Energy function analysis of dynamic programming neural networks

All analytical examination of the energy function associated with a dynamic programming neural network is presented. The analysis is carried out in two steps. First, the locations and numbers of the minimum states for different components of the energy function are investigated in the extreme cases. A clearer insight into the energy function can be gained through the minimum states of different components. Secondly, the locations of the minimum states of the energy function using different parameter values are derived. It is shown that the minimum states can reside in regions which are regarded as valid solutions with certain conditions. Examples and simulation results are given to justify the validity of the theories developed.     

A new simplified threshold-voltage model for n-MOSFETs with nonuniformly doped substrate and its application to MOSFET's miniaturization

The formulation, verification, and application of a new simplified 2-D threshold voltage model for n-MOSFETs with nonuniformly doped substrate profile are provided, in which the averaged normal field at the Si/SiO/sub 2/ interface in the active channel is quoted from a simplified solution of two-dimensional Poisson equation using the Green function technique. Starting with the expression of this average normal field, a simple threshold-voltage model for short-channel n-MOSFETs with nonuniformly doped substrate profile is explicitly expressed in terms of device structures and terminal voltages by considering parabolic source-drain boundary potentials. Moreover, the effects of the junction depth on the threshold voltage are examined in detail. It is shown that the DIBL effect cannot be completely eliminated by simply increasing the substrate doping concentration. Comparisons among developed model, 2-D numerical analysis, and experimental data have been made and the accuracy of the developed analytical model has been verified. In addition, a direct extension of our model to the case of uniformly doped substrates leads to a new constraint equation for device miniaturization.<>     

Ausgleich der Volatilität der Erneuerbaren Energien mittels Wärmespeichern auf Salzbasis

To compensate for the volatility of renewable energies, solutions beyond fossil reserve power plants are needed. One approach is salt-based heat storage with phase change materials (PCM). In order to achieve economic efficiency, ambitious targets are required. A sensible and latent heat storage system for the temperature range around 400 °C based on salts and thermal oils with high development potential is presented.     

Support vector classification of proteomic profile spectra based on feature extraction with the bi-orthogonal discrete wavelet transform

Automatic classification of high-resolution mass spectrometry data has increasing potential to support physicians in diagnosis of diseases like cancer. The proteomic data exhibit variations among different disease states. A precise and reliable classification of mass spectra is essential for a successful diagnosis and treatment. The underlying process to obtain such reliable classification results is a crucial point. In this paper such a method is explained and a corresponding semi automatic parameterization procedure is derived. Thereby a simple straightforward classification procedure to assign mass spectra to a particular disease state is derived. The method is based on an initial preprocessing stage of the whole set of spectra followed by the bi-orthogonal discrete wavelet transform (DWT) for feature extraction. The approximation coefficients calculated from the scaling function exhibit a high peak pattern matching property and feature a denoising of the spectrum. The discriminating coefficients, selected by the Kolmogorov–Smirnov test are finally used as features for training and testing a support vector machine with both a linear and a radial basis kernel. For comparison the peak areas obtained with the it ClinProt-System ¹ [33] were analyzed using the same support vector machines. The introduced approach was evaluated on clinical MALDI-MS data sets with two classes each originating from cancer studies. The cross validated error rates using the wavelet coefficients where better than those obtained from the peak areas².     

Linear Bidirectional On-Line Construction of Affix Trees

Affix trees are a generalization of suffix trees that are based on the inherent duality of suffix trees induced by the suffix links. An algorithm is presented that constructs affix trees on-line by expanding the underlying string in both directions and that is the first known algorithm to do this with linear time complexity.     

Surfactant effects on gas absorption in a coke-oven gas treatment process

The effect of organic impurities in industrial stripped coal water (SCW) on the absorption of CO₂ was measured experimentally. Removal of these impurities via activated carbon showed a marked improvement in interphase mass transfer of a vertical wetted-wall column absorber. However, this benefit was not found in a stirred-cell absorber, in which a different flow pattern from that in wetted-wall column absorber is expected. An ad hoc systematic study on the effects of three deliberately added surfactants on gas absorption by pure water in three different absorbers with different flow patterns was thereafter conducted. The experimental results reveal that absorption deterioration also prevails only in a vertical, wetted-wall column absorber and the reduction in liquid phase mass transfer by the addition of surfactant can be satisfactorily correlated with surface pressure of solutions. This indicates that the effect of the industrial impurities in SCW on gas absorption may successfully be simulated under the same flow pattern by a surfactant solution with the same surface pressure. A possible modification of the existing coke-oven gas (COG) treatment process for the benefit of absorption enhancement was finally proposed.     

Linear and quadratic programming neural network analysis

Neural networks for linear and quadratic programming are analyzed. The network proposed by M.P. Kennedy and L.O. Chua (IEEE Trans. Circuits Syst., vol.35, pp.554-562, May 1988) is justified from the viewpoint of optimization theory and the technique is extended to solve optimization problems, such as the least-squares problem. For quadratic programming, the network converges either to an equilibrium or to an exact solution, depending on whether the problem has constraints or not. The results also suggest an analytical approach to solve the linear system Bx =b without calculating the matrix inverse. The results are directly applicable to optimization problems with C(2) convex objective functions and linear constraints. The dynamics and applicability of the networks are demonstrated by simulation. The distance between the equilibria of the networks and the problem solutions can be controlled by the appropriate choice of a network parameter.