Subway short-term passenger flow forecast can help the subway operation department to optimize the train driving interval, improve the operation level and save the station air-conditioning electric ...energy. According to the different characteristics of passenger flow change on workdays and holidays, in order to avoid the influence of different characteristics between data, the prediction model of workdays and holidays passenger flow data are built respectively, and the Pearson correlation coefficient was used to analyze the correlation degree between the historical passenger flow data and the predicted value. Due to the large fluctuation of passenger flow data, there will be large error in directly predicting the original data. Adopt complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm to decompose passenger flow data, then based on the timing change of the passenger flow, bi-directional long short-term memory network (BLSTM) improved by LSTM is employed to predict each decomposed series. Finally, all the predicted outputs are merged as the overall output. To verify the superiority of the model, the LSTM, BLSTM, EMD-BLSTM, STL-BLSTM model is compared with the CEEMDAN-BLSTM model proposed in this paper, and the results show that the proposed model has a significant accuracy improvement compared to the traditional prediction model.
In the field of rapidly developing endovascular technique and technology, accurate assessment of surgical operation is essential for improving the efficiency of endovascular surgery and the ...performance of endovascular surgery robots. Existing methods of assessment have taken into consideration of a variety of indicators such as path length of operation, operation time and so on. The indicators that have been considered all come from surgeon's operation itself. However, the characteristics of specific patients' blood vessels are not considered for objective assessment. So in this paper, operating difficulty of different blood vessels was described for operation through the aortic arch by machine learning k-means models. Then clustering results were verified with external and internal metrics. Based on this study, difficulty levels of blood vessels can be taken as an important indicator for surgeons' endovascular operation evaluation in the future research.
The success rate of the vascular interventional surgery (VIS) depends largely on the skill level of the surgeon. Surgeons with different skill levels will have differences in generating movement ...trajectory inside blood vessels. The operation skills and skill levels of surgeons during VIS can be evaluated through the images that include the movement trajectory of the distal part of the catheter. Thus, it is very meaningful to propose a method to correctly distinguish the operations of experienced surgeons from the operations of inexperienced surgeons. This paper presents a method to differentiate surgical skills of surgeons in vascular interventional surgery. In our study, the movement trajectory of the guidewire in the images based on the two-dimensional vascular models was firstly collected. Then, these images were manually annotated and the Elan software was used to annotate the operation time. In addition, whether the guidewire deformed when it collided with the vascular wall during the operation was obtained indicate the significant differences between the two groups. Corner detection algorithm was used to obtain the motion coordinates of the distal part of the guidewire in each operation. The coordinates of the distal part were drawn on a picture, that is, the distal end trajectory in an operation is generated. The above method was used to obtain all the movement trajectories of experienced and inexperienced operations. Finally, the VGG network was used to classify them and the results were obtained. Finally, the classification accuracy of the proposed method can reach 97.4% from the experimental results, which proved that the proposed method was effective and feasible.
Singular Value Decomposition was widely used in recommendation system because of the Netflix Prize competition. The method decomposed the user item rating matrix into two matrix with low rank. In ...order to avoid overfitting the observed user item ratings. It used ℓ2 regularization method to regularize the learned parameters by penalizing their magnitudes. It can solve the problem of sparsity and reduce the dimension of user item rating matrix. It obtain good result using the Root Mean Square Error (RMSE) as evaluation index. But the method cost a lot of time. In this paper, we proposed ℓ1 regularization method and combine ℓ1 and ℓ2 regularization method to regularize the learned parameters of SVD. ℓ1 regularization method show great superiority in the problem of sparsity. Experimental results on XMU News data set and Movie lens data set demonstrate the efficiency and effectiveness of the proposed model. ℓ1 regularization method can represent the users' and items' implicit relation with fewer feature. Combining ℓ1 and ℓ2 regularization method perform well on the RMSE and costing time.
Cloning, Expression, and Renaturation Studies of Reteplase Zhao, Y.C; Kong, Y; Zhang, C.K. (Shandong University, Jinan, China);Ge, W. (Shandong Younglong Biotechnology Co., Jinan, Republic of Korea)
Journal of microbiology and biotechnology,
12/2003, Volume:
13, Issue:
6
Journal Article
Peer reviewed
Recombinant human tissue plasminogen activator deletion mutein is a clinically promising thrombolytic drug, drug, Reteplase cDNA was subcloned into a bacteria expression system. and the resultant ...recombinant was biologically characterized. The Reeplase was expressed in Escherichia coli as an inclusion body, and the downstream processes of the Reteplase inclusion body included denaturation, renaturation, and purification
Using the neural networks algorithms to control automatically is one important direction in intelligent vehicle's steering control. This paper presents the installation, mechanical design and ...processing of intelligent vehicle electronic steering control system. According to the characteristics of intelligent vehicle, the neural networks steering control model is designed, programmed and implemented on computer. Experiments testify the efficiency of the proposed neural networks control algorithms.
Both unipolar and bipolar modulation schemes are widely used in single-phase grid-connected H-bridge inverters. This paper makes a comprehensive comparison between the aforementioned modulation ...strategies in aspects of basic switching action and key points, effects on operation mode, current harmonics and efficiency et al. Furthermore, a novel hybrid modulation scheme with the fusion of the unipolar and bipolar modulations is proposed in this paper. Numerical and experimental results are given to demonstrate the effectiveness and validity of the proposed method.
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