1582results about How to "Reduce the number of iterations" patented technology

There is disclosed a software system for specifying design of a product or process, the system having a first software module 1a for specifying design of the product or process according to a first set of design criteria, and a second software module 16 independent of the function of the first software module la for specifying design of the product or process according to a second set of design criteria. The first set of design criteria and the second set of design criteria are different to one another. The first software module 1a has a predetermined parameter having a limit which should not normally be breached. Conversion means interconnects the first software module and the second software module to enable data representing the product or process specified by either the first software module or the second software module to be recognised in the other of the software modules. Feedback means provides feedback when specification of said product or process by the second software module will breach the limit of said predetermined parameter.

The invention discloses an authoritative author and high-quality paper recommending system and recommending method. According to each preset theme, the recommending system calculates an author authoritative value and a paper quality paper through a plurality of factors including an author level, a citation rate, publish date and a publish periodical or a meeting level of a paper, and hereby recommends an authoritative author and a high-quality paper with the specified theme, so as to avoid authors with low authoritative values or papers with low quality values in an author or paper recommending list, and to reduce system calculation burden and improve system response time. The system and the method, on the basis of properties of academic papers, introduce relates algorithms after taking various factors affecting the author authoritative value and the paper high-quality value into comprehensive consideration, so as to improve accuracy of recommended results, which not only recommends high-quality papers to users but also remarkably shortens calculation time, and the system and the method are good in a real-time updating effect; furthermore, the system can enhance diversity of recommended results in the paper recommending list, and overcome a shortcoming of an existing system which is narrow in user view.

A reduced complexity Turbo multi-user detector (MUD) processing system in multiple access communications channels that decreases the likelihood of improper decoding of the final values of interest and decreases the computation complexity for each iteration, thereby allowing for a reduction in the number of iterations performed and lowers the overall complexity without negatively impacting performance. In one form the present invention comprises a multi-user detector coupled to two or more decoder sections, two ore more recoders, and a compare and adjust section in such a manner that data flows iteratively to correct for errors in a computationally efficient manner.

The invention discloses a neural network method for diagnosing analog circuit failures which is based on a particle swarm algorithm, and comprises the following steps: imposing an actuating signal to an analog circuit to be tested, measuring an actuating response signal in the testing nodes of the circuit, extracting the candidate signal of failure characteristics by implementing noise elimination and then wavelet packet transformation on the measured actuating response signal, extracting the failure characteristics information by further implementing orthogonal principal component analysis and normalization processing on the candidate signal of failure characteristics, and sending the failure characteristics information as samples to the neural network for implementing classification. The method adopts the particle swarm algorithm instead of a gradient descent method in traditional BP algorithms, thus leading the improved algorithm to be characterized in that the algorithm avoids the local minimum problem and has better generalization performance. The BP neural network method for diagnosing the analog circuit failures which is optimized on the basis of particle swarm can obviously reduce iteration times in the algorithm, improve the precision of network convergence, and improve diagnosis speed and precision.

A quantizer finds a quantization threshold using a quantization loop with a heuristic approach. Following the heuristic approach reduces the number of iterations in the quantization loop required to find an acceptable quantization threshold, which instantly improves the performance of an encoder system by eliminating costly compression operations. A heuristic model relates actual bit-rate of output following compression to quantization threshold for a block of a particular type of data. The quantizer determines an initial approximation for the quantization threshold based upon the heuristic model. The quantizer evaluates actual bit-rate following compression of output quantized by the initial approximation. If the actual bit-rate satisfies a criterion such as proximity to a target bit-rate, the quantizer sets accepts the initial approximation as the quantization threshold. Otherwise, the quantizer adjusts the heuristic model and repeats the process with a new approximation of the quantization threshold. In an illustrative example, a quantizer finds a uniform, scalar quantization threshold using a quantization loop with a heuristic model adapted to spectral audio data. During decoding, a dequantizer applies the quantization threshold to decompressed output in an inverse quantization operation.

The invention discloses a certificate picture camera and a certificate picture photographing method. The certificate picture camera comprises a parameter configuration module, a data obtaining module, a detection reminding module, an intelligent cutting module and a photographing quality grading module. The certificate picture photographing method comprises the following steps: detecting whether photographing parameters of an intelligent terminal camera satisfy certificate picture photographing requirements or not, and if not, configuring the photographing parameters; obtaining sensor data of an intelligent terminal, portrait characteristic data, luminance data, definition data and chromaticity distortion data of a preview frame image, judging whether relevant standard requirements of a legal certificate picture are satisfied or not, and if so, obtaining original image data after pressing a photographing button; cutting the original image data so as to obtain a standard-sized original picture of the certificate picture; grading the shooting quality of the original picture of the certificate picture, and giving a suggestion according to a comprehensive grading result. According to the invention, users can photograph the certificate picture satisfying government and public security industrial standard requirements in a self-service manner.

A pre-decoder applied to a turbo decoder for decoding a punctured turbo code. The turbo code consists of a data bit stream and a plurality of parity bit streams, parts of which are punctured. The pre-decoder has an arithmetic unit for calculating estimated parity bit streams by carrying out, with respect to the data bit stream, the same algorithm used by a turbo encoder to produce the parity bit streams, a comparison unit for comparing the plurality of parity bit streams with the estimated parity bit streams, and a recovery unit for substituting estimated parity symbols for corresponding punctured parts of the parity symbol streams when related non-punctured bits of the parity bit streams are identical with corresponding estimated non-punctured parity bits. The punctured parity symbols are recovered by the pre-decoder completely, or at least partially, and provided to the turbo decoder. Accordingly, the decoding performance of the turbo decoder is enhanced.

The invention discloses a scattered workpiece recognition and positioning method based on point cloud processing, and the method is used for solving a problem of posture estimation of scattered workpeics in a random box grabbing process. The method comprises two parts: offline template library building and online feature registration. A template point cloud data set and a scene point cloud are obtained through a 3D point cloud obtaining system. The feature information, extracted in an offline state, of a template point cloud can be used for the preprocessing, segmentation and registration of the scene point cloud, thereby improving the operation speed of an algorithm. The point cloud registration is divided into two stages: initial registration and precise registration. A feature descriptor which integrates the geometrical characteristics and statistical characteristics is proposed at the stage of initial registration, thereby achieving the uniqueness description of the features of a key point. Points which are the most similar to the feature description of feature points are searched from a template library as corresponding points, thereby obtaining a corresponding point set, andachieving the calculation of an initial conversion matrix. At the stage of precise registration, the geometrical constraints are added for achieving the selection of the corresponding points, therebyreducing the number of iteration times of the precise registration, and reducing the probability that the algorithm falls into the local optimum.

A reduced complexity Turbo multi-user detector (MUD) processing system in multiple access communications channels that decreases the likelihood of improper decoding of the final values of interest and decreases the computation complexity for each iteration, thereby allowing for a reduction in the number of iterations performed and lowers the overall complexity without negatively impacting performance. In one form the present invention comprises a multi-user detector coupled to two or more decoder sections, two ore more recoders, and a compare and adjust section in such a manner that data flows iteratively to correct for errors in a computationally efficient manner.

The invention discloses that based on open IFFT / FFT lower reserved subcarrier OFDM system PAPR methods and devices, and the system is using IFFT / FFT transform to a peak frequency domain Consumers sequence in the transmitter to reduce OFDM system ratio of power PAPR, the method described in steps including data distribution , IFFT transformation, realizing OFDM modulation, gained time-domain signal, judgment threshold and whether the number of iterations is to achieve settings, limited Consumers peak processing, extracted through FFT frequency domain reduce-peak sequence. The installations include the transmitter and receiver, the transmitter unit increased data distribution, data option modules, judgment unit, a unit reduce-peak sequence, sequence reduce-peak insert modules; Receiver increased information extraction unit. This invention in a process of elimination-series will not lead to the increase in bit error rate, the effect of significantly reducing the PAPR, the device is simple, running FFT / IFFT the iteration times is less.

The invention relates to an AGV optimization scheduling method based on mixed particle swarm optimization. First of all, a mathematics model is abstracted from the work process of an AGV, and an object function of a scheduling scheme is determined, and secondly, the model is solved by use of the mixed particle swarm optimization based on a genetic algorithm, a stimulated annealing algorithm and ant colony optimization, and an optimization scheduling scheme is generated. According to the invention, a contrast analysis is made between the mixed particle swarm optimization and standard particle swarm optimization through examples, and the variation operation of the mixed optimization employs an ant colony optimization thinking mode, ensures intersection of individual best and group best in an intersection operation process, ensures the feasibility of the mixed particle swarm optimization and has the validity for solving large-scale scheduling tasks.

The invention discloses item recommendation method and system based on a user-item bipartite model. The method includes: extracting acquired user behavior information to establish a user-item bipartite model; establishing an item-to-user inverted list model on the basis of the user-item bipartite model, calculating item weights, and establishing a user-item weight list; calculating weights of item pairs corresponding to users and common user weights of the item pairs according to the established user-item weight, and establishing an item pair-common user weight list; operating to obtain an inter-item similarity list according to a preset similarity algorithm; querying the established user-item bipartite model to obtain recommended user-mapped items, querying an inter-item similarity list according to the user-mapped items to be recommended to obtain query result, and generating a recommendation list according to the query result. By the use of the method and system, calculation needed by recommendation can be reduced and recommendation efficiency can be improved.

The invention elates to a human face character position method in the field of image processing technology. It first uses human face detecting method to find the human face area in the image, and then it detects eyes of human face area to fide the location of two eyes, then it according to the middle location of the two eyes, the distance between the two eyes and the angle of the two eyes to do imitate-injecting transformation to the initial ASM module so that the initial location of ASM module is near to the module which is formed by real character points, at last it dose ASM searching to the initial location after imitate-injecting transformation so as to obtain the location of human face character point.

The invention relates to a pavement crack image detection method. The method comprises the steps of: carrying out graying and filtering processing on a collected pavement image, constructing a pulse coupling neural network PCNN model, utilizing a genetic algorithm to rapidly find advantages of an optimal solution in a non-linear manner in a solution space so as to optimize important parameters of the model, and rapidly and accurately segmenting cracks and a background in the image; then according to the characteristics of the image after the segmentation, carrying out connected domain detection on the whole image, and filtering out the interference of noise and background textures; and finally, extracting a crack skeleton, calculating the maximum widths of the cracks along the normal line of the skeleton, and making marks in the original image. According to the invention, the digital image processing technology is adopted, the genetic algorithm is utilized to optimize the parameters of the PCNN model, optimization searching is accelerated, the iteration times f the PCNN are reduced, and the iteration is more liable to come to convergence, the interference resistance of the segmentation effect is relatively high, and the segmentation is more accurate; in addition, the modes of connected domain rectangularity, circularity filtering and irregular noise filtering are utilized to filter out a large number of irregular patches, and convenience is provided for the crack detection.

The invention relates to a PFH and inertial navigation based mobile context real-time three-dimensional reconstruction method. The PFH and inertial navigation based mobile context real-time three-dimensional reconstruction method is divided into three stages below: a first stage of designing a PFH based point cloud compression algorithm; a second stage of designing an inertial navigation based point cloud registration algorithm; and a third stage of finishing point cloud fusion. The PFH and inertial navigation based mobile context real-time three-dimensional reconstruction method can realize a relatively high compression ratio and can effectively reduce number of input point sets of an ICP algorithm, thereby saving computation time for point cloud registration and point cloud fusion. The number of iterations of the ICP algorithm is reduced, the rate of convergence is increased, and the real-time performance of the whole reconstruction algorithm is consequently improved.

The invention relates to an optical fiber wavelength demodulating light spectrum peak value positioning method, belonging to the technical field of optical fiber sensing. The method comprises the following steps: firstly, carrying out maximum value detection on obtained diffraction light spectrum energy data; determining a suitable threshold value according to a maximum value and utilizing the threshold value to carry out peak searching treatment; constructing a fitting array by the processed data; and utilizing an LM peak value positioning algorithm to carry out accurate peak value positioning on the spectrum. Compared with the existing power weighted algorithm and Gaussian-polynomial fitting algorithm, the algorithm has the advantages of high-precision positioned light spectrum peak value; and the method is easy to realize on a digital television through a simplified data processing process.

The invention discloses a method for segmenting a multi-dimensional texture image on the basis of fuzzy C-means FCM clustering and spatial information and mainly solves the problem of poor quality of image segmentation. The realizing process comprises the following steps of: inputting the texture image to be segmented, carrying out two-dimensional discrete wavelet transformation to the image, and calculating the characteristic vector corresponding to each wavelet coefficient; segmenting the coarsest scale of wavelet transformation; calculating spatial coordinate factors corresponding to the coefficients of the coarsest scale, adding the spatial coordinate factors into an objective function of a traditional FCM clustering algorithm and obtaining the segmenting result marker mapping and the marking field of the scale; obtaining the segmenting result marker mapping of the next scale by adopting the multiple dimensional segmenting method determined by an adaptive scale until the obtained segmenting result marker mapping is at the finest scale; and outputting the segmenting result of the finest scale as the final segmenting result. The method has the advantages of accurate segmenting edge and good consistency of segmenting regions and can be used for segmenting texture images, SAR images including texture information, remote sensing images and medical images.

The invention discloses a fast color image segmentation method based on combination of clustering analysis and an image pyramid. The method comprises the following steps: 1) constructing a Gauss and Laplace image pyramid according to a source image, and obtaining a low-resolution image and an edge image; 2) transforming the low-resolution image to an HSV (hue, saturation and value) space, obtaining the clustering number and an initial clustering center through histogram analysis, and performing clustering segmentation on the low-resolution image in the HSV space by use of a clustering algorithm; 3) performing up-sampling on the segmentation result obtained in the step 2), projecting to the original resolution, performing spatial filtering and removing the excessively small area to obtain the original resolution area segmentation result; and 4) integrating the edge image obtained in the step 1) and the area segmentation result obtained in the step 3) to obtain the final segmentation result. The method disclosed by the invention guarantees the segmentation quality while increasing the color image segmentation speed, and is suitable for a digital image processing system with high requirements on real-time property.

The invention discloses an improved target tracking method, and solves the technical problems in the prior art that a Mean-shift algorithm tracking window is fixed and Kalman prediction is sensitive in target adhesion. The specific method includes: using Kalman filtering to predict a target position, reducing a research range, then matching a target through binary image features, correcting kernel function bandwidth of a Mean-shift algorithm, updating the Kalman model, searching a matched target in the vicinity of the predicted position, and completing target tracking by Mean-shift vector iteration. The improved target tracking method provided by the invention combines the advantages of Kalman prediction and the Mean-shift algorithm, is small in calculated amount and has good real-time performance, and effectively guarantees tracking accuracy under a circumstance of target adhesion.

The invention provides a static voltage stability margin analyzing and system fault ordering method of a power system. The static voltage stability margin analyzing and system fault ordering method includes the following steps that the Newton iteration method based on the optimal multiplier is used for determining a static voltage collapse point; according to the characteristic of iteration convergence, the type of the voltage collapse point is judged; according to the requirement for stability margin, system fault danger conditions are checked and ordering of stability faults is performed; the faults are parameterized and the iteration method is used for giving out the order of seriousness degrees of instability faults. According to the static voltage stability margin analyzing and system fault ordering method of the power system, voltage stability margins of the power system can be rapidly given online, real-time effective monitoring is carried out on the voltage stability and the comprehensive order of the stability faults and the instability faults can be used for guiding a power generator to adjust online and switching reactive power compensation devices when the system encounters faults, can also guide sub-circuit parameter adjusting, line increasing and decreasing, configuration of an FACTS device and the like off line and have great significance in operation and planning of the power system.

The invention discloses a green building intelligent energy-saving assessment management system based on a Web geographic information system (GIS) and belongs to the technical field of assessment of green buildings. The green building intelligent energy-saving assessment management system based on the Web GIS is characterized in that: buildings with different characteristics are classified by using a clustering method; primary indexes of all classes of items are determined by using a principal component analysis method; corresponding secondary indexes and tertiary indexes are established through layered breakdown by using a work breakdown structure (WBS) method; the primary index which corresponds to each class is stored, and an index database is established; credibility and compatibility are improved on the basis of the conventional analytic hierarchy process, and a weight optimization model is established; a calculation model of an energy-saving performance comprehensive index is established by using a variable fuzzy evaluation method; the weight optimization model is introduced for determination of building index systems and weights under different levels; and a set of internet-based green building assessment system is developed on a Web GIS platform. The invention has the advantages that: the original information of each expert is reserved to the largest extent; the green building intelligent energy-saving assessment management system based on the Web GIS is convenient for operation and better in universality and practicability; information is shared; and the importance of the past experience on project management is emphasized.

The invention provides a neural network model training method and device, electronic equipment and a computer readable storage medium. The neural network model training method of the neural network model comprises the following steps: executing initial training by utilizing a first training sample set to obtain an initial neural network model; performing prediction on the second training sample set by utilizing the initial neural network model to obtain a prediction result of each training sample in the second training sample set; determining a plurality of preferred samples from the second training sample set based on the prediction result; receiving a labeling result for the plurality of preferred samples, and adding the labeled plurality of preferred samples into a first training sample set to obtain an expanded first training sample set; performing update training by using the extended first training sample set to obtain an updated neural network model; under the condition that a training ending condition is met, ending the training; and repeating the prediction step, the preferred sample determination step, the sample expansion step and the training updating step under the condition that the training ending condition is not met.

An apparatus and method for automatically focusing a lens of a digital imaging device, such as a digital still or video camera or camera phone, that reduces the auto focus time while not compromising image sharpness quality and simultaneously reducing device power consumption due to focus actuator movement, all without tuning or adjustment of thresholds or parameters is disclosed. The present invention automatically computes a set of digital band-pass filters matched to the imaging device specifications along with corresponding step-size magnitudes. A filter-switching search for the in-focus focus actuator position is formulated such that filters are switched and step-sizes are reduced as the search gets closer to the in-focus position by utilizing local estimates of the first and second-order differential information of the focus value surface.

The invention provides an allocation algorithm of joint beam and power for 5G unmanned aerial vehicle communication, and belongs to the field of communication. The method comprises the following steps: firstly, constructing unmanned aerial vehicle communication architecture, sending, by a millimeter wave base station, a communication signal, calculating an SINR of each miniature unmanned aerial vehicle in an unmanned aerial vehicle group based on adaptive beam forming, and separately calculating the respective allocated link capacity; then, in combination with the link capacity of each miniature unmanned aerial vehicle, constructing constraint conditions and an objective function to maximize the system capacity of the communication architecture; and finally solving the objective function,and performing beam and beam power resource allocation on the miniature unmanned aerial vehicle group within a millimeter wave band. By adoption of the allocation algorithm provided by the invention,the number of iterations is reduced, the fast beam and power joint allocation is achieved, the fairness of the unmanned aerial vehicles is ensured, and the system capacity is also improved.

The invention brings forward a frequency domain three-dimensional irregular earthquake data reconstruction method. The method is characterized in that first of all, three-dimensional earthquake data in a time domain is converted to a frequency domain by use of Fourier transform, and then, a projection onto convex set (POCS) algorithm is employed and curvelet transform capable of describing localized features of the earthquake data is introduced; and in an iteration process, a new threshold parameter attenuating according to an index rule is brought forward, and each frequency slice is individually reconstructed by use of a soft threshold operator, such that the iteration frequency is reduced, the reconstruction precision is improved, and the purpose of reconstructing the three-dimensional earthquake data is realized. According to the invention, the new threshold parameter attenuating according to the index rule is brought forward and each frequency slice is reconstructed in individually by use of the soft threshold operator, such that the disadvantage of quite slow convergence speed of a conventional threshold parameter is overcome, the calculation complexity of an algorithm is reduced, the calculation efficiency is substantially improved, and the operation time is reduced.

The invention discloses a short-term power load predicting method based on a grey theory. The method includes the following steps: recognizing defective data in load data, and performing completion and correction on defective and mutant load data; according to the completed and corrected load data, verifying modeling feasibility of a GM (1,1) model, and utilizing a logarithm processing method to correct unqualified sequences; correcting (img file=' 2013106975443100004dest_path_image002.TIF' wi='26' he=' 23' / ) parameters in the GM (1,1) model built in step 2; forming different predicting schemes by selecting data sequences from different prospectives and utilizing the GM (1,1) model after (img file=' 297985dest_path_image002. TIF' wi=' 26' he=' 23' / ) parameters corrected for predicting, sectioning a predicating day, calculating an average value of correlation coefficients of the schemes in each time section, and selecting the scheme with a biggest correlation coefficient as a predicating scheme of the time section; testing accuracy of the GM (1,1) model by utilizing a posterior difference checking method. By the short-term power load predicting method, the objectives of universality in predicting use and high accuracy are achieved.

The invention discloses a rectangular coordinate Newton method load flow calculation method with a changeable Jacobian matrix. The method includes the following steps that original data input and voltage initialization are conducted; a node admittance matrix is formed; power and voltage deviations are calculated, and the maximum amount of unbalance delta Wmax is obtained; the Jacobian matrix J is formed; a correction equation is solved and a real part e and an imaginary part f of voltage are corrected; node and circuit data are output. According to the rectangular coordinate Newton method load flow calculation method, a Jacobian matrix calculation method different from that used in the following iteration processes is adopted in the initial iteration process, and the convergence problem of rectangular coordinate Newton method load flow calculation in analyzing a system with a small-impedance branch circuit is solved. When misconvergence happens with conventional rectangular coordinate Newton method load flow calculation, the rectangular coordinate Newton method load flow calculation method with the changeable Jacobian matrix can achieve reliable convergence, and the number of iterations is fewer compared with the prior art. The rectangular coordinate Newton method load flow calculation method with the changeable Jacobian matrix can effectively solve the convergence problem of the conventional rectangular coordinate Newton method load flow calculation in analyzing the system with the small-impedance circuit branch, and meanwhile load flow calculation can be performed on normal systems, and adverse effects are avoided.

The invention discloses a cloud service distributed data center system which comprises a system state monitoring module, a load admission control module, a load routing distribution module, a load dispatching module and a multi-source energy supply management module. The system state monitoring module is used for acquiring cloud service requests from different users, recording the cloud service requests and information related to the cloud service requests and transmitting the cloud service requests to the load admission control module, the load admission control module is used for selecting parts of the cloud service requests according to the number of the received cloud service requests and transmitting the cloud service requests to the load routing distribution module, and the load routing distribution module is used for distributing the received cloud service requests to corresponding data centers for processing. The cloud service distributed data center system can solve the problems that a data center energy supply system in an existing system is high in long-term operating expenditure and unstable in new energy supply, electricity price fluctuates, and system data or stable distribution needs to be acquired in advance.

The invention relates to a modeling method of plate rolling in an online control model, which carries out the modeling by a rigid-plastic finite element method. The modeling method comprises the following steps: taking a central line of a plate as an x shaft and the thickness direction of the plate as a y shaft to build a two-dimension plane strain rolling model; inputting rolling conditions and parameters; dividing finite element grids in a rolling deformation region at the lower side of the rolling contact region by adopting a quadratic element and carrying out finite element pretreatment; setting the initial speed field of the finite element; building a rigid-plastic finite element energy functional by taking the initial speed field as an initial value, iterating and solving a minimum value point of the energy functional by adopting the damping Newton method, and obtaining the actual speed field; calculating the strain field and the strain field according to the actual speed field, further calculating online control parameters of the rolling force, the rolling torque and the forward slip value, and obtaining the plate rolling model. The invention improves the calculation speed of the finite element, realizes the online rapid calculation and control of the rigid-plastic finite element of the plate rolling and has strong antijamming capacity and good stability.