In Low Earth orbit （LEO）satellite multi-beam communication scenario， the traditional fixed resource allocation algorithm can not meet the differences in channel capacity requirements of different users. In order to meet the requirements of users， the optimization model of minimum supply-demand difference of combining channel allocation， bandwidth allocation and power allocation was established， and Pattern Division Multiple Access technology （PDMA）was introduced to improve the utilization of channel resources. In view of the non-convex characteristic of the model， the optimal resource allocation strategy learned by the Q-learning algorithm was used to allocate the channel capacity suitable for each user， and a reward threshold was introduced to further improve the algorithm， speeding up the convergence and minimizing the difference between supply and demand when the algorithm converged. The simulation results show that the convergence speed of the improved algorithm is about 3.33 times that before improvement； the improved algorithm can meet larger user requirement， about 14% higher than the Q-learning algorithm before improvement， about 2.14 times that of the traditional fixed algorithm.

To solve the imbalance problem of wind turbine dataset, a Borderline Synthetic Minority Oversampling Technique-Sequence (BSMOTE-Sequence) sampling algorithm was proposed. In the algorithm, when synthesizing new samples, the space and time characteristics were considered comprehensively, and the new samples were cleaned, so as to effectively reduce the generation of noise points. Firstly, the minority class samples were divided into security class samples, boundary class samples and noise class samples according to the class proportion of the nearest neighbor samples of each minority class sample. Secondly, for each boundary class sample, the minority class sample set with the closest spatial distance and time span was selected, the new samples were synthesized by linear interpolation method, and the noise class samples and the overlapping samples between classes were filtered out. Finally, Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) were used as the fault detection models of wind turbine gear box, and F1-Score, Area Under Curve (AUC) and G-mean were used as performance evaluation indices of the models, and the proposed algorithm was compared with other sampling algorithms on real wind turbine datasets. Experimental results show that, compared with those of the existing algorithms, the classification effect of the samples generated by BSMOTE-Sequence algorithm is better with an average increase of 3% in F1-Score, AUC and G-mean of the detection models. The proposed algorithm can be effectively applicable to the field of wind turbine fault detection where the data with sequential rule is imbalanced.

Non-Volatile Memory (NVM) attracts people's attention because of its large capacity, persistence, bit addressability and low read latency. However, it also has some disadvantages, such as limited writes and asymmetric reading and writing speed. When the traditional linear hash index is implemented directly on NVM, it will lead to a great number of random write operations. To solve this problem, a new NVM-friendly linear hash index called NVM-LH (NVM-oriented Linear Hashing) was proposed. The cache friendliness was achieved by NVM-LH through the cache line alignment during storing data. And a log-free data consistency guaranteeing strategy was presented in NVM-LH. In addition, the split and delete operations were optimized in NVM-LH to minimize the NVM write operations. Experimental results show that NVM-LH outperforms the state-of-the-art NVM-aware hash index CCEH (Cacheline-Conscious Extendible Hashing) in terms of space utilization (30% higher) and NVM write number (about 15% lower), showing better NVM-friendliness.

Although ontology-based dynamic requirement traceability methods can improve the accuracy of trace links compared with Information Retrieval (IR), but it is rather complicated and tedious to construct a reasonable and effective ontology, especially domain ontology. In order to reduce time cost and labor cost brought by the domain ontology construction, a Modifier Ontology-based Keyword Semantic Judgment Method (MOKSJM) which combined modifiers with general ontology was proposed. Firstly, the collocation relationship between keywords and modifiers was analyzed. Then, the semantics of keywords were determined by combining modifier ontologies with rules, so as to avoid the bias of dynamic requirements traceability results caused by the polysemy of keywords. Finally, based on results of the above analysis, the semantics of keywords were adjusted and reflected by similarity scores. The number of modifiers is small in the requirements document, design documents, etc., so the time cost and labor cost brought by establishing the modifier ontology is relatively small. The experimental results show that compared to domain ontology-based dynamic requirement traceability method, MOKSJM has a small gap in precision with the same recall rate, and when compared to Vector Space Model (VSM) method, MOKSJM can effectively improve the accuracy of the requirements traceability result.

Widely used WebKit of excellent architecture has been ported to many embedded platforms, with excellent cross-platform features. Due to the diversity of hardware for embedded platforms, WebKit open source version does not take full advantage of the characteristics of embedded platforms. Through studying WebKit render architecture, taking full advantage of the embedded hardware feature and the benefit of the software rendering design, a hardware and software fusion render layer was designed. This layer sped up the browser rendering on the embedded platform and improved the user experience. The layer was verified, the time of opening website was reduced by 48% and the speed of rendering of html animation increased by 130% compared to the original WebKit.

Due to the lack of information of Automatic Identification System (AIS) equipment, the location of a vessel cannot be accurately judged by intelligent supporting command system based on AIS. It is difficult to accurately issue the traffic signal from it. Meanwhile, due to the narrow and winding features in controlled waterway, it is difficult for traditional Kalman filter to accurately predict track of moving vessel. In this situation, the real-time estimation of system noise in Kalman filter algorithm was proposed to increase the accuracy of track prediction of moving vessel. Simulation analysis was carried out on the tracking effect of the traditional Kalman filter and improved Kalman filter. The results indicate that the proposed algorithm can solve the lack in information of AIS equipment, and accurately predict the location of a vessel. The accuracy and the reliability of intelligence supporting command system can be ensured in controlled waterway.

To solve the problems of inefficiency and bad-adaptability for the existing outlier mining algorithms based on grid, this paper proposed an outlier mining algorithm based on data partitioning and grid. Firstly, the technology of data partitioning was applied. Secondly, the non-outliers were filtered out by cell and the intermediate results were temporarily stored. Thirdly, the structure of the improved Cell Dimension Tree (CD-Tree) was created to maintain the spatial information of the reserved data. Afterwards, the non-outliers were filtered out by micro-cell and were operated efficiently through two optimization strategies. Finally, followed by mining by data point, the outlier set was obtained. The theoretical analysis and experimental results show that the method is feasible and effective, and has better scalability for dealing with massive and high dimensional data.