The main goal of this investigation is to improve the tracking accuracy of the stage of a linear motor. A DC brushless linear motor is used to actuate a gantry stage to perform printing. To compensate for the tracking error of the gantry stage that is associated with nonlinear friction, the dynamics of the nonlinear static friction are formulated using the Hsieh-Pan model. Particle swarm optimization (PSO), genetic algorithm, and real-coded genetic algorithm-based optimization problems are investigated to evaluate the parameters of the nonlinear friction model. The use of PSO-based optimization to tune the parameters of a disturbance-observer-based variable structure controller is also discussed to improve the tracking response. To check the consistency of the proposed controller, it is implemented in real time and an improved positional accuracy better than 0.1 μm is readily achieved.

Traditional network attack and hacking models are constantly evolving to keep pace with the rapid development of network technology. Advanced persistent threat (APT), usually organized by a hacker group, is a complex and targeted attack method. A long period of strategic planning and information search usually precedes an attack on a specific goal. Focus is on a targeted object and customized specific methods are used to launch the attack and obtain confidential information. This study offers an attack detection system that enables early discovery of the APT attack. The system uses the NSL-KDD database for attack detection and verification. The main method uses principal component analysis (PCA) for feature sampling and the enhancement of detection efficiency. The advantages and disadvantages of using the classifiers are then compared to detect the dataset, the classifier supports the vector machine, naive Bayes classification, the decision tree and neural networks. Results of the experiments show the support vector machine (SVM) to have the highest recognition rate, reaching 97.22% (for the trained subdata A). The purpose of this study was to establish an APT early warning model mechanism, that could be used to reduce the impact and influence of APT attacks.