) is incorporated as a preprocessing step and Short Time Fourier Transform (STFT) is used for denoising the signal adequately. Feature extraction of EEG signals is accomplished on the basis of three parameters namely, Standard Deviation, Correlation Dimension and Lyapunov Exponents. The Artificial Neural Network (ANN) is trained by incorporating Levenberg-Marquardt(LM) training algorithm into the backpropagation algorithm that results in high classification accuracy. Experimental results reveal that the methodology will improve the clinical service of the EEG recording and also provide better decision making in epileptic seizure detection than the existing techniques. The proposed EEG signal classification using feed forward Backpropagation Neural Network performs better than to the EEG signal classification using Adaptive Neuro Fuzzy Inference System (ANFIS) classifier in terms of accuracy, sensitivity, and specificity.

• 한국음향학회지
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• 제41권2호
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• pp.235-241
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• 2022

수동 음향 관측을 통해 수집된 방대한 양의 데이터에서 해양포유류의 소리를 탐지하고 식별하기 위해 합성곱 신경망(Convolutional Neural Network, CNN)을 활용한 연구가 많이 수행되고 있다. 본 연구는 2017년 8월부터 2018년 8월까지 동시베리아 해에서 수집된 수중음향 스펙트럼 이미지를 기반으로 CNN을 활용하여 턱수염물범 소리의 분류 자동화 가능성을 확인해 보았다. 학습 데이터로서 다른 소음이 거의 포함되지 않은 뚜렷한 턱수염물범 소리를 사용하였을 때, 암기로 인한 과적합이 발생하였다. 일부 데이터를 소음이 포함된 데이터로 교체하여 학습시켜 수집된 전체 데이터로 평가한 결과 정확도(0.9743), 정밀도(0.9783), 재현율(0.9520)으로 모델이 이전보다 일반화되어 과적합이 방지되는 것을 확인하였다. 본 연구를 통해 물범신호 분류는 학습 데이터에 소음이 포함되었을 때 성능이 증가하는 것으로 나타났다.

• 한국군사과학기술학회지
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• 제23권6호
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• pp.554-564
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• 2020

In environment of torpedo firing, underwater acoustic signal is generated by target and noise. Sound wave which is generated from acoustic signal is propagated by seawater and it is received through the sonar(sound navigation and ranging) system mounted on torpedo. In the ocean, acoustic signal or sound wave from target that is generated by the spread of broadband can be attenuated by ambient noise and can be lost by medium and environment. This research is designed to support teamwork training in Naval operations by constructing a simulation system that is more similar to the real-world conditions. This paper attempts to research the modeling of target detection and to develop the simulation of torpedo sonar(TOSO). In order to develop the realistic simulation, we researched the broadband sound modeling of target and noise source, the modeling of acoustic transmission loss by chemical component of seawater, and the modeling of signal attenuation by ambient noise environment which is approximated by experimental measurements in seawater surrounding the Korea Peninsular and by experience of Navy's actual torpedo firing. This research contributed to constructing more practical simulation of torpedo firing in real time and the results of this research were used to develop a teamwork training system for the Navy and their education.

• 한국전기전자재료학회논문지
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• 제18권10호
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• pp.965-975
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• 2005

In the case of the fault in stator windings of a high voltage motor. it facilitates certain destructive characteristics in insulations. This will result in a decreased reliability in power supplies and will prevent the generation of electricity, which will result in huge economic losses. This study simulates motor windings using normal windings and four faulty windings for an actual fault in stator winding of a high voltage motor. The partial discharge signals produced in each faulty winding were measured using an 80 PF epoxy/mica coupler sensor. In order to quantified signal waves its a way of feature extraction for each faulty signal, the signal wave of winding was quantified to measure the degree of skewness shape and kurtosis, which are both types of statistical parameters, using a discrete wavelet transformation method for each faulty type. Wave types present different types lot each faulty type, and the skewness and kurtosis also present different quantified values. The result of feature extraction was used as a preprocessing stage to identify a certain fault in stater windings. It is evident that the type of faulty signals can be classified from the test results using faulty signals that were randomly selected from the signal, which was not applied in the training after the training and learning period, by applying it to a back-propagation algorithm due to the supervising and learning method in a neural network in order to classify the faulty type. This becomes an important basis for studying diagnosis methods using the classification of faulty signals with a feature extraction algorithm, which can diagnose the fault of stator windings in the future.

• 대한임베디드공학회논문지
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• 제18권6호
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• pp.277-283
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• 2023

Vital signals provide essential information regarding the health status of individuals, thereby contributing to health management and medical research. Present monitoring methods, such as ECGs (Electrocardiograms) and smartwatches, demand proximity and fixed postures, which limit their applicability. To address this, Non-contact vital signal measurement methods, such as CW (Continuous-Wave) radar, have emerged as a solution. However, unwanted signal components and a stepwise processing approach lead to errors and limitations in heart rate detection. To overcome these issues, this study introduces an integrated neural network approach that combines noise removal, demodulation, and dominant-frequency detection into a unified process. The neural network employed for signal processing in this research adopts a MLP (Multi-Layer Perceptron) architecture, which analyzes the in-phase and quadrature signals collected within a specified time window, using two distinct input layers. The training of the neural network utilizes CW radar signals and reference heart rates obtained from the ECG. In the experimental evaluation, networks trained on different datasets were compared, and their performance was assessed based on loss and frequency accuracy. The proposed methodology exhibits substantial potential for achieving precise vital signals through non-contact measurements, effectively mitigating the limitations of existing methodologies.

• The Journal of the Acoustical Society of Korea
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• 제23권4E호
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• pp.108-111
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• 2004

This paper has interest in the effect of a fine frequency offset, defined in ITU-T G.225, to the training performance of an adaptive equalizer. This paper uses Hilbert filter in configuring a transmission system model in order to let it get a frequency offset. Also additive white Gaussian noise and band-limited filter are considered. The signal received from the above transmission system applies to an adaptive equalizer with LMS algorithm, and its training procedures are investigated. As a result, we could find that even small fine frequency offset can severely deteriorate training performance of adaptive algorithm.

• 한국생산제조학회지
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• 제6권1호
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• pp.27-33
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• 1997

A neural network approach has been developed to determine the depth of a surface breaking crack in a steel plate from ultrasonic backscattering data. The network is trained by the use of feedforward three-layered network together with a back-scattering algorithm for error correction. The signal used for crack insonification is a mode converted 70$^{\circ}$transverse wave. A numerical analysis of back scattered field is carried out based on elastic wave theory, by the use of the boundary element method. The numerical data are calibrated by comparison with experimental data. The numerical analysis provides synthetic data for the training of the network. The training data have been calculated for cracks with specified increments of the crack depth. The performance of the network has been tested on other synthetic data and experimental data which are different from the training data.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2521-2525
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• 2005

In this paper, we presented a predictive control technique, which is based on wavelet neural network (WNN), for the control of chaotic systems whose precise mathematical models are not available. The WNN is motivated by both the multilayer feedforward neural network definition and wavelet decomposition. The wavelet theory improves the convergence of neural network. In order to design predictive controller effectively, the WNN is used as the predictor whose parameters are tuned by error between the output of actual plant and the output of WNN. Also the training method for the finding a good WNN model is the Extended Kalman algorithm which updates network parameters to converge to the reference signal during a few iterations. The benefit of EKF training method is that the WNN model can have better accuracy for the unknown plant. Finally, through computer simulations, we confirmed the performance of the proposed control method.

• Journal of information and communication convergence engineering
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• 제4권2호
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• pp.58-61
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• 2006

A key issue toward mobile multimedia communications is to create technologies for broadband signal transmission that can support high quality services. Such a broadband mobile communications system should be able to overcome severe distortion caused by timevarying multi-path fading channel, while providing high spectral efficiency and low power consumption. For these reasons, an adaptive suboptimum decision feedback equalizer (DFE) for the single-carrier shortburst transmissions system is considered as one of the feasible solutions. For the performance improvement of the system with the short-burst format including the short training sequence, in this paper, the multiple-training least mean square (MTLMS) based DFE scheme with soft decision feedback is proposed and its performance is investigated in mobile wireless channels throughout computer simulation.

• 정보통신설비학회논문지
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• 제10권3호
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• pp.92-97
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• 2011

In this paper, a controller recognizing human gestures using EMG signal is shown. The tiny and band-type controller is developed for image training to excercise the specific area in the body, and uses a dry-type silver fiber electrode easy to be attached or detached itself to a skin. The captured EMG signals are converted to 10-bit digital values via amplification and frequency filtering processes within the controller, and are transmitted to the server by wireless. As the gesture recognition ratio using the proposed controller on biceps is up to 80%, we expect the practical potential of the controller is very promising.