• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.1
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• pp.80-82
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• 2016

In this letter, we evaluate the performances of both GOOK and GFSK de/modulation techniques which are candidates for ULP communications in WPAN systems. For the performance evaluation, we define the system model for each of GOOK and GFSK de/modulations and perform computer simulations based on the proposed models. From the computer simulations, we show that GOOK de/modulation technique has 15 dB out-of-band emission gain and is robust over frequency offsets in terms of BER compared to GFSK de/modulation technique.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.11
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• pp.2554-2561
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• 2015

In this paper, we strengthen the advantages of a simple PID controller as a study on the formation control of mobile robots and propose an adaptive PID controller with robust performance at the dynamics characteristics of following robot. Simulation studies show that the adaptive PID controller has better keeping constant distance and angle such as tracking performance of following robot for the formation control than a conventional PID controller. This is the proposed adaptive PID controller to change the gains is found to represent the best performance. This is able to verify that the performance of the proposed adaptive PID controller is excellent.

• Journal of Digital Contents Society
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• v.17 no.6
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• pp.509-521
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• 2016

A novel Samples Balanced Genetic Algorithm combined with Extreme Learning Machine (SBGA-ELM) for Parkinson's Disease diagnosis and detecting bio-markers is presented in this paper. Proposed approach uses genes' expression data of 22,283 genes from open source ParkDB data base for accurate PD diagnosis and detecting bio-markers. Proposed SBGA-ELM includes two major steps: feature (genes) selection and classification. Feature selection procedure is based on proposed Samples Balanced Genetic Algorithm designed specifically for genes expression data from ParkDB. Proposed SBGA searches a robust subset of genes among 22,283 genes available in ParkDB for further analysis. In the "classification" step chosen set of genes is used to train an Extreme Learning Machine (ELM) classifier for an accurate PD diagnosis. Discovered robust subset of genes creates ELM classifier with stable generalization performance for PD diagnosis. In this research the robust subset of genes is also used to discover 24 bio-markers probably responsible for Parkinson's Disease. Discovered robust subset of genes was verified by using existing PD diagnosis approaches such as SVM and PBL-McRBFN. Both tested methods caused maximum generalization performance.

• The Journal of the Acoustical Society of Korea
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• v.22 no.7
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• pp.519-527
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• 2003

Speech is much influenced by the existence of outliers which are introduced by such an unexpected happenings as additive background noise, change of speaker's utterance pattern and voice detection errors. These kinds of outliers may result in severe degradation of speaker recognition performance. In this paper, we proposed the GMM based on robust principal component analysis (RPCA-GMM) using M-estimation to solve the problems of both ouliers and high dimensionality of training feature vectors in speaker identification. Firstly, a new feature vector with reduced dimension is obtained by robust PCA obtained from M-estimation. The robust PCA transforms the original dimensional feature vector onto the reduced dimensional linear subspace that is spanned by the leading eigenvectors of the covariance matrix of feature vector. Secondly, the GMM with diagonal covariance matrix is obtained from these transformed feature vectors. We peformed speaker identification experiments to show the effectiveness of the proposed method. We compared the proposed method (RPCA-GMM) with transformed feature vectors to the PCA and the conventional GMM with diagonal matrix. Whenever the portion of outliers increases by every 2%, the proposed method maintains almost same speaker identification rate with 0.03% of little degradation, while the conventional GMM and the PCA shows much degradation of that by 0.65% and 0.55%, respectively This means that our method is more robust to the existence of outlier.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.18 no.3
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• pp.227-243
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• 2005

This paper presents a hybrid control system combining lead rubber bearings and hydraulic actuators for seismic response control of a cable stayed bridge. Because multiple control devices are operating, a hybrid control system could improve the control performances. However, the overall system robustness may be impacted negatively by additional active control devices. Therefore, a secondary on-off type controller according to the responses of lead rubber bearings is combined with LQG algorithm to improve the controller robustness. Numerical simulation results show that control performances of the hybrid system controlled by an on off type LQG algorithm are improved compared to those of the passive and active control systems and are similar to those of performance oriented hybrid system controlled by a LQG algorithm with the similar peak and normed control forces. Furthermore, it is verified that the hybrid system with an on-off type LQG controller is more robust for stiffness matrix perturbation than conventional hybrid control of system, and there are no signs of instability in the overall system. The proposed control system also maintains the control performance under not only the design earthquakes but also the other earthquakes. Therefore, the hybrid control system using on-off type LQG algorithm could be proposed as an improved control strategy for seismically excited cable-stayed bridges containing many uncertainties.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.580-582
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• 2004

스펙트럼 차감과정에서 발생하는 잔류 잡음을 제거하는 방법으로 파라메터를 이용하는 적응 스펙트럼 차감법이 있다. 이는 파라메터를 증가시켜 잔류 잡음을 감소시키는 방법이지만 파라메터를 과도하게 증가시킬 경우 음성 왜곡이 발생한다. 따라서, 적절한 파라메터를 추출하기 위하여 SNR이나, 마스킹 효과 등을 이용한 방법들이 제안되었으나 과도한 잡음의 제거로 인한 음성 왜곡 문제와 낮은 SNR에서 부정확한 파라메터의 추출 문제는 여전히 해결해야 할 과제로 남아있다. 본 논문은 기존의 SNR을 이용한 방법에 마스킹 효과를 적용한 수정된 적응 스펙트럼 차감법을 제안한다. 제안된 방법에서는 마스킹 임계치를 이용하여 잡음 추정값을 재 계산 항으로써 SNR을 향상시켰고, 이를 이용하여 파라메터를 추출함으로써 성능을 개선했다 성능평가 결과, 제안한 차감법을 적용한 음성신호를 고립단어 음성인식 시스템에 적용했을 때 기존의 방법 보다 인식률이 향상된 것을 확인할 수 있었다.

• Proceedings of the KIEE Conference
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• 2008.04a
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• pp.131-132
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• 2008

오실로메트릭 방법으로 휴대용 혈압 측정 시 빈번하게 발생하는 잡음에 의한 오실레이션 신호의 왜곡을 줄이기 위해 새로운 잡음 제거 알고리즘을 제안하였다. 제안된 잡음 제거 알고리즘은 선형 예측기 구조 기반의 적응 필터를 이용한다. 제안된 잡음 제거알고리즘의 성능을 평가하기 위해 왜곡된 오실레이션 신호에 선형보간법을 사용하는 기존의 방법과 적응 필터를 사용하는 제안된 방법을 적용하여 잡음 제거 성능을 비교하였다. 제안된 방법은 잡음이 오실레이션과 중첩되어 나타난 경우에 기존의 방법과 달리 잡음에 강인한 특징을 보여주었다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.05a
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• pp.639-641
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• 2012

People detection using HOG linear SVM classification has been successfully applied. Also, HOG combined with LBP, which reflects texture informations, shows improved performance. In this paper, we analyze various gamma correction methods. We also analyze results obtained using HOG+LBP methods.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.386-389
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• 2007

EBGM은 자세와 포즈, 조명 변화에 강인한 얼굴 인식 기법중의 하나이다. 하지만 EBGM을 통한 얼굴 인식 시스템은 얼굴의 특징점을 추출하기 위해 주어지는 초기값에 상당한 영향을 받는다. 이러한 문제를 해결하기 위해서 얼굴의 윤곽 추출에 우수한 성능을 보이는 AAM을 통하여 EBGM의 초기값을 주고 EBGM을 통하여 개선하는 방법을 제안하였었다. 본 논문에서는 등록자마다 다른 경계값을 갖는 방법을 제안한다. 기존의 경계값에 비해 성능의 향상이 어느 정도 이뤄지는가에 대해 다룰 것이다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.193-196
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• 2009

여러 개의 영상으로부터 스케일, 조명, 시점 등의 환경변화를 고려하여 대응점을 찾는 일은 쉽지 않다. SURF는 이러한 환경변화에 불변하는 특징점을 찾는 알고리즘중 하나로서 일반적으로 성능이 우수하다고 알려진 SIFT와 견줄만한 성능을 보이면서 속도를 크게 향상시킨 알고리즘이다. 하지만 SURF는 그레이공간 상의 정보만 이용함에 따라 컬러공간상에 주어진 많은 유용한 특징들을 활용하지 못한다. 본 논문에서는 강인한 컬러특정정보를 포함하는 확장된 SURF알고리즘을 제안한다. 제안하는 방법의 우수성은 다양한 조명환경과 시점변화에 따른 영상을 SIFT와 SURF 그리고 제안하는 컬러정보를 적용한 SURF알고리즘과 비교 실험을 통해 입증하였다.