• Archives of design research
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• v.18 no.1 s.59
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• pp.5-16
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• 2005

As for the public, it is hard to understand semiotics because of the scope of an enormous semiotics education. This study is making semiotics of Charles Sanders Peirce the subject. I thought that utilization can hold his semiotics study in a visual design field. First of all, this study considered design related papers related to the existing semiotics again and study found an error of the existing semiotics study and understanding did category concept with re-definition about semiotics of Peirce. Explained a symbol mark through understanding of semiotics of Peirce. This study was able to get the following conclusion through these results. 1) A symbol mark means one product, sonics, company oneself with a custom. Therefore, it is a rule symbol in the Representation side. 2) A symbol mark symbolizes an object with one symbol, so a symbol mark is a symbol in an object. 3) Because a symbol mark exists through a social rule, in semiotics definition of Peirce, this must become understanding with a Argument symbol. 4) A symbol mark is what a company or an organization field used from the past, and the public are recognizing this how. Therefore, it works as fact a company attaches a symbol mark to own product, and to show the public a symbol mark. A symbol mark is Dicent Sign in Interpretant. A rule and understanding about a lot of types which have various mutual relation, Peirce classification and understanding of a symbol mark tells to demand is holding that understanding a type of semiotics with the concept that is not an image to us.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.79-84
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• 2019

Video image based technology is being used in various fields with continuous development. The demand for vision system technology that analyzes and discriminates image objects acquired through cameras is rapidly increasing. Image processing is one of the core technologies of vision systems, and is used for defect inspection in the semiconductor manufacturing field, object recognition inspection such as the number of tire surfaces and symbols. In addition, research into license plate recognition is ongoing, and it is necessary to recognize objects quickly and accurately. In this paper, propose a recognition model through the rotational alignment of objects after checking the angle value of the tilt of the object in the input video image for the recognition of inclined objects such as numbers or symbols marked on the surface. The proposed model can perform object recognition of the rotationally sorted image after extracting the object region and calculating the angle of the object based on the contour algorithm. The proposed model extracts the object region based on the contour algorithm, calculates the angle of the object, and then performs object recognition on the rotationally aligned image. In future research, it is necessary to study template matching through machine learning.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.837-846
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• 2003

This paper deals with plain figures on the architectural drawings of apartment. This kind of architectural drawings consist of main walls represented by two parallel bold lines, symbols (door, window, $\cdots$), dimension line, extension line, and dimensions represent various numerical values and characters. This paper suggests a method for recognizing main wall which is a backbone of apartment in an architectural drawing. In this thesis, the following modules are realized : an efficient image barbarization, a removal of thin lines, a vectorization of detected lines, a region bounding for main walls, a calculation of extension lines, a finding main walls based on extension line, and a field expansion by searching other main walls which are linked with the detected main walls. Although the windows between main walls are not represented as main walls, a detection module for the windows is considered during the recognition period. So the windows are found as a part of main wall. An experimental result on 9 different architectural drawings shows 96.5% recognition of main walls and windows, which is about 5.8% higher than that of Karl Tombre.

• The Journal of the Acoustical Society of Korea
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• v.24 no.7
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• pp.410-415
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• 2005

Recently, most of the speaker verification systems are based on the pattern recognition approach method. And performance of the pattern-classifier depends on how to classify a variety of speakers' feature parameters. In order to classify feature parameters efficiently and effectively, it is of great importance to enlarge variations between speakers and effectively measure distances between feature parameters. Therefore, this paper would suggest the positively mixed model scheme that can enlarge inter-speaker variation by searching the individual model with world model at the same time. During decision procedure, we can maximize inter-speaker variation by using the proposed mixed model scheme. We also make use of a symbol probability weighting function in this system so as to reduce vector quantization errors by measuring symbol probability derived from the distance rate of between the world codebook and individual codebook. As the result of our experiment using this method, we could halve the Detection Cost Function (DCF) of the system from $2.37\%\;to\;1.16\%$.

• Journal of the Korea Society of Computer and Information
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• v.17 no.3
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• pp.11-25
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• 2012

We present the method to infer Context-Free Grammars by applying genetic algorithm to the Binary Third-order Recurrent Neural Networks(BTRNN). BTRNN is a multiple-layered architecture of recurrent neural networks, each of which is corresponding to an input symbol, and is combined with external stack. All parameters of BTRNN are represented as binary numbers and each state transition is performed with any stack operation simultaneously. We apply Genetic Algorithm to BTRNN chromosomes and obtain the optimal BTRNN inferring context-free grammar of positive and negative input patterns. This proposed method infers BTRNN, which includes the number of its states equal to or less than those of existing methods of Discrete Recurrent Neural Networks, with less examples and less learning trials. Also BTRNN is superior to the recent method of chromosomes representing grammars at recognition time complexity because of performing deterministic state transitions and stack operations at parsing process. If the number of non-terminals is p, the number of terminals q, the length of an input string k, and the max number of BTRNN states m, the parallel processing time is O(k) and the sequential processing time is O(km).

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.107-123
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• 2006

We propose the Generalized Binary Second-order Recurrent Neural Networks(GBSRNNf) being equivalent to regular grammars and ?how the implementation of lexical analyzer recognizing the regular languages by using it. All the equivalent representations of regular grammars can be implemented in circuits by using GSBRNN, since it has binary-valued components and shows the structural relationship of a regular grammar. For a regular grammar with the number of symbols m, the number of terminals p, the number of nonterminals q, and the length of input string k, the size of the corresponding GBSRNN is $O(m(p+q)^2)$ and its parallel processing time is O(k) and its sequential processing time, $O(k(p+q)^2)$.

• The Transactions of the Korea Information Processing Society
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• v.3 no.4
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• pp.720-732
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• 1996

Spatial match retrieval methods for iconic image databases recognize an image document as several icon symbols. Therefore the iconic symbols are used as primary keys to index the image document. When a user requires content-based retrieval ofimages, a spatial match retrieval method converts a query image into iconic symbols and then retrieves relevant images by accessing stored images. In order to support content-based image retrieval efficiently, we, in this paper, propose spatial match retrieval methods using signatures for iconic image databases. For this, we design new index representations of two-dimensional iconic images and explain implemented system.. In addition, we compare the conventional 9-DLT and our two-dimensional image retrieval method in terms of retrieval precision and recall ratio. We show that our method is more efficient than the conventional method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.10
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• pp.2409-2416
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• 2014

In this paper, an automatic recognition algorithm for linearly modulated signals like PSK, QAM under noncoherent asynchronous condition is proposed. Frequency, phase, and amplitude characteristics of digitally modulated signals are changed periodically. By using this characteristics, cyclic moments and higher order cumulants based features are utilized for the modulation recognition. Hierarchial decision tree method is used for high speed signal processing and totally 4 feature extraction parameters are used for modulation recognition. In the condition where the symbol number is 4,096, the recognition accuracy of the proposed algorithm is more than 95% at SNR 15dB. Also the proposed algorithm is effective to classify the signal which has carrier frequency and phase offset.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.9
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• pp.2009-2019
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• 2013

In this paper, we propose an automatic modulation recognition algorithm based on cyclic moment and new modified cumulant for analog and digital modulation signals. It is noteworthy that each modulated signal has different cycle frequency characteristics according to its order of cyclic moment. By means of this characteristics as classification features, various modulated signals can be efficiently classified. Also, to identify modulated signals having the same cycle frequency characteristics, we take advantage of the additional classification factors such as variations of envelope and phase as well as modified cumulant. The proposed algorithm was evaluated by considering the number of symbols, SNR, and frequency offset. In the simulation condition where the number of gathered symbols was about 819, and SNR and frequency offset were above 10dB and below 25%, respectively, the average accuracy of the proposed algorithm was more than 95%.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.279-283
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• 2004

본 논문에서는 일반적인 비디오 스트림에서 자동으로 인간의 제스처를 인식하는 알고리즘에 대하여 기술한다. 본 알고리즘은 입력된 비디오 영상으로부터 추출된 신체영역의 2차원적 특징 벡터를 사용하며, 주성분 분석법(Principle Component Analysis)을 통하여 모델 제스처 공간(Model Gesture space)을 구성함으로서 제스처를 통계학적으로 분석/표현하며, 이 제스처 공간에서 새로 입력되는 영상을 같은 방법으로 투영시키고, HMM(Hidden Markov Model) 이론을 적용하여 심볼화함으로써 최종적으로 제스처를 인식하게 된다. 본 방법은 기존의 제스처 인식 방법들과는 달리 전체적인 영상 정보(Global Information)와 세부적인 영상 정보(Partial Information)를 조합하여 사용한다는데 특징이 있으며, 본 알고리즘을 통해 보다 정확하게 강건한 제스처 인식 기술을 실생활에 적용할 수 있을 것이다.