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163results about How to "Improve training efficiency" patented technology

Machine learning model training method and device

The invention discloses a machine learning model training method and device. The method comprises the following steps: on the basis of initialized first weight and second weight of each sample in a training set, and with features of each sample as granularity, training a machine learning model; on the basis of prediction loss of each sample in the training set, determining a first sample set, where corresponding target variables are predicated inaccurately, and a second sample set, where corresponding target variables are predicated accurately; on the basis of the prediction loss of each sample in the first sample set and the corresponding first weight, determining overall prediction loss of the first sample set; on the basis of the overall prediction loss of the first sample set, improving the first weight and the second weight of each sample in the first sample set; and inputting the updated second weight of each sample in the training set and features of each sample and the target variables into the machine learning model, and with the features of each sample as granularity, training the machine learning model. Through the machine learning model training method and device, prediction accuracy and training efficiency of the machine learning model can be improved.
Owner:TENCENT TECH (SHENZHEN) CO LTD

Multifunctional welding piece fixture dedicated to welding training

The invention relates to the work fixture technology, in particular to a multifunctional welding piece fixture dedicated to welding training, comprising a pedestal, an upright post, a transverse arm, a clamping part; the bottom of the upright post and the pedestal are connected into a whole, the transverse arm comprises a transverse beam and a sleeve, one end of the transverse beam is connected with the sleeve, and the other end of the transverse beam is connected with the clamping part, the sleeve is sheathed on the upright post, the outside of the sleeve is provided with a tapped hole penetrating through the pipe, the tapped hole is connected with a sleeve fastening handle by a thread, and the sleeve fastening handle is screwed up to clamp the sleeve on the upright post in a firm way. The invention can adjust the height and angle of the welding piece; a pipe fixture is connected with the transverse arm and the transverse beam by a bolt, a plate fixture is connected with the transverse arm and the transverse beam in a suiting way, thus being capable of changing the welding position of the specimen and realizing simulating multiple welding positions; the clamping operation is convenient and simple, thus improving training efficiency; the clamping is steady, and the specimen is uneasy to sway and fall off, thus being safe and reliable.
Owner:WUHAN YIYE STEEL STRUCTURE

Automated training device and method for behavior learning of non-human primates

InactiveCN101715739AEasy to operateImprove training efficiencyTaming and training devicesNon human primateBehavior learning
The invention discloses an automated training device for behavior learning of non-human primates, wherein a personal computer (PC) is provided with two displays, wherein one of the displays and a camera are respectively positioned in front of an animal seat; a water supply control system comprises a water receiver, a driving control circuit and an electromagnetic valve, wherein the water receiver is connected with the electromagnetic valve through a guide tube, the electromagnetic valve is electrically connected with the driving control circuit, and the driving control circuit is electrically connected with a microcontroller; a swinging rod and a water faucet are fixed on the animal seat, a pressure sensor is fixed on a handle of the swinging rod, the pressure sensor and a potentiometer of the swinging rod are electrically connected with the microcontroller respectively, and the water faucet is connected with the electromagnetic valve of the water supply control system through the guide tube; and the PC is electrically connected with the camera and the microcontroller respectively. The invention can realize a painless automated animal behavior training method, and the camera can be used for displaying and monitoring the animal behavior in real time, thereby greatly improving the training speed and saving human resources.
Owner:ZHEJIANG UNIV

Mechanical structure for multi-axis wireless movement method of flight simulator

The invention discloses a mechanical structure for a multi-axis wireless movement method of a flight simulator. The mechanical structure employs a plurality of gyroscope-type ring structures and comprises an outer ring, an inner ring, a central ring, a base, servo motors, a simulator cockpit, a power supply system and a wireless receiving and transmitting apparatus, wherein the base is fixedly connected with the outer ring; an X-direction servo motor and an X-direction support shaft are connected with a Y-direction support shaft together so as to control X-direction rotation; a Y-direction servo motor on the Y-direction support shaft and the Y-direction support shaft are staggered from X direction by 90 degrees, and are internally connected with the central ring so as to control Y-direction rotation; a Z-direction servo motor and a Z-direction support shaft on the central ring are staggered from Y direction by 90 degrees, and are internally connected with the simulator cockpit so as to control Z-direction rotation. The simulator cockpit can rotate around three axes of X, Y and Z in a three-dimensional coordinate, thus movement change situation of an airplane can be simulated. The mechanical structure for the multi-axis wireless movement method of the flight simulator disclosed by the invention has the advantages of improving the training efficiency, saving the training cost, guaranteeing flying security, reducing pollution, being simple in structure and low in cost.
Owner:徐强

Language recognition method based on language model and text classification method and device

The invention relates to a language recognition method and device based on a language model, a text classification method and device, computer equipment and a storage medium. The method comprises thesteps of obtaining a training word vector corresponding to a training statement, and inputting the training word vector into a to-be-trained first model and a trained second model to obtain a featurematrix output by each first network layer of the first model and a feature matrix output by each second network layer of the second model, wherein the first network layers and the second network layers are in one-to-one correspondence, and the network layer number of the first model is smaller than that of the second model; performing similarity calculation on the feature matrix output by each first network layer and the feature matrix output by the second network layer corresponding to each first network layer; and obtaining each similarity, adjusting model parameters of the first model basedon each similarity until the updated target similarity meets a convergence condition, obtaining a trained first model, and performing language recognition through the first model. The method can improve the model training efficiency.
Owner:TENCENT TECH (SHENZHEN) CO LTD

Dense stacking target detection method based on automatic labeling and transfer learning

The invention discloses a dense stacking target detection method based on automatic labeling and transfer learning. The dense stacking target detection method comprises the following steps: establishing a labeled training image set by high-resolution image segmentation; inputting the labeled training image set into a pre-trained target detection model YOLOv3, optimizing a priori frame size and a loss function of the YOLOv3 model, and performing fine adjustment on the model by using the training image set; and finally, inputting a to-be-detected image into the fine-tuned YOLOv3 model, outputting the classification of the target sub-regions and the positions of the sub-regions, splicing output result images to recover the output result images into an original image, and counting a total counting result. The dense stacking target detection method provided by the invention has strong anti-interference performance and robustness, and has low requirements on an image photographer and a photographing illumination condition; through an unsupervised learning method, quasi-automatic annotation of the image is realized, and the workload of manual annotation is greatly reduced, and the model training efficiency is improved; and the dense stacking target detection method can be used for image recognition of a large number of densely stacked targets which are mutually shielded, and is suitable for automatic counting scenes of various densely stacked targets.
Owner:NANJING COLLEGE OF INFORMATION TECH

Auxiliary volleyball training device for sports

The invention relates to the technical field of sport equipment, in particular to an auxiliary volleyball training device for sports; the auxiliary volleyball training device for sports comprises a mounting plate, wherein supporting columns are fixedly connected to the bottoms of the left end and the right end of the mounting plate respectively; trundles are arranged at the bottom of the supporting columns; a lifting mechanism fixedly connected with the mounting plate is arranged between the supporting columns on the two sides; a shell is fixedly connected to the top of the mounting plate; a ball releasing mechanism is arranged at the top of the shell; and a ball serving mechanism connected with the ball releasing mechanism is arranged on the inner side of the shell. Due to the arrangementof the ball releasing mechanism, the volleyball balls in a ball releasing bucket can fall into a ball releasing tube one by one, so that the ball serving is more ordered; the ball serving mechanism is arranged, so that the volleyball balls can be automatically sent out during use, the waste of manpower in ball serving training is avoided, ball serving training is can be performed continuously, and the training efficiency of athletes is greatly improved; and by arranging an adjusting mechanism, the ball releasing tube can move up and down, the hit position of the volleyball can be adjusted, and different rotation of the volleyball can be realized.
Owner:SUZHOU YIKADI SPORTS EQUIP CO LTD

Fly-by-wire device for trainer aircraft

ActiveCN101707021AImprove training efficiencyAchieving conciseness requirementsTeaching apparatusDriver/operatorTransverse axis
The invention relates to a fly-by-wire device for a trainer aircraft, which is characterized in that: a longitudinal linkage rod, a transverse linkage rod and a course linkage rod passes through two sides of each of front and back cockpits; and a displacement sensor, an eddy-current damper and an adjustment mechanism are arranged on the front and back cockpits respectively. The device has the advantages that: 1) the mechanism linkage is realized in both the front and back cockpits operated by a longitudinal axis, a transverse axis and a course axis, so the mechanical and electrical properties of the front and back cockpits are completely consistent, which contributes to the improvement of training efficiency and the safe operation of the aircraft; 2) the operating rod systems of the front and back cockpits are changed by a few driving rod systems to pass through from two sides of a control console, so the simple layout requirements of the cockpits are met; and 3) the driving displacement sensor, the eddy-current damper, the adjustment mechanism and the like are distributed in the front and back cockpits on the premise of ensuring the important operating properties of the systems, so the whole driver operation device can make a flexible and high-efficiency use of the space of the front and back cockpits in the front and back cockpits.
Owner:JIANGXI HONGDU AVIATION IND GRP

Short text sentiment classification method based on CNN-BiMGU model

The invention discloses a short text sentiment classification method based on a CNN-BiMGU model, and belongs to the technical field of deep learning and natural language processing. The CNN-BiMGU model mainly comprises an embedding layer, a convolution layer, a pooling layer, a BiMGU layer, an attention mechanism layer, a full connection layer and a classification layer, wherein the embedding layer encodes a data set containing commodity comments into word vectors, the convolution layer extracts text feature matrixes from the word vectors through a CNN channel, the pooling layer performs dimension reduction on the text feature matrixes, and the BiMGU layer is used for obtaining hidden state vectors; wherein the attention mechanism layer is used for strengthening important information, thefull connection layer is used for splicing output of the pooling layer and the attention mechanism layer, and the classification layer is used for obtaining final emotion categories in a classified mode. According to the CNN-BiMGU model, the CNN channel and the BiMGU channel are connected in parallel, the defect that the CNN channel ignores the relation before and after the features is overcome, the attention mechanism is fused into the BiMGU channel, important features in context semantic features are highlighted, and the accuracy of short text emotion classification is improved.
Owner:NANJING UNIV OF POSTS & TELECOMM

Table tennis ball accompanying training robot with visual recognition function

The invention discloses a table tennis ball accompanying training robot with a visual recognition function. The robot comprises a table tennis ball serving module, a table tennis ball recovery storagemodule, a visual module and a main control module; the table tennis ball serving module comprises a holder, a table tennis ball conveying mechanism and a ball serving mechanism; the holder realizes all-directional serving; the table tennis ball conveying mechanism is composed of a conveying pipe and a shifting wheel, and conveys a table tennis ball to the ball serving mechanism from a ball storage box on a machine frame; the ball serving mechanism is composed of a serving pipe and two friction wheels, and realizes the control of the ball speed and the self rotation of the table tennis ball byadjusting the speed of a motor; the visual module comprises a camera and a processor; the camera collects images and transmits the images to the processor, recognizes the orientation of a user through a visual recognition algorithm built in the processor, and transmits orientation information to the main control module; and the main control module processes data transmitted by the visual module through an algorithm, and controls the table tennis ball serving module to perform serving. By simulating an actual table tennis court, the training efficiency is improved.
Owner:XIAN JIAOTONG LIVERPOOL UNIV

A short-term load prediction method considering somatosensory temperature and radiation intensity

ActiveCN109886567AReduce the dimensionality of the neural networkImprove training efficiencyResourcesTraining loadPrediction methods
The invention discloses a short-term load prediction method considering somatosensory temperature and radiation intensity. The short-term load prediction method comprises the following steps of 1) inquiring sample data such as historical load and weather; 2) calculating historical somatosensory temperature data and a daily load level; 3) selecting an optimal'mode similar day 'from the historical sample data set on the basis of the day type information and meteorological data of the to-be-predicted day, and finally calculating to obtain a normalized load curve; 4) establishing a neural networkprediction model considering the somatosensory temperature and the sunlight intensity to obtain the load level of the to-be-predicted day; and 5) calculating load data of the to-be-predicted day through the normalization curve and the load level. According to the method, the influence on the somatosensory temperature of the load and the sunlight intensity of the distributed photovoltaic power generation are fully considered, the change rule of the historical load is fully considered, the load level and the load mode are separately predicted, the input dimension of the neural network is reduced, the network training load is reduced, and the calculation efficiency is improved.
Owner:NARI TECH CO LTD +4

Intelligent insertion plate, rehabilitation training system and rehabilitation interconnection training system

The invention relates to an intelligent insertion plate, a rehabilitation training system and a rehabilitation interconnection training system. The intelligent insertion plate comprises a shell, a plurality of induction capacitor modules and a processor; a plurality of blind holes are formed in the shell; the induction capacitor modules correspond to the blind holes one to one; the processor is electrically connected with the induction capacitor modules, and when insertion parts are inserted into the blind holes and the corresponding induction capacitor modules are triggered, the processor acquires state information of the corresponding induction capacitor modules and generates training data according to the state information. According to the intelligent insertion plate, collection of thetraining data of a trainer in the rehabilitation training process is achieved, the trainer can better judge the training condition of the trainer according to the collected training data and give more specific training task information, the problem is solved that traditional training is completely dependent on the trainer so that the efficiency can be low, and the training efficiency is improved.
Owner:广州市章和智能科技有限责任公司

SerDes link parameter automatic debugging method

ActiveCN108933600AImprove training efficiencyParallel/series conversionCoding detailsHigh rateSelf adaptive
Provided is a SerDes link parameter automatic debugging method. The method includes steps: selecting corresponding coding and decoding modes, a forward error correction method and a check code for a link clock and a channel characteristic, then determining a pre-emphasis parameter of a TX side of a link through a backtracking method, experiencing a code stream learning process, and determining anadaptive configuration parameter through training at an RX side of the link. According to the method, strict sequential control of the training of the TX side and the RX side is performed, the link parameter configuration deviation caused by interference generated by bidirectional communication of the link is avoided, and the accuracy of the set parameter of the link can be effectively guaranteed;furthermore, the TX side performs backtracking training on the pre-emphasis parameter currently selected by the link according to an error reporting condition of the FEC decoding of the RX side link,the parameter configuration selected by each time of training can be determined according to the characteristic of the link, and the training efficiency is improved. The method is applicable to various router backboards, and automatic debugging of various link parameters is realized for high-rate SerDes links.
Owner:SANECHIPS TECH CO LTD

Network traffic classification method based on feature strong correlation

The invention provides a network traffic classification method based on feature strong correlation, which comprises the following four steps of: inputting data to be trained, and performing feature extraction on the input data to form a multi-dimensional feature vector set; calculating the correlation between the features by using the mutual information between the features and the response variables; calculating redundancy among the features according to the mutual information, and selecting the feature with the highest score as a final feature vector through iterative calculation; and constructing a network traffic classification model based on feature strong correlation according to the classification target and obtaining a classification result. According to the method, the correlationbetween features can be fully utilized, and the feature with the maximum correlation and the minimum redundancy is extracted in the learner training process. Under the same classification model, theclassification efficiency can be effectively improved on the premise that the classification precision is guaranteed, and the defect caused by the fact that correlation between features is not considered in an existing feature selection method based on heuristic search is overcome.
Owner:NANJING UNIV OF POSTS & TELECOMM

Dialogue generation method based on near-end strategy optimization and adversarial learning

The invention relates to a dialogue generation method based on near-end strategy optimization and adversarial learning, and belongs to the field of computer natural language processing. The dialogue generation method comprises the steps of firstly pre-training a generation model and a discrimination model of an adversarial generative network; then, adopting a Monte Carlo sampling method to calculate awards corresponding to each word in the generated sentence, wherein the size of an award value represents the quality of word generation; secondly, taking the training process of the adversarial generative network as a reinforcement learning process, and training the adversarial generative network by using a near-end strategy optimization algorithm, so that awards obtained by the discrimination model can guide the generation of the generative model, and dialogues obtained by the generative model can guide the training of the discrimination model; and finally, training the generation modelby using a forced guidance method. According to the dialogue generation method, the training efficiency of the model is improved by controlling self-adaptive multiple iterations of the generation model, and the complexity of the sample is improved through the near-end strategy optimization algorithm, and the dialogue generation quality is further improved, and the dialogue closer to human beings can be generated.
Owner:KUNMING UNIV OF SCI & TECH

Multi-component radar signal intra-pulse modulation mode identification method

The invention relates to the field of automatic recognition algorithms of deep learning, in particular to a multi-component radar signal intra-pulse modulation mode recognition method. The method comprises the following steps of acquiring time-frequency images of the single-component or overlapped multi-component radar signals of several different intra-pulse modulation modes; preprocessing the radar signal time-frequency image by using an image processing algorithm, and making a training set and a test set by using the signal type contained in the radar signal as a label; designing a pre-training network based on a convolutional neural network to extract radar signal time-frequency image features, and designing a multi-component signal classification network based on reinforcement learning to obtain a classification and recognition result; training, testing and perfecting a network structure and parameters; classification and identification of multi-component signals are realized. Themulti-component radar signal identification algorithm provided by the invention has a wide radar signal type application range and relatively high identification accuracy under the condition of low signal-to-noise ratio, and realizes intra-pulse modulation mode identification of randomly overlapped multi-component radar signals.
Owner:HARBIN ENG UNIV

Publication PDF layout analysis and recognition method based on mixing of multiple neural networks

The invention relates to a publication PDF layout analysis and recognition method based on mixing of multiple neural networks, belongs to the technical field of image recognition and PDF layout analysis. The method comprises: using a multi-task training mode to recognize, segmenting and marking PDF layouts including paragraphs, titles and illustrations, locating text lines and then recognizing texts. According to the method, on the aspect of layout recognition, through a multi-task training mode, row and structure recognition annotation is completed at the same time, manual participation is not needed in the whole process, and PDF text structure information is effectively reserved. According to data with PDF text structure information obtained through layout analysis, a common Chinese dictionary for version data is constructed, and a text recognition model is trained in a targeted manner, so that the recognition precision of the model in a PDF printed text recognition task is greatly improved. The recognized text also has structural information, an original PDF layout structure is restored, and subsequent secondary editing, electronic book manufacturing and book content knowledge mining are also facilitated.
Owner:重庆华龙网海数科技有限公司
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