38results about How to "Reduce callouts" patented technology

The invention discloses a model training method for cross-domain sentiment analysis based on a convolutional neural network, belongs to the field of cross-domain sentiment classification, and aims tosolve the problem of cross-domain sentiment analysis. The method comprises: S1, preprocessing a text; S2, training a word vector model; S3, performing cross-domain model migration; wherein the step S3is carried out in sequence; a neural network model is trained through a source domain; migrating the trained model. Firstly, a weight value of a convolution kernel in a model is shared, a convolutionkernel weight trained in a source domain is used for extracting corresponding features in a target domain, a small part of data in the target domain is trained again, parameters of a full connectionlayer weight of the previously trained model are adjusted, and the effect is that model migration is conducted on a cross-domain emotion text.

The invention discloses a Tibetan speech corpus labeling method and a Tibetan speech corpus labeling system based on cooperative batch active learning. The system comprises a sample selection module,a manual labeling module, a labeling decision-making module, a labeling person evaluation module and a training set generation module. According to the method and the system, the construction of a sample evaluation function and the proving of the submodular function property of the sample evaluation function are solved through the adjacent optimal batch sample selection method, and the construction of a labeling decision function and the modeling of a labeling person evaluation model and a labeling person auxiliary learning model are solved through the labeling committee collaborative labelingmethod. In addition, the system disclosed by the invention can realize the functions inducing the optimal selection of a sample, the labeling and evaluation of a user, the sharing of labeling information and Tibetan speech knowledge, auxiliary learning of the labeling person and the like, so that the labeling quality of the Tibetan speech data is improved, and the construction of the speech corpus is accelerated.

The invention relates to a handing method and device of a locomotive replacing operation based on a CTC3.0 system. The handing method comprises the steps that a locomotive detaching-hanging function module is increased in the CTC3.0 system, the locomotive detaching-hanging function is realized by using an original plan, operation flow and route state information in the CTC3.0 system; according tothe interlocking route information, the operation arrangement conditions of a lead locomotive entering and leaving the warehouse are designed as the basis for planning, train planning train number information and the line information and signals or track occupancy conditions of the locomotive entering and leaving the warehouse are utilized, the internal related information of the lead locomotive and the train number are discovered, the calculation of planned orders for the lead locomotive detaching-hanging operation is realized, and the handling of detaching-hanging and route of locomotives through the detaching-hanging operation plan is completed. Compared with the prior art, according to the handling method and device of locomotive replacing operation based on the CTC 3.0 system, the problem of unmanned operation of the locomotive operation plan at an existing general speed hub station is solved, and the advantage that the safety jaw control is carried out when the locomotive detaching-hanging operation is handled is realized.

The invention discloses a behavior recognition method based on a multi-instance markov model. The behavior recognition method includes the following steps that local features of each video are abstracted and a feature histogram of a local video block is used for expressing certain local movement of behavior; a random sampling manner is used for obtaining multiple local video blocks, the local video blocks form multiple markov chains and the markov chains are expressed as continuous movement of some local movement in time; under a frame of multi-instance study, the model selects the markov chain with the most recognition performance to express behavior. In the time of testing, multiple markov chains are composed in the same manner to express the videos and the scores of the markov chains are calculated. If the scores of the markov chains are larger than a certain threshold value, the markov chains belong to the behavior, and otherwise, the markov chains do not belong to the behavior. According to the multi-instance markov model, the aim of behavior recognition under complicated scenes is achieved and labels of the videos are reduced.

The invention relates to a training method of a pulmonary nodule detection model, and belongs to the technical field of medical image processing. The training method comprises the steps that an original CT image sample is acquired, the original CT image sample comprises a labeled sample and an unlabeled sample, the labeled sample is cut, and a pulmonary nodule block sample is obtained; training apositioning model according to the labeled samples; pre-training the classification model according to the pulmonary nodule block sample; sequentially inputting unlabeled samples into the trained positioning model and the pre-trained classification model, and classifying the pulmonary nodule blocks; and combining the classified pulmonary nodule blocks and pulmonary nodule block samples into training set data, and training the classification model again to obtain a final classification model. Unlabeled pulmonary nodule blocks are classified through the pre-trained classification model, more training samples are obtained, the classification model is trained through the more training samples, the labeling process is reduced, and the training efficiency of the pulmonary nodule detection modelis improved.

The invention discloses a model training method and device, equipment and a storage medium. The model training method comprises the following steps: performing model training based on a training data set to obtain an intermediate model; based on a preset data acquisition type, acquiring data from the to-be-labeled data set as to-be-tested data, and generating a model reasoning result of the to-be-tested data based on the current intermediate model; based on the dynamic test set and the fixed test set, testing and evaluating the model reasoning result; and if the evaluation result of the fixed test set does not meet the standard condition, adjusting a preset data acquisition type based on the evaluation result of the dynamic test set, performing iterative training on the intermediate model based on the to-be-tested data corresponding to the adjusted preset data acquisition type until the evaluation result of the fixed test set meets the standard condition, and obtaining a target model. Under the driving of small sample annotation data, dynamic model evaluation is carried out, model iteration is rapidly carried out, and then the effect of rapidly generating a model conforming to annotation is achieved.

The invention provides a key point detection model training method, a key point detection method and a device thereof, and the training method comprises the steps: obtaining a training sample set; the training samples of the first type comprise images, key point positions and key point availability, and the training samples of the second type comprise images; a teacher network is trained by using the training sample of the first type and the first total loss function; the first total loss function comprises loss for constraining the position and availability of the key point; when the training sample is selected as a second type, the training sample is input into the trained teacher network and student network to obtain a key point position and availability prediction result; the prediction result is input into a second total loss function so as to train a student network through knowledge distillation; and the second total loss function comprises a key point position for constraining the student network to learn the teacher network and the loss of the availability prediction result. Therefore, the marking time consumption can be reduced, the low-quality image detection effect is improved, and the detection real-time performance is improved.

The invention discloses a 3D point cloud semantic segmentation and migration method based on meta-learning, and relates to the technical field of robot navigation. The method comprises the following steps: constructing a Point Net network model; selecting a training data set; for each training data set, forming a training task set by utilizing different types of data; constructing a meta-learningframework; according to the meta-learning framework, training the Point Net network model through each training task set; selecting a test task set; and inputting the test task set into the trained Point Net network model for testing until the gradient update value of the model converges. According to the method, a trained model is loaded in a new environment task, optimal similar task parametersare used, and the training efficiency of the indoor scene semantic segmentation method of the new task is high; the semantic segmentation capability of different tasks is learned through a meta-learning framework, it is ensured that learning features can be suitable for different migration environments, and the generalization performance of the model is improved.

The invention relates to the field of image processing in artificial intelligence, and discloses a target tracking method and device, computer equipment and a storage medium. The method comprises thesteps that a real-time mechanical arm state and an image sequence containing a target object are acquired according to a preset sampling period; the real-time mechanical arm state and the image sequence containing the target object are input into a target object tracking model, and motion parameters output by the target object tracking model are obtained, wherein the target object tracking model is a prediction model constructed based on a DDPG algorithm; and the motion state of the mechanical arm is controlled according to the motion parameters to enable the mechanical arm to move following the target object. The target tracking method and device can reduce the development cost of the target tracking, improve the training efficiency of the target tracking model, and can be applied to theconstruction of the smart city. At the same time, the target tracking method and device also relates to a blockchain technology.

The invention provides an integrated meter tail structure which includes a wire fixing portion, a lead, a first meter tail portion and a second meter tail portion. The wire fixing portion is of a plate-shaped structure provided with a wire fixing round hole, a cantilever rod and a buckle. The lead passes through the wire fixing round hole and is integrally fixed in the wire fixing portion to forma wire concentration area. The wire fixing portion is fixedly connected with the first meter tail portion through the cantilever rod and the buckle, and the first meter tail portion is disposed abovethe second meter tail portion. In the structure, the lead is integrally fixed on the wire fixing portion and forms an integrated structure together with the meter tail portions, to ensure that the position of a component is fixed, so that the lead can be soldered in one time by a wave soldering technology in the same manner as other components, the repeated soldering is avoided, and the productionefficiency of an electric meter is improved. The invention further provides the electric meter with the integrated meter tail structure. The electric meter is simple in structure and convenient to assemble, internal elements are connected firmly, and it is ensured that the electric meter has good overall usability.

The embodiment of the invention provides an RPA and AI combined question and answer knowledge base updating method and device, equipment and a storage medium. The method comprises the steps of obtaining to-be-audited questions; calculating an uncertain index of each to-be-audited question in a preset question and answer knowledge base; determining a target question in the to-be-audited questions according to the uncertain indexes; and obtaining knowledge points matched with the target question, and adding the target question into a preset question and answer knowledge base according to the matched knowledge points so as to update the preset question and answer knowledge base. Because a small number of to-be-audited questions with relatively high uncertainty are screened out from a large number of to-be-audited questions as target questions according to the uncertainty indexes of the to-be-audited questions, and the knowledge points of the small number of target questions are labeled and matched by a data trainer, the processes of manually screening and labeling the knowledge points of the to-be-audited questions can be reduced. Therefore, the matching efficiency of the knowledge points corresponding to the to-be-audited questions is improved, the updating efficiency of the question-answer knowledge base is improved, and the performance of the question-answer knowledge base canbe rapidly improved.

The embodiment of the invention discloses a training method for enhancing an image generation model, and an image processing method and device. The training method of the enhanced image generation model comprises the following steps: acquiring a to-be-processed sample image, and determining the category of the to-be-processed sample image, wherein the category comprises a plain scanning image and an enhanced image; identifying a region of interest of the to-be-processed sample image to obtain a sample image including the region of interest; and training an initial model based on the sample image including the region of interest and the category of the sample image to obtain an enhanced image generation model. Through the technical scheme disclosed by the embodiment of the invention, the contrast of the enhanced image and the application flexibility of the plain-scan image are improved, the injury to a patient due to multiple times of injection of the enhanced contrast agent is avoided, and the diagnosis efficiency of a doctor is improved.

The invention relates to the technical field of computers, in particular to a data annotation method and device of a target element, terminal equipment and a computer readable storage medium, and the method comprises the following steps: determining a to-be-annotated target element based on a game; the method comprises the following steps: determining to-be-labeled data after screen recording in a game process, labeling a target element in the to-be-labeled data to form labeled data, labeling at least four point locations when labeling the target element, forming a rectangular frame when the four point locations are connected end to end, and enabling the target element to be in the rectangular frame; preprocessing the annotation data through the script to form training data, wherein the training data at least comprises a plurality of to-be-annotated pictures; establishing a training model according to the training data, and training the training model through a script; and training a new model according to the training data, the script and the trained training model. The invention provides a method capable of reducing manual labeling of target elements.

The invention relates to the artificial intelligence technology, and discloses a text brief report generation method comprising the following steps: using a first brief report set and a first categorylabel set to train a pre-constructed original label classification network so as to obtain a standard label classification network, using the standard label classification network to execute label classification operation on a second brief report set, obtaining a second category label set, summarizing the first category label set and the second category label set to obtain a category label set, dividing the first brief report set and the second brief report set by using the category label set to obtain a category brief report set, generating a brief report template by using the category briefreport set, receiving a brief report text to be generated, and executing named entity identification on the brief report text to be generated to obtain an entity set, and executing a combination operation on the entity set and the brief report template to obtain a brief report. The invention further provides a text brief report generation device, electronic equipment and a computer readable storage medium. According to the invention, the calculation resource can be saved, and the brief report generation accuracy is improved.

The invention discloses a cross-supervised model training method, an image segmentation method and related equipment. The cross-supervised model training method comprises the following steps: acquiring a sample image; the sample image is input into a basic twin network, the twin network comprises a first network and a second network which are the same in network structure, and basic parameters of the first network are different from basic parameters of the second network; calculating the loss of the second network by taking the prediction result of the first network as the label of the second network, and calculating the loss of the first network by taking the prediction result of the second network as the label of the first network so as to obtain the loss of the twin network; iteratively updating the parameters of the first network and the second network based on the loss of the twin network until a training cut-off condition is met; and taking the trained and updated first network or second network as a target model. By means of the mode, annotation of the sample images can be reduced, and the model operation accuracy is improved.

The embodiment of the invention discloses an image semantic understanding method and device, equipment and a storage medium. The method comprises the steps that target image information is acquired; the target image information is input into a pre-trained semantic understanding model, a semantic classification result output by the semantic understanding model is obtained, and the semantic understanding model is obtained through training based on associated image information and text information; and determining a semantic understanding result according to the semantic classification result. According to the method provided by the embodiment of the invention, the target image information is classified through the semantic understanding model obtained by training directly based on the obtained associated image information and text information, so that the annotation amount during model training is simplified, and the semantic understanding model with accurate classification can be obtained by training with less annotation.