Abstract: Disclosed is a method for preparing a bimetallic perovskite loaded grapheme-like carbon nitride photocatalyst, comprising: 11) dissolving SbCl3 and AgCl in HCl solution under heating and constant stirring; then adding CsCl in the heated solution to form sediment on the bottom of the beaker; collecting the sediment and wash it with ethanol, and finally drying in an oven to obtain Cs2AgSbCl6 powder; 12) adding melamine into an aluminum oxide crucible and placing it into a muffle furnace for calcination and finally cooling to room temperature naturally to obtain g-C3N4 samples; 13) adding the Cs2AgSbCl6 bimetallic perovskite and the g-C3N4 into a solvent, and stirring after subjecting to ultrasound, and drying after centrifuging to obtain the photocatalyst. Provided is a new idea for the combination of bimetallic halide perovskite and photocatalytic material, and the preparation method has mild conditions, simple operation, and is favorable for large-scale production.

Abstract: Provided herein is a tunnel electromagnetic joint scanning detection method and a system thereof. It introduces a new tunnel electromagnetic detection system called TEJS, realizes three-dimensional joint inversion of multi-component, time domain and frequency domain signals, and forms tunnel joint scanning imaging. This method adopts the mode of surface transmission, underground reception, multi-source transmission and multi-component reception. Based on an observation system, a large number of stochastic models are constructed and numerically simulated, and a large number of training data sets are constructed by using the simulated data to complete the training of UNet model. This model can realize real-time and fast imaging of the position of the low-resistance anomalous body in three-dimensional space. An algorithm forms a dual checking mechanism through surface imaging and underground imaging, which constrains the three-dimensional spatial position of anomalous body together to prevent misjudgment.

Abstract: The present disclosure discloses a three-dimensional inversion method of airborne transient electromagnetics based on deep learning. The method of the present disclosure proposes two strategies that focus on training datasets, to improve the performance of deep learning models, including divide and conquer strategy and random models generating. Through a large of reasonable structural models, appropriate network setups, a more generalized result can be obtained through our proposed U-Net framework, which has been demonstrated to be effective on both synthetic and field data. This scheme can realize the rapid prospecting of three-dimensional resistivity structure in large-area target region, and solve the problem of low efficiency of traditional three-dimensional inversion calculation of ATEM and poor migration ability of three-dimensional inversion based on deep learning developed by predecessors.

Abstract: A moving target focusing method and system based on a generative adversarial network are provided. The method includes: generating, using a Range Doppler algorithm, a two-dimensional image including at least one defocused moving target, as a training sample; generating at least one ideal Gaussian point in a position of at least one center of the at least one defocused moving target in the two-dimensional image, to generate a training label; constructing the generative adversarial network, the generative adversarial network includes a generative network and a discrimination network; inputting the training sample and the training label into the generative adversarial network to perform repeated training until an output of the generative network reaches a preset condition, to thereby obtain a trained network model; and inputting a testing sample into the trained network model, to output a moving target focused image.

Abstract: A Mg—Ta based dielectric ceramic for multi-layer ceramic capacitor (MLCC) and a low-temperature preparation method thereof are provided. By providing a glass additive with high matching with a Mg—Ta ceramic, a modifier A+12CO3—B2+O—C3+2O3—SiO2 (A=Li, K; B=MnO, CuO, BaO; C=B, Al) is intruded in to a main material MgO—Ta2O5, which can significantly reduce the sintering temperature and provide a negative temperature coefficient of dielectric constant of ?100±30 ppm/° C., and reduce the deterioration factors of loss caused by an additive for sintering, and prepare a dielectric material applied to RF MLCC with low loss, low cost and good process stability.

Abstract: A low-temperature sintered microwave dielectric ceramic material and a preparation method thereof are provided. The ceramic material includes a base material and a low-melting-point glass material; a general chemical formula of the base material is (Zn0.9Cu0.1)0.15Nb0.3(Ti0.9Zr0.1)0.55O2; a percent by weight of the low-melting-point glass material is in a range of 1 wt. % to 2 wt. %; chemical compositions of the low-melting-point glass material include A2CO3-M2O3—SiO2, A of which includes at least two of a lithium ion, a sodium ion, and a potassium ion, M of which includes at least one of a boron ion and a bismuth ion; and a sintering temperature of the ceramic material is in a range of 850° C. to 900° C. The microwave dielectric ceramic material has the advantages of low dielectric loss, simple and controllable process, etc., has good process stability, and can meet requirements for radio communication industry.

Abstract: A modified Ni—Ti—Ta dielectric material for multi-layer ceramic capacitor (MLCC) and a low-temperature preparation method thereof are provided. By using characteristics that radii of the Cu2+ ion and (Al1/2Nb1/2)4+ ion are close to those of Ni and Ti elements, respectively, Cu2+, Al3+ and Nb5+ ions are introduced into a Ni0.5Ti0.5TaO4 matrix for partial substitution, a negative temperature coefficient of dielectric constant of ?220±30 ppm/° C. is provided while a sintering temperature is significantly reduced, and deterioration factors of loss caused by sintering aids is reduced, so that the dielectric material applied to radio frequency MLCC with low loss, low cost and good process stability is prepared.

Abstract: This application provides a born-again TSK fuzzy classifier based on knowledge distillation. The born-again TSK fuzzy classifier based on knowledge distillation is denoted as CNNBaTSK, and a fuzzy rule of CNNBaTSK includes two parts: an antecedent part based on soft label information and a consequent part based on original data. A method for constructing the fuzzy rule of CNNBaTSK includes following steps: Step 1: taking, by the CNNBaTSK, the original data as input, obtaining a probability distribution of an output layer through a layer-by-layer neural expression, and introducing a distillation temperature to generate soft label information of DATASET; Step 2: partitioning the soft label information into five fixed fuzzy partitions to construct the fuzzy rule in a fuzzy part of the CNNBaTSK; Step 3: introducing the original data to calculate a consequent parameter, and optimizing the consequent parameter of CNNBaTSK using a non-iterative learning method.

Abstract: An automatic cleaning method and a cleaning bed for a patient of gatism are provided. The cleaning bed includes a bed body, a bed plate is arranged on the bed body and is provided with a through groove therein, a bed bottom plate is arranged on a lower part of the bed body, a lifting mechanism is arranged on the bed bottom plate and is provided with a soft cushion thereon, a fabric bracket is arranged under the bed plate and is provided with a material roller thereon, a pressing roller bracket is arranged under the bed plate and at one side close to the fabric bracket and is provided with provided with a first pressing roller and a second pressing roller.

Abstract: A traffic flow forecasting method based on Deep graph Gaussian processes includes: S1, with respect to the dynamics existing in a spatial dependency, using an attention kernel function to describe a dynamic dependency among vertices on a topological graph, and using the attention kernel function as a covariance function in an Aggregation Gaussian process to extract dynamic spatial features; S2, obtaining a Temporal convolutional Gaussian process from weights at different times and a convolution function that obeys the Gaussian processes, and obtaining temporal features in traffic data by combining the Aggregation Gaussian process; S3, constructing a Deep graph Gaussian process method integrating a Gaussian process and a depth structure from the Aggregation Gaussian process, the Temporal convolutional Gaussian process and a Gaussian process with a linear kernel function, inputting a data sample to be forecasted into the Deep graph Gaussian process method to obtain a forecasted result.

Abstract: Disclosed is an in-situ vacuum reaction system for dynamically detecting defects, which includes an electron paramagnetic resonance spectrometer, an in-situ vacuum reaction chamber, a gas supply unit, a vacuum unit, an illumination unit, a temperature control unit and a mixing bottle, the in-situ vacuum reaction chamber is arranged inside a detection cavity of the electron paramagnetic resonance spectrometer, the gas supply unit is connected to the mixing bottle through a pipeline, and the mixing bottle is connected to a gas inlet of the in-situ vacuum reaction chamber through a pipeline, the vacuum unit is connected to the gas inlet of the in-situ vacuum reaction chamber through a pipeline, the illumination unit is arranged corresponding to a detachable window of the electron paramagnetic resonance spectrometer, and the temperature control unit is connected to the electron paramagnetic resonance spectrometer through a pipeline.