61 results about "Parametric transformation" patented technology

Methods, systems, and articles of manufacture for viewing annotations created for data objects appearing in a data source using a second application configured to access and view the data source. Some embodiments present users viewing a data source with an indication of data objects in the data source that have been by other applications accessing the data source. An annotation system may use a mapping component to transform a set of parameters suitable for the first application to identify an annotated data object into a set of parameters suitable for the second application to view an annotated data object in a common data source.

A method and system for modeling an integrated circuit. The method includes converting a representation of the integrated circuit into design shapes of design levels of a design of the integrated circuit; adding control shapes to the design, the control shapes not defining any physical part of the integrated circuit; extracting layout-dependent stress parameters of the devices from the design levels of the design based on the control shapes and the design shapes; converting the layout-dependent stress parameters to stress parameters using a stress algorithm; generating stressed device parameters from the stress parameters using a compact model; and simulating performance of the integrated circuit using the stressed device parameters in a simulation model of the integrated circuit design.

The invention discloses a seven-parameter conversion method between a national three-dimensional coordinate system and a random local plane coordinate system. The seven-parameter conversion method between the national three-dimensional coordinate system and the random local plane coordinate system aims at the problems that an existing algorithm cannot directly obtain a high-precision single seven-parameter conversion parameter, and further the achievements of a local coordinate system cannot be obtained in real time based on CORS measured data, and provides a seven-parameter convention model to which addition constants are additionally added. According to the method, through a series of data processing methods, the additionally added addition constants greatly decrease the influence of projection deformation; the influence of a geodetic height on parameter solving precision, and the like are decreased through a three-parameter conversion model; the precision of a conversion parameter is greatly improved; moreover, the method accords with the requirement that a measurement handbook in the current market can only accept single four / seven-parameter input; the achievements of a national coordinate system, which are obtained through measurement based on a CORS network, can be converted into the achievements of the local coordinate system in real time; in this way, the expansion of the application spaces of provincial and municipal CORSs in a measurement practice process is facilitated.

The invention discloses a visual application component arrangement method and system, and belongs to the field of computer software, and the method comprises the following steps: S1, visual configuration; S2, parameter verification; S3, conversion of parameters; and S4, application deployment. In the step S1, the visual operation form is a connection and dragging form carried out on a visual interface, in the step S1, parameter configuration work is completed while visual operation is carried out, and the dependency relationship of the configuration basic application is the service and component on which the configuration basic application depends. According to the invention, the application components are arranged in a visual manner, the workload and the error rate of deployment personnelcan be reduced, the efficiency is improved, deployers do not need to consider the deployed configuration file format, the parameter conversion engine can automatically generate the corresponding configuration file in the Json or Yaml format according to user requirements, and due to the fact that the parameter configuration uses the interface form, the user experience is improved, and the use difficulty of green hands is reduced.

An avionics interface data adaptation conversion system disclosed by the invention is few in computing resources, high in running speed and high in test efficiency, and is realized through the following technical scheme: selecting a man-machine operation interface unit to be read by the interior of the system and converted into an internal source code, sealing a conversion function for realizing parameter conversion between ICDs in a framework of the system, and displaying a mapping relation existing in the mapping project; enabling the ICD mapping project unit to store the ICD mapping scheme as a project file in an XML format; and enabling the ICD conversion code generation unit to convert an ICD mapping relation contained in an ICD mapping project file in the ICD mapping project unit into a source code, achieving parameter conversion between ICDs in a function mode, enabling the ICD conversion code editing window unit to directly edit received binary data in a code editing window according to a message name, converting element relations, establishing a relative mapping relationship, and outputting a conversion result by the interface data conversion system.

The invention relates to the data processing technology, and provides a parameter conversion method and a device based on a rule engine, computer equipment and a storage medium, and the method comprises the steps: obtaining a target parameter conversion rule corresponding to a rule publishing instruction when the rule publishing instruction is received; analyzing the target parameter conversion rule to obtain a target parameter set; inputting the target parameter set into a pre-trained link calculation model to obtain an optimal execution link; receiving and analyzing the parameter conversion request to obtain a target interface parameter corresponding to the parameter conversion request; calling the optimal execution link according to the target interface parameter to obtain a parameter conversion result; and outputting the parameter conversion result. The method can improve the parameter conversion efficiency, and promotes the quick development of a smart city.

The invention provides a neural network model quantification method and device, and electronic equipment, and relates to the technical field of machine learning, and the method comprises the steps: obtaining a neural network model, wherein the neural network model comprises a convolution layer, a normalization layer and a quantization activation layer, and the output feature of the quantization activation layer is an integer type feature; converting the parameters of the convolution layer into integer type parameters; combining the normalization layer and the quantization activation layer to obtain a combined layer; and converting the parameters of the merging layer into integer type parameters to obtain a quantized neural network model. According to the method, the operation complexity ofthe neural network model is reduced, so that the quantized neural network model can be accelerated to run on target equipment only supporting fixed-point or low-bit-width operation.

A method of recognizing a first shape in a second shape. The method includes decomposing the first shape into at least one subshape belonging to one of a plurality of subshape groups, and searching the second shape for a parametric transformation of the subshape.

The invention discloses a method and device for determining configuration parameters of an air purifier. The method comprises the steps that contextual parameters are obtained, wherein the contextualparameter are bases for generating the configuration parameters of the air purifier, and the configuration parameters are bases for the operation of the air purifier; each parameter in the contextualparameters is converted into an input for a neural network model, wherein the neural network model is derived through training of training data, and each set of training data comprises a contextual parameter and a corresponding configuration parameter under the contextual parameter; the outputs of the neural network model are acquired; the outputs are converted into the configuration parameters, and the configuration parameters are sent to the air purifier. According to the method and device for determining the configuration parameters of the air purifier, the technical problems that in the related art, the intelligent degree of the air purifier is low, and the user experience is low due to tedious use are solved.

The invention provides a filter S parameter dephasing loading method and a system, a medium and equipment, and relates to the technical field of filter modeling simulation design. The method comprisesthe steps that a second admittance parameter Yq'is converted into a second scattering parameter, a coupling matrix M is converted into a third scattering parameter, and a comprehensive evaluation function obj (alpha beta) is established according to the second scattering parameter and the third scattering parameter; and a phase shift factor pair (alpha beta) closest to a theoretical value is found according to the comprehensive evaluation function obj (alpha beta). According to the method, a stable dephasing loading method is established, and an intelligent optimization algorithm is combined,so that a globally optimal solution of a phase shift factor during modeling parameter extraction of the microwave filter is found. The original S parameter dephasing loading meets the multi-attributeconstraint condition of filter extraction modeling, so that the algorithm is more stable, and the calculation precision is higher.

The invention discloses a method and device for determining configuration parameters of a dehumidifier. The method comprises the steps that scene parameters are acquired, wherein the scene parametersare the basis for generating the configuration parameters of the dehumidifier, and the configuration parameters are the basis for operation of the dehumidifier; each parameter in the scene parametersis converted into the input of a neural network model, wherein the neural network model is acquired by training data, and each group of training data comprises the scene parameters and the corresponding configuration parameters under the scene parameters; the output of the neural network model is acquired; and the output is converted to the configuration parameters, and the configuration parameters are sent to the dehumidifier. The method solves the technical problems that the intelligentization degree of a dehumidifier in the related art is low, and the user experience is low due to the factthat the dehumidifier is tedious to use.

The invention provides a method for converting transformer zero-sequence test data into zero-sequence parameters used for computation. By designing a transformer zero-sequence test data list and a nameplate parameter list, the transformer test data and nameplate parameters are managed in a standardized mode, and according to the PSD-SWNT and PASAP requirements, the transformer zero-sequence test data are converted into the transformer zero-sequence parameters used for stability computation. By means of the method, the transformer zero-sequence test data can be conveniently converted to be in the data format needed by PSD-SWNT and PSASP transformer models and transformer zero-sequence parameters obtained through actual measurement can be all stored into a database. According to practical application, the method efficiently manages transformer zero-sequence information such as the zero-sequence branch connecting mode and zero-sequence test parameters, and transformer zero-sequence branches used for stabilizing a simulation program can be formed automatically through programs. Thus, the accuracy rate and the efficiency at which simulation computation workers prepare transformer stability computation data are improved.

The information processing apparatus (2000) of the example embodiment 1 includes an acquisition unit (2020), a clustering unit (2040), a transformation unit (2060) and modeling unit (2080). Until a predetermined termination condition is determined, the clustering unit (2040) repeatedly preforms: 1) optimizing the posterior parameters for clustering assignment for each data streams; 2) optimizes the posterior parameters for each determined cluster and for each time frame; 3) optimizes the posterior parameters for individual responses for each data stream; 4) optimizes the posterior parameters for latent states, via approximating the observation model through non-conjugate inference. The transformation unit (2060) transforms the latent states into parameters of the observation model, through a transformation function. The modeling unit (2060) generates the model data, which including all the optimized parameters of all the model latent variables, optimized inside the clustering unit (2040).

The invention discloses a neural machine translation decoding acceleration method based on a discrete attention mechanism, and the method comprises the steps: constructing a neural machine translationmodel for training a parallel corpus and based on an attention mechanism; generating a machine translation word list through the parallel corpus; carrying out the further training to obtain a model parameter after training convergence, and enabling the model parameter to serve as a baseline system; converting partial parameters of the attention mechanism in the network into integer values by scaling the model parameter values, and mapping floating-point numbers into the integer section of the integer values; replacing the normalized part which cannot be subjected to integer calculation with alinear structure which is beneficial to integer calculation; controlling whether all numerical values participate in operation or not through a threshold value before the linear structure; and adjusting the selected threshold value on 1%-2% of data randomly extracted from the training data, so that a better translation result can be achieved. According to the method, the real-time corresponding speed can be improved on the basis of the latest implementation of rapid reasoning and on the premise that the model performance is hardly reduced.

The invention discloses a system (1) for processing images (4) comprising a main neural network (2), preferably convolution-based (CNN), and at least one preprocessing neural network (6), preferably convolution-based, upstream of the main neural network (2), for carrying out before processing by the main neural network (2) at least one parametric transformation f, differentiable with respect to its parameters, this transformation being applied to at least part of the pixels of the image and being of the form P' = f(V(p), Theta) where p is a processed pixel of the original image or of a decomposition of this image, p' the pixel of the transformed image or of its decomposition, V(p) is a neighborhood of the pixel p, and Theta is a vector of parameters, the preprocessing neural network (6) having at least part of its learning be performed simultaneously with that of the main neural network (2).

The analyzing apparatus: generates first internal data; converts a position of first feature data in a feature space, based on the first internal data and a second learning parameter; reallocates, based on a result of first conversion and the first feature data, the first feature data to a position obtained through the conversion in the feature space; calculates a predicted value of a hazard function of analysis time in a case where the first feature data is given, based on a result of reallocation and a third learning parameter; optimizes the first to third learning parameters, based on a response variable and a first predicted value; generates second internal data, based on second feature data and the optimized first learning parameter; converts a position of the second feature data in the feature space, based on the second internal data and the optimized second learning parameter; and calculates importance data.

The invention discloses a method and device for determining configuration parameters of a water purifier, and the method is characterized by comprising the following steps of obtaining scenario parameters, wherein the scenario parameters are a basis for generating configuration parameters of the water purifier, and the configuration parameters are a basis for the operation of the water purifier; converting each parameter in the scenario parameters into an input of a neural network model, wherein the neural network model is obtained by training the training data, and each group of the trainingdata includes scenario parameters and corresponding configuration parameters under the scenario parameters; obtaining the output of the neural network model; converting the output into configuration parameters, and sending the configuration parameters to the water purifier. The method and device for determining configuration parameters of the water purifier provided by the invention solve the technical problem that the water purifier in the related art has a low degree of intelligence and is relatively cumbersome to use, resulting in a low user experience.

The invention discloses an underdetermined aliasing matrix estimation method based on a neural network and a genetic algorithm. The method comprises the following steps: carrying out short-time Fourier transform on observation signals; calculating a tangent value of each observation signal obtained after transform at each moment; constructing a competitive neural network, and with the tangent values being as input, carrying out preliminary clustering on the input tangent values; carrying out optimization on the preliminary clustering result through the genetic algorithm; and according to the optimized clustering result, calculating an estimated value of an aliasing matrix through arctangent. The method can carry out accurate separation on underdetermined blind source signals under the conditions of low-dimensional observation and unstable mixed signals; by converting the high-dimensional solution parameters into the one-dimensional tangent value, computation complexity is reduced; and through clustering analysis with the neural network and the genetic algorithm being combined, the clustering effect is more obvious, and solution accuracy of the aliasing matrix is improved.

The invention discloses a material modeling system based on a PBR material, and the system comprises: a material manufacturing module which carries out the visual PBR material parameter adjustment of a material template, so as to manufacture a new PBR material; a real-time rendering and previewing module which is used for performing online real-time rendering and previewing an online rendering effect in real time by applying a new PBR material; a data conversion module which is used for converting PBR material parameters corresponding to a new PBR material rendered by the online real-time rendering engine into offline rendering parameters adopted by the offline rendering engine, and aligning a light field adopted by the online real-time rendering engine with a light field required to be adopted by the offline rendering engine; an off-line rendering and preview module which is used for performing off-line rendering by adopting the off-line rendering parameters and the light field needing to be adopted so as to obtain and preview an off-line rendering effect; and a confirmation module which is used for confirming the designed new PBR material by comparing the online rendering effect and the offline rendering effect to be the same. The material modeling system can improve the material modeling efficiency and ensure the material modeling quality.

The invention discloses an image classification method based on a Bayesian neural network random addition decomposition structure, which comprises the following steps: scaling attribute parameters of a Bayesian neural network to obtain scaled attribute parameters, and converting the scaled attribute parameters into random bit stream data; determining the input number of a reference multiplexer based on the network structure of the Bayesian neural network, determining the input number and quantity of a middle multiplexer according to the input number of the reference multiplexer, and determining an inner product scaling factor of an inner product operation unit based on a parameter scaling factor and the input number and quantity of the middle multiplexer; acquiring random bit stream data, determining an inner product operation output result of an inner product operation unit according to the random bit stream data and an intermediate multiplexer, determining an output scaling factor based on an inner product scaling factor and a parameter scaling factor, and then outputting a final output result of the Bayesian neural network so as to complete image classification. Image classification is carried out through adoption of the above classification method, the hardware implementation cost during classification is greatly reduced.

The invention provides a filter effect generation method and device, electronic equipment and a storage medium, and relates to the technical field of computers. The filter effect generation method comprises the steps of determining a target configuration file corresponding to a target filter effect in response to a selection operation on the target filter effect; determining an original filter parameter corresponding to the target filter effect according to the target configuration file; obtaining a pre-configured filter parameter table, and converting the original filter parameter into a target filter parameter according to the filter parameter table; and generating the target filter effect according to the target configuration file and the target filter parameter. According to the technical scheme of the embodiment of the invention, the diversity of the filter effect of the client can be improved, and the user experience is improved.

The invention discloses a software test case automatic generation method based on a clustering and evolutionary algorithm, wherein the method comprises the steps: analyzing a to-be-tested program, and constructing a control flow diagram, a coding path and an initialized access matrix of the program; initializing population and algorithm parameters; performing k-medoids clustering on the individuals of the population; performing inter-cluster and intra-cluster variation and crossover operation; recording access information of the individuals of the population one by one; calculating an access matrix cost function according to the access matrix, converting population individual parameters into binary systems, and calculating Hamming distances among the individuals of the population and an overall fitness function; updating the mutation operator of each individual in the population, performing selection operation, and enabling the selected new individuals to form the next generation; detecting whether all the paths are completely covered or reach the set maximum cycle number, and if all the paths are not completely covered or reach the set maximum cycle number, returning to repeat the execution; and if all the paths are completely covered or reach the set maximum cycle number, outputting a test case set covering each path of the to-be-tested program. According to the invention, automatic generation of the test sample is realized, and the software test efficiency is improved.

The invention discloses a task migration method and device, electronic equipment and a storage medium, and relates to the field of data processing, and the method can comprise the steps: obtaining a task submitted by a user; if the task is a Hadoop task and it is determined that task conversion needs to be conducted, convertingHadoop parameters in the task into parameters capable of being recognized by Spark; and injecting the conversion result into a predetermined toolkit, and submitting the conversion result to a Spark cluster. By applying the scheme provided by the invention, the workload of a user can be reduced, and the processing efficiency is improved.

The invention provides an online correction method for a conversion curve based on segmented Spline fitting. The method is used for correcting a conversion curve of a measurement system and comprisesthe following steps: firstly, calibrating a to-be-corrected measurement system, namely calibrating the to-be-corrected measurement system, selecting a segmented fitting region interruption point according to a first-order linear fitting error of a calibration result, carrying out Spline fitting, converting parameters obtained by fitting into a binary format, and writing the parameters into an FPGAlookup table; and executing a correction parameter calling process: when the actual measurement system works, judging the interval where the measurement result of the system is located, calling the correction parameter of the corresponding segmented interval in the lookup table, and obtaining the corrected measurement result through hardware calculation.

An apparatus for locating a target object using parametric inversion employs a signal injector (606), wherein at least a portion of the target object (610) is buried, a magnetic field receiver (802), a matching processor (604) for matching to a magnetic field received by the magnetic field receiver (802) a parametric magnetic field distribution model corresponding to the target object and at least one other object, and a locating processor (604) for locating the target object (610) using parameters from the parametric magnetic field distribution model. A method for locating a target object (610) using parametric inversion injects a signal into the target object (610), at least a portion of which is buried, receives a magnetic field radiated from the target object, matches to the received magnetic field a parametric magnetic field distribution model corresponding to the target object and at least one other object, and locates the target object (610) using parameters from the parametric magnetic field distribution model.

The invention discloses a model parameter conversion method and device, and the method can obtain model parameters under a parameter type required by a to-be-constructed model, transmits the model parameters into the to-be-constructed model, carries out the format conversion through a pre-deployed parameter conversion relation in the to-be-constructed model based on the model parameters, and obtains the model parameters of the to-be-constructed model. The method comprises the following steps: acquiring a first parameter in a standard format according to a to-be-constructed model to obtain the first parameter in the standard format, further converting the first parameter in the standard format through a preset conversion function in the to-be-constructed model to obtain a first parameter in a parameter type required by a user-defined calculation layer, and transmitting the first parameter into the user-defined calculation layer of the to-be-constructed model; according to the method and the device, the to-be-constructed model containing the user-defined computing layer is constructed, the target service corresponding to the to-be-constructed model is executed through the constructed to-be-constructed model, and the user-defined computing layer is a network layer additionally configured for the target service, so that the model deployment efficiency is improved.

The present invention provides novel techniques for optimizing and controlling production plants using parametric multifaceted models. In particular, the parametric multifaceted models may be configured to convert a first set of parameters (e.g., control parameters) relating to a production plant into a second set of parameters (e.g., optimization parameters) relating to the production plant. In general, the first set of parameters will be different than the second set of parameters. For example, the first set of parameters may be indicative of low-level, real-time control parameters and the second set of parameters may be indicative of high-level, economic parameters. Utilizing appropriate parameterization may allow the parametric multifaceted models to deliver an appropriate level of detail of the production plant within a reasonable amount of time. In particular, the parametric multifaceted models may convert the first set of parameters into the second set of parameters in a time horizon allowing for control of the process plant by a control system based on the second set of parameters.

The invention relates to a cross-system claim settlement data processing method and device, electronic equipment and a computer readable medium. The method comprises the following steps: obtaining a claim settlement request of an external system, wherein the claim settlement request comprises initial claim settlement parameters; performing field conversion on the initial claim settlement parameteraccording to a preset mapping table to obtain a target claim settlement parameter; converting the initial claim settlement parameters into a target claim settlement parameter group; obtaining a multi-way tree architecture, and determining a target node and a target hierarchy of the claim settlement request in the multi-way tree architecture according to the target claim settlement parameter group; processing the target claim settlement parameter according to a calculation algorithm of the target node to obtain a processing result of the target node; and summarizing the processing results of the target nodes according to the upward hierarchical sequence of the target hierarchies to obtain a claim settlement result of the claim settlement request. The technical scheme provided by the invention can provide a set of efficient, easy-to-configure, easy-to-expand and automatic-to-execute cross-system calculation engine.

An embodiment of the present application provides a matching and screening method, device, electronic device, and computer-readable storage medium. The matching and screening method includes: the electronic device obtains an initial matching set, and the initial matching set is derived from an initial matching result between image pairs; At least one clipping module selects a matching subset from the initial matching set, the correct matching ratio in the matching subset is higher than the correct matching ratio in the initial matching set, and at least one clipping module is used to obtain the consistency of each initial matching in the initial matching set sex information; this matching subset is used to process image tasks associated with image pairs. The embodiment of the present application can improve the processing effect of the parametric transformation model processing image task.

The invention discloses a seven-parameter conversion method between a national three-dimensional coordinate system and a random local plane coordinate system. The seven-parameter conversion method between the national three-dimensional coordinate system and the random local plane coordinate system aims at the problems that an existing algorithm cannot directly obtain a high-precision single seven-parameter conversion parameter, and further the achievements of a local coordinate system cannot be obtained in real time based on CORS measured data, and provides a seven-parameter convention model to which addition constants are additionally added. According to the method, through a series of data processing methods, the additionally added addition constants greatly decrease the influence of projection deformation; the influence of a geodetic height on parameter solving precision, and the like are decreased through a three-parameter conversion model; the precision of a conversion parameter is greatly improved; moreover, the method accords with the requirement that a measurement handbook in the current market can only accept single four / seven-parameter input; the achievements of a national coordinate system, which are obtained through measurement based on a CORS network, can be converted into the achievements of the local coordinate system in real time; in this way, the expansion of the application spaces of provincial and municipal CORSs in a measurement practice process is facilitated.