Abstract: The disclosure provides a method, an apparatus and a computer device for three-dimensional reconstruction of an indoor structure based on a two-dimensional video, wherein the method comprises: generating a color point cloud of three-dimensional structure of a house by using the video data of the house; regularizing the color point cloud of the house; extracting a plan of the house based on the color point cloud of the house; and constructing a three-dimensional model of the house in real life. The disclosure realizes autonomous and rapid construction of a three-dimensional model of a house by the general public, and realizes a low-cost, efficient and convenient three-dimensional visual digital representation of the house.

Abstract: A preparation method of a zeolite/polyimide composite membrane includes: synthesizing a zeolite-doped polyamic acid precursor casting solution by condensation polymerization synthesis; coating a substrate with the obtained casting solution, and obtaining a zeolite/polyamic acid composite porous membrane by non-solvent induced phase separation; and obtaining the zeolite/polyimide composite membrane by performing thermal imidization on the zeolite/polyamic acid composite porous membrane through gradient heating.

Abstract: The disclosure relates to a method for simulating intraplate volcanism. A technical solution is: mixing 47-60 wt % of calcium oxide powder, 35 wt % of alumina powder and 5-18 wt % of silica powder uniformly to obtain a mixed powder; putting the mixed powder in a corundum crucible, placing the crucible in a high-temperature furnace provided with an observation window outside which an industrial camera with a depression angle of 30-45° is provided, heating to 1,500-1,900° C. at a rate of 1-30° C./min under an air atmosphere at a normal pressure, holding for 0.5-5 h; recording intraplate volcanism formed by upwelling of a melt of the mixed powder along an inner wall of the crucible during the holding with the industrial camera to obtain a simulated process of the intraplate volcanism.

Abstract: A quantitative detection method of rare earth doped calcium phosphate fluorescent nanoparticles (RE-nCaP) in organisms includes establishing a fluorescent intensity-concentration standard curve of rare earth ions, preparing samples to be tested and the blank control group into homogenate, performing centrifuging and testing the fluorescent intensity of supernatants, calculating the fluorescent intensity values per unit mass or volume of the samples and the blank control group, and performing significant difference analysis; if P is greater than or equal to 0.05, determining that the RE-nCaP content in the samples is 0, and if P is smaller than 0.05, testing the tissue extraction rate of RE-nCaP; and comprehensively considering the tissue extraction rate, the homogenate volume, the fluorescent intensity value per until mass or volume, the homogenate dilution ratio, and the doping amount to obtain the accurate content of the RE-nCaP in biological tissue samples.

Abstract: The present invention discloses a collaborative design method using an event-triggered scheme (ETS) and a Takagi-Sugeno (T-S) fuzzy H? controller in a network environment. For the problem about the unmanned surface vehicle control based on a switching T-S fuzzy system under an aperiodic DoS attack, the present invention provides an H? controller design method based on the event-triggered scheme. The characteristics of the unmanned surface vehicle system under the DoS attack are analyzed, and external disturbance in the navigation process is added into an unmanned surface vehicle motion model to establish an unmanned surface vehicle switching system model. The stability of the system is analyzed by piecewise Lyapunov functionals, such that controller gain and event-triggered scheme weight matrix parameters are obtained, thus ensuring that a networked unmanned surface vehicle navigation system has the ability to resist the DoS attack and the external disturbance.

Abstract: A transformer fault diagnosis method and system using induced ordered weighted evidence reasoning is provided. The method includes the following steps. A typical data sample of transformer sweep frequency response analysis is loaded and a diagnostic label is set as an identification framework. Test data of a device to be diagnosed is loaded. Basic probability assignment is calculated and a reliability decision matrix is constructed. An induced ordered weighted averaging operator and its induction vector are calculated according to a sample source of the data. An index weight vector is calculated. All evidence is fused by the induced ordered weighted evidence theory and reliability of comprehensive evaluation is calculated, so as to determine a diagnosis result. The disclosure realizes fault identification, fault type distinction and fault position of power equipment by interpreting detection waveforms.

Abstract: The present invention discloses a method and system for multicarrier signal tracking based on deep learning and high precision positioning. Using the data characteristics of S-curve, and using S-curve which contains multipath signals as feature data for training deep learning networks under different signal-to-noise ratios. The delay regression results of receiving signal can be directly obtained by the S-curve of real-time receiving signal and the pre-trained network. The motivation of this method is to fully utilize the advantages of deep learning networks in accurately regressing complex problems with a large amount of data, fundamentally solving the impact of multipath signals on the delay estimation of the main path signal in traditional software defined receivers, extracting the corresponding relationship between the delay of main path and S-curve under the influence of different signal-to-noise ratios and different multipath signals.

Abstract: A method and a system for fault diagnosis with small samples of power equipment based on virtual and real twin spaces are disclosed, which belong to the field of fault diagnosis of power equipment. The method includes: test samples containing different locations, types and severity levels of fault of power equipment are acquired to form a real physical space; a virtual mirror space is acquired by simulation according to a simulation model of the equipment to be diagnosed; the training set in the real physical space is spatially integrated with the sample set in the virtual mirror space to obtain a training sample set in the twin spaces; the training sample set in the twin spaces serves as the supplement to the training set in the real physical space, and the fault type and fault location serve as diagnostic labels to be input to the deep neural network for training.

Abstract: The present disclosure provides a short-process high-performance forming method of a high-strength aluminum alloy, and use thereof. In the present disclosure, pre-hardening treatment is conducted on an obtained W-temper aluminum alloy sheet blank after a solution treatment and quenching, to obtain a pre-hardened aluminum alloy sheet blank for batch supply. The pre-hardened aluminum alloy sheet blank is subjected to plastic forming, to obtain a component with satisfactory performances. After the pre-hardening treatment, a high-strength aluminum alloy sheet blank forms a GPII zone that is completely coherent with a matrix, and has a room-temperature formability exceeding that off traditional soft sheet blank. Moreover, the GPII zones interact with dislocations during the forming, resulting in planar slips. In this way, large-scale dynamic recovery is more effectively suppressed, thus enhancing a work hardening ability of a formed component.

Abstract: A method and a system for predicting a gas content in transformer oil based on a joint model are provided and belong a field of transformer failure prediction. The method includes the following: determining a type and a time series of gas to be predicted related to a failure, processing an original series by adopting empirical mode decomposition (EMD) and local mean decomposition (LMD) for a non-stationarity characteristic of a dissolved gas concentration series in oil; performing normalization on each sub-series component, dividing a training sample and a test sample; and establishing a deep belief network (DBN) prediction model for each of the sub-series components for training, performing superposition and reconstruction on the established DBN prediction model to perform characteristic extraction and classification on multi-dimensional data of the failure, evaluating prediction performance of the prediction model through calculating an error index.

Abstract: A reliability evaluation method and system for a microgrid inverter IGBT based on segmented long short-term memory (LSTM) is disclosed, including steps as follows. An electrothermal coupling model is constructed to obtain real-time junction temperature data. The original LSTM algorithm is improved to obtain a segmented LSTM prediction network for the aging characteristics of the IGBT. The monitoring value of the IGBT aging parameter is used to perform segmented LSTM prediction to obtain the predicted aging process, and the threshold values of different aging stages are categorized. An aging correction is performed on the aging parameter of the electrothermal coupling model to ensure the accuracy of the junction temperature data. Rainflow-counting algorithm is used to calculate real-time thermal stress load distribution of the IGBT. The fatigue damage theory and the Lesit life prediction model are combined to calculate the real-time cumulative damage and predicted life of the IGBT.

Abstract: The invention discloses an unmanned surface vessel (USV) formation path-following method based on deep reinforcement learning, which includes USV navigation environment exploration, reward function design, formation pattern keeping, a random braking mechanism and path following, wherein the USV navigation environment exploration is realized adopting simultaneous exploration by multiple underactuated USVs to extract environmental information, the reward function design includes the design of a formation pattern composition and a path following error, the path following controls USVs to move along a preset path by a leader-follower formation control strategy, and path following of all USVs in a formation is realized by constantly updating positions of the USVs.

Abstract: A pulp diagnosis-treatment assistance method and system are disclosure, and relates to the field of medical device technologies.

Abstract: A power transformer fault diagnosis method based on a stacked time series network, includes: collecting gas-in-oil data of a transformer in each substation; performing z-score normalization on the collected data to obtain a normalized matrix; dividing the normalized matrix into a training set and a test set in proportion; constructing a stacked time series network based on Xgboost and a bidirectional gated neural network, and inputting the training set and the test set to perform network training; and normalizing real-time collected data to obtain trainable data to predict a fault and update network parameters. The gas-in-oil data is predicted by using Xgboost and a gated neural network, obtains prediction data of a power transformer from two time series networks by using a meta learner, and obtains a fault diagnosis result of the transformer by using a Softmax layer. The neural network has accurate fault diagnosis performance and stable robustness.

Abstract: A method and a system of predicting an electric system load based on wavelet noise reduction and empirical mode decomposition-autoregressive integrated moving average (EMD-ARIMA) are provided. The method and the system belong to a field of electric system load prediction. The method includes the following steps. Raw load data of an electric system is obtained first. Next, noise reduction processing is performed on the load data through wavelet analysis. The noise-reduced load data is further processed through an EMD method to obtain different load components. Finally, ARIMA models corresponding to the different load components are built. Further, the ARIMA models are optimized through an Akaike information criterion (AIC) and a Bayesian information criterion (BIC). The load components obtained through predicting the different ARIMA models are reconstructed to obtain a final prediction result, and accuracy of load prediction is therefore effectively improved.

Abstract: The present invention provides a long-term streamflow forecast method and system based on process-data synergic drive.

Abstract: The invention discloses a failure diagnosis method for a power transformer winding based on a GSMallat-NIN-CNN network.

Abstract: A wind power prediction method and system for optimizing a deep Transformer network by whale optimization algorithm are disclosed. The sequence data of wind power and related influence factors are taken as sample data which is divided into a training set and a test set, where the data is trained and predicted by a Transformer network model established according to values of the initialized hyper-parameters, and an average absolute error of wind power prediction is taken as a fitness value of each whale group. A local optimal position is determined according to the initial fitness value of individual whale group, and the current optimal position is updated by utilizing whale group optimization, and the best prediction effect is obtained by comparing the local optimal solution with the global optimal solution. An optimal hyper-parameter combination is obtained after multiple iterations of the whale optimization algorithm, and the wind power is predicted.

Abstract: A method and a system for diagnosing a fault of a three-phase three-level rectifier are relate to the technical field of fault diagnosis of power electronic equipment, and provided to implement identification and location of an open-circuit fault of a power switching device thereof. A deviation between an expected value and an actual value of a phase-to-phase voltage is adopted as a diagnosis variable. The diagnosis variable is calculated by adopting a screening technique, thereby reducing calculation error to ensure accuracy of diagnosis. Only existing voltage current signals in a control system of the rectifier are required to calculate the diagnosis variable, so no additional hardware is required and low-cost fault diagnosis can be implemented. Different voltage thresholds are adopted for different fault characteristic sections, and the voltage thresholds are updated in real time according to a direct current side voltage, which improves diagnosis speed while ensuring higher robustness.

Abstract: The present invention relates to the field of inland vessel identification and ranging technology, and discloses a method, system, medium, equipment and terminal for identifying and ranging inland vessels. In the stage of vessel identification, based on the classical YOLO-V4 network model, the MobileNetV1 network is used to replace the feature extraction network CSPDarknet53 of the YOLO-V4 model; In the stage of vessel ranging, a binocular stereo vision ranging model is established, and the FSRCNN is used for super-resolution reconstruction of the original image pairs to enhance the vessel feature information; the ORB algorithm is used to achieve feature detection and matching at the sub-pixel level to obtain the parallax value between image pairs, and the depth information of the vessel target is obtained by triangulation principle and coordinate conversion.