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Core competency has become the keynote of curriculum reform in education field both at home and abroad. The integration of core competency with various disciplines has become increasingly developing. In order to meet the needs of social development and employers for Business English professionals, this paper adopts theoretical deduction method to summarize and refine the educational model of Business English discipline based on the core competency. The educational model constructed in this study, to some extent, is the first time to integrate core competency with Business English discipline, which has a certain innovative significance in the development of Business English programme. Hopefully, this educational model can provide a new pathway for the teaching reform of Business English discipline.

In order to overcome the problems of low accuracy and efficiency in traditional passenger frequent pattern mining methods, a multi-scale passenger frequent pattern mining method based on single mode data is proposed. Set up a passenger travel data acquisition framework to obtain the travel displacement and time data of passengers, and denoise the relevant data. Based on the denoised passenger data, the frequent pattern mining database is constructed, and the hash index process is set up. Finally, the travel characteristics are extracted, and the PrefixSpan algorithm is used to complete the passenger frequent pattern mining. Experimental results show that, compared with traditional mining methods, the proposed mining method has higher mining accuracy and efficiency, and has higher practical application value.

In order to solve the problem of high error rate in the traditional teaching reform scheme implementation effect evaluation method, a multi-level grey model based evaluation method of teaching reform scheme implementation effect is proposed. Firstly, the general mode of teaching reform scheme is analyzed, and then the evaluation system of teaching reform scheme implementation effect is constructed. By determining the index weight, the evaluation sample matrix is constructed, and the scheme implementation effect is divided into five grades, which are regarded as the evaluation grey class. Finally, according to the calculation results of grey evaluation weight vector and weight matrix, a multi-level grey model is constructed to obtain the evaluation results. The experimental results show that the method can quickly and accurately obtain the evaluation results of the implementation effect of the teaching reform program, and can be further promoted and used in practice.

In order to solve the problems of low accuracy and poor effect of the current network capacity limit prediction results, the capacity limit prediction method of wireless sensor networks is studied by combining sensing technology. Firstly, the standard capacity parameters of wireless sensor network nodes are calculated by combining genetic algorithm, and the node distribution density of the network structure is calculated according to the calculation results, thus obtaining the network capacity limit according to the node density and node capacity. In order to ensure the accuracy of the calculation, the capacity prediction process of the wireless sensor network is optimized, the node capacity detection path allocation steps are simplified, and the capacity limit of the wireless invasive network is predicted. Finally, experiments prove that the capacity limit prediction method based on sensing technology is more accurate and practical than the traditional method.

The weak signal and low signal-noise ratio (SNR)in the vertical attitude measurement system are the major two factors lowing the measurement accuracy greatly. Based on the defect analysis of the conventional attitude measurement system for vertical position, a novel system based on four Euler rotations is presented to increase the amplitude and SNR of original signal. As a first step, the theoretical model of the proposed system is established. Then the effect on the amplitude and SNR of original signal using the proposed model in the vertical measurement system is studied. Finally, the measurement error of the proposed system is tested. Experimental results suggest that the proposed system can effectively enhance the original signal amplitude over 20 times and improve the overall accuracy of the vertical attitude measurement system.

We combined the finite rate of innovation (FRI) method with singular value decomposition (SVD) theory and got an improved sampling algorithm of non-band limited signals. It used SVD instead of annihilating filter in FRI method to reduce noise. We took streams of diracs signal as an example and deduced the detailed sampling and reconstruction process in the improved algorithm. It first found DFT coefficients of the samples, and constructed a Hankel data matrix. Then the matrix was decomposed according to SVD technique and the position information of diracs was gotten. Finally it computed weight coefficients from the Vandermonde system. The simulation results indicate that the original signal can also be reconstructed well in the presence of noise if only the sample rate is not less than its innovation rate. The sampling method based on SVD has good antinoise performance. It also saves power consumption and computational complexity. In some communication systems such as UWB and CDMA, a very narrow pulse which is like diracs signal very much is used to carry information. So this FRI algorithm based on SVD can be applied in their receivers.