• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.4065-4083
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• 2021

Super-resolution can improve the clarity of low-resolution (LR) images, which can increase the accuracy of high-level compute vision tasks. Portable devices have low computing power and storage performance. Large-scale neural network super-resolution methods are not suitable for portable devices. In order to save the computational cost and the number of parameters, Lightweight image processing method can improve the processing speed of portable devices. Therefore, we propose the Enhanced Information Multiple Distillation Network (EIMDN) to adapt lower delay and cost. The EIMDN takes feedback mechanism as the framework and obtains low level features through high level features. Further, we replace the feature extraction convolution operation in Information Multiple Distillation Block (IMDB), with Ghost module, and propose the Enhanced Information Multiple Distillation Block (EIMDB) to reduce the amount of calculation and the number of parameters. Finally, coordinate attention (CA) is used at the end of IMDB and EIMDB to enhance the important information extraction from Spaces and channels. Experimental results show that our proposed can achieve convergence faster with fewer parameters and computation, compared with other lightweight super-resolution methods. Under the condition of higher peak signal-to-noise ratio (PSNR) and higher structural similarity (SSIM), the performance of network reconstruction image texture and target contour is significantly improved.

• Journal of Electrical Engineering and Technology
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• 제11권2호
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• pp.416-424
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• 2016

In this study, automatic detection of stator winding inter-turn short circuit fault (SWISCFs) in surface-mounted permanent magnet synchronous motors (SPMSMs) and automatic classification of fault severity via a pattern recognition system (PRS) are presented. In the case of a stator short circuit fault, performance losses become an important issue for SPMSMs. To detect stator winding short circuit faults automatically and to estimate the severity of the fault, an artificial neural network (ANN)-based PRS was used. It was found that the amplitude of the third harmonic of the current was the most distinctive characteristic for detecting the short circuit fault ratio of the SPMSM. To validate the proposed method, both simulation results and experimental results are presented.

• Journal of Power Electronics
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• 제14권6호
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• pp.1254-1262
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• 2014

Magneto-electric encoders have been widely used in industry and military applications because of their good shock resistance, small volume, and convenient data processing. However, the characteristics of a magneto-electric encoder's signal generator and hall sensor changes minimally with temperature variation. These changes cause an angle drift. The main purpose of this study is to construct the compensation system of a neural network and constantly update weight coefficients of temperature correction by finite iteration calculation so that the angle value modified can approach the angle value at the target temperature. This approach is used in adaptive correction of the angle value.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1999년도 추계학술대회 논문집 학회본부 B
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• pp.727-729
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• 1999

We have developed a realtime phonetic typewriter implemented on IBM PC with sound card based on Windows 95. In this system, analyzing of speech signal, learning of neural network, labeling of output neurons and visualizing of recognition results are performed on realtime. The developing environment for speech processing is established by adding various functions, such as editing, saving, loading of speech data and 3-D or gray level displaying of spectrogram. Recognition experimental using Korean phone had a 71.42% for 13 basic consonant and 90.01% for 7 basic vowel accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.842-847
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• 2005

This paper proposes a novel real-time EMG pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To cope with the nonstationary signal property of the EMG, features are extracted by wavelet packet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of PCA and SOFM. The dimensionality reduction by PCA simplifies the structure of the classifier, and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features to a new feature space with high class separability. Finally a multilayer neural network is employed as the pattern classifier. We implement a real-time control system for a multifunction virtual hand. From experimental results, we show that all processes, including virtual hand control, are completed within 125 msec, and the proposed method is applicable to real-time myoelectric hand control without an operation time delay.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권12호
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• pp.834-838
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• 2004

This paper proposes an algorithm for EEG pattern classification using the Multi-template method, which is a kind of speaker adaptation method for speech signal processing. 10-channel EEG signals are collected in various environments. The linear prediction coefficients of the EEGs are extracted as the feature parameter of human sensibility. The human sensibility classification algorithm is developed using neural networks. Using EEGs of comfortable or uncomfortable seats, the proposed algorithm showed about 75% of classification performance in subject-independent test. In the tests using EEG signals according to room temperature and humidity variations, the proposed algorithm showed good performance in tracking of pleasantness changes and the subject-independent tests produced similar performances with subject-dependent ones.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -2
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• pp.818-821
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• 2002

In this paper, we propose Neuro-Fuzzy discrimination method of the new and old bill using bill money acoustic data. The concept of the histogram is introduced to improve the processing time into the proposal system. The adaptative filter is used in order to remove the motor sound from an observed bill money acoustic data. The output signal of this adaptive digital filter is converted into not only a spectrum but also a histogram. It became easy that features of the paper money sound were extracted from the bill money acoustic data. The spectral data and the histogram is obtained like this, and it become an input pattern of the neural network(NN). Then, the discrimination result of the NN is finally judged by the fuzzy inferece in the new bill or the exhaustion bill.

• 한국마린엔지니어링학회:학술대회논문집
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• 한국마린엔지니어링학회 2002년도 춘계학술대회논문집
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• pp.213-217
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• 2002

Recently the subject of "wavelet analysis" has be drawn by both mathematical and engineering application fields such as Signal Processing, Compression/Decomposition, Wavelet-Neural Network, Statistics and etc. Even though its similar to courier analysis, wavelet is a versatile tool with much mathematical content and great potential for applications. Especially, wavelet transform uses localizable various mother wavelet functions in time-frequency domain. Therefore, wavelet transform has good time-analysis ability for high frequency component, and has good frequency-analysis ability for low frequency component. Using the discriminative ability is more easy method than other conventional techniques. In this paper, Morlet wavelet transform was applied to discriminate the kind of line fault by acquired data from real power transformation network. The experimental result presented that Morlet wavelet transform is easier, and more useful method than the FFW (Fast courier Transform).ransform).

• Journal of Electrical Engineering and Technology
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• 제5권3호
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• pp.493-496
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• 2010

At present, Metal Oxide gas Sensors (MOXs) are widely used in gas detection because of its advantages, including high sensitivity and low cost. However, MOX presents well-known problems, including lack of selectivity and environment effect, which has motivated studies on different measurement strategies and signal-processing algorithms. In this paper, we present an artificial neural network (ANN) that models an MOX sensor (TGS822) used in a dynamic environment. This model takes into account dependence in relative humidity and in gas nature. Using MATLAB interface in the design phase and optimization, the proposed model is implemented as a component in an electronic simulator library and accurately expressed the nonlinear character of the response and that its dependence on temperature and relative humidity were higher than gas nature.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2005년도 전력전자학술대회 논문집
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• pp.192-194
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• 2005

Development of nonlinear loads and compensation instruments make PQ(Power Quality) problem into important issue. Few studies by signal processing and pattern classification as NN(Neural Network), Wavelet Transform, and Fuzzy present feature extraction. A lot of Input features make not always good result and they are difficult to make realtime system. Thus, The dimentionality reduction is indispensable process. PCA(Principal Component Analysis) reduces high-dimensional input features onto a lower-dimensional subspace effectively. It will be useful to apply to realtime system and NN.