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 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.

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.