44 results about "Dimensional weight" patented technology

An improved dimensional detection system is portable and can be used to characterize a workpiece. The dimensional detection system employs as few as a single focused light source and as few as a single camera along with a calibration data set to convert the illuminated pixels of an image of a beam on the workpiece into a cloud of real world points in space on an outer surface of the workpiece. The cloud of points can be processed to characterize the workpiece, such as by determining the right hexahedron that would encompass all of the real world points in space and which could be used to determine a dimensional weight of the workpiece.

An improved dimensional detection system is portable and can be used to characterize a workpiece. The dimensional detection system employs as few as a single focused light source and as few as a single camera along with a calibration data set to convert the illuminated pixels of an image of a beam on the workpiece into a cloud of real world points in space on an outer surface of the workpiece. The cloud of points can be processed to characterize the workpiece, such as by determining the right hexahedron that would encompass all of the real world points in space and which could be used to determine a dimensional weight of the workpiece.

Methods, apparatus and computer program products provide efficient techniques for reconstructing surfaces from data point sets. These techniques include reconstructing surfaces from sets of scanned data points that have preferably undergone preprocessing operations to improve their quality by, for example, reducing noise and removing outliers. These techniques include reconstructing a dense and locally two-dimensionally distributed 3D point set (e.g., point cloud) by merging stars in two-dimensional weighted Delaunay triangulations within estimated tangent planes. The techniques include determining a plurality of stars from a plurality of points pi in a 3D point set S that at least partially describes the 3D surface, by projecting the plurality of points pi onto planes Ti that are each estimated to be tangent about a respective one of the plurality of points pi. The plurality of stars are then merged into a digital model of the 3D surface.

A method is disclosed for defining tracks on a rotating magnetic disk medium of a disk drive. Reference tracks are followed using a servo control loop while writing servo burst patterns defining a first target servo track. The servo control loop includes a two-dimensional digital state compensator having a first input that receives position error signals, a first output that generates control signals for positioning a transducer head, a second output that generates track-following state variables, and a second input that receives processed and stored track-following state variables. The first target track is followed using the servo control loop while servo burst patterns are written, and while the processed and stored track-following state variables corresponding to the servo burst patterns defining the first target track are applied to the second input.

The invention discloses a coarse-grained reconfigurable system-oriented convolution neural network loop convolution calculation data reuse system, including a main controller and a connection control module, an input data reuse module, a convolution loop operation processing array, and a data transmission Pathways in four parts. During the convolution cycle operation, the essence is to multiply multiple two-dimensional input data matrices with multiple two-dimensional weight matrices. Generally, these matrices are large in size, and the multiplication takes up most of the time of the entire convolution calculation. The present invention utilizes a coarse-grained reconfigurable array system to complete the convolution calculation process. After receiving the convolution operation request instruction, the register rotation method is used to fully explore the reusability of the input data in the convolution cycle calculation process, which improves the data utilization rate. It also reduces the bandwidth access pressure, and the designed array unit is configurable, and can complete convolution operations with different cyclic convolution scales and step sizes.

Disclosed is a method and system for incorporating a carbon offset calculation into a shopping cart subsystem of a merchant website that calculates an estimated cost to offset a calculated estimate of the carbon emissions generated from shipping purchased products in order to allow the incorporation of the estimated carbon offset cost into the cost for the purchased products and allocate at least a portion of the payment of the estimated carbon offset cost to an entity building, managing, or operating a carbon offsetting application. Some embodiments may permit the customer purchasing the products to optionally select to incorporate the carbon offset costs in the costs for the purchased products. The estimate of carbon emissions generated calculation is based on specific information for the purchased products and the selected shipping methods for shipping the purchased products such as the shipping weight of the purchased products, a distance the purchased products are being shipped, and a designation of the shipping or transportation method. Additional embodiments may include dimensional weights of the purchased products, additional factors to account for the specific aspects of the selected transportation method for shipping the purchased products, and permitting the customer to select a specific carbon offsetting application to fund.

The invention discloses a receiving-transmitting type robust dimensionality-reducing self-adaptive beam forming method of coherent MIMO (Multiple Input Multiple Output) radar. The receiving-transmitting type robust dimensionality-reducing self-adaptive beam forming method comprises the following steps: firstly, separating a transmitting-receiving two-dimensional weight vector in the coherent MIMO radar into a transmitting weight vector and a receiving weight vector to realize dimensionality reduction; then utilizing a two-order convex optimization algorithm to deduce a double-secondary cost function and a constraint condition based on a bilateral guiding vector error model; calculating an optimal weight vector needed by beam forming by using a double iteration method; and finally, robustly realizing self-adaptive beam forming by using the optimal weight vector. According to the receiving-transmitting type robust dimensionality-reducing self-adaptive beam forming method, the disadvantages that a very great sample quantity and a great calculation quantity are needed when a traditional self-adaptive beam forming method is applied to the coherent MIMO radar are overcome. Compared with the traditional self-adaptive beam forming method, the needed sample quantity and calculation quantity are reduced to the great extent, the convergence speed is improved greatly and the robustness is very good.

An object pose estimating and matching system is disclosed for estimating and matching the pose of an object highly accurately by establishing suitable weighting coefficients, against images of an object that has been captured under different conditions of pose, illumination. Pose candidate determining unit determines pose candidates for an object. Comparative image generating unit generates comparative images close to an input image depending on the pose candidates, based on the reference three-dimensional object models. Weighting coefficient converting unit determines a coordinate correspondence between the standard three-dimensional weighting coefficients and the reference three-dimensional object models, using the standard three-dimensional basic points and the reference three-dimensional basic points, and converts the standard three-dimensional weighting coefficients into two-dimensional weighting coefficients depending on the pose candidates. Weighted matching and pose selecting unit calculates weighted distance values or similarity degrees between said input image and the comparative images, using the two-dimensional weighting coefficients, and selects one of the comparative images whose distance value up to the object is the smallest or whose similarity degree with respect to the object is the greatest, thereby to estimate and match the pose of the object.

The invention discloses a front collision avoiding system based on driving behavior of a front vehicle driver and a vehicle collision avoiding algorithm. The front collision avoiding system consists of laser radar, a front vehicle driving information acquisition camera and a video processor hardware platform, wherein the laser radar is used for acquiring the distance between a vehicle self and a front vehicle, the speed of the front vehicle and the acceleration of the front vehicle; the front vehicle driving information acquisition camera is used for acquiring a front vehicle left turn light, a front vehicle right turn light, front vehicle brake lights and vehicles in adjacent lanes of the front vehicle. By analyzing driving parameters of the front vehicle and information of vehicles in adjacent lanes of the front vehicles, a prediction model of driving behavior of the front vehicle driver can be established, and a collision avoiding scheme set can be designed. Two evaluation indexes of security and stability can be established. A collision avoiding optimization function based on two-dimensional weight can be established, particle swarm optimization is adopted to optimize collision avoiding schemes, and thus optimal collision avoiding schemes can be obtained.

The invention discloses a dendriform solid decomposition and graded framework extraction method, which comprises the following steps: a maximum central point diagram is constructed through integral-type boundary distance conversion and integral-type single seed point distance conversion of a solid; a dendriform clustering scheme is generated, the clustering scheme is combined with the maximum central point diagram to extract the central representative point of each clustering, and a classification framework is constructed for classification; branch points are taken as seed points for integral type distance conversion, thus constructing the section of each branch; equidistant faces of the sections are constructed through curved surface unidirectional distance conversion, thus acquiring divisional planes and completing decomposition of the solid, namely, the solid is decomposed into a plurality of blocks; finally, a three-dimensional weighted directed graph is constructed through precise Euclidean distance conversion, the cost value of a compact center is calculated, and the minimal spanning tree is extracted; the central frameworks of the blocks and a connecting framework reaching the centers of the divisional planes are acquired, thus finally getting the graded framework. The invention can measure form and structure, and represent shapes accurately according to test specifications of a plurality of data sets.

An object pose estimating and matching system is disclosed for estimating and matching the pose of an object highly accurately by establishing suitable weighting coefficients, against images of an object that has been captured under different conditions of pose, illumination. Pose candidate determining unit determines pose candidates for an object. Comparative image generating unit generates comparative images close to an input image depending on the pose candidates, based on the reference three-dimensional object models. Weighting coefficient converting unit determines a coordinate correspondence between the standard three-dimensional weighting coefficients and the reference three-dimensional object models, using the standard three-dimensional basic points and the reference three-dimensional basic points, and converts the standard three-dimensional weighting coefficients into two-dimensional weighting coefficients depending on the pose candidates. Weighted matching and pose selecting unit calculates weighted distance values or similarity degrees between said input image and the comparative images, using the two-dimensional weighting coefficients, and selects one of the comparative images whose distance value up to the object is the smallest or whose similarity degree with respect to the object is the greatest, thereby to estimate and match the pose of the object.

The present invention relates to building structure and construction method. It includes one wall structure comprising rigid net frame with shaped steel and steel wire member and planar sandwich layer with foamed glass; integrally cast floor board connected via steel wire net; combined construction process of assembling prefabricated members and site casting;a nd movable and easy-to-connect wall for variable bay and fitting setting. The present invention has high integral performance, short construction period and low building cost.

The invention discloses a weak signal enhancement detection method based on a complementary stochastic resonance filter, which comprises the steps of (1) dividing a signal with the length being 2N points in a half-and-half manner into two sub-signals S1(t), S2(t), wherein the signal is acquired by preprocessing, and the length of each sub-signal is N points; (2) building a complementary stochastic resonance filter; (3) calculating a value of the weighted spectral kurtosis of a main channel output signal x(t); and (4) searching a maximum value in a six-dimensional weighted spectral kurtosis matrix, wherein the output signal x(t) corresponding to the maximum value is an optimal filtering output signal of the complementary stochastic resonance filter, and performing spectral analysis on the signal, thereby being capable of judging the fault type of a bearing according to fault characteristic frequency of the bearing. According to the invention, the more advanced double-channel complementary stochastic resonance filter is adopted, appropriate system parameters are adjusted in an adaptive manner through a weighted spectral kurtosis indicator, and weak periodic signals of a main channel are enhanced by using phase difference noises of complementary channels, so that enhancement for the weak fault characteristic frequency of the bearing is realized, and the accuracy of fault diagnosis of the bearing is improved.

The invention relates to the technical field of node positioning in wireless sensor networks, in particular to a three-dimensional weighted centroid positioning method based on a mass-spring model. The positioning method includes the steps: in an initial phase, amending a weight of the three-dimensional weighted centroid positioning method, and estimating the initial positions of unknown nodes by the weighted centroid positioning method with the amended weight; in an optimizing phase, amending the measuring distance by the aid of error factors, adjusting the unknown nodes to the best positions by the method of the mass-spring model. The positioning method is applicable to positioning geometric peripheral nodes comprising anchor nodes and has the advantages of high positioning accuracy and strong robustness.

The invention discloses an image reconstruction method based on three-dimensional weighted filtering back projection and statistical iteration. The method comprises the following steps: firstly obtaining projection data, obtaining a projection angle, a taper angle and a fan angle of a ray corresponding to each projection data, then pre-weighting the projection data, then carrying out line-by-linefiltering on the pre-weighted projection data, then carrying out three-dimensional weighting, and carrying out back projection to obtain an initial reconstructed image, and calculating projection estimation values at all angles according to the obtained reconstructed image, comparing the projection estimation values with actual projection data, calculating an error, correcting the reconstructed image according to the error, and repeatedly iterating the process until the reconstructed image meeting the condition is obtained.

The invention relates to a nonnegative matrix factorization-based method for detecting anomalies in the stock market. The method includes the following steps that: a stock index matrix is built; the stock index data matrix is factorized through using NMF (nonnegative matrix factorization), so that a base matrix U representing stock index feature bases and a coefficient matrix V representing low-dimension weight coefficients are obtained; nonnegative matrix factorization is performed on a stock time sequence Xn*m, so that a base matrix Un*r and a coefficient matrix Vr*m can be obtained; wavelet transformation is performed on a weight coefficient vector Vi, so that multi-level waveforms of different granularities are obtained; the fluctuation amplitude of the waveforms is detected, anomaly conditions are judged according to the amplitude of the waveforms; and after an anomaly position of the sequence is determined, empirical analysis is carried out: the detected anomaly fluctuation position relative to the sequence is found out in the sequence of which the weight coefficient vector Vi has been subjected to wavelet transformation, the time point of an abnormal event is marked in a corresponding position in original matrix data, the transformation conditions of stock market indexes at the time point are inspected, and therefore, the accuracy of detection is judged.

A universal storage system that includes a magnetic holding means and a universal storage device. The magnetic holding means is applied to or incorporated into an office stamp, tool, or other article, which is then stored on the universal storage device. The universal storage device includes a mounting surface of magnetically attractive material and securely holds the office stamp, tool, etc. in place, yet readily releases it when it is needed. The magnetic holding means is well-suited for mounting all types of conventional self-inking, flash inked, pre-inked, as well as wood-handled stamps, of any size, weight and shape, as well as other office and household articles and tools.

The invention discloses a neural network column sparse method based on weight saliency. When the neural network model carries out feedforward operation, the four-dimensional weight tensor can be expanded into a two-dimensional matrix in a tensor expansion mode, and therefore convolution operation is converted into matrix multiplication. Therefore, on the basis of a tensor expansion operation mode,the invention discloses a method for carrying out column sparsity on a weight matrix, namely deleting columns of the weight matrix in a neural network parameter layer to obtain a sparse model, and the dimension of the operation matrix is reduced at the moment, thereby reducing the model scale of the neural network and accelerating the operation of the neural network. Network re-training is performed on the remaining weight parameters to callback the accuracy, and re-training is stopped when the accuracy of the model is not increased any more to obtain a final model. According to the method, the deep learning model can be deployed on mobile and embedded equipment, a certain actual acceleration effect is obtained, and the application of an intelligent algorithm on a mobile terminal is promoted.

The invention discloses a meteorological radar wind power plant clutter rejection method based on two-dimensional combined interpolation. The method comprises the following steps: under the conditionof low signal to noise ratio, firstly carrying out zero setting on a distance unit containing wind turbine clutter (WTC) and meteorological signals simultaneously, taking the distance unit as a center, selecting two adjacent distance units at two ends respectively, and thus improving the signal to noise ratio through coherent integration; then calculating the one-dimensional interpolation meteorological signal estimated value of a distance domain and the one-dimensional interpolation meteorological signal estimated value of a Doppler domain respectively, and finally carrying out two-dimensional combined interpolation according to a two-dimensional weight coefficient calculated by a least square method. Simulation experiments indicate that the interpolation precision can be improved effectively under the condition of low signal to noise ratio, accurate recovery of meteorological signals is realized, the calculation quantity is small, and the method is applicable for engineering application.

The invention discloses a building edge point detection method based on double-domain graph signal filtering. The method comprises the following steps: collecting airborne laser radar data and generating three-dimensional point cloud data; based on the local feature statistical histogram and the elevation value of the vertex, constructing a double-domain signal graph of a spatial domain and a value domain; constructing a three-dimensional space weighted undirected signal graph model through a histogram cross kernel and a Gaussian function; carrying out edge perception smoothing on the signal graph model by utilizing bilateral filtering, and removing perigee and noise points; and based on the double-domain signal graph model, constructing a double-threshold high-pass filter to automaticallyextract building edge points. According to the invention, an airborne laser radar system and a POS system are used for collecting three-dimensional point cloud data of a surface building; and then athree-dimensional weighted double-domain signal graph is constructed based on a local feature statistical histogram, and bilateral filtering smoothing and double-threshold high-pass filtering edge detection is performed by using unused filters, thereby achieving the purpose of directly and accurately extracting building edge points from original point cloud data.

The invention discloses a multi-language scene text detection and recognition method. The method comprises: processing an input image through a text detector to obtain a series of text candidate boxes; adaptively generating a one-dimensional weight vector and a two-dimensional weight matrix through a feature selection and long-range dependency extraction module so as to pay more attention to channels from which text information is extracted and areas containing texts, and acquiring global information by capturing long-range dependency; and by the operation of the feature enhancement module, enabling the network to have better distinguishing performance on the text / non-text, thereby reducing false alarms; finally, predicting a series of text candidate boxes by using a plurality of convolution respectively; and carrying out text recognition and text category recognition on the text candidate boxes subjected to threshold processing and zooming through a text recognizer and a text category recognizer to obtain text contents and text categories. The method has high text detection and recognition performance and is suitable for multi-language application scenarios.

A system and method for assisting a party (e.g., a freight carrier) in determining whether dimensional weight or actual weight should be used for shipping particular freight. A weighing device may be associated with a forklift, and may also include a DIM comparator in association with the forklift. The DIM comparator may have an input and a display of information, and may also include a CPU unit for storing and processing data. The DIM comparator may allow an operator to input various data through selectable menus and tables. The DIM comparator then determines a theoretical dimensional weight of the freight and compares it to the actual weight as measured by the weighing device associated with the forklift. This comparison is then displayed to the operator and a prompt is provided if a dimensional weight should be determined.

The present invention provides a deep learning accelerator comprising: a plurality of processing components (PE) divided into a plurality of PE groups for performing a computation of a convolution layer by applying multidimensional weights to input activations to generate output activations; the scheduler is used for scheduling the input data in the input activation and the non-zero weight in themulti-dimensional weight to the processing component according to a control mask; wherein the control mask specifies a position of a zero weight of the multi-dimensional weights, and the plurality ofPE groups generate output data of each output channel in the output activation and share the same control mask specifying the same position of the zero weight. Correspondingly, the invention further provides a method for accelerating the deep learning operation. By adopting the invention, the system performance can be improved.

The invention discloses measuring equipment for material size and weight, and relates to the technical field of material detection equipment. The measuring equipment comprises a feeding mechanism, a measuring mechanism, an X-axis conveying mechanism, and a grading material receiving mechanism. The measuring mechanism comprises a size measuring stage and a weighing stage which are arranged side byside; a plane size measuring device is arranged above the size measuring stage, and a thickness measuring device is arranged at the side part of the size measuring stage; a weighing module is arrangedon the weighing stage; the X-axis conveying mechanism comprises an X-axis mechanical arm used for transferring the materials to the size measuring stage and the weighing stage; the grading material receiving mechanism is arranged at the downstream position of the measuring mechanism, and comprises a rotary disc, a plurality of collection boxes are arranged on the periphery of the rotary disc, anda material receiving mechanical arm used for transferring the materials into the corresponding collection box is arranged on the rotary disc. According to the measuring equipment for material size and weight, the detection precision is high, the grading is fine, and the material is not easily damaged.

A method and apparatus for assessing the health of a process is provided where the health of a client's process is assessed across a plurality of dimensions based on computing a process health index. The process health index is computed by determining a sub-process health index across a plurality of dimensions for the sub-processes comprising the process. The sub-process health index across different dimensions is determined based, at least in part, on responses to a set of evaluators and the dimensional weight value assigned to each of the dimensions. The response to each evaluator is an option selected from a plurality of weighted options. The sub-process health index is then stored. A process health index is calculated based on the sub-process health index for each sub-process across a plurality of dimensions and the sub-process weight value assigned to each of the sub-processes. The process health index is stored and used to assess the health of a client's process.

A computer implemented method for estimating the dimensional weight of a package is disclosed. The method provides a user interface for inputting one or more dimensional attributes of the package such as length, width, height and mass weight of the package. The net dimensional weight of the package is determined using an algorithm that includes calculating a first value by multiplying the length, width and the height of the package; calculating a second value by dividing the first value by the dimensional weight constant and calculating the dimensional weight of the package by subtracting the mass weight of the package from the second value.

The invention provides a method and a device for image fusion classification. The method comprises the following steps: obtaining a three-dimensional weight matrix of a hyperspectral image by a support vector machine classifier; Performing superpixel segmentation on the hyperspectral image to obtain K superpixel images, wherein K is a positive integer; Regularizing the three-dimensional weight matrix using a partitioning method of the superpixel map to obtain a regular matrix; The category to which the sample belongs is determined according to the regular matrix. By using the method provided by the invention, the problem of estimating the number of the superpixels can be avoided, furthermore, the spatial structure information of the ground objects contained in different superpixel maps isfused in a cascade manner, and the distinguishing power of the features is remarkably improved.

Systems and methods for loading freight containers are disclosed. The inventive system and method is used to reduce excess shipping costs resulting from the use of dimensional weight in calculating freight charges. The invention is comprised of various loading combinations for boxes optimally sized to fit on standard palettes. Through use of the invention, the higher shipping costs increased dimensional weight of lighter packages can be alleviated.

The invention relates to a scanning mode switching system. The system comprises a front-end processing assembly which comprises a CT scanning device, a CT image obtaining device, a format conversion device and a data reading device; the format conversion device is connected with the CT image obtaining device and the data reading device, and the CT scanning device is connected with the CT image obtaining device; The first back-end processing assembly comprises three-dimensional volume data set generation equipment and three-dimensional weight construction equipment; The second back-end processing assembly comprises boundary extraction equipment, boundary fitting equipment and data display equipment; And the mode switching equipment is respectively connected with the CT scanning equipment and the structure capturing equipment and is used for determining the scanning mode of the CT scanning equipment based on the on-site capturing type and realizing switching of the scanning mode of the CT scanning equipment based on the determined scanning mode. According to the invention, the scanning mode of the CT scanning equipment can be flexibly switched.