• 융합신호처리학회논문지
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• 제1권1호
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• pp.1-6
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• 2000

본 논문은 HMM(Hidden Markov Model)을 이 용하여 인식을 수행할 경우의 오류를 최소화 할 수 있는 후처리 과정으로 신경망을 결합시켜 HMM 단독으로 사용하였을 때 보다 높은 인식률을 얻을 수 있는 HMM과 신경망의 하이브리드 시스템을 제안한다 HMM을 이용하여 학습한 후 학습에 참여하지 않은 데이터를 인식하였을 때 오인식 데이터를 정인식으로 인식하도록 HMM의 출력으로 얻은 각 출력확률을 후처리에 사용될 신경망의 학습용으로 사용하여 신경망을 학습하여 HMM과 신경망을 결합한 하이브리드 시스템을 만든다 이와 같은 HMM과 신경망을 결합한 하이브리드 모델을 사용하여 단독 숫자음에서 실험한 결과 HMM 단독으로 사용하였을 때 보다 MLP에서는 약 $4.5\%$ RBFN에서는 약 $2\%$의 인식률 향상이 있었다. 기존의 하이브리드 시스템이 갖는 많은 학습시간이 소요되는 문제점과 실시간 음성인식시스템을 구현할 패의 학습데이터의 부족으로 인한 인식률 저하를 해결할 수 있는 방법임을 확인할 수 있었다

• 방송공학회논문지
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• 제19권5호
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• pp.687-698
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• 2014

ATSC는 방송망 기반의 서비스호환 3DTV 방송서비스 표준완료 이후 최근 이종망 환경에서 하이브리드 3DTV 방송서비스에 대한 표준화를 진행 중에 있다. 본 논문에서는 기존의 디지털방송 화질열화 없이 Full HD 3D 화질을 보장하기 위한 방송망 및 IP망 연동형 3D 비디오 방송방식을 제안한다. 특히, 본 논문에서는 ISO/IEC 23009-1 DASH를 활용한 3D 부가영상 전송, 이종망 환경 하에서 안정적인 3D 비디오 동기화 및 하이브리드 3DTV 수신기 개발을 위한 시스템 타겟 디코더 모델을 기술한다. 실험결과, 제안된 기술은 하이브리드 3DTV 방송 표준화에 직접적으로 적용될 수 있으며 안정적인 하이브리드 3DTV 인코더 및 수신기 개발을 위한 참조 모델로 활용될 수 있음을 확인하였다.

• 분석과학
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• 제15권5호
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• pp.399-409
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• 2002

글로우방전 (Glow Discharge)을 이용한 원자방출 분광계의 On-line 분광분석을 위해 개발된 본 시스템을 위한 프로그램은 주변 광학기기들을 제어하는 부분과 스펙트럼의 비선형적인 오차를 줄여 보다 정확한 결과를 얻기 위해 인공지능 기법을 도입한 스펙트럼 해석 부분으로 구성되어져 있다. McPHERSON 207 Monochromator를 GPIB 통신 프로토콜로서 제어하였으며, (주)Photon_Tek에서 제작한 A/D Amplifier를 사용하여 PMT로부터 검출 신호를 측정할 수 있었다. 인공지능 기법인 HNN(Hybrid Neural Network)을 스펙트럼 해석 부분에 도입하여 P, Cu, Fe, Cr, 등의 정성 분석과, Cd 10 ppb의 미량 검출을 통한 정량분석을 기존의 상용화된 방법보다 정확하게 수행할 수 있었다.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권3호
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• pp.1213-1237
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• 2019

Vehicular Ad-hoc Network (VANET) facilities envision future Intelligent Transporting Systems (ITSs) by providing inter-vehicle communication for metrics such as road surveillance, traffic information, and road condition. In recent years, vehicle manufacturers, researchers and academicians have devoted significant attention to vehicular communication technology because of its highly dynamic connectivity and self-organized, decentralized networking characteristics. However, due to VANET's high mobility, dynamic network topology and low communication coverage, dissemination of large data packets (e.g. multimedia content) is challenging. Clustering enhances network performance by maintaining communication link stability, sharing network resources and efficiently using bandwidth among nodes. This paper proposes a mobility-based, multi-hop clustering algorithm, (MBCA) for multimedia content broadcasting over an IEEE 802.11p-LTE-enabled hybrid VANET architecture. The OMNeT++ network simulator and a SUMO traffic generator are used to simulate a network scenario. The simulation results indicate that the proposed clustering algorithm over a hybrid VANET architecture improves the overall network stability and performance, resulting in an overall 20% increased cluster head duration, 20% increased cluster member duration, lower cluster overhead, 15% improved data packet delivery ratio and lower network delay from the referenced schemes [46], [47] and [50] during multimedia content dissemination over VANET.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권4호
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• pp.220-227
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• 2005

This paper presents hybrid artificial intelligent(HAI) controller based on the vector controlled IPMSM drive system. And it is based on artificial technologies that adaptive neural network fuzzy(A-NNF) is to speed control and artificial neural network(ANN) is to speed estimation. The salient feature of this technique is the HAI controller The hybrid action tolerates any inaccuracies in the fuzzy logic assignment rules or in the neural network stationary weights. Speed estimators using feedforward multilayer and artificial neural network(ANN) are compared. The back-propagation algorithm is easy to derived the estimated speed tracks precisely the actual motor speed. This paper presents the theoretical analysis as well as the simulation results to verify the effectiveness of the new hybrid intelligent control.

• 한국소음진동공학회논문집
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• 제23권9호
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• pp.814-822
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• 2013

For the fault diagnosis of a mechanical system, pattern recognition methods have being used frequently in recent research. Hidden Markov model(HMM) and artificial neural network(ANN) are typical examples of pattern recognition methods employed for the fault diagnosis of a mechanical system. In this paper, a hybrid method that combines HMM and ANN for the fault diagnosis of a mechanical system is introduced. A rotating blade which is used for a wind turbine is employed for the fault diagnosis. Using the HMM/ANN hybrid model along with the numerical model of the rotating blade, the location and depth of a crack as well as its presence are identified. Also the effect of signal to noise ratio, crack location and crack size on the success rate of the identification is investigated.

• Journal of Power Electronics
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• 제14권5호
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• pp.1008-1027
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• 2014

An electric scooter with a V-belt continuously variable transmission (CVT) driven by a permanent magnet synchronous motor (PMSM) has a lot of nonlinear and time-varying characteristics, and accurate dynamic models are difficult to establish for linear controller designs. A PMSM servo-drive electric scooter controlled by a novel hybrid modified recurrent Legendre neural network (NN) control system is proposed to solve difficulties of linear controllers under the occurrence of nonlinear load disturbances and parameters variations. Firstly, the system structure of a V-belt CVT driven electric scooter using a PMSM servo drive is established. Secondly, the novel hybrid modified recurrent Legendre NN control system, which consists of an inspector control, a modified recurrent Legendre NN control with an adaptation law, and a recouped control with an estimation law, is proposed to improve its performance. Moreover, the on-line parameter tuning method of the modified recurrent Legendre NN is derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, two optimal learning rates for the modified recurrent Legendre NN are derived to speed up the parameter convergence. Finally, comparative studies are carried out to show the effectiveness of the proposed control scheme through experimental results.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 춘계학술대회 및 임시총회
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• pp.1-5
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• 2002

본 논문은 수족이 불편한 장애인의 편리성을 위해 휠체어에 음성인식 모듈을 개발하는데 목표로 하고 있다. 본 시스템의 주프로세서는 TMS320C32를 이용하였고, 전처리단계에서 잡음환경의 특성을 고려하여 Winer 필터를 적용해서 잡음을 제거하였고 특징추출과정에서는 LPC&Cepstrum을 이용하여 프레임당 12차의 특징패턴을 추출하였다. 그 후 인식부에서는 기존의 알고리즘 중 고립단어에서 흔히 사용하는 DTW(Dynamic Time Warping)과 오인식률 발생을 방지하기 위해 NN(Neural Network)를 결합한 Hybrid 형태로 구현하였다. 본 연구에서는 DTW와 Hybrid형태를 각각 실험한 결과 잡음환경에서 고립단어 인식률이 평균 96%이상 나타났다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.448-450
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• 2007

Peer-to-Peer system has emerged as a popular model aiming at further utilizing Internet information and resources, complementing the available client-server services. However, the mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical location aware topology can cause serious performance degradation. In this paper, we consider the network distance between peers so that it helps peers select neighbors located at the nearest when they exchange queries for sharing of resources. To reduce the unnecessary signaling traffic and delay of query exchange, we propose a location aware topology based Hybrid P2P system. This system calculates the network distance which combines the direct measurement such as RTT (Round Trip Time) with geographic space of peers using IP address