Obtaining bifurcation diagrams with a thermoacoustic network model

Linear techniques can predict whether the non-oscillating (steady) state of a thermoacoustic system is stable or unstable. With a sufficiently large impulse, however, a thermoacoustic system can reach a stable oscillating state even when the steady state is also stable. A nonlinear analysis is required to predict the existence of this oscillating state. Continuation methods are often used for this but they are computationally expensive. In this paper, an acoustic network code called LOTAN is used to obtain the steady and the oscillating solutions for a horizontal Rijke tube. The heat release is modelled as a nonlinear function of the mass flow rate. Several test cases from the literature are analysed in order to investigate the effect of various nonlinear terms in the flame model. The results agree well with the literature, showing that LOTAN can be used to map the steady and oscillating solutions as a function of the control parameters. Furthermore, the nature of the bifurcation between steady and oscillating states can be predicted directly from the nonlinear terms inside the flame model. Copyright © 2012 by ASME.

Forecasting Daily Chlorophyll a Concentration during the Spring Phytoplankton Bloom Period in Xiangxi Bay of the Three-Gorges Reservoir by Means of a Recurrent Artificial Neural Network

A recurrent artificial neural network was used for 0-and 7-days-ahead forecasting of daily spring phytoplankton bloom dynamics in Xiangxi Bay of Three-Gorges Reservoir with meteorological, hydrological, and limnological parameters as input variables. Daily data from the depth of 0.5 m was used to train the model, and data from the depth of 2.0 m was used to validate the calibrated model. The trained model achieved reasonable accuracy in predicting the daily dynamics of chlorophyll a both in 0-and 7-days-ahead forecasting. In 0-day-ahead forecasting, the R-2 values of observed and predicted data were 0.85 for training and 0.89 for validating. In 7-days-ahead forecasting, the R-2 values of training and validating were 0.68 and 0.66, respectively. Sensitivity analysis indicated that most ecological relationships between chlorophyll a and input environmental variables in 0-and 7-days-ahead models were reasonable. In the 0-day model, Secchi depth, water temperature, and dissolved silicate were the most important factors influencing the daily dynamics of chlorophyll a. And in 7-days-ahead predicting model, chlorophyll a was sensitive to most environmental variables except water level, DO, and NH3N.

Modeling phytoplankton dynamics in the River Darling (Australia) using the radial basis function neural network

A radial basis function neural network was employed to model the abundance of cyanobacteria. The trained network could predict the populations of two bloom forming algal taxa with high accuracy, Nostocales spp. and Anabaena spp., in the River Darling, Australia. To elucidate the population dynamics for both Nostocales spp. and Anabaena spp., sensitivity analysis was performed with the following results. Total Kjeldahl nitrogen had a very strong influence on the abundance of the two algal taxa, electrical conductivity had a very strong negative relationship with the population of the two algal species, and flow was identified as one dominant factor influencing algal blooms after a scatter plot revealed that high flow could significantly reduce the algal biomass for both Nostocales spp. and Anabaena spp. Other variables such as turbidity, color, and pH were less important in determining the abundance and succession of the algal blooms.

The design algorithm of an associative memory neural network

A design algorithm of an associative memory neural network is proposed. The benefit of this design algorithm is to make the designed associative memory model can implement the hoped situation. On the one hand, the designed model has realized the nonlinear association of infinite value pattern from n dimension space to m dimension space. The result has improved the ones of some old associative memory neural network. On the other hand, the memory samples are in the centers of the fault-tolerant. In average significance the radius of the memory sample fault-tolerant field is maximum.

The image coding scheme based on neural network and SVM

This paper applies data coding thought, which based on the virtual information source modeling put forward by the author, to propose the image coding (compression) scheme based on neural network and SVM. This scheme is composed by "the image coding (compression) scheme based oil SVM" embedded "the lossless data compression scheme based oil neural network". The experiments show that the scheme has high compression ratio under the slightly damages condition, partly solve the contradiction which 'high fidelity' and 'high compression ratio' cannot unify in image coding system.

Lossless data compression scheme based on neural network and SVM

First, the compression-awaited data are regarded Lis character strings which are produced by virtual information source mapping M. then the model of the virtual information source M is established by neural network and SVM. Last we construct a lossless data compression (coding) scheme based oil neural network and SVM with the model, an integer function and a SVM discriminant. The scheme differs from the old entropy coding (compressions) inwardly, and it can compress some data compressed by the old entropy coding.

Measurement and analysis of microwave frequency response of semiconductor optical amplifiers - art. no. 682406

The measurement and analysis of the microwave frequency response of semiconductor optical amplifiers (SOAs) are proposed in this paper. The response is measured using a vector network analyzer. Then with the direct-subtracting method, which is based on the definition of scattering parameters of optoelectronic devices, the responses of both the optical signal source and the photodetector are eliminated, and the response of only the SOA is extracted. Some characteristics of the responses can be observed: the responses are quasi-highpass; the gain increases with the bias current; and the response becomes more gradient while the bias current is increasing. The multisectional model of an SOA is then used to analyze the response theoretically. By deducing from the carrier rate equation of one section under the steady state and the small-signal state, the expression of the frequency response is obtained. Then by iterating the expression, the response of the whole SOA is simulated. The simulated results are in good agreement with the measured on the three main characteristics, which are also explained by the deduced results. This proves the validity of the theoretical analysis.

Analysis of leakage current in GaAs micro-solar cell arrays

The output characteristics of micro-solar cell arrays are analyzed on the basis of a modified model in which the shunt resistance between cell lines results in current leakage. The modification mainly consists of adding a shunt resistor network to the traditional model. The obtained results agree well with the reported experimental results. The calculation results demonstrate that leakage current in substrate affects seriously the performance of GaAs micro- solar cell arrays. The performance of arrays can be improved by reducing the number of cells per line. In addition, at a certain level of integration, an appropriate space occupancy rate of the single cell is recommended for ensuring high open circuit voltages, and it is more appropriate to set the rates at 80%-90% through the calculation.

Processing network models of energy/environment systems

Processing networks are a variant of the standard linear programming network model which are especially useful for optimizing industrial energy/environment systems. Modelling advantages include an intuitive diagrammatic representation and the ability to incorporate all forms of energy and pollutants in a single integrated linear network model. Added advantages include increased speed of solution and algorithms supporting formulation. The paper explores their use in modelling the energy and pollution control systems in large industrial plants. The pollution control options in an ethylene production plant are analyzed as an example. PROFLOW, a computer tool for the formulation, analysis, and solution of processing network models, is introduced.

Analysis of orthogonal memory patterns

It is well known that the storage capacity may be large if all memory patterns are orthogonal to each other. In this paper, a clear description is given about the relation between the dimension N and the maximal number of orthogonal vectors with components +/-1, and also the conception of attractive index is proposed to estimate the basin of attraction. Theoretic analysis and computer simulation show that each memory pattern's basin of attraction contains at least one Hamming ball when the storage capacity is less than 0.33N which is better than usual 0.15 N.

Analysis of leakage current in GaAs micro-solar cell arrays

The output characteristics of micro-solar cell arrays are analyzed on the basis of a modified model in which the shunt resistance between cell lines results in current leakage. The modification mainly consists of adding a shunt resistor network to the traditional model. The obtained results agree well with the reported experimental results. The calculation results demonstrate that leakage current in substrate affects seriously the performance of GaAs micro- solar cell arrays. The performance of arrays can be improved by reducing the number of cells per line. In addition, at a certain level of integration, an appropriate space occupancy rate of the single cell is recommended for ensuring high open circuit voltages, and it is more appropriate to set the rates at 80%-90% through the calculation.

Sand storms: CFD analysis of Reynolds stress and collision stress of particles near sand bed

Sand storm is a serious environmental threat to humans. Sand particles are transported by saltation and suspension, causing soil erosion in one place and deposition in another. In order to prevent and predict sand storms, the causes and the manners of particle motions must be studied in detail. In this paper a standard k-epsilon model is used for the gas phase simulation and the discrete element method (DEM) is used to predict the movements of particles using an in-house procedure. The data are summarized in an Eulerian-Eulerian regime after simulation to get the statistical particle Reynolds stress and particle collision stress. The results show that for the current case the Reynolds stress and the air shear stress predominate in the region 20-250 mm above the initial sand bed surface. However, in the region below 3 mm, the collision stress must be taken into account in predicting particle movement. (C) 2010 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

Artificial neural network classification based on high-performance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer

Nucleosides in human urine and serum have frequently been studied as a possible biomedical marker for cancer, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. Fifteen normal and modified nucleosides were determined in 69 urine and 42 serum samples using high-performance liquid chromatography (HPLC). Artificial neural networks have been used as a powerful pattern recognition tool to distinguish cancer patients from healthy persons. The recognition rate for the training set reached 100%. In the validating set, 95.8 and 92.9% of people were correctly classified into cancer patients and healthy persons when urine and serum were used as the sample for measuring the nucleosides. The results show that the artificial neural network technique is better than principal component analysis for the classification of healthy persons and cancer patients based on nucleoside data. (C) 2002 Elsevier Science B.V. All rights reserved.

基于随机网络编码的无线广播重传方案及性能分析

针对自动最复重传(ARQ)机制在无线广播系统中吞吐量性能不佳的缺陷,提出一种基于随机网络编码的广播重传方案RNC-ARQ。对于广播节点,采用随机线性码对所有丢失包进行编码组合重传。对于接收节点,当接收的编码包累积到一定数量后可通过解码操作恢复出原始数据。该方案可有效减少重传次数,改善无线广播的吞吐量性能。基于Gilbert-Elliott模型描述的突发错误信道,建立了信道状态和节点接收处理流程合并的多状态马尔可夫模型,并以此为基础推导了RNC-ARQ方案的TQ吐量闭合解。最后,使用NS-2模拟器评估RNC-ARQ方案的性能,结果表明在突发差错信道下,基于随机网络编码重传方案的吞吐量优于传统的选择重传ARQ方案和基于异或编码的重传方案。

Preparation and Characterization of Interpenetration Polymer Network Films Based on Poly(vinyl alcohol) and Poly(acrylic acid) for Drug Delivery

A series of full interpenetrating polymer network (full-IPN) films of poly(acrylic acid) (PAA)/poly (vinyl alcohol) (PVA) were prepared by radical solution polymerization and sequential IPN technology. Attenuated total reflectance-Fourier transform infrared spectroscopy, swelling properties, mechanical properties, morphology, and glass transition temperature of the films were investigated. FTIR spectra analysis showed that new interaction hydrogen bonds between PVA and PAA were formed. Swelling property of the films in distilled water and different pH buffer solution was studied. Swelling ratio increased with increasing PAA content of IPN films in all media, and swelling ratio decreased with increasing PVA crosslink degree. Tensile strength and elongation at break related not only to the constitution of IPNs but also to the swelling ratio of IPNs.