32results about How to "Ensure training efficiency" patented technology

The invention discloses an attention mechanism and hash-based image retrieval method and device and a storage medium, and the method comprises the steps: obtaining the discrete hash code of each imagebased on a pre-trained attention mechanism-based hash feature extraction model for each image in an original data set, and building an image feature library; obtaining a discrete hash code of a to-be-detected image based on a pre-trained hash feature extraction model based on the attention mechanism; and querying data closest to the discrete hash code Hamming distance of the to-be-detected imagein an image feature library, wherein the image corresponding to the data is a retrieval result. The Hash feature extraction model based on the attention mechanism gives full play to the ability of thedeep convolutional neural network to extract the abstract semantic features of the image; and by embedding the attention module, the network can focus on the visual content capable of expressing semantic information of the whole image as much as possible in the image, the expression effect of hash coding is improved, and image retrieval is more accurate and faster.

The invention discloses an image classification method of a spiking learning model based on a dynamic threshold. The method comprises the following steps: obtaining an image data set, converting imageinformation into a pulse excitation sequence by adopting a phase delay coding method, establishing and training a spiking learning model based on a dynamic threshold, and performing classification processing on images to be classified by utilizing the trained spiking learning model. According to the invention, image information is converted into a pulse excitation sequence by using a phase delaycoding method; according to the image classification method and device, the dynamic threshold is set up, the spiking learning model based on the dynamic threshold is set up for training, the trained spiking learning model is used for image classification, the robustness of the learning model can be remarkably improved while the training efficiency and accuracy of the learning model are guaranteed,and the image classification efficiency is further improved.

The invention relates to a hand fine motion training guidance system and method based on vision. The system comprises a data processing module, a 3D hand posture recognition module, a data analysis module and a training guidance module. The system detects the hand fine movement ability of a user in real time through a computer vision method, guides the user to perform hand fine movement rehabilitation training in real time, guarantees the training efficiency and intensity, accurately controls training parameters, and objectively evaluates the rehabilitation training effect through quantitativedata.

The embodiment of the invention belongs to the field of big data, is applied to the field of smart medical treatment, and relates to a training corpus generation method of an intention recognition model and related equipment of the training corpus generation method. Wherein the customer answer corpus comprises an inquiry related corpus and a non-inquiry related corpus; establishing an inquiry related corpus and a non-inquiry related corpus; adjusting the inquiry-related corpus and the non-inquiry-related corpus based on the similarity between the non-inquiry-related corpus and the inquiry-related corpus to obtain a target inquiry-related corpus and a target non-inquiry-related corpus; establishing a first training sample based on the target inquiry related corpus; establishing a second training sample based on the intention label and a non-inquiry related corpus; and taking the first training sample and the second training sample as training corpora and outputting the training corpora.Wherein the training corpus can be stored in a block chain. The quality of the training corpus is improved.

The invention relates to the technical field of virtual teaching and in particular to a VR-based teaching and training system comprising at least one group of teaching units for storing teaching documents, a classification unit for classifying data in the teaching units, and a plurality of learning units connected with the classification unit to identify the teaching data classified by the classification unit, wherein each learning unit is provided therein with a question feedback unit, the classification unit is provided therein with a feedback receiving unit, the feedback receiving unit includes an automatic feedback question module and a manual solution module which transmits the feedback questions to the teaching units. The VR-based teaching and training system automatically records and answers the training questions, can record the questions to prevent the same questions from being required to be manually answered, can automatically answer the feedback questions, and has high solution efficiency, high accuracy and high reliability.

The invention discloses a pelvis correction rehabilitation training robot rehabilitation training control method comprising the following steps: step 1, before rehabilitation training, using a pressure sensor to collect the maximum muscle force value of a patient, and using a photoelectric encoder to collect the maximum motion angle value of the hip joint of the patient; step 2, setting a maximummuscle strength value and a maximum movement angle value of a hip joint, repeatedly outputting motor torque and rotating speed in a range smaller than the maximum muscle strength value and the maximummovement angle value of the hip joint, measuring the muscle strength of the patient and the movement angle of the hip joint, giving a pelvic deformation condition evaluation result and rehabilitationtraining data of the patient, and generating an evaluation report; and step 3, setting rehabilitation training modes and parameters according to the evaluation report, generating a control instruction, completing setting of motor torque and rotating speed, and enabling the motor to drive the patient to complete corresponding actions. A servo motor is adopted to drive flexion and extension movement of hip joints of a robot, and the robot is used for replacing a treatment practitioner to assist a patient in rehabilitation training of pelvis correction.

The invention discloses a neural network training method and device, a storage medium and an electronic device. The method comprises the steps that an offline sample set used for training a neural network in a human-computer interaction application is acquired, and the offline sample set comprises offline samples meeting preset configuration conditions; The initial neural network is trained offline by using the offline sample set to obtain an object neural network, and in the human-computer interaction application, the processing capability of the object neural network is higher than that of the initial neural network; And the object neural network is accessed to an online operation environment of the human-computer interaction application for online training to obtain a target neural network. According to the invention, the technical problem of low training efficiency in the neural network training method provided by the related technology is solved.

The invention discloses a pelvis correction rehabilitation training robot. The robot comprises a supporting body, a hip joint correction module and a leg supporting module, wherein the hip joint correction module comprises a motor, a speed reducer and a hip joint correction part; the motor is installed on the supporting body; an output shaft of the motor is connected with the input end of the speed reducer; the output end of the speed reducer is connected with the hip joint correction part; and the hip joint correction part is connected with the leg supporting module. A servo motor is adoptedto drive flexion and extension movement of hip joints of the robot, and the robot is used for replacing a therapist to assist a patient in rehabilitation training of pelvis correction, so that the therapist can be liberated from a heavy training process, the training efficiency can be guaranteed, and the rehabilitation training robot has the great significance.

The embodiment of the invention discloses a neural network model adjustment method and device, equipment and a storage medium, and relates to the technical field of neural networks, and the method comprises the steps: inputting a target number of sample data into a neural network model, and updating the model parameters of the neural network model according to a first output result of the neural network model, wherein the model parameter before updating is a first parameter, and the model parameter after updating is a second parameter; and inputting the target number of application data into the updated neural network model, and correcting model parameters of the neural network model according to a second output result and a second parameter of the updated neural network model, the model parameters before correction being the first parameter, and the model parameters after correction being the third parameter. By adopting the method, the technical problem that the performance of the deep learning model cannot be ensured when the deep learning model is applied to different scenes in the prior art can be solved.

The embodiment of the invention provides a mammary gland X-ray image antisymmetric generation analysis model training method and device. The problems that an existing mammary gland X-ray image analysis mode is low in efficiency and accuracy are solved. The mammary gland X-ray image antisymmetric generation analysis model training method comprises: respectively extracting original bilateral mammarygland image features of bilateral mammary gland X-ray images based on a pre-trained feature extraction model, wherein the bilateral mammary gland X-ray images correspond to a left breast and a rightbreast respectively; inputting the original bilateral mammary gland image features into a neural network model to obtain an antisymmetric analysis result opposite to the feature information of the original bilateral mammary gland image features; calculating a loss function value between the generated antisymmetric analysis result and original bilateral mammary gland image features; and adjusting parameters of the neural network model based on the loss function value.

The invention discloses a marine rescue device and an intelligent rescue system. The invention relates to a maritime rescue device, which belongs to the field of maritime rescue, and comprises more than one intelligent bracelet with a waterproof function, a positioning module and a vital sign monitoring module, the lifesaving equipment can be automatically inflated after being ejected; the launcher comprises a rotary table, a vertical rod, a shooting barrel and a driving mechanism, the vertical rod is rotationally connected with the shooting barrel, a rotating wheel and a thrust structure are arranged in the shooting barrel, a rope is wound on the rotating wheel, one end of the rope is connected with the lifesaving equipment, and the thrust structure is used for launching the lifesaving equipment; the console comprises a microprocessor, and a storage module and a communication module which are electrically connected with the microprocessor, and the console is in wireless communication connection with the intelligent bracelet and is electrically connected with the driving mechanism.