• 전자공학회논문지B
• /
• 제31B권4호
• /
• pp.1-8
• /
• 1994

In this paper, we propose an efficient two-step pattern matching method which promises shape distortion-tolerant recognition of handwritten of handwritten Hangul syllables. In the first step, nonlinear shape normalization is carried out to compensate for global shape distortions in handwritten characters, then a preliminary classification based on simple pattern matching is performed. In the next step, nonlinear pattern matching which achieves best matching between input and reference pattern is carried out to compensate for local shape distortions, then detailed classification which determines the final result of classification is performed. As the performance of recognition systems based on pattern matching methods is greatly effected by the quality of reference patterns. we construct reference patterns by combining the proposed nonlinear pattern matching method with a well-known averaging techniques. Experimental results reveal that recognition performance is greatly improved by the proposed two-step pattern matching method and the reference pattern construction scheme.

• 한국의류산업학회지
• /
• 제13권5호
• /
• pp.734-742
• /
• 2011

This study has been undertaken as a basic research for automatic pattern design for baseball uniforms manufactured under custom-MTM system, propose building up of a system whereby various partial patterns are combined under an automatic design system and develop a multi-combination type pattern searching algorithm which allows development of a various designs. As a result of this, type classification based on pattern design elements includes side, open, collar, facing and panel type. Design have been divided into coarse classification ranging from level 1 to 7 according to pattern design elements, based on a design distribution chart. Out of 7 such levels, 3 major types determining design which are, more specifically, level 1 sleeve type, level 2 open type and level 3 collar type, have been taken and combined to determine a total of 12 types to be used for design classification codes. Respective name of style and patterns have been coded using alphabet and numerals. Totally, pattern searching algorithm of multi-combination type has been developed whereby combination of patterns belonging to a specific style can be retrieved automatically once that style name is designated on the automatic pattern design system.

• 한국산업정보학회논문지
• /
• 제4권2호
• /
• pp.1-6
• /
• 1999

화상 데이터의 특성을 표현하는데 적합한 깁스분포를 바탕으로 특징벡터를 추출하여 패턴을 분류하는 새로운 알고리즘을 제안하였다. 특징벡터는 화상의 크기, 위치, 회전에 대해서 불변이며 접영에 대해서도 덜 민감한 특징을 갖는 2차원 모멘트들의 원소로 만들어진다. 알고리즘은 공간정보를 갖는 2차원 모멘트를 이용하여 특징벡터를 추출하는 과정과 거리함수를 이용하여 패턴을 분류하는 과정으로 구축하였다. 특징벡터는 깁스분포의 묘수를 추정하여 2차원 조건부 모멘트를 추출하여 구성한다. 패턴 분류 과정은 추출된 특징벡터로부터 제안된 판별거리함수를 계산하여 여러 원형 패턴 가운데 최소거리를 산출한 미지의 패턴을 원형패턴으로 분류한다. 제안된 방법의 성능을 검증하기 위하여 대문자와 소문자 52자로 구성된 훈련 데이터를 만들어 SUN ULTRA 10 워크스테이션에서 실험을 한 결과 98%이상의 분류성능이 있음을 밝혔다.

• 대한기계학회논문집
• /
• 제17권7호
• /
• pp.1783-1793
• /
• 1993

Pattern recognition technique using fuzzy c-means algorithm and multilayer perceptron was applied to classify tool wear states in turning. The tool wear states were categorized into the three regions 'Initial', 'Normal', 'Severe' wear. The root mean square(RMS) value of acoustic emission(AE) and current signal was used for the classification of tool wear states. The simulation results showed that a fuzzy c-means algorithm was better than the conventional pattern recognition techniques for classifying ambiguous informations. And normalized RMS signal can provide good results for classifying tool wear. In addition, a fuzzy c-means algorithm(success rate for tool wear classification : 87%) is more efficient than the multilayer perceptron(success rate for tool wear classification : 70%).

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• 제24권3호
• /
• pp.321-330
• /
• 2020

Data classification and clustering is one of the most common applications of the machine learning. In this paper, we aim to provide the insight of the classification for Turing pattern image, which has high nonlinearity, with feature engineering using the machine learning without a multi-layered algorithm. For a given image data X whose fixel values are defined in [-1, 1], X - X³ and ∇X would be more meaningful feature than X to represent the interface and bulk region for a complex pattern image data. Therefore, we use X - X³ and ∇X in the neural network and clustering algorithm to classification. The results validate the feasibility of the proposed approach.

• 전자공학회논문지CI
• /
• 제39권3호
• /
• pp.32-43
• /
• 2002

본 논문에서는 신경망에 기반한 디지털 신호를 위한 패턴분류 시스템을 제안한다. 제안하는 시스템은 두 가지 신경망 모델로 구성된다. 첫 번째 부분은 특징 추출의 역할을 하는 웨이블릿 신경망이다. 이 부분을 위해 기존의 웨이블릿 신경망 모델들을 비교한 후, 특징 추출을 위한 새로운 웨이블릿 신경망 모델을 제안한다. 다른 부분은 패턴 분류를 위한 웨이블릿 신경망이다. 패턴 분류에 적용하기 위해 기존의 웨이블릿 신경망 구조를 수정하고 학습 방법을 제안한다. 패턴 분류 웨이블릿 신경망의 입력은 특징 추출 신경망의 은닉노드의 연결강도, 확장 및 이동 파라미터로 구성되었다. 또 출력은 특징 추출 신경망의 입력 신호가 속한 부류를 나타낸다. 제안한 시스템을 EEG 신호를 주파수에 따라서 분류하는 문제에 적용하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• 제11권3호
• /
• pp.204-210
• /
• 2011

This paper proposes a fuzzy support vector machine ($FSVM_n$) pattern classifier to classify the arrhythmia patterns of an electrocardiograph (ECG). The $FSVM_n$ is a pattern classifier which combines n-dimensional fuzzy membership functions with a slack variable of SVM. To evaluate the performance of the proposed classifier, the MIT/BIH ECG database, which is a standard database for evaluating arrhythmia detection, was used. The pattern classification experiment showed that, when classifying ECG into four patterns - NSR, VT, VF, and NSR, VT, and VF classification rate resulted in 99.42%, 99.00%, and 99.79%, respectively. As a result, the $FSVM_n$ shows better pattern classification performance than the existing SVM and FSVM algorithms.

• Journal of Electrical Engineering and Technology
• /
• 제11권2호
• /
• pp.309-319
• /
• 2016

This paper presents a new recognition and classification method for power quality (PQ) disturbances on the basis of pattern linguistic values. This method solves the difficulty of recognizing disturbances rapidly and accurately by using fuzzy logic. This method uses classification disturbance patterns to define the linguistic values of fuzzy input variables and used the input variables of corresponding disturbance pattern to set membership functions. This method also sets the fuzzy rules by analyzing the distribution regularities of the input variable values. One characteristic of this method is that the linguistic values of fuzzy input variables and the setting of membership functions are not only related to the input variables but also to the character of classification disturbance and the classification results. Furthermore, the number of fuzzy rules is equal to the number of disturbance patterns. By using this method for disturbance classification, the membership function and design of fuzzy rules are directly related to the objective of classification, thus effectively reducing the complexity of the design process and yielding accurate classification results. The classification results of the simulation and measured data verify the feasibility and effectiveness of this method.

• 융합보안논문지
• /
• 제1권1호
• /
• pp.37-45
• /
• 2001

In this paper, separated and optimized pattern database model is proposed. In order to improve efficiency of Network-based IDS, pattern database is classified by proper basis. Classification basis is decided by the specific Intrusions validity on specific target. Using this model, IDS searches only valid patterns in pattern database on each captured packets. In result, IDS can reduce system resources for searching pattern database. So, IDS can analyze more packets on the network. In this paper, proper classification basis is proposed and pattern database classified by that basis is formed. And its performance is verified by experimental results.

• 대한의용생체공학회:의공학회지
• /
• 제33권1호
• /
• pp.15-24
• /
• 2012

Classification of human thought is an emerging research field that may allow us to understand human brain functions and further develop advanced brain-computer interface (BCI) systems. In the present study, we introduce a new approach to classify various mental states from noninvasive electrophysiological recordings of human brain activity. We utilized the full spatial and spectral information contained in the electroencephalography (EEG) signals recorded while a subject is performing a specific mental task. For this, the EEG data were converted into a 2D spatiospectral pattern map, of which each element was filled with 1, 0, and -1 reflecting the degrees of event-related synchronization (ERS) and event-related desynchronization (ERD). We evaluated the similarity between a current (input) 2D pattern map and the template pattern maps (database), by taking the inner-product of pattern matrices. Then, the current 2D pattern map was assigned to a class that demonstrated the highest similarity value. For the verification of our approach, eight participants took part in the present study; their EEG data were recorded while they performed four different cognitive imagery tasks. Consistent ERS/ERD patterns were observed more frequently between trials in the same class than those in different classes, indicating that these spatiospectral pattern maps could be used to classify different mental states. The classification accuracy was evaluated for each participant from both the proposed approach and a conventional mental state classification method based on the inter-hemispheric spectral power asymmetry, using the leave-one-out cross-validation (LOOCV). An average accuracy of 68.13% (${\pm}9.64%$) was attained for the proposed method; whereas an average accuracy of 57% (${\pm}5.68%$) was attained for the conventional method (significance was assessed by the one-tail paired $t$-test, $p$ < 0.01), showing that the proposed simple classification approach might be one of the promising methods in discriminating various mental states.