141 results about "Si model" patented technology

The invention discloses a transverse federated learning system optimization method and device, equipment and a readable storage medium. The method comprises the steps that a target type of local modelparameter updating needing to be sent by each participating device in each round of model updating is determined from parameter updating types according to a preset strategy, and the parameter updating types comprise model parameter information and gradient information; sending indication information for indicating the target type to each participation device, so that each participation device performs local training according to the indication information and returns local model parameter update of the target type; and fusing the local model parameter updates of the target type received fromeach participating device, and sending the global model parameter updates obtained by fusion to each participating device, so that each participating device performs model updating according to the global model parameter updates. According to the method, the advantages of a gradient averaging algorithm and a model averaging algorithm are combined, and a hybrid federated averaging mechanism is realized.

The invention relates to an experiment method for jointly and synchronously measuring model motion track, object plane pressure and space velocity field during pitching-rolling two-degree-of-freedom dynamic simulation motion control process. A pitching motion mechanism and a model support rod are simultaneously driven through an industrial personal computer to respectively drive the model to make pitching and rolling motion along preset tracks. When the model moves to a position requiring data acquisition, the industrial personal computer sends an instruction to pressure measuring equipment and PIV (Particle Image Velocimetry) equipment at multiple points, and the actual motion position, the object plane pressure and the space velocity field of the model are collected synchronously. Experiment results show that two-degree-of-freedom coupled simulation motion has high precision under the control of PID (Proportion Integration Differentiation) closed loop feedback, and the error of synchronous measurement can be reduced by transmitting an external trigger signal of synchronous measurement according to a partitioning method. The joint and synchronous experiment technology provides an effective research means for the analysis of the large angle-of-attack unsteady flow mechanism.

The invention provides an outdoor three-dimensional scene combined construction method based on image content parsing. The outdoor three-dimensional scene combined construction method comprises the following steps of: acquiring scene layout information by parsing an image map, acquiring geometric structure information of scene objects by parsing a plurality of internet images of a scene, and combining the scene layout information with the structure information of the objects to obtain a description of three-dimensional information of the integral scene; and constructing a three-dimensional object material library, retrieving a model in the library by semantic and model feature information, converting each object or object part in the described scene into a specific three-dimensional model, carrying out operations of deformation and the like according to the layout and structure information of the scene, and finally, obtaining a combined construction result of the integral outdoor three-dimensional scene . The three-dimensional scene description method provided by the invention can effectively describe the layout, structure and semantic information of the three-dimensional scene and provides basics for retrieval; and on the other hand, the invention also provides a model retrieving and matching reference method.

The embodiment of the invention provides a model training method, a model prediction method and a model control system. Configuration information of the target model is obtained, the configuration information comprises a first storage path and training resource information, a training sample set and training model information required for training the target model are read from a storage space corresponding to the first storage path, and the training model information is used for determining a model training program required for training the target model; and based on the training resource information, n training nodes are selected from the server cluster, n is greater than 1, and through the n training nodes and the model training program, based on the training sample set, a target model is obtained through training. Automatic model training is realized, and the convenience of model training is improved.

The invention discloses a method and a system for efficiently estimating the near-surface PM2.5 (particular matter 2.5) concentration. The method includes a model building step and a model estimating step. The model building step further includes a data compressing sub-step, extracting main spectral signal structure characteristics of remotely sensed data; a data matching sub-step, extracting corresponding remotely sensed information according to spatial coordinates of PM2.5 ground monitoring data; a model building sub-step, building estimation models according matched data sets. The model estimating step further includes an estimation requesting sub-step, preprocessing estimation input data; an estimating sub-step, estimating the near-surface PM2.5 concentration according to estimation requests and outputting estimation results. The method and the system have the advantages that the near-ground PM2.5 concentration can be estimated according to the MODIS (moderate resolution imaging spectroradiometer) observation data on the basis of the artificial neural network models, and accordingly remote sensing operational monitoring requirements can be met; the method and the system can support importing of meteorological factors, and accordingly the near-surface PM2.5 concentration can be quickly and efficiently dynamically monitored on a large scale.

A method for modelling a saturation dependant property in a sample by obtaining a spatially resolved measurement of fluid saturation in the sample; determining a measured value of a saturation dependant property from the measurement; fitting a model to the measured value to obtain a model value for the measured value; and optimising the fit of the model to the measured value by minimizing an error between the model and the measured value where the error is a distance between the measured value and the model value.

The invention discloses a machine learning model selection method, mainly comprising the steps of model setting, training testing, model evaluation, model selection, prediction reasoning and model monitoring. The adopted algorithm model is selected through the model selection strategy and the model evaluation result, and the model selection process is decomposed into multiple aspects such as resource consumption, performance and business risks, and necessary and key processes involved in machine learning model selection are covered more widely, and the machine learning model selection method can be suitable for selection of various types of machine learning algorithm models and is high in universality. Meanwhile, multiple dimensions such as resource consumption, performance and business risk are adopted as the basis of model selection, and the engineering cost and the business risk are introduced into the model selection process besides the conventional model performance, and the highengineering availability and the low application risk of the algorithm model are effectively ensured, and the practical value is high.

The invention discloses an image vehicle attitude estimation method based on a fine three-dimensional model. The method comprises steps: first, the position of the vehicle being detected from the image, and initializing the vehicle three-dimensional posture parameters, rendering the vehicle three-dimensional model to the image plane using the current posture parameters, extracting the contours ofthe image vehicle and the model vehicle respectively, using the matching error between the contours to construct the energy function, and adopting the Gauss-Newton algorithm to optimize the posture parameters, solving the energy function minimization problem, and obtaining the final result. In contour matching, the improved random sampling consistency (RANSAC) algorithm is used to fit the contourof the curve model with piecewise straight lines, and then the contour is matched with the real vehicle contour. The invention can accurately and robustly recover the three-dimensional posture parameters of the vehicle relative to the camera in the surveillance video in the complex surveillance scene, which is of great significance to the understanding and automatic processing of the surveillancevideo.

The invention discloses a multi-model control method based on self learning. The multi-model control method comprises the steps that (1) a model base is built, and the model base consists of a group of local models of a non-linear model; (2) a group of controllers are built, and a group of local controllers are designed according to the local model in the model base; (3) the performance evaluation is executed: output errors and differences between system output y and model output yi are observed, and a performance feedback or value function is calculated or sent to an API (application program interface) module on the basis of signals; and (4) a similar policy iteration algorithm is executed: performance feedback signals are observed, error signals between reference output and system output are received, the signals are used as the Markov decision process states, and meanwhile, the states are fed back to become return signals for enhancing the leaning. The multi-model control method has the advantages that the principle is simple, the application range is wide, the reliability is high, the general performance and the convergence of the control can be ensured, and the like.

The automatic modeling method of the business service model comprises the steps of constructing a model, training the model, evaluating the model and applying the model, and performing continuous dataexploration and model optimization in the process of training the model and the application model; Constructing a model: carrying out data cleaning, data mining, data conversion and data writing by uploading a training set, automatically and quickly matching an algorithm model, and providing multi-dimension assessment model quality; Training a model: training the model through the test set by using a machine learning technology; And model application: applying the model to a real business scene to verify the model, and outputting a visual chart report according to business requirements afterthe model is evaluated and optimized through a test set. The invention provides a big data technology automatic modeling method covering a data mining program module, a machine learning module and anartificial intelligence program module for the whole industry.

The embodiment of the invention provides a three-dimensional simulation method and a system based on virtual reality. The method comprises the following steps: creating an equipment initial model based on equipment basic information; optimizing the equipment initial model to obtain a model optimization result; rendering the equipment initial model to obtain a model rendering result; segmenting theequipment initial model to obtain a model segmentation result; importing the model optimization result, the model rendering instance and the model segmentation result into preset software to obtain preset model import information; performing simulation scene building on the preset model import information based on the environment elements to obtain a model simulation scene building result; and performing scene motion simulation interaction design on the model simulation scene building result by adopting a hybrid collision detection algorithm to obtain an equipment three-dimensional simulationresult. According to the embodiment of the invention, three-dimensional display is carried out by combining equipment with VR interactive operation, so that the authenticity of the equipment is improved, and the user experience is improved by using vivid simulation animation.

The invention discloses a multi-view three-dimensional model retrieval method and a multi-view three-dimensional model retrieval system based on pairing depth feature learning. The multi-view three-dimensional model retrieval method comprises the steps of: acquiring two-dimensional views of a to-be-retrieved three-dimensional model at different angles, and extracting an initial view descriptor ofeach two-dimensional view; aggregating the plurality of initial view descriptors to obtain a final view descriptor; extracting potential features and category features of the final view descriptor respectively; performing weighted combination on the potential features and the category features to form a shape descriptor; and performing similarity calculation on the obtained shape descriptor and ashape descriptor of the three-dimensional model in a database to realize retrieval of the multi-view three-dimensional model. According to the multi-view three-dimensional model retrieval method, a multi-view three-dimensional model retrieval framework GPDFL is provided, potential features and category features of the model are fused, and the feature recognition capability and the model retrievalperformance can be improved.

The invention provides a method, a device and equipment for executing an automatic machine learning process. The method comprises the following steps: providing a model training operator and a model prediction operator which are independent of each other; training a machine learning model by using the model training operator based on training data; and providing a prediction service for predictiondata by using the model prediction operator and the trained machine learning model.

The invention discloses a random forest integration method improved through a width neural network, and is suitable for the field of machine learning. The method mainly comprises two parts: model design and model training. The model design mainly comprises two parts: the design of a feature mapping layer and an enhancement layer, and the design of an output weight. A neural network node composed of a random forest and a complete random forest is designed so as to adaptively adjust the width of a model. A local weight is obtained through the mean accuracy of the nodes, and the output weight iscalculated, and finally a final output vector is solved. The method is high in automation degree, adaptively decides the size of the model through the training, is easy for theoretical analysis, is good in interpretability and is strong in parallelization capability.

The invention discloses an opportunity spectrum access method based on a sideband observation information multi-arm tiger machine model. The method comprises the following steps: firstly, aiming at asecondary user access problem under the conditions that cognitive network channel information is unknown and channel perception is imperfect, carrying out detection perception on N channels, and selecting a channel problem suitable for access to be modeled as MABP-SI model; sensing all authorized channels by a secondary user, recording a sensing observation result, updating a probability vector, and recording a channel set which is sensed to be idle; creating a candidate set of spatial probability vector estimated values, and selecting an estimated value of an idle probability vector; judgingwhether the idle channel set is empty or not at the time slot t: if the set is empty, not accessing the secondary user to any channel, and if the set is not empty, selecting k authorized channels withthe maximum coefficient to access; and finally, updating t to t + 1, and circulating. According to the method, the income loss of channel access under the statistical asymptotic condition is reduced,and the method has the advantage of statistical asymptotic effectiveness.

A method includes obtaining (402) operating data associated with operation of a cross-directional industrial process controlled by at least one model-based process controller (106, 204). The method also includes, during a training period (502a, 502b), performing (406) closed-loop model identification with a first portion of the operating data to identify multiple sets of first spatial and temporalmodels. The method further includes identifying (408) clusters (604) associated with parameter values of the first spatial and temporal models. The method also includes, during a testing period (504a, 504b), performing (410) closed-loop model identification with a second portion of the operating data to identify second spatial and temporal models. The method further includes determining (412) whether at least one parameter value of at least one of the second spatial and temporal models falls outside at least one of the clusters. In addition, the method includes, in response to such a determination (414), detecting that a mismatch exists between actual and modeled behaviors of the industrial process.

A method for compressing a machine learning model by an electronic device. The method may comprise determining a compression parameter of a set hidden layer in a model based on a pruning number of respective channels included in the set hidden layer and a pruning loss of each hidden layer of the model; and compressing the model based on the compression parameter of the set hidden layer. The compression parameter may be related to a pruning of the model.

The invention discloses a blast furnace molten iron silicon content prediction method and a device based on LSTM & DNN. The method comprises the following steps: dividing for obtaining a time lag attribute, a related attribute and a redundancy attribute based on a Pearson correlation coefficient; respectively constructing an LSTM blast furnace silicon content model and a DNN blast furnace siliconcontent model by utilizing the divided attribute data; performing weighted fusion on the LSTM model and the DNN model through a BP neural network to obtain a blast furnace molten iron silicon contentprediction model; according to the method, attribute division is carried out on the basis of the Pearson correlation coefficient, redundant attributes are removed, and the correlation attributes are stripped, so that the pressure of the LSTM model can be effectively relieved, the calculation speed is increased, and the model prediction effect is improved; the long-term and short-term memory capability of the LSTM network is utilized to effectively solve the large time lag characteristic of the blast furnace data; the DNN model is used for mining high-dimensional features of related attributes,so that the LSTM & DNN-based blast furnace molten iron silicon content prediction model has memory ability and generalization ability.

The embodiment of the invention provides a BIM model and GIS model registration method, and the method comprises the steps: respectively extracting the feature points of a BIM model and a GIS model, carrying out the rough matching of the feature points of the BIM model and the feature points of the GIS model to obatin a point pair after rough matching; performing accurate matching on the point pairs after rough matching, and establishing a one-to-one correspondence point pair relationship between the feature points of the BIM model and the feature points of the GIS model; and based on the one-to-one correspondence point pair relationship, performing spatial registration on the BIM model and the GIS model, and solving registration attitude parameters. According to the embodiment of the invention, the BIM model and the GIS model are automatically and accurately matched, and the attitude registration parameters of the BIM model and the GIS model are solved, so that the BIM model is coordinated with the scene position and orientation of the original GIS model and does not conflict with the original GIS model after being arranged, the efficiency is ensured, the manual intervention intensity is reduced, and the automation degree is improved.

A technical system is described by several abstract or concrete models which have a mutual relationship. Information / attributes can be associated with model elements and “inherited” through these relationships. While the approach can basically be applied to different types and models of machines, it is described more particularly for hierarchical component models. Model elements in these models are referred to as components, assemblies, etc.

The present disclosure relates to a computer-based method for 3D simulation of oil and gas operations. According to an aspect, the method comprises:—selecting from a database comprising data related to a plurality of equipments and a plurality of environments, one environment and at least one equipment;—loading, using a processor, core data and 3D models related to the selected environment and equipment(s), wherein the core data and 3D models are stored in the database;—determining, using the processor, the position of the selected equipment(s) in the selected environment, based on the core data of the equipment(s) and environment;—generating, using the 3D models and the determined position of the equipment(s), a 3D representation of a scene comprising the selected environment and equipment(s);—displaying views and / or animations related to the equipment(s) and / or environment upon request of a user, wherein the views and / or animations are derived from the 3D models.

The embodiment of the invention discloses a searching method of a machine learning model and a related device, and equipment. Specifically, the invention relates to the technical field of artificial intelligence, the method comprises the following steps of: searching and quantifying a model; generating a plurality of pure bit models according to the to-be-quantized model, further obtaining an evaluation parameter of each layer structure in the plurality of pure bit models; further, selecting one candidate model from the candidate set for training and testing; obtaining a target model, the quantitative weight of each layer of structure in a target model can be determined based on the network structure of the target model and the evaluation parameter of each layer of structure in the targetmodel; therefore, the layer structure with the maximum quantization weight in the target model is quantified, the quantified model is added into the candidate set, frequent information interaction with the terminal can be reduced, and the efficiency of model search and model quantization is improved.

The invention relates to an optimized traffic flow prediction model based on a space-time diagram convolutional network. Traffic flow prediction is defined as follows: for a specific road network structure, traffic flow data of several time steps in the future are predicted according to traffic flow data of several time steps recorded historically, wherein the model establishment comprises spatial correlation modeling; the structure of the graph is represented through a self-adaptive adjacency matrix obtained through model training; time correlation modeling is carried out, the calculation process of the gate and the hidden state of the GRU is full-connection operation, and GCN is used for replacing the gate and the hidden state of the GRU; a TPA mechanism is introduced; loss function is adopted, and the purpose of designing and training the model is to minimize an error between a model prediction value and a real value of a road node. According to the method, the accurate prediction precision of the short-time traffic flow is improved, the capability of the model for analyzing the data of the graph structure is enhanced, and the time-space dependence of the traffic flow can be fully mined, so that the prediction precision of the short-time traffic flow and the convergence speed of the model are improved.

The invention provides a shipbuilding-oriented one-stop construction system and method for multiple industrial mechanism models. The system comprises a modeling preparation layer, a basic model construction layer, a model reconstruction layer and a model storage layer, wherein the modeling preparation layer is used for collecting and summarizing model demand characteristic information; the basic model construction layer is used for modeling a basic model; the model reconstruction layer is used for further editing and reconstructing the basic model; and the model storage layer is used for analyzing the types of the models generated by the basic model construction layer and the model reconstruction layer, and storing the models in corresponding model libraries in a classified manner according to the types of the models. According to the one-stop construction system for the multiple industrial mechanism models for shipbuilding, the multiple industrial mechanism models involved in the industry are rapidly constructed and regenerated through a one-stop basic model development and model regeneration technology, so the construction speed and quality of the models are improved, and high-quality development of shipbuilding is assisted.

The invention belongs to the field of computer graphics and statistics, and particularly discloses a casting three-dimensional feature extraction and similarity measurement method in combination withprocess parameters. The casting three-dimensional feature extraction and similarity measurement method comprises the following steps: S1, performing preliminary matching on a to-be-matched casting model and models in a model library so as to select a plurality of models from the model library as preliminary matching models; S2, obtaining the overall modulus, the average wall thickness and the maximum wall thickness of each preliminary matching model and the to-be-matched casting model, and calculating the overall modulus distance value, the average wall thickness distance value and the maximumwall thickness distance value of each preliminary matching model and the to-be-matched casting model respectively; and S3, calculating a weighted distance value between each preliminary matching model and the to-be-matched casting model, and sorting the preliminary matching models again, thereby determining a final matching model. The casting three-dimensional feature extraction and similarity measurement method completes similarity measurement of the casting model in combination with casting process parameters, overcomes the defects that an existing matching method is insensitive to model scale changes and individual models cannot be distinguished, and is good at the matching effect.

The invention discloses a wind tunnel test device which at least comprises a model, a wind tunnel test section and a model position adjusting part, the wind tunnel test section is located over the model position adjusting part, and the model position adjusting part is connected with the model and can send the model into or take the model out of the wind tunnel test section. The function of bringing the model into or out of the wind field after the flow field is established in the wind tunnel test section is realized, so that the impact load caused by the opening and closing of the supersonic wind tunnel is eliminated, and the test safety of the large-wingspan aircraft model is ensured.

The invention discloses a test device for measuring thrust of a shock tunnel. The test device comprises a supporting seat, a balance, a model balance adapter, a fairing and an accelerometer. The supporting seat, the balance and the model balance adapter are sequentially arranged from bottom to top, the fairing is fixedly mounted on the supporting seat and surrounds the outer sides of the balance and the model balance adapter, and the inner wall of the fairing is not in contact with the balance and the model balance adapter. The accelerometer is installed on the balance. According to the test device provided by the invention, the two balances are combined to measure the thrust acting on the model, the thrust measuring requirements of models with different lengths can be met by adjusting the distance between the two balances, the adaptability of the balances is good, the manufacturing cost of the balances is saved, the measurement signal is compensated through the accelerometer, and the measurement requirement of the shock tunnel in extremely short effective test time is met.

The invention relates to the related field of machine learning of privacy protection, in particular to a vertical federated modeling method based on a LightGBM algorithm, which comprises vertical federated modeling preparation work, initiator P1 data preparation work, partner P0 data preparation work, model training work and model evaluation work. The invention provides a novel longitudinal federated learning system structure based on a tree model, the two parties are allowed to construct a joint training model on the premise of protecting data privacy, and the two parties are enabled to train the joint model without the help of a trusted coordinator, so that a gradient value is protected, and the security of a protocol is improved; the architecture of the method is easy to expand, and besides two-party model training, the architecture of the method supports multi-party joint modeling; and according to the longitudinal federation learning based on the lightGBM algorithm, safety, speed, accuracy, support category features and continuous features are comprehensively considered, a large amount of training data can be processed under the same data set and features, and the method is suitable for engineering.

The invention relates to a method for applying a cut triangular patch to ridge repair of a three-dimensional model, which adopts a computer for processing and comprises the following steps: acquiring a to-be-processed model file; wherein the to-be-processed model file is a model file intersected with the target ridge; wherein the to-be-processed model file comprises corresponding model file data; wherein the model file data comprises geometric information of the model file and texture information of the model file; wherein the geometric information of the model file comprises coordinates of vertexes and texture coordinates of triangles in a spatial triangulation network; carrying out thread task allocation on the to-be-processed model file to obtain a plurality of thread tasks; executing a plurality of thread tasks to obtain a second ridge line; wherein the second ridge line is a ridge line after the target ridge line is repaired; wherein each thread task is to obtain a triangle corresponding to the model file according to a target ridge drawn in advance by one model file, and fuse the model files according to the triangle.