Abstract Due to the development of the novel materials, the past two decades have witnessed the rapid advances of soft electronics. The soft electronics have huge potential in the physical sign monitoring and health care. One of the important advantages of soft electronics is forming good interface with skin, which can increase the user scale and improve the signal quality. Therefore, it is easy to build the specific dataset, which is important to improve the performance of machine learning algorithm. At the same time, with the assistance of machine learning algorithm, the soft electronics have become more and more intelligent to realize real-time analysis and diagnosis. The soft electronics and machining learning algorithms complement each other very well. It is indubitable that the soft electronics will bring us to a healthier and more intelligent world in the near future. Therefore, in this review, we will give a careful introduction about the new soft material, physiological signal detected by soft devices, and the soft devices assisted by machine learning algorithm. Some soft materials will be discussed such as two-dimensional material, carbon nanotube, nanowire, nanomesh, and hydrogel. Then, soft sensors will be discussed according to the physiological signal types (pulse, respiration, human motion, intraocular pressure, phonation, etc.). After that, the soft electronics assisted by various algorithms will be reviewed, including some classical algorithms and powerful neural network algorithms. Especially, the soft device assisted by neural network will be introduced carefully. Finally, the outlook, challenge, and conclusion of soft system powered by machine learning algorithm will be discussed.
Based on the mathematical model of the doubly fed induction generation system,a nonlinear control algorithm using the input-output linearizing and decoupling control strategy is introduced.By using nonlinear coordinate transform and state variable feedback control laws,the input-output linearization of the doubly fed generation system is implemented,the full decoupling of active and reactive power under both steady state and transient state is achieved,and the dynamic performance of the system is improved,which is represented by faster response speed and smaller ripples of system active and reactive power.Since the proposed nonlinear control law only relates to the ratio of generator inductance,the parameter robustness of the control system is improved.The control strategy is implemented and tested by a 7.5 kW wind power generation test rig,and the experiment results validate that the input-output linearizing and decoupling control scheme is superior to the conventional vector control scheme.
We demonstrate a broadband spectrum absorber using random structures on refractory plasmonic material (Tungsten) resulting in the absorption efficiency over 90% in the wavelength range from 200 nm to 1100 nm. Numerical simulations for the structure with same parameters agree well with the experimental results. Random nanostructures provide more freedom for enhancing absorption and spectrum selectivity than periodic nanostructures.
Flexible metamaterials (FMMs) at optical frequencies can conform to a wide range of target geometries whilst allowing their optical properties to be tuned post-fabrication. Here we discuss this potential by presenting our recent and preliminary results, obtained for FMMs at visible frequencies for sensing, filtering and imaging applications.
The light's orbital angular momentum (OAM) is a consequence of the spiral flow of the electromagnetic energy. In this paper, an analysis of light beams with OAM used for free space optics (FSO) is conducted. The basic description and conception of light's OAM are reviewed. Both encoding information into OAM states of single light beam and encoding information into spatial structure of the mixed optical vortex with OAM are discussed, and feasibility to improve the FSO's performance of security and obstruction of line of sight is examined.
Radiative heat transfer between two polar nanostructures at different temperatures can be enhanced by resonant tunneling of surface polaritons. Here we show that the heat transfer between two nanoparticles is strongly varied by the interactions with a third nanoparticle. By controlling the size of the third particle, the time scale of thermalization toward the thermal bath temperature can be modified over 5 orders of magnitude. This effect provides control of temperature distribution in nanoparticle aggregation and facilitates thermal management at nanoscale.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
Plasmonic tweezers have become an important tool for the capture, manipulation, and characterization of nanoparticles (NPs), biomolecules, and viruses. However, the physical environment in the microscale strongly affects the trapping performance of plasmonic nanostructures. We demonstrated the trapping of gamma-aminobutyric acid (GABA) functionalized gold NPs at different solution temperatures and viscosities. Experimental results show that the trapping stiffness decreases from 0.3 to 0.1 fN/nm as the temperature increases from 25°C to 37°C. For solutions with higher viscosity, the trapping stiffness increases but the trapping efficiency would be reduced due to hindered movement of particles.
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