• 인터넷정보학회논문지
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• 제20권4호
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• pp.21-27
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• 2019

RNA 시퀀싱 데이터 (RNA-seq)에서 수집된 많은 양의 데이터에 변별력이 확실한 특징 패턴 선택이 유용하며, 차별성 있는 특징을 정의하는 것이 쉽지 않다. 이러한 이유는 빅데이터 자체의 특징으로써, 많은 양의 데이터에 중복이 포함되어 있기 때문이다. 해당이슈 때문에, 컴퓨터를 사용하여 처리하는 분야에서 특징 선택은 랜덤 포레스트, K-Nearest, 및 서포트-벡터-머신 (SVM)과 같은 다양한 머신러닝 기법을 도입하여 해결하려고 노력한다. 해당 분야에서도 SVM-기반 제약을 사용하는 서포트-벡터-머신-재귀-특징-제거(SVM-RFE) 알고리즘은 많은 연구자들에 의해 꾸준히 연구 되어 왔다. 본 논문의 제안 방법은 RNA 시퀀싱 데이터에서 빅-데이터처리를 위해 SVM-RFE에 강화학습의 Q-learning을 접목하여, 중요도가 추가되는 벡터를 세밀하게 추출함으로써, 변별력이 확실한 특징선택 방법을 제안한다. NCBI-GEO와 같은 빅-데이터에서 공개된 일부의 리보솜 단백질 클러스터 데이터에 본 논문에서 제안된 알고리즘을 적용하고, 해당 알고리즘에 의해 나온 결과와 이전 공개된 SVM의 Welch' T를 적용한 알고리즘의 결과를 비교 평가하였다. 해당결과의 비교가 본 논문에서 제안하는 알고리즘이 좀 더 나은 성능을 보여줌을 알 수 있다.

• 전자공학회논문지C
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• 제34C권7호
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• pp.70-81
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• 1997

In this paper, we propose HMCNN(hybrid multiple component neural networks) that enhance performance of MCNN by adapting new pattern partitioning algorithm which can cluster many input patterns efficiently. Added neural network performs similar learning procedure that of kohonen network. But it dynamically determine it's number of output neurons using algorithms that decide self-organized number of clusters and patterns in a cluster. The proposed network can effectively be applied to problems of large data as well as huge networks size. As a sresutl, proposed pattern partitioning network can enhance performance results and solve weakness of MCNN like generalization capability. In addition, we can get more fast speed by performing parallel learning than that of other supervised learning networks.

• International journal of advanced smart convergence
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• 제6권3호
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• pp.17-21
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• 2017

In this paper, the service execution accuracy was compared by ontology based rule inference method and machine learning method, and the amount of data at the point when the service execution accuracy of the machine learning method becomes equal to the service execution accuracy of the rule inference was found. The rule inference, which measures service execution accuracy and service execution accuracy using accumulated data and pattern matching on service results. And then machine learning method measures service execution accuracy using cross validation data. After creating a confusion matrix and measuring the accuracy of each service execution, the inference algorithm can be selected from the results.

• 대한전자공학회논문지SD
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• 제41권5호
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• pp.81-90
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• 2004

본 연구는 테스트 패턴 생성 알고리즘에서 결정 과정을 안내하는 데 이용되는 새로운 테스트 용이도 척도 계산법을 제안한다. 이 테스트 용이도 척도는 학습에 의해 얻어지는 회로의 구조적 정보를 이용한다. 제안된 테스트 용이도 척도는 오직 하나의 해결책이 존재할 경우 모순조건을 조기에 찾아내는 패턴을 유도하며, 반면에 다수의 해결책들이 존재할 경우 최소한의 모순이 발생토록 유도한다. 제안된 테스트 용이도 척도는 기존의 방법과 동일한 고장 검출율을 얻는 패턴을 얻는데 소요되는 CPU 시간을 상당히 감소시킨다.

• 한국컴퓨터산업학회논문지
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• 제2권3호
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• pp.285-296
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• 2001

본 논문에서는 초등학교 학생이나 외국인을 위해 한글을 익히는 교육용 시스템을 개발하고자 한다. 모델이 될 수 있는 표준 문자 패턴을 선정하고 이의 특징을 추출하여 데이터베이스화한다. 이를 위해 환경에 불변인 전처리과정의 수행과 특징 추출 그리고 유사도 함수를 정의한다. 최종적으로 초등학생이나 외국인이 쓴 문자에 대해 표준 문자 패턴과의 유사도를 계산하여 필기체 문자의 미적 평가를 행한다. 본 시스템은 특정 서체에 대해서도 표준 문자 패턴을 선정하여 서체 익히기까지 행할 수 있도록 확정이 가능한 시스템이며 실험에 의해 본 논문의 유용성을 입증하고자 한다.

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

• 경영과학
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• 제18권2호
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• pp.25-38
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• 2001

The well-known back-propagation algorithm for multi-layered neural network has successfully been applied to pattern c1assification problems with remarkable flexibility. Recently. the multi-layered neural network is used as a powerful data mining tool. Nevertheless, in many cases with complex boundary of classification, the successful learning is not guaranteed and the problems of long learning time and local minimum attraction restrict the field application. In this paper, an Improved learning procedure of multi-layered neural network is proposed. The procedure is based on the generalized delta rule but it is particular in the point that the architecture of network is not fixed but enlarged during learning. That is, the number of hidden nodes or hidden layers are increased to help finding the classification boundary and such procedure is controlled by entropy evaluation. The learning speed and the pattern classification performance are analyzed and compared with the back-propagation algorithm.

• 전자공학회논문지B
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• 제29B권9호
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• pp.68-75
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• 1992

In this paper the back-propagation algorithm through omitting redundant learning has been proposed to improve learning speed. The proposed algorithm has been applied to XOR, Parity check and pattern recognition of hand-written numbers. The decrease of the number of patterns to be learned has been confirmed as learning proceeds even in early learning stage. The learning speed in pattern recognition of hand-written numbers is improved more than 2 times in various cases of hidden neuron numbers. It is observed that the improvement of learning speed becomes better as the number of patterns and the number of hidden numbers increase. The recognition rate of the proposed algorithm is nearly the same as that conventional method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권1호
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• pp.305-326
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• 2019

Discriminative correlation filter (DCF) based tracking algorithms have recently shown impressive performance on benchmark datasets. However, amount of recent researches are vulnerable to heavy occlusions, irregular deformations and so on. In this paper, we intend to solve these problems and handle the contradiction between accuracy and real-time in the framework of tracking-by-detection. Firstly, we propose an innovative strategy to combine the template and color-based models instead of a simple linear superposition and rely on the strengths of both to promote the accuracy. Secondly, to enhance the discriminative power of the learned template model, the spatial regularization is introduced in the learning stage to penalize the objective boundary information corresponding to features in the background. Thirdly, we utilize a discriminative multi-scale estimate method to solve the problem of scale variations. Finally, we research strategies to limit the computational complexity of our tracker. Abundant experiments demonstrate that our tracker performs superiorly against several advanced algorithms on both the OTB2013 and OTB2015 datasets while maintaining the high frame rates.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2011년도 춘계학술발표대회
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• pp.346-349
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• 2011

In this paper, we design a perceptually invisible dot pattern layout and its recognition scheme, and we apply the recognition scheme into a smart self learning aid for interactive learning aid. To increase maximum information capacity and also increase robustness to the noises, we design a ECC (error correcting code) based dot pattern with directional vector indicator. To make a smart self-learning aid, we embed the micro dot pattern (20 information bit + 15 ECC bits + 9 layout information bit) using K ink (CMYK) and extract the dot pattern using IR (infrared) LED and IR filter based camera, which is embedded in the smart pen. The reason we use K ink is that K ink is a carbon based ink in nature, and carbon is easily recognized with IR even without light. After acquiring IR camera images for the dot patterns, we perform layout adjustment using the 9 layout information bit, and extract 20 information bits from 35 data bits which is composed of 20 information bits and 15 ECC bits. To embed and extract information bits, we use topology based dot pattern recognition scheme which is robust to geometric distortion which is very usual in camera based recognition scheme. Topology based pattern recognition traces next information bit symbols using topological distance measurement from the pivot information bit. We implemented and experimented with sample patterns, and it shows that we can achieve almost 99% recognition for our embedding patterns.