64 results about "Model synthesis" patented technology

A system and method are provided which dynamically adapts to a changing economic environment by selecting or synthesizing an economic model from a set of economic models based on the selected model's ability to make accurate predictions about an actual economic market. The method and system each forms a space of different economic models, forms a behavioral landscape by extracting observables from executions of the economic models, and performs model selection and composite model synthesis through optimization over the behavioral landscape.

The invention discloses a coupling-bionics-based food crispness tester which comprises a chewing simulator, a cracking signal acquisition system and a data processing and analysis system, wherein the chewing simulator is used for simulating the food chewing of a human chewing system; the cracking signal acquisition system is used for acquiring a pressure signal and a sound signal, which are generated during the chewing of the chewing simulator; the data processing and analysis system is used for receiving the signals, and performing data processing and analysis to acquire a predetermined food crispiness result. The coupling-bionics-based food crispness tester is established by simulating the human chewing system and an auditory system by a bionic technology, and is used for acquiring mechanical and acoustic signals when the food is chewed, establishing a sensory crispness evaluation and mechanical and acoustic signal prediction model and comprehensively determining the crispness of the food by utilizing the model; an integrated manual technical system with the characteristic of achieving maximum environmental adaptability with low energy consumption is constructed; a test is carried out from a crispness formation mechanism, and is superior to crispness evaluation from other indexes such as cracks and simply from the mechanical signals.

The invention discloses a multi-model integrated flood forecasting method based on propagation time clustering analysis and belongs to the technical field of hydrologic forecasting. The method includes adopting a derived dynamic time warp matching method for floor process similarity analysis; estimating flow propagation time of each station in the upstream and the downstream; decomposing a sample into a plurality of clusters by performing clustering analysis on the flow propagation time; respectively building an SVM regression model for each sub-flow sequence to simulate the flood forming process; combining the sub-models into a comprehensive model. Comparing a comprehensive forecasting result acquired by the method with a model forecasting result acquired by a single model under conventional conditions and on the basis of flow clustering, the comparison result shows that the comprehensive model is better in comprehensive performance.

The invention provides a multi-model comprehensive prediction method for photovoltaic powder based on synchronous extrusion wavelet transformation. The multi-model comprehensive prediction method comprises the following steps: dividing photovoltaic historical data into four types including sunny day, cloudy day, rainy day and cloudy day according to different weather conditions; preprocessing eachtype of photovoltaic powder data by virtue of a synchronous extrusion wavelet transformation method, and decomposing the data into a series of modal functions with mutually exclusive characteristics;carrying out normalization processing on each modal function; determining an input variable set of each modal function; establishing a BP neural network, support vector machine and Gaussian process regression integrated multi-model comprehensive prediction method for each modal function; and overlapping prediction results of different modal functions, so as to obtain a final photovoltaic power short-term predicted value. According to the multi-model comprehensive prediction method, the prediction precision is effectively increased, the reliability of a prediction result is improved, and the problem of short-term prediction of the photovoltaic powder of a power system can be well solved.

An approach and tool integrate cyber-physical systems design based on the function-behavior-state (FBS) methodology where multi-domain simulation models capturing both the behavioral and structural aspects of a system are automatically generated from its functional description. The approach focuses on simulation-enabled FBS models using automatic and context-sensitive mappings of functional basis elementary functions to simulation components described in physical modeling languages. Potentially beneficial process loops are recognized and inserted in the functional model.

The invention relates to a method for constructing a state evaluation and remaining life prediction model of an IGBT module. The method comprises the following steps: measuring electric heating parameters of the IGBT module under different aging degrees through testing; establishing a functional model of average crust thermal resistance change rate-average power cycle index and a functional modelof average saturation pressure drop change rate-average power cycle index of test samples of the IGBT module; evaluating the aging state of the IGBT module according to the above functional models, and expressing the aging state of the IGBT by a real number on the interval [0, 1] to obtain a state evaluation result; and establishing a calculation model of the remaining life of the IGBT module according to the state evaluation result. The method provided by the invention establishes mathematical models of electrical parameters, thermal parameters and power cycle indexes according to a power cycle accelerated aging test, and the models comprehensively consider the influence of electrical parameters and thermal parameters on the evaluation results to make up for the deficiency of single parameter evaluation, thereby accurately obtaining the aging state of the IGBT module under the condition of thermal resistance and saturation voltage drop of a certain crust.

The invention discloses a comprehensive optimization method of a simulation credibility evaluation model of an electric propulsion system. The comprehensive optimization method comprises the following steps: decomposing the electric propulsion system into multiple subsystems; decomposing the subsystems under various standards of a simulation system with an establishment process of system simulation as an evaluation standard until obtaining a minimum unit; carrying out topology modeling on the electric propulsion system; collecting branch voltages, a phase angle, active power and passive power of the electric propulsion system; adopting corresponding intelligent algorithms to optimize different attributes of the electric propulsion system to obtain a credibility evaluation optimization model; collecting data of an actual system under the same working condition, and using the credibility evaluation optimization model to test the actually collected data to obtain credibility; if the credibility is smaller than a credibility index n, carrying out topology modeling on the electric propulsion system again; and if the credibility is larger than the credibility index n, the credibility evaluation is terminated. The comprehensive optimization method disclosed by the invention is high in stability and is high in reliability.

The invention discloses a fire image detection method based on a mixed color model and a neural network. The method comprises the following steps: firstly, performing an inter-frame difference methodon a monitoring video image to obtain a moving foreground region; extracting an area conforming to flame color characteristics through the mixed color model, and performing secondary color area expansion to obtain candidate areas filtered by the color model; taking a union set of the motion foreground area and the color candidate area to obtain a suspected flame area; and finally, constructing a dense connection convolutional neural network, and comprehensively judging whether a suspected flame region has a fire behavior or not through the trained network model and the secondary mixed color model. According to the method, the flame candidate area with high precision is obtained through motion detection and the mixed color model, the detection speed is high, the deep neural network guarantees the flame detection accuracy and the generalization capability, the method can be widely deployed in an actual intelligent security system, fire hazards are early warned, and losses caused by the fire hazards are reduced.

A speaker recognition method through emotional model synthesis based on Neighbors Preserving Principle is enclosed. The methods includes the following steps: (1) training the reference speaker's and user's speech models; (2) extracting the neutral-to-emotion transformation / mapping sets of GMM reference models; (3) extracting the emotion reference Gaussian components mapped by or corresponding to several Gaussian neutral reference Gaussian components close to the user's neutral training Gaussian component; (4) synthesizing the user's emotion training Gaussian component and then synthesizing the user's emotion training model; (5) synthesizing all user's GMM training models; (6) inputting test speech and conducting the identification. This invention extracts several reference speeches similar to the neutral training speech of a user from a speech library by employing neighbor preserving principles based on KL divergence and combines an emotion training speech of the user using the emotion reference speech in the reference speech, improving the performance of the speaker recognition system in the situation where the training speech and the test speech are mismatched, and the robustness of the speaker recognition system is increased.

The invention relates to the field of software monitoring of rack operation and maintenance, in particular to a model fault detection method and device, computer equipment and a storage medium. The method comprises the steps of obtaining a stability index and a correctness index of a model to be evaluated and a stability index and a correctness index of a model variable in the model to be evaluated; Obtaining a model score according to a to-be-evaluated model, the stability index and the correctness index of the model variable and a preset fuzzy judgment matrix; Obtaining a model scoring weight parameter; And obtaining a model comprehensive score according to the model score and the model score weight parameter, and when the model comprehensive score is lower than a preset threshold, determining that the model has a fault. According to the model evaluation, the score of the overall health degree of the model is obtained by comprehensively considering various parameters such as the stability and correctness of the model and the stability and correctness of variables. a comprehensive evaluation result of the model health degree is output based on the comprehensive evaluation system so as to judge whether the model is in a normal state.

The invention relates to a high-intensity focused ultrasound spherical phased array with array elements annually and intensively distributed, comprising a rigid spherical crown body and a plurality of array elements, wherein one array element is independently arranged in the center of the spherical crown body, and the other array elements are evenly and dispersedly distributed on a plurality of layers of concentric rings which take the center of the spherical crown body as an original point; the angular spacings among all ring layers are not equal, and the spacings among all ring layers and the spacing between adjacent array elements in the same ring are set according to an array element intensive distribution principle; the position of the original array element of each layer of concentric rings is randomly set, the other array elements are subsequently, intensively and evenly distributed on the concentric rings according to equiangular spacing, and each array element has independent electric excitation signal feeder lines which are respectively connected to a phased signal excitation system. The invention calculates the phase position and the voltage amplitude distribution of the array elements by adopting a focus model synthesis method based on a pseudo-inverse matrix theory, realizes acoustic beam electron focusing and scanning, generates required single-focus or multi-focus sound field control modes, and the like and is applied to heating and fusing target tumor focuses in the human body.

The invention discloses a multi-model integrated intelligent controlling method of a heavy-duty generator unit. The method comprises (1) constructing a model library and a controller library, wherein the model library comprises N submodels corresponding to the N operation conditions of the generator unit, the controller library comprises N submodel fuzzy controllers, each is designed a control rule corresponding to one submodel; each submodel fuzzy controller is the controller with double inputs and double outputs, the inputs are power angle deviation amount e delta and terminal voltage deviation amount u2, and the outputs are excitation regulation amount uf and valve opening regulation amount u2; (2) acquiring the real-time operating conditions data of the generator unit, and judging the actual operating conditions value based on the variable (delta, Vt); and (3) calculating the matching degree fn of the actual operating conditions value (delta, Vt) and N submodels in the model library, which is used as a weighting coefficient wn (n is an integer) of the integration of the integrated controller. The invention has the advantages of simplicity, good reliability, good real-time performance, high control accuracy, and good practical performance.

The invention discloses an arch dam safety evaluation method based on a geomechanical model comprehensive method testThe method comprises the following steps: S1, building a comprehensive method testsafety evaluation index system according to detected data; s2, constructing an abrupt change model for evaluating the safety degree of the arch dam; s3, standardizing each underlying index of the comprehensive method test, and converting the underlying indexes into dimensionless data between 0 and 1; s4, performing mutation level calculation by using a normalization formula, and solving a total mutation level value of the target layer; s5, repeating the steps S1-S4, respectively calculating arch dam safety total abrupt change level values under different overload coefficients in the comprehensive method test, drawing a relation curve graph of the total abrupt change level values and the overload coefficients, and obtaining a high arch dam comprehensive method test safety coefficient according to inflection points of the relation curve of the total abrupt change level values and the overload coefficients; and S6, evaluating the overall stability and safety of the arch dam project by integrating the factors. According to the invention, the existing deformation monitoring data in the model comprehensive method test is utilized to establish a comprehensive evaluation system for the safety degree of the high arch dam comprehensive method test.

The invention discloses a method for modeling an overload performance evaluation model of a transformer in consideration of multi-factor, comprising the following steps of collecting data based on a scheduling operation and production management information platform; establishing a model based on a multi-factor index; evaluating the performance of the model; utilizing a small value K between a determined maximum load rate K1 and a determined maximum load rate K2, and inputting K as an independent variable into economic loss and economic income formulae in an economic profit and loss model to realize overload performance evaluation of the transformer from the angle of economic profit and loss. The model established by the invention comprehensively considers the multi-factors, which influence the overload performance of the transformer, such as hot-spot temperature limitation, failure rate limitation and economic profit and loss indexes; and compared with an existing overload evaluation model, the constraint conditions are comprehensive. Compared with a traditional heat conduction model, the precision for evaluating the hot-spot temperature aspect of the transformer can be effectively improved in the improved hot-spot temperature model.

The invention relates to a power system transient stability evaluation method based on a short-time disturbed trajectory after fault removal. The method is characterized by comprising the following steps: firstly, selecting the short-time disturbed trajectory of the electric quantity at the end of the generator after the fault is removed, and secondly, searching the characteristic arrangement modeof the short-time trajectory when the evaluation accuracy is optimal, so that the local characteristic extracted by the model is more robust, and optimizing the parameters of the network window by taking the optimal comprehensive evaluation index of the model as the target, so that the generalization ability of the model is enhanced, and finally establishing the mapping relation between the short-time trajectory and the transient stability, so that rapid and accurate transient stability rapid evaluation of the power system can be realized. According to the method, misjudgment missing samplesof model evaluation can be effectively reduced, and compared with a traditional machine learning evaluation method, the method is more accurate and efficient, and the transient stability can be evaluated based on the short-time disturbed trajectory, and a certain time margin is reserved for a dispatcher to take a control measure.

The invention discloses an R language-based optimal extraction technology for extracting saponin and total flavonoids in liquorice. A central composite design experiment in a response surface method is adopted, the saponin content and flavone content in liquorice are used as detection indexes, and an entropy weight method in an R language environment is adopted for weight assignment of the two indexes, a BP neural network and genetic algorithm mathematical model is established, and target optimizing is performed for the extraction technology to obtain an optimal extraction technology. The optimal extraction conditions are as follows: the ammonia concentration is 0.62%, the ethanol concentration is 64%, the reflux time is 1.8h, and the liquid-solid ratio is 12:1. Relative error between the comprehensive evaluation predicted value of the model and the average comprehensive evaluation value of a verification experiment is 1.43%, proving relatively good predictability of the neutral network and genetic algorithm. In the invention, the established mathematical model searching for the optimal extraction conditions for extracting saponin and total flavonoids in liquorice is scientific and feasible, and new reference and idea are provided for optimizing multiple targets of the chemical components and even effective substance basis of traditional Chinese medicine.

The invention discloses a pipe bending neutral layer offset position determining method. The method comprises the following steps of static stress balance analysis, elastic deformation analysis, plastic deformation analysis and analytical-numerical hybrid modeling for solving neutral layer offset. The pipe bending neutral layer offset position determining method is based on an axial force balancemethod, a hybrid analytical-numerical neutral layer frame is built, a neutral layer offset angle alpha is determined, an analytical-numerical hybrid neutral layer offset solving model is built, the model comprehensively considers pipe bending geometrical parameters and complex material performance parameters, the neutral layer offset position can be rapidly and accurately determined, the neutral layer offset rule and mechanism can be rapidly analyzed, and a theoretical basis is laid for innovative development of the pipe bending new technology and improvement of the forming ability.

Converting from transaction level model to register transfer level. A method comprises accessing a transaction level model description. Function calls in the transaction level model description are automatically converted to signal accesses in a register transfer level description. Interlace logic is automatically generated in the register transfer level description. The interface logic provides an interface between a bus interface and the signal accesses. The register transfer level description is stored in a computer readable medium.

The invention discloses an augmented reality-based dressing-up and try-on system used for game role play. The augmented reality-based dressing-up and try-on system comprises a camera shooting unit, an equipment costume model forming unit, a live model forming unit, a model synthesis unit, a game scene rendering unit and a display unit. The augmented reality-based dressing-up and try-on system used for game role play has the advantages that an equipment costume three-dimensional model and a human body three-dimensional model are synthesized into a three-dimensional effect model, a game player can beneficially learn the effect after game costume is tried on and game equipment is equipped, and an interest of a game player on a reality simulation role in a game playing process is raised; and by combining the game scene rendering effect with the three-dimensional effect model together, the simulation dressing-up and try-on three-dimensional effect is enhanced and is displayed by virtue of the display unit, and the game player can beneficially and visually know and simulate the real effect of a corresponding game role, so that the game player has more immersive game experience feeling, game entertainment excitement can be more beneficially improved, and higher requirements of the game player on game experience can be met.

The invention discloses a virtual fitting system based on augmented reality, comprising a first camera unit, a clothes model forming unit, a human body model forming unit, a model synthesis unit, a live-action rendering unit, a second camera unit, and a display unit. The output end of the first camera unit is connected to the input ends of the clothes model forming unit and the human body model forming unit. The output ends of the clothes model forming unit and the human body model forming unit are connected to the input end of the model synthesis unit. The output ends of the model synthesis unit and the second camera unit are connected to the input end of the live-action rendering unit. The output end of the live-action rendering unit is connected to the input end of the display unit. By synthesizing a clothes 3D model and a human body 3D model into a 3D effect model, a person can know the corresponding effect of clothes that the person tries on, and the fitting time is reduced. By combining the live-action rendering effect and the 3D effect model, the reality effect of virtual fitting is enhanced.

The invention relates to a model synthesis method, in particular to a global optimization-based three-dimensional model synthesis method, which utilizes the structural information of a three-dimensional model sample and a global optimization algorithm to synthesize various target three-dimensional models similar to the initial model. The method first utilizes a model structure analysis method to split the three-dimensional model sample and obtain the class number of each spatial block, then establishes a global model synthesis energy equation on the basis of the constraint relation between the class numbers obtained from the three-dimensional model sample, and adopts an expectation maximization algorithm for iterative optimization to work out a global energy minimum, and the final result is a synthesized model. The method can effectively keep the continuity of target three-dimensional model structures and the integrity of the visual effect.

The invention discloses a 3D printing method and system with human image scanning by a mobile phone. The method comprises: a camera of a mobile terminal scans a human body image in different directions; human body images obtained by scanning in different directions are synthesized into a 3D human body model; the 3D human body model is segmented into a human head model and a human body model; a Q-version model, the head model and the body model are synthesized to obtain a Q-version 3D human body model; and the Q-version 3D human body model is displayed for a user, costs needed by 3D printing of the Q-version 3D human body model are calculated, and the cost information is displayed for the user. The method and system have the following beneficial effects: the human body image is scanned by a mobile phone, the human body image obtained by scanning is synthesized into the 3D human body model, the 3D human body model is segmented and Q-version model synthesis is carried out, and then the 3D human body model with a special effect is printed; different Q-version special effects, different printing materials, and different processes can be selected; the diversified selection options are provided; the automation degree is high; and the 3D human body model printing by a user becomes convenient.

The invention discloses a traffic flow prediction method fusing spatial and temporal features. The method comprises the following steps: step 1, preprocessing data; step 2, introducing an automatic encoder to obtain data characteristics; step 3, introducing an SAEs model and acquiring spatial feature ; step 4, introducing an LSTM model, and obtaining time features; step 5, synthesizing the SAEs model and the LSTM model to obtain an ideal hybrid model, and establishing a hybrid deep learning model SAES-LSTM to predict the traffic flow of an urban expressway. Time and space information is comprehensively utilized. The collected information of a database is analyzed and utilized more fully, and therefore a prediction result can be more accurate.

The two-dimensional coordinates of a vertex are extracted in each of a top view and front view and, if their X-coordinates are equal to each other, the combination of their Y-coordinate values is determined to be the two-dimensional coordinates of a candidate vertex in a side view. Then, candidate line segments for the side view are extracted from the line segments connecting two candidate vertices, excepting not only those line segments for which no corresponding line segment exists in the top and front views, but a so those line segments for which corresponding horizontal or vertical line segments exist in the top and front views, and which a e not horizontal or vertical in the side view.

The invention provides a human body scene image eigen-decomposition and relighting method and device, and the method comprises the steps: making a human body model through employing Clo3d, carrying out the light tracing through employing blender, and carrying out the rendering, and obtaining a training data set; pre-training a normal prediction network by using a single image; taking the original image and the normal graph obtained through prediction as input of an image eigen-decomposition network, and obtaining a reflectivity graph, a shadow graph and spherical harmonic illumination parameters through prediction; re-illuminating the original image by using the new spherical harmonic illumination parameter; and finally, decomposing a single picture into a reflectivity picture and a shadow picture, so the application of picture relighting, 3D model synthesis, model surface texture reconstruction and the like can be achieved. The shadow of the input picture can be removed in the field of three-dimensional reconstruction, and the surface texture quality of a three-dimensional reconstruction model is improved.

The invention discloses a method for measuring the reliability of a mechanical / electronic product, relating to a digital computation method especially suitable for special applications. In the method disclosed by the invention, through comprehensively processing probability density functions and membership functions based on a generalized stress-generalized strength interference model, the comprehensive processing on random information and fuzzy information in the process of measuring the reliability of the product is realized so as to obtain the reliability value of the mechanical / electronic product, and the reliability curve and reliability value of the product are intuitively displayed by a computer, thereby overcoming the defect that in the existing product reliability calculation method, under the condition that the random information and the fuzzy information coexist, the reliability of the product is difficult to measure.

A system and method for predicting passive, high-frequency sonar array performance at any underwater location, implemented as a software package running on a computer. An operator selects an arbitrary geographical location, specifies a sonar array and defines a target. The necessary bathymetry, and associated oceanographic, environmental and meteorological data are automatically acquired or generated, along with appropriate models for ocean surface and bottom reflectivity, and vertical noise directionality. A Comprehensive Acoustic System Simulation (CASS) software module performs appropriate ray tracing, which is used to produce Cross-Spectral Density (CSD) matrices for the target and the anisotropic ambient noise. These are used to generate graphic and numeric representations of the array's performance at the selected location. A time of year may also be specified and the appropriate anisotropic noise field for conditions expected at that location at that time of year will be generated. Shipping realizations may be incorporated.

The invention discloses a four-step-coupled middle and long term hydrologic forecasting method. The four-step-coupled middle and long term hydrologic forecasting method comprises the following steps:step (1) forecasting: carrying out middle and long term hydrologic forecasting by adopting various models according to watershed hydrologic and meteorological data; step (2) carrying out error correction: carrying out the error correction on a forecasting result by adopting an AR (Augmented Reality) model, so as to obtain a corrected forecasting result; step (3) integrating various models: carrying out weighting processing on the forecasting result corrected by the various models according to errors of the corrected forecasting result of all the models, so as to obtain a comprehensive forecasting result; step (4) evaluating the reliability: based on the comprehensive forecasting result, quantitatively evaluating uncertain factors in a forecasting process by adopting a hydrologic uncertainty processor (HUP), so as to obtain an interval forecasting result under a certain confidence degree. The forecasting result can provide abundant uncertainty information, and more scientific and reasonable decision-making evidences are provided for water resource dispatching and configuration; the forecasting result has important actual meaning.

An approach and tool integrate cyber-physical systems design based on the function-behavior-state (FBS) methodology where multi-domain simulation models capturing both the behavioral and structural aspects of a system are automatically generated from its functional description. The approach focuses on simulation-enabled FBS models using automatic and context-sensitive mappings of functional basis elementary functions to simulation components described in physical modeling languages. Potentially beneficial process loops are recognized and inserted in the functional model.

The invention discloses a time-based microblog document expansion method oriented to microblog retrieval, and relates to the technical field of microblog retrieval. The microblog document expansion method aims to solve the problem that the improvement of microblog retrieval performance is limited since a function on estimating a microblog document model by the document expansion can be weakened due to adverse effects brought to the document expansion by the short microblog. The time-based microblog document expansion method utilizes the time characteristic expansion document of a relevant microblog, and puts forward the time-based microblog document model. The model comprehensively considers the characteristics of the explosiveness, which is integrally presented on an aspect of time, of the relevant microblog and the adjacency, which is presented on an aspect of time, of an individual, utilizes the distribution of words on a microblog in an outbreak period and the microblog which is in near neighbor with the microblog in the outbreak period to obtain the weight of a document expansion word, and selects a query expansion word by a method based on machine learning so as to estimate an accurate document model. The method can better avoid influence brought to the document expansion by the short microblog.