101results about How to "Shorten the training period" patented technology

The invention discloses a polyphone model training method for speech synthesis, and a speech synthesis method and device. The method comprises the following steps of processing a voice data set and a text set so as to generate a training corpus set, wherein the text set corresponds to the voice data set, and the training corpus set comprises texts and Pinyin sequences corresponding to the texts; extracting feature information of the texts; and training polyphone models according to the feature information and the Pinyin sequence. According to the polyphone model training method for speech synthesis, in a polyphone model training process, manual labeling on Pinyin of the texts is not required, a training period of the polyphone models is greatly shortened, meanwhile, the circumstance that the trained polyphone models are inaccurate due to wrong manual labeling is avoided, and accuracy of the trained polyphone models is improved.

The invention discloses a real terrain simulation-based parachuting landing training system and method. A parachuting landing training simulation system closer to actual parachuting conditions is built by adopting the advanced technologies of parachuting landing simulation, virtual reality, real terrain simulation and the like, and the landing training problem is solved; the training system solves the problem that lift-off parachuting training is influenced by the airplane guarantee and the weather, the in-air and landing scenes of parachuting are simulated by using a ground device, thus the training cycle can be effectively shortened, the training consumption and security risks are decreased, and the training effect is obvious; and the training system integrates the landing site judging training, parachute controlling training and landing buffer movement training which are independent in the original ground training way, thereby being capable of closer to actual lift-off parachuting conditions, the training level of parachuters is effectively improved, and the mental tension of parachuters is alleviated.

A portable drilling simulation system includes a main control computer, a graphic processing computer, a choke console and a blowout preventer console. The main control computer, the graphic processing computer, the choke console and the blowout preventer console are interconnected by a network and serial ports. The blowout preventer console includes a blowout preventer control panel. On the left of the blowout preventer control panel is provided a blowout preventer control zone. On the upper right is provided a throttling manifold control zone. On the lower right is provided a high-pressure manifold control zone. The flow plug console includes a flow plug control panel. A main control program runs on the main control computer and a graphic processing program runs on the graphic computer. The regular blowout preventer console, the regular throttling manifold, the regular high-pressure manifold and the regular flow plug console are integrated into the portable drilling simulation system, thus the simulated operation of several apparatuses on the control panel is realized.

The invention provides a method for training an acoustic model based on CTC (Connectionist Temporal Classification). The method comprises the steps of 1, training an initial GMM (Gaussian Mixture Model), wherein time point forced alignment on text annotation of training data by using the GMM to obtain a time region corresponding to each phoneme; 2, inserting a blank symbol associated with the phoneme behind each phoneme, wherein each phoneme has a unique blank symbol; 3, constructing a CTC forward and backward calculated search path diagram for a phoneme annotation sequence with the blank symbols being added by adopting a finite state machine; 4, restricting the appearance time range of each phoneme according to a time alignment result, pruning the search path diagram, and cutting off thepath with the phoneme position exceeding the time restrictions so as to obtain a final search path diagram required by calculating a network error in CTC; and 5, performing acoustic model training byadopting the combination of a time-delay neural network (TDNN) structure and the CTC method to obtain a final TDNN-CTC acoustic model.

The invention discloses a data model dual-drive GFDM receiver and method. The method comprises the steps: respectively obtaining a channel estimation and a signal detection neural network; taking thereal-number result of a matrix comprising transmitted pilot frequency information and a received time domain pilot frequency vector as the input of a channel estimation neural network, and outputtingan estimation of the frequency domain channel state information; obtaining an equivalent channel matrix, taking the real-number result of the equivalent channel matrix and the received time domain signal vector as the input of a signal detection neural network, and outputting the result as the estimation of a GFDM symbol; establishing a demapping neural network, taking an estimation of the GFDM symbol outputted by the signal detection neural network as the input, and outputting the estimation as the estimation of the original bit information; determining the output of the demapping network andthe size of a set threshold, and outputting a detection result of the original bit information according to a determination result. The method has the advantages that the training parameters do not change with the data dimension, the training speed is fast, and the adaptability to different channel environments is strong.

The invention relates to the technical field of biological recognition, and provides a body posture recognition method based on LSTM. The body posture recognition method comprises the steps: obtainingan action video of a to-be-recognized main body; extracting action feature information in the acquired action video of the to-be-identified main body through OpenPose, wherein the action feature information at least comprises skeleton key point information; and recognizing an action specification degree corresponding to the action feature information according to the action feature information and a pre-trained and generated body posture recognition model, wherein the body posture recognition model is a target neural network model generated according to a preset standard action, and the target neural network model is generated by training according to standard action feature information arranged according to a time sequence. According to the body posture recognition method, video actionsdo not need to be cut into isolated features to be recognized, and learning recognition is carried out through cooperation with the neural network model, and the body posture recognition process is rapid and accurate, and the user experience is improved.

The invention discloses a high-speed railway onboard signal simulation training system and method. The high-speed railway onboard signal simulation training system comprises an external environment simulation device, an internal processing device and a training effect feedback device, wherein the external environment simulation device is configured to simulate a ground environment and a train operating state so as to provide train operating conditions to the internal processing device, and to receive train protection information outputted by the internal processing device; the internal processing device is configured to compute in accordance with operating signals of a train driver and the train operating conditions so as to generate the train protection information; and the training effect feedback device is configured to conduct quantitative evaluation on the operations of the train driver in accordance with computation data of the internal processing device and the train operating conditions. The high-speed railway onboard signal simulation training system and method provided by the invention can reduce training cost and shorten a practical training cycle.

The invention discloses a recommendation model training method and device, computer equipment and a storage medium, and belongs to the technical field of computers. According to the method, the secondpreference information of the user for the unpurchased object is preliminarily predicted based on the first preference information of the user for the purchased object, and the features of the user dimension and the features of the object dimension are extracted from the obtained first preference information and second preference information respectively; then the currently acquired preference information based on the features of the two dimensions is corrected to obtain third preference information with higher accuracy, and the training data is generated according to the third preference information so that data modeling is not required to be directly performed according to original online data, and the complexity and time consumption of a training data generation process are reduced; therefore, the training period of the model is shortened, the model can be updated in time according to the latest user data, and the output result of the model can better accord with the current user preference.

Embodiments of the invention provide a patient inquiry conversation simulation method and apparatus, a terminal device and a storage medium. The patient inquiry conversation simulation method comprises the steps of obtaining an inquiry statement input by a user, searching for a first response statement corresponding to the inquiry statement in an inquiry matching template, and if the first response statement corresponding to the inquiry statement exists in the inquiry matching template, outputting the first response statement to the user; and if the first response statement corresponding to the inquiry statement does not exist, determining a corresponding second response statement according to a semantic attribute type of the inquiry statement, and outputting the second response statementto the user. Therefore, the training cost can be reduced and the training cycle can be shortened.

The invention discloses a spinal intervertebral foramen surgery model. The model comprises an artificial spine composed of multiple segments of artificial vertebral bones,an artificial intervertebraldisc located between every two adjacent artificial vertebral bones,an intervertebral disc protrusion simulated object bonded to each artificial intervertebral disc and artificial tissue wrapped on theexterior of the artificial spine; the intervertebral disc protrusion simulated objects are made of viscose fibers and bonded to arbitrary positions of the corresponding artificial intervertebral discs. The intervertebral disc protrusion simulated objects made of the viscose fibers can be glued to appropriate positions outside the corresponding intervertebral discs to simulate different types of intervertebral disc protrusions,and the shape and size of the intervertebral disc protrusion simulated objects can be adjusted according to needs to be better in line with the real state of the human body. Compared with a traditional mode of using corpses or non-dedicated models for training,the model has the advantages that the cost of training doctors can be greatly reduced,the repeatability is excellent,surgical modes are diversified,and the training period can be shortened.

The invention discloses skydiving simulation training equipment, which mainly comprises a suspension bracket system, a control system, a dynamic system, a visual system, a software system, a sound equipment system, a command control system and an evaluation system. According to the invention, in a suspension bracket and harness system, when a parachutist jumps out of a cabin, the flying direction is controlled through a control belt. A sensor is operated to transmit the operation information to a computer. A motion system is driven after operation, and the simulated innervation is provided. Meanwhile, the computer outputs visual images to a head-mounted display, and an immersion type virtual environment is provided. A teacher sets a system initial condition through a teacher table and monitors all students. The invention further provides a simulation training method based on the utilization of the above training equipment. By the adoption of the method and the equipment, the training safety of the skydiving person can be guaranteed. The training period is greatly shortened, and the skydiving training cost is reduced. Great military benefits, economic benefits and social benefits can be generated.

The invention discloses a balanced sample set construction method and device, a duplicated image recognition method and device, equipment and a medium. The method comprises the steps of obtaining an unbalanced duplicated sample set and a preset target balanced sample number; inputting the unbalanced copying sample set into a DVAE-GAN network model in the balanced sample set construction model fortraining; recording the converged DVAE-GAN network model as a trained DVAE-GAN network model until the total loss value of the DVAE-GAN network model reaches a preset convergence condition; if the target equalization sample number is greater than the positive sample number and the negative sample number at the same time, randomly generating a positive copy simulation sample and a negative copy simulation sample by the trained DVAE-GAN network model; and obtaining a balanced copy sample set for training a copy recognition model. According to the invention, the sufficient number of balanced copying samples are generated through the balanced sample set construction model to be trained by the copying recognition model, the time for collecting the copying samples can be shortened, and the recognition accuracy and the hit rate of the copying recognition model are improved.

The invention discloses a picture set classification method and device. The method comprises the steps that a first picture set is acquired from a to-be-classified picture set, the picture types of the first picture set are known, and the number of the picture types is a preset number; target models matched with the preset number are selected from preset classification models, and the target models are branch networks divided according to the type number in the preset classification models; model parameters of the target model are updated through the first picture set; and the second picture set is classified by using the target model of which the model parameters are updated to obtain a classification result of the second picture set, the second picture set being a partial or total picture set remaining after the first picture set is removed from the to-be-classified picture set. According to the invention, the technical problem of low picture classification efficiency due to the factthat a computer image processing method for deep learning in the prior art needs a long time to carry out model training is solved.

The invention discloses a UAV human body health monitoring method and a UAV human body health monitoring system based on intelligent visual reconstruction. The UAV human body health monitoring methodcomprises the steps of: step 1, constructing a human body natural physical sign database and a human body extreme sports database; step 2, utilizing human body heights obtained by means of a ZED camera carried on a UAV to screen a human body group to be identified; step 3, step 3, performing rapid identification on human body face images to be identified by utilizing a generalized regression neural network based on a Hadoop platform; step 4, tracking identified human bodies by means of the UAV, acquiring health data of human bodies to be identified and transmitting the health data to a remoteserver by means of wearable equipment; step 5, and inputting the acquired health data into a human body health assessment model based on a PID neural network, so as to obtain health detection degreesof the human bodies to be identified. The UAV human body health monitoring method and the UAV human body health monitoring system are based on a big data platform, employ the wearable equipment, the UAV and the binocular camera to acquire outdoor sports human body health information, and perform real-time health monitoring, so as to make a reasonable decision.