13results about How to "Solve balance problems" patented technology

The utility model discloses a kind of mine dump truck antenna support, comprising: support assembly, vertical pole, support base, transition base, buffer base, buffer pad, antenna installation support;Support assembly is frame structure, support assembly is provided with assembly connecting end head in bottom, vertical pole lower end is hinged with support base, vertical pole upper end is hinged with assembly connecting end head;Transition base is hinged in support assembly lower part, transition base, assembly connecting end head are located at the both sides of support assembly respectively;Antenna installation support is connected in support assembly top, buffer base is connected in support assembly, buffer base is located between antenna installation support, transition base, buffer pad is installed between support assembly, buffer base. The utility model can effectively solve the balance and stability problem of antenna in driving and unloading process, provide reliable guarantee for high-precision positioning of unmanned mine dump truck.

The application belongs to the technical field of robot control, and particularly relates to a master-slave operating arm with electromagnetic force feedback for precision assembly and a bidirectional transparent control method, which comprises a master-slave robot unit, an information transmission unit, a master-slave control unit, an electromagnetic force feedback unit, a bidirectional transparent control unit, a singular point processing unit, an adaptive path planning unit and a master-slave proportion matching unit. The electromagnetic force feedback unit obtains environmental force information through a magnetic coupling component and transmits the information to the master control unit; the bidirectional transparent control unit constructs a master-slave control closed loop, balances the system transparency and stability under communication delay, detects and avoids joint singular points through a Jacobian matrix, and constructs a potential field function to generate an optimal motion path. The application realizes high-precision control and force feedback under a communication delay environment, and improves the accuracy and safety of remote operation.

This invention provides an amino-functionalized transparent impact-resistant isophthalic resin and its preparation method, belonging to the field of functional polymer material synthesis. The amino-functionalized transparent impact-resistant isophthalic resin is a multi-component copolymer of styrene, isoprene, and a p-chloromethylstyrene derivative monomer containing tertiary amine functional groups. The preparation method employs a classic anionic polymerization method. First, an amino-functionalized polystyrene active segment SN1 is prepared. Then, it is added to a styrene / isoprene mixed monomer for a two-stage reaction to obtain a block copolymer segment SN1-SP with alternating styrene / isoprene sequences. Finally, styrene and the amino-functionalized monomer are added for a three-stage reaction or directly coupled with a coupling agent to obtain a special block copolymer SN1-SP-SN2, successfully yielding the amino-functionalized transparent impact-resistant isophthalic resin. By controlling the length of the special alternating sequence molecular chain and the block composition ratio, the mechanical properties of the product are controlled. Simultaneously, the tertiary amine polar groups are quantitatively introduced into the molecular chains, successfully achieving high impact resistance and transparency, and also enhancing the resin polarity. Using a lithium-based polymerization method, the resin particles are free of monomer and harmful impurities.

The application discloses a knowledge-based temporal convolution neural network (KB-TGCN) blast furnace fault diagnosis method. The method can solve the space-time dependence and sample imbalance problem of the blast furnace ironmaking process. First, the calculated variables that can further reflect the process state are calculated by using the variables directly detected by the sensor. Then, the knowledge-based graph structure is constructed according to the spatial position and calculation relationship of the variables. Subsequently, a one-dimensional time information extraction module is embedded in the graph convolutional neural network. Therefore, the TGCN can capture time information while maintaining the original spatial relationship. By using the knowledge-based graph structure, the KB-TGCN can fully mine the spatial information of different blast furnace positions. In addition, the method uses a focal loss function with an adaptive balance factor instead of a traditional cross-entropy loss function to overcome the sample imbalance problem.

This invention belongs to the field of materials science, specifically relating to a crystal structure generation method based on variational autoencoders and Cartesian coordinate derivation. The aim is to construct a crystal structure design method driven by target properties. The method includes: acquiring real crystal structure data and performing data augmentation processing; training a variational autoencoder using the crystal structure dataset as the real sample and the target material properties as the input conditions; converting the real crystal structure data in the crystal structure dataset into crystal diagrams, performing feature derivation on the edges of the crystal diagrams based on the geometric information of atoms in the Cartesian coordinate system, and training a graph neural network based on the edge-derived features; inputting the target material properties into the fully trained variational autoencoder to generate candidate crystal structures; inputting the candidate crystal structures into the fully trained graph neural network for property prediction; comparing the predicted property values ​​with the target material properties within a preset target tolerance range and selecting the desired crystal structure.

This invention provides an intelligent prediction method for low-resistivity oil reservoirs based on hierarchical ensemble learning. This method uses small-layer data as a benchmark, integrates heterogeneous data from multiple sources such as well logging, well logging, and production data, and constructs a labeled dataset after standardized preprocessing. Multi-dimensional features are automatically extracted using the Tsfresh framework, and key feature subsets are selected using random forest. Subsequently, generative adversarial networks that integrate noise filtering, adaptive clustering, and residual connections are used to optimize sample distribution, addressing data imbalance and insufficient sample problems. A model cluster containing traditional machine learning models and convolutional hybrid neural networks is constructed, adapting to static and temporal feature learning respectively. A hierarchical stacking ensemble strategy is adopted, using the unbiased prediction results of five types of base learners as meta-features, and integrating the advantages of each model through meta-learners. Furthermore, this invention also provides an intelligent prediction device for low-resistivity oil reservoirs based on hierarchical ensemble learning. The technical solution provided by this invention can improve the prediction accuracy and generalization ability of low-resistivity oil reservoirs under complex geological conditions, thereby increasing the recovery rate.

This invention belongs to the field of deep learning clustering, specifically relating to an ASD neural typing method based on synthetic brain map data augmentation. The method includes: acquiring brain map data for training and inputting it into a pre-trained dual-decoder graph autoencoder to obtain latent embeddings; inputting the latent embeddings into a pre-trained latent space conditional diffusion model to obtain an augmented brain map; training a deep embedding clustering model based on MBVAE or a deep graph clustering model based on HGDAE using the augmented brain map to obtain a trained deep embedding clustering model based on MBVAE or a deep graph clustering model based on HGDAE; acquiring brain map data to be detected and inputting it into the trained deep embedding clustering model based on MBVAE or a deep graph clustering model based on HGDAE to obtain the ASD neural typing result. This invention effectively solves the gender imbalance and multi-site heterogeneity problems existing in ASD data, enhancing the robustness and generalization ability of the model.

The application provides a multi-dimensional identity recognition method and device, electronic equipment and program product, and relates to the technical field of artificial intelligence. The method comprises the following steps: collecting video stream data containing a to-be-recognized object, extracting multi-dimensional feature information of the to-be-recognized object based on a first target image in the video stream data, recognizing the identity of the to-be-recognized object according to face information, if the recognition fails, performing target tracking on the to-be-recognized object based on a second target image before the first target image in the video stream data to determine whether a third target image containing the identity recognition result of the to-be-recognized object exists in the second target image, and if not, recognizing the identity of the to-be-recognized object according to body key point information. Through face recognition, target tracking and identity recognition based on body key points, the high accuracy of face recognition is retained, and when face recognition fails, identity recognition can be accurately performed based on body key points, so that the balance between the high accuracy and the easy failure of face recognition is achieved.

The application discloses a kind of sludge solidifying agent using carbon fixation desulfurization ash, by cement clinker 12~15%, mineral powder 50~55%, steel slag powder 15~20%, carbon fixation desulfurization ash 10~15%, solid additive 1~2%, liquid additive 1~2% composition by mass percentage.The application uses desulfurization ash to fix CO2 in industrial tail gas to improve the activity of desulfurization ash, then carbon fixation desulfurization ash is compounded with cement clinker, mineral powder, steel slag powder, solid additive, liquid additive to obtain sludge solidifying agent.The application does not need to use various chemical means of flotation agent to enrich the carbon fixation component in desulfurization ash, and the cementation activity of desulfurization ash after carbon fixation is greatly improved, can better play sulfate activation effect, and with additive play superposition coupling effect, and with cement clinker, a kind of cementing material system, can jointly prepare sludge solidifying agent, to waste treat waste at the same time, improve the solidification strength of the sludge solidifying agent to sludge and the solidification effect of heavy metals in sludge.

A deep learning-based method and system for detecting rail damage belongs to the field of rail transit monitoring and damage identification technology. The method includes: acquiring an ultrasonic two-dimensional image of the rail to be detected; inputting the two-dimensional image into a pre-trained neural network model, outputting detection results including damage category and location coordinates; neural network model training includes: acquiring a training sample set containing labeled information; inputting the training sample set into the neural network model, and having the model output predicted bounding boxes representing the predicted location and size of the damage; calculating the error between the predicted bounding box and the ground truth bounding box using a bounding box regression loss function, and updating the model parameters through backpropagation; the bounding box regression loss function includes an overlap loss term, a center distance loss term, and a size penalty term, and is adjusted by a dynamic weighting factor. This invention solves the problems of imbalanced positive and negative samples and uneven distribution of easy and difficult samples during training, improving the detection accuracy and robustness for small targets and hidden damage.

The present application belongs to the technical field of medical image processing and artificial intelligence, and particularly relates to a frequency domain enhanced in-situ biological hyperspectral feature extraction and classification method. By fusing the adaptive spatial-frequency feature extraction capability of fractional Fourier transform and the local-global spectral attention mechanism, the medical hyperspectral image can be deeply featured and efficiently classified, and the rapid and accurate diagnosis of early pathological lesions can be realized. The method is particularly suitable for real-time analysis of complex non-stationary pathological signals and multi-scale spectral-spatial information in a clinical environment, and provides accurate lesion classification and diagnosis assistance for doctors.

This invention discloses a lightweight single-image super-resolution method based on attention sharing and information distillation. The method includes: acquiring a low-resolution image to be processed; inputting the low-resolution image into a pre-trained super-resolution model to obtain a high-resolution image output by the super-resolution model; wherein the super-resolution model is obtained by training a pre-constructed deep learning network using a sample dataset; the sample dataset includes clear and blurred image pairs obtained by preprocessing the acquired original image data; and the deep learning network includes a shallow feature extraction module, an information distillation block stacking module, and an upsampling reconstruction module. This achieves lightweight image reconstruction.