探寻“神曲”之“真神”——中国流行歌曲创作的民族文化溯源

我国流行歌曲的创作,应将具有鲜明传统民族特色与文化内涵的民族民间音乐、戏曲、曲艺、诗词及历史典故等元素和内容融入其中,这样不仅可以扩大艺术作品受众面,得到更广泛人群的认同和喜爱,同时还可以通过对其音乐的欣赏更好地传承和发扬优秀传统文化,在享受作品形式美的同时巧妙地实现音乐作品的社会教育功能和文化传承功能。继承和发展民族传统音乐文化,既是音乐创作的最佳手段,也是最终目的。

New Algorithm for Image Target Recognition Based on Fractal Feature Fusion

By combining fractal theory with D-S evidence theory, an algorithm based on the fusion of multi-fractal features is presented. Fractal features are extracted, and basic probability assignment function is designed. Comparison and simulation are performed on the new algorithm, the old algorithm based on single feature and the algorithm based on neural network. Results of the comparison and simulation illustrate that the new algorithm is feasible and valid.

Design of hydraulic motor speed control system based on co-simulation of AMESim and Matlab_Simulink

In order to design an effective hydraulic motor speed control system, Matlab_Simiulink and AMESim co-simulation technology is adopted to establish more accurate model and reflect the actual system. The neural network proportion-integration-differentiation （PID） control parameters on-line adjustment is utilized to improve system accuracy, celerity and stability. Simulation results indicate that with the control system proposed in this paper, the system deviation is reduced, therefore accuracy is improved; response speed for step signal and sinusoidal signal gets faster, thus acceleration is rapidly improved; and the system can be restored to the control value in case of interfering, so stability is improved.

ADAPTIVE NEURAL NETWORK ATTITUDE CONTROL FOR UNMANNED HELICOPTER

Adaptive flight control technology, feedback linearization, model inversion theory are reviewed and the error dynamic characteristics are analyzed, and an adaptive on-line neural network attitude control system is presented. The model inversion is under the hover condition. And the adaptive control law based on the neural network is designed to guarantee the boundedness of tracking error and control signals. Simulation results demonstrate that the nonlinear neural network augmented model inversion can self-adapt to the uncertainty and modeling errors of unmanned helicopters. Results are compared while the parameters of PD controller and robustness items are changed.

Phenomenological Simulation Study of Neuronal Activity Synchronization in 110 Elements Network

The phenomenon of activity synchronization in biological neural network is considered. Simulation of neurons dynamics in the 6-layer neural network with 110 elements in different regimes： regular spikes, chaotic spikes, regular and chaotic bursting, etc was performed. Izhykevich＇s phenomenological model that displays different types of activity inherent for real biological neurons was used for simulation. Space-time diagram for the entire network and raster plots for the whole structure and for each layer separately were built for visual inspection of neural network activity synchronization. Synchronization coefficients based on cross-correlation times of action potentials for all neurons pairs were calculated for the whole neural system and for each layer separately.

Integration of Neural Networks and Cellular Automata for Urban Planning

This paper presents a new type of cellular automa ta (CA) model for the simulation of alternative land development using neural netw orks for urban planning. CA models can be regarded as a planning tool because th ey can generate alternative urban growth. Alternative development patterns can b e formed by using different sets of parameter values in CA simulation. A critica l issue is how to define parameter values for realistic and idealized simulation . This paper demonstrates that neural networks can simplify CA models but genera te more plausible results. The simulation is based on a simple three-layer netw ork with an output neuron to generate conversion probability. No transition rule s are required for the simulation. Parameter values are automatically obtained f rom the training of network by using satellite remote sensing data. Original tra ining data can be assessed and modified according to planning objectives. Altern ative urban patterns can be easily formulated by using the modified training dat a sets rather than changing the model.

Chemical Components from the Fungus Engleromyces goetzei

A new ceramide, (2S,3S,4R, 10E)-2-[(2'R)-2'-hydroxytetracosanoyl amino]-10-octadecene-1, 3,4-triol (1), together with twelve known compounds, cerebroside A, cerebroside B, cerebroside D, cytochalasin D, epoxycytochalasin D, cytochalasin C, loganin, cerevisterol, ergosta-7,22-dien-3beta, 5alpha, 6alpha-triol, ergosta-4,6,8(14),22-tetraen-3-one, ergosterol peroxide and ergosta-5,7,22-trien-3-ol, was isolated from ethyl acetate fraction of Engleromyces goetzei P. Henn. The structure of new ceramide (1) was elucidated by spectral data and chemical method, especially by 2D-NMR techniques. All of the compounds except cytochalasin D were first obtained from this fungus.

Simulation of phytoplankton biomass in Quanzhou Bay using a back propagation network model and sensitivity analysis for environmental variables

Prediction and sensitivity models,to elucidate the response of phytoplankton biomass to environmental factors in Quanzhou Bay,Fujian,China,were developed using a back propagation(BP) network.The environmental indicators of coastal phytoplankton biomass were determined and monitoring data for the bay from 2008 was used to train,test and build a three-layer BP artificial neural network with multi-input and single-output.Ten water quality parameters were used to forecast phytoplankton biomass(measured as chlorophyll-a concentration).Correlation coefficient between biomass values predicted by the model and those observed was 0.964,whilst the average relative error of the network was-3.46% and average absolute error was 10.53%.The model thus has high level of accuracy and is suitable for analysis of the influence of aquatic environmental factors on phytoplankton biomass.A global sensitivity analysis was performed to determine the influence of different environmental indicators on phytoplankton biomass.Indicators were classified according to the sensitivity of response and its risk degree.The results indicate that the parameters most relevant to phytoplankton biomass are estuary-related and include pH,sea surface temperature,sea surface salinity,chemical oxygen demand and ammonium.

Design and Simulation of Single Neuron Adaptive PID Controller

The purpose of this paper is to design a neuron adaptive PID controller based on the theory of intelligent control of the extens- ive research on the characteristics of neuronss, neurons and PID controller. Artificial neurons have the adaptive, parallel processing, selflearning learning, and mare fault-tolerant characteristics. When the artificial neurons are used to control the process, the syste^n will enabled to en-sure that the accused has strong anti-interference capability and ro. bustness.

Eukaryotic Promoter Recognition Using Back propagation Neural Network

A new system is developed to recognize promoter sequences from non promoter sequences based on position weight matrix and backpropagation neural network in this paper. The system performs significantly better on the training set and the test set, the mean recognition rate is as high as 99% on the training set and 97% on the testing set. Experimental results demonstrate the effectiveness of the system to recognize the promoter sequences that have been trained and the promoter sequences that have not been seen previously.

Fully Coupled Time-Domain Simulation of Dynamic Positioning Semi-Submersible Platform Using Dynamic Surface Control

A fully coupled 6-degree-of-freedom nonlinear dynamic model is presented to analyze the dynamic response of a semi-submersible platform which is equipped with the dynamic positioning(DP) system. In the control force design, a dynamic model of reference linear drift frequency in the horizontal plane is introduced. The dynamic surface control(DSC) is used to design a control strategy for the DP. Compared with the traditional back-stepping methods, the dynamic surface control combined with radial basis function(RBF) neural networks(NNs) can avoid differentiating intermediate variables repeatedly in every design step due to the introduction of a first order filter. Low frequency motions obtained from total motions by a low pass filter are chosen to be the inputs for the RBF NNs which are used to approximate the low frequency wave force. Considering the propellers' wear and tear, the effect of filtering frequencies for the control force is discussed. Based on power consumptions and positioning requirements, the NN centers are determined. Moreover, the RBF NNs used to approximate the total wave force are built to monitor the disturbances. With the DP assistance, the results of fully coupled dynamic response simulations are given to illustrate the effectiveness of the proposed control strategy.

Simulation and experimental research of digital valve control servo system based on CMAC-PID control method

Digital valve control servo system is studied in this paper. In order to solve the system problems of poor control precision and slow response time,a CMAC-PID( cerebellar model articulation controller-PID) compound control method is proposed. This compound controller consists of two components: one is a traditional PID for the feedback control to guarantee stability of the system; the other is the CMAC control algorithm to form a feed-forward control for achieving high control precision and short response time of the controlled plant. Then the CMAC-PID compound control method is used in the digital valve control servo system to improve its control performance. Through simulation and experiment,the proposed CMAC-PID compound control method is superior to the traditional PID control for enhancing stability and robustness,and thus this compound control can be used as a new control strategy for the digital valve control servo system.

Control strategy of hybrid fuel cell/battery distributed generation system for grid-connected operation

This paper presents a control strategy of a hybrid fuel cell/battery distributed generation (HDG) system in distribution systems. The overall structure of the HDG system is given, dynamic models for the solid oxide fuel cell (SOFC) power plant, battery bank and its power electronic interfacing are briefly described, and controller design methodologies for the power conditioning units and fuel cell to control the power flow from the hybrid power plant to the utility grid are presented. To distribute the power between the fuel cell power plant and the battery energy storage, a neuro-fuzzy controller has been developed. Also, for controlling the active and reactive power independently in distribution systems, the current control strategy based on two fuzzy logic controllers has been presented. A Matlab/Simulink simulation model is developed for the HDG system by combining the individual component models and their controllers. Simulation results show the overall system performance including load-following and power management of the HDG system.

Construction of eukaryotic expression vector with brain-derived neurotrophic factor receptor trkB gene

Objective: To construct an eukaryotic expression vector carrying rat brain-derived neurotrophic factor receptor trkB gene. Methods: Using the total RNA isolated from rat brain as template, the trkB gene was amplified by reverse-transcription-polymerase chain reaction (RT-PCR) with a pair of specific primers which contained the restrictive sites of EcoR I and BamH I. The amplified fragment of trkB gene was digested with EcoR I and BamH I, and then subcloned into cloning vector pMD18-T and expression vector pEGFP-C2 respectively. The recombinant plasmids were identified by restriction endonuclease enzyme analysis and PCR. Results: The amplified DNA fragment was about 1461 bp in length. Enzyme digestion and PCR analysis showed that the gene of trkB had been successfully cloned into vector pMD18-T and pEGFP-C2. Conclusions: The trkB gene of rat has been amplified and cloned into the eukaryotic expression vector pEGFP-C2.