Using Satcom-On-The-Move (SOTM) antenna on moving carriers to track communication satellites is a rapidly developing technology. The normal running of the SOTM system requires its antenna beam to track the target communication satellite accurately at all times. However, due to the errors of the measurement system, tracking deviation will inevitably occur, especially when the moving carrier is in ahigh maneuvering state, which may cause communication failures. In this paper, we propose a synthetic deviation correction algorithm; when the carrier is in the high maneuvering state, the measurement error is converted into the deviation of the azimuth as well as the pitch of the antenna that needs to be corrected to correct the pointing of the SOTM antenna. Finally, the proposed algorithm is verified by experiments. The experimental results show that the proposed algorithm has a good isolation effect on the high maneuverability of the carrier, which means that the pointing to the communication satellite is more accurate and achieves better communication quality under the high maneuvering state. The effectiveness of the algorithm is illustrated.
When the construction wastes were used as raw materials of Recycled concrete, the type and replacement ratio of recycled aggregates should be considered in addition to mix proportion. It is very difficult to describe the complicated nonlinear relationship between different indexes. Through analyzing design process of BP neural network model, the appropriate network parameters were selected, the BP neural network model about performance of recycled concrete is established. After the BP neural network was trained, the 7-20-3 BP neural network model is established to realize nonlinear mapping about performance of recycled concrete. The results show that the established BP network model can accurately predict the recycled concrete slump, 28d compressive strength and elastic modulus, which has a good applicability to the concrete. InstructionEvery year to billion tons of construction wastes in our country were produce, it is very difficult to handle. Through screening and crushing the waste brick and concrete in the construction waste, the recycled aggregate was produced which can replaced natural aggregate to produce recycled concrete, and it can solved the problems of the construction waste. Domestic and foreign scholars have done a lot of experimental study on the basic properties of recycled concrete, but the source of the recycled aggregates, crushing technology, physical properties are different. In addition to mix proportion, the physical properties of recycled aggregate which include apparent density, bulk density, water absorption and crushing index and replacement ratio of recycled aggregates must be considered. So we need a kind of intelligent analysis tool to realize the recycled concrete strength and working performance and mixture ratio, and the nonlinear mapping between classification of recycled aggregate performance.Artificial neural network theory regards biological neural network as the prototype, analyzing the microstructure, imitating the running mechanism of the biological neural networks, then the numerical calculation model is got which can solve the complex nonlinear relationship between simple input signals. This paper uses the BP neural network model to realize the study on the properties of recycled concrete, the non-linear relation between recycled concrete working performance and various influencing factors was build. And the BP network model is adopted to the practical application to make a prediction of the recycled concrete slump, 28d compressive strength and elastic modulus and verify the applicability of the model. Setting of BP Neural Network Parameters Selection the network structureMany factors affecting the performance of recycled concrete, such as water cement ratio, sand ratio, types and replacement ratio of recycled aggregate, and cement varieties and dosage, additive, etc.
A scheme of piece-wise linearization PID control of aero-engine software defined control system based on wireless network is proposed in this paper. The characteristic of the system is that the PID controller is separated and distributed among the nodes of the wireless network, each node has the function of computing, memory and wireless communication, no core control unit in the system and the whole wireless network acts as a distributed PID controller. The linearization model and corresponding tuning parameters at different working points in flight envelope of the aero-engine are established offline using the memory resource of each node to make the node task parameters switch when the aero-engine working at different points. Finally, MATLAB/Simulink software tool is used to conduct the digital simulation analysis, the result shows that the proposed scheme achieves good dynamic and steady-state performance, and achieves good control effect.
Aero-engine control systems generally adopt centralized or distributed control schemes, in which all or most of the tasks of the control system are mapped to a specific processor for processing. The performance and reliability of this processor have a significant impact on the control system. Based on the aero-engine distributed control system (DCS), we propose a decentralized controller scheme. The characteristic of this scheme is that a network composed of a group of nodes acts as the controller of the system, so that there is no core control processor in the system, and the computation is distributed throughout the entire network. An LQR output feedback control is constructed using system input and output, and the control tasks are executed on each node in the decentralized controller. The constructed LQR output feedback is equivalent to the optimal LQR state feedback. The primal-dual principle is used to tune the parameters of each decentralized controller. The parameter tuning algorithm is simple to calculate, making it conducive for engineering applications. Finally, the proposed scheme was verified by simulation. The simulation results show that a high-precision feedback gain matrix can be obtained with a maximum of eight iterations. The parameter tuning algorithm proposed in this paper converges quickly during the calculation process, and the constructed output feedback scheme achieves equivalent performance to the state feedback scheme, demonstrating the effectiveness of the design scheme proposed in this paper.
Highly efficient and stable hybrid white organic light-emitting diodes (HWOLEDs) with a mixed bipolar interlayer between fluorescent blue and phosphorescent yellow emitting layers are demonstrated. The bipolar interlayer is a mixture of p-type diphenyl (10-phenyl-10H-spiro [acridine-9,9'-fluoren]-3'-yl) phosphine oxide and n-type 2',2"-(1,3,5-benzinetriyl)-tris(1-phenyl-1-H-benzimidazole). The electroluminance and Commission Internationale de l'Eclairage (CIE1931) coordinates' characteristics can be modulated easily by adjusting the ratio of the holepredominated material to the electron-predominated material in the interlayer. The hybrid WOLED with a p-type:n-type ratio of 1:3 shows a maximum current efficiency and power efficiency of 61.1 cd/A and 55.8 lm/W, respectively, with warm white CIE coordinates of (0.34, 0.43). The excellent efficiency and adaptive CIE coordinates are attributed to the mixed interlayer with improved charge carrier balance, optimized exciton distribution, and enhanced harvesting of singlet and triplet excitons.
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