The different importance of the activities in the business process in real world is not taken into account by the existing cost functions， so that in the alignment process of model and log， alignment cost may deviates from perceived cost significantly. To solve this problem， a concept of important synchronization cost function was proposed based on the typical flow characteristic of the behaviors in business processes， and an alignment method that can improve efficiency was proposed under this function. Firstly， the important synchronization cost function was defined based on the concept of perceived cost. Then， the important matching sub-sequence to segment the process model and the log trace was determined according to the log trace and the typical flow characteristic of the behaviors in the process model. Finally， based on the important synchronization cost function， the segmented sub-process and the corresponding log trace subsequence were aligned， and the segmented alignment results were combined to obtain the final alignment result. The experiments were carried out to verify the proposed method from the perspectives of accuracy and efficiency. In terms of accuracy， compared with the existing standard cost function and maximum synchronous cost function， the proposed cost function improved the alignment accuracy by up to 17.44 percentage points， and when the event log contained mixed noise， the proposed cost function had the highest average alignment accuracy of 88.67%. The efficiency of alignment was verified by comparing the time consumed by alignment. The average time of the existing two functions were 1.58 s and 2.21 s respectively， while that of the proposed method was 0.63 s， which was improved by 150.79% and 250.79% respectively. Experimental results show that the proposed method can satisfy the accuracy demand and improve the efficiency of alignment at the same time.

In order to break the limitation of the path and graph structure in process repository based process modeling recommendation method， extract more useful recommendation information from a process repository for modelers， and assist modelers in building a business process model with higher quality， a process modeling recommendation method based on behavioral profile definition target rules was proposed. Firstly， a target profile matrix for formalizing and abstracting business interaction rules was developed through business presentation. Then， by comparing all the behavioral profile matrices in the behavioral profile matrix set with the target profile matrix， the processes in the process repository that satisfy the target profile matrix were identified as candidate process set. Finally， the process with the highest similarity to the current modeling model in the candidate process repository was calculated by the behavioral profile metric method， and the next node of the current modeling node in these processes was selected as the recommendation node. The proposed method was evaluated on a real dataset. The evaluation of both recommendation ability and recommendation accuracy shows that compared with the independent path matching method， the proposed method can provide more useful recommendation information for modelers while meeting the practical application requirements in terms of accuracy.

By removing irrelevant features from the original dataset and selecting good feature subsets， feature selection can avoid the curse of dimensionality and improve the performance of learning algorithm.In the process of feature selection， only the dynamically change information between the selected features and classes is considered， and interaction relevance between the candidate features and the selected features is ignored by Dynamic Change of Selected Feature with the class （DCSF） algorithm. To solve this problem， a Dynamic Relevance based Feature Selection （DRFS） algorithm was proposed. In the proposed algorithm， conditional mutual information was used to measure the conditional relevance between the selected features and classes， and interaction information was used to measure the synergy brought by the candidate features and the selected features， so as to select relevant features and remove redundant features then obtain good feature subsets. Simulation results show that， compared with existing algorithms， the proposed algorithm can effectively improve classification accuracy of feature selection.

National Food Safety Standards(NFSS) are not only the operation specifications of food producers, but also the law enforcement criteria of food safety supervision. However, there are various NFSSs with a wide range of contents and complicated inter-reference relationships. To systematically study the contents and structures of NFSSs, it is necessary to extract the knowledges and mine the reference relationships in NFSSs. First, the contents of the standard files and the reference relationship between the standard files were extracted as knowledge triplets through the Knowledge Graph(KG) technology, and the triplets were used to construct the NFSS knowledge graph. Then, this knowledge graph was linked to the food production process ontology which was made manually based on Hazard Analysis Critical Control Point(HACCP) standards, so that the food safety standards and the related food production processes can be referenced to each other. At the same time, the Louvain community discovery algorithm was used to analyze the standard reference network in the knowledge graph, and the standards with high citations as well as their types in NFSSs were obtained. Finally, a question answering system was built using gStore's Application Programming Interface(API) and Django, which realized the knowledge retrieval and reasoning based on natural language, and the high-impact NFSSs in the graph could be found under specified requirements.

In the practical application scenario, the wheat kernel image background is single, and the imperfect characteristics of wheat imperfect grains are mostly local features while most of the image features are not different from normal grains. In order to solve the problems, an imperfect wheat kernel recognition method based on detail Image Enhancement (IE) was proposed. Firstly, the alternate minimization algorithm was used to constrain the L0 norms of the original image in the horizontal and vertical directions to smooth the original image as the base layer, and the original image was subtracted from the base layer to obtain the detail layer of the image. Then, the detail layer was delighted and superimposed with the base layer to enhance the image. Finally, the enhanced image was used as the input of the Convolutional Neural Network (CNN), and the CNN with Batch Normalization (BN) layer was used for recognition of the image. The classic classification networks LeNet-5, ResNet-34, VGG-16 and these networks with the BN layer were used as classification networks, and the images before and after enhancement were used as input to carry out classification experiments, and the accuracy of the test set was used to evaluate the performance. Experimental results show that by adding the BN layer and using the same input, all three classic classification networks have the accuracy of the test set increased by 5 percentage points, and when using the images with enhanced detail as input, the three networks have the accuracy of the test set increased by 1 percentage point, and when the above two are used together, all the three networks obtain the accuracy of the test set improved by more than 7 percentage points.

In recent years, significant progress has been made in human interaction recognition based on RGB video sequences. Due to its lack of depth information, it cannot obtain accurate recognition results for complex interactions. The depth sensors (such as Microsoft Kinect) can effectively improve the tracking accuracy of the joint points of the whole body and obtain three-dimensional data that can accurately track the movement and changes of the human body. According to the respective characteristics of RGB and joint point data, a convolutional neural network structure model based on RGB and joint point data dual-stream information fusion was proposed. Firstly, the region of interest of the RGB video in the time domain was obtained by using the Vibe algorithm, and the key frames were extracted and mapped to the RGB space to obtain the spatial-temporal map representing the video information. The map was sent to the convolutional neural network to extract features. Then, a vector was constructed in each frame of the joint point sequence to extract the Cosine Distance (CD) and Normalized Magnitude (NM) features. The cosine distance and the characteristics of the joint nodes in each frame were connected in time order of the joint point sequence, and were fed into the convolutional neural network to learn more advanced temporal features. Finally, the softmax recognition probability matrixes of the two information sources were fused to obtain the final recognition result. The experimental results show that combining RGB video information with joint point information can effectively improve the recognition result of human interaction behavior, and achieves 92.55% and 80.09% recognition rate on the international public SBU Kinect interaction database and NTU RGB+D database respectively, verifying the effectiveness of the proposed model for the identification of interaction behaviour between two people.

To eliminate the drawbacks of traditional small feature culling algorithm which processing granularity are triangles and can't efficiently cope with the number of vertexes and triangles up to hundreds of millions in a certain time period, an improved small feature culling algorithm for large scale process plant based on octree was proposed. Based on the component primitive characteristics and spatial characteristics of the process plant model, the value of screen for quantizing the size of component was proposed, and the established octree and the value of screen were combined to estimate the upper limit of the number of pixels, so as to quickly determine whether the component would be culled or not. The experimental results show that the proposed algorithm is simple and effective. Compared with the current popular review software after loading the factory model with 10000 pipelines, the frame rate is increased by at least 50%, which greatly improves the platform's fluency. Process factory industry and graphics platform as a whole to enhance the level of design has a positive meaning.

In order to solve the problem that using the file suffix and file feature to identify file type may cause a low recognition accuracy rate, a new content-based file-type detection algorithm was proposed, which was based on Principal Component Analysis (PCA) and K Nearest Neighbors ( KNN). Firstly, PCA algorithm was used to reduce the dimension of the sample space. Then by clustering the training samples, each file type was represented by cluster centroids. In order to reduce the error caused by unbalanced training samples, KNN algorithm based on distance weighting was proposed. The experimental result shows that the improved algorithm, in the case of a large number of training samples, can reduce computational complexity, and can maintain a high recognition accuracy rate. This algorithm doesn't depend on the feature of each file, so it can be used more widely.

Studying the constructing mechanism of micro-blog transmission network help to understand the information spreading process on the micro-blog platform deeply, and then obtain effective strategies and suggestions. As for this issue, a directed and weighted network model was proposed. In the model building process, according to the phenomenon that micro-blogs can be transmitted more than one time, triad formation was introduced. Different directions of links were used to represent the various characteristics of active and famous users. Besides, the dynamic evolution process of the link weight was considered. The theory analysis and simulation experiment results indicate the strength distribution, the degree distribution and the correlation of strength and degree obey power-law distribution, and the power exponents are between 1 and 3. Also, this model is characterized by high clustering coefficient and short average path length. Average clustering coefficient is 0.7, and average length is less than 6. As well, actual data of micro-blog transmission were collected to prove the model's correctness.

Since the energy consumption of joint processing in uplink multi-base cooperative communication system is excessively high, an Inter-Cell Interference Rejection based Uplink Multi-Base Cooperative Energy Efficiency Algorithm (ICIR-UMBCEEA) was proposed. Firstly, equivalent noise and Coordinated Multi-Point (CoMP) estimated channel were gotten by DeModulation Reference Signal (DMRS) sequence, and the Interference Rejecting Combining (IRC) filtering matrix of CoMP channel was deduced; Secondly, an equivalent interference model was established and the average inter-cell interference was obtained by using IRC filtering matrix; Finally, interference level of user in each cell to non-CoMP set was calculated, and a joint processing against strong interference users was made. In the comparison experiments with Uplink Multi-Base Cooperative Algorithm of Optimal Water Filling Control (UMBCA-OWFC), the normalized average interference of ICIR-UMBCEEA decreased by 19.2% in center users and 24.5% in edge users, and the energy efficiency of it increased by 25.48% in center users and 18.03% in edge users; ICIR-UMBCEEA had less energy consumption, and had higher throughput in center users and not much difference in edge users. The theoretical analysis and simulation results show that ICIR-UMBCEEA can effectively improve the energy efficiency of communication system in engineering.

Concerning the problem that single UML model can not test the software sufficiently, this paper proposed a new method of automatically generating software test clues by combining the characteristics of UML2.0 interaction overview diagram. First, this paper gave the formal definition of UML class diagrams, sequence diagrams and Interaction Overview Diagrams (IOD) . Second, the Node Control Flow Graph (NCFG) was constructed by extracting the process information of the interaction overview diagram while the Message Sequence Diagrams (MSD) were constructed by extracting the object interaction information. The testable model of IOD was constructed by embedding the MSD's message path into NCFG. At last, the paper adopted two-two coverage criterion to generate test clues. The experiment verifies that this method which automatically generates test clues avoids the combinatorial explosion while guaranteeing the test adequacy.

In order to effectively improve the recognition accuracy of the speech emotion recognition system, an improved speech emotion recognition algorithm based on Support Vector Machine (SVM) was proposed. In the proposed algorithm, the SVM parameters, penalty factor and nuclear function parameter, were optimized with genetic algorithm. Furthermore, an emotion recognition model was established with SVM method. The performance of this algorithm was assessed by computer simulations, and 91.03% and 96.59% recognition rates were achieved respectively in seven-emotion recognition experiments and common five-emotion recognition experiments on the Berlin database. When the Chinese emotional database was used, the rate increased to 97.67%. The obtained results of the simulations demonstrate the validity of the proposed algorithm.

An unsupervised extraction method is proposed in order to extract bead-like particles regions of fly ash from scanning electron microscope image, which is based on region growing with gray similarity bounded by gradient and shape. The process is automatic, including seeds selecting , regions growing and shape distinguishing. The experimental error is measured by the acreage probability of missing segmentation and false segmentation. The minimum error rate of the experimental results is 6.8%, and the average error rate is 8%. The time of extraction from 60 SEM images is within 10 minutes. The method is effective for the content estimate of fly ash in the material.

Concerning the characteristics of Bayesian networks classifier, a discriminative parameter learning algorithm of Bayesian networks classifier based on parameters ensemble named PEBNC was proposed to improve the classification performance of Bayesian classifier. This algorithm regarded the parameter learning as a regression problem, applied the additive regression model to the parameter learning of Bayesian networks classifier, and realized a discriminative parameter learning of Bayesian networks classifier. The experimental results indicate that the PEBNC classifier can improve the classification performance in most cases. Furthermore, compared with the general Bayesian classifier ensemble, PEBNC requires less space because there is no need to save parameters of individual classifiers.