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With the development of EMC technology, EMC assessment has become increasingly important in EMC design. Although numerous EMC assessment models are available today, few of them can provide a tradeoff between efficiency and accuracy for the specific case of military vehicular communication systems. Face to this situation, a modified four-level assessment model is proposed in the paper. First, the development of EMC assessment technology is briefly reviewed, and the theoretical mechanism of EMI environment is introduced. Then, the architecture of the proposed model is outlined, and the assessment methods are explored. To demonstrate the application of it, an example involving a communication system in a military vehicle is presented. From the physical layer to the signal layer, a hierarchical assessment on the entire system is successfully performed based on the proposed model, and we can make a qualitative EMC assessment on the EMC performance of the system. Based on a comparison with the traditional model, we conclude that the proposed model is more accurate, more efficient and less time-consuming, which is suitable for the EMC assessment on military vehicular communication systems. We hope that the proposed model will serve as a useful reference for system-level EMC assessments for other systems.

In this paper, the LMS Virtual.Lab Motion multi-body dynamics simulation software was used to analyze the mid-low speed maglev train current collection system of the collector shoe and the conduct rail. After establishing the dynamic simulation model of the collector shoe and the whole vehicle respectively, the dynamic simulation model of the current collection system is established with considering the irregularity of the running rail and the conduct rail at the same time. The model uses contact force, contact resistance and offline rate to simulate the influence of vehicle running speed on the current collection quality. The following conclusions are obtained by analyzing the simulation results: The current collection system has good current collection characteristics with low speed; fluctuations in contact force and contact resistance and offline rate increases with the increasing speed, resulting in a decrease in the current collection quality; especially at high speeds, contact force, contact resistance fluctuates drastically and the offline rate increases significantly; the current collection system is not suitable for high-speed operation; the current collection system is more sensitive to track irregularity at high speed.

In this paper, the LMS Virtual. Lab Motion was used to analyze the dynamics of the bogie of Medium-speed maglev trains with a mid-mounted linear motor. Firstly, the multi-rigid-body model of the single bogie, the decentralized levitation control scheme and the magnet-rail relationship model were built in LMS Virtual. Lab Motion. The modal frequencies and modal shapes were obtained by using the linearization analysis function of the software. The modal analysis results were verified with the vibration response of the frequency sweeping analysis while applying a vertical force on the mid-mounted linear motor.

Efficient and accurate analysis of the traffic data contained in the network is the key measure to detect the abnormal behavior, resist the invasion and protect the information security. In this paper, we make a comprehensive utilization of the characteristics of port mapping identification, payload identification, statistical analysis and SVM machine learning, and propose the dynamic hybrid strategy. Firstly, the machine learning training samples are obtained through port mapping and load feature recognition. Then, on the basis of information gain feature selection, the SVM machine learning model is built and trained. Finally, through the voting mechanism, we achieve comprehensive analysis of the traffic data. The experimental results show that the accuracy of the proposed algorithm is as high as 99.1%, and the number of manual decision analysis is greatly reduced at the same time.

Cross domain data such as numerical or categorical types are ubiquitous in practical network. Network anomaly detection based on cluster analysis exist some difficulties, for example, the initial center of cluster analysis is sensitive and easy to fall into the local optimal solution. Cross domain data involved great information, but can't be effectively used, which will influence the performance of detection. In this paper, we proposed the dynamic hierarchical clustering method. Firstly, the feature selection based on information gain was used to reduce the feature dimension. Next, to measure the cross domain data, we defined the generalized Euclidean distance to extend the traditional Euclidean distance. Thirdly, dynamic clustering accuracy was set to guide the dynamic hierarchical clustering, guaranteeing the clustering accuracy as well as the convergence of clustering. Finally, Anomaly detection and classification model was constructed by using the training sample data. Simulation results show that the proposed algorithm can achieve higher detection rate and lower false detection rate.

In this paper, we introduce a watermarking algorithm for halftone image. The arithmetic is based on HVS and multiscale error diffusion. We insert special binary image in the halftone image and study on it after printing and scanning. Using the proposed methods, several hundred information bits can be embedded into images with perfect recovery against the print-scan operation. Different printing resolution, different scanning resolution and printing paper type are experimented. The experiments show that the watermarking algorithm is robust to print-scan process.