35results about How to "Solve training problems" patented technology

The invention provides a method for preparing cartilage with a high mechanical property based on 3D bioprinting. The method comprises the following steps: preparing silk fibers, preparing a gelatin solution, preparing ciological ink containing cells, carrying out 3D cartilage printing, performing cell culture and differentiation on a printed tissue to form the cartilage. The method solves the problems of polarization of 2-dimension culture for cells and high body and surface ratio of cells through the 3D bioprinting technology; sodium alginate and calcium ions form ionic bonds, and gelatin and methacrylic anhydride form covalent bonds, so that the 3D bioprinted cartilage is good in mechanical property and is not easily broken; the gelatin has biocompatibility and degradability of normal biological material hydrogel, has bioactivity, and is good for cell growth and differentiation and realization of cell functions.

The invention discloses a simulation system for dynamically generating a three-dimensional scene on the basis of Unity. The simulation method comprises a three-dimensional simulation model library, a scene configuration file library, a three-dimensional simulation module and a data communication module, wherein the three-dimensional simulation module comprises a scene display module and a scene operation module; and the output end of the three-dimensional simulation model library and the output end of the scene configuration file library are connected with a data input end of the scene display module. The implementation method of the simulation system comprises the following steps: 1) creating a three-dimensional model; 2) reading a user-defined configuration file from the Unity, and automatically generating the three-dimensional scene simulation module; 3) newly building a monitoring thread in the three-dimensional scene simulation module, and obtaining required external real-time data from an external equipment simulation program; and 4) operating equipment or repairing equipment faults in a three-dimensional scene, and sending operation information to the external equipment simulation program for simulation calculation and judgment. The simulation system has the advantages of being visual in observation, can receive and send simulation data in real time and is tightly combined with actual power system communication work.

The invention discloses a neuromorphic system based on variable-resistance devices and adaptive-excited neurons and a realization method. The system includes a crossed array of the variable-resistance devices, the anterior neurons, the posterior neurons, a global dynamic threshold value control circuit, a control logic module, a voltage regulation module, a sample input, a tag input and a result output. According to the system, the variable-resistance devices are used as electronic synapses, and a new structure and operation mode of the adaptive-excited neurons are provided, thus the system is optimized on area and operation aspects, and training problems faced by the same type of systems are solved.

The invention discloses a hysteroscopic surgery simulator, which comprises an examining table, a female human model, a uterine cavity resectoscope, a rotation sensor, a displacement sensor, a force feedback touch simulator, data receiving processing equipment, a 3D (three-dimensional) model processing server, a 3D platform system server and display equipment. The hysteroscopic surgery simulator is characterized in that the uterine cavity resectoscope is put in the model; the lower part of the resectoscope body is provided with the displacement sensor and the rotation sensor; the tail end of the resectoscope body is provided with the force feedback touch simulator; the displacement sensor, the rotation sensor and the force feedback touch simulator are respectively and bi-directionally related with the data receiving processing equipment arranged in the examining table. Various 3D stereoscopic models of normal tissues and diseases of a virtual uterine cavity and 3D models for an operation process are stored in the 3D model processing server, and a hysteroscopy and surgery operation interface and entering and playback software are stored, and are output to the display equipment through the 3D platform system server to show vivid virtual 3D images; the hysteroscopic surgery simulator also has a vivid force feedback touch, so that the effects of hysteroscopy, and surgery skill training and assessment can be obviously improved.

The invention discloses an air quality prediction model training method and device, an air quality prediction model prediction method and device, equipment, a program and a medium, and relates to thetechnical field of computers, in particular to the deep learning technology. The method comprises the following steps: dividing a target monitoring range into a plurality of areas; pre-training an airquality prediction model by adopting a pre-training sample and a pre-training target function, the pre-training sample comprising the measured value; adopting a formal training sample and a formal training objective function to train a pre-trained air quality prediction model, wherein the formal training sample comprises the actual measurement value, and the air quality prediction model is used for predicting the air quality of the plurality of regions according to the space information, the historical information and the environment information. According to the invention, the optimized training target can be achieved by different pre-training samples and target functions.

The invention discloses a portable multimedia training and examination system, and particularly relates to the technical field of petrochemical enterprise education and training. The portable multimedia training and examination system comprises an integrated box body, a hardware system and a software system. The hardware system comprises a host, a man-machine interaction device, a wireless answerdevice, an identity recognizer and a multimedia unit, and the software system comprises a plan management module, a training examination module, a record query module, a data management module, an offline use module and an equipment detection module. The specific application process is as follows: before training and examination, preparing multiple forms of training contents and examination questions, and storing the training contents and the examination questions in the portable multimedia training and examination system in the form of videos, animations or documents; inputting the information of the examinees into the portable multimedia training examination system through the identity recognizer; recording sign-in of the examinees, generating a sign-in record and recording the sign-in record. The defect that an existing petrochemical enterprise training examination is poor in adaptability is overcome.

The invention belongs to a billiard training device, in particular to a grid-type billiard aiming training device, which comprises a grid-type aiming plate and a support seat under the grid-type aiming plate. The grid-type aiming plate is formed via staggered arrangement of three horizontal bars or horizontal lines with the same spacing and three vertical bars or vertical lines with the same spacing, the horizontal bars or horizontal lines are as long as the vertical bars or vertical lines, and the length of the horizontal bars or horizontal lines and the vertical bars or vertical lines are equal to the diameter of a billiard body. The training problems of three aiming habits can be solved since: 1, the bottom corner of the grid-type aiming plate is a goal point along a table surface; 2, intersection points of the vertical bars or vertical line on the sides and the horizontal bars or horizontal lines in the middle are goal points of a cue ball during collision; and 3, the vertical bars or vertical line on the sides are central axial lines of the cue ball during collision.

The invention discloses a face spoofing detection method and system based on meta-pseudo labels and illumination invariant features. The method comprises the steps that: data is preprocessed to obtain an RGB color channel graph and a PLGF graph; the RGB color channel graph is divided into labeled samples, unlabeled samples and enhanced unlabeled samples, the labeled samples, the unlabeled samples and the enhanced unlabeled samples are sent to a teacher learning module to obtain teacher semi-supervised loss, false labels of the unlabeled samples and enhanced unlabeled loss, and parameters of a student model and a teacher model are updated; the PLGF map is sent to an illumination invariant feature extraction network to obtain a feature vector and a classification vector, and a network model and parameters are stored after triple loss and cross entropy loss are used for supervised training; a threshold value is determined by using a verification set; and test data is loaded to the student model and the illumination invariant feature extraction network to obtain a corresponding RGB classification score and a PLGF classification score, weighted summation is performed to obtain a classification score, and a classification result is judged according to a threshold value. According to the method, the robustness of the face spoofing detection model is improved under the condition that the training samples are insufficient.

The invention provides a method for improving the activity of nitrification function microbiology in activated sludge through feeding iron hydroxide during in situ preparation. The method comprises a cultivation stage of the nitrification function microbiology and a long-time running activity maintenance stage. The cultivation stage is characterized in that: the activity of the nitrification function microbiology is improved through in situ preparation of active sludge system equipment and feeding of ultramicron iron hydroxide. The long-time running activity maintenance stage refers to feeding ferric salt chemical agent into an activated sludge mixture directly so as to replenish ferrum lost due to excess sludge emission. The in situ preparation of iron hydroxide is realized through chemical reaction between ferric trichloride solution and sodium bicarbonate solution which are mixed in a centrifugal pump. According to the method, the problems that in the existing saprobiont denitrification system, the running stability is poor, and low temperature has impact on the activity of the nitrification function microbiology, can be effectively solved, the biochemical reaction activity of the nitrification function microbiology is improved, and the low temperature impact resistant capacity of the biology denitrification system is improved.

The invention discloses a model training method and device, a task processing method, a storage medium and a processor. The method comprises the steps that first data is used for training to obtain a first model, the first data are data with labels, and the first model is a teacher model; performing anti-disturbance on the first model based on the first data to obtain second data, the second data being data with pseudo labels; and training by adopting the first data and the second data to obtain a second model, the second model being a student model. The method solves the technical problems that a model training method in the related technology cannot realize model training under a small amount of marked data, and the active perception and defending capability of the anti-risk is poor.

The embodiments of the invention disclose an exam and training system based on banking services. The system includes a server and a client. The server includes a personal information database, an exam question database and a scoring module. The personal information database stores person information of users. The exam question database stores exam questions and training questions. The scoring module is intended for reading the exam questions stored in the server so as to score the exam questions corresponding to the client. The client includes a login module, an input module, an exam module and a display module. The login module is an interface for logging in the exam and training system. The input module allows the user to select and input. The exam module provides exam questions to the user. The display module is a window which presents at least exam questions. The exam question database includes a past exam question database and a training exam question database. According to the embodiment of the invention, the system addresses the problems existed in the exam and training for bank workers.

The invention provides an accurate and small-computation remote sensing image super-resolution reconstruction method combining a two-parameter beta process with a dictionary, and belongs to the technical field of remote sensing image processing. The method comprises the following steps: S1, inputting a low-resolution image to be reconstructed, a high-resolution image dictionary D(x), a low-resolution image dictionary D(y) and a mapping matrix A; S2, performing sparse coding on the input low-resolution image according to a dictionary D (y) to obtain a low-resolution sparse coefficient, mappinga high-resolution sparse coefficient corresponding to the low-resolution sparse coefficient by using a matrix A, and reconstructing a high-resolution image by using the high-resolution sparse coefficient and a dictionary D (x); the acquisition method of the dictionary D (x), the dictionary D (y) and the matrix A comprises the steps that according to paired high-resolution and low-resolution training images, the dictionary D (x), the dictionary D (y) and the matrix A are acquired through a double-parameter beta process, the matrix A is a mapping matrix from sparse coefficients of the dictionaryD (y) to sparse coefficients of the dictionary D (x), and the sparse coefficients are products of coefficient weights and dictionary atoms.

The invention belongs to the field of scene text recognition, and relates to a scene text recognition method based on generative adversarial style migration, which is implemented by a processor executing a program instruction and comprises the following steps: in a preheating training stage, implementing supervised learning training on a scene text recognition network by using a first scene text picture data set; in the synchronous training stage, setting a co-training network at the input end of the scene text recognition network, and using a second scene text picture data set for conducting synchronous training based on generative adversarial on the co-training network and an overall network formed by the co-training network and the scene text recognition network, wherein the co-training network comprises a generator for providing scene text pictures for the scene text recognition network; according to the accuracy of the scene text recognition network on the verification set after synchronous training, selecting model parameters of the scene text recognition network, and using the scene text recognition network to recognize text information of the scene text picture under the model parameters. According to the method, the scene identification problem under the condition of less real data can be effectively solved.

A movable type true fire training system comprises a vehicle-mounted platform, wherein a control and device machine room is arranged at the front-portion position the vehicle-mounted platform and connected with a training room, a vacant portion is reserved at the rear portion of the vehicle-mounted platform, an electrical control system and a gas supply system are arranged in the control and device machine room, a first burning plate and a second burning plate are arranged in the training room, a flashover plate is arranged at the top of the training room, and the training room is provided with three inlets. The movable type true fire training system can be conveniently transported to a unit having the training demand for training and is small in occupied area and convenient to use. A scene can be quickly customized in training space according to different training demand and is close to life. True fire fuel is liquefied petroleum gas, the effect is life-like, and the fire can be obtained in a training field simply and safely.

The invention discloses a method for training users through a training system, which includes: learning the rules of the training system; improving personal data and upgrading to become an expert user; performing commission task training, and obtaining points to upgrade the user level by completing the set commission task; Carry out dividend task training. Client users share with friends through social tools. Friends download and promote the client to complete the commission task. Users who promote the client get dividend income and accumulate dividend income points to improve user level; improve the quality of users in the circle of relatives and friends; Consumption training guides the dividends earned by users of the promotion client to be withdrawn as change in the promotion client, and their user level can be improved by accumulating points through consumption. The present invention can quickly solve user training problems through rewards and upgrade guidance, can automatically guide users to grow rapidly in batches, can improve the operation level of the entire promotion client, and can quickly improve the growth and competitiveness of the promotion client.

The invention discloses a target detection training method and system, electronic equipment and a computer readable storage medium, and the method comprises the steps: extracting features from an image through a backbone, and obtaining an image feature; obtaining noise features of the image features through a noise method; optimizing the RPN network by using a consistency regularization method according to the image features and the noise features; outputting proposals by the RPN network of which the image features; obtaining noise proposals by the proposals through a noise method; optimizingthe roi_heads network by using a consistency regularization method according to the proposals and the noise proposals, and optimizing the roiheads network by using a consistency regularization methodaccording to the roiheads and the noise proposals; and obtaining a final full classification result and a final position result through the roiheads network by the proposals. On the basis of a consistency regularization technical route in the field of image classification, unlabeled data can be used for network training in the field of target detection, so that the precision of a network structureis improved, and higher detection performance is achieved.

The invention relates to a substation training device based on a PLC (programmable logic controller). The substation training device comprises a base, an operating display screen, an alternating current air switch, an alternating current contactor, an intermediate relay, the PLC, a grounding fault setting device, a wiring terminal block, PLC signal input devices and PLC signal output devices, wherein the base is provided with a power plug, and the operating display screen comprises a box shell, a simulation circuit board, a warning device, an isolation switch operating rocking rod, a displayer I, a displayer II and a plurality of voltmeters. The substation training device has the advantages that through controlling the operating display screen by the PLC, one-time analogue simulation training of a substation is realized, the training problem of electricity utilization operating personnel is solved, the educational and training quality of trainees is fast promoted, the acceptability of electricians for network access operation is greatly improved, the operation is visual and vivid, the trainees can easily grasp the learned key points, a notable effect is produced, and the problem that the actual operation is very complex and is difficult to grasp is solved.