• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.883-884
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• 2008

This paper presents a method for classification of underwater transient signals using, which employs a binary image pattern of the mel-frequency cepstral coefficients(MFCC) as a feature vector and a neural network as a classifier. A feature vector is obtained by taking DCT and 1-bit quantization for the square matrix of the MFCC sequences. The classifier is a feed-forward neural network having one hidden layer and one output layer, and a back propagation algorithm is used to update the weighting vector of each layer. Experimental results with some underwater transient signals demonstrate that the proposed method is very promising for classification of underwater transient signals.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.943-946
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• 1988

A simple method is proposed for determining the frequency response function G(j.omega.) of a system using a pair of characteristic M-sequences (maximum length linear feed back shift register sequence). When a characteristic M-sequence is sampled with q$_{1}$ and q$_{2}$ both of which are coprime with N, where N is the period of the M-sequence, the obtained pair of sequences have conjugate complex frequency spectrum. Making use of this fact, two charcteristic M-sequences having conjugate complex frequency spectrum are applied to a system to be measured. Since the magnitude of spectrium of M-sequence is known, the gain of G(j.omega.) is directly obtained from the Fourier transform of the system output. The phase of G(j.omega.) is obtained simply by taking the average of the two phases of output spectrum.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.363-366
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• 2003

We propose the system that automatically adjusts the white balance on display products to a standard value according to several nations. We replace manual or semi-auto method with fully automatic method using windows application program. And we use RS-232C serial interface to communicate PC with display products which we want to adjust white balance. The PC generates patterns for measuring color information and Color Analyzer measures color and brightness. This value is transmitted through RS-232C serial interface to PC. The PC's algorithm analyzes this information and then decides which RGB Gain value is best for optimal white balance. This RGB Gain value is transmitted through RS-232C serial interface to display products. The modified color value is measured again and feed back to PC. This sequence is repeated until optimum white balance is obtained.

• Journal of Sensor Science and Technology
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• v.13 no.3
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• pp.236-243
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• 2004

In this paper, we proposed a brain-computer interface system using EEG signals. It can generate 4 direction command signal from EEG signals captured during imagination of subjects. Bandpass filter used for preprocessing to detect the brain signal, and the power spectrum at a specific frequency domain of the EEG signals for concentration status and non-concentration one is used for feature. In order to generate an adequate signal for controlling the 4 direction movement, we propose a new interface system implemented by using a support vector machine and a time-multiplexing method. Moreover, bio-feed back process and on-line adaptive pattern recognition mechanism are also considered in the proposed system. Computer experimental results show that the proposed method is effective to recognize the non-stational brain wave signal.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.11
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• pp.56-65
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• 1993

This paper presents the design consideration of the MFNNs(Multilayer Feed forward Neural Networks) based on the distribution of the given teching patterns. By extracting the feature points from the given teaching patterns, the structure of a network including the netowrk size and interconnection weights of a network is initialized. This network is trained based on the modified version of the EBP(Error Back Propagation) algorithm. As a result, the proposed method has the advantage of learning speed compared to the conventional learning of the MFNNs with randomly chosen initial weights. To show the effectiveness of the suggested approach, the simulation result on the approximation of a two demensional continuous function is shown.

• Structural Engineering and Mechanics
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• v.29 no.1
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• pp.77-89
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• 2008

Presence of high degree of orthotropy enhances shear lag phenomenon in laminated composite box-beams and it persists till failure. In this paper three key parameters governing shear lag behavior of laminated composite box beams are identified and defined by simple expressions. Uniqueness of the identified key parameters is proved with the help of finite element method (FEM) based studies. In addition to this, for the sake of generalization of prediction of shear lag effect in symmetrical laminated composite box beams a feed forward back propagation neural network (BPNN) model is developed. The network is trained and tested using the data base generated by extensive FEM studies carried out for various b/D, b/tF, tF/tW and laminate configurations. An optimum network architecture has been established which can effectively learn the pattern. Computational efficiency of the developed ANN makes it suitable for use in optimum design of laminated composite box-beams.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.457-463
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• 2016

In this paper, a method of enhancing the bandwidth of a double-dipole quasi-Yagi antenna (DDQYA) using a modified integrated balun is presented. The modified integrated balun consists of a microstrip (MS) line inserted along the center of a coplanar strip (CPS) line and the end of the MS line is connected to the CPS line through a shorting pin at the feed point. The geometry of the modified integrated balun is adjusted to improve the bandwidth of the DDQYA. In addition, the performance of the proposed balun in a back-to-back configuration is compared with a conventional balun. The proposed antenna with the optimized modified integrated balun is fabricated on an FR4 substrate, and the experiment results show that the antenna has a frequency band of 1.56-3.04 GHz(64.4%) for a VSWR < 2, which shows enhanced bandwidth compared to the DDQYA with the conventional balun.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.6_2
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• pp.1083-1095
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• 2023

Recently, the demand for low-voltage, high-capacity ESS is rapidly increasing due to the revitalization of the e-mobility industry, which is mainly powered by electricity. In addition, the demand for portable power banks is rapidly increasing due to the revitalization of leisure industries such as camping and fishing. The ESS with this structure consists of a small number of series cells and many parallel cells, resulting in a system with a large rated current. Therefore, the number of power devices for cell balancing configured in series is small, but a balancing device with a large current capacity is required. Construction of a constant temperature device in such a low-voltage, high-current ESS is difficult due to economic issues. The demand for an active balancing system that can solve the passive balancing heating problem is rapidly increasing. In this paper, propose a power feedback fly-back topology that can solve the balancing heating problem. The characteristic of the proposed topology is that a series-connected voltage sharing voltage is used as the input of the flyback converter, and the converter output is connected to one transformer. In this structure, the converter output for cell voltage balancing shares magnetic flux through one high-frequency transformer, so the cell voltage connected to the converter automatically converges to the same voltage.

• Journal of Korean Society on Water Environment
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• v.21 no.1
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• pp.21-28
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• 2005

The training and prediction modeling using an artificial neural network was implemented to predict the turbidity of treated water as well as to estimate the optimized feed concentration of polyaluminium chloride (PACl) in a water treatment plant. The parameters used in the input layers were pH, temperature, turbidity and alkalinity, while those in output layers were PACl and turbidity of treated water. Levenberg-Marquadt method of feedforward back-propagation perceptron in the neural network toolbox of MATLAB program was used in this study. Correlation coefficients of the training data with the measured data were 0.9997 for PACl and 0.6850 for turbidity and those of the testing data with measured data were 0.9140 for PACl and 0.3828 for turbidity, when four parameters at input layer, 12-12 nodes each at both the first and the second hidden layers, and two parameters(PACl and turbidity) at output layer were used. Although the predictability of PACl was improved, compared to that of the previous studies to use the only coagulant dose as output layer, turbidity in treated water could not be predicted well. Acquisition of more data through several years obtained with the advanced on-line measuring system could make the artificial neural network useful and practical in actual water treatment plants.

• The Journal of the Korea Contents Association
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• v.1 no.1
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• pp.8-14
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• 2001

In this paper, we suggests a algorithms of recognizing the disease region by extracting particular organ from medical image. This method can extract liver region in spite of input image including many organs and charged format by using multi-threshold of feed-back-structure for segmentation liver region, and suggest the recognition of disease region in extracted liver, using multi-neural network structured by RBF and BP, overcoming the defect of single-neural network. The algorithm in this paper is proficient in adaptation for a multi form change of input medical image. This algorithm can be used at tole-medicine through automatic recognition after recognizing of the disease region by real-tire medical Image.