Browsing by Author "Jan, JC"

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Augmented IFN learning model
(2000-01-01)
Hung, SL
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Jan, JC
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土木工程學系
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Department of Civil Engineering
Solving engineering problems is a creative, experiential process. An experienced engineer generally solves a new problem by recalling and reusing some similar instances examined before. According to such a method, the integrated fuzzy neural network (IFN) learning model was developed and implemented as a computational model for problem solving. This model has been applied to design problems involving a complicated steel structure. Computational results indicate that, because of its simplicity, the IFN model can learn the complicated problems within a reasonable computational time. The learning performance of IFN, however, relies heavily on the values of some working parameters, selected on a trial-and-error basis. In this work, we present an augmented IFN learning model by integrating a conventional IFN learning model with two novel approaches-a correlation analysis in statistics and a self-adjustment in mathematical optimization. This is done to facilitate the search for appropriate working parameters in the conventional IFN. The augmented IFN is compared with the conventional IFN using two steel structure design examples. This comparison reveals a superior learning performance for the augmented IFN learning model. Also, the problem of arbitrary trial-and-error selection of the working parameters is avoided in the augmented IFN learning model.
Damage detection of structures using unsupervised fuzzy neural network
(2005)
Wen, CM
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Hung, SL
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Huang, CS
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Jan, JC
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土木工程學系
This work presents an artificial neural network (ANN) approach for detecting structural damage. An unsupervised neural network which incorporates the fuzzy concept (named the Unsupervised Fuzzy Neural Network, UFN) is adopted to detect localized damage. The structural damage is assumed to take the form of reduced elemental stiffness. The damage site is demonstrated to correlate with the changes in the modal parameters of the structure. Therefore, a feature representing the damage location termed the Damage Localization Feature (DLF) is presented. When the structure experiences damage or change in the structural member, the measured DLF is obtained by analyzing the recorded dynamic responses of the structure. The location of the structural damage then can be identified using the UFN according to the measured DLF information. This study verifies the proposed model using an example involving a five-storey frame building. Both single and multiple damaged sites are considered. The effects of measured noise and the use of incomplete modal data are introduced to inspect the capability of the proposed detection approach.
Direct experimental evidence of hybridization of Pb states with O 2p states in ferroelectric perovskite oxides
(2005-07-04)
Jan, JC
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Tsai, HM
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Pao, CW
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Chiou, JW
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Asokan, CK
This work presents the O K- and Ti L-3,L-2-edge x-ray absorption near-edge structure (XANES) spectra of PbxSr1-xTiO3 (PxSTO) and BaxSr1-xTiO3 (BxSTO) compounds with various Pb and Ba concentrations. The result provides direct evidence that the Pb-O bonding strongly affects O 2p-Ti 3d hybridization in the TiO6 octahedron of PxSTO. In contrast, the Ba-O bonding does not substantially affect O 2p-Ti 3d hybridization in BxSTO. The Ti L-3-edge XANES spectra show the splitting of the e(g) band for PxSTO with x greater than or similar to 0.5, which provides an evidence of Pb-induced tetragonal distortion in the TiO6 octahedron. In contrast, e(g) band splitting is absent in BxSTO. (c) 2005 American Institute of Physics.
Effect of the Ca content on the electronic structure of Pb1-xCaxTiO3 perovskites
(2003-10-20)
Jan, JC
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Kumar, KPK
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Chiou, JW
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Tsai, HM
;
Shih, HL
This study performs O K- and Ti L-3,L-2-edge x-ray absorption near-edge structure (XANES) measurements and first-principles pseudopotential calculations for the electronic structures of ABO(3)-type Pb1-xCaxTiO3 (x=0-1) perovskites. The features in the O K-edge XANES spectra are found to be contributed primarily by hybridization between O 2p and Ti 3d, Pb 6p, and Ca 3d orbitals. The O K-edge XANES spectra reveal that partial substitution of A cations, Pb, by Ca not only decreases O 2p-Pb 6p but also O 2p-Ti 3d hybridization. The Ti L-3,L-2-edge measurements find that the off-center displacement of Ti, and hence, ferroelectricity persist up to a Ca concentration between 0.3 and 0.4. (C) 2003 American Institute of Physics.
High-order MS_CMAC neural network
(2001-05-01)
Jan, JC
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Hung, SL
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土木工程學系
;
Department of Civil Engineering
A macro structure cerebellar model articulation controller (CMAC) or MS_CMAC was developed by connecting several one-dimensional (1-D) CMACs as a tree structure, which decomposes a multidimensional problem into a set of 1-D subproblems, to reduce the computational complexity in multidimensional CMAC, Additionally, a trapezium scheme is proposed to assist MS_CMAC to model nonlinear systems. However, this trapezium scheme cannot perform a real smooth interpolation, and its working parameters are obtained through cross-validation. A quadratic splines scheme is developed herein to replace the trapezium scheme in MS_CMAC, named high-order MS_CMAC (HMS_CMAC), The quadratic splines scheme systematically transforms the stepwise weight contents of CMACs in MS_CMAC into smooth weight contents to perform the smooth outputs, Test results affirm that the HMS_CMAC has acceptable generalization in continuous function-mapping problems for nonoverlapping association in training instances. Nonoverlapping association in training instances not only significantly reduces the number of training instances needed, but also requires only one learning cycle in the learning stage.
Machine learning in engineering design - An unsupervised fuzzy neural network case-based learning model
(1997)
Hung, SL
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Jan, JC
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土木工程學系
;
Department of Civil Engineering
Engineering design is a creative and experience oriented process. Facing a new design case, an experienced designer will recall the similar cases in case base been solved before. Then, the designer will attempt to find the solution from these similar cases in a way of adaptation or synthesis. In this paper, an unsupervised fuzzy neural network (UFN) case-based learning model has been developed to perform the aforementioned design process and implemented in two steps. The UFN learning model has been applied to the domain of engineering design. The learning results show that the learning performance of the new learning model is superior to that of a supervised learning model only in complicated or discrete domains. Also, the unsupervised fuzzy neural network learning model can learn complicated design problems within a reasonable CPU time.
MS_CMAC neural network learning model in structural engineering
(1999-01-01)
Hung, SL
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Jan, JC
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土木工程學系
;
Department of Civil Engineering
The present American Institute of Steel Construction specifications use the alignment charts and approximate formulas conveniently to determine some coefficients in design, such as moment gradient coefficient C(b) for beams of I-shaped section and effective length factor K of columns. In these methods, the coefficients are unconservative when the boundary conditions are different from the development of specifications. The governing equations, numerical approaches, on the K and C(b) coefficients provide more accurate results. The approaches, however, are not readily available for structural engineers to use in design. Applying neural network computing toward structural engineering problems has received increasing interest, with particular emphasis placed on supervised neural networks. The cerebellar model articulation controller (CMAC), one of the supervised neural network learning models, is mostly used in the domain of control. In this work, we use a newly developed Macro Structure CMAC (MS(-)CMAC) neural network learning model to aid steel structure design. The topology of the novel learning model is constructed by a number of time inversion CMACs as a tree structure. The learning performance of the MS(-)CMAC is first compared with a stand-alone time inversion CMAC using one structural engineering example. That comparison indicates not only superior prediction-but also fast learning propriety for the MS-CMAC neural network learning model. In addition, the MS-CMAC neural network learning model is applied to two steel design problems. It is shown that the MS-CMAC not only can learn structural design problems within a reasonable central processing unit time but also can estimate more accurate coefficients than that estimated through alignment charts and approximate formulas in American Institute of Steel Construction specifications.
Neural network forecast model in deep excavation
(2002-01-01)
Jan, JC
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Hung, SL
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Chi, SY
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Chern, JC
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土木工程學系
Diaphragm wall deflection is an important field measurement in deep excavation. The monitoring data are applied to evaluate the construction performance to avoid a supporting system failure or damages incurred to adjacent structures. Despite the numerous case histories of construction projects and several forecasting methods, no method accurately forecasts the performance of construction due to the complicated geotechnical and construction factors affecting the behavior of the diaphragm wall. This work predicts the diaphragm wall deflection by using the adaptive limited memory-Broyden-Fletcher-Goldfarb-Shanno supervised neural network. Eighteen case histories of deep excavations with four to seven excavation stages are selected for training and verification. In addition, the knowledge representation adopts measured wall deflections of previous excavation stages as inputs to the network. Doing so substantially reduces the importance of soil parameters, which are often extremely fluctuating and difficult to assess. Simulation results indicate that the artificial neural network can reasonably predict the magnitude, as well as the location, of maximum deflection of the diaphragm wall.
Temperature induced stress of ZnSe quantum dots in glass matrix thin films grown by pulsed laser deposition
(2001-02-01)
Jan, JC
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Kuo, SY
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Yin, SB
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Hsieh, WF
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光電工程學系
By analyzing the temperature dependent spectral shifts and broadenings of both photoluminescence (PL) and Raman modes from the ZnSe-doped glass thin films grown by pulsed laser deposition, we found that temperature induced stress causes excess shifts of the PL emission bands to higher energies when the samples were kept below 100K. The compressed stress may be a result of the different thermal expansion of the ZnSe nanocrystal and the glass matrix; it is further confirmed by the temperature dependent excess Raman shift in this quantum dot sample as compared with the crystal data in the high-pressure experiment. In addition, we found the activation energies of the nonradiative decay channels associated with the edge-emission and deep-level bands are close to 1TO and 1LO of ZnSe phonon energies.