101 results about "Model composition" patented technology

An automated method for the generation of (i) human models comprehensive of shape and joint centers information and/or (ii) subject specific models from multiple video streams is provided. To achieve these objectives, a kinematic model is learnt space from a training data set. The training data set includes kinematic models associated with corresponding morphological models. A shape model is identified as well as one or more poses of the subject. The learnt kinematic model space and the identified shape model are combined to generate a full body model of the subject starting from as few as one-static pose. Further, to generate a full body model of an arbitrary human subject, the learnt kinematic model space and the identified shape model are combined using a parameter set. The invention is applicable for fully automatic markerless motion capture and generation of complete human models.

Techniques for composing an application resource model in a data stream processing system are disclosed. The application resource model may be used to understand what resources will be consumed by an application when executed by the data stream processing system. For example, a method for composing an application resource model for a data stream processing system comprises the following steps. One or more operator-level metrics are obtained from an execution of a data stream processing application in accordance with a first configuration. The application is executed by one or more nodes of the data stream processing system, and the application is comprised of one or more processing elements that are comprised of one or more operators. One or more operator-level resource functions are generated based on the obtained one or more operator-level metrics. A processing element-level resource function is generated based on the one or more generated operator-level resource functions. The processing element-level resource function represents an application resource model usable for predicting one or more characteristics of the application executed in accordance with a second configuration.

The invention discloses an evanescent die process. The evanescent die process is characterized by comprising the following steps of: selecting foaming plastic beads, manufacturing a model; enabling the beads to expand to be in a certain size through hot water pre-frothing, steam pre-frothing and vacuum pre-frothing, curing and frothing forming; clustering model composition, coating a model; vibrating and shaping; pouring and displacing; and cooling and cleaning, wherein the full mold casting ramoff is simple, a casting piece can be hoisted out when a sand box is inclined or can be directed hoisted out form the sand box, the casting piece and dried sand are naturally separated, and the separated dried sand is reused after treatment. According to the process, a die is not required to be taken out, and a parting plane and a sand core are not required, and thus the casting piece has no flash, no burrs and no pattern tapers, and the dimension error caused by core combination is reduced; and compared with the conventional sand casting method, for the evanescent die process, the 40%-50% machining time is reduced.

It is an object of the present invention to provide an image processing device capable of capturing an image of various objects and common scenes with ideal compositions and attractive compositions. The image processing device predicts an attention region 52 for a through image 51, based on a saliency map S having a plurality of feature quantity maps Fc, Fh, and Fs integrated therein (steps Sa to Sc). The image processing device extracts line components (e.g., edge SL) of an edge image 53 (step Se, Sf) corresponding to the through image 51. The image processing device uses the attention region 52, the line components (e.g., edge component SL), or the like and identifies, from among a plurality of model composition suggestions, a model composition suggestion that resembles the through image 51 in regard to a state of positioning of the principal object.

A method includes combining at least two 3-D models, given the positions of the models and constraint areas of the models that should not be changed by the combination. The combining includes generating a weighted graph representation of the models at least in a transition volume and including at least a portion of the constraint areas and finding a minimum cut which separates the weighted graph into two cut graphs representing cut versions of the models.

The invention relates to a sound event recognition method based on optimized parallel model combination. The sound event recognition method includes 1) recording data of a sound event, acquiring a GMM (Gaussian mixture model) according to clean sound event training, and establishing a clean sound event template; 2) acquiring noise data in current environment in indoor actual noisy environment, acquiring a GMM according to the noise data training, and establishing a noise template; 3) processing the noise template and the clean sound event template by the method of optimized parallel model combination, and obtaining a template of a sound event with noise; 4) sampling to obtain sample signals of the sound event with noise, recognizing sound of the sample signals according to the parameters in the template of the sound event with noise. According to the sound event recognition method, a GMM capable of describing background noise feature distribution better is established as one input in a PMC (portable media center) method, a clean GMM of five sound events is established at another input in the PMC. Meanwhile, robustness of a recognition system to noises is guaranteed.

There is provided a voice recognition device and a voice recognition method that enhance the function of noise adaptation processing in voice recognition processing and reduce the capacity of a memory being used. Acoustic models are subjected to clustering processing to calculate the centroid of each cluster and the differential vector between the centroid and each model, model composition between each kind of assumed noise model and the calculated centroid is carried out, and the centroid of each composition model and the differential vector are stored in a memory. In the actual recognition processing, the centroid optimal to the environment estimated by the utterance environmental estimation is extracted from the memory, model restoration is carried out on the extracted centroid by using the differential vector stored in the memory, and noise adaptation processing is executed on the basis of the restored model.

A modelling composition, in which there is no cracking likely to cause the modelling composition to disintegrate either during drying or during drying of the objects made from said composition, contains a compound made up of the components starch, an adhesive agent, common salt, water, calcium chloride, sodium benzoate, potassium hydrogen sulphate, liquid paraffin, propylene glycol and bittern.

The invention relates to a modeling composition consisting of binder which is present as a plastisol and optionally further additives, wherein the plastisol is composed essentially of PVC and plasticizer and wherein the composition comprises at least one phthalate-free plasticizer.

The present invention presents two new model types and a new method for evaluating a model used in the control application. These include a compound model, a hybrid model and a directional change coefficient model. The present invention allows the mixing of models with different inputs and outputs and the switching between these models based criteria for measuring optimization accuracy. The present invention allows switching between these models. The compound model is a model type that allows zooming in on the process to model parts of the data space with higher fidelity or resolution without loosing the capability to model the complete data space. The modeler does not loose any functionally over a regular neural network, but instead gains the ability to define the conditions when the model should use network weights best matched to the defined local conditions. The hybrid model is an extended version of a compound model. A hybrid model allows the combining of one or more models into a single model for purposes of interrogation or optimization. Within the hybrid model may reside a compound model itself. The directional change model (DCC) allows better evaluation of the predictive capability of Compound Models. It may also be used with any other model type.

The invention discloses a data processing method for real-time prediction of the number of people in an airport security check, comprising: performing preprocessing on the used data including data cleaning, structural processing and data integration; performing feature extraction on the preprocessed data; inputting the extracted features Multiple prediction sub-models predict the number of people in airport security checks, and output the prediction results respectively; respectively evaluate the prediction results output by multiple prediction sub-models, assign weights to each prediction sub-model according to the evaluation results and form a combined model, and based on the combined model Predict the number of people in airport security checks and output matching prediction results. The present invention adopts multi-model combination to predict, uses time series model, random forest and GBDT to evaluate the prediction results, and then assigns the weight of each model to form a combined prediction model to achieve higher prediction accuracy. At the same time, the present invention also provides a processing device for predicting the number of people in airport security checks in real time.

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 invention discloses an eight-section impedance model based body composition analysis method. The body composition analysis method comprises the following steps: according to input currents and measured voltages, acquiring six valid body impedance expressions through the eight-section body impedance model; acquiring the difference value between the resistance values of left and right upper limbs as well as the difference value between the resistance values of left and right lower limbs through a five-section body impedance model; calculating to obtain the body impedance expression of each section; according to at least two groups of different input currents and the body impedance expression of each section, obtaining at least two groups of body impedance values; selecting the optimal group of eight-section impedance values, and determining a fitting model according to the selected optimal group of eight-section impedance values; conducting training on a plurality of known samples in the fitting model to obtain unknown coefficients of the fitting model as well as a body composition predicting formula; according to the body composition predicting formula, analyzing the unknown samples to obtain body composition parameters. Through the adoption of the body composition analysis method, the obtained the body composition is accurate.

The invention relates to the technical field of remote sensing image processing, in particular to a PM2.5 inversion method based on MODIS and machine learning model fusion. An MODIS image and PM2.5 monitoring data are acquired; the PM2.5 data are interpolated into PM2.5 interpolation image; the MODIS image is subjected to cloud detection; a training set and a test set are constructed; performanceindicators are calculated according to the training set and the test set; a histogram of the performance indicators is made; all corresponding models in a histogram interval with the highest frequencyin the histogram are selected and taken as the optimal model combination; the optimal model combination is used for the full MODIS image for inversion of model fusion. In the method, the relation between the remote sensing image and actually measured PM2.5 is directly constructed on the basis of data of the remote sensing image through a machine learning algorithm and model fusion, so that the inversion result with higher accuracy is realized. Error transferring is avoided and the inversion accuracy is high.

The invention provides an antimony ore grade soft-measurement method based on the selective fusion of a heterogeneous classifier. The method comprises the step of together forming a feature space based on the pretreatment of antimony flotation froth image feature data and production data related to the grade of the antimony ore. According to the method, firstly, some feathers are randomly selected to form a plurality of sub-sample spaces. Secondly, a plurality of different sub-samples in each sub-sample space are sampled through the bootstrap sampling process. At the same time, the PCA analysis is conducted on each sub-sample to obtain key features that are high in sensitivity to grade change and free of/weak in dependency. Thirdly, two KELMs are conducted respectively for each sub-sample set to construct a candidate sub-model, based on an RBF kernal of better learning ability and a polynomial kernel type KELM of better generalization ability. Fourthly, each candidate sub-model is endowed with a weight based on the method of information entropy. Finally, all candidate sub-models are sorted from small to large based on the RMSE, and then an optimal weighted sub-model combination is selected as a final model for the prediction on the grade of the antimony ore.

The invention relates to a gasoline blending optimization method based on molecular composition. The gasoline blending optimization method has the advantages of being simple and efficient, greatly saving the analysis and detection time and cost, directly performing molecular computing on the macroscopic property from each component without acquiring the macroscopic property of a blending componentin advance, thus saving more than 75% of the analysis and detection time and more than 50% of labor, being simpler to use, and being higher in efficiency, being high in universality and accuracy of ablending physical property computing model, wherein the physical property model can calculate the conventional gasoline components, ether-containing gasoline, methanol gasoline, and ethanol gasoline;and being high in applicability. Besides, the gasoline blending optimization method based on molecular composition can automatically select blending components from any one component pool for optimization without fixed components. The gasoline blending optimization method based on molecular composition has strong versatility, can optimize the conventional national standard gasoline, can also optimize the methanol gasoline and the ethanol gasoline, and can optimize the blended gasoline containing the distillation range index constraint. The gasoline blending optimization method based on molecular composition is high in the credibility of the optimization result and greatly improves the primary blending success rate of gasoline blending.

The invention discloses a 3D vertebra CT image active contour segmentation method fusing weighted random forest, and relates to the field of medical image processing. For the problem of sensitivity ofa vertebra CT image segmentation method to an initial contour, a method for automatically locating a vertebra and segmenting a vertebra CT image is proposed. The method comprises the steps of firstly, proposing a weighted random regression and classification forest algorithm to determine a vertebra center; secondly, putting an initial contour ball of active contour segmentation in the vertebra center, and segmenting out the vertebra in the image by adopting a 3D active contour segmentation method in combination with an energy function; and finally, performing combination output on trained models to obtain a complete vertebra CT image segmentation model. A spinal CT segmentation model proposed in the method can automatically locate the vertebra center and can perform automatic 3D segmentation on the vertebra, so that the spinal CT image segmentation steps and processes are simplified.

The invention relates to a modeling method for combustion optimization of a porous medium combustor, and provides a modeling method considering both model prediction accuracy and generalization capacity aiming at the bottleneck problem in the combustion optimization of the porous medium combustor. Before modeling, modeling data is selected according to uniform distribution and number equalization in topological structure, and proper preprocessing is performed, so that prediction capacity and generalization capacity of a model are guaranteed; combustion characteristic models of the porous medium combustor are established by applying a support vector machine and a radial machine neural network aiming at different fuels respectively; the support vector machine and the radial machine neural network model are integrated by applying a weighted average method to form a combustion optimization characteristic model of the porous medium combustor, wherein a weighting coefficient is obtained through a particle swarm optimization algorithm; and finally, the combustion optimization models of different fuels are combined to form an integral model. By using the method, the combustion optimization characteristic model of the porous medium combustor, which has higher accuracy and generalization capacity, can be established.

A modeling composition consisting of a wax-based and oil-based binder, a filler, and a coloring agent, wherein the filler is substantially a light filler comprised of hollow microbeads. The binder is comprised of solid wax, pasty wax, and a liquid component of a wax base, an oil base, or a wax and oil base. The binder is present in the modeling composition in an amount of 45 to 90% by weight. The modeling composition can be used as play dough for children or as a therapeutic composition in the medical field for training and rehabilitation.

The invention provides a two-dimensional animation production method. The method comprises the following steps of: drawing a plurality of limb element models of each limb element; selecting suitable limb element models and combining the models into a role; selecting limb presentation of each limb element according to posture requirements of the role, and placing the body presentation of each limb element at a corresponding time point; and generating body presentation of each limb element between any two adjacent role postures through ordered changes to complete continuous changes of role postures, and performing animation demonstration through a playhead. The invention further discloses the limb element model and the role. According to the method, a small number of limb element models and internal states of the limb element models are designed, the limb element models and the internal states of the element models are selected to form the role postures at the time points, and continuity processing is performed on the role postures, so that animation production is implemented; and the design is simple, the workload is greatly reduced, and requirements for designers and computer configurations are not high, thereby facilitating popularization.

The invention discloses a general topic-embedding-model joint-training method. The method comprises the following steps: step 1, preprocessing an input original document corpus to obtain target text;step 2, constructing a vocabulary table for the target text; step 3, initializing network structures, initializing parameter matrices of models, and constructing a negative sampling table; and step 4,carrying out joint modeling on the topic embedding models, and training the models in a manner of multiple iterations, wherein each iteration process is divided into the following three steps: step 1, using an expectation-maximization (EM) algorithm to train a topic model part; step 2, using a stochastic-gradient-descent algorithm to train an embedding model part; and step 3, using a complete-gradient-descent algorithm to train a regularization term part. The invention can provide a general manner for jointly training the topic model and the embedding model, and problems such as the problemsthat existing model combination manners are too dependent on unique models, generality is insufficient, and two models are difficult to improve at the same time are solved.