In response to the inability of existing 3D shape reconstruction models to effectively fuse global spatio-temporal information， a Depth Focus Volume （DFV） module was proposed to retain the transition information of focus and defocus， on this basis， a Global Spatio-Temporal Feature Coupling （GSTFC） model was proposed to extract local and global spatio-temporal feature information of multi-depth-of-field image sequences. Firstly， the 3D-ConvNeXt module and 3D convolutional layer were interspersed in the shrinkage path to capture multi-scale local spatio-temporal features. Meanwhile， the 3D-SwinTransformer module was added to the bottleneck module to capture the global correlations of local spatio-temporal features of multi-depth-of-field image sequences. Then， the local spatio-temporal features and global correlations were fused into global spatio-temporal features through the adaptive parameter layer， which were input into the expansion path to guide and generate focus volume. Finally， the sequence weight information of the focus volume was extracted by DFV and the transition information of focus and defocus was retained to obtain the final depth map. Experimental results show that GSTFC decreases the Root Mean Square Error （RMSE） index by 12.5% on FoD500 dataset compared with the state-of-the-art All-in-Focus Depth Net （AiFDepthNet） model， and retains more depth-of-field transition relationships compared with the traditional Robust Focus Volume Regularization in Shape from Focus （RFVR-SFF） model.

Concerning the problems of the lack of standard words， fuzzy semantics and feature sparsity in news topic text， a news topic text classification method based on Bidirectional Encoder Representations from Transformers（BERT） and Feature Projection network（FPnet） was proposed. The method includes two implementation modes. In mode 1： the multiple-layer fully connected layer features were extracted from the output of news topic text at BERT model， and the final extracted text features were purified with the combination of feature projection method， thereby strengthening the classification effect. In mode 2， the feature projection network was fused in the hidden layer inside the BERT model for feature projection， so that the classification features were enhanced and purified through the hidden layer feature projection. Experimental results on Toutiao， Sohu News， THUCNews-L、THUCNews-S datasets show that the two above modes have better performance in accuracy and macro-averaging F1 value than baseline BERT method with the highest accuracy reached 86.96%， 86.17%， 94.40% and 93.73% respectively， which proves the feasibility and effectiveness of the proposed method.

Traditional machine learning methods fail to fully dig out semantic information and association information when classifying the sentiment polarity of online comment text. Although the existing deep learning methods can extract the semantic information and contextual information， the process is often one-way and there are some deficiencies in the process of obtaining the deep semantic information of comment text. Aiming at the above problems， a text sentiment analysis method was proposed by combining generalized autoregressive pretraining for language understanding model （XLNet） and RCNN （Recurrent Convolutional Neural Network）. Firstly， XLNet was used to represent the text features. And by introducing the segment-level recurrence mechanism and relative position information encoding， the contextual information of comment text was fully considered， thereby improving the expression ability of text features effectively. Then， RCNN was used to train the text features in both directions and extract the context semantic information of the text at a deeper level， thereby improving the comprehensive performance in the sentiment analysis task. The experiments with the proposed method were carried out on three public datasets weibo-100k， waimai-10k and ChnSentiCorp. The results show that the accuracy reaches 96.4%， 91.8% and 92.9% respectively， which proves the effectiveness of the proposed method in the sentiment analysis task.

In order to solve the problems of feature selection ReliefF algorithm， such as poor algorithm stability and low classification accuracy for selected feature subsets caused by using Euclidean distance to select the nearest neighbor samples， an MICReliefF （Maximum Information Coefficient-ReliefF） algorithm based on Maximum Information Coefficient （MIC） was proposed. At the same time， the classification accuracy of the Support Vector Machine （SVM） model was used as the evaluation index， and the optimal feature subset was automatically determined by multiple optimizations， thereby realizing the interactive optimization of the MICReliefF algorithm and the classification model， that is the MICReliefF-SVM automatic feature selection algorithm. The performance of the MICReliefF-SVM algorithm was verified on several UCI public datasets. Experimental results show that the MICReliefF-SVM automatic feature selection algorithm cannot only filter out more redundant features， but also select the feature subsets with good stability and generalization ability. Compared with Random Forest （RF）， max-Relevance and Min-Redundancy （mRMR）， Correlation-based Feature Selection （CFS） and other classical feature selection algorithms， MICReliefF algorithm has higher classification accuracy.

Aiming at the poor real-time performance of mobile robot path planning in complex indoor environment, a further improvement on A ^* algorithm was proposed by analyzing and comparing Dijkstra algorithm, traditional A ^* algorithm and some improved A ^* algorithms. Firstly, the estimated path cost of the current node and its parent node were weighted in exponentially decreasing way. In this way, when the current code was far away from the target, the improved algorithm could search towards to the target quickly instead of searching around the start node. While the current code was near to the target, the algorithm could search the target carefully to ensure that the target was reachable. Secondly, the generated path was smoothed by quintic polynomia to further shorten the path and facilitate robot control. The simulation results show that compared with the traditional A ^* algorithm, the proposed algorithm can reduce the searching time by 93.8% and reduce the path length by 17.6% and get the path without quarter turning point, so that the robot could get to the destination along the planned path without a break. The proposed algorithm is verified in different scenarios, and the results show that the proposed algorithm can adapt to different environments and has good real-time performance.

Concerning the present situation that Quality of Service (QoS) evaluation methods ignore the implicit service quality assessment and lead to inaccurate results, a service evaluation method that comprehensively considered explicit and implicit quality attributes was put forward. Explicit quality attributes were expressed in vector form, using service quality assessment model, after quantization, normalization, then evaluation values were calculated; and implicit quality attributes were expressed according to the evaluation on similar users' recommendation. The users' credibility and difference between old and new users were considered in the evaluation process. Finally the explicit and implicit quality evaluation was regarded as the QoS evaluation results. The experiments were performed in comparison with three algorithms by using one million Web Service QoS data. The simulation results show that the proposed method has certain feasibility and accuracy.

Although the normal cloud model is universally used, it faces some difficulties when describing some monotonic rise/fall conceptions. This model also has big subjective influence under multiple conditions and large computation consumption. To overcome these shortcomings, a new kind of exponential cloud model was provided along with a weight based cloud reasoning algorithm. By splitting the multi-condition generator to several single-condition generators, the algorithm firstly used Analytic Hierarchy Process (AHP) method to get weight of each property, and then used them to calculate weighted average of single-condition generator output to quantitfy value. The validation and effectiveness of this method is checked through a comparison between fuzzy reasoning and stimulation of torpedo avoid system.

In order to avoid transmission collisions and improve energy efficiency for periodic report Wireless Sensor Network (WSN), a Medium Access Control (MAC) protocol with network utility maximization and collision avoidance called UM-MAC was proposed. UM-MAC used Time Division Multiple Access (TDMA) scheduling mechanism and introduced the utility model into the slot assignment process. A utility maximization problem of joint link reliability and energy consumption optimization based on utility model was put forward. To handle it, a heuristic algorithm was proposed to make the network to quickly find out a slot scheduling strategy which maximize network utility and avoid transmission collisions. Comparison experiments among UM-MAC, S-MAC and CA(Collision Avoidance)-MAC protocols were conducted under networks with different nodes, where UM-MAC got larger network utility and higher average packet successful delivery ratio, the lifetime of UM-MAC was between S-MAC and CA-MAC, while its average transmission delay increased under networks with defferent loads. The simulation results show that UM-MAC can achieve collision avoidance and improve network performance in terms of packet successful delivery ratio and energy efficiency; meanwhile, the TDMA-based protocol is not better than competition-based protocol in low load networks.

Concerning the classification problems of complex data distribution of scientific practice, economic life and many other fields, the correlation between variables could not be well described by using traditional Support Vector Machine (SVM), which would influence the classification performance. For this situation, Q-Gaussian function that was a parametric generalization of Gaussian function was put forward as the kernel function of SVM, and a financial early-warning model based on SVM with Q-Gaussian kernel was presented. Based on the financial data of A-share manufacturing listed companies of the Shanghai and Shenzhen stock markets, T-2 and T-3 financial early-warning model were constructed in experiments, the significance test was used to select some suitable indicators and the Cross Validation (CV) was used to determine model parameters. Compared to SVM model with Gaussian kernel, the forecasting accuracies of T-2 and T-3 model constructed by SVM with Q-Gaussian kernel were improved about 3%, and high-cost type I errors were reduced by at most 14.29%.

A kind of area matching method that combined weights matrix with similarity coefficient matrix was proposed in this article. The article was organized as follows: first of all, the method got the weights matrix by using color similarity and distance proximity, and the value of the matrix was corrected with an edge matrix for improving correction of the edge pixels. Then a similarity coefficient matrix was adaptively obtained according to each point pairs sum of absolute difference in matching window between left image and right image. Finally, the method was investigated by matching four stereo images (Tsukuba, Venus, Teddy, and Cones) with ground truth provided in Middlebury stereo database and the rate of overall accuracy reaches 91.82%,96.19%,76.6%,86.9%，respectively.

This paper presented a sophisticated power saving scheme based on system-level resource management for TH-1A supercomputer system. The scheme introduced a uniform framework for centralized management of various power-consuming resources, i.e. computing elements, communication components, power supply and cooling devices. And many efficient management policies such as LRU etc. were applied within the framework.

To overcome the shortcomings of conventional algal bloom forecast system in acquiring data, this study applied the Internet of Things (IoT) technology to establish a data transmission network with three-layer structure, and thus secured data continuity. With improved retrieval approach of water quality parameters, technology of Wireless Sensor Network (WSN) and forecast model of algal bloom, the blue-green algal bloom forecast platform was developed. The evaluation demonstrates that the platform achieves an overall accuracy of 80% in forecasting blue-green blooms in Taihu Lake in next three days.

In the paper, on the basis of analyzing the structure of dynamic information database, and in combination with the feature of the power system, the key technologies of concurrency data processing, memory-mapped file, disk cache management mechanism and associated data storage were discussed, and the data sampling flow and hybrid compression algorithm were also introduced in detail. The application case in the automatic system of power grid dispatching was introduced and the result proves that the dynamic information database can meet the performance requirement of high-speed data processing.

In this paper, a new joint Maximum A Posterior (MAP) formulation was proposed to integrate image registration into blind image Super-Resolution (SR) reconstruction to reduce image registration errors. The formulation was built upon the MAP framework, which judiciously combined image registration, blur identification and SR. A cyclic coordinate descent optimization procedure was developed to solve the MAP formulation, in which the registration parameters, blurring function and High Resolution (HR) image were estimated in an alternative manner given to the two others, respectively. The experimental results indicate that the proposed algorithm has considerable effectiveness in terms of both quantitative measurement and visual evaluation.

An adaptive filter was proposed to detect and remove pepper and salt noise in an image based on Pulse Coupled Neural Network (PCNN) firing matrix. The PCNN was simplified and a unidirectional decaying threshold was proposed to avoid complex parameter selection and improve processing speed. The noise-polluted pixels were detected through analyzing the PCNN firing matrix, and then only the noisy pixels were filtered by a median filter while protecting image edges and details. The window size of the filter and the filtering time were adaptively determined by calculating the noise intensity of the contaminated image. The experimental results show that the proposed method performs better in removing noise while conserving detailed information than traditional filters do.

Considering that the classical rough set theory can only process the discrete data, the degree of general importance of an attribute and attribute subsets was presented. And then a generalization rough set theory was proposed based on the general near neighborhood relation. The theory partitioned the universe into the tolerant modules and formed lower approximation and upper approximation of the set under general near neighborhood relationship which avoided the discretization in Pawlak's rough set. Furthermore, the definition of attribute reduction in generalization rough set and its greedy algorithm were proposed. Finally, results of some examples show the correctness and validity of this method.

With the introduction of the Service Oriented Architecture (SOA), the integration and development speed of software systems will become quicker and quicker. But the security mechanism of software systems has to be rebuilt when they are developed. Moreover, security mechanism becomes more complex when the number of software systems increases rapidly. A Business Process Execution Language (BPEL)-based method for general security control module design was proposed, which reduced the development and management work. The operation mechanism of right module was exemplified by the account integration of Enterprise Resource Planning (ERP) in manufacturing with e-business system.