• The Journal of Korean Association of Computer Education
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• v.6 no.4
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• pp.37-47
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• 2003

In this paper. we propose a new digital watermarking technique which uses the self-similarity of OCT(Discrete Cosine Transform) coefficients for the ownership protection of an image, similar coefficients are classified by SOM(Self-Organizing Map) out of Neural Network. The watermark is inserted into the selected cluster among clusters which consist of coefficients. Generally, the inserted watermark in high frequency regions of an image is eliminated by the compression process such as JPEG compressions, and the inserted watermark in low frequency regions of an image causes the distortion of an image quality. Therefore, the watermark is inserted into the cluster that has many coefficients in the middle frequency regions. This algorithm reduces the distortion of an image quality because of inserting the watermark into an image according to the number of coefficients in selected cluster. To extract watermarks from the watermarked image, the selected cluster is used without an original image. In the experiment, the new proposed algorithm have a good quality and endure attacks(JPEG compressions, filtering. zoom in, zoom out, cropping, noises) very well.

• Proceedings of the Korea Concrete Institute Conference
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• 2008.04a
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• pp.1009-1012
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• 2008

The fiber dispersion performance in fiber-reinforced cementitious composites is a crucial factor with respect to achieving desired mechanical performance. However, evaluation of the fiber dispersion performance in the composite PVA-ECC(Polyvinyl alcohol-Engineered Cementitious Composite) is extremely challenging because of the low contrast of PVA fibers with the cement-based matrix. In the present work, an enhanced fiber detection technique is developed and demonstrated. Using a fluorescence technique on the PVA-ECC, PVA fibers are observed as green dots in the cross-section of the composite. After capturing the fluorescence image with a Charged Couple Device(CCD) camera through a microscope. The fibers are more accurately detected by employing a series of process based on a categorization, watershed segmentation, and morphological reconstruction.

• The Journal of the Acoustical Society of Korea
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• v.14 no.2
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• pp.101-112
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• 1995

In order to construct a feature map-based phoneme classification system for speech recognition, two procedures are usually required. One is clustering and the other is labeling. In this paper, we present a phoneme classification system based on the Kohonen's Self-Organizing Feature Map (SOFM) for clusterer and labeler. It is known that the SOFM performs self-organizing process by which optimal local topographical mapping of the signal space and yields a reasonably high accuracy in recognition tasks. Consequently, SOFM can effectively be applied to the recognition of phonemes. Besides to improve the performance of the phoneme classification system, we propose the learning algorithm combined with the classical K-mans clustering algorithm in fine-tuning stage. In order to evaluate the performance of the proposed phoneme classification algorithm, we first use totaly 43 phonemes which construct six intra-class feature maps for six different phoneme classes. From the speaker-dependent phoneme classification tests using these six feature maps, we obtain recognition rate of $87.2\%$ and confirm that the proposed algorithm is an efficient method for improvement of recognition performance and convergence speed.

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.37-50
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• 2007

In this paper, we designed and implementation of the high speed neuron processor for real time object recognition in the making automatic system. and we designed of the PE(Processing Element) used residue number system without carry propagation for the high speed operation. Consisting of MAC(Multiplication and Accumulation) operator using residue number system and sigmoid function operator unit using MAC(Mixed Radix conversion) is designed. The designed circuits are descript by C language and VHDL(Very High Speed Integrated Circuit Hardware Description Language) and synthesized by compass tools and finally, the designed processor is fabricated in $0.8{\mu}m$ CMOS process. we designed of MAC operation unit and sigmoid proceeding unit are proved that it could run time 0.6nsec on the simulation and improved to the speed of the three times and decreased to hardware size about 50%, each order. The designed neuron processor can be implemented of the object recognition in making automatic system with desired real time processing.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.2
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• pp.169-176
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• 2010

Nonstationary power quality(PQ) signals which the Sag, Swell, Impulsive Transients, and Harmonics make sometimes the operations of the industrial power electronics equipment, speed and motion controller, plant process control systems in the undesired environments. So, this PQ problem might be critical issues between power suppliers and consumers. Therefore, We have studied the PQ recognition system in order to acquire, analyze, and recognize the PQ signals using the software, i.e, MATLAB, Simulink, and CCS, and the hardware. i.e., TMS320C6713DSK(TI), The algorithms of the PQ recognition system in the Wavelet transforms and Backpropagation algorithms of the neural networks. Also, in order to verify the real-time performances of the PQ recognition system under the environments of software and hardware systems, SIL(Software In the Loop) and PIL(Processor In the Loop) were carried out, resulting in the excellent recognition performances of average 99%.

• The Journal of the Acoustical Society of Korea
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• v.15 no.4
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• pp.43-49
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• 1996

This research proposes a system for speaker independent Korean continuous speech recognition with 247 DDD area names using keyword spotting technique. The applied recognition algorithm is the Dynamic Programming Neural Network(DPNN) based on the integration of DP and multi-layer perceptron as model that solves time axis distortion and spectral pattern variation in the speech. To improve performance, we classify word model into keyword model and non-keyword model. We make an experiment on postprocessing procedure for the evaluation of system performance. Experiment results are as follows. The recognition rate of the isolated word is 93.45% in speaker dependent case. The recognition rate of the isolated word is 84.05% in speaker independent case. The recognition rate of simple dialogic sentence in keyword spotting experiment is 77.34% as speaker dependent, and 70.63% as speaker independent.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.816-819
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• 2005

This paper proposes a coordinate correction technique using the transformation of 3D parabolic coordinate function and BP(Back Propagation) neural network in order to solve space distortion problem caused by using catadioptric camera. Although Catadioptric camera can obtain omni-directional image at all directions of 360 degrees, it makes an image distorted because of an external form of lens itself. Accordingly, To obtain transformed ideal distance coordinate information from distorted image on 3 dimensional space, we use coordinate transformation function that uses coordinates of a focus at mirror in the shape of parabolic plane and another one which projected into the shape of parabolic from input image. An error of this course is modified by BP neural network algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.322-323
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• 2022

The most necessary thing for the optimal operation of a water purification plant is to accurately predict the pattern and amount of tap water used by consumers. The required amount of tap water should be delivered to the drain using a pump and stored, and the required flow rate should be supplied in a timely manner using the minimum amount of electrical energy. The short-term demand forecasting required from the point of view of energy optimization operation among water purification plant volume predictions has been made in consideration of seasons, major periods, and regional characteristics using time series analysis, regression analysis, and neural network algorithms. In this paper, we analyzed energy management methods through AI-based complex sensor applicability analysis such as LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Units), which are types of cyclic neural networks.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.5
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• pp.265-271
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• 2023

Recently, location information services are gradually expanding due to the development of information and communication technology. RSSI is widely used to extract indoor and outdoor locations. The indoor and outdoor location estimation methods using RSSI are less accurate due to the influence of radio wave paths, interference, and surrounding wireless devices. In order to improve this problem, a distance estimation method that takes into account the wireless propagation environment is necessary. Therefore, in this study, we propose a distance estimation algorithm that takes into account the radio wave environment. The proposed method estimates the distance by learning RSSI input and output considering the RBF neural network and the propagation environment. To evaluate the performance of the proposed method, the performance of estimating the location of the receiver within a range of up to 55[m] using a BLE beacon transmitter and receiver was compared with the average filter and Kalman filter. As a result, the distance estimation accuracy of the proposed method was 6.7 times higher than that of the average filter and Kalman filter. As shown in the results of this performance evaluation, if the method of this study is applied to location services, more accurate location estimation will be possible.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.1-6
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• 2014

In this study, we propose optimized RBFNNs based on night vision face recognition simulator with the aid of $(2D)^2$ PCA algorithm. It is difficult to obtain the night image for performing face recognition due to low brightness in case of image acquired through CCD camera at night. For this reason, a night vision camera is used to get images at night. Ada-Boost algorithm is also used for the detection of face images on both face and non-face image area. And the minimization of distortion phenomenon of the images is carried out by using the histogram equalization. These high-dimensional images are reduced to low-dimensional images by using $(2D)^2$ PCA algorithm. Face recognition is performed through polynomial-based RBFNNs classifier, and the essential design parameters of the classifiers are optimized by means of Differential Evolution(DE). The performance evaluation of the optimized RBFNNs based on $(2D)^2$ PCA is carried out with the aid of night vision face recognition system and IC&CI Lab data.