• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.11
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• pp.3001-3011
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• 1996

In this paper, using DBNN algorithm which is used in the binary pattern classification or speech signal processing the digital DBNN circuit is designed having the variable expansion depending the size of input data and pattern type. The processing elemen(PE) of the proposed network consists of the synapse and MAXNET circuits for the similarity measurement between reference and input pattern. Global MAXNET selects the global winner among the local winners which is selected in each PE. Through the several simultions, and thus each PE and global MAXNET search the reference pattern that was the most simlar to input pattern for the discord of the pattern.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.1
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• pp.58-64
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• 2012

Instead of the surface pattern arranged repeatedly in two axial direction on a plane, we propose double patterns superposing two one-axial linear patterns as a reference target for surface encoding. A upper layer of the superposed pattern is the transparent glass with grooves cut in it at a fixed pitch. The position is sensed by detecting a shift of beam due to difference of a refractive index. And a lower layer is the aluminum with color-coated grooves. The amount of beam reflected on the layer varies according to its targeting position and is detected for encoding. For the above reference pattern, we can detect two-axial positions using only the single beam. Furthermore, the pattern size can be expanded with a size of the detector kept constant, meaning that the measured range can be expanded easily. In this paper, we review the existing optical encoding methods for grid pattern, and discuss the hardware implementation of the suggested surface encoding method.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.6
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• pp.685-694
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• 2015

In this paper, a new dynamic reference scheme is proposed to improve the read voltage margin better than the previous static reference scheme. The proposed dynamic reference scheme can be helpful in compensating not only the background pattern dependence but also the cell position dependence. The proposed dynamic reference is verified by simulating the CMOS-memristor hybrid circuit using the practical CMOS SPICE and memristor Verilog-A models. In the simulation, the percentage read voltage margin is compared between the previous static reference scheme and the new dynamic reference scheme. Assuming that the critical percentage of read voltage margin is 5%, the memristor array size with the dynamic scheme can be larger by 60%, compared to the array size with the static one. In addition, for the array size of $64{\times}64$, the interconnect resistance in the array with the dynamic scheme can be increased by 30% than the static reference one. For the array size of $128{\times}128$, the interconnect resistance with the proposed scheme can be improved by 38% than the previous static one, allowing more margin on the variation of interconnect resistance.

• Journal of Korea Multimedia Society
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• v.18 no.3
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• pp.312-322
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• 2015

In this paper a new defect detection method for flat panel display that does not require any separately prepared reference images and shows robustness against problems with regard to pixel tolerance and nonuniform illumination condition is proposed. In order to perform defect detection under any magnification value of camera, the proposed method automatically obtains the value of pattern interval through an image analysis. Using the information for pattern interval, an advanced PCSR-G method presented in this paper utilizes neighboring patterns as its reference images instead of utilizing any separately prepared reference images. Also this paper proposes a scheme to improve the performance of the conventional PCSR-G method by extracting and applying additional information for pixel tolerance and intensity distribution considering the value of pattern interval. Simulation results show that the performance of the proposed method utilizing pixel tolerance and intensity distribution is superior to that of the conventional method. Also, it is proved that the proposed method that is implemented using parallel technique based on GPGPU can be applied to real system.

• Journal of radiological science and technology
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• v.30 no.4
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• pp.419-426
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• 2007

In this paper, the input pattern vectors are extracted and the learning algorithms is designed to recognize taste(bitter, sweet, sour and salty) pattern vectors. The signal intensity of taste are used to compose the input pattern vectors. The SOM(Self Organizing Maps) algorithm for taste pattern recognition is used to learn initial reference vectors and the ot-star learning algorithm is used to determine the class of the output neurons of the sunclass layer. The weights of the proposed algorithm which is between the input layer and the subclass layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVQ(Learning Vector Quantization) algorithm. The pattern vectors are classified into subclasses by neurons in the subclass layer, and the weights between subclass layer and output layer are learned to classify the classified subclass, which is enclosed a class. To classify the pattern vectors, the proposed algorithm is simulated with ones of the conventional LVQ, and it is confirmed that the proposed learning method is more successful classification than the conventional LVQ.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.95-103
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• 2006

In this paper, the input pattern vectors are extracted and the learning algorithms is designed to recognize EEG pattern vectors. The frequency and amplitude of alpha rhythms and beta rhythms are used to compose the input pattern vectors. And the algorithm for EEG pattern recognition is used SOM to learn initial reference vectors and out-star learning algorithm to determine the class of the output neurons of the subclass layer. The weights of the proposed algorithm which is between the input layer and the subclass layer can be learned to determine initial reference vectors by using SOM algorithm and to learn reference vectors by using LVQ algorithm, and pattern vectors is classified into subclasses by neurons which is being in the subclass layer, and the weights between subclass layer and output layer is learned to classify the classified subclass, which is enclosed a class. To classify the pattern vectors of EEG, the proposed algorithm is simulated with ones of the conventional LVQ, and it was a confirmation that the proposed learning method is more successful classification than the conventional LVQ.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.10
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• pp.95-104
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• 1993

IPA(interpattern association) model is a method of feature extraction using a neural network. Even in the case that the reference patterns are simuklar to one another, this model can recover the reference patterns effectively. However, when the pattern whose feature pixels are lost is used as input, this model can not guarantee perfect recovery of the reference pattern. It is proposed a improved interpattern association(IPA) model for the feature extraction using neural network. The improved IPA model that combines the first interconnection weight matrix of the IPA model with the second additional weight matrix is proposed here to overcome the recovery problem of the original IPA model. The results of computer simulation and optical experiment are advanced.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.7
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• pp.83-91
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• 2004

The determination of camera position and orientation from known correspondences of 3D reference points and their images is known as pose estimation in computer vision or space resection in photogrammetry. This paper discusses estimation of transform parameters using the pattern matching method with 2D images only. In general, the 3D reference points or lines are needed to find out the 3D transform parameters, but this method is applied without the 3D reference points or lines. It uses only two images to find out the transform parameters between two image. The algorithm is simulated using Visual C＋＋ on Windows 98.

• Journal of information and communication convergence engineering
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• v.8 no.3
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• pp.339-342
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• 2010

An iris image pattern positioning with preserved edge detector, ring zone and clock zone, frequency distribution and overlay matching is presented in this paper. Edge detector is required to be powerful and detail. That is proposed by overlaying Canny with LOG (CLOG). The two reference patterns are made from allocating each gray level on the clock zone and ring zone respectively. The normalized target image is overlaid with the clock zone reference pattern and the ring zone pattern to extract overlapped number, and make a matched frequency distribution to look through a symptom and position of human organ and tissue. The iterating experiments result in the ring and clock zone positioning evaluation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.10
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• pp.1973-1979
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• 2016

As technologies evolve, diverse 3D measurement techniques using cameras and pattern projectors have been developed continuously. In 3D measurement, high accuracy, fast speed, and easy implementation are very important factors. Recently, 3D measurement using multi-frequency fringe patterns for absolute phase computation has been widely used in the fringe projection profilometry. This paper proposes an improved method to compute the object's absolute phase using the reference plane's absolute phase and phase difference between the object and the reference plane. This method finds the object's absolute phase by adding the difference between the reference plane's wrapped phase and the object's wrapped phase to the reference plane's absolute phase already obtained in the calibration stage. Through this method, there is no need to obtain multi-frequency fringe patterns about new object for the absolute phase computation. Instead, we only need the object's phase difference relative to the reference planes's phase in the measurement stage.