In order to avoid the flower pollination algorithm falling into local optimum in the identification process of the extreme learning machine, an extreme learning machine algorithm based on cloud quantum flower pollination was proposed. Firstly, cloud model and quantum system were introduced into the flower pollination algorithm to enhance the global search ability of the flower pollination algorithm, so that the particles were able to perform optimization in different states. Then, the cloud quantum flower pollination algorithm was used to optimize the parameters of the extreme learning machine in order to improve the identification accuracy and efficiency of the extreme learning machine. In the experiments, six benchmark functions were used to simulate and compare several algorithms. It is verified by the comparison results that the performance of proposed cloud quantum flower pollination algorithm is superior to those of other three swarm intelligence optimization algorithms. Finally, the improved extreme learning machine algorithm was applied to the identification of oil and gas layers. The experimental results show that, the identification accuracy of the proposed algorithm reaches 98.62%, and compared with the classic extreme learning machine, its training time is shortened by 1.680 2 s. The proposed algorithm has high identification accuracy and efficiency, and can be widely applied to the actual classification field.

In the traditional way to design the energy storage spring of the circuit breaker the method of experience trial calculation is mainly adopted, which may easily lead to unreasonable parameters of the spring structure, large volume of circuit breaker and poor breaking performance. Therefore, An improved cloud particle swarm optimization algorithm combined with catfish effect was applied to optimize the parameters of energy storage spring of circuit breaker. Firstly, according to the working principle of energy storage springs, the mathematical optimization design model of the energy storage springs and the constraints of the spring parameter design were deduced. Then, improving the algorithm based on the optimization model, on the basis of the traditional particle swarm optimization algorithm, catfish effect strategy was introduced to produce various candidate solutions, avoiding the algorithm falling into local optimal value and the optimization speed weighting factor was adjusted combined with the cloud model to speed up the convergence of the algorithm and improve the ability of global search solutions. Finally, the improved algorithm was used to simulate the optimization model of the energy storage spring of circuit breakers and calculate the corresponding spring parameters. The results show that the improved particle swarm optimization algorithm can achieve miniaturization and better breaking performance of circuit breakers.

The Sparsity Rank Singular Value Decomposition (SRSVD) method and Semi-Exact Augmented Lagrange Multiplier (SEALM) algorithm cannot fit the node trajectory of Wireless Sensor Network (WSN) accurately when the sampling rate is small, the sparse noise is large, and the Gaussian noise exists. In order to solve the problems, a novel Regularized Weighted Incomplete Robust Principal Component Analysis (RWIRPCA) method was proposed. Firstly, the Incomplete Robust Principal Component Analysis (IRPCA) was applied to the fitting of node trajectory. Then, on the basis of IRPCA, in order to better describe the low rank and sparsity of matrices, as well as the anti-Gauss noise performance of enhanced model, the low rank matrix and the sparse matrix were weighted respectively. Finally, the F norm of Gaussian noise matrix was used as a regular term and applied to the fitting of node trajectory. The simulation results show that, the fitting effects of IRPCA and RWIRPCA are better than those of SRSVD and SEALM in the case that the sampling rate is small and the sparse noise is large. Especially, the proposed RWIRPCA can still obtain accurate and stable results when both sparse noise and Gaussian noise exist at the same time.

In session-based Collaborative Filtering (CF), a user interaction history is divided into sessions using fixed time window and user preference is expressed by sequences of them.But in tourism data, there is no interaction in some sessions and it is difficult to select neighbors because of high sparsity. To alleviate data sparsity and better use the characteristics of the tourism data, a new tourism route recommendation method based on dynamic clustering was proposed. Firstly, the different characteristics of tourism data and other standard data were analyzed. Secondly, a user interaction history was divided into sessions by variable time window using dynamic clustering and user preference model was built by combining probabilistic topic distribution obtained by Latent Dirichlet Allocation (LDA) from each session and time penalty weights. Then, the set of neighbors and candidate routes were obtained through the feature vector of users, which reflected the characteristics of tourist age, route season and price. Finally, routes were recommended according to the relevance of probabilistic topic distribution between candidate routes and tourists. It not only alleviates data sparsity by using variable time window, but also generates the optimal number of time windows which is automatically obtained from data. User feature vector was used instead of similarity of tourism data to select neighbors, so as to the avoid the computational difficulty caused by data sparsity. The experimental results on real tourism data indicate that the proposed method not only adapts to the characteristics of tourism data, but also improves the recommendation accuracy.

The distributed state of noise existing among different bands of hyperspectral remote sensing image is complex, so the traditional denoising methods are hard to achieve the desired effect. In order to solve this problem, based on Principal Component Analysis (PCA), a novel denoising method for hyperspectral data was proposed combining with noise estimation and dictionary learning. Firstly, a group of the principal component images were achieved from the original hyperspectral data by using the PCA transform, which were divided into clear image group and noisy image group according to the corresponding energy. Then, according to any band image from noisy hyperspectral data, the noise standard deviation of the image was estimated via a noise estimation method based on Singular Value Decomposition (SVD). Meanwhile, combining this noise estimation method with denoising method via K-SVD dictionary learning, a new dictionary learning denoising method with adaptive noise estimation characteristics was proposed and applied to denoise those images from noisy image group with low energy where noise mainly existed. Finally, the final denoising image was obtained by weighted fusion according to the corresponding energy of each principal component image. The experimental results on simulated and real hyperspectral remote sensing data show that, compared with PCA, PCA-Bish and PCA-Contourlet, the Peak Signal-to-Noise Ratio (PSNR) of the image denoised by the proposed algorithm is improved by 1-3 dB, and more detailed information and better visual effect of the denoised image by the proposed method are achieved.