Applied Mechanics and Materials Vols. 40-41

Abstract: Take data driven method as theoretical basis, study multi-source information fusion technology. Using online and off-line data of the fusion system, does not rely on system's mathematical model, has avoided question about system modeling by mechanism. Uses principal component analysis method, rough set theory, Support Vector Machine(SVM) and so on, three method fusions and supplementary, through information processing and feature extraction to system's data-in, catches the most important information to lower dimensional space, realizes knowledge reduction. From data level, characteristic level, decision-making three levels realize information fusion. The example indicated that reduced computational complexity, reduced information loss in the fusion process, and enhanced the fusion accuracy.

Abstract: A new type of electromagnetic linear actuator for vehicle active suspension system is designed. Combined with the finite element analysis tool and electromagnetic induction principle, the finite element model and mathematic model are built up individually. Based on the simulation results, a prototype of electromagnetic linear actuator is developed. By comparison of the finite element simulation results and the experimental data, it shows the correctness of actuator design and simulation model.

Abstract: Based on the periodically forced dynamic qualities of the blood circulation system, this paper considered from network and hemodynamics, establishes plane structure diagram of the cerebral circulation. According to the electric circuit graph theory and blood dynamics equation, applies the averaging method to find an approximate solution of this equation. We apply this method in the cerebrovascular network that may help to explain the development processes of venous diseases. Simulation shows that computing result is consistent with blood flow change phenomenon of the clinical observation cerebral infarction.

Abstract: To remove the noises in ECG and to overcome the disadvantage of the denoising method only based on empirical mode decomposition (EMD), a combination of EMD and adaptive noise cancellation is introduced in this paper. The noisy ECG signals are firstly decomposed into intrinsic mode functions (IMFs) by EMD. Then the IMFs corresponding to noises are used to reconstruct signal. The reconstructed signal as the reference input of adaptive noise cancellation and the noisy ECG as the basic input, the de-noised ECG signal is obtained after adaptive filtering. The de-noised ECG has high signal-to-noise ratio, preferable correlation coefficient and lower mean square error. Through analyzing these performance parameters and testing the denoising method using MIT-BIH Database, the conclusion can be drawn that the combination of EMD and adaptive noise cancellation has considered the frequency distribution of ECG and noises, eliminate the noises effectively and need not to select a proper threshold.

Abstract: In this paper, we study positive solutions to the repulsive singular perturbation Hill equations with impulse effects. It is proved that such a perturbation problem has at least one positive impulsive periodic solution by a nonlinear alternative of Leray--Schauder.

Abstract: With the rapid development of automotive ECUs(Electronic Control Unit), the fault diagnosis becomes increasingly complicated. And the link between fault and symptom becomes less obvious. In order to improve the maintenance quality and efficiency, the paper proposes a fault diagnosis approach based on data mining technologies. By making full use of data stream, we firstly extract fault symptom vectors by processing data stream, and then establish a diagnosis decision tree through the ID3 decision tree algorithm, and finally store the link rules between faults and the related symptoms into historical fault database as a foundation for the fault diagnosis. The database provides the basis of trend judgments for a future fault. To verify this approach, an example of diagnosing faults of entertainment ECU is showed. The test result testifies the reliability and validity of this diagnostic method and reduces the cost of ECU diagnosis.

Abstract: In order to offer reference for the construction of energy transportation channels in China, we evaluated the existing system with the information entropy theory. By calculating the information entropy for the scale of construction of energy transportation channels with the data of annual investment in the fixed assets from 1986 to 2008, we analyzed the relation between transportation channels and the information entropy combined with the maximum entropy methods. Empirical results show that we should not let the rail transportation channels have excessive investment growth, and then secondly transfer some of the road investment to the transportation channels of waterway or pipeline while finally advance the construction step of the pipeline transportation channels. The quantitative evaluation of the construction scale of energy transportation channels is a important supplement to the traditional research and provide evidence for further development.

Abstract: In this paper, a model for evaluating the quality of Baikal skullcap root based on the chromatographic fingerprint and pharmacological effect correlation mode was established by using multivariate polynomial fitting technique. This result is new and the accuracy of the model is tested by comparing the modeled results with the experimental data. In addition, a related piece of software was developed. This paper also provides us with a new modelling method for the quality evaluation of traditional Chinese herbal medicine.

Abstract: The evaluation of clustering validity is important for clustering analysis, and is one of the hottest spots of cluster analysis. The quality of the evaluation of clustering is that optimal number of clusters is reasonable. For fuzzy clustering, the paper surveys the widely known fuzzy clustering validity evaluation based on the methods of fuzzy partition, geometry structure and statistics.

Abstract: Under deregulated environment, accurate electricity price forecasting is a crucial issue concerned by all market participants. Experience shows that single forecasting model is very difficult to improve the forecasting accuracy due to the complicated factors affecting electricity prices. In this paper, a particle swarm optimization based GM(1,1) method on short-term electricity price forecasting with predicted error improvement is proposed, in which the moving average method is used to process the raw data, the particle swarm optimization based GM(1,1) model is used to the processed series, and the time series analysis is used to further improve the predicted errors. The numerical example based on the historical data of the PJM market shows that the method can reflect the characteristics of electricity price better and the forecasting accuracy can be improved virtually compared with the conventional GM(1,1) model. The forecasted prices accurate enough to be used by electricity market participants to prepare their bidding strategies.