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While the industrial robotic manipulator is a kind of multi-input and multi-output human-like operation and highly autonomous control system. It is widely used in medical care, home service, industrial manufacturing and other fields. With the integration of cyber-physical system networks and the Internet in recent years, the control commands of the industrial robotic arm control system can be totally exposed to the Internet. Under these circumstances, the chances of successful attacks by attackers to systems are increasing year by year. Compared to the security of traditional cyber physical system, the security of manipulator control system is a very challenging problem. In this paper, a covert attack method of 7 degrees of freedom (7-DOF) manipulator control system was proposed. Firstly, based on the inverse kinematics equation of the manipulator, the motion planning and modeling of 7-DOF manipulator, which communicated by EtherCAT, was carried out. Secondly, according to the research and analysis of particle swarm optimization method, a 7-DOF manipulator system PID parameter identification algorithm based on chaotic theory for multi- swarm particle swarm optimization was proposed. Parameter identification mainly identified the PID parameters of each joint. The principle and derivation process of the algorithm were described in detail. Finally, the experimental platform of manipulator control system was built and the identified parameters were used in combination with the covert attack principle to conduct the experiment. The proposed method was compared with other traditional attack methods, such as state machine attack and traditional sine attack. The results show that the covert attack model of the proposed 7-DOF manipulator can destroy the data integrity and accuracy of the manipulator system, and has a good concealment, which verifies the effectiveness and feasibility of the established attack model. The attack experiment platform constructed in this paper provides the physical basis for the attack and defense experiment of the manipulator, and it has certain reference significance for similar researchers.

Based on financial informatization, financial information security concept, current major risks to financial information security in domestic and international were overviewed and summarized, including network security, data security and business & services continuity, etc. Some effective techniques and control measures in this areas were introduced. Finally, relevant implications and recommendations were proposed.

A plot experiment including four treatments, CK (N 105 kg ha−1 as urea, including a basal N application of 35 kg ha−1 and a topdressing N 70 kg ha−1 at turned green stage) and optimized N management (OPT1, OPT2 and OPT3, applied two-thirds, one-third and two-fifths N at jointing stage, respectively, total N 60 kg ha−1), was conducted to evaluate the effects of nitrogen management on growth and N uptake of winter wheat (Triticum aestivum), Dongnong 1, which is the first highly cold tolerant winter wheat in China. Index of population quality, N uptake and yield were determined. The ear-bearing tiller rate was increased by above 12%, and the leaf area index, biomass and N uptake were significantly decreased (P

For the recognition of armored targets in complex battlefield environments, how to reduce missed and false alarms while achieving real-time is an urgent issue. To this end, the GCD-YOLOv5 algorithm is innovatively proposed. Firstly, array lidar is used to acquire the armor target data. Secondly, the armor target data is expanded with an improved GAN(Generative Adversarial Network) to increase the diversity of training data. Afterward, the expanded dataset is fed into the GCD-YOLv5(You Only Look Once) for training. And the GCD-YOLOv5 is reflected in the following aspects. Firstly, the CBAM(Convolutional Block Attention Module) and the multi-scale feature fusion are added to improve the feature extraction capability and detection efficiency, increasing the recognition capability of small and obscured targets. Secondly, combining with DETR(Detection Transformer) to lighten YOLOv5 to achieve the real-time requirement. Thirdly, the YOLOv5 loss function and prediction box filtering method are improved to increase the detection accuracy and the confidence of the detection boxes. The experimental results show that the GCD-YOLOv5 algorithm has higher accuracy and real-time, the mAP(mean Average Precision) can reach 99.7%, and fps is 68.56% higher compared to YOLOv5, which significantly improves the recognition capability of armored targets in complex battlefield environments.

The noise suppression of high-resolution range profile (HRRP) is a prerequisite for Geiger-mode of avalanche photodiodes (GM-APD) lidar to achieve precise sensing. However, it is difficult to balance the suppression effect and the integrity of detailed information. Considering this problem, we propose a Bayesian network-based improved loop filter (BNILF) for abnormal noise suppression. Based on the ILF, the Bayesian non-average local filtering model is established to calculate a distance of Pearson distance, which gives the criterion of noise judgment. Furthermore, the block preselection is used to accelerate identify abnormal noise and complete range profile noise suppression. To evaluate the performance of this algorithm, simulation and physical system experiments are carried out. The results show that the proposed algorithm has a better noise suppression effect and a higher detailed information protection ability in comparison with the existing typical approaches.