259 results about "Algorithm" patented technology

A first display displays a predetermined number of symbols among a plurality of symbols including at least one special symbol. A storage stores a first number of times that a special symbol combination is appeared on the first display, the special symbol combination includes a first symbol combination in which at least one special symbol is included and a second symbol combination which is a predetermined combination of the symbols. A determinant determines whether the first number of times stored in the storage is not less than a second, predetermined number. A signal generator generates an award signal for causing the gaming machine to provide an award to a player in accordance with the special symbol combination displayed on the first display, when the determinant determines the first number of times is not less than the second number of times.

Methods and systems represent constraint as an Ising model penalty function and a penalty gap associated therewith, the penalty gap separating a set of feasible solutions to the constraint from a set of infeasible solutions to the constraint; and determines the Ising model penalty function subject to the bounds on the programmable parameters imposed by the hardware limitations of the second processor, where the penalty gap exceeds a predetermined threshold greater than zero. Such may be employed to find quantum binary optimization problems and associated gap values employing a variety of techniques.

The invention discloses a convolutional neural network optimization and rapid target detection method and device. The optimization method comprises the steps of obtaining a first convolutional model trained by adopting floating point type sample image data; wherein a BN layer operation in the first convolution model is located behind convolution layer operation and is used for normalizing a numerical value obtained after the convolution layer operation to a preset data range; adjusting parameters of a convolutional layer according to the parameters of the BN layer; deleting the BN layer to obtain an adjusted second convolution model; adding a quantization layer before the convolution operation of the convolution layer in the second convolution model to obtain a third convolution model; wherein the quantization layer is used for quantizing input data to integer data of a predetermined bit, and enabling a convolution layer to perform convolution operation by using the integer data of thepredetermined bit. According to the method, the problems of large model body quantity and low operation speed are solved by optimizing the convolution operation.

The invention relates to an online fault detection method for a reduced set-based downsampling unbalance SVM (Support Vector Machine) transformer. At present, the research of improving the performance of an unbalance data downsampling SVM algorithm comprises upsampling and downsampling. The SVM model calculating cost of the upsampling algorithm is increased. The downsampling algorithm is selected improperly sometimes, and thus the poor classifying effect is caused. The online fault detection method comprises the following steps: (1), acquiring a vibration signal of a transformer; (2), obtaining a noise reduction vibration signal; (3), obtaining multiple groups of fault detection feature data; (4), clustering by using a K-mean algorithm; (5), figuring out a weight value of each sample; (6), establishing a majority sample reduction vector solution optimization model; (7), obtaining an SVM fault diagnosis model; and (8), inputting a sample to be tested to an unbalance SVM detector trained in the step 7, analyzing a result output from the detector to obtain a working state of the transformer, and realizing online fault detection of the transformer. The online fault detection method is used for detecting the fault of the transformer online.

The invention provides an unmanned aerial vehicle route planning method based on an improved bat algorithm. According to the method, the optimization success rate is introduced to change the speed updating mode of the individual bats based on the conventional bat algorithm; meanwhile, the chaotic method is applied to initialize the distribution of the individual bats in the search space and the concept of the artificial potential field is utilized to simulate the gravitational field of the ending point and the repulsive field of the starting point and the obstacle so as to accelerate the speedof the individual bats to the optimal solution; and finally the improved bat algorithm based on the chaotic artificial potential field is proposed. Compared with the conventional bat algorithm, the track length is shortened for 36.56%, the planning time is shortened for 56.05% and the obstacle avoidance fitness value is reduced for 49.53% by the method; and compared with the differential evolutionary bat algorithm, the track length is shortened for 27.16%, the planning time is shortened for 27.30% and the obstacle avoidance fitness value is reduced for 42.46% by the method in the unmanned aerial vehicle route planning task so that the method is a route planning algorithm with practical significance.

A computer-implemented method, computer program product and computing system for defining a model that includes a plurality of variables. At least one variable of the plurality of variables is conditioned based, at least in part, upon a conditioning command received from a user, thus defining a conditioned variable. The model is inferenced based, at least in part, upon the conditioned variable.

The invention provides a query word classifying method and a query word classifying device. The query word classifying method comprises the following steps of acquiring an inputted query word; generating a leaf industry category distribution vector which corresponds to the query word and comprises industry categories of at least one leaf node corresponding to the query word; determining the industry category of the leaf node of the query word according to category word vectors of the industry categories of various leaf nodes corresponding to the query word; and determining the industry category of the query word according to the determined industry category of the leaf node and a preset mapping relation of various industry categories. Various industry categories of various nodes correspond to corresponding category word vectors, and each category word vector comprises a plurality of category word. By the query word classifying method and the query word classifying device, an industry query result is accurate, and the query efficiency is high.

The invention discloses a clustering method and device, wherein the clustering method comprises the steps of: classifying types of a plurality of users through a MinHash clustering model, storing a Hash function of the MinHash clustering model, storing a correspondence of Hash values of the plurality of users and the types; determining Hash values of new users through the Hash function of the MinHash clustering model; and determining types of the Hash values of the new users according to the correspondence of the Hash values and the types. Through determining the Hash function of the MinHash clustering model and the correspondence of the Hash value and the type, the new users are rapidly re-clustered by using the existing MinHash clustering model without re-generating a clustering model, thus the clustering efficiency of the new users is increased.

The invention discloses a failure prediction method based on ICA reconstruction, which includes the following steps: step 1, calculating a separative matrix W; step 2, calculating the statistic value I<2>(k), SPE(k) or I<2>e(k) of the real-time data Xnew(k) through adopting the formulas I<2>(k)=S'newd(k)<T> S'newd(k), I<2>e=S'newe(k)<T>*S'newe(k), SPE(k)=(xnew(k)-x'new(k))<T>*(xnew(k)-x'new(k)), S'newd(k)=Wd*xnew(k), and S'newe(k)=We*xnew(k), wherein Wd refers to the matrix formed by the lines expect the first d lines of the separative matrix W, We refers to the matrix formed by the lines except the first d lines of the separative matrix W, and X'new(k)=Q<-1>BdWd*xnew(k), Bd=(WdQ<-1>)<T>, Be=(WeQ<-1>)<T>, and Q refers to a whitening matrix; and step 3, calculating the nuclear density of I<2>(k), SPE(k) or I<2>e(k), and detecting failures as per the control limit. The method provided by the invention solves the problem that the traditional flue gas turbine prediction method can not utilize the multidimensional valid data, takes the multi-channel vibration data into consideration, can be used for directly predicating failures, and improves the prediction accuracy compared with the PCA reconstruction method.

The invention provides a malicious software API (Application Program Interface) call sequence detection method based on graph convolution. The method comprises the following steps: acquiring and recording API call sequence information of processes and sub-processes when a large number of software samples run; performing vectorization processing on the API calling sequence information; extracting aparameter relationship, a dependency relationship and a sequence relationship of the API function; establishing an API call graph; inputting the API call graph into a graph convolutional neural network for training to obtain a malicious software detection network model; collecting API calling sequence information of processes and sub-processes when the executable file to be detected runs; constructing an API call graph of the executable file to be detected, then inputting the API call graph of the executable file to be detected into the malicious software detection network model, If the output result of the malicious software detection network model is 1, indicating that the judgment result is malicious software; If the output result of the malicious software detection network model is 0,indicating that the judgment result is normal software.

The invention is suitable for the technical field of ice shape prediction, and provides a deep belief network-based airfoil profile icing ice shape prediction method and device. The method comprises the following steps: constructing and training a Fourier coefficient deep belief network model and an upper and lower limit deep belief network model in advance; performing data normalization on the icing condition to be predicted to obtain a normalized icing condition; inputting the normalized icing condition into the Fourier coefficient depth confidence network model and the upper and lower limitdepth confidence network model; substituting ai, bi, xiu and xil into an ice-shaped curve Fourier series expansion formula to obtain a wing-shaped icing ice-shaped curve; the Fourier coefficient deepbelief network model and the upper and lower limit deep belief network model are composed of a plurality of restricted Boltzmann machines and a BP neural network layer. According to the method, the network training time is greatly reduced, the network prediction precision is improved, and the technical problems that gradient disappearance and local minimization are likely to happen to a pure BP neural network are solved.

A method and apparatus for automatically generating a target sequence of a plurality of elements selected in accordance with a plurality of user-defined constraints such as a play list of songs. The apparatus comprises a user interface (102) for inputting a plurality of user-defined constraints, a storage device (106) for storing a plurality of elements or music files; and a processor (104) for automatically generating the target sequence or play list by an iterative process in which a solution sequence is found at each iteration to best fit the user-defined constraints. A small, random or biased change is made to the sequence found and if this improves the solution, the new solution is used for the next iteration. The biased change is used to help predict a better result for user-defined constraints which are, ordinarily, difficult to satisfy.

The invention discloses a method, system and device for compiling an AI chip and a storage medium, and the method comprises the steps of carrying out the quantification of a network model of the AI chip, and carrying out the precision adjustment of the quantified network model based on the weight of the network model; allocating calculation of the convolutional neural network to different MAC calculation units of the AI chip in the network model after precision adjustment so as to optimize a calculation process of the network model; and acquiring hardware architecture parameters, and generating a continuous calculation flow according to the hardware architecture parameters and the network model. According to the invention, the AI chip is quantified and the precision is adjusted, so that the performance is more perfect; the calculation of the network is optimized to the maximum extent, the calculation steps are reduced, and the utilization rate of the MAC calculation unit is maximized;the utilization rate of the AI equipment is improved by generating the calculation flow capable of enabling the calculation equipment to be always in the busy state in the network reasoning process.

The invention discloses a method for matching character string patterns. The method includes acquiring M1 by bad character movement rules in pattern string retrieval procedures and acquiring M2 by end character movement rules in the pattern string retrieval procedures. The maximum values of the M1 and the M2 are the maximum movable distances of pattern strings. Compared with the traditional KMP (Knuth-Morris-Pratt) algorithms, Horspool algorithms and Boyer-Moore algorithms, the method has the advantages that excellent pattern string movement effects can be realized, retrieval means are abundant and changeable, occurrence locations of pattern strings in text strings can be quickly positioned, the method has efficient character string retrieval functions, and accordingly the experience of users can be enhanced.

The invention discloses a sparse code division multiple access signal detection method based on a depth neural network. First, the relationship between users and resources is expressed as factor graph, Then the iterative transfer of information on the factor graph is transformed into the forward transfer of neural network, and then the signal detection error is estimated and the estimated value isused as the input data of the neural network, and then the gradient descent method is used to train the neural network to obtain better coefficients, and finally the trained network is used for SCMAsignal detection. Because of the use of neural network architecture, this method can be implemented on the corresponding AI chip. Compared with the traditional methods, this method can improve the performance and transfer the related computations to the high-speed parallel processing AI chip, which can effectively reduce the time delay caused by SCMA signal detection.

Disclosed herein is a method of generating an encoded data packet over GF(2). The method comprises determining 303 a plurality of data packets in dependence on a Latin rectangle, wherein the plurality of data packets have equal length; and generating 305 an encoded data packet by bitwise XORing the determined plurality of data packets. The efficiency of encoding, decoding, and transmission over a network of data packets are all improved, as well as the security properties of the transmitted information.

The invention discloses an automatic electrocardiosignal analyzing and processing method based on deep learning. The method comprises the operation steps of: preprocessing an original electrocardiosignal, segmenting a fixed-length waveform to sort electrocardiosignal data into vectors with a plurality of consistent dimensions, and training the sorted data through a deep learning model of a full connected neural network or a full convolutional neural network; and finally, comprehensively and accurately outputting different electrocardio index categories. By means of the method, the workload ofdoctors during interpretation can be reduced, the doctors are helped to improve the interpretation accuracy, and the method becomes an important auxiliary scheme for doctors to diagnose heart diseases.

A system that can detect when abnormal trading activity is occurring and take action to halt the detected abnormal trading activity without human intervention using a computer-implemented anomaly detection and action stage that performs an exponential weighted averaging of trade order flow, on a per symbol basis within a sliding volume based window and a volume based exponential weighted averaging, on a per symbol basis, on trade update messages received during the sliding volume based window and checks for an inflection in covariance between them. A related method is also described.

The invention discloses a data enhancement method applied to neural machine translation. The method comprises the steps that word alignment processing is conducted on bilingual training linguistic data to obtain word alignment information; minimum translation units contained in the bilingual training linguistic data are determined according to the word alignment information; all the minimum translation units are calculated according to a preset vector representation calculation rule, and corresponding vector values are obtained; the similarity value between all the minimum translation units iscalculated through a cosine formula; whether the similarity value exceeds a threshold or not is judged, and if yes, the position of the minimum translation unit, corresponding to the similarity valueexceeding the threshold, in the bilingual training linguistic data is exchanged, and new bilingual training linguistic data is obtained. By means of the method, the new bilingual linguistic data canbe formed, and the training linguistic data can be effectively increased; the structure information of the original linguistic data is enriched, and the improvement of the low-resource language translation quality of neural machine translation is achieved. The invention further discloses a data enhancement system and device and a computer readable storage medium which have the above advantages.

Exemplary embodiments can include methods, devices, and executable instructions for autoscaling displays containing moving data (e.g., data that changes with respect to time). Embodiments make use of counters and an algorithm to autoscale a display in a manner that can approximate the behavior of a user. For example, embodiments may autoscale a display under conditions that would cause a typical user to manually autoscale the display.

The invention relates to an interest point check-in prediction method fusing deep learning with a factorization machine and belongs to the field of location check-in prediction. The method comprises the following steps: S1, acquiring check-in data of a user; S2, performing embedding processing on input discrete data; S3, performing sparse elimination processing on the discrete data, and learning implicit second order relations among the data; S4, learning addition of continuous characteristics into a full connection hidden layer, and selecting an appropriate excitation function; S5, inputtinga result obtained by processing discrete characteristics and a result obtained by processing the continuous characteristics and adding the results as an input of a hidden layer h1; S6, enabling an output l1 of the hidden layer h1 to pass a first-order linear and characteristic interaction structure and adding as an input of a hidden layer h2; and S7, receiving an input by a hidden layer h3 from outputs l1 and l2 of the hidden layers h1 and h2, adding a shortcut structure at the same time for guaranteeing gradient stability during parameter learning, determining the best model structure, and finally outputting a prediction result. The method provided by the invention fully excavates and learns check-in rules and predicts interest point check-in problems by analyzing check-in information ofthe user.

An apparatus for constant quality rate control in moving picture compression. First, a total activity measure for the current picture to be encoded in a set of consecutive pictures is calculated. Based on the total activity measure of the current picture and an activity-to-complexity ratio of a previously encoded picture of the same type in the set of consecutive pictures, a complexity measure of the current picture is estimated. Thus, an instantaneous complexity measure is updated using the complexity measure and then an instantaneous rate is calculated. A target bit budget is allocated to the current picture depending on the instantaneous rate, the complexity measure and the instantaneous complexity measure. After encoding, the activity-to-complexity ratio for the just encoded picture is computed based on the total activity measure, actual bits consumed by the picture and the average of actual quantization step sizes used to encode the picture.

The invention discloses a method for building a fractal dimension-based multi-phase mixed effect prediction model, which comprises the following steps of: (1) acquiring a multi-phase stirring mixing real-time pattern by using electron tomography or a high-rate camera; (2) computing the fractal dimension of the acquired real-time pattern by using a compiled box dimension computation program; (3) acquiring previous relative data of each period at every interval by using the method in step (2) to obtain a corresponding time sequence; (4) computing a time delay variable, an embedded dimension and a bandwidth by using the time sequence obtained in step (3) and by using a phase reconstruction method; (5) processing the time sequence in the step (3) by using a one-step-forward method and kernel regression prediction to obtain an entire time sequence; and (6) testing and verifying a prediction error by using an experimental method. The invention provides a reliable and practical prediction method for predicting the mixed effect of chemical engineering experiments and theoretically instructing stirring reactor design, stirring operation and correction experiments.