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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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🏛️國立陽明交通大學機構典藏

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    Recurrent neural-network-based physical model for the chin and other plucked-string instruments
    (2000-11-01)
    Liang, SF
    ;
    Su, AWY
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    電控工程研究所
    ;
    Institute of Electrical and Control Engineering
    A new physical model with neural networks is presented. The structure of the network is designed for the analysis of plucked-string instruments, and this network is also used as the corresponding synthesis engine. The proposed approach also provides a general and automatic way of determining suitable synthesis parameters by using a supervised neural network training algorithm with recorded sounds of a specific played instrument as the training vector. This is a general method and can be used for any plucked-string instrument. A traditional Chinese plucked-string instrument, called the Chin, is used as the target instrument to demonstrate this new synthesis method.
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    A novel neural-network-based image resolution enhancement
    (2003)
    Pu, HC
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    Lin, CT
    ;
    Liang, SF
    ;
    Kumar, N
    ;
    電控工程研究所
    In this paper, a novel HVS-directed neural-network-based adaptive interpolation scheme for natural image is proposed. A fuzzy decision system built from the characteristics of the human visual system (HVS) is proposed to classify pixels of the input image into human perception nonsensitive class and sensitive class. High-resolution digital images along with supervised learning algorithms are used to automatically train the proposed neural network. Simulation results demonstrate that the proposed new resolution enhancement algorithm can produce higher visual quality of the interpolated image than the conventional interpolation methods.
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    EEG-based fuzzy neural network estimator for driving performance
    (2004)
    Wu, RC
    ;
    Lin, CT
    ;
    Liang, SF
    ;
    Huang, TY
    ;
    Jung, TP
    Accidents caused by drivers' drowsiness have a high fatality rate because of the marked decline in the drivers' vehicle control abilities. Preventing accidents caused by drowsiness is highly desirable but requires techniques for continuously detecting, estimating, and predicting the level. of alertness of drivers. This paper proposes a brain-machine interface that combines electroencephalographic power spectrum estimation, principal. component analysis, and fuzzy neural networks to estimate/predict drivers' drowsiness level in a virtual-reality-based driving simulator. The driving performance is defined as deviation between the center of the vehicle and the center of the cruising lane. Our results demonstrated that the proposed method is feasible to accurately estimate quantitatively driving performance in a realistic driving simulator.
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    Estimating driving performance based on EEG spectrum and fuzzy neural network
    (2004)
    Wu, RC
    ;
    Lin, CT
    ;
    Liang, SF
    ;
    Huang, TY
    ;
    Chen, YC
    The growing number of traffic fatalities in recent years has become a serious concern to society. Accidents caused by drivers' drowsiness behind the steering wheel have a high fatality rate because of the marked decline in the drivers' abilities of perception, recognition! and vehicle control abilities while sleepy. Preventing accidents caused by drowsiness requires a technique for detecting, estimating, and predicting the level of alertness of a driver and a mechanism for maintaining his/her maximum performance. This paper describes a system that combines electroencephalographic (EEG) power spectrum estimation, principal component analysis, and fuzzy neural network model to estimate/predict drivers' drowsiness level in a driving simulator. Our results demonstrated that, for the first time, it is feasible to accurately estimate task performance, accurately estimate quantitatively measured driving performance, expressed as deviation between the center of the vehicle and the center of the cruising lane, in a realistic driving simulation.
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    Classification of driver's cognitive responses from EEG analysis
    (2005)
    Liang, SF
    ;
    Lin, CT
    ;
    Wu, RC
    ;
    Huang, TY
    ;
    Chao, WH
    During the past years, the growing number of traffic fatalities has become an important issue in public security. In this paper, we develop a quantitative analysis for ongoing assessment of cognitive response by investigating the neurobiological brain dynamics in traffic-light experiments. A single-trial event-related-potential (ERP)-based fuzzy neural network (FNN) is applied to recognize different brain potentials stimulated by red/green/yellow traffic-light events. The system consists of a dynamic virtual-reality (VR)-based motion simulation platform, EEG signal detection and analysis units, and FNN-based classifier. The independent component analysis (ICA) algorithms are used to obtain noise-free ERP signals from the multi-channel EEG signals. A novel temporal filter is also proposed to solve time-alignment problems of ERP features and principle component analysis (PCA) is used to reduce dimension of features, which were then fed into a FNN classifier. Experimental results demonstrate the feasibility of detecting and analyzing multiple streams of ERP signals that organize operators' cognitive responses to task events. Comparisons of three kinds of linear and nonlinear classifiers show that our proposed FNN-based classifier can achieve a satisfactory and superior recognition rate (85%). The classification results can be further transformed as the control/biofeedback signals of intelligent driving systems.
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    Assessment of driver's driving performance and alertness using EEG-based fuzzy neural networks
    (2005)
    Lin, CT
    ;
    Chen, YC
    ;
    Wu, RC
    ;
    Liang, SF
    ;
    Huang, TY
    Traffic fatalities in recent years have become a serious concern to our society. Accidents caused by drivers' drowsiness have a high fatality rate due to the decline of drivers' abilities in perception, recognition, and vehicle control abilities while sleepy. Preventing such an accident requires a technique for detecting, estimating, and predicting the level of alertness of a driver and a mechanism to maintain the driver's maximum performance of driving. This paper proposed a system that combines electroencephalogram (EEG) power spectra estimation, independent component analysis and fuzzy neural network models to estimate drivers' cognitive state in a dynamic virtual-reality-based driving environment. Experimental results show that the quantitative driving performance can be accurately and successfully estimated through analyzing driver's EEG signals by the proposed system.
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    Fuzzy neural network design using support vector regression for function approximation with outliers
    (2005)
    Lin, CT
    ;
    Liang, SF
    ;
    Yeh, CM
    ;
    Fan, KW
    ;
    電控工程研究所
    A fuzzy neural network based on support vector learning mechanism for function approximation is proposed in this paper. Support vector regression (SVR) is a novel method for tackling the problems of function approximation and regression estimation based on the statistical learning theory. SVR has been shown to have robust properties against noise. A novel support-vector-regression based fuzzy neural network (SVRFNN) by integrating SVR technology into FNN is developed. The SVRFNN combines the high accuracy and robustness of support vector regression (SVR) and the efficient human-like reasoning of FNN for function approximation. Experimental results show that the proposed SVFNN for function approximation can achieve good approximation performance with drastically reduced number of fuzzy kernel functions.
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    Adaptive feature extractions in an EEG-based alertness estimation system
    (2005)
    Lin, CT
    ;
    Chao, WH
    ;
    Chen, YC
    ;
    Liang, SF
    ;
    腦科學研究中心
    During the past years, the public security has become an important issue, especially, the safe manipulation and control of various vehicles. Maintaining high cognition is particularly important for the drivers behind the steering wheel. It requires an optimal estimation system to online continuously detect drivers' cognitive state related to abilities in perception, recognition, and vehicle control. In this paper, we proposed an EEG-based alertness estimation system with automatic feature selection mechanism. The independent component analysis (ICA) is used first to decompose the measured electroencephalogram (EEG). Then, a time-frequency analysis is performed to evaluate the time-frequency characteristic of each ICA component. We also proposed a new adaptive feature extracting mechanism for selections of frequency bands and ICA components. Different ranges of the alpha rhythm of subjects can be evaluated by the adaptive feature extracting mechanism according to the correlation coefficient between the ICA time-frequency response and the driving performance. The extracted features are then trained both by linear regression model and Self-cOnstructing Neuro-Fuzzy Inference Network (SONFIN) for the estimation of driving performance. The training and testing results of SONFIN are 96% and 91%, while the results of linear regression model are 90% and 85%, respectively. It demonstrates that the proposed adaptive feature extracting mechanism can achieve a great performance in alertness estimation with frequency band and component selection.