Search

Phellinus is a kind of fungus and known as one of the elemental components in drugs to avoid cancer. With the purpose of finding optimized culture conditions for Phellinus production in the lab, plenty of experiments focusing on single factor were operated and large scale of experimental data was generated. In previous work, we used regression analysis and GA Gene-set based Genetic Algorithm (GA) to predict the production, but the data we used depended on experimental experience and only little part of the data was used. In this work we use the values of parameters involved in culture conditions, including inoculum size, PH value, initial liquid volume, temperature, seed age, fermentation time and rotation speed, to establish a high yield and a low yield classification model. Subsequently, a prediction model of BP neural network is established for high yield data set. GA is used to find the best culture conditions. The forecast accuracy rate more than 90% and the yield we got have a slight increase than the real yield.

Welan gum is a kind of novel microbial polysaccharide, which is widely produced during the process of microbial growth and metabolism in different external conditions. Welan gum can be used as the thickener, suspending agent, emulsifier, stabilizer, lubricant, film-forming agent and adhesive usage in agriculture. In recent years, finding optimal experimental conditions to maximize the production is paid growing attentions. In this work, a hybrid computational method is proposed to optimize experimental conditions for producing Welan gum with data collected from experiments records. Support Vector Regression (SVR) is used to model the relationship between Welan gum production and experimental conditions, and then adaptive Genetic Algorithm (AGA, for short) is applied to search optimized experimental conditions. As results, a mathematic model of predicting production of Welan gum from experimental conditions is obtained, which achieves accuracy rate 88.36%. As well, a class of optimized experimental conditions is predicted for producing Welan gum 31.65g/L. Comparing the best result in chemical experiment 30.63g/L, the predicted production improves it by 3.3%. The results provide potential optimal experimental conditions to improve the production of Welan gum.

Identifying protein coding regions in DNA sequences by computational methods is an active research topic. Welan gum produced by Sphingomonas sp. WG has great application potential in oil recovery and concrete construction industry. Predicting the coding regions in the Sphingomonas sp. WG genome and addressing the mechanism underlying the explanation for the synthesis of Welan gum metabolism is an important issue at present. In this study, we apply a self adaptive spectral rotation (SASR, for short) method, which is based on the investigation of the Triplet Periodicity property, to predict the coding regions of the whole-genome data of Sphingomonas sp. WG without any previous training process, and 1115 suspected gene fragments are obtained. Suspected gene fragments are subjected to a similarity search against the non-redundant protein sequences (nr) database of NCBI with blastx, and 762 suspected gene fragments have been labeled as genes in the nr database.

Phellinusis a kind of fungus and is known as one of the elemental components in drugs to avoid cancers. With the purpose of finding optimized culture conditions forPhellinusproduction in the laboratory, plenty of experiments focusing on single factor were operated and large scale of experimental data were generated. In this work, we use the data collected from experiments for regression analysis, and then a mathematical model of predictingPhellinusproduction is achieved. Subsequently, a gene-set based genetic algorithm is developed to optimize the values of parameters involved in culture conditions, including inoculum size, PH value, initial liquid volume, temperature, seed age, fermentation time, and rotation speed. These optimized values of the parameters have accordance with biological experimental results, which indicate that our method has a good predictability for culture conditions optimization.

To overcome the disadvantages of full-wave Fourier algorithm that can not filter decaying DC component and has bad frequency characteristic, Morlet complex wavelet amplitude algorithm is studied. After analyzing the influence of parameters m, c and N to Morlet complex wavelet amplitude algorithm and the question that one-circle Morlet complex wavelet amplitude algorithm can not filter the two times harmonic component, difference filter adding one-circle Morlet complex wavelet amplitude algorithm is presented. The performance of full-wave Fourier algorithm, difference filter adding one-circle Morlet complex wavelet amplitude algorithm and three-circle Morlet complex wavelet amplitude algorithm are compared by simulation. The results of simulation indicate that Morlet complex wavelet amplitude algorithm can filter decaying DC component, has better frequency characteristic and higher calculating precision. It can acquire more precise results than the full-wave Fourier algorithm.

The problem of estimating the frequency, damping, phase, and amplitude of exponentially damped sinusoidal signal is considered. An expression of asymptotic Cramer-Rao lower bound (CRLB) for any unbiased estimator of the estimation problem under consideration is derived. A simplified maximum likelihood Gauss Newton algorithm is developed. The analytic and numerical studies show that the algorithm provides asymptotically efficient estimates of the parameters. A numerical example is presented to illustrate the performance of the proposed estimation procedure. A comparison to the Cramer-Rao lower bounded performance is also presented using the simple expression for the asymptotic CRLB covariance matrix derived in the paper. >

In this paper, stability studies of a multiple channel active noise control system are approximated in terms of multivariable state space function. By considering the presence of the acoustic channel, a multiple channel filtered x LMS adaptive notch filter is used with a specified plant model. It has been shown that the system behaves as a linear time-invariant system with a flow adaptation. The convergence coefficient and the estimation error of the plant phase characterize the behavior of the system. >