Advanced Control for Applications最新文献

To address the IoT security problem, in this paper we propose and evaluate the DDoS attack mitigation method based on blockchain, and construct a DDoS abnormal information detection and sharing model. The obtained experimental results show that when the number of decision trees increases, the training time of the DDoS attack detection model based on the RF model grows with a minimum trend of 14 s. The testing time is finally maintained at 1 s, and the recognition accuracy of DDoS attacks keeps improving, ultimately reaching over 99.8%. If the amount of DDoS abnormal traffic information exceeds 100 pieces and 2000 pieces, it only takes 0.1 and 5 s to sign the DDoS abnormal traffic information using ECDSA algorithm digitally. The signature verification only takes 0.1 and 9 s, respectively. And compared to conventional network physical system IoT security architecture, a network physical system IoT security architecture that integrates AI empowerment, blockchain, and SDN integration has a higher joint defense success rate. It can be explained that this scheme will be conducive to promoting joint defense against DDoS attacks and ensuring the security of the Internet of Things.

To systematically and efficiently study the relationship between the design parameters and the strength and performance of the laminated coupling, it is expected to provide a theoretical basis for the design and selection of the coupling. Based on ANSYS parametric design language, parametric modeling and analysis were carried out on four different types of membranes, namely circular, multilateral, joint and plum-pattern membranes. The loads and constraints were parameterized and the automatic grid division and analytical parameter control were realized, so as to obtain the stress distribution rule of the membranes. Based on Python language and parameterized commands, the secondary development of the membrane parametric finite element analysis tool was provided. The influence law of the factors such as angular displacement, torque, thickness, and the number of holes on the membrane performance was studied respectively, so as to achieve efficient finite element analysis. Results show that stress concentration exists near the hole edge of the membranes under the action of torque and angular displacement. Both the increase of torque and angular displacement will maximize the equivalent stress of the membranes, and appropriately reducing the thickness of the single membrane can lower the equivalent stress of the membranes.

At present, most of the parts of mechanical equipment are made of metal, so the accurate detection and extraction of important characteristic parameters of metal parts is the key to determine the quality of mechanical equipment. In order to accurately extract and measure the feature size of metal parts, this research first carried out image preprocessing. Threshold segmentation is performed, and the target traits are extracted from the image to obtain the ROI. Finally, the preprocessed image is extracted from the image by the sub-pixel edge extraction technology to extract the feature size of the circle to be measured. The difference between the measured value of the characteristic dimension of the spring bearing seat and the gasket and the real value measured by the research method is within the range of 5 and 4 μm, respectively, and both of them meet the requirements of the characteristic dimension accuracy. When the feature size of the two is repeatedly measured, the variation range of the detection results of the spring bearing seat and the gasket is within 9 and 7 μm respectively, and the detection value is much smaller than the feature size tolerance. The detection accuracy of the method can reach 95.394%. The $��F_1�$ score was 96.029 and the AUC value was 0.93, both higher than other methods. The results show that the use of sub-pixels on metal parts The edge extraction method is extremely accurate. The research method can effectively improve the detection accuracy of the feature size of metal parts, which is of great significance to the development of the entire metal parts processing industry.

At present, hydraulic transmission loaders have high automatic adaptability and convenient operation, but their transmission efficiency is low, and the intelligent degree of energy consumption regulation is limited. Strengthening the research of locking device in torque converter is an important measure to effectively reduce energy consumption. Therefore, the research uses MATLAB software and torque converter parameters to build the external characteristic curves of the engine and torque converter, and builds the dynamic model based on the working principle and locking process of the lockup clutch. The research and experimental analysis are carried out from three aspects of the lockup dynamic process of the torque converter, the lock-up and unlocking conditions, and the change of the lockup oil pressure of the clutch. The results show that the speed difference range between pump wheel and turbine (233.08–365.44 rpm) is much lower than that of other cases (409.68–616.23 rpm) when the engine and torque converter are matched. And the fuel rate is the highest when the throttle opening of the loader is 70%, about 38%. The above results show that the locking structure of the hydraulic torque converter of the loader can effectively reduce the working vibration and improve the service life and fuel rate. And its energy-saving application effect has an important enlightening effect on the performance optimization and resource utilization of construction machinery, and provides new ideas for improvement.

In order to effectively solve the problem of energy autonomy of patrol robots in open-air working environment and free them from the limitation of power lines on their activity range. In this article, a solar-powered patrol robot based on a robot operating system is designed. The system adopts a modular design, using a master–slave distributed controller to control the robot's motion, and building an intelligent sensing system for the robot with various sensors. Meanwhile, flexible thin-film solar cells and lithium batteries form a folded multi-mode power supply system, which solves the problem of timely power supply. In addition, a simple and practical human–computer interaction interface is designed to facilitate viewing the robot's status. Finally, in order to verify the performance of the robot, the stability of the designed solar-powered patrol robot is verified through different experiments, which has a reference value and practical significance for the development of patrol robots.

With the progress of manufacturing capability and the requirements of modern project construction clustering and safety, unmanned technology has become a major development direction for construction machinery. The study analyzes the defects of the ACO applied to path planning, uses the basic ant colony method combined with the APF algorithm to construct comprehensive heuristic information to improve the efficiency of the initial iteration, and adds a pseudo-random proportional transfer rule to improve its transfer rule, in addition to proposing an optimal ant pheromone release rule to ensure that the pheromone update can effectively improve the effect of the later iteration. In the 20 × 20 raster map, the optimal path of both the basic and potential field ant colony algorithms is 31.52, while the basic ant colony algorithm does not converge to the optimal path after 200 iterations in the 30 × 30 raster map, and the comparison algorithm converges to the optimal path after 156 iterations, while the potential field ant colony algorithm converges to the optimal path after 25 iterations at 47.52. And the potential field ant colony The potential field ant colony algorithm converges to the optimal path of 47.52 after 25 iterations, and the number of turns in both raster maps of different sizes is only about 50% of the basic ant colony algorithm, which is also better than that of the comparison algorithm. The proposed algorithm improves the ability of the ant colony algorithm to explore and exploit raster maps, enhances its path planning capability, and improves the construction efficiency well in practical applications.

As a vital component in the shipping industry, coastal container terminals must be developed in a sustainable manner. The container receiving process is a crucial aspect of container loading and transportation and is the cornerstone of container terminal operations. This process is characterized by its discrete random events. To model this process, the testbed was designed and developed using a Hierarchical Generalized Stochastic Petri Net (HGSPN) workflow engine to drive the data and simulate the container receiving process through the interplay of resources and changes. Random indexing was utilized to mimic the discrete randomness of the operations. The performance of the container receiving process was validated and evaluated through the construction of an operational energy and efficiency-focused container receiving evaluation model. Results showed that the intelligent container receiving system, tested on the in-loop platform, can effectively evaluate the strengths and weaknesses of container receiving, leading to continuous improvement of the system and demonstrating its significant practical value.

CNC machining realizes the automatic control of machine tools with digital information, which is an advanced technology widely used in today's machinery manufacturing industry. In the process of NC lathe machining of parts, thin-walled workpieces are prone to deformation due to poor rigidity and thinner wall thickness. Therefore, it is necessary to study the deformation suppression of NC parts. By combining fuzzy control with single neuron PID and introducing fast non dominated sorting genetic algorithm, the parameters of thin-walled workpiece milling are optimized. The results show that the neuron PID algorithm of fuzzy control has no overshoot in the environment with or without interference, and has fast response speed and strong anti-interference ability. In the case verification, the cutting force controlled by fuzzy neuron PID can quickly reach the reference 240 N and remain stable. The removal rate fluctuates less in the range of 8000–12,000. It can improve the metal removal rate while maintaining a constant cutting force, so as to restrain the machining deformation of parts. At the same time, the introduced fast non dominated sorting genetic algorithm can increase the maximum rotational speed to 10,486.5, the maximum feed rate per tooth from 0.075 to 0.112, and the rotational speed is nearly doubled, effectively improving the processing quality, providing a technical reference for the stable control of the NC machining system, and providing a new idea and method for part deformation suppression.

Gears are the basic units in modern power systems. The detection and diagnosis of surface defects and faults in gears are conducive to improving product quality, ensuring the safety of mechanical equipment, and reducing maintenance costs. However, the accuracy of manual and traditional automated target detection algorithms is not satisfactory. Therefore, this research uses the R-CNN algorithm for gear detection, improves its non-maximum suppression algorithm and multi-task loss function, and obtains the improved Faster R-CNN algorithm. The test was carried out on the built data set. The actual measurement shows that the recall rate of the improved Faster R-CNN is up to 0.951 and the lowest is 0.816. Its AP value is as low as 0.677 and as high as 0.858, and the mAP value is 0.843. Horizontal comparison, the comparison results show that the mAP of Faster R-CNN is 0.80 1, second only to R-CNN among the tested algorithms, and 8.83% higher than the original Faster R-CNN. Under the condition of [email protected]:0.95, among all the tested algorithms, its AR index is the highest at 54.3, and the detection speed is 18 FPS/s. Although the detection speed has decreased, the detection and recognition accuracy has been significantly improved, which provides feasibility for the R-CNN series of algorithms new optimization directions. The research provides a better automatic detection method for product quality inspection in gear manufacturing industry.

In this technical note we study the computation of the Maximal Output Admissible Set for linear systems subject to polynomial constraints. The computation of an inner approximation of the Maximal Output Admissible Sets requires the determination of constraint redundancy. We use a procedure to determine polynomial constraint redundancy based on a consequence of Putinar's Positivstellensatz. Further, we present a modification of the algorithm to compute the Maximal Output Admissible Set with improved performance. Lastly, demonstrate the potential for practical applications in two case studies of spacecraft rendezvous and control of an electromagnetic actuator.