To remove Gibbs artifacts in Magnetic Resonance Imaging （MRI）， a Self-attention connection UNet based on Self-Distillation training （SD-SacUNet） algorithm was proposed. In order to reduce the semantic gap between the encoding and decoding features at both ends of the skip connection in the UNet framework and help to capture the location information of artifacts， the output features of each down-sampling layer at the UNet encoding end was input to the corresponding self-attention connection module for the calculation of the self-attention mechanism， then they were fused with the decoding features to participate in the reconstruction of the features. Self-distillation training was performed on the network decoding end， by establishing the loss function between the deep and shallow features， the feature information of the deep reconstruction network was used to guide the training of the shallow network， and at the same time， the entire network was optimized to improve the level of image reconstruction quality. The performance of SD-SacUNet algorithm was evaluated on the public MRI dataset CC359， with the Peak Signal-to-Noise Ratio （PSNR） of 30.261 dB and the Structure Similarity Index Measure （SSIM） of 0.917 9. Compared with GRACNN （Gibbs-Ringing Artifact reduction using Convolutional Neural Network）， the proposed algorithm had the PSNR increased by 0.77 dB and SSIM increased by 0.018 3； compared with SwinIR （Image Restoration using Swin Transformer）， the proposed algorithm had the PSNR increased by 0.14 dB and SSIM increased by 0.003 3. Experimental results show that SD-SacUNet algorithm improves the image reconstruction performance of MRI with Gibbs artifacts removal and has potential application values.

A deterministic algorithm for two-stage cutting layout with same-shape block was proposed to solve the large-scale two-dimensional cutting problems. The algorithm used dynamic programming to generate the optimal same-shape block, solved the knapsack problem to determine the optimal layout of the same-shape block in the same-shape section and the same-shape section in the same-shape segment, and optimally selected the two segments to compose the optimal same-shape block layout. The algorithm was tested through 43 benchmark problems, and compared with the classic two-stage and the three-block algorithms. The experimental results indicate that the algorithm is superior in both computation time and material usage rate, it not only meets the requirements of shearing and punching process, but also achieves good results within reasonable time.

The weak and low-frequency Electrocardiogram （ECG） signals which can be used for disease diagnosis after preprocessing are susceptible to interference from the external environment. The wavelet decomposition and re-construction can not effectively filter out the 50Hz frequency and EMG interference of which the frequency band is the same with the ECG, the wavelet thresholding method can not effectively filter out the baseline drift, and this method can cause Pseudo-Gibbs phenomenon in the signal singular points. In order to denoise effectively, a wavelet denoising method based on aTrous algorithm was proposed in this paper, and this method was a comprehension of the wavelet reconstruction and decomposition, wavelet thresholding and 50Hz notch filter. The clinical ECG simulation results show that this method can effectively remove the ECG baseline drift, 50Hz frequency interference and EMG interference, while reducing the Gibbs phenomenon.

The data model, business model, execution schema and performance metric of TPC-DS benchmark for next generation Decision Support System (DSS) application performance evaluation were introduced. The implementation architecture and key technologies for a configurable TPC-DS performance testing tool were put forward, including configuration file, query execution control and data maintenance mechanism. By testing practices in different Database Management Systems (DBMSs), the configurability and usability of the proposed tool for implementation strategies were verified.

A robust 3D reconstruction method based on image sequence was presented. First, an optimal parameter estimation of two images was applied. Then, along with the number of images increasing, the sparse bundle adjustment was adopted to reduce the minimum geometric error of the measurements of image coordinates. Finally, the 3D structure and camera parameters were processed with a global optimization method to improve the robustness of the reconstruction. The experiment results demonstrate that the proposed method can effectively improve the accuracy and robustness of reconstruction, and factually represent 3D model of objects.