93 results about "Pattern vector" patented technology

A method and related system for correcting measurements obtained from an induction logging device. In one embodiment, the method comprises: receiving conductivity measurements from a multi-array induction tool; constructing a measurement pattern vector from the conductivity measurements; determining one or more geometry parameters associated with an approximation of the measurement pattern vector; finding borehole geometrical factors using current values of the one or more conductivity parameters and of the one or more geometry parameters; and calculating improved estimates of the one or more conductivity parameters using current values of the borehole geometric factors. The geometry parameters may be determined by finding a geometry pattern vector that best fits the measurement pattern vector. The determining, finding, and calculating operations may be repeated until a predetermined condition is satisfied. Finally, the conductivity measurements may be corrected for environment effects using calculated conductivity parameters and geometry parameters.

The invention relates to an SVG technology-based distribution network modeling system. The single-line diagram of the distribution network modeling system is generated according to the following steps: SVG entities are respectively constructed with different patterns according to different types of distribution network structures contained in a distribution model; according to the branch information contained in a distribution network wiring diagram, the basic branch entities of an SVG topological diagram are constructed; node entities and branch entities are respectively constructed with different colors and solid and dotted patterns according to operating states; the entity shape of each equipment in a distribution network is customized in advance; the data of a power grid model are accessed and read; and the single-line diagram of the distribution network is formed by merging, and a closed loop model is automatically generated. The SVG-based pattern vector display technology disclosed by the invention can efficiently and visually display distribution network wiring diagrams in real time. The SVG technology-based distribution network modeling system can effectively increase working efficiency and provide a variety of appropriate topological diagram display modes for users, and is used in various power grid analysis works.

The invention belongs to the technical field of automatic radar HRRP (high resolution range profile) object identification and particularly relates to compressive sensing based range profile object identification. Range profile object identification includes the steps: constructing a matching dictionary according to a radar echo model, selecting an appropriate test matrix for compressive sensing of a training sample range profile and to-be-identified test sample range profile which are known in type information so as to achieve data dimension reduction; then, subjecting data subjected to compressive sensing to sparse reconstruction so as to obtain sparse coefficients of the training sample range profile and the test sample range profile under the matching dictionary; utilizing the sparse coefficient of the training sample range profile as a pattern vector, and identifying the test sample range profile according to a nearest neighbor method. By the aid of compressive sensing based range profile object identification, since sparse coefficient characteristics of objects under the dictionary are extracted, redundancy is avoided, calculating amount is decreased and unnecessary noise is avoided.

An exemplary embodiment of the invention relates to a method of using pattern vectors for image coding and decoding. The method comprises converting a block of image data into a set of transform coefficients, quantizing the transform coefficients such that a number of the coefficients become zero, constructing a single entity or bit vector indicating which coefficients are non-zero, coding the single entity or bit vector as an integer using an adaptive, semi-adaptive or non-adaptive arithmetic coder, coding the values of the coefficients in any fixed order, using an adaptive, semi-adaptive or non-adaptive arithmetic coder, or some other coder, and coding all coefficients except the zero coefficients. The system and method of decoding data relate to the corresponding hardware and process steps performed by the decoder when decoding a bitstream coded as described herein.

An exemplary embodiment of the invention relates to a method of using pattern vectors for image coding and decoding. The method comprises converting a block of image data into a set of transform coefficients, quantizing the transform coefficients such that a number of the coefficients become zero, constructing a single entity or bit vector indicating which coefficients are non-zero, coding the single entity or bit vector as an integer using an adaptive, semi-adaptive or non-adaptive arithmetic coder, coding the values of the coefficients in any fixed order, using an adaptive, semi-adaptive or non-adaptive arithmetic coder, or some other coder, and coding all coefficients except the zero coefficients. The system and method of decoding data relate to the corresponding hardware and process steps performed by the decoder when decoding a bitstream coded as described herein.

The invention discloses a sending method and apparatus, and a receiving method and apparatus of uplink data. The sending method includes: performing non-orthogonal multiple access coding modulation on the uplink data through channel coding by employing a non-orthogonal multiple access pattern vector corresponding to the uplink data, wherein the uplink data through the non-orthogonal multiple access coding modulation refers to modulation symbols of the non-orthogonal multiple access pattern vector; and generating an OFDM symbol of each antenna port through power adjustment, transmission layer mapping, MIMO pre-coding and resource mapping of the modulation symbols. According to the methods and apparatuses, the non-orthogonal multiple access technology and the uplink MIMO technology are combined, characteristics of the non-orthogonal multiple access technology in the time-frequency domain, the coding domain, the power domain and the airspace etc. are fully utilized, simultaneous data transmission at the same time-frequency resource by more terminals can be supported, and the system capacity or the number of accessed terminals can be increased.

The invention discloses a method and apparatus for transmitting and receiving an uplink demodulation pilot carrier. The method comprises: determining non-orthogonal multiple access basic transmission units of a transmission service at the time of transmission, wherein each of the basic transmission units uses a non-orthogonal multiple access pattern vector to transmit data; using a demodulation pilot carrier resource to transmit a demodulation pilot signal in each non-orthogonal multiple access basic transmission unit; at the time of reception, acquiring an uplink demodulation pilot signal from a received signal on a time-frequency resource occupied by the non-orthogonal multiple access basic transmission units. The method and apparatus solve the need for uplink demodulation pilot resource growth in the case that high-load multi-user data multiplexes the time-frequency resources in a non-orthogonal manner.

The present invention provides a method for obtaining an accurate detected value of a similarity, such as an hitting sound. The method includes the steps of: creating original standard / input pattern vectors each having a feature quantity of an hitting sound; creating a skewness-weighting vector and a kurtosis-weighting vector based on a reference pattern vector of a reference shape; calculating a skewness-weighted standard pattern vector and a kurtosis-weighted standard pattern vector by product-sum operation using component values of the skewness-weighting vector and the kurtosis-weighting vector and a component value of the original standard pattern vector; creating a dual and weighted standard pattern vector based on these vectors and similarly creating a dual and weighted input pattern vector; creating dual and selected standard / input pattern vectors based on the dual and weighted standard / input pattern vectors; and setting an angle between the dual and selected standard and input pattern vectors as a geometric distance value between the original standard and input pattern vectors.

A method of detecting the identity of video programming is described, whereby known video programming is converted into a set of pattern vectors stored in a database and incoming detected video programming is converted into a set of pattern vectors that are used to search the database for matching pattern vectors indicating a match with the known video programming.

An embodiment of the invention provides a network data flow type detection method comprising the following steps: acquiring the header of a first data packet of a current data flow, and acquiring the pattern vector of the current data flow from the header; comparing at least one characteristic dimension in the pattern vector of the current data flow with the corresponding characteristic dimension in the pattern vector of at least one historical data flow to get at least one pattern similarity of the current data flow; predicting the length of the current data flow according to the at least one pattern similarity of the current data flow and the length of at least one corresponding historical data flow; and comparing the predicted length of the current data flow with a preset threshold, and judging whether the current data flow is a large data flow or a small data flow according to the result of comparison. According to the invention, the type of a data flow can be detected by detecting the header of the first data packet of the data flow, the type of a network data flow can be judged in real time, and the accuracy of judgment is improved.

A conventional human body sensing system using a tremor sensing-type human body sensor within a house has a problem such that the system cost becomes high because two human body sensors in each room or one human body sensor in each room plus one human body sensor in a corridor are used in order to sense a stationary person in a room so as to carry out a room exit determination. In a room exit determination part 250, a determination of presence is made in the case that a movement pattern vector consisting of the room ID of movement origin, the room ID of movement destination and the movement period of time is not received from the non-specified room response sensing part 230 within a predetermined period of time and a determination of exit from the room is made only when the movement period of time in a movement pattern vector agrees with the movement period of time of past exits from the room stored in the movement time storage part 240 in the case that this vector is received from the non-specified room response sensing part 230.

Disclosed are an apparatus and a method for recognizing traffic signs. The apparatus and the method for recognizing traffic signs includes an image sensor, a neuromorphic system in which a plurality of neurons storing a feature pattern vector and a content pattern vector of the traffic sign are connected by a parallel bus, and a control unit that normalizes a window of a predetermined size for a region of interest set in an image frame inputted from the image sensor unit by making the window slide in such a way to overlap by a predetermined pixel value, generates a first input vector that vectorizes, extracts a candidate region of the traffic sign based on feature pattern information of a neuron having a feature pattern vector most similar to the inputted first input vector among the plurality of neurons stored in the neuromorphic system, stores the coordinates of the extracted traffic sign candidate region, converts the image size of the extracted candidate region, normalizes a window of a predetermined size for the candidate region of the converted image size, by making the window slide in such a way to overlap by a predetermined pixel value, generates a second input vector that vectorizes the normalized window, determines traffic sign content information of a neuron having a content pattern vector most similar to the inputted second input vector among the plurality of neurons stored in the neuromorphic system, stores the determined traffic sign content information, and recognizes the location and content of the traffic sign based on the coordinates of the stored candidate regions and the content information of the stored traffic sign when the traffic sign disappears.

The invention discloses a power grid fault identification method based on WAMS (Wide Area Measurement System) time sequence decomposition, which comprises the following steps: S1, acquiring the real-time data of a power grid by using a phasor measurement unit and extracting after the acquired real-time data is subjected to time sequence decomposition to obtain a power grid real-time feature vector; S2, respectively comparing the power grid real-time feature vector obtained by extraction with a plurality of benchmark fault mode vectors in a preset benchmark fault mode vector set to identify whether the power grid fails or not; S3, if the power grid fails, obtaining a fault content in combination with the benchmark fault mode vector corresponding to the fault and implementing the step S4, and otherwise, implementing the step S1; and S4, updating a power grid network topology and the preset benchmark fault mode vector set according to the identified fault content. The power grid fault identification method based on the WAMS time sequence decomposition has the advantages of high and accurate identification speed and timely reflection and can be widely applied to power grid fault identification.

The invention discloses a detection method for user group behavior rules based on stay places in a mobile trajectory. The method comprises the following steps: firstly, receiving multi-user trajectory data from a data collector; secondarily, extracting stay place sequences from the trajectory data, and transforming the stay place sequences into place preference vectors which represent user schedule behaviors; then extracting schedule behavior vectors, and using a weighted average method to calculate schedule behavior pattern vectors; finally, calculating similarities among users based on schedule behavior patterns, and using a Kmeans clustering algorithm to detect behavior rule patterns of user groups. According to the invention, the similarities among the users are calculated by using the schedule behavior patterns based on the stay places to solve the problem of nonoverlapping of user stay areas, so that the behavior rule patterns of the user groups can be effectively detected.

The invention provides a lending fraud detection model training method, a lending fraud detection method and a lending fraud detection device. The lending fraud detection model training method comprises the following steps: obtaining identity information of a plurality of sample users, user bank pipeline information and fraud label information corresponding to each user; Constructing identity feature vector and user behavior pattern vector according to identity information; According to the user behavior pattern vector, the second vector transformation matrix and the user bank pipeline information, the pipeline characteristic vector is constructed. Generating a target feature vector according to a user behavior pattern vector and a pipeline feature vector; The target eigenvector is inputted into the target neural network, and the fraud detection result of the target eigenvector is obtained. Then the target neural network, the first vector transformation matrix and the second vector transformation matrix are trained to obtain the debit and credit fraud detection model. The application can improve the identification efficiency and the identification accuracy of the fraudulent user bythe credit platform, and greatly save the human cost.

The invention discloses a growing hierarchical self-organizing map (GHSOM)-based intrusion detection method for a neural network and belongs to the technical field of the safety of network information. A method for training a GHSOM intrusion detection model comprises the following key points of: 1) designing a new mixed vector structure which enables an improved GHSOM neural network to treat a mixed input mode vector which comprises a value type member and a character type member; and 2) designing a new control mechanism which replaces a parameter tau1 by a tension and mapping ratio (TMR), so that the GHSOM neural network supporting the mixed input mode vector automatically controls the growth of a nerve cell. Due to the application of an improved neural network in intrusion detection technology, detection rate is increased.

An embodiment of the invention provides a method, a device and a system for distributing downlink power. The method comprises the steps of defining a user which requires pairing according to positions of users in a cell; defining a PDMA pattern matrix, distributing different PDMA pattern vector groups for each user, and forming a power distribution pattern matrix according to the PDMA pattern vector group; defining a power distribution factor of each user in one pair according to the power distribution pattern matrix, and defining the downlink transmitting power of each user according to the power distribution factor. On the condition that user fairness is considered, different PDMA pattern vector groups and powers can be distributed to the users according to the positions of the users through PDMA technology, and difference between a coding domain and a power domain is sufficiently used for receiving, thereby improving average and edge user spectrum effectiveness of the cell and increasing number of accessing users of the cell.

Disclosed are an apparatus and method for providing additional degrees of freedom for diffraction gratings of an output waveguide in a near-eye display device. The near-eye display device includes animager to generate an image based on light from a light source. The device further includes a waveguide to input a light wave representing the image received from the imager and to output the light wave representing the image toward an optical receptor of a user. The waveguide includes a plurality of diffractive optical elements (DOEs) in a common light path from an input of the waveguide to an output of the waveguide. The DOEs include a plurality of periodic diffraction patterns. Each of the periodic diffraction patterns is represented by a diffraction pattern vector. The periodic diffractionpatterns are determined such that a vector summation of the diffraction pattern vectors equals zero.

The invention discloses a power distribution network load space-time characteristic visual analysis method which comprises the following steps: accessing power distribution transformer load characteristic data from a power distribution network service system, including electrical quantity data and complaint data, to form a power distribution network load characteristic data set; analyzing and processing the electrical quantity data by adopting a dynamic color spot pattern generation technology to form a color spot pattern vector layer file; a scatter diagram generation technology is adopted for complaint data, a map layer of a scatter diagram style is dynamically created, and the two map layers are displayed through a GIS front end; superposing the image layers to form a correlation analysis image taking the color spot image layer as a base image and the scatter image layer as a foreground; deploying a color spot image layer generation service on the GIS platform, and periodically generating a new color spot image layer; statistics and visual display are carried out on the overall indexes, and multi-dimensional sorting analysis is carried out on all the distribution transformer devices in the selected geographic area. According to the method, distribution network business personnel can conveniently interpret the load rate horizontal distribution condition in the area range andguide distribution network planning, transformation and electric energy quality treatment work.

A method of iris recognition using a cumulative-sum-based change point analysis and an apparatus using the same are disclosed. The method includes: transforming an iris image to a polar coordinated image having n×m pixel size and dividing the polar coordinated image into at least one cell; grouping the divided iris images into at least one of first groups having a predetermined number of cells, and at least one of second groups each having more cells than the first group has; performing a cumulative-sum-based change point analysis using a predetermined characteristic pattern value of each cell as a representative value; and generating a pattern vector by assigning a predetermined value to a cell having the change point and assigning a different value to other cells.

The invention relates to a phylogenetic tree construction method based on a sequence pattern mining algorithm. The phylogenetic tree construction method based on the sequence pattern mining algorithmcomprises the following steps: mining a specific pattern which is hidden in a sequence set and can be used for measuring sequence similarity to obtain an initial pattern set; filtering an unclosed frequent pattern in the initial patter set to acquire an optimized pattern set capable of representing the sequence set; and constructing a patter vector set and calculating the distance between numericvectors so as to construct a distance matrix for producing a phylogenetic tree. The sequence pattern which frequently appears in the sequence set is extracted by a sequence patter mining algorithm, the sequence set is converted into binary system by the filtered pattern set or the distance matrix is calculated in the form of giving weight information to the pattern vector set, and then the phylogenetic tree is constructed. For the large-scale and low-similarity sequence set, the more representative pattern in the sequence set can be mined by utilizing a pattern growth strategy, so that the extraction of a redundancy pattern which is useless for measuring sequence similarity is voided and measurement on the similarity among the sequences within the global range is optimized.

An embodiment of the invention provides a method of distributing uplink power, equipment and a system thereof. The method comprises the following steps of according to a position of a user in a cell, determining a user who needs to be paired; determining a PDMA pattern matrix, distributing different PDMA pattern vector groups for each user, and according to the PDMA pattern vector groups, forming a power control pattern matrix; and according to the power control pattern matrix, determining a power control factor of each user in each pair, and according to the power control factor, determining uplink emission power of each user. During a combined usage process of PDMA and an uplink power control technology of LTE, a technology of determining the uplink emission power of the terminal is provided, terminal power consumption is reduced, an uplink cell average and edge user frequency spectrum efficiency are increased, and cell access user numbers are increased too.

In a room exit determination, a determination of presence of a body in a room is made if a movement pattern vector consisting of a room ID of movement origin, a room ID of movement destination and a movement period of time is not received from the a non-specified room response sensor within a predetermined period of time. A determination of exit from the room of a body is made only when the movement period of time in the movement pattern vector agrees with a movement period of time of past exits from the room stored in the a movement time storage if this vector is received from the non-specified room response sensor.

Provided is an information processing apparatus including a message generation unit that generates a message based on a pair of multi-order multivariate polynomials F=(f1, . . . , fm) defined in a ring K and a vector s that is an element of a set Kn, a message supply unit that supplies the message to a verifier storing the pair of multi-order multivariate polynomials F and vectors y=(y1, . . . , ym)=(f1(s), . . . , fm(s)), a response supply unit that supplies the verifier with response information corresponding to a verification pattern which the verifier selects from among k (where k≧3) verification patterns. The vector s is a secret key. The pair of multi-order multivariate polynomials F and the vectors y are public keys.

The invention relates to a method for generating a test graphic vector of a CPU device, comprising the following steps: using assembly language or C language to write a source program in sections; software simulation to form a final source program and a burnable file supported by a memory; building an integrated circuit tester A development system composed of an auxiliary circuit board; guide and run the final source program, output the result; collect and record the input and output information of the CPU to form the initial test graphic vector; get rid of the auxiliary circuit board, and simulate the external memory by the integrated circuit tester to generate the test Graphic vector. The present invention simulates the external memory through the integrated circuit testing machine, and transmits the program to the CPU according to the read and write sequence of the memory, which not only can effectively avoid the influence of peripheral devices, but also simplifies the test program, without prior programming of the CPU, and can directly Test on the machine. At the same time, the source program is written in sections, which not only reduces the complexity of the source program, but also can effectively locate the fault.

A method of iris recognition using a cumulative-sum-based change point analysis and an apparatus using the same are disclosed. The method includes: transforming an iris image to a polar coordinated image having n×m pixel size and dividing the polar coordinated image into at least one cell; grouping the divided iris images into at least one of first groups having a predetermined number of cells, and at least one of second groups each having more cells than the first group has; performing a cumulative-sum-based change point analysis using a predetermined characteristic pattern value of each cell as a representative value; and generating a pattern vector by assigning a predetermined value to a cell having the change point and assigning a different value to other cells.

The invention provides a log anomaly detection method, and relates to the field of data processing. The method comprises the following steps: acquiring historical log data and real-time log data; preprocessing the historical log data; obtaining an initial data set and a stream data set based on the historical log data set; obtaining an initial log mode library based on the initial data set; updating the initial log mode library to obtain a log mode library; acquiring a mode library mode vector based on the log mode library; obtaining a streaming data mode vector based on the streaming data set; processing the historical log data set based on the mode library mode vector to obtain a log data mode; obtaining a dependency relationship library based on the two mode vectors; obtaining a threshold of anomaly detection based on the two mode vectors; preprocessing the real-time log data; and respectively detecting a real-time log data set based on the log data mode, the threshold value, the log mode library and the dependency relationship library. According to the method, the interpretability is relatively high when the log abnormal condition is detected.

A method and related system for correcting measurements obtained from an induction logging device. In one embodiment, the method comprises: receiving conductivity measurements from a multi-array induction tool; constructing a measurement pattern vector from the conductivity measurements; determining one or more geometry parameters associated with an approximation of the measurement pattern vector; finding borehole geometrical factors using current values of the one or more conductivity parameters and of the one or more geometry parameters; and calculating improved estimates of the one or more conductivity parameters using current values of the borehole geometric factors. The geometry parameters may be determined by finding a geometry pattern vector that best fits the measurement pattern vector. The determining, finding, and calculating operations may be repeated until a predetermined condition is satisfied. Finally, the conductivity measurements may be corrected for environment effects using calculated conductivity parameters and geometry parameters.