Quantum Dynamics Framework （QDF） is a basic iterative process of optimization algorithm with representative and universal significance， which is obtained under the quantum dynamics model of optimization algorithm. Differential acceptance is an important mechanism to avoid the optimization algorithm falling into local optimum and to solve the premature convergence problem of the algorithm. In order to introduce the differential acceptance mechanism into the QDF， based on the quantum dynamics model， the differential solution was regarded as a potential barrier encountered in the process of particle motion， and the probability of particles penetrating the potential barrier was calculated by using the transmission coefficient in the quantum tunneling effect. Thus， the differential acceptance criterion of quantum dynamics model was obtained： Potential Barrier Estimation Criterion （PBEC）. PBEC was related to the height and width of the potential barrier and the quality of the particles. Compared with the classical Metropolis acceptance criterion， PBEC can comprehensively estimate the behavior of the optimization algorithm when it encounters the differential solution during sampling. The experimental results show that， the QDF algorithm based on PBEC has stronger ability to jump out of the local optimum and higher search efficiency than the QDF algorithm based on Metropolis acceptance criterion， and PBEC is a feasible and effective differential acceptance mechanism in quantum optimization algorithms.

Automated Test Case Generation for Path Coverage （ATCG-PC） problem is a hot topic in the field of automated software testing. The fitness functions commonly used by swarm intelligence evolutionary algorithms in ATCG-PC problem are highly similar with each other， but the existing swarm intelligence evolutionary algorithms for solving ATCG-PC problem do not consider this similarity feature yet. Inspired by the similarity feature， the two similar fitness functions were treated as two tasks， so that ATCG-PC problem was transformed into a multi-task ATCG-PC problem， and a new swarm intelligence evolutionary algorithm called Multifactorial Backtracking Search optimization Algorithm （MFBSA） was proposed to solve multi-task ATCG-PC problem. In the proposed algorithm， the memory population function of multifactorial selection Ⅰ was used to improve the global search ability， and the similar tasks were able to improve each other’s optimization efficiency through knowledge transfer by assortative memory mating. The performance of MFBSA was evaluated on six fog computing test programs and six natural language processing test programs. Compared with Backtracking Search optimization Algorithm （BSA）， Immune Genetic Algorithm （IGA）， Particle Swarm Optimization with Convergence Speed Controller （PSO-CSC） algorithm， Adaptive Particle Swarm Optimization （APSO） algorithm and Differential Evolution with Hypercube-based learning strategies （DE-H） algorithm， MFBSA has the total test cases used to cover the paths on 12 test programs reduced by 64.46%， 66.64%， 67.99%， 74.15%， and 61.97%， respectively. Experimental results show that the proposed algorithm can effectively reduce testing cost.

Face verification is widely used in various scenes in life， and the acquisition of ordinary RGB images is extremely dependent on illumination conditions. In order to solve the interference of illumination and head pose， a convolutional neural network based Siamese network L2-Siamese was proposed. Firstly， the paired depth images were taken as input. Then， after using two convolutional neural networks that share weights to extract facial features respectively， L2 norm was introduced to constrain the facial features with different poses on a hypersphere with a fixed radius. Finally， the fully connected layer was used to map the difference between the features to the probability value in （0，1） to determine whether the group of images belonged to the same object. In order to verify the effectiveness of L2-Siamese， a test was conducted on the public dataset Pandora. Experimental results show that L2-Siamese has good overall performance. After the dataset was grouped according to the size of head pose interference， the test results show that the prediction accuracy of L2-Siamese is 4 percentage points higher than that of the state-of-the-art algorithm fully-convolutional Siamese network under the maximum head pose interference， illustrating that the accuracy of prediction has been significantly improved.

Moving object detection aims to separate the background and foreground of the video， however， the commonly used low-rank factorization methods are often difficult to comprehensively deal with the problems of dynamic background and intermittent motion. Considering that the skewed noise distribution after background subtraction has potential background correction effect， a moving object detection model based on the reliability low-rank factorization and generalized diversity difference was proposed. There were three steps in the model. Firstly， the peak position and the nature of skewed distribution of the pixel distribution in the time dimension were used to select a sub-sequence without outlier pixels， and the median of this sub-sequence was calculated to form the static background. Secondly， the noise after static background subtraction was modeled by asymmetric Laplace distribution， and the modeling results based on spatial smoothing were used as reliability weights to participate in low-rank factorization to model comprehensive background （including dynamic background）. Finally， the temporal and spatial continuous constraints were adopted in proper order to extract the foreground. Among them， for the temporal continuity， the generalized diversity difference constraint was proposed， and the expansion of the foreground edge was suppressed by the difference information of adjacent video frames. Experimental results show that， compared with six models such as PCP（Principal Component Pursuit）， DECOLOR（DEtecting Contiguous Outliers in the Low-Rank Representation）， LSD（Low-rank and structured Sparse Decomposition）， TVRPCA（Total Variation regularized Robust Principal Component Analysis）， E-LSD（Extended LSD） and GSTO（Generalized Shrinkage Thresholding Operator）， the proposed model has the highest F-measure. It can be seen that this model can effectively improve the detection accuracy of foreground in complex scenes such as dynamic background and intermittent motion.

Aiming at the problem that the model in the judicial field relation extraction task does not fully understand the context of sentence and has weak recognition ability of overlapping relations， based on Criminal-Efficiently learning an encoder that classi?es token replacements accurately （CriElectra）， an encoding-decoding relationship extraction model was proposed. Firstly， referred to the training method of Chinese Electra， CriElectra was trained on one million criminal dataset. Then， the word vectors of CriElectra were added to Bidirectional Long Short-Term Memory （BiLSTM） model for feature extraction of judicial texts. Finally， the vector clustering was performed to the features through Capsule Network （CapsNet）， so that the relationships between entities were extracted. Experimental results show that on the self-built relationship dataset of intentional injury crime， compared with the pre-trained language model based on Chinese Electra， CriElectra has retraining process on judicial texts to make the learned word vectors contain richer domain information， and the F1-score increased by 1.93 percentage points. Compared with the model based on pooling clustering， CapsNet can effectively prevent the loss of spatial information by vector operation and improve the recognition ability of overlapping relationships， which increases the F1-score by 3.53 percentage points.

Precise train stopping is a key technology of automatic train operation. On the basis of analyzing the train stopping phase, the delay characteristics of brake model and constraint conditions of train characteristics were considered, using generalized predictive control theory, a multi-objective predictive controller with constraints was designed taking account of train speed and distance as control targets and combining the control constraint conditions. The simulation results show that the proposed controller can accurately track the train stopping curve to achieve high precision stopping requirements and higher comfort.

A new sparse decomposition algorithm was presented to get a sparser representation of the signal. In the procedure of the algorithm, it established the two dictionary sets consisting of the selected dictionary set and the unselected dictionary set. The proposed algorithm added a more strict process which selected the best kernel from the unselected dictionary set to the original Repeated Weighted Boosting Search (RWBS), so the proposed algorithm could produce a sparser model while reserving the advantages of the original algorithm. The effectiveness of the proposed algorithm is illustrated through several examples.

Signal decomposition is a process that obtains information from signals and it is a foundational and key technique for many fields such as pattern recognition, intelligent system and machinery fault diagnosis. It is very important to study non-stationary signal decomposition which always includes lots of information that can reflect the changing of the system and widely exists. After improving the sparsity of signal representation, the engineering background of feature extraction for non-stationary signal was studied in this paper, the characteristics, mechanisms, development history and current and future challenges of five types of methods were analyzed in depth, the models of these methods were compared, together with the state-of-the-art of feature extraction models in signal processing and analysis and some successful applications available were systematically reviewed. Finally, several main problems and a few deficiencies were pointed out, and future research directions were anticipated.

To avoid the problem of the carton missing in the process of cigarette production, this paper introduced a new method of pattern matching based on machine. Using the method could avoid the effects of the random reflecting light on images. After getting the taboo area of the image, the result of the pattern matching was used to determine whether some cartons miss or not. In addition, the taboo matching method could also adjust the image and get the template image automatically without considering the pattern or color of the carton. The taboo matching would reduce the error detection rate in a real system and provide a way of solving problems of the similar kind.

Concerning that the resource in mobile Ad Hoc network is limited and the existing ant routing algorithms are complex, a basic ant routing algorithm was proposed. Based on the analysis of ant routing process, it only maintained basic ant routing mechanism, without any extra overhead, discussed pheromone update and pheromone use which were two key components of the algorithm; moreover, it analyzed their impact on performance by simulation. Finally, the experiment results show that it can get a performance closed to other routing protocols under a lower overhead.

A new sparseness-based method was proposed for mixing matrix estimation, in the case of poor sparseness of speech signals with increasing number of sources. The time-frequency bins with only one source were detected by Principal Component Analysis (PCA), and then were exploited to estimate the mixing matrix to improve the estimation performance. The proposed method is especially applicable to underdetermined blind speech separation. The reasons deteriorating the performance of blind speech separation were also pointed out. The simulation results demonstrate the conclusions above.

With the consideration of the features of open, multiple-domain and layout regularity of bilingual resources on Web, a mixture probabilistic alignment model was proposed to reveal the domain-specific and position-specific characteristic for aligning texts. Compared to the traditional lengthen-based aligning model, the model in this paper achieves 37% and 40.4% improvement on precise and recall respectively with the extensive experiments.

In practice terms, it is impossible to avoid a great deal of spatial data processing when publishing with SVG document through Internet. Therefore, it not only slows down the executing speed but also expend too much the resource of computer system when searching the spatial data in SVG document. It was proposed to store the SVG document into ORDBMS, and to use the extended SQL (SQL3/SQL99) to make searching. At the same time, another storing and searching method based on native XML database (NXD) was also executed, using XQuery language for searching approach. The results show that both of SVG searching methods of database are very efficient and take less system resources than publishing the document directly on client side.