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Memristors have emerged as key candidates for beyond-von-Neumann neuromorphic or in-memory computing owing to the feasibility of their ultrahigh-density three-dimensional integration and their ultralow energy consumption. A memristor is generally a two-terminal electronic element with conductance that varies nonlinearly with external electric stimuli and can be remembered when the electric power is turned off. As an alternative, light can be used to tune the memconductance and endow a memristor with a combination of the advantages of both photonics and electronics. Both increases and decreases in optically induced memconductance have been realized in different memristors; however, the reversible tuning of memconductance with light in the same device remains a considerable challenge that severely restricts the development of optoelectronic memristors. Here we describe an all-optically controlled (AOC) analog memristor with memconductance that is reversibly tunable over a continuous range by varying only the wavelength of the controlling light. Our memristor is based on the relatively mature semiconductor material InGaZnO (IGZO) and a memconductance tuning mechanism of light-induced electron trapping and detrapping. We demonstrate that spike-timing-dependent plasticity (STDP) learning can be realized in our device, indicating its potential applications in AOC spiking neural networks (SNNs) for highly efficient optoelectronic neuromorphic computing., Comment: 28 pages, 12 figures

Particle trap is an important measure to restrain the damage of moving metal particles to the insulation of DC gas insulated transmission lines (GIL). However, there is still no clear guiding principle for the design of particle trap in DC GIL, and there is little theoretical research on particle trap. In this paper, a particle trap design, wedge-shaped particle trap, is proposed. Compared with traditional particle trap, the results show that wedge-shaped particle trap can shield the electric field at the bottom of the trap more efficiently and effectively, and can block spherical particles escape. Furtherly, wedge-shaped particle traps are optimized based on particle capture probability. Considering the strong randomness of particle motion, it is difficult to achieve the optimization through experiments, so the simulation based on Monte Carlo idea is used to optimize the trap parameters, and random release of a large number of spherical metal particles to simulate the random collision motion of particles. The trap parameters are optimized with the capture rate as the optimization objective. The results show that when the width of the trap slots is 4 mm and the inclination is 50 degree, the trap have the best acquisition probability. Therefore, The particle trap design optimization method can provide guidance for the design of DC GIL particle traps with different voltage levels.

The electrical performance of DC GIL using SF 6 gas mixture or alternative gas as insulation medium will be deteriorated by free metal particles. The analysis of the factor affecting the insulation strength of SF 6 /N 2 and C 4 F 7 N/CO 2 gas mixtures is mainly performed in this paper. The breakdown experiments with particle were conducted with the ball-bowl electrode under the DC electric field, which included different gas compositions such as C 4 F 7 N/CO 2 (4%/96%) gas mixture, SF 6 /N 2 (volume ratio of SF 6 includes: 20%, 30%, 50%, and 70%, respectively) gas mixtures and pure SF 6 gas. The concept of Discharge Sensitivity by Particles(DSP) were proposed to evaluate the sensitivity of the insulation strength of different compositions gas to the electric field distortion caused by metal particles. The experiment results show that in the pressure range of 0.1 to 0.5 MPa, the dielectric strength of 4%C 4 F 7 N/96%CO 2 is equivalent to that of 30%SF 6 /70%N 2 in the absence of particle, while due to the presence of particle, the DSP of 4%C 4 F 7 N/6%CO 2 gas mixture is higher than that of 20%SF 6 /80%N 2 gas mixture and lower than that of 30%SF 6 /70%N 2 gas mixture, accompanied by the appearance of discharge current of double peak. The high dielectric strength and low sensitivity to particle discharge of C 4 F 7 N/CO 2 gas mixture is closely related to the strong electronegativity and high attachment coefficient of C 4 F 7 N. Combined with the physical model of moving particle-triggered discharge, the emergence of the double peak characteristics of gap breakdown current is also elaborated in this paper.

Compared with the fiber Bragg grating (FBG), the tilted fiber grating (TFBG) has the coupling between core mode and cladding mode or the radiation mode because of the tilt angle, which improves the sensitivity to the external environment. B ased on the coupled mode theory and the simulation software OptiGrating , the spectral characterist ics of the TFBG with different tilt angles were studied. When the tilt angle is small, the resonant peaks on the transmission spectrum move in the long wave direction with the increase of the tilt angle. What ’ s more, the transmission loss and transmission width decrease and the group delay decreases. W hen the tilt angle is large enough, the TFBG has strong bifurcation effect, the Bragg resonant peak disappears while the cladding mode resonant peak appears in pairs, and the high order cladding mode birefringence effect is stronger than the low order clad ding mode. In the end of the paper, different angles of sensing applications were analyzed.

The safety and reliability of roller bearing always have significant importance in rotating machinery. It is needful to build an efficient and excellent accuracy method to monitoring and diagnosis the baring failure. A novel method is presented in this paper to classify the fault feature by wavelet function and extreme learning machine(ELM) that take into account the high accuracy and efficient. The morlet wavelet function was constructed as the activation function of ELM neural nodes. In order to construct the best wavelet basis function. The minimum Shannon entropy and SVD methods are used to select the optimal shape factor and scale parameter for the morlet wavelet, respectively. The proposed method is applied to practical classification and fault diagnosis of roller bearing. The result show that the proposed method is more reliable and suitable than conventional neural networks and other ELM methods for the defect diagnosis of roller bearing.

In order to improve the reliability and accuracy in using ultrasonic flaw detector, this paper designed a system for detecting the flaw detector performance parameters of the transmitter, to achieve the testing and calibration of parameters for detector output characteristics of the transmitter. The core of the system is the IPC, using acquisition card PCI-5124 to collect output signal of detector's transmitter. Based on VC++, this paper designed interface of output parameters for detector's transmitter, achieved the design of driver of acquisition card better and the design of detection algorithm has various parameters. Calibration results show that: the system completed testing and calibration parameters better, such as the pulse peak, pulse rise time and duration, pulse repetition frequency, center frequency and bandwidth, the equivalent output impedance transmitter.