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.

In this study, a set of methods for the inspection of a working motor in real time was proposed. The aim was to determine if ball-bearing operation is normal or abnormal and to conduct an inspection in real time. The system consists of motor control and measurement systems. The motor control system provides a set fixed speed, and the measurement system uses an accelerometer to measure the vibration, and the collected signal data are sent to a PC for analysis. This paper gives the details of the decomposition of vibration signals, using discrete wavelet transform (DWT) and computation of the features. It includes the classification of the features after analysis. Two major methods are used for the diagnosis of malfunction, the support vector machines (SVM) and general regression neural networks (GRNN). For visualization and to input the signals for visualization, they were input into a convolutional neural network (CNN) for further classification, as well as for the comparison of performance and results. Unique experimental processes were established with a particular hardware combination, and a comparison with commonly used methods was made. The results can be used for the design of a real-time motor that bears a diagnostic and malfunction warning system. This research establishes its own experimental process, according to the hardware combination and comparison of commonly used methods in research; a design for a real-time diagnosis of motor malfunction, as well as an early warning system, can be built thereupon.

This study discusses a circular trajectory tracking function through a proposed pneumatic artificial muscle (PAM)-actuated robot manipulator. First, a dynamic model between a robot arm and a PAM cylinder is introduced. Then the parameters thereof are identified through a genetic algorithm (GA). Finally, PID is used along with a high-order sliding-mode feedback controller to perform circular trajectory tracking. As the experimental results show, the parameters of sampling time and moment of inertia are set to accomplish the trajectory tracking task in this study. In addition, the maximum error between the objective locus and the following locus was 11.3035 mm when applying theta-axis control to the circular trajectory of the robot arm with zero load or lower load. In an experiment of controller comparison, the results demonstrate that a high-order sliding-mode feedback controller is more robust in resisting external interference and the uncertainty of modeling, making the robot arm have good performance when tracking.

Ball bearings are important parts of all modern rotating machines. Their function is to reduce friction, support rotating shafts and spindles, and bear loads. Bearing damage can result in abnormal vibrations, cause machine malfunction, and even be dangerous. In this study, analysis of four different ball-bearing conditions was carried out: normal bearings and bearings with inner ring, rolling body, and outer ring malfunction. This was based on electromechanical vibration signals produced on a fault diagnosis simulation platform. The objective was to use a series of signal processing analytical methods to build a set of identification models used to forecast malfunction. Wavelet packet transform technology was first used to process the vibration signal. The signals were pre-processed and analyzed before eigenvalue calculation was done to analyze the signal changes which allowed determination of the nature of the bearing malfunction to be made. The extracted eigenvalues and ball-bearing status categories were input to the support vector machine for model training and testing. Finally, the constructed model parameters were integrated with particle swarm optimization, and the artificial fish-swarm algorithm was used to obtain the optimal parameters for the classifier, and this improved the accuracy of malfunction classification.

In this paper, the multi-objective Hybrid Taguchi-genetic Algorithm is used to search for the best processing parameters with specified processing accuracy. The experimental cutting parameters used for the L9 orthogonal table process are cutting depth, cutting velocity and feed rate. The surface roughness of the machined workpiece surface was measured according to the standard of centerline average roughness. The Material Removal Rate (MRR) will be calculated by measuring the diameter of the processed workpiece from the formula to give the MRR. A linear regression model is constructed from the processed quality and the processing parameters of the orthogonal table and the reliability of the model is confirmed by analysis of variance (ANOVA). A Hybrid Taguchi Genetic Algorithm (HTGA) was used to calculate the optimal cutting parameters for multi-objective processing. The results of the experiments indicate that HTGA gave better convergence and robustness than the conventional Genetic Algorithm (GA) using the same number of iterations. This process produces multiple combinations of optimal cutting parameters for material removal rate and surface roughness. As the enhancement of material removal rate improved efficiency on the production line, the optimal cutting parameters were based on the tolerance range of Ra 1.6μm ~ 3.2μm according to the international standard of surface roughness. After actual processing with the selected optimum cutting parameters, the quality of processing is even better than the experimental design of the L9 Orthogonal table.