303 results about "Incidence matrix" patented technology

The invention discloses a collaborative filtering recommendation method introducing video popularity and user interest change. The method comprises the following steps of: acquiring and processing user behavior data, and thus obtaining a user-video binary incidence matrix; acquiring a video popularity weight and a user interest weight on the basis of the matrix, and introducing the video popularity weight and the user interest weight into a user similarity calculation process; searching the first K neighbors maximally similar to a target user, and predicting an interest value of the target user in a video which does not generate an effective behavior according to the magnitude of the similarity between the target user and a neighbor user; and selecting N videos with the maximum interest value to form a recommendation list, and providing a personalized recommendation for the user. In full consideration of the characteristics of difference in the popularity of the videos in the system and the time-dependent change of user interest, the method is in accordance with an objective fact, so that the user similarity can be accurately calculated, the quality of a collaborative filtering recommendation is improved, and a personalized video recommendation in accordance with the user interest is provided for a video user.

The invention relates to the technical field of operation and control of an electric power system and discloses a method for detecting and identifying dynamic bad data of the electric power system. The method comprises the steps of A, reading a current network parameter and a network topology of the electric power system and forming a node admittance matrix and a branch circuit-node incidence matrix; B, establishing an equivalent circuit according to the network topology of the electric power system and configuring measurement functions and a power management unit (PMU) of the electric power system, wherein system measurement includes node voltage amplitude measurement, node current amplitude measurement, node power injection measurement and node trend measurement; C, detecting and identifying the dynamic bad data of the electric power system; D, judging the convergence condition. By means of the method, accidentally arisen bad data from a supervisory control and data acquisition (SCADA) system and in the PMU measurement can be found and eliminated, and accordingly an accurate operation state, voltage amplitude and phase angles of nodes of a power grid are obtained.

The invention discloses a multi-target track method based on moving target detection in video monitoring. The method comprises the following steps: firstly, using a background removing method to detect foreground moving targets; then establishing an incidence matrix between a foreground target block mass of the current frame and the target detected in a previous frame so as to judge various states of the targets (such as target missing, target keeping initial condition, target overlap, target separation and the like), and secondarily tracking the targets which are in the separation condition; and at last, updating the positions and areas of the targets, kernel weighted color histograms and other characteristics to achieve tracking multiple targets. The method of the invention can be used for effectively and reliably tracking sheltered targets and the multiple targets which are mutually overlapped and then separated in real time, and improving stability and robustness of a video monitoring system.

The invention discloses a method and a device for analyzing an abnormal state of a spacecraft in the operating process. The method comprises the following steps of: comparing telemeasuring data in a time period to be detected with historical data in a normal state so as to determine an abnormal condition of the telemeasuring data; under the condition that the telemeasuring data is abnormal, according to a correlation coefficient of sequences corresponding to the two groups of data, determining abnormality degree information of corresponding parameters of the telemeasuring data; according to spacecraft design data, establishing an incidence matrix of the corresponding parameters of the telemeasuring data and stand-alone equipment; according to associated data corresponding to the incidence matrix and the abnormality degree information, determining a fault probability of each piece of stand-alone equipment so as to analyze the possibility that the abnormal condition occurs to each piece of stand-alone equipment. The method and the device utilize the telemeasuring data so as to analyze the abnormal condition of the telemeasuring data, and solve the problem that due to shortage of a method for analyzing the abnormal state of the spacecraft in the operating process, a fault part cannot be determined when the spacecraft is abnormal in the operating process.

The invention relates to the technical field of electric power system operation and control and discloses an electric power system dynamic state estimation method based on a synchronous phase-angle measuring device. According to the technical scheme, the method comprises the steps of A, reading current network parameters of an electric power system and a network topological structure of the electric power system, and therefore forming a node admittance matrix and a branch-node incidence matrix; B, establishing an equivalent circuit according to the network topological structure of the electric power system, and configuring a measurement function of the electric power system and a PMU of the electric power system, wherein the measurement of the system comprises node voltage amplitude measurement, node current amplitude measurement, node power injection measurement and node load flow measurement; C, conducting dynamic state estimation on the system on the basis of extended Kalman filtering; D, judging conditions of convergence. According to the electric power system dynamic state estimation method based on the synchronous phase-angle measuring device, due to the introduction of the PMU, real-time and accurate measurement information such as the voltage and the phase angel can be supplied to the system, the higher measurement redundancy rate of the system is obtained, and therefore the precision of the state estimation is improved. The electric power system dynamic state estimation method based on the synchronous phase-angle measuring device has the advantages of being good in robustness, high in state estimation precision and good in convergence performance.

The invention discloses a network attack path analysis method based on incidence matrixes, which is characterized in that potential attack paths in a network system are acquired by acquiring network topology information, threat information and vulnerability information of devices and interrelation between the threat and the vulnerability, and building incidence matrix models. The method provided by the invention is utilized to greatly improve comprehensive analysis capacity of network risks and effectively ensure safety of network information systems. The method is applied to large-scale network environments.

The invention discloses a preventative device maintenance method based on dynamic reliability and belongs to the technical field of petrochemical equipment management. The preventative device maintenance method based on the dynamic reliability comprises the following steps of 1 establishing a device basic database including equipment accounts, historical maintenance and repair data, a fault strength analysis data, operation monitoring data and the like; 2 utilizing basic data to conduct operation time analysis, fault strength analysis and alarm state and operation trend analysis on devices; 3 utilizing results to establish a device reliability level and fault strength incidence matrix, formulate DRBPM data analysis logic, automatically screen preventative maintenance devices and generate preventative maintenance planning reports of the devices; 4 examining, verifying and executing the preventative maintenance planning reports of the devices. The preventative device maintenance method based on the dynamic reliability can be used for dynamic analysis and management of states of petrochemical equipment, facilitate formulation of scientific and effective device maintenance measures and provide guarantee for timely device fault elimination and production continuity reliability and can be widely applied to the technical field of petrochemical equipment management.

The invention discloses a low SNR(Signal to Noise Ratio) motion small target tracking and identification method. The method comprising the following steps: providing a method of extracting a target from a single frame image of a video sequence, and influences of backgrounds and noises can be reduced or eliminated; providing weak target motion information extracting and state predicting modeling; establishing an incidence matrix of the image motion small target between two frames; based on information fusion of overlapped multi-frame images, providing a large-scale image and video image motion small target tracking algorithm and an identification method by using fuzzy push-down automation chain slot stack recursion calculation. The low SNR(Signal to Noise Ratio) motion small target tracking and identification method is advantageous in that the target identification and image processing personnel can be aware of the target motion law, the active level, and the influence on other targets conveniently, and then can give the corresponding decisions, and therefore the searching, the inhibiting, and the eliminating of the influences of the bad factors on other important targets can be very necessary, and in addition, the important experiences and the important references can be provided for the target identification and tracking in the military field, the civil field, the public security system, and the road traffic field based on the video system.

The invention discloses a path planning determining method based on cladogram topological road network construction, which comprises the steps of: obtaining city complete road network data, constructing a road network full-connected graph G expressing original geographic information by applying an adjacent topological cladogram; classifying and dividing the road network full-connected graph G into a Y-type road network topological cladogram; obtaining the minimal branching tree comprising all target nodes; and eliminating invalid branching nodes by adopting a branching delimiting searching strategy for the minimal branching tree comprising all target nodes in a road planning problem, reducing incidence matrix dimensionality and obtaining a planning result. The invention has the advantagesof optimizing algorithm complexity of the road planning process and improving road planning efficiency.

The invention relates to a multiscale prediction method for the mechanical property of a woven composite material. The method comprises the following steps that 1), an initial parameter is input; 2), elastic stiffness matrixes of a fiber/basal body scale, a fiber bundle scale and a unit cell scale are computed sequentially according to a bottom-to-top homogenization process; 3), a multiscale incidence matrix is constructed; 4), stress-strain analysis is performed on unit cell entirety, stress is decomposed by the multiscale incidence matrix from top to bottom, and stress-strain fields under all the scales are obtained synchronously; 5), whether an ingredient material is in a failure is judged according to respective failure criteria of a fiber and a basal body; if so, the corresponding ingredient material is subjected to stiffness reduction; 6), whether the unit cell entirety is in a failure is judged, and if so, Step 7) is executed, or else, a next displacement increment is added and Step 2) is returned; and 7), stiffness and strength computation results of the woven composite material are output. Compared with the prior art, the method has the advantages of high computation efficiency, high precision, high universality and the like.

The invention discloses a method and a system for excavating information of academic journal paper authors. The method comprises the steps: firstly selecting an objective subject field and establishing an OWL (Web Ontology Language) field ontology; secondly extracting author information from an academic journal paper of the objective subject field; thirdly performing the format conversion to the extracted author information, storing the information into an author information bank and calculating out the unique author ID (Identity); finally obtaining an incidence matrix of an author and an academic paper, an academic developing route chart of the author, a cooperator network diagram of the author, an academic cooperation distances among authors, a hot spot research direction map and an academic reputation map of the author by above information. According to the invention, the data source of the author information excavating method is changed; the OWL filed ontology technology is introduced into calculation processes of the academic cooperation distances among authors and the hot spot research direction; and the semantic calculation effect is improved.

The invention discloses an identifying method of brain cognitive states based on tensor locality preserving projection (Tensor Locality Preserving Projection, TLPP). The method comprises the following steps: 1) pretreating and grouping of fMRI (functional Magnetic Resonance Imaging) data of the brain cognitive states; 2) constructing a neighbor graph G and a corresponding incidence matrix S; 3) calculating characteristic decomposition of a training sample set, solving corresponding characteristic transformation matrix and calculating low dimensional imbedding of training samples; 4) classifying and identifying: calculating low dimensional imbedding of the training sample sets, and distinguishing and classifying the training sample sets by a tensor distance-based neighbor classifier. According to the method, dimensionality reduction and characteristic extraction are directly carried out on multidimensional tensors by TLPP algorithm, and characteristic dimensionality reduction is carried out on collected brain cognitive fMRI data, so that the brain cognitive states are effectively identified and classified. By combining the tensor distance-based neighbor classifier, the classifying accuracy is improved. The method not only inherits advantages of conventional methods, but also greatly reduces complex of time and space and overcomes curse of dimensionality. The method is less in calculated amount, less in memory consumption and shorter in time consumed.

The invention discloses a game theory-based SCMA system codebook and power allocation method. The method comprises the steps that 1, it is assumed that in an SCMA down link cellular system, a given SCMA block orthogonal resource block has M orthogonal subcarriers, and H nonzero elements exist in an SCMA encoder, the transmission layer number is the codebook number J, the downlink user number is K,each user performs data transmission of the user only through one transmission layer, then K=J, and a corresponding relation between the J codebooks and the M orthogonal subcarriers meets an incidence matrix F; 2, codebook allocation is conducted on the basis of the maximum sum of interference powers and noise powers, and iteration is conducted multiple times until all the codebooks are allocated; and 3, for the codebooks allocated to the users, a game model is built on the basis of a game theory to conduct power allocation, and iteration is conducted multiple times until all the powers are allocated. The method has the advantages of being capable of effectively increasing the throughput capacity and speed of an SCMA system and decreasing the bit error rate of the system.

The invention provides a power grid large-scale topological structure construction method and system. A stored power grid topological incidence matrix table is read to obtain sparse matrixes, MapReduce is utilized to process the sparse matrixes so as to obtain a stored power grid topological node analysis result, a power grid topological island connecting relation is calculated to obtain a power grid topological network result, and finally a power grid large-scale topological structure is established according to the power grid topological network result. Strict processing computation and processing process are adopted in the whole process, and accurate establishment is ensured. By utilizing a MapReduce expansibility advantage, the matrix computation complexity is simplified, a computation ending condition is optimized, quickness of large-scale sparse matrix computation is ensured, the large-scale power grid topological structure is quickly computed so as to facilitate accurate analysis on topological nodes and topological islands of a large power grid, and finally the power grid large-scale topological structure is efficiently and accurately established.

The invention discloses a method for evaluating risks of an MOV (metal oxide varistor) of a series capacitive compensator. The method includes the steps of (1) acquiring parameters including preventive trial data and electric monitoring data of the MOV of the series capacitive compensator; (2) establishing characteristic quantity including failure modes and probability of risk occurrence, establishing an incidence matrix about the probability of risk occurrence and failure modes, and establishing the risk evaluation for the MOV of the series capacitive compensator. Based on preventive trials, unbalanced current and historical data, the method can evaluate series compensation risks, simultaneously breaks through design and manufacture problem of international high-energy high-capacity MOVs, and improves operation reliability of the series compensation system.

The invention provides a method for analyzing DC power flow in a dynamictopology electric grid, belonging to the electric grid safety technical field. The method is characterized in that the method sets up an incidence matrix and a transmission line admittance matrix; according to net injection powers of electric grid nodes, the generalized inverse solution power flow equation is used. The method can be applied to connected or disconnected electric grids, and the analysis for the topological connectivity of the electric grid is unnecessary, and the selection of reference nodes in the electric grid is also unnecessary, thereby the method brings great convenience for analyzing DC power flow in the dynamictopology electric grid.

The invention discloses a workflow modeling method based on log record mining and combination verification. The method comprises the following steps of: deriving workflow logs from a database, deleting redundant information, and classifying the logs; scanning loop-free workflow logs, counting the occurrence frequency of task nodes, and recording following task nodes of each task node, and the co-occurrence frequency of the task node and the following task nodes; constructing a directed task graph according to each task node and a following task node set; adding a control structure to the directed task graph according to the occurrence frequency of the task nodes and the co-occurrence frequency of the task nodes and the following task nodes; and converting the directed task graph into a workflow network, and verifying the accuracy of a mined model by using an incidence matrix and a state equation of the workflow network. By adoption of the method, a sequence structure, a selection structure, a circulation structure and a parallel structure in a workflow can be effectively constructed, formal verification is performed, and the accuracy of the model is ensured.

The invention discloses a radar multi-target tracking optimization method based on a data correlation method, comprising: separately determining a total number T' of radar tracking targets; determining a measurement number nk corresponding to k time; successively calculating the candidate measurement set Zt' (k) of a t-th object at k time after optimization, a vector C (k) formed by appearing times of nk measurements in respective correlation window corresponding to T' targets at k time, and a vector C t' (k) formed by appearing times of candidate measurements in an nk*T' dimensional measurement-target incidence matrix Omega, the candidate measurements falling in a t-th object correlation window at k time; furthermore successively calculating the appearing times cit' (k) of an i-th candidate measurement in the nk*T' dimensional measurement-target incidence matrix Omega, the i-th candidate measurement falling in the t-th object correlation window at k time, a probability beta it (k) that an i-th candidate measurement zit' (k) in the candidate measurement set Zt' (k) of a t-th object at k time after optimization is from the t-th object, and a probability beta 0t (k) that no candidate measurement in the candidate measurement set Zt' (k) of the t-th object at k time after optimization is from the t-th object; and furthermore successively calculating a state equation X <^> t (k|k) of the t-th object at k time, and an error covariance matrix Pt(k|k) of the t-th object at k time.

The invention discloses a millimeter wave radar target tracking method in a complex traffic environment. The method is suitable for tracking a radar target in the complex traffic environment. According to the method, a measurement selection mode and associated event generation conditions in a traditional JPDA algorithm are improved, so that the algorithm becomes simple, the calculation amount is greatly reduced, retention of an effective flight path is increased, the possibility that the flight path is a false alarm is lower, and meanwhile, tracking stability is also improved. The method mainly comprises the following steps: 1) updating the track state in a radar target library in real time; 2) generating a confirmation matrix according to the flight path and new measurement; 3) judging whether the flight path is associated with measurement or not through the confirmation matrix, updating the life state of the successfully associated flight path, not tracking the flight path, the lifestate Lt of which is smaller than or equal to 0, and continuously tracking the flight path, the life state Lt of which is larger than 0; 4) generating an incidence matrix according to the continuouslytracked track and the measurement, and calculating incidence probability, and 5) dynamically estimating the motion state of the track.

The invention discloses a workshop manufacturing system multi-target scheduling method based on a time-assigned Petri network. The workshop manufacturing system multi-target scheduling method comprises the following steps that conducting modeling on a multi-target workshop manufacturing system through the Petri network; reading an attribute value corresponding to each library in the Petri networkmodel, and solving an association matrix between the library in the Petri network model and the transition; and based on the incidence matrix and the A * search algorithm, expanding the sub-nodes fromthe initial node until all the target nodes are found, thereby finishing the multi-target scheduling of the system. According to the invention, a workshop manufacturing system timing Petri net modelis taken as an object; a multi-target heuristic scheduling method is adopted, a non-dominated scheduling scheme most meeting requirements is found out by comprehensively judging a plurality of attributes of a target, the method can solve all non-dominated solutions according to different attributes, and the heuristic multi-target A * algorithm is adopted, so that the system scheduling scheme meeting the requirements can be obtained without expanding all nodes of the system.

Disclosed is an enterprise credit evaluation method based on gray fuzzy, which includes the following steps: initializing multi-dimension time series data; dividing credit evaluation grade standards, and confirming various credit evaluation indexes; mapping values of various credit evaluation indexes to a certain value interval by utilizing simple mathematical function transformation in a same credit index system; confirming reference sequences and comparison sequences, calculating gray correlation coefficient, and calculating gray correlation degree to obtain gray incidence matrix composed of credit evaluation values; and converting the gray incidence matrix into fuzzy similar matrix, subjecting the fuzzy similar matrix to square self-synthesis method to be converted into fuzzy equivalent matrix, selecting a confidence level value lambda belonging to a range from 0 to 1, and calculating lambda stage matrix of the fuzzy equivalent matrix. When the rij is less than or equal to the lambda, a sample xi and a sample zj can be combined into a same class and the obtained classification is an equivalent classification on the lambda level, accordingly different evaluation results are achieved. The enterprise credit evaluation method has the advantages of reducing the calculating complexity, having good timeliness, and effectively improving the reliability.

The invention discloses an aspect-level sentiment analysis method based on a convolutional neural network. The method comprises the steps of establishing an aspect-level sentiment analysis model; constructing a relative position matrix of aspect-level information in the text through the model; fusing the text codes with the text codes; extracting a part of incidence matrixes corresponding to aspect-level information in the incidence matrixes; wherein the aspect-level emotion analysis model comprises a word embedding module, a relative position coding module, an aspect-level attention module and an sentiment classification module, and the aspect-level sentiment analysis model comprises a word embedding module, a relative position coding module, an aspect-level attention module and a sentiment classification module. According to the method, the sentiment of the aspect-level information in the text is modeled, so that the aspect-level emotion of the text is efficiently and accurately analyzed.

The invention discloses a method for diagnosing lithium battery faults by the Petri net. For diagnosis of electric quantity nonuniformity of single lithium batteries, a Petri net system is adopted for constructing a fault diagnosis model, and fault information for analysis comes from external battery voltage, current, temperature and system operating time. According to the method for diagnosing the lithium battery faults, various factors are considered in fault diagnosis, and diagnosis operation is quick; high efficiency shows up in various complicated systems; the Petri net based fault diagnosis model is capable of quickly working out a fault position according to a corresponding incidence matrix and calling an associated judgment model for confirming fault occurrence, so that real-time monitoring of battery operating conditions can be well realized in an operation process of a battery pack system, and fault information can be processed timely.

The invention provides a method of calculating an equivalent circuit, which reduces the number of elements constituting a network to a large extent with a target accuracy secured. The method of the invention calculates an equivalent circuit by a computer, with regard to an object that has a conductor, a dielectric to support the conductor, and plural input/output terminals to the outside. Step 1 in the method receives inputs of geometry information to specify a shape of a conductor in a circuit board being the object of determining the equivalent circuit, and material information to specify a material of the circuit board; Step 2 partitions the shape of the objective conductor into meshes on the basis of the geometry information received, and creates mesh data to be expressed; Step 3 calculates a nodal admittance matrix from the mesh data, and stores the result; step 4 calculates the number of nodes and the number of independent networks on the basis of the mesh data, determines an incidence matrix, and determines the structure of an equivalent network by means of the calculated nodal admittance matrix and incidence matrix; and Step 5 determines the values of elements of the equivalent network.