• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.774-779
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• 2012

Brain-computer interface based on motor imagery is a system that transforms a subject's intention into a control signal by classifying EEG signals obtained from the imagination of movement of a subject's limbs. For the new paradigm, we do not know which positions are activated or not. A simple approach is to use as many channels as possible. The problem is that using many channels causes other problems. When applying a common spatial pattern (CSP), which is an EEG extraction method, many channels cause an overfit problem, in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest a binary particle swarm optimization with channel impact factor in order to select channels close to the most important channels as channel selection method. This paper examines whether or not channel impact factor can improve accuracy by Support Vector Machine(SVM).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.201-207
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• 2012

A RAM-based neural network is a weightless neural network based on binary neural network(BNN) which is efficient neural network with a one-shot learning. RAM-based neural network has multiful information bits and store counts of training in BNN. Supervised learning based on the RAM-based neural network has the excellent performance in pattern recognition but in pattern categorization with unsupervised learning as unsuitable. In this paper, we propose a unsupervised learning algorithm in the RAM-based neural network to perform pattern categorization. By the proposed unsupervised learning algorithm, RAM-based neural network create categories depending on the input pattern by itself. Therefore, RAM-based neural network for supervised learning and unsupervised learning should proof of all possible complex models. The training data for experiments provided by the MNIST offline handwritten digits which is consist of 0 to 9 multi-pattern.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.3
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• pp.269-281
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• 2001

This thesis deals with a novel font-dependent Hangul recognition algorithm invariant to position translation, scaling, and rotation using circular pattern vectors. The proposed algorithm removes noise from input letters using binary morphology and generates the circular pattern vectors. The generated circular pattern vectors represent spatial distributions on several concentric circles from the center of gravity in a given letter. Then the algorithm selects the letter minimizing the distance between the reference vectors and the generated circular pattern vectors. In order to estimate performances of the proposed algorithm, the completed Batang Hangul 2,350 letters were used as test images with scaling and rotational transformations. Experimental results show that the proposed algorithm are better than conventional algorithm using the ring projection in the recognition rates of Hangul letters with scaling and rotational transformation.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.191-196
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• 2009

In this paper, we proposed a feature selection method using Binary Particle Swarm Optimization(BPSO) and Mutual information. This proposed method consists of the feature selection part for selecting candidate feature subset by mutual information and the optimal feature selection part for choosing optimal feature subset by BPSO in the candidate feature subsets. In the candidate feature selection part, we computed the mutual information of all features, respectively and selected a candidate feature subset by the ranking of mutual information. In the optimal feature selection part, optimal feature subset can be found by BPSO in the candidate feature subset. In the BPSO process, we used multi-object function to optimize both accuracy of classifier and selected feature subset size. DNA expression dataset are used for estimating the performance of the proposed method. Experimental results show that this method can achieve better performance for pattern recognition problems than conventional ones.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.9
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• pp.963-971
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• 1992

A (4,6) block binary line code is proposed. In addition to being de-free and runlength-limited, the new code called MB46 is strictly bandwidth-limited to the Nyquist frequency, thus achieving improved bandwidth efficiency over previously known binary line code. A technique specially employed in the design of the code is described in depth, and some performance measures including the eye pattern and the power spectrum are presented as obtained by simulation.

• Journal of Broadcast Engineering
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• v.21 no.4
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• pp.562-568
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• 2016

In this paper, we focus on recognition of speed-limit signs among a few types of traffic signs because speed-limit sign is closely related to safe driving of drivers. Although histogram of oriented gradient (HOG) and local binary patterns (LBP) are representative features for object recognition, these features have a weakness with respect to rotation, in that it does not consider the rotation of the target object when generating patterns. Therefore, this paper propose the fast rotation-invariant binary patterns (FRIBP) algorithm to generate a binary pattern that is robust against rotation. The proposed FRIBP algorithm deletes an unused layer of the histogram, and eliminates the shift and comparison operations in order to quickly extract the desired feature. The proposed FRIBP algorithm is successfully applied to German Traffic Sign Recognition Benchmark (GTSRB) datasets, and the results show that the recognition capabilities of the proposed method are similar to those of other methods. Moreover, its recognition speed is considerably enhanced than related works as approximately 0.47second for 12,630 test data.

• Journal of the Korea Society of Computer and Information
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• v.8 no.3
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• pp.73-79
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• 2003

This paper proposes speaker recognition algorithm which includes both the pitch detection and the fuzzified pattern matching. This study utilizes pitch pattern using a pitch and speech parameter uses binary spectrum. In this paper. makes reference pattern using fuzzy membership function in order to include time variation width for non-utterance time and performs vocal track recognition of common character using fuzzified pattern matching.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.11 no.4
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• pp.170-175
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• 2001

It was possible to grow the $Al_{2}O_{3}$ based $Y_{3}A_{5}O_{12}(YAG),ZrO_{2}$ binary and ternary eutectic fibers using micro-pulling down method with a growing rate of 0.1~15 mm/min. While $Al_{2}O_{3}/ZrO_{2}$ showed cellular-lamellar structure, $Al_{2}O_{3}$/YAG and $Al_{2}O_{3}$/YAG/$ZrO_{2}$ternary eutectic fibers showed homogeneous Chinese script lamellar structures. The microstructures of $Al_{2}O_{3}/ZrO_{2}$ binary eutectic fibers changed with solidification rate from lamellar pattern to cellular structure. The interlamellar spacing agreed with the inverse-square-root dependance on pulling rate according to $\lambda$=$kv_p\;{-1/2}$. $Al_{2}O_{3}/ZrO_{2}$ binary eutectic fibers recorded the highest tensile strength of about 1560MPa at room temperature. $Al_2O_3/YAG/ZrO_2$ternary eutectic fiber showed excellent thermal stability to $1200^{\circ}C$ without significant decrease. The maximum strength of ternary eutectic fibers recorded were 1100MPa at $25^{\circ}C$ and 970MPa at $1200^{\circ}C$, respectively.

• Proceedings of the Acoustical Society of Korea Conference
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• 1993.06a
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• pp.121-124
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• 1993

This paper is a study on the word recognition using neural network. A limited vocabulary, speaker independent, isolated word recognition system has been built. This system recognizes isolated word without performing segmentation, phoneme identification, or dynamic time wrapping. It needs a static pattern approach to recognize a spatio-temporal pattern. The preprocessing only includes preceding and tailing silence removal, and word length determination. A LPC analysis is performed on each of 24 equally spaced frames. The PARCOR coefficients plus 3 other features from each frame is extracted. In order to simplify a structure of neural network, we composed binary code form to decrease output nodes.