Surface defects of bearing have a significant impact on performance and stability of electromechanical equipment. Aiming at the issues of low recognition accuracy of small targets and low detection speed in current surface defect detection process for bearings， a tiny defect detection algorithm of bearing surface based on RT-DETR （Real-Time DEtection TRansformer） — FECS-DETR （Faster Expand and Cross hierarchical-scaled feature Screening DETR） algorithm was proposed. Firstly， a lightweight FasterNet-T1 was employed to reconstruct the backbone network of RT-DETR for reducing computational overhead. Secondly， an Attention-embedded Expand Residual Fusion （AERF） module was designed for deep feature extraction， thereby enhancing the description capability of small-scale abstract features. Thirdly， a Cascaded Group Attention （CGA） was applied to further reduce computational redundancy and improve operational efficiency of the model. Fourthly， a Cross hierarchical-scaled Information Screening Feature Pyramid Network （CIS-FPN） was proposed to address the issue of information loss during feature fusion and enhance feature integration capability. Finally， a joint regression loss optimization strategy combining Normalized Wasserstein Distance （NWD） and improved Inner-MPDIoU was employed to accelerate model’s convergence and improve model accuracy for small-scale targets. Experimental results show that on the bearing surface tiny defect dataset， compared with the original RT-DETR algorithm， FECS-DETR algorithm has the mean Average Precision （mAP） improved by 2.5 percentage points， the computation complexity reduced by 28.8%， and the detection speed increased by 20.8%. It can be seen that the proposed algorithm achieves a balance between accuracy and real-time performance， and satisfies the requirements for detection of bearing surface tiny defects in industrial environment.

This paper transfered the problem of the 3D craniofacial registration into the one in 2D parameter domain by using surface parameterization. Firstly, six landmarks on the craniofacial surfaces were calibrated according to the physiological characteristics, and the pose and size of the craniofacial surfaces were normalized by projecting the craniofacial surfaces into a unified coordinate system which was determined by using the six landmarks. Secondly, Least Squares Conformal Mapping (LSCM) was performed for a reference craniofacial surface by pinning two outer corners of the eyes, by which the 2D parameters of the six landmarks were computed. Thirdly, any craniofacial surface could be mapped into a 2D domain using LSCM by pinning the six landmarks. Finally, the 3D point correspondences were obtained by mapping the 2D correspondences into the 3D surfaces. To validate the proposed method, the reference model was deformed into the target one by the Thin Plate Spline (TPS) transform with the corresponding vertices being control points, and the average distance between two corresponding point sets after deformation was computed. By the average distance, the proposed method was compared with the principal axes analysis based ICP (Iterative Closest Point) and the random sampling control points based iterative TPS registration methods. The comparison shows that the proposed approach is more accurate and effective.

In order to further improve the efficiency of the population diversity in the implementation process of the Particle Swarm Optimization (PSO), an Adaptive PSO (APSO) algorithm based on diversity feedback was proposed. APSO adopted a new population diversity evaluation strategy which enabled the automatic control of the inertia weight with population diversity in the search process to balance exploration and the exploitation's process. In addition, an elite learning strategy was used in the globally best particle to jump out of local optimal solution. It not only ensured the convergence rate of the algorithm, but also adaptively adjusted the search direction to improve the accuracy of solutions. The simulation results on a set of typical test functions verify the validity of APSO.

Concerning the Virtual Machine (VM) migration problems in Wide Area Network (WAN), a live migration model was proposed. The link state between nodes was continuously detected, and the migration time of disk, memory, CPU status and network were optimized. The disk cycle synchronization, unidirectional tunnel and virtual machine localization were implemented. The experimental results show that the model reduces amount of migration data and shortens redirection path in WAN. The total time and pause time are close to the manner of shared storage under simulated conditions.

Only using estimation of leading edge for target will cause vertical and horizontal ambiguity. Therefore, a new method of Synthetic Aperture Radar (SAR) target aspect estimation based on Radon transform of leading edge was proposed. The new method was introduced to eliminate the ambiguity of horizontal and vertical aspect estimation based on the length of the target region. It is difficult to separate the long leading edge from the short one. By introducing the discrimination rule of the target leading edge, the problem that many traditional algorithms try to settle was solved due to the estimation algorithm of Radon transform. The experimental results on the MSTAR data prove the precision and robustness of the algorithm.

Test case generation of EFSM (Extended Finite State Machine Models) includes test path generation and test data generation. However, nowadays most research into EFSM testing focuses on test path generation. In order to explore the automatic test generation, a test data generation method oriented to the path of EFSM models was proposed. A Tabu Search (TS) strategy was adopted to automatically generate test data, and the key factors that affect the performance of test data generation in EFSM models were analyzed. Moreover, the test generation efficiency was compared with that of Genetic Algorithm (GA). The experimental results show that the proposed method is promising and effective, and it is obviously superior to the GA in the test generation for EFSM models.

A management model of virtual machines was proposed based on Peer-to-Peer (P2P) structure and its prototype system was implemented. Host nodes were organized in P2P structure and resource discovery was achieved by multicast. Live migration algorithm of virtual machines was proposed and live migrations between nodes were automatically triggered. The requests of cloud computing user were mapped to the host by elected root node, and the on-demand operations of creating, deleting, and stopping a virtual machine were achieved. The experimental results show that: the model has the features of rapid convergence, low bandwidth utilization and high availability. Load balance of cloud computing resources can be achieved.

Basic Shuffled Frog Leaping Algorithm (SFLA) algorithm easily traps into local optimum and has a low convergent precision when being used to address complex functions. To overcome these above shortcomings, an improved SFLA based on mutation idea in Differential Evolution (DE) was proposed. The proposed algorithm used beneficial information of the other individuals in sub-group to disturb updating strategy locally. The experimental results show that the improved SFLA has a better capability to solve complex functions than other algorithms. It has high optimization efficiency, good global performance, and stable optimization outcomes, and is superior to the other algorithms.

Standard Particle Swarm Optimization (PSO) algorithm falls into local optima easily and has low convergence accuracy when it is used to address the problem of complex functions optimization. In order to overcome the shortcomings, an improved PSO algorithm was proposed. The proposed algorithm integrated the attractionrepulsion mechanism in the field of biology into PSO algorithm and took full advantage of the mutual influence between particles to modify velocity updating formula, and thus maintained population diversity and enhanced the ability of particle to escape from the local optima. The experimental results demonstrate that the proposed algorithm outperforms two existing variants of the PSO algorithm in terms of convergence accuracy while improving the velocity of convergence in the later evolution phase and avoiding premature convergence problem effectively.