110 results about "Algorithmic skeleton" patented technology

A novel algorithmic framework is presented for the simulation of hyperelastic soft tissues that drastically improves each aspect discussed above compared to existing techniques. The approach is robust to large deformation (even inverted configurations) and extremely stable by virtue of careful treatment of linearization. Additionally, a new multigrid approach is presented to efficiently support hundreds of thousands of degrees of freedom (rather than the few thousands typical of existing techniques) in a production environment. Furthermore, these performance and robustness improvements are guaranteed in the presence of both collision and quasistatic / implicit time stepping techniques. The result is a significant advance in the applicability of hyperelastic simulation to skeleton driven character skinning.

The invention provides a variable neighborhood search algorithm for solving multi depot vehicle routing problem with time windows. The variable neighborhood search algorithm is characterized in that in a local searching progress, an or-opt and 2-opt mixed operator is utilized for local searching; a Metropolis rule in a simulated annealing algorithm model is introduced into a basic frame of VNS (variable neighborhood search) algorithm for accepting partial poorer solutions under a certain condition, so that the possibility that the algorithm is trapped into local optimization is reduced; in this way, the reliability of the algorithm is improved; and a rear optimization progress is added into the algorithm framework, as a result, both the solving quality is improved and the convergence speed of the algorithm is increased in a way.

Disclosed is an abstract convex lower-bound estimation based protein structure prediction method. The method includes: firstly, aiming for high-dimensional conformational spatial sampling problems for proteins, adopting a series of transform methods to transform an ECEPP / 3 force field model into an increasing radial convex function in unit simple constraint conditions; secondly, based on an abstract convex theory, proving and analyzing to give out a supporting hyperplane set of the increasing radial convex function; thirdly, constructing a lower-bound underestimate supporting plane on the basis of population minimization conformation subdifferential knowledge under a differential evolution population algorithm framework; fourthly, by the aid of a quick underestimate supporting plane extreme point enumeration method, gradually decreasing a conformational sampling space to improve sampling efficiency; fifthly, utilizing the lower-bound underestimate supporting plane for quickly and cheaply estimating an energy value of an original potential model to effectively decrease evaluation times of a potential model objective function; finally, verifying effectiveness of the method by methionine-enkephalin (TYR1-GLY2-GLY3-PHE4-MET5) conformational spatial optimization examples. The abstract convex lower-bound estimation based protein structure prediction method is high in reliability, low in complexity and high in computation efficiency.

The invention discloses a method for predicting a protein three-dimensional structure based on Monte Carlo local shaking and fragment assembly. The method comprises the following steps that firstly, according to the difficult problem that search space of protein high-dimensional conformation space is complex, the effectiveness of fragment replacement is judged under a Rosetta force field model through the Monte Carlo statistical method according to a protein database configuration fragment bank; under a differential evolution group algorithm framework, the complexity of the search space is reduced through fragment assembly, meanwhile, false fragment assembly is removed through the Monte Carlo statistical method, and the conformation search space is gradually reduced through the diversity of an evolutionary algorithm, and therefore the searching efficiency is improved; meanwhile, a module with coarseness is adopted, a side chain is ignored, and cost of a search is effectively reduced. The method for predicting the protein three-dimensional structure based on Monte Carlo local shaking and fragment assembly can effectively obtain optimal local stable conformation and is high in predicting efficiency and good in convergence correctness.

The invention relates to the technical field of high-performance computers, and discloses a concurrent computational system for multi-scale discrete simulation. In respect to the multi-scale structure and discrete characteristics of the complex system in the real world, the system maintains the consistency and similarity among simulation object, computation model, frame of algorithm and computer architecture. The system describes the local behavior of the systems in different layers with a large number of adjacent discrete units and describes the collective behavior of the systems in different layers with long-range restraint and correlation, and two-way feedback is applied between the upper layer unit and the lower layer unit. Correspondingly, the special reconfigurable vectorized accelerator group is used as the bottom layer computation hardware, the universal processor array is used as the upper layer computation hardware, and the adjacent processors or accelerators in the same layer can directly communicate or share memory with the processors or accelerators in the part of adjacent layers. Therefore, the system can efficiently and truly simulate complex process and system.

The invention discloses a Caffe based extraction method of stack denoising self-encoding gene information characteristics. The Caffe based extraction method of the stack denoising self-encoding gene information characteristics comprises specific steps as follows: S1, data is prepared; S2, a Caffe based stack denoising self-encoding gene characteristic extraction model is built; S3, the stack denoising self-encoding gene characteristic extraction model is trained layer by layer; S4, the stack denoising self-encoding gene characteristic extraction model is subjected to fine adjustment according to labeled gene data; S5, a gene trait identification system is built by utilizing the well trained gene characteristic extraction model. According to the technical scheme, a deep learning algorithm framework is adopted, and on the basis of image characteristic extraction, to-be-detected genes can be subjected to characteristic extraction and further classification and recognition through training of a classification and identification model.

In an exemplary embodiment, some of the main aspects of the present invention are the following: (i) Data model: We introduce tensor streams to deal with large collections of multi-aspect streams; and (ii) Algorithmic framework: We propose window-based tensor analysis (WTA) to effectively extract core patterns from tensor streams. The tensor representation is related to data cube in On-Line Analytical Processing (OLAP). However, our present invention focuses on constructing simple summaries for each window, rather than merely organizing the data to produce simple aggregates along each aspect or combination of aspects.

For digital pathology imaging, intelligent processing, such as automatic recognition or content-based retrieval, is one significant benefit that drives the wide application of this technology. Before any intelligent processing on pathology images, every image is converted into a feature vector which quantitatively capture its visual characteristics. An algorithm characterizing pathology images with statistical analysis of local responses of neural networks is described herein. The algorithm framework enables extracting sophisticated textural features that are well adapted to the image data of interest.

The invention provides a distributed hydrology model multi-time flooding parameter calibration method based on DSS database reading and writing, and belongs to the technical field of hydrology model parameter calibration. The distributed hydrology model multi-time flooding parameter calibration method comprises the steps of 1, building a multi-target genetic algorithm eNSGA-II frame to be used forparameter multi-target optimization and calibration of an HEC-HMS model; 2, based on a parameter optimizing frame, utilizing a JAVA language to write individuals in a parameter species into a basin file to input model parameters, after the model is run, extracting a simulation result from a DSS database to be fed back to the frame to calculate the parameter individual adaptability to generate a new parameter species, so that coupling of the model and the parameter optimizing frame is achieved, and the whole parameter automatic calibration process is completed; 3, selecting an appropriate parameter input model in a Pareto solution set according to standards like whether parameter physical meaning conforms to drainage basin properties and evaluating precision of the simulation result or not. The application limit of the model is broken through, the calibration time and manpower are greatly saved, and the model is easily applied and popularized.

The invention relates to a method for obtaining a face image light source orientation based on an android platform. The method comprises: pretreatment is carried out on an image in a database to obtain a binary image with face information removed; segmentation is carried out on the image and pixel gray values of sub images are combined with image light source information to form a training set; and libsvm training is carried out on the training set and then the training set is used for detecting light source orientations in other images. According to the invention, convenient embedding to the existing pixel domain algorithm frame is realized; and the error can be guaranteed to be less than + / -5 degrees based on the model.