Neural Networks

In multiresolution analysis(MRA)by wavelet function Daubechies (db), we decompose the signal in two parts, the low and high-frequency content,respectively. We remove the high-frequency content and reconstruct a new ¡§de-noise¡¨ signal by using inverse wavelet transform. In order to improve the forecasting accuracy of ANN (Artificial Neural Network) model ,we use the concept of tidal constituent phase-lags, and the new ¡§de-noise¡¨ signal was used as the input data set of ANN. Besides, we also use wavelet spectrum, conventional energy spectrum (Fast Fourier Transform, FFT),and harmonic analysis to analyze the character of tidal data . The results show that the concept of tidal constituent phase-lags can improve ANN model of tidal forecasting and supplement, but in the wavelet analysis , the improvement is insignificant .The reason is that the energy of higher frequency noise is very small compared to the energy of the diurnal and the semi- diurnal tidal components. In other word , the ANN model has a certain tolerance of noise effect .

Liquefied Natural Gas (LNG) is becoming an increasing factor in the U.S. natural gas market. For 30 years LNG imports into the U.S. have remained fairly flat. There are currently 18 permit applications being filed in the U.S. and another 10 permit applications being filed in Canada and Mexico for LNG import terminals. The EIA (Energy Information Agency) estimates by 2025 that LNG will make up 21% of the total U.S. Natural Gas Supply. This study developed a neural network approach to forecast LNG imports into the U.S. Various input variables were gathered, organized into groups based on similarity, and then a correlation matrix was generated to screen out redundant variables. Since a limited number of data points were available I used a restricted number of input variables. Based on this restriction, I grouped the input variables into four different scenarios and then generated a forecast for each scenario. These four different scenarios were the $/MMBTU model, natural gas energy consumption model, natural gas consumption model and the energy stack model. The standard neural network approach was also used to screen the input variables. First, a correlation matrix determined which variables had a high correlation with the output, U.S. LNG imports. The ten most correlated input variables were then put into correlation matrix to determine if there were any redundant variables. Due to the lack of data points only the five most highly correlated input variables were used in the neural network simulation. A number of interesting results were obtained from this study. The energy stack model and the consumption of natural gas forecasted a non-linear trend in U.S. LNG imports, compared to the linear trend forecasted by the EIA. The energy stack model and consumption of natural gas model predicted that in 2025 U.S. LNG imports will be about 6.5 TCF, while the other three models prediction is about three times as less. The energy stack model is the most realistic model due its non-linear trend, when the rapid increase of LNG imports is going to occur, and the quantity of U.S. LNG imports predicted in 2025.

Thesis (M. Phil.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 45-48).

Thesis (M.P.H.)--University of Hong Kong, 2009. / Includes bibliographical references (p. 36-39).

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references. Available also from UMI/Dissertation Abstracts International.

Thesis (M. Phil.)--University of Hong Kong, 1978.

Thesis (Ph. D.)--University of Queensland, 2002. / Includes bibliographical references.

The work described in this thesis concerns the study of the evolution of simple forms of learning behaviour in artificial agents. Our interest in the phylogeny of learning has been developed within the theoretical framework provided by the "ecological approach" to the study of learning. The latter is a recent theoretical and methodological perspective which, contrary to that suggested by the classical approaches in animal and comparative psychology, has reconsidered the importance of the evolutionary analysis of learning as a species- niche-specific adaptive process, which should be investigated by employing the conceptual apparatus originally developed by J. J. Gibson within the context of visual perception. However, it has been acknowledged in the literature that methodological difficulties are hindering the evolutionary ecological study of learning. We argue that methodological tools - i. e., artificial agent based models - recently developed within the context of biologically-oriented cognitive science can potentially represent a complementary methodology to investigate issues concerning the evolutionary history of learning without losing sight of the complexity of the ecological perspective. Thus, the experimental work presented in this thesis contributes to the discussion on the adaptive significance of learning, through the analysis of the evolution of simple forms of associative learning in artificial agents. Part of the work of the thesis focuses on the study of the nature of the selection pressures which facilitate the evolution of associative learning. The results of these simulations suggest that ecological factors might prevent the selection from operating in favour of those elements of the "learning machinery" which, given the varying nature of the environment, are of potential benefit for the agents. Other simulations highlight the properties of the agent control structure and the characteristics of particular features of the ecology of the learning scenario which facilitate the evolution of learning agents

Neural stem cell (NSC) transplantation strategy offers great potential to treat spinal cord injury (SCI). NSCs may replace lost neurons or oligodendrocytes, and act as a source of neurotrophic factors to support the survival of remaining cells. Their efficiency was limited by poor survival after transplantation, and they had more tendencies to differentiate into astrocytes, but not neurons and oligodendrocytes. This project investigated whether activated microglia is a factor that contributes to this phenomenon, and studied the potential role of minocycline, a widely used antibiotic drug, to modify the negative effects of microglia on NSCs. In the first part of this study, we used organotypic spinal cord slice (SCS) culture to mimic in vivo local environment after SCI, and NSCs were grafted on their surface or shared culture medium with them. After specific depletion of microglia with clodronate loaded liposome, more grafted NSCs survived, and in the co-culture system, the NSC neuronal differentiation rate increased while glial differentiation rate decreased, the apoptosis rate also decreased. This suggested that activated microglia may impair NSC survival, and neuronal differentiation, but improve glial differentiation. In the second part of this study, we first tested the direct effects of minocycline on NSC apoptosis, proliferation and differentiation in vitro, to test whether minocycline has any side effect on NSCs. The results showed that at the concentration 10μg/ml or lower, minocycline did not affect NSC survival and proliferation, but impaired neuronal differentiation. Then we treated primary microglia culture with LPS or LPS plus minocycline, and collected the conditioned mediums (CM-LPS and CM-LPSMC) to test their effects on NSC　apoptosis and differentiation. The results showed that compared with CM-LPS, CM-LPSMC resulted in a significantly lower apoptotic rate of NSCs, also allowed NSC neuronal differentiation. This suggested that minocycline may impair the pro-apoptotic effect of activated microglia on NSCs. In conclusion, our study showed that activated microglia may impair NSC survival and neuronal differentiation. This indicated that in NSC transplantation strategy for SCI, microglia would be a target to be manipulated to improve graft survival and neuronal differentiation. Although minocycline may suppress NSC differentiation towards neurons, it has the potential to protect NSCs from the toxic effects of activated microglia. This showed the therapeutic potential of minocycline in NSC transplantation strategies for SCI. / published_or_final_version / Anatomy / Doctoral / Doctor of Philosophy