A Hybrid Bird Swarm Algorithm （HBSA） was proposed to minimize the makespan more efficiently for Permutation Flowshop Scheduling Problem （PFSP）. Firstly， to improve the quality and diversity of initial population， a new population initialization method was put forward by combining a NEH （Nawaz-Enscore-Ham） based heuristic algorithm and chaotic mapping. Secondly， to deal with the discrete scheduling problem by the algorithm， the Largest Ranked Value （LRV） rule was adopted to convert continuous position values to discrete job permutation. Finally， to enhance the ability of the algorithm to explore the solution space， local search methods for the individual best job permutation and population best job permutation were proposed on the basis of the ideas of Variable Neighborhood Search （VNS） and Iterative Greedy （IG） algorithms respectively. The proposed algorithm was simulated and tested on the widely used benchmark test set Rec and compared with Hybrid Differential Evolution algorithm proposed by Liu et al （L-HDE） algorithm， Hybrid Symbiotic Organisms Search （HSOS） algorithm， Discrete Wolf Pack Algorithm （DWPA） and Multi-Class Teaching-Learning-Based Optimization （MCTLBO） algorithm， which are the effective meta-heuristic algorithms for PFSP. The results show that the average values of Best Relative Error （BRE） and Average Relative Error （ARE） achieved by HBSA are at least 73.3% and 76.8% lower than those of the above four algorithms， thus proving that HBSA has stronger search ability and better stability. It is worth mentioning that， for Rec25 and Rec27 test instances， only HBSA achieves the currently known optimal solutions， which further proves its superiority.

The current real-time semantic segmentation algorithm has the high computational cost and large memory footprint， which cannot meet the applications requirements of actual scenes. In order to solve the problems， a new type of shallow lightweight real-time semantic segmentation algorithm — AEFNet （Real-time segmentation algorithm based on Attention mechanism and Effective Factorized convolution） was proposed. Firstly， one-dimensional non-bottleneck structure （Non-bottleneck-1D） was adopted to construct a lightweight factorized convolution module to extract rich contextual information and reduce the amount of calculation. At the same time， the learning ability of the algorithm was enhanced in a simple way and the extraction of detailed information was facilitated. Then， the pooling operation and Attention Refinement Module （ARM） were combined to construct a global context attention module to capture global information and refine each stage of the algorithm to optimize the segmentation effect. The algorithm was verified on the public datasets cityscapes and camvid， and the precision of 74.0% and the inference speed of 118.9 Frames Per Second （FPS） were obtained on the cityscapes test set. Compared with Depth-wise Asymmetric Bottleneck Network （DABNet）， the proposed algorithm has the precision increased by about 4 percentage points， and the inference speed increased by 14.7 FPS. Compared with the recent efficient Enhanced Asymmetric Convolution Network （EACNet）， the proposed algorithm has the precision slightly lower by 0.2 percentage points， but has the inference speed increased by 6.9 FPS. Experimental results show that the proposed algorithm can more accurately identify the scene information， and can meet the real-time requirements.

Recently， the leading human pose estimation algorithms are heatmap-based algorithms. Heatmap decoding （i.e. transforming heatmaps to coordinates of human joint points） is a basic step of these algorithms. The existing heatmap decoding algorithms neglect the effect of systematic errors. Therefore， an error compensation based heatmap decoding algorithm was proposed. Firstly， an error compensation factor of the system was estimated during training. Then， the error compensation factor was used to compensate the prediction errors including both systematic error and random error of human joint points in the inference stage. Extensive experiments were carried out on different network architectures， input resolutions， evaluation metrics and datasets. The results show that compared with the existing optimal algorithm， the proposed algorithm achieves significant accuracy gain. Specifically， by using the proposed algorithm， the Average Precision （AP） of the HRNet-W48-256×192 model is improved by 2.86 percentage points on Common Objects in COntext （COCO）dataset， and the Percentage of Correct Keypoints with respect to head （PCKh） of the ResNet-152-256×256 model is improved by 7.8 percentage points on Max Planck Institute for Informatics （MPII）dataset. Besides， unlike the existing algorithms， the proposed algorithm did not need Gaussian smoothing preprocessing and derivation operation， so that it is 2 times faster than the existing optimal algorithm. It can be seen that the proposed algorithm has applicable values to performing fast and accurate human pose estimation.

To solve the problems of large space, low transmission efficiency, high maintenance frequency in existing coal mine belt conveyor transmission system, a kind of direct drive structure using outer-rotor Permanent Magnet Synchronous Motor (outer-rotor PMSM) was proposed, and a model-free adaptive control with cascade structure was applied to the speed control system of belt conveyor. According to the requirements of mine belt conveyor running, the detailed design parameters and mathematical model of this motor were given, and the ideal speed curves of starting and the steady state were set. By using model-free adaptive control algorithm, a cascade model-free adaptive control law was designed, and the cascade control system structure was also given. The simulation of the ideal starting curve in direct-drive system in coal mine belt conveyor for outer-rotor PMSM was conducted using Matlab software. The results show that the cascade model-free adaptive control algorithm reduces the speed tracking error and improves control precision, which suppresses the system noise and the disturbance for load changing effectively. This method achieves good startup and steady-state characteristics of belt conveyor.

To address unified authentication and authorization, Single Sign-On (SSO) of multiple applications and that the Role-Based Access Control (RBAC) model can not be directly applied to the SSO problems in regional health information platform, this paper proposed a method based on a combination of role group control strategy and JASIG-CAS unified identity authentication system. Central Authentication Service (CAS) server used MyBatis technology to effectively show the subsystem information. Axis2 was used between the various application systems to keep consistency of user information. Besides, the authors made use of Session to store each users permissions to reduce the frequency of database access, thus significantly improving the performance of the platform. The SSO system achieved functions including unified user management, uniform assignment of permissions, unified platform features and so on. At last, the professional pressure testing platform LoadRunner8.0 was applied to the test and analysis of performance. The testing results show the performance of the system is stable and the design of the platform is reasonable.