• 한국환경생태학회지
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• 제24권1호
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• pp.78-86
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• 2010

국내 주요 도시에서 수행된 비오톱유형 분류는 생물서식처 관점보다는 토지이용 개념에 한정되어 분류되었고, 생태적 가치에 따라 상세하게 분류되지 못하였다. 비오톱유형은 지역적 특성을 고려하여 생물서식처 관점에 따라 분류되어야 한다. 본 논문은 비오톱유형 분류에 사용되는 분류위계, 분류항목, 분류요인, 분류지표, 분류기준, 분류key 등 분류인자의 개념적 틀을 명확하게 설정하고 사례도시의 비오톱유형 분류결과에 적용하여 문제점을 고찰하고 비오톱유형 분류체계 개선방향을 제안하였다. 비오톱유형 분류체계는 위계별로 분류의 기본 수준과 기준을 마련하여 일관된 분류 특성을 가져야 한다. 분류지표는 생물적 요인, 무생물적 요인, 인간행태적 요인을 고려하여 적용되어야 한다. 비오톱유형은 일반인들과 계획가들이 이해하기 쉽도록 분류지표와 분류key의 표준화와 더불어 지역 비오톱 특성을 반영할 수 있도록 분류key와 분류기준의 특성화를 반영하는 것이 필요하다.

• 지능정보연구
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• 제1권1호
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• pp.61-72
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• 1995

Dynamic and explicit ordering of strategies is a key process in modeling knowledge-level problem-solving behavior. This paper addressed the important problem of howl to make the scheduler more knowledge-intensive in a way that facilitates the acquisition, integration, and maintenance of the scheduler control knowledge. The solution a, pp.oach described in this paper involved formulating the scheduler task as a heuristic classification problem, and then implementing it as a classification expert system. By doing this, the wide spectrum of known methods of acquiring, refining, and maintaining the knowledge of a classification expert system are a, pp.icable to the scheduler control knowledge. One important innovation of this research is that of recursive heuristic classification : this paper demonstrates that it is possible to formulate and solve a key subcomponent of heuristic classification as heuristic classification problem. Another key innovation is the creation of a method of dynamic heuristic classification : the classification alternatives that are selected among are dynamically generated in real-time and then evidence is gathered for and aginst these alternatives. In contrast, the normal model of heuristic classification is that of structured selection between a set of preenumerated fixed alternatives.

• 스마트미디어저널
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• 제9권1호
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• pp.23-29
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• 2020

This paper presents an approach for dynamic hand gesture recognition by using algorithm based on 3D Convolutional Neural Network (3D_CNN), which is later extended to 3D Residual Networks (3D_ResNet), and the neural network based key frame selection. Typically, 3D deep neural network is used to classify gestures from the input of image frames, randomly sampled from a video data. In this work, to improve the classification performance, we employ key frames which represent the overall video, as the input of the classification network. The key frames are extracted by SegNet instead of conventional clustering algorithms for video summarization (VSUMM) which require heavy computation. By using a deep neural network, key frame selection can be performed in a real-time system. Experiments are conducted using 3D convolutional kernels such as 3D_CNN, Inflated 3D_CNN (I3D) and 3D_ResNet for gesture classification. Our algorithm achieved up to 97.8% of classification accuracy on the Cambridge gesture dataset. The experimental results show that the proposed approach is efficient and outperforms existing methods.

• Structural Engineering and Mechanics
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• 제84권1호
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• pp.1-16
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• 2022

An efficient and accurate classification method for failure modes of reinforced concrete (RC) columns was proposed based on key characteristic parameters. The weight coefficients of seven characteristic parameters for failure modes of RC columns were determined first based on the support vector machine-recursive feature elimination. Then key characteristic parameters for classifying flexure, flexure-shear and shear failure modes of RC columns were selected respectively. Subsequently, a support vector machine with key characteristic parameters (SVM-K) was proposed to classify three types of failure modes of RC columns. The optimal parameters of SVM-K were determined by using the ten-fold cross-validation and the grid-search algorithm based on 270 sets of available experimental data. Results indicate that the proposed SVM-K has high overall accuracy, recall and precision (e.g., accuracy>95%, recall>90%, precision>90%), which means that the proposed SVM-K has superior performance for classification of failure modes of RC columns. Based on the selected key characteristic parameters for different types of failure modes of RC columns, the accuracy of SVM-K is improved and the decision function of SVM-K is simplified by reducing the dimensions and number of support vectors.

• 정보보호학회논문지
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• 제28권6호
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• pp.1427-1437
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• 2018

최근 생체 인증 시스템이 확대됨에 따라, 생체 정보를 이용하여 공개키 기반구조(Bio-PKI)에 적용하는 연구들이 진행 중이다. Bio-PKI 시스템에서는 공개키를 생성하기 위해 생체 정보로부터 암호학적 키를 생성하는 과정이 필요하다. 암호학적 키 생성 방법 중 특성 정보를 숫자로 정량화하는 기법은 데이터 손실을 유발하고 이로 인해 키 추출 성능이 저하된다. 이 논문에서는 다중 분류 모델을 이용하여 생체 정보를 분류한 결과를 이용하여 키를 생성하는 방법을 제안한다. 제안하는 기법은 특성 정보의 손실이 없어 높은 키 추출 성능을 보였고, 여러 개의 분류 모델을 이용하기 때문에 충분한 길이의 키를 생성한다.

• Journal of Electrical Engineering and Technology
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• 제12권2호
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• pp.830-839
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• 2017

Support vector machine (SVM) is introduced as an effective fault diagnosis technique based on dissolved gases analysis (DGA) for oil-immersed transformers with maximum generalization ability; however, the applicability of the SVM is highly affected due to the difficulty of selecting the SVM parameters appropriately. Therefore, a novel approach combing SVM with improved imperialist competitive algorithm (IICA) for fault diagnosis of oil-immersed transformers was proposed in the paper. The improved ICA, which is proved to be an effective optimization approach, is employed to optimize the parameters of SVM. Cross validation and normalizations were applied in the training processes of SVM and the trained SVM model with the optimized parameters was established for fault diagnosis of oil-immersed transformers. Three classification benchmark sets were studied based on particle swarm optimization SVM (PSOSVM) and IICASVM with four multiple classification schemes to select the best scheme for transformer fault diagnosis. The results show that the proposed model can obtain higher diagnosis accuracy than other methods. The comparisons confirm that the proposed model is an effective approach for classification problems.

• 대한전자공학회논문지TC
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• 제45권4호
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• pp.22-30
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• 2008

본 논문은 웨이브릿 변환을 이용하여 사전정보 없이 입사하는 디지털 신호의 변조타입 자동식별 방법에 관한 것이다. 변조인식에 사용되는 특징(key features)은 변조타입에 대한 민감도가 우수하고, SNR에 대한 변화가 적은 속성을 가져야 한다. 잡음에 대한 변화가 적은 속성을 가진 웨이브릿 변환 계수에서 변조인식을 위해 4개의 특징(key features)을 선정하였다. 또한 선정된 특징들을 이용하여 총 8종의 디지털변조 신호를 분류하기 위해 시뮬레이션을 수행하였다. 소프트웨어 라디오의 변조인식 모듈 탑재를 고려하여, 3 타입의 변조인식기에 대한 인식 정확도 및 수행시간을 비교 분석하였다. 시뮬레이션 결과 전체 인식시간은 MDC(Minimum Distance Classifier)와 DTC(Decision Tree Classifier)가 빠르게 수행되었고, 인식정확도는 MDC와 SVMC(Support Vector Machine Classifier)가 우수하게 제시되었다.

• 정보과학회 논문지
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• 제42권5호
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• pp.558-565
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• 2015

최근 새롭게 제작되는 악성코드 수의 증가와 악성코드 변종들의 다양성은 악성코드 분석가의 분석에 소요되는 시간과 노력에 많은 영향을 준다. 따라서 효과적인 악성코드 분류는 악성코드 분석가의 악성코드 분석에 소요되는 시간과 노력을 감소시키는 데 도움을 줄 뿐만 아니라, 악성코드 계보 연구 등 다양한 분야에 활용 가능하다. 본 논문에서는 악성코드 분류를 위해 중요 연산부호를 이용하는 방법을 제안한다. 중요 연산부호란 악성코드 분류에 높은 영향력을 가지는 연산부호들을 의미한다. 실험을 통해서 악성코드 분류에 높은 영향력을 가지는 상위 10개의 연산부호들을 중요 연산부호로 선정할 수 있음을 확인하였으며, 이를 이용할 경우 지도학습 알고리즘의 학습시간을 약 91% 단축시킬 수 있었다. 이는 향후 다량의 악성코드 분류 연구에 응용 가능할 것으로 기대된다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권8호
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• pp.3917-3941
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• 2019

The recent interest in artificial intelligence and machine learning has partly contributed to an interest in the use of such approaches for hyperspectral remote sensing (HRS) imagery classification, as evidenced by the increasing number of deep framework with deep convolutional neural networks (CNN) structures proposed in the literature. In these approaches, the assumption of obtaining high quality deep features by using CNN is not always easy and efficient because of the complex data distribution and the limited sample size. In this paper, conventional handcrafted learning-based multi features based on expert knowledge are introduced as the input of a special designed CNN to improve the pixel description and classification performance of HRS imagery. The introduction of these handcrafted features can reduce the complexity of the original HRS data and reduce the sample requirements by eliminating redundant information and improving the starting point of deep feature training. It also provides some concise and effective features that are not readily available from direct training with CNN. Evaluations using three public HRS datasets demonstrate the utility of our proposed method in HRS classification.

• Journal of Information Processing Systems
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• 제15권2호
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• pp.422-432
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• 2019

A hybrid kernel function of support vector machine is proposed to improve the classification performance of power quality disturbances. The kernel function mathematical model of support vector machine directly affects the classification performance. Different types of kernel functions have different generalization ability and learning ability. The single kernel function cannot have better ability both in learning and generalization. To overcome this problem, we propose a hybrid kernel function that is composed of two single kernel functions to improve both the ability in generation and learning. In simulations, we respectively used the single and multiple power quality disturbances to test classification performance of support vector machine algorithm with the proposed hybrid kernel function. Compared with other support vector machine algorithms, the improved support vector machine algorithm has better performance for the classification of power quality signals with single and multiple disturbances.