34 results about "Kernel model" patented technology

Systems and methods are disclosed that facilitate producing probabilistic outputs also referred to as posterior probabilities. The probabilistic outputs include an estimate of classification strength. The present invention intercepts non-probabilistic classifier output and applies a set of kernel models based on a softmax function to derive the desired probabilistic outputs. Such probabilistic outputs can be employed with handwriting recognition where the probability of a handwriting sample classification is combined with language models to make better classification decisions.

The present invention relates to a PET-CT multimodal nasopharyngeal carcinoma image segmentation method based on a hypergraph model, comprising: extracting grayscale information and position information of pixels in a nasopharyngeal tumor image to construct a data set, and constructing a data set according to the data set Sparsely represent the model and solve it to obtain the reconstruction coefficient matrix and construct the hyperedge, use the Gaussian kernel model to calculate the similarity of data samples as the hyperedge weight value, solve the hyperedge order and vertex order to construct the hypergraph Laplacian matrix; then the nasopharynx The tumor image is marked and the label vector is obtained at the same time. A semi-supervised learning model is constructed according to the label vector, and then the optimal cutting vector is obtained by solving the least squares problem. Finally, the classification result is returned to the pixel level, that is, the segmentation of the tumor image is completed. The segmentation method of the present invention has higher segmentation accuracy than single modality, and at the same time, the hypergraph model based on the combination of sparse representation and Gaussian kernel has higher segmentation accuracy for nasopharyngeal tumor image data than other simple graph models or hypergraph models precision.

The invention provides a slab continuous casting secondary cooling and dynamic soft reduction off-line simulation system, which mainly solves the technical problem that the adjustment cost of dynamic soft reduction parameters is too high in actual production. The computer of the simulation system includes: the simulator core unit, which realizes the simulation of the process parameters of the main pouring conditions; the process model parameter setting unit, which is used to set the process and control parameters in the model; the model calculation core unit, combined with Simulate the pouring condition parameters to calculate the process model; the monitoring and display unit mainly completes the interface display of the simulation results; the simulator kernel unit and the model calculation kernel unit realize the interaction of process parameter data through the shared memory, and the model calculation kernel unit and the monitoring display unit pass The Ethernet local area network interacts with the simulation result data, the model calculation core unit and the process model parameter setting unit realize the process model parameter interaction through the model parameter database. The invention is mainly used for simulating the specific control process of secondary cooling water distribution and dynamic soft reduction of slab continuous casting.

The invention discloses an M module low-level (LL) driver layer realization method for M module-based local area network (LAN)-based extensions for instrumentation (LXI) equipment, which belongs to the field of automatic test and aims to solve the problem of independence between a conventional M module driver software architecture for the LXI equipment and hardware. In the method, an M module hardware interface control logic is realized by a field programmable gate array (FPGA); the LL driver layer driving of the M module software architecture is used for realizing an M module interface operating function; in the driving of a high-level (HL) driver layer, a function of an LL driver layer is called; a testing function related application layer repackages a function interface provided by the HL driver layer; a testing equipment related application layer realizes testing functions; the LL driver layer is divided into two architectures comprising a user model and a kernel model, the user model adopts a uClinux application program to perform FPGA setting and register configuration, and the kernel model adopts uClinum character driving to perform the setting of an advanced RISC machine (ARM) processor and the register configuration; and a system calls and interrupts corresponding functions of the user model and the kernel model.