Sea salt aerosols are mostly distributed over the oceans and they can significantly affect the atmospheric radiative transfer. This article investigated sea salt aerosol impact on the polarization state of radiance at the top of the atmosphere (TOA) through the use of sea salt particle models. Specifically, six models of sea salt aerosols, including a homogeneous sphere, two super-spheroids, and three inhomogeneous spheres with both spherical and nonspherical cores, were considered and their optical properties were computed using the Lorenz–Mie theory and the invariant imbedding T-matrix method. The polarized radiance at the TOA was simulated by using a vector adding–doubling radiative transfer model. It was demonstrated that the inhomogeneous sphere-modeled TOA polarized radiance had a minimum at the backscattering angles ranging from 170° to 175°, whereas such features disappear when a homogeneous sphere or nonspherical model is used. To prove this effect, the satellite marine aerosol vertical feature mask data from the Cloud Aerosol Light Detection and Ranging (Lidar) and Infrared Pathfinder Satellite Observations (CALIPSO) over global ocean areas were collocated with the measurements from Polarization and Anisotropy of Reflectance for Atmospheric Science Coupled with Observations from a Lidar (PARASOL). It was found that the PARASOL polarized radiance also had negative values at the backscattering angles ranging from 170° to 175°. Thus, the obvious negative polarized radiances at these backscattering angles could be indicative of inhomogeneous sea salt aerosols. Both homogeneous and inhomogeneous sea salt models related to ambient relative humidity (RH) should be considered for accurate radiative transfer simulations.
With the emergence of large-size complex structures, conventional discrete sensors can’t meet the requirement of structure health monitoring because they can only sense the strain in a single direction. In this paper, based on sensing and covering properties of carbon fiber smart material (CFSM), an idea of a sensitive layer placed on the structure surface was proposed. By setting finite electrodes on the edge of the sensitive layer, the stress field of tested structure is transformed to electric field which is apt to be tested, and with resistivity tomography technology (ERT), field(global) monitoring on civil engineering structure can be realized. To avoid impact resulting from measuring errors caused by misc factors in experiment, CFSM ERT system was utilized in virtual experiments. Virtual Experiments were conducted on ANSYS finite element software aided by its excellent abilities in coupled field analysis. The virtual experiments included two cases: circular plate simply supported at its perimeter under single loading of different values in the center, and circular plate simply supported at its perimeter under multipoint loading in different positions. In the virtual experiments current incentive in adjacent electrodes and voltage measurement in other adjacent electrodes were implemented, and the measured voltage data was transmitted to the ERT system to obtain the contour plot of resistivity distribution. It indicates that for the single loaded CFSM virtual experiment with tensile strain, its resistivity is increased with the load increase. Compared with 1st and 2nd principal strain distribution in structure tested area, resistivity distribution will qualitatively reflect force field of structure. In multipoint loaded CFSM virtual experiment with compress strain, resistivity descends. Compared with 3rd and 2nd principal strain distribution in structure tested area, low resistivity area just locates at area of biggest strain. Based on virtual experiment, efficiency of CFSM ERT system is demonstrated, greatly supporting the consequent practical application.
Advanced carbon materials have played an important function in the field of energy conversion and storage. The green and low-carbon synthesis of elemental carbon with controllable morphology and microstructure is the main problem for carbon materials. Herein, we develop a green and low-carbon method to synthesize porous carbon by reacting CO2 with LiAlH4 at low temperatures. The starting reaction temperatures are as low as 142, 121, and 104 °C for LiAlH4 reacting with 1, 30, and 60 bar CO2, respectively. For the elemental carbon, the porosity of elemental carbon gradually decreased, whereas its graphitization degree increased as the CO2 pressure increased from 1 bar to 60 bar. CO2 serves as one of the two reactants and the CO2 pressure can adjust the thermodynamic and kinetic properties of the formation reaction for synthesizing elemental carbon. The mechanism for CO2 pressure-dependent microstructure and morphology of carbon is discussed on the basis of the formation reaction of elemental carbon and gas blowing effect of H2 and CO2. The elemental carbon with different morphology and microstructure exhibits distinct electrochemical lithium storage performance including reversible capacity, rate capability, cycling stability, and Coulombic efficiency, owing to their different lithium storage mechanism. The elemental carbon synthesized at 30 bar CO2 delivers the highest reversible capacity of 506 mAh g−1 after 1000 cycles even at 1.0 A g−1. Advanced energy storage technology based on the green and low-carbon synthesis of carbon materials is a requisite for providing a stable and sustainable energy supply to meet the ever-growing demand for energy.
Sliding mode-like fuzzy logic control (SMFC) algorithm for nonlinear systems is presented in this paper. Firstly dead zone parameters of sliding mode control (SMC) are self-tuned by proper adaptive laws and then combined into fuzzy logic system (FLS) to compose the opportune fuzzy logic control (FLC), which is equivalent to the pre-designed SMC controller with self-tuning parameters. Robustness and invariance to the uncertainties of the closed-loop systems are improved and chattering of the SMC is eliminated. Finally simulation results of numerical examples show that the proposed control algorithm is efficient and feasible.
A new scheme of small integrated navigation system based on micro inertial measurement unit (MIMU), global position system (GPS) is presented. The characteristic of these sensors and the structure of system are introduced respectively. The TI high performance floating point DSP TMS320C6713B is used as core processor, which is designed to realize both the data collecting and the navigation calculating. According to the error models of inertial navigation system, an integrated navigation algorithm used Kalman filter is proposed to fuse the information from all of the sensors. The simulation test results show the feasibility of the system design.
For systems described by Controlled Auto-Regresive Integrated Moving Average model, a novel variable structure control (VSC) strategy based on general sliding mode prediction is presented. By creating a special sliding mode prediction model, introducing a suitable reference sliding mode trajectory, and combining with general predictive control (GPC) approach, the variable structure control law is constructed. Simulation results illustrate that the closed-loop systems have the advantages of VSC and GPC, while relief the drawbacks of VSC. Such as the systems have strong robustness, the systems states have fast convergence, the control signals in the systems are optimal, the disgusting chattering phenomena is eliminated, and the algorithm is convenient to deal with time-delay.
This document mainly describes the control equipment of microwave anechoic chamber RCS measurement system, which can be expanded into a generic and automatic multi-channel control system. The conventional control equipment is mainly operating in manual mode with computer control feature as secondary. Such feature causes the problems of difficult operation, complicated structure and no software portability. This generic control system is designed with DAM-E3000 series of module which is an interface module from generic sensor to computer, uses remote Ethernet connection solution, supports TCP/IP and UDP protocols, and can realize remote I/O control. This generic control system has the automatic control feature through network by using this mentioned module. In addition, the hardware structure design is simplified, and the software portability and expandability are enhanced, so that the codes programming difficulty is lowered. Therefore the system can conform to more industrial I/O signal protocols and can be used widely in various industrial environments.
This study addresses the completely distributed consensus control problem for the heterogeneous nonlinear multi-agent system (MAS) with disturbances under switching topology. First, a global sliding mode manifold (GSMM) is designed for the overall MAS dynamic, which maintains stability without oscillations during topology switching after achieving the sliding mode. Subsequently, a consensus sliding mode control protocol (SMCP) is proposed, adopting the common sliding mode control (SMC) format and ensuring the finite-time reachability of the GSMM under topology switching. Finally, the proposed GSMM and SMCP are applied to the formation control of multiple-wheeled mobile robots (WMRs), and simulation results confirm their feasibility and effectiveness. The proposed SMCP design demonstrates key advantages, including a simple control structure, complete robustness to matched disturbance, and reduced-order dynamics under the sliding mode.
Fringe signals like laser stripes exist in many fields, such as line structured light measurement, lane line detection, and so on. Line structured light measurement is an essential active vision technology, which is used in a variety of industry fields, such as chip appearance inspection and rail wear detection, in which the image of the light strip is very complex. Traditional methods fail to achieve high accuracy and high robustness when dealing with complicated environments. In this paper, a fast and robust method based on structured-light vision and deep learning is proposed. Our main contributions are: Firstly, we offer a fast laser stripe detection neural network (Fast-LSDNN). By applying a dual-channel network and knowledge distillation to our network, the speed and accuracy of laser stripe region positioning are greatly improved and carefully balanced. Secondly, we improve the traditional Gray-Gravity-method, and the running time is significantly reduced. We also build a dataset for chip appearance inspection independently, which contains different kinds of chip inspection scenes and can be transferred to other tasks directly by using our work. The experimental results show that our approach is robust and applicable.
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