11results about How to "Good recognition effect" patented technology

The invention proposes a small sample image generation method based on semantic mask reconstruction and category semantic learning, and relates to the technical field of image processing, and the method comprises the steps: S1, constructing a network model comprising a generator and a discriminator based on a LoFGAN network; s2, extracting depth features of the input image by using a generator, and fusing the depth features of the input image into depth semantic features through local feature fusion; s3, semantic mask reconstruction is carried out on the depth semantic features, and a corresponding image is generated through a decoder; s4, sending a real image and a corresponding image generated by the decoder into a discriminator to train the network model; s5, inputting the small sample image into the trained network model to obtain a generated image similar to the input image; according to the method, the extraction precision of the semantic information can be effectively enhanced, the understanding ability of the generation model for a complex semantic scene is improved, and the fidelity and definition of the image are improved.

The invention relates to a distribution network maintenance topology identification system based on multi-source data fusion, and the system achieves the effective integration and precise processing of multi-source power distribution network data through the cooperative work of a multi-source data collection module, a data preprocessing module and a measurement generation module, and remarkably improves the temporal-spatial resolution of the state quantity of a distribution network. According to the method, a confidence evaluation system is constructed, and an algorithm confidence evaluation system is used for carrying out confidence scoring on an output identification structure, so that the uncertainty of an identification result can be quantified, and the availability and reliability of the system in an actual complex environment are greatly improved; through seamless connection and data interaction between a database and a marketing system and a scheduling system, the applicability and effectiveness of the system in an actual maintenance scene are ensured, accurate and reliable topology identification support can be provided for power distribution network maintenance, and the maintenance efficiency and the power supply reliability are improved; the method has the advantages of being high in self-adaption, accurate and reliable.

The embodiment of the invention provides a face recognition model construction method, a portrait changing method and a related device, and the method comprises the steps: carrying out the processing of the definition of each source face image, and obtaining a blurred face image and a high-definition face image corresponding to each source face image; storing all face blurred images into a blurred data set, and storing all face high-definition images into a high-definition data set; according to a set sampling period, determining a first sampling probability and a second sampling probability adopted by each time of sampling in the sampling period; acquiring sample images from the high-definition data set and the fuzzy data set in each sampling according to the corresponding first sampling probability and second sampling probability and a set sample number so as to obtain a sample set of each sampling; and training the initial network model by using the sample set to obtain a face recognition model. Therefore, the face recognition model meeting the face changing task requirement is obtained, and the AI face changing effect can be improved.

The invention provides a BMS terminal voltage identification method based on a WPD-GMDH neural network, and belongs to the technical field of lithium battery management system identification. The technical problems of high difficulty and low precision of identifying the lithium battery management system are solved. According to the technical scheme, the method comprises the following steps of 1) obtaining sample data through a simulation experiment; 2) preprocessing data, constructing a data set, and dividing the data set into a training set and a test set; and step 3) training the data set through the WPD-GMDH neural network to obtain a WPD-GMDH model for real-time estimation. The beneficial effects of the invention are that the WPD-GMDH neural network used in the invention separates signals of different frequencies through the WPD technology, thereby finding the nonlinear relation of different features, and improving the learning capability of the GMDH neural network model.

The application relates to the technical field of direct-current power supply, and discloses a method for intelligently identifying and dynamically distributing power of a direct-current load. The method synchronously collects voltage and current waveforms when the load is connected, extracts transient and steady-state characteristics to construct a characteristic identifier, and compares the characteristic identifier with a pre-stored characteristic library to determine the load type or mark the load as a new type; according to the determination result, a corresponding power distribution strategy is executed, and the efficiency and state matching degree indexes are continuously monitored during the strategy execution process, so that re-identification based on new data and dynamic adjustment of the strategy are triggered, forming a closed loop. The application can accurately identify unknown or dynamically changing direct-current loads, realize adaptive power distribution and online optimization, and help improve the compatibility, energy efficiency and operation stability of a power supply system.

The utility model discloses a wheel hub motor with LED lighting light, including wheel hub casing, wheel hub end cover, rotary light emitting subassembly and side surface light emitting disc, wheel hub casing one side is equipped with wheel hub end cover, is equipped with a circle lamp hole on the wheel hub end cover, and the side surface light emitting disc is installed in the inboard of wheel hub end cover, is equipped with a circle LED lamp pearl on the side surface light emitting disc, and LED lamp pearl and lamp hole one to one correspondence, and LED lamp pearl is embedded from inboard lamp hole, two groups of rotary light emitting subassembly are installed in the both sides symmetry of wheel hub casing, and rotary light emitting subassembly includes base and LED lamp stick, and the base is integrally formed with wheel hub casing, and the detachable connection of LED lamp stick is installed on the base, and LED lamp stick vertical points to the central axis of wheel hub casing, and LED lamp stick rotates synchronously with the rotation of wheel hub casing, the utility model discloses the LED lighting light is integrated on wheel hub motor, and the battery of vehicle itself supplies power, can continuously emit light, and the connection is stable, and will not occur and throw off phenomenon because of high -speed riding, and the safety is high, and the lighting effect is good.

The invention relates to the technical field of rapid detection of diseases, in particular to a tomato leaf disease real-time data rapid detection method which comprises the steps that a data set is generated based on a leaf image of tomato diseases, and the diseases comprise early blight, leaf mold and late blight of tomatoes; constructing a matrix expression model according to each leaf image feature in the data set, and performing feature filtering on the matrix expression model by using a volume sliding mode surface function; based on the filtered image feature model, feature re-extraction is carried out on the image feature model by using a normalization function; for the image feature model after feature re-extraction, calculating an observation error by using a model compensation observer; a control input vector of a volume sliding mode filtering fixed time is designed according to an observation error, and a tomato disease detection model is constructed in combination with a Lyapunov function, so that the model is higher in small target recognition rate and better in convergence, the tomato leaf recognition rate and detection speed are improved, and the tomato sampling leaf data image transmission and acquisition stability is improved.

The invention relates to the technical field of system identification, in particular to a parameter identification method for a data missing output error system. The parameter identification method comprises the steps that based on a model expression of an output error system, an information vector and a parameter vector are defined, a linear regression equation of the output error system is generated, and the output error system comprises white noise; dividing the collected data set into a measurable data set and a hidden data set according to whether the input and output data pair is missing or not; based on the measurable data set and the hidden data set, adopting an expectation maximization algorithm to construct a Q function used for calculating current parameter vector conditional expectation; and based on the linear regression equation and the Q function, iteratively calculating estimated values of a parameter vector and a noise variance until convergence. According to the parameter identification method provided by the invention, an excellent parameter identification effect can be achieved when the output data is missing.

A distribution network line parameter identification method based on GA-SVR includes: S1, setting the parameters of each branch, including the resistance and reactance of each branch, and obtaining the index data of the voltage amplitude, active power, reactive power, active loss and reactive loss of the head and end nodes of each branch through the node flow calculation program; S2, extracting each index data and sorting them into a line parameter data group; S3, normalizing the data in the line parameter data group; S4, combining the normalized line parameter data group with the GA algorithm to optimize the penalty factor c and kernel function parameter g of the support vector regression machine SVR to form a GA-SVR; S5, identifying the distribution network line parameters through GA-SVR. Effective identification of distribution network line parameters is achieved; and simulation results show that compared with the traditional SVR method, this method greatly improves the identification accuracy of line parameters and has a better line parameter identification effect.

This invention relates to the fields of functional materials and intelligent sensing technology, and discloses a method for intuitively visualizing the stretching amount of a flexible adhesive. The method includes: providing a flexible adhesive with an elastic substrate capable of elastic deformation along the stretching direction; setting multiple patterned marking regions corresponding to different preset stretching thresholds on the surface of the elastic substrate; forming microgroove structures with differentiated structural parameters at corresponding positions on the elastic substrate to generate a differentiated stress concentration effect; forming color-developing layers containing mechanochromic materials with different bulk crosslinking densities in each patterned marking region, with each color-developing layer collectively constituting a patterned color-developing functional layer; through the synergistic effect of the differentiated stress concentration effect of the microgroove structure and the difference in bulk crosslinking density of the color-developing layers, each patterned marking region changes from a hidden state to a visible state when the corresponding stretching threshold is reached, thereby characterizing the stretching amount of the flexible adhesive through pattern visualization. This invention provides clear thresholds and high readability, and is suitable for scenarios such as motion protection, flexible electronics, intelligent packaging, and structural condition early warning.