• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2272-2274
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• 1998

This paper presents recognition and classification of high impedance fault(HIF) patterns in the electrical power systems based on chaotic features. Chaotic features are obtained from two dimensional chaos attractors reconstructed from fault current waveform. The RBFN is trained with the two types of HIF data generated by the electromagnetic transient program and measured from actual faults. The RBFN successfully classifies normal and the three types of fault patterns based on the binary chaotic features.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.11
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• pp.130-137
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• 1992

This paper presents the word recognition method using a neural-fuzzy pattern matching, in order to make a proper speech pattern for a spectrum sequence and to improve a recognition rate. In this method, a frequency variation is reduced by generating binary spectrum patterns through associative memory using a neural network, and a time variation is decreased by measuring the simillarity using a fuzzy pattern matching. For this method using binary spectrum patterns and logic algebraic operations to measure the simillarity, memory capacity and computation requirements are far less than those of DTW using a conventional distortion measure. To show the validity of the recognition performance for this method, word recognition experiments are carried out using 28 DDD city names and compared with DTW and a fuzzy pattern matching. The results show that our presented method is more excellent in the recognition performance than the other methods.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.3
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• pp.109-119
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• 1996

We propsoed a multiplexed optical correlation filter method for an optical parallel addition based on symbolic substitution. In the proposed mthod, we used redundant binary number which was easy to minimize the number of the symbolic substitution rules. We chose MACE filter which had very low sidelobes and good correlation peak compared with SDF filter as the optical correlation recognition filter and encoded input numbers properly to increase the discrimination capability. In order to minimize the number of symbolic substitution rules, sixteen input patterns were divided into six groups of the same addition results and six filters for recognizing the input patterns were used. these filters were multiplexed in two MMACE filter planes and the corresponding substitution method was proposed. Through the computer simulation, we confirmed the proposed method was suitable to implement the optical parallel adder.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.14 no.1
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• pp.6-11
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• 2001

We studied the feasibility of optical lithography for the critical layers of 0.12${\mu}{\textrm}{m}$ DRAM assuming ArF excimer laser as a light source. To enhance the fidelity of aerial image and process margin, Phase shift mask (PSM) patterns as well as binary mask patterns are corrected with in-house developed Optical Proximity Correction (OPC) software. As the result, w found that the aerial image of critical layers of DRAM cell with 0.12${\mu}{\textrm}{m}$ design rule could not be reproduced with binary masks. But if we use PSM or optical proximity corrected PSM, the fidelity of aerial image ,resolution and process margin are so much enhanced that they could be processed with optical lithography.

• ETRI Journal
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• v.37 no.5
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• pp.990-1000
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• 2015

This paper presents an overlapping pattern substitution (PS) method. The original overlapping PS method as a reversible data hiding scheme works well with only four pattern pairs among fifteen possible such pairs. This paper generalizes the original PS method so that it will work well with an optimal pair from among the fifteen possible pattern pairs. To implement such an overlapping PS method, changeable and embeddable patterns are first defined. A class map is virtually constructed to identify the changeable and embeddable pairs. The run-lengths between consecutive least probable patterns are recorded. Experiments show that an implementation of our overlapping PS method works well with any possible type of pairs. Comparison results show that the proposed method achieves more embedding capacity, a higher PSNR value, and less human visual distortion for a given embedding payload.

• Annual Conference of KIPS
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• 2015.10a
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• pp.1358-1360
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• 2015

본 논문에서는 MB-LBP(Multi-scale Block Local Binary Patterns)와 공간피라미드를 이용하여 생성된 특징을 랜덤 포레스트(Random Forest) 분류기에 적용하여 영상내의 표지판 속도를 인식하는 알고리즘을 제안한다. 입력 영상에서 표지판 영역은 다양한 위치와 크기를 가지며 주위 배경이 후보 영역에 포함되므로 먼저 입력 영상에 원형 Hough Transform을 적용하여 원형의 표지판 후보 영역만을 검출한다. 그 후 영상의 화질을 향상시키기 위해 히스토그램 평활화와 모폴로지 연산을 적용하여 표지판의 숫자 영역과 배경 영역의 대비를 높이도록 한다. 표지판의 크기 변화에 강건한 시스템의 구현을 위해 후보 영역에서 LBP(Local Binary Patterns)보다 우수한 성능을 보이는 MB-LBP를 적용하고, 다양한 크기의 속도 표지판을 인식하기 위해 공간 피라미드를 사용하여 지역적 특징과 전역적 특징 모두를 추출하였다. 추출된 특징은 랜덤 포레스트(Random Forest)를 이용하여 각 9개의 속도 표지판으로 분류, 각 속도별 클래스에 대한 인식 성능을 측정하였다.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.100-105
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• 1992

Texture is one of the important characteristics that has been used to identify objects or regions of interest in an image. Tire tread patterns can be considered as a kind of texture, and these are classified with a texture analysis method. In this sense, this paper proposes a new algorithm for the classification of tire tread pattern. For the classification, cooccurrence matrix for the binary image is used. The performances are tested by experimentally 8 different tire tread pattern and the robustness is examined by including some kinds on noise.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3312-3327
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• 2020

Plant diseases are a significant yield and quality constraint for farmers around the world due to their severe impact on agricultural productivity. Such losses can have a substantial impact on the economy which causes a reduction in farmer's income and higher prices for consumers. Further, it may also result in a severe shortage of food ensuing violent hunger and starvation, especially, in less-developed countries where access to disease prevention methods is limited. This research presents an investigation of Directional Local Quinary Patterns (DLQP) as a feature descriptor for plants leaf disease detection and Support Vector Machine (SVM) as a classifier. The DLQP as a feature descriptor is specifically the first time being used for disease detection in horticulture. DLQP provides directional edge information attending the reference pixel with its neighboring pixel value by involving computation of their grey-level difference based on quinary value (-2, -1, 0, 1, 2) in 0°, 45°, 90°, and 135° directions of selected window of plant leaf image. To assess the robustness of DLQP as a texture descriptor we used a research-oriented Plant Village dataset of Tomato plant (3,900 leaf images) comprising of 6 diseased classes, Potato plant (1,526 leaf images) and Apple plant (2,600 leaf images) comprising of 3 diseased classes. The accuracies of 95.6%, 96.2% and 97.8% for the above-mentioned crops, respectively, were achieved which are higher in comparison with classification on the same dataset using other standard feature descriptors like Local Binary Pattern (LBP) and Local Ternary Patterns (LTP). Further, the effectiveness of the proposed method is proven by comparing it with existing algorithms for plant disease phenotyping.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.11
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• pp.810-820
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• 2001

Conventional binary adder requires a carry propagation to the most significant bit, and leads to serial addition. However, optical adder using a modified signed digit(MSD) number system has been Proposed to reduce the carry propagation chain encountered in binary adder. In this paper, in order to minimize the number of symbolic substitution(SS) rules, nine input patterns were divided into five groups of the same addition results. For recognizing the input reference patterns, serial connections of joint spatial encoded patterns and masks without any other spatial operations are used.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.2
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• pp.44-57
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• 1995

In this paper GFI (Generalized Fuzzy Isodata) and FI (Fuzzy Isodata) algorithms are studied and applied to the tire tread pattern classification problem. GFI algorithm which repeatedly grouping the partitioned cluster depending on the fuzzy partition matrix is general form of GI algorithm. In the constructing the binary tree using GFI algorithm cluster validity, namely, whether partitioned cluster is feasible or not is checked and construction of the binary tree is obtained by FDH clustering algorithm. These algorithms show the good performance in selecting the prototypes of each patterns and classifying patterns. Directions of edge in the preprocessed image of tire tread pattern are selected as features of pattern. These features are thought to have useful information which well represents the characteristics of patterns.