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나희승,박영진 대한기계학회 1992 대한기계학회논문집 Vol.16 No.7
본 연구에서는 Fig.3과 같은 다층 퍼셉트론을 사용하기로 한다. 그리고 위 에서 언급한 세가지점에서 다층퍼셉트론을 다시 살펴보아 해결하고자 하는 문제에 맞 도록 다층퍼셉트론을 개선시켜 보기로 한다. 따라서 본 연구의 목적은 제한조건을 갖는 문제를 풀기위한 새로운 형태의 다층퍼셉트론 설계 및 이에 적합한 학습규칙을 적용하여 보다 간단한 구조와 빠른 학습시간을 갖는 신경망을 구성하는데 있다. The conventional neural networks are built without considering the underlying structure of the problems. Hence, they usually contain redundant weights and require excessive training time. A novel neural network structure is proposed for symmetric problems, which alleviate some of the aforementioned drawback of the conventional neural networks. This concept is expanded to that of the constrained neural network which may be applied to general structured problems. Because these neural networks can not be trained by the conventional training algorithm, which destroys the weight structure of the neural networks, a proper training algorithm is suggested. The illustrative examples are shown to demonstrate the applicability of the proposed idea.
고속열차(TGV) 주행시 연변에서의 소음예측 및 방음시설설계
나희승 한국소음진동공학회 1999 소음 진동 Vol.9 No.6
This paper sums up the study of the soundproof facilities (noise barriers) to be placed on the test track section within the Seoul-Pusan H.S.T. project. The objective of this study is to determine optimum design of soundproof including height, length, location, sound absorbing materials for test track(chonan-taejon). This paper shows the model to design the shape and materials of noise barrier for high speed trains(TGV, ICE, ect). The design of soundproof facilities is to be conducted by MITHRA for the prediction of noise impact of the TGV and for optimising noise barriers in order to reduce the noise generated by high speed trains. A number of computer simulations are carried out in order to determine the specification of noise barrier on test track.
나희승,박영진 한국소음진동공학회 1994 소음 진동 Vol.4 No.1
A new LMS algorithm titled constrained LMS' is proposed for problems with constrained structure. The conventional LMS algorithm can not be used because it destroys the constrained structures of the weights or parameters. Proposed method uses error-back propagation, which is popular in training neural networks, for error minimization. The illustrative examplesare shown to demonstrate the applicability of the proposed algorithm.
나희승,Na, Hui-Seung 대한기계학회 2000 大韓機械學會論文集A Vol.24 No.5
Diffraction systematically causes error in acoustic measurements. Most probes are designed to reduce this phenomenon. On the contrary, this paper proposes a spherical probe a] lowing acoustic inten sity measurements in three dimensions to be made, which creates a diffracted field that is well-defined, thanks to analytic solution of diffraction phenomena. Six microphones are distributed on the surface of the sphere along three rectangular axes. Its measurement technique is not based on finite difference approximation, as is the case for the ID probe but on the analytic solution of diffraction phenomena. In fact, the success of sound source identification depends on the inverse models used to estimate inverse diffraction phenomena, which has nonlinear properties. In this paper, we propose the concept of nonlinear inverse diffraction modeling using a neural network and the idea of 3 dimensional sound source identification with better performances. A number of computer simulations are carried out in order to demonstrate the diffraction phenomena under various angles. Simulations for the inverse modeling of diffraction phenomena have been successfully conducted in showing the superiority of the neural network.