As generalization ability of the out-dated traditional data processing technology is weak, and the technology did not take into account the multi-source data security issues, a blockchain oriented online federated incremental learning algorithm was proposed. Ensemble learning and incremental learning were applied to the framework of federated learning, and stacking ensemble algorithm was used to integrate the local models and the model parameters in model training phase were uploaded to the blockchain with fast synchronization. This made the accuracy of the constructed global model only fall by 1%, while the safety in the stage of training and the stage of storage was improved, so that the costs of the data storage and the transmission of model parameters were reduced, and at the same time, the risk of data leakage caused by model gradient updating was reduced. Experimental results show that the accuracy of the model is over 91.5% and the variance of the model is lower than 10 ^-5, and compared with the traditional integrated data training model, the model has the accuracy slightly reduced, but has the security of data and model improved with the accuracy of the model guaranteed.

To improve the performance of gait recognition based on smartphone accelerometer, a recognition method based on Multiple Classifier Fusion (MCF) was proposed. Firstly, as the gait features extracted from the existing methods were relatively simple, the speed variation of the relative gradual acceleration extracted from each single gait cycle and the acceleration variation per unit time were taken as two new types of features (16 in total). Secondly, combing the new features with the frequently-used time domain and frequency domain features to form a new feature set, which could be used to train multiple classifiers with excellent recognition effect and short training time. Finally, Multiple Scale Voting (MSV) was used to fuse the output of the multiple classifiers to obtain the final classification result. To test the performance of the proposed method, the gait data of 32 volunteers were collected. Experimental results show that the recognition rate of new features for a single classifier is increased by 5.95% on average, and the final recognition rate of MSV fusion algorithm is 97.78%.

Current analytical method based on short-text can not describe the evolution process of burst-event in a simple and accurate manner. In order to solve the problem, a new method was proposed to express the evolution process of burst-event based on short-text data sets. Firstly, a method of measuring event status was proposed to describe the state of event at each time for analyzing the development process of the event. Secondly, according to the structured information of short-text, the value of event status was set from two aspects: text information and user information. Thirdly, with the consideration of the impact factor of text information, the weight of text information was calculated by constructing related formulas. Fourthly, with the consideration of the impact factor of user information, a modified PageRank algorithm was proposed, and users were divided into different layers to calculate the weight of user information by constructing related formulas. Finally, the weight of text information and the weight of user information were combined to calculate the value of event status. The experimental results show that considering user information in turn, the modified PageRank algorithm, and the idea of dividing the users into different layers all can correct 1~2 points of description and improve the accuracy of expressing the evolution process of event.

Because of the literature search system failing to comprehend users' real-time demands, a method to find users' real-time demands for literature search systems was proposed. Firstly, this method analyzed the users' personalized search behaviors such as browsing and downloading. Secondly, it established users' real-time Requirement Documents (RD) based on the relations between users' search behaviors and users' requirements. And then it extracted keyword network from requirement documents. Finally, it gained users' demand graphs which were formed by core nodes extracted from keyword network by means of random walk. The experimental results show that the method by extracting demand graphs increases the F-measure by 2.5%, in the comparison of the K-medoids algorithm on average, under the condition that users' demands are emulated in the experiment. And it also increases the F-measure by 5.3%, in the comparison with the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm on average, under the condition that users really searches for papers. So, when the method is used in literature search systems where users' requirements are stable, it will be able to gain users' demands to enhance users' search experiences.

An improved Canny operator was proposed to ensure the accuracy of the Light Emitting Diode (LED) wafer edge detection applied in the vision system of automatic LED die bonder. In the traditional Non-Maxima Suppression (NMS) process, inaccuracy would be caused by selecting two pixels near gradient direction as contrastive points. To solve this problem, bilinear interpolation based on the center pixel and three pixels around was made along gradient direction to implement new NMS process. In addition, the gray histogram of LED wafer was characterized by typical three peaks, and then the high threshold and the low threshold could be got by Otsu dual threshold method with new evaluation function to replace traditional artificial adjustment. The experimental results indicate that greater wafer outline and poles edges can be got by the improved Canny algorithm which is appropriate for LED wafer edge detection.

The suitability analysis of matching area plays a significant role in the field of vision-based navigation. There are many feature indexes that can only unilaterally describe the suitability of matching area. An algorithm was proposed to integrate several feature indexes to solve conflicts among different feature indexes and provide a kind of method that can measure the suitable confidence and unsuitable confidence of a feature.And then the confidences were fused by using the Dempster-Shafer (DS) rules. At last the algorithm was verified by simulation experiment.

Due to the multi-hierarchy, multi-attribute process of product design, the key technology of knowledge reuse in product design based on axiomatic design was studied. With the axiomatic design framework, the design process was decomposed, and the Z map of the requirement domain, functional domain, structure domain, process domain of the product design was put forward. Then, a case-based knowledge reuse method with overall consideration of inter-domanin and intra-domain design information was presented, which adopted the similarity-based retrieving in knowledge reuse algorithm, and got the best reuse objects. Finally, a case was provided to validate and prove the efficiency and feasibility of the method.

In this paper, the authors studied the continuous berth allocation problem for the container terminals with assumption of quay crane being dynamically allocated and establish a dynamic berth allocation scheme for minimizing vessel's time in port.Based on brother-son method, an algorithm was developed to solve the berth allocation problem with presetting the position of the vessels and quay crane being dynamically allocated in the right position without crossing. In comparison to the existing method，the method can reduce the vessel's time with the same example, which show that the proposed model and algorithm are more efficient.

In the super-peer network, the super-peer topology structure and its mechanism of dynamic maintenance and search routing are important factors affecting network performance and search efficiency. In this paper, a new structure named k-Perfect Difference Graph (PDG) was proposed by analyzing the characteristics and the deficiencies of PDG, new Super-peer Network based on k-PDG (KPDGN) was constructed, and then the mechanism of dynamic maintenance and search routing was presented in KPDGN. The analysis and simulation results show that compared with current supper-peer topology, KPDGN has good performance with constant degree and fixed adjacent nodes, which reduces the bandwidth consumption during searching and the cost of topology construction and maintenance.

A kind of Boolean operational method based on a three-dimensional grid model was proposed. Firstly, through collision detection algorithm based on hierarchical bounding box tree of Oriented Bounding Box (ORB), the intersecting triangles could be got. Through the intersection test of the triangles, the intersecting lines could be obtained and the intersecting lines topology relations with the triangles could be established. Secondly, a regional division for the intersecting triangles was made through processing the three types of intersecting lines, so as to get a series of polygons, and carry out Delaunay triangulations for polygon to get the result area. Lastly, relation adjacency list was constructed based on solid containing relations, the polygons internal relation and external relation with other entities were judged, and the triangles were located according to the mesh model topology relations. Simultaneously, according to such Boolean operations as the intersection, union, and differences, according to the grid model topology relations were judged, the position of the triangles were judged and then the final results could be obtained. Experimental results show that this algorithm can achieve better results. Experimental results show that the lithology of intersecting parts is consistent with the entities and can verify the correctness and feasibility of the algorithm.

This paper proposed a stable and precise algorithm for 3D mesh model. Firstly, the algorithm, based on the original topology of mesh model, realized quick location of intersectional area of mesh curve by combining the intersection test of the layers of nodes bounding box. Then the algorithm utilized the improved triangular intersection algorithm to calculate the discrete intersectional segments and re-triangulates every intersectional triangle. Through building the topology of intersectional segments and triangles, the algorithm could quickly trace and pick up the discrete segments, and classify and combine the local area, and realize the precise Boolean operation. The algorithm could effectively deal with all kinds of instances, and could be implemented in programs easily. And the experimental results prove that the algorithm accords with requirements of the project.

 Automatic interpretation of synthetic aperture radar (SAR) images was one of the most important application fields in image processing. Focusing on the medium resolution SAR images and combining with the previous algorithms, a novel technique to detecting ship targets from coastal regions based on fuzzy theory was proposed. In this new method, the input image was first processed with improved fuzzy enhancement algorithm so as to alter the characteristics of the gray-level distribution; then, with the threshold method, the sea and land regions could be separated; following that, the maximum entropic algorithm was employed to further process the image and regions of interest which contained candidate ship targets could be extracted; Then ROIs were segmented and the features of ship targets were extracted. Finally the ship targets could be detected based on fuzzy reasoning technique.