67results about How to "Improve training effect" patented technology

The application discloses an age classification model generation method, an age classification method and system, and a training method. i Input into a face feature extraction network to obtain a face feature map f i ; the face feature map f i is input into a feature discrimination module D f for first optimization processing; the face feature map corresponding feature vector is input into a first full connection layer for second optimization processing; and iterative training is performed to obtain an age classification model. In terms of training data, the weight setting of data sampling is performed on face images of different age stages, and the data proportion of different age stages is balanced; in model optimization, a feature discrimination module and a ViT module are introduced to reduce the ambiguity information of face images of different age stages, and accurate and stable prediction is realized.

The utility model discloses a kind of intelligent lower limbs reciprocating type strength training equipment, including training frame, the top of training frame is fixedly connected with cushion, the side of cushion is fixedly connected with movable frame, the side of movable frame is installed with backrest, the top of training frame is away from the side of cushion and is equipped with mounting groove, the top of training frame is movably connected with sliding frame by mounting groove, the front of training board is equipped with sliding slot, the front and back of sliding frame are fixedly connected with displacement sensor, the inner wall of mounting groove is equipped with moving groove, the inner side of mounting groove is fixedly connected with tension sensor, the side of tension sensor is fixedly connected with telescopic link, one end of telescopic link is fixedly connected with tension spring. Through the tension sensor, telescopic link and displacement sensor set, improve training effect, so that lower limbs reciprocating type strength training equipment can be intelligently adjusted according to the power change of trainer, avoid the damage caused to trainer in training process.

Embodiments of the present application disclose a text recognition method and device, computer equipment and a computer readable storage medium. The method comprises: obtaining a text image sample; performing image index calculation according to image attribute information of the text image sample, and determining a reference sample index based on a calculation result; performing image feature extraction processing on the text image sample by using a feature extraction model to obtain image feature information; performing attention feature extraction based on the image feature information by using the feature extraction model to obtain attention feature information of a context information of interest; predicting a prediction sample index based on the attention feature information; and training the feature extraction model according to the prediction sample index and the corresponding reference sample index, so as to extract the attention feature information of a to-be-recognized text image by using the trained feature extraction model to perform image text recognition. The method can train the feature extraction model by using a large number of unlabeled text image samples, and can enhance the training effect of the feature extraction model.

The utility model discloses a kind of shock-absorbing martial arts training pile, it is related to martial arts training instrument technical field, including pile body, pile hand and pile leg, the bottom of pile body is provided with rubber pad, the number of rubber pad is four groups, four groups rubber pad enclose circle, the bottom outer wall of four groups rubber pad is all set up with butt joint hole, the inside of butt joint hole is clamped with butt joint block, butt joint block and butt joint hole cross section are regular hexagon, the end of butt joint block away from butt joint hole is fixedly connected with clamping seat, the top of rubber pad is provided with buffer assembly and be used for the locking assembly of the fixed pile body, the utility model can play good shock-absorbing effect in the process that people hit training pile, avoid the situation that fixed bolt appears loosening in the process that wooden dummy is used for a long time, guarantee the stability in the process that training pile is used for a long time.

This invention discloses a smart production training system and method for facility horticulture based on digital twins, belonging to the field of smart production training technology. It includes: a smart horticulture production training module, a virtual twin module, a smart control module, and a training and intelligent evaluation module. The invention uses the smart horticulture production training module to collect environmental, equipment, and crop growth data and execute control commands; the virtual twin module constructs a one-to-one mapped virtual three-dimensional scene, realizing real-time virtual-real mapping between physical entities and the virtual scene; the smart control module generates control strategies based on simulated data and training conditions and issues control commands; the training and intelligent evaluation module provides operation guidance, visual display, data recording, and automated scoring and comprehensive evaluation, improving the safety of smart production training in facility horticulture and solving the problems of low safety in existing technologies due to long cycles, high costs, slow rotation, and high risks.

This disclosure provides a model training method, an advertising effectiveness evaluation method, an apparatus, and equipment, which can be applied to the fields of artificial intelligence, the Internet, advertising, and model training. The model training method includes: acquiring training samples; using the feature extraction module of the model to be trained to extract features from the user attribute information of the samples to obtain target sample features; using the evaluation module of the model to be trained to evaluate the target sample features to obtain an evaluation result representing the degree of interest of the user samples in the advertisement; based on reference features, identifying feature differences in the target sample features to determine the feature alignment loss value of the model to be trained; and adjusting the parameters of the model to be trained based on the feature alignment loss value and the evaluation loss value to obtain the trained model.

The utility model discloses a kind of front landing gear torsion arm dismounting practical operation rack, it includes fixed support, operating plate, torsion arm practical operation model and torsion arm pin assembly on the torsion arm of aircraft front landing gear, the upper surface of fixed support is fixed with two operating plates, the lower of operating plate is equipped with fixed tray, torsion arm practical operation model is respectively equipped in the front of operating plate, the connection between torsion arm practical operation model and operating plate can be disassembled, the front end of torsion arm practical operation model is equipped with left and right torsion arm, torsion arm pin assembly is connected on each torsion arm practical operation model left and right torsion arm. Through the utility model, the entire dismounting process of torsion arm pin assembly of aircraft front landing gear torsion arm can be carried out practical operation training.

This disclosure relates to the field of computer science, providing a method, apparatus, electronic device, and medium for training a multimodal contrastive learning model. The method includes: acquiring multimodal data including text data and image data; processing the multimodal data to obtain serialized marker data corresponding to the text data and image patch data corresponding to the image data; inputting the serialized marker data and image patch data into a feature encoder to obtain initial text features and initial image features; inputting the initial text features and initial image features into a preset routing-based expert layer to obtain multimodal features; inputting the multimodal features into an encoding layer to obtain encoded text features and encoded image features; and training a contrastive learning network based on the encoded text features and encoded image features to obtain a trained multimodal contrastive learning model. This disclosure allows a single model to handle multiple single / multimodal tasks simultaneously, avoiding catastrophic forgetting problems and improving work efficiency and training effectiveness.

The application discloses a skin type prediction method for small sample data autonomous learning, and relates to the technical fields of deep learning and medical beauty skin type. The initial model is obtained by a convolutional neural network through a small number of labeled samples, and the subsequent new samples are accepted through the initial model, and whether the new samples have labeling value is judged according to the prediction result of the model, so that only the samples determined to be inaccurate by the model are selected for labeling and retraining. Compared with the existing skin type method, a single model can be used to obtain a more perfect result, the model is continuously iteratively optimized by using the enhanced unit in the actual use process, and the idea of active learning is used to select the samples with greater uncertainty for labeling, so that the number of samples needing expert labeling can be greatly reduced, and the labor cost is reduced. According to the reliability module, when the samples are expanded, the skin type with insufficient reliability is preferred, so that the final typing effect is improved.

The application belongs to the technical field of medical treatment and can be used for sample data enhancement in the scene of human body, tissue organ modeling and the like in the medical field, and particularly relates to an image data enhancement method, a training method and device of a binocular stereo matching model. The method comprises the following steps: obtaining a target sample image pair, the target sample image pair comprising a first image and a second image; generating a first random mask and a second random mask; obtaining a first disordered image and a second disordered image; obtaining a first data enhancement image based on the first random mask, the second disordered image and the first image; obtaining a second data enhancement image based on the second random mask, the first disordered image and the second image; and combining the first data enhancement image and the second data enhancement image to obtain a data enhancement sample image pair. The above method and device improve the adaptability of the introduced noise to the training sample set, so that the expanded training sample has better training effect.

The present application belongs to the technical field of nuclear power plant physical simulation and simulation, and particularly relates to a nuclear power plant core instrument bay equipment simulation body and a simulation method. The simulation body comprises a driving unit, a group selector assembly and a path selector assembly, the driving unit is connected to the path selector assembly through the group selector assembly, the cable outlet end of the driving unit is connected to the group selector inlet end through a stainless steel pipe and a sleeve joint, and the group selector outlet end of the group selector assembly is connected to the path selector inlet end of the path selector assembly through a stainless steel pipe and a sleeve joint. The present application has the beneficial effect of improving training effect and efficiency, the RIC system simulation body can simulate the real operating environment of the nuclear power plant core, so that the trainees can learn and operate in a work scene close to the actual one, thereby more intuitively understanding the working principle and operation process of the core measurement system.

This invention discloses a substation acoustic signature fault diagnosis method based on LSTM-BHPSO. The method involves collecting substation acoustic signature signals and extracting features using MFCC to obtain a feature dataset, which is then divided into training and testing sets. An LSTM-based substation acoustic signature fault diagnosis model is constructed. The optimal initial parameters are obtained by iteratively optimizing the particle positions using the number of feature points extracted by MFCC, the number of batch samples, the number of network layers, and the number of hidden and fully connected layers. These optimal initial parameters are then input into the LSTM-based substation acoustic signature fault diagnosis model. Finally, the trained LSTM-based substation acoustic signature fault diagnosis model is used to diagnose substation acoustic signature faults. This invention offers good processing efficiency, avoids local optima, and facilitates accurate fault diagnosis results.

This invention discloses a firearm shooting training simulator and its working method, which can provide shooters with near-realistic shooting training. It includes a receiver and a simulated barrel. The receiver houses a firing pin. The simulated barrel includes a simulated chamber tube, a circuit board tube, and a solenoid valve data cable tube. The simulated chamber tube contains a simulated cartridge, which includes a simulated cartridge case. A signal rod slides within the cavity of the simulated cartridge case. The circuit board tube contains a circuit board with a chip and a switch. A solenoid valve data cable is connected to the circuit board and is connected to a solenoid valve. The recoil simulator includes a rear end cover, a piston cylinder, and a front end cover. The rear end cover has an air inlet connector that communicates with the cavity of the piston cylinder. The air inlet connector is connected to a high-pressure gas cylinder via a solenoid valve. A lead-core piston slides within the piston cylinder. The lead-core piston includes a piston section and a piston rod. A piston return spring is fitted onto the piston rod. The piston section is driven by high-pressure gas.

This invention discloses a method for preparing bio-organic fertilizer that reduces the incidence of tobacco root-knot nematodes. The bio-organic fertilizer consists of a fermentation substrate, microbial inoculant, and earthworm castings in a mass ratio of 10:0.2-0.4:2-3. The invention uses Bacillus subtilis, Trichoderma harzianum, and Bacillus mucilaginosus in a mass ratio of 15-25:8-13:30-40 to prepare the microbial inoculant. The fermentation substrate and microbial inoculant are mixed and fermented. The fermentation substrate includes cow dung, wheat bran, straw, and earthworm castings. The bio-organic fertilizer of this invention can reduce the incidence of tobacco root-knot nematodes and also promotes tobacco root growth and increases tobacco leaf yield.

This application provides a training method, apparatus, device, storage medium, and computer program product for a conversion prediction model, applicable to various conversion prediction scenarios such as cloud technology, artificial intelligence, smart transportation, and vehicle-mounted systems. The method includes: acquiring recommended information samples with a conversion waiting time of a first duration up to the current training time and conversion tags for the recommended information samples; extracting first sample features of the recommended information samples based on the conversion prediction model to be trained, and extracting second sample features of the recommended information samples based on a second feature extraction module in an auxiliary training model, wherein the auxiliary training model is trained with a second duration shorter than the first duration; predicting a first predicted conversion probability corresponding to the sample fusion feature of the first and second sample features based on the conversion prediction model to be trained; and training the conversion prediction model to be trained based on the difference between the first predicted conversion probability and the conversion tag, thereby obtaining the conversion prediction model. This application can improve the accuracy of the model.

This application discloses a method, apparatus, device, and medium for identifying street tree species in street view images, relating to the field of image recognition technology. The method includes: acquiring a street view dataset of the study area; acquiring a dataset of individual tree species; training a YOLO supervised model using the individual tree species dataset; adding a global domain adaptive module and an enhanced pseudo-label adapter to a semi-supervised object detection framework to obtain a semi-supervised domain adaptation framework; inputting the street view dataset, the individual tree species dataset, and the trained YOLO supervised model into the semi-supervised domain adaptation framework for training to obtain a tree species identification model; and using the tree species identification model to identify street tree species. This application utilizes a street view dataset and a dataset of labeled individual tree species for training, which reduces the cost of dataset labeling; it uses a global domain adaptive module to align features and an enhanced pseudo-label adapter to optimize pseudo-labels, which improves the training effect of the model and thus improves the accuracy of tree species identification in street view images.

The application relates to a model training method and device, equipment and a storage medium, and relates to the technical field of deep learning. The method comprises the following steps: a training image can be acquired; an occlusion image and a non-occlusion image are images in different scenes; then, a first processing image and a second processing image can be determined based on an image processing network; subsequently, the first processing image and the non-occlusion image are input into a first image discrimination network to obtain a first loss, and the second processing image and the occlusion image are input into a second image discrimination network to obtain a second loss; thus, a to-be-trained model can be trained based on a loss set to obtain an image processing model; the image processing model is used for removing occlusions in a to-be-processed image. Therefore, the image processing model for removing occlusions can be trained through the occlusion image and the non-occlusion image in different scenes, so that the model for removing occlusions is more convenient to train.

The utility model discloses a respiratory muscle training device relates to the technical field of breathing training instrument, including device main part, airflow regulating valve, breathing check valve and breathing mouth, the airflow channel is arranged along the length direction in device main part, and one end of device main part is provided with airflow regulating valve, and airflow regulating valve is used for making the airflow channel and the outside communication, and the airflow size is adjusted, and one end of airflow regulating valve is provided with breathing mouth away from device main part, and breathing mouth is used for the breathing training of trainee, and the other end of device main part is provided with breathing check valve. The utility model can simulate the breathing resistance of different altitudes, and improve the training effect.

This application discloses a model processing method, a model processing device, a computer program product, and an equipment. The method includes: acquiring content data of multimedia resources and interaction data of sample objects with respect to the multimedia resources; adaptively training a recommendation model using the content data and interaction data to obtain a first recommendation model; acquiring collaborative features; the collaborative features include at least one of the following: multimedia collaborative features of multimedia resources and object collaborative features of sample objects, wherein the multimedia collaborative features represent the content representation of multimedia resources in the online recommendation scenario, and the object collaborative features represent the object representation of sample objects in the online recommendation scenario; optimizing and training the first recommendation model using the collaborative features to obtain a trained recommendation model; and using the trained recommendation model to recall multimedia resources recommended to objects. Using this application can improve the accuracy of recommending multimedia resources to objects.

The embodiment of the application provides a deep learning model training method, an object detection method, an apparatus and an electronic device, relates to the technical field of computers, and adjusts the resolution of the obtained to-be-processed image data of multiple data sources according to a preset resolution, thereby obtaining first image data, so that the resolution of the final sample image data is adjusted according to the preset resolution, and based on attribute information of each data source, the fusion proportion of each data source is determined, for each data source, a corresponding number of first image data is selected from the first image data of the data source according to the fusion proportion of the data source, sample image data of the data source is obtained, so that the data of multiple data sources is integrated, and finally, the deep learning model is trained by using the sample image data of each data source obtained through integration, so that the deep learning model training effect can be improved.

The application relates to the technical field of artificial intelligence, and discloses an intelligent simulation training method and device based on dynamic portraits, which comprises the following steps: obtaining basic portrait information of a target student, generating training demand information of the target student based on the basic portrait information and a time sequence knowledge graph; obtaining real-time interaction information of the target student, determining corresponding evaluation analysis results based on the real-time interaction information and the basic portrait information; generating dynamic portrait information of the target student according to the evaluation analysis results and the basic portrait information, then generating simulation training parameters, and performing real-time simulation operation on the target student based on the simulation training parameters to obtain real-time simulation results corresponding to the target student; and generating simulation training evaluation results of the target student according to the real-time simulation results. It can be seen that the application can perform real-time simulation training to improve the real-time performance of simulation training, thereby being beneficial to improving the accuracy and reliability of simulation training, and further improving the training effect of simulation training.

The application discloses a hair key point prediction method and system based on Kalman filtering and a storage medium, which comprises the following steps: acquiring a current frame as an input image, performing key point prediction processing on the input image, and obtaining predicted face key point coordinate information A t and predicted t-time hair key point coordinate information Z t ; obtaining the inter-frame motion vector of the face key point according to the face key point coordinate information A t‑1 and the predicted t-time face key point coordinate information A t ; inputting the inter-frame motion vector of the face key point into a pre-trained mapping network B to obtain the inter-frame motion vector of the predicted hair key point; and calculating the output result of the hair key point according to the inter-frame motion vector of the predicted hair key point and a preset Kalman filtering formula. The application can effectively improve the detection effect of the relatively unstable hair key point in a video stream.

This invention discloses a polyp segmentation method based on edge guidance and deep-supervised PVT. For colonoscopy images containing polyps, this method utilizes a constructed polyp segmentation network to output the location and shape of the polyps. The polyp segmentation network uses PVT-v2 as the backbone network for feature extraction and also includes a context fusion decoding branch, a boundary extraction branch, a boundary awareness module, and a cascaded feature fusion branch. The context fusion decoding branch acts as an image decoder, receiving the semantic feature map output by the PVT-v2 backbone network. The boundary extraction branch uses a thinning module to enrich the content of the detail feature map, obtaining edge prediction results. The boundary awareness module fuses the thinned features with the output feature map of the context fusion decoding to obtain a boundary-aware prediction result. The cascaded feature fusion branch fuses features at different scales layer by layer in a deep-to-shallow order, outputting the final polyp segmentation prediction result P5.

This invention discloses a machine learning-based intelligent driving behavior method. It acquires basic data, including driving behavior data from real-world road scenarios. Based on this data, it calculates feature vectors for each driving scenario. Then, based on these feature vectors, it uses a reinforcement learning algorithm to select driving scenarios. Finally, it generates driving behaviors within these scenarios using the same reinforcement learning algorithm. Finally, it updates the strategy to obtain the optimal model. This method aims to optimize the environmental perception and decision-making capabilities of the intelligent driving system, thereby improving its adaptability and reliability in real-world driving scenarios.

The utility model relates to a fitness stool which comprises a base, a backrest, a force transmission seat, a pull rope, a guide piece and a resistance rope. The first end of the backrest is rotatably connected with the base, and the second end can move relative to the base; the force transmission seat and the inner side surface of the backrest are arranged at intervals; the pull rope is connected with the force transmission seat, the two ends of the pull rope are force application ends, and when external force acts on the two force application ends, the force transmission seat can move relative to the backrest; the guide piece is installed on the inner side of the backrest and located on the side, close to the first end of the backrest, of the force transmission base, and the guide piece and the inner side surface of the backrest are arranged at intervals; the resistance rope is located on the inner side of the backrest, one end of the resistance rope is close to the second end of the backrest and connected with the backrest, and the other end of the resistance rope extends from the side, close to the backrest, of the force transmission base to the first end of the backrest, and is connected with the force transmission base after being wound from the side, facing the backrest, of the guide piece to the other side away from the backrest. By optimizing the connecting mode of the resistance rope, the structure is more compact, and the risk that the resistance rope falls off is reduced.

This invention proposes a multi-scale feature enhancement and training method and apparatus for underwater sonar small target detection. The method includes: constructing a backbone network for a target detection model, with the network outputting four layers of multi-scale features at different resolutions; implementing sub-pixel-based skip connections to fuse high-level features; implementing sub-pixel-based context enhancement to fuse features at all scales; implementing channel attention sharing guidance to optimize the generated feature pyramid; constructing an overall target detection model, loading a pre-trained model and a sonar image dataset, and starting training; weighting the multi-scale loss values, selecting the two scales with the highest variance reduction rate for enhancement, and increasing their weights; calculating the overall loss, backpropagating to update the model parameters, and obtaining the target detection model after training. This invention achieves good detection accuracy in underwater sonar small target detection tasks.

The utility model discloses a kind of auxiliary up and down stairs sky rail self-adapting gravity center following device, including walking track and nose component, walking track is along stair arrangement, and is installed in the top of stair by sky rail hanger, nose component is used to generate power and move along walking track, nose component is connected with patient by sling and provides dynamic weight loss effect for patient;Measurement grating is arranged on the side of stair, measurement grating is used to detect the position of patient;A plurality of RFID chip modules are arranged on walking track, a plurality of RFID chip modules and the multiple steps of stair one-to-one correspondence, card reader is arranged on nose component, card reader is used to read the information of RFID chip module;External control system controls nose component movement according to the signal output by measurement grating and the information read by card reader;The utility model can adaptively assist cooperation patient training, achieve synchronization with patient's walking, make patient occupy initiative in the process of climbing stair training and can train alone, make training reasonable, effective and reliable.

This utility model discloses a building fire simulation training device, including an upper simulation area and a lower simulation area, both constructed using shipping containers. The lower simulation area includes a furniture fire simulation training room, a kitchen fire simulation training room, an electrical fire simulation training room, and an oil fire simulation training room. The outer wall of the oil fire simulation training room has a gas storage chamber, and a gas delivery pipeline is installed on the gas storage chamber, one end of which is connected to the gas storage chamber. The gas delivery pipeline surrounds the outer wall of the lower simulation area. A bed fire model is installed in the furniture fire simulation training room directly opposite the igniter; a kitchen fire model is installed in the kitchen fire simulation training room directly opposite the igniter; and an electrical fire model is installed in the electrical fire simulation training room directly opposite the igniter. This utility model provides a variety of training scenarios and good training results, with the upper simulation area used for comprehensive fire simulation training and the lower simulation area used for real fire simulation training.