2813 results about "Computation process" patented technology

A system and method automatically generate an on-line document from raw text into an engaging, interactive form for a plurality of viewers. Unstructured articles are read from an information feed. A computation process extracts and tags proper names of people, products, organizations, and places and categorizes them. An image database is used to link these proper names with image files. The image database consists of a series of attribute-value pairs for active searching of names. A URL query string is inputted to the database to extract the location of the image in the database file system. An Extensible Markup Language (XML) file is created from the raw text of the article, the list of proper names in the processed data and the image file references. The XML file is stored in a file system. An Extensible Stylesheet Language (XSL) file provides templates containing computational relationships between the text and images. The XML and XSL style sheets are combined to generate a Hypertext Markup Language (HTML) file containing an on-line story of the unstructured articles in a Java Applet which allows the system to provide a variety of interactive behaviors for a final presentation available by a viewer from a browser.

Methods and systems for performing semiconductor metrology directly on device structures are presented. A measurement model is created based on measured training data collected from at least one device structure. The trained measurement model is used to calculate process parameter values, structure parameter values, or both, directly from measurement data collected from device structures of other wafers. In some examples, measurement data from multiple targets is collected for model building, training, and measurement. In some examples, the use of measurement data associated with multiple targets eliminates, or significantly reduces, the effect of under layers in the measurement result, and enables more accurate measurements. Measurement data collected for model building, training, and measurement may be derived from measurements performed by a combination of multiple, different measurement techniques.

The invention discloses a three-dimensional reconstruction method based on coding structured light, comprising the following steps: 1) projecting structured light to an object to be measured, and capturing an image modulated by the object to be measured by a camera; 2) matching an optical template, comprising: (2.1) positioning the optical strip boundary, scanning along each row of the image, determining a pixel point with strong gray variation as a candidate marginal point, and searching a local domain; and (2.2) matching the optical strip: adopting a color cluster method to build a color matching proper vector, comparing image color with a projected color, and defining Euclidean distance between the color proper vector and the cluster center to distribute the colors of red, green, blue and white of the candidate optical strip; and 3) using a calibrated system parameter for three-dimensional reconstruction of the object to be measured, determining the relation between a space point coordinate and the image coordinate point thereof by the calibrated conversion matrix parameter; and restoring three-dimensional spatial coordinate from the image coordinate of a feature point. The invention can simplify calculation process and has high matching precision and high reconstruction precision.

A decision system for providing customized deployment architectures to users of a cloud computing environment. The decision system can identify one or more parameters for analyzing applications and processes running in a cloud, monitor the applications and processes executed in the cloud, and collect information such as usage of cloud resources, number and type of computing processes instantiated, software programs utilized by the computing processes. The decision system can then generate customized deployment architectures based on the collected information.

A method and an apparatus for easily and efficiently setting an evaluation index suitable for an evaluation purpose when a business or a business portfolio is evaluated. In the method and apparatus, investment distribution is easily and visually confirmed during computation of the evaluation index when an object to be computed is the business portfolio. A table for defining a computation expression model for the evaluation index and a correspondence relationship between the evaluation index and the evaluation purpose is managed by a database to extract and provide an evaluation index model conforming to the evaluation purpose specified by a user. When the business portfolio is evaluated, business investment distributions are represented by a pie chart. The investment distribution information is updated and the evaluation index is computed, by adjusting a distribution surface of the represented pie chart.

The invention provides a deep convolutional neural network computing method applicable to hardware design implementation. The deep convolutional neural network computing method puts forward that a deep convolutional neural network computing structure is readjusted in advance through relevant adjustment parameters and breaks through the constraint that a computing window structure is fixed in a traditional convolutional neural network, so that data, involved in computation, in every computing layer can reach firstly, and computing parallelism in the deep convolutional neural network and streamlining among the computing layers are fully exploited to reduce storage of a great number of intermediate results effectively. The deep convolutional neural network computing method applicable to hardware design implementation has the advantages that a deep convolutional neural network computing structure adjusted by the method is more beneficial to implementation of efficient parallel streamlining in terms of special hardware design, and the problems of resource waste and effective computing delay due to various filling operations during computation are solved effectively, so that system power consumption can be reduced effectively and computing and processing speed can be increased greatly.

The invention relates to a submerged line tracking method for flood evolution simulation in a complicated river channel landform area. The submerged line tracking method comprises four process of generating a digital river channel multi-resolution grid segmentation model; modeling and computing a simulation forecast model for a drainage area flood evolution state, wherein the process is operated in parallel with the process of generating the digital river channel multi-resolution grid segmentation model; accurately searching the submerging boundary of the complicated river channel landform; and dynamically identifying a drainage area flood evolution submerged area. The submerged line tracking method performs accurate searching on the submerging boundary of the complicated river channel landform through establishing the digital river channel multi-resolution grid segmentation model and the simulation forecast model, thereby realizing dynamic identification on the drainage area flood evolution submerged area, accurately simulating complicated river channels at various landforms, and quickly simulating a flood submerged area. Quick, accurate and visual forecast on time, water depths and flow and even flow velocity in flood evolution to different river segments are realized. A scientific basis is laid for disaster prevention and mitigation operation before flooding.

The invention discloses a monocular vision range finding method based on geometric relation, and the method comprises the following steps: S1, calculating the distance between a section straight line of a target object and a camera lens in a horizontal plane; S2, calculating the distance between a target object and the optical axis of a monocular lens through the triangle similarity principle. According to the projection model of a camera, the method obtains the relation between a road surface coordinate system and an image coordinate system through geometric derivation, is simple in calculation process, is low in calculation complexity, and is lower in requirements for hardware. Meanwhile, the method can reach higher precision, and the error of the method is less than 0.5m. The method can effectively calculate the distance between the front vehicles or other obstacles under the conditions that the hardware configuration condition is very simple and almost no additional load and electric quantity is added, thereby achieving the effective avoidance. The method and device are good in application prospect, such as the technical field of automobile automatic driving or intelligent robot walking.

The invention relates to an object deformation real-time simulation graphic processing technique, particularly a soft tissue deformation simulation method based on coupling of mesh-free Galerkin and mass spring, which comprises the following steps: in the pretreatment process, establishing a linear viscoelasticity biomechanical model for soft tissues; in the deformation computation process, dynamically partitioning a mesh-free region and a mass spring region according to the load carried by the soft tissues, establishing a transitional unit of the connection region between the mesh-free region and mass spring region, and constructing a transitional unit approximation displacement function, thereby implementing self-adapting coupling of a mesh-free Galerkin method and a mass spring method;and in the after-treatment process, outputting the state of the mass or node of each time step in the deformation process onto a screen, carrying out illumination rendering, and finally displaying the real-time deformation process of the soft tissue organ under stressed conditions on the screen, thereby implementing visualization effect of dynamic deformation. By utilizing the advantage of high efficiency in the mass spring method and the advantages of high precision and no need of mesh reconstruction in the mesh-free Galerkin method, the invention overcomes the defect that the Galerkin method is not suitable for solving a large-scale problem, thereby effectively lowering the complexity of computation in the soft tissue deformation simulation and enhancing the operation efficiency.

The invention relates to a self-positioning technology in wireless sensor network, especially a distributed method without anchor position. Various network nodes measure the values of distance from each other to get the neighbor information within the scope of their respective perception, and exchange their estimated coordinates many times. The invention adopts the distributed calculation method, firstly estimates the approximate location of nodes, and then each node itself makes iterative calculation of the estimated coordinate location by using the particle spring optimization model. The invention provides a practical method for the wireless sensor network to get geographic information. In addition, the calculation process is implemented on each node without the large cost of communication required by the centralized control, and has a very low complexity of calculation, so that the consumption of positioning calculation is lower, which is suitable for the self-organizing wireless sensor network with nodes measuring distance.

The invention discloses a control method of a three-phase grid-connected inverter based on a modified proportional resonant regulator. The method comprises the following steps of: firstly, computing according to given active and reactive powers of the inverter and a voltage of a power grid to obtain required d-axis and q-axis currents under a synchronous coordinate system, and performing coordinate transformation by combining with phase information which is measured by a phase locking loop and a measured three-phase voltage signal of the power grid to obtain a current reference signal under a stationary alpha-beta coordinate system; secondly, performing coordinate transformation on an output current signal of the inverter to the alpha-beta coordinate system, controlling and tracking the current reference signal by the modified proportional resonant regulator with a harmonic compensator; and finally, returning to an abc coordinate system by coordinate transformation to generate a pulse-width modulation (PWM) pulse-control three-phase full-bridge inverter output, so as to make a distributed generation system grid-connected for power generation. By the method, a large number of complicated computation processes are eliminated, and a control structure is simplified. The control method of the three-phase grid-connected inverter based on the modified proportional resonant regulator is an accurate and high-efficiency novel inverter control method and has a bright development future.

The invention discloses an on-line calibration system for an external parameter of a monocular camera. The system comprises a straight line detection module, a lane extraction module, a fast-perspective-removal (FPR) module, an optimal value iterative module, and an external parameter output module. The straight line detection module obtains an effective straight line position of an inputted infrared image; the lane extraction module carries out effective straight line searching and determination at a possibly occurring lane position; the FPR module carried out perspective transformation removing on a lane based on an imaging principle; the optimal value iterative module carries out loop iteration to enable a fitness function to be optimized, thereby obtaining an angle with the smallest error relative to a practical value; and the external parameter output module is used for outputting the angle obtained by optimal iteration. No calibration object is needed; the influence on the calibration precision by any external factor can be reduced; dependence on the straight line information of the lane is only needed during the calculation process; the flexibility is high; and on-line calibration on the camera can be realized. Moreover, no control by an external device and no strict experiment condition are needed during the calculation process. The system and method are simple and convenient to realize.

The invention discloses a method for identifying and locating fruit to be picked based on depth target detection, comprising the following steps: image acquisition, image annotation. data Set Preparation. Feature extraction includes using depth convolution neural network CNN to extract the fruit features in the image first; VGG16 is used as a convolution neural network for feature extraction. Model training, For all the training samples, the feature maps on each convolution layer are extracted firstly, and the prediction boxes are generated for the extracted feature maps, the categories of thetargets in the prediction boxes are calculated, the distance between the prediction boxes and the real annotation boxes is calculated, and the joint category loss and the prediction box offset loss are used as the target loss function of the training during the training process. A training is completed based on the complete one-time calculation process described above, and the training ends whenthe number of training times reaches a predetermined threshold or the loss is less than the predetermined threshold.