Ring artifact is one of the most common artifacts in various types of CT （Computed Tomography） images， which is usually caused by the inconsistent response of detector pixels to X-rays. Effective removal of ring artifacts， which is a necessary step in CT image reconstruction， will greatly improve the quality of CT images and enhance the accuracy of later diagnosis and analysis. Therefore， the methods of ring artifact removal （also known as ring artifact correction） were systematically reviewed. Firstly， the performance and causes of ring artifacts were introduced， and commonly used datasets and algorithm libraries were given. Secondly， ring artifact removal methods were divided into three categories to introduce. The first category was based on detector calibration. The second category was based on analytical and iterative solution， including projection data preprocessing， CT image reconstruction and CT image post-processing. The last category was based on deep learning methods such as convolutional neural network and generative adversarial network. The principle， development process， advantages and limitations of each method were analyzed. Finally， the technical bottlenecks of existing ring artifact removal methods in terms of robustness， dataset diversity and model construction were summarized， and the solutions were prospected.

Sparse-view projection can reduce the scan does and scan time of Cone-Beam Computed Tomography （CBCT） effectively but brings a lot of streak artifacts to the reconstructed images. Sinogram inpainting can generate projection data for missing angles and improve the quality of reconstructed images. Based on the above， a Residual Encoder-Decoder Generative Adversarial Network （RED-GAN） was proposed for sinogram inpainting to reconstruct sparse-view CBCT images. In this network， the U-Net generator in Pix2pixGAN （Pix2pix Generative Adversarial Network） was replaced with the Residual Encoder-Decoder （RED） module. In addition， the conditional discriminator based on PatchGAN （Patch Generative Adversarial Network） was used to distinguish between the repaired sinograms from the real sinograms， thereby further improving the network performance. After the network training using real CBCT projection data， the proposed network was tested under 1/2， 1/3 and 1/4 sparse-view sampling conditions， and compared with linear interpolation method， Residual Encoder-Decoder Convolutional Neural Network （RED-CNN） and Pix2pixGAN. Experimental results indicate that the sinogram inpainting results of RED-GAN are better than those of the comparison methods under all the three conditions. Under the 1/4 sparse-view sampling condition， the proposed network has the most obvious advantages. In the sinogram domain， the proposed network has the Root Mean Square Error （RMSE） decreased by 7.2%， Peak Signal-to-Noise Ratio （PSNR） increased by 1.5% and Structural Similarity （SSIM） increased by 1.4%； in the reconstructed image domain， the proposed network has the RMSE decreased by 5.4%， PSNR increased by 1.6% and SSIM increased by 1.0%. It can be seen that RED-GAN is suitable for high-quality CBCT reconstruction and has potential application value in the field of fast low-dose CBCT scanning.

In the ride-hailing platform， matching is a core function，and the platform needs to increase the number of matched orders as much as possible. However， the demand distribution of ride-hailing is usually extremely uneven， and the starting points or end points of orders show the characteristic of high concentration in some time periods. Therefore， an incentive mechanism with early warning was proposed to encourage drivers to take orders across regions， thus achieving the purpose of rebalancing the platform cross-regional transport capacity. The order information was analyzed and processed in this strategy， and an early warning mechanism of transport capacity in adjacent regions was established. To reduce the number of unmatched orders in the region during the period of tight transport capacity and improve the platform utility and passenger satisfaction， drivers in adjacent regions were encouraged to accept cross-regional orders when regional transport capacity was tight. Experimental results on instances show that the proposed rebalancing mechanism improves the average utility by 15% and 38% compared with Greedy and Surge mechanisms， indicating that the cross-regional transport capacity rebalancing mechanism can improve the platform revenue and driver utility， rebalance the supply-demand relationship between regions to a certain extent， and provide a reference for the ride-hailing platform to balance the supply-demand relationship macroscopically.

The original Rival Penalized Competitive Learning (RPCL) algorithm ignores the influence of the geometry structure of a dataset on the weight variation of its nodes. A new RPCL algorithm proposed by Wei Limei et al. (WEI LIMEI, XIE WEIXIN. A new competitive learning algorithm for clustering analysis. Journal of Electronics, 2000, 22(1): 13-18) overcame the drawback of the original RPCL by introducing the density of samples to adjust the weights of nodes, while the density was not much objective. This paper defined a new density for a sample according to the pattern distribution of samples in a dataset, and introduced the density into the adjusting for the weights of nodes in RPCL to overcome the disadvantages of the available RPCL algorithms. The authors' improved RPCL algorithm was tested on some well-known datasets from UCI machine learning repository and on some synthetic data sets with noisy samples. The accuracy of determining the number of clusters of a dataset and the run time and the clustering error of the algorithms were compared. The Rand index, the Jaccard coefficient and the Adjust Rand index were used to analyze the performance of the algorithms. The experimental results show that the improved RPCL algorithm outperforms the original RPCL and the new RPCL proposed by WEI LIMEI et al. greatly, and achieves much better clustering results and has a stronger anti-interference performance for noisy data than that of the other two RPCL algorithms. All the analyses demonstrate that the improved RPCL algorithm can not only determine the right number of clusters for a dataset according to its sample distribution, but also uncover the suitable centers of clusters and advance the clustering accuracy as well as approximate the global optimal clustering result as fast as possible.

To protect privacy in E-government information resource sharing, a privacy protection model was proposed. The model could classify information resource sharing into two types: one is decision making business based on data mining or statistics and the other is business collaboration. The model adopted data preprocessing method to generalize privacy and a business collaboration simulator to determine the minimum privacy set for business collaboration respectively to protect privacy in the two types of businesses. The analytical results show the proposed method is effective in privacy protection.

As a criterion of feature selection, F-score does not consider the influence of the different measuring dimensions on the importance of different features. To evaluate the discrimination of features between classes, a new criterion called D-score was presented. This D-score criterion not only has the property as the improved F-score in measuring the discrimination between more than two sets of real numbers, but also is not influenced by different measurement units for features when measuring their discriminability. D-score was used as a criterion to measure the importance of a feature, and Sequential Forward Search (SFS) strategy, Sequential Forward Floating Search (SFFS) strategy, and Sequential Backward Floating Search (SBFS) strategy were, respectively, adopted to select features, while Support Vector Machine (SVM) was used as the classification tool, so that three new hybrid feature selection methods were proposed. The three new hybrid feature selection methods combined the advantages of Filter methods and Wrapper methods where SVM played the role to evaluate the classification capacity of the selected subset of features via the classification accuracy, and leaded the feature selection procedure. These three new hybrid feature selection methods were tested on nine datasets from UCI machine learning repository and compared with the corresponding algorithms with F-score as criterion of the discriminability of features. The experimental results show that D-score outperforms F-score in evaluating the discrimination of features, and can be used to implement the dimension reduction without compromising the classification capacity of datasets.

This paper presented an algorithm, based on multi-layer Hopfield neural network, for determining unit commitment. By constructing an appropriate energy function, a single layer Hopfield neural network can solve the problem of assigning output power of generators at any given time. Based on this single layer Hopfield neural network, a multi-layer Hopfield neural network was presented. The multi-layer Hopfield neural network can solve the problem of power system unit commitment. The energy functions of single layer and multi-layer Hopfield neural network and the corresponding algorithm were given. The restricted conditions of the balance between power supply and demand, maximum and minimum outputs of power plants were considered in the energy function. An example shows that the result got by Hopfield neural network is like to that got by genetic algorithm, but the calculation time is much less.