• Structural Engineering and Mechanics
• /
• 제63권2호
• /
• pp.237-249
• /
• 2017

The Self-Organization Feature Map as an unsupervised network is very widely used these days in engineering science. The applied network in this paper is the Self Organization Feature Map with constant weights which includes Kohonen Network. In this research, Reinforced Concrete Shear Wall buildings with different stories and heights are analyzed and a database consisting of measured fundamental periods and characteristics of 78 RC SW buildings is created. The input parameters of these buildings include number of stories, height, length, width, whereas the output parameter is the fundamental period. In addition, using Genetic Algorithm, the structure of the Self-Organization Feature Map algorithm is optimized with respect to the numbers of layers, numbers of nodes in hidden layers, type of transfer function and learning. Evaluation of the SOFM model was performed by comparing the obtained values to the measured values and values calculated by expressions given in building codes. Results show that the Self-Organization Feature Map, which is optimized by using Genetic Algorithm, has a higher capacity, flexibility and accuracy in predicting the fundamental period.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제13권10호
• /
• pp.5078-5094
• /
• 2019

In recent years, the development of drone technologies has promoted the widespread commercial application of drones. However, the ability of drone to carry explosives and other destructive materials may bring serious threats to public safety. In order to reduce these threats from illegal drones, acoustic feature extraction and classification technologies are introduced for drone sound identification. In this paper, we introduce the acoustic feature vector extraction method of harmonic line association (HLA), and subband power feature extraction based on wavelet packet transform (WPT). We propose a feature vector extraction method based on combined HLA and WPT to extract more sophisticated characteristics of sound. Moreover, to identify drone sounds, support vector machine (SVM) classification with the optimized parameter by genetic algorithm (GA) is employed based on the extracted feature vector. Four drones' sounds and other kinds of sounds existing in outdoor environment are used to evaluate the performance of the proposed method. The experimental results show that with the proposed method, identification probability can achieve up to 100 % in trials, and robustness against noise is also significantly improved.

• Journal of Electrical Engineering and Technology
• /
• 제13권5호
• /
• pp.2099-2106
• /
• 2018

This study presents a novel approach of discriminative feature vectors based on manifold learning using nonlinear dimension reduction (DR) technique to improve loss function, and combine with the Adversarial examples to regularize the object function for image classification. The traditional convolutional neural networks (CNN) with many new regularization approach has been successfully used for image classification tasks, and it achieved good results, hence it costs a lot of Calculated spacing and timing. Significantly, distrinct from traditional CNN, we discriminate the feature vectors for objects without empirically-tuned parameter, these Discriminative features intend to remain the lower-dimensional relationship corresponding high-dimension manifold after projecting the image feature vectors from high-dimension to lower-dimension, and we optimize the constrains of the preserving local features based on manifold, which narrow the mapped feature information from the same class and push different class away. Using Adversarial examples, improved loss function with additional regularization term intends to boost the Robustness and generalization of neural network. experimental results indicate that the approach based on discriminative feature of manifold learning is not only valid, but also more efficient in image classification tasks. Furthermore, the proposed approach achieves competitive classification performances for three benchmark datasets : MNIST, CIFAR-10, SVHN.

• 한국생산제조학회지
• /
• 제7권1호
• /
• pp.69-74
• /
• 1998

In order to monitor the tool wear in milling operation, cutting force is measured as the tool wear increased. The digital signal processing methods are used to detect the tool wear . As AR parameter extract the feature of tool wear , it can be used as input parameter of pattern classifier. The FFT monitor the tool wear exactly , but it can not do real time signal processing. The band energy method can be used to real time monitoring of tool wear ,but int can degrade the exact monitoring.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1996년도 한국자동제어학술회의논문집(국내학술편); 포항공과대학교, 포항; 24-26 Oct. 1996
• /
• pp.1253-1256
• /
• 1996

In this paper, a new nonlinear feedback linearization control scheme for induction motors is developed. The control scheme employs a fuzzy nonlinear identification scheme based on fuzzy basis function expansion to adoptively compensate the parameter variations, i.e. rotor resistance, mutual and self inductance etc. An important feature of the proposed control scheme is to incorporate the sliding mode controller into the scheme to speed up convergence rate. Simulation tests show the robust behavior of the proposed controller in the presence of the parameter uncertainties of the machine.

• 전자공학회논문지CI
• /
• 제38권5호
• /
• pp.58-64
• /
• 2001

현재 많은 정보들이 비디오 데이터로 전송 또는 저장되고 있으며 고성능 PC의 보급과 internet과 같은 통신망의 대중화로 이런 비디오 데이터는 급속도로 증가하고 있다. 본 논문에서는 이런 비디오 데이터의 검색을 위하여 비디오 스트립을 분석하여 shot을 찾아내고 이들 중 key frame을 찾는 방법에 대하여 연구하고 이로서 사용자의 질의에 부합하는 비디오를 검색한다. 본 논문에서는 shot 경계 검출을 위해 객체의 움직임에 강인하면서 shot 내에서의 칼라의 변화에 둔감한 새로운 feature를 제안하고, shot frame에서 구한 각 feature들의 통계적 특성을 이용하여 스트립의 특징에 따라 weight를 부가하여 구해진 characterizing value의 시간 변화량을 구한다. 구해진 변화량의 local maxima와 local minima는 비디오 스트림에서 각각 가장 특정적인 frame과 평균적인 frame을 나타낸다. 이 순간의 short frame을 구함으로서 효과적이고 빠른 시간 내에 key frame을 추출한다. 추출되어진 key frame에 대하여 원 영상을 복원한 후, 색인을 위하여 다수의 parameter를 구하고, 사용자가 질의한 영상에 대해서 이들 parameter를 구하여 key frame들과 가장 유사한 대표영상들을 검색한다. 실험결과 일반적인 방법보다 더 나은 결과를 보였고, 높은 검색율을 보였다.

• 한국소음진동공학회논문집
• /
• 제24권7호
• /
• pp.555-561
• /
• 2014

Acoustic Emission technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the bearing problems and wavelet transform is a powerful method to detect faults occurred on rotating machinery. However, exact method for AE signal is not developed yet for the rotating machinery diagnosis. Therefore, in this paper two methods which are processed by Hilbert transform and DET for feature extraction. In addition, we evaluate the classification performance with varying the parameter from 2 to 15 for feature selection DET, 0.01 to 1.0 for the RBF kernel function of SVR, and the proposed algorithm achieved 94 % classification of averaged accuracy with the parameter of the RBF 0.08, 12 feature selection.

• 한국음향학회지
• /
• 제26권5호
• /
• pp.207-213
• /
• 2007

본 연구에서는 스펙트럼 분석과 신경망을 이용한 효과적인 음성/음악 분류 방법을 제안한다. 제안하는 방법은 스펙트럼을 분석하여 스펙트럴 피크 트랙에서 지속성 특징 파라미터인 MSDF(Maximum Spectral Duration Feature)를 추출하고 기존의 특징 파라미터인 MFSC(Mel Frequency Spectral Coefficients)와 결합하여 음성/음악 분류기의 특징으로 사용한다. 그리고 신경망을 음성/음악 분류기로 사용하였으며, 제안하는 방법의 성능 평가를 위해 학습 패턴 선별과 양, 신경망 구성에 따른 다양한 성능 평가를 수행하였다. 음성/음악 분류 결과 기존의 방법에 비해 성능 향상과 학습 패턴의 선별과 모델 구성에 따른 안정성을 확인할 수 있었다. MSDF와 MFSC를 특징 파라미터로 사용하고 50초 이상의 학습 패턴을 사용할 때 음성에 대해서는 94.97%, 음악에 대해서는 92.38%의 분류율을 얻었으며, MFSC만 사용할 때보다 음성은 1.25%, 음악은 1.69%의 향상된 성능을 얻었다.

• 한국멀티미디어학회논문지
• /
• 제12권1호
• /
• pp.50-57
• /
• 2009

본 논문은 3차원 다각형 모델에서 특징 선을 추출하기 위한 방법에 대해 제안한다. 이산 곡면으로 이루어진 다각형 모델에서 특징 선을 추출하기 위하여 기존 방법에서는 전역적인 음함수 곡면 맞춤 기법(Implicit Surface Fitting)을 이용하여 모델의 꼭지점에서 곡률과 곡률 미분 값을 측정하였다. 이러한 방법은 다각형 모델의 꼭지점에서 음함수 곡면으로 정확하게 투영할 수 있도록 사용자의 정의 파라미타를 찾아야 하며, 특징 추출을 위한 많은 계산 시간을 요구한다. 그러나 제안 방법은 지역적 음함수 곡면 맞춤 기법을 이용하여 모델의 꼭지점에 근사된 곡면을 통해 미분 정보를 측정한다. 측정된 미분 정보를 통해 쉽게 각각의 모서리에서 제로-클로싱을 통해 특징 점을 추출하고, 곡률 방향을 따라 추출된 점들을 연결하여 특징 선을 생성한다. 여러 가지 다각형 모델에서 실험을 하였고 기존 방법보다 빠르며 높은 품질의 특징 선을 추출한다.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
• /
• pp.536-538
• /
• 2006

The most important factor of mobile robot is to build a map for surrounding environment and estimate its localization. This paper proposes a real-time localization and map building method through 3-D reconstruction using scale invariant feature from monocular camera. Mobile robot attached monocular camera looking wall extracts scale invariant features in each image using SIFT(Scale Invariant Feature Transform) as it follows wall. Matching is carried out by the extracted features and matching feature map that is transformed into absolute coordinates using 3-D reconstruction of point and geometrical analysis of surrounding environment build, and store it map database. After finished feature map building, the robot finds some points matched with previous feature map and find its pose by affine parameter in real time. Position error of the proposed method was maximum. 8cm and angle error was within $10^{\circ}$.