141 results about "Decision graph" patented technology

Methods and systems are disclosed for learning a regression decision graph model using a Bayesian model selection approach. In a disclosed aspect, the model structure and / or model parameters can be learned using a greedy search algorithm applied to grow the model so long as the model improves. This approach enables construction of a decision graph having a model structure that includes a plurality of leaves, at least one of which includes a non-trivial linear regression. The resulting model thus can be employed for forecasting, such as for time series data, which can include single or multi-step forecasting.

The present invention relates to an arrangement for monitoring performance in a network comprising a number of network devices or nodes, services and users, in real-time. The arrangement comprising information collecting means, generating means for generating performance KPI indicators depending on a number of definition performance parameters for one or more of said network nodes. The generating means comprise or communicate with decision graph generating means adapted to generate decision graph structures representing respective KPI values and combination probabilities in dependence of KPI definition performance parameters and in dependence of additional or correlation parameters. The generating means comprise decision graph content updating means adapted to update the decision graph structure or structures using said collected information to enable prediction of KPI status / behavior and message sending means are provided which are adapted to send out messages or notifications relating to one or more prediction results.

The invention provides a reliability modeling method for a related multi-mode system based on a failure mechanism. The method comprises the following steps: analyzing system components and confirming and limiting various states of the system and the components; respectively confirming the failure mechanism of each component and the related relation of each failure mechanism under the working environments of the limited states and the function conditions, and establishing a binary decision graph model related to the failure mechanism for each component; calculating the state probability of each component according to the binary decision graph model established in the step (2) under the condition of the service life distribution of each known component under the independent effect of each failure mechanism and establishing a multi-mode multi-value decision graph model based on each component for the system according to the logic relation of each component in the system; substituting the state probability of each component acquired in the step (3) into the logic expressed by the multi-mode multi-value decision graph model established in the step (4) and calculating the state probability of the whole system and the system reliability.

A method of rule processing includes defining / entering attributes, enumerations, and / or relationships; packaging the definitions in a reduced canonical form suitable for propositional logic manipulation using zero-suppressed binary decision diagrams (Zdd) to produce a prime Zdd; and / or (iii) executing the rule by applying a series of user inputs to the prime Zdd to determine a result that preferably includes conflict and selection advice to guide the user to satisfaction. Elective events, such as but not limited to the display of messages or the performance of calculations, may optionally be packaged along with the prime rule or components thereof, and presented during execution to help guide the end user to satisfaction or compliancy when choosing among possible selections. The invention automates determination of a complex rule having a combinatorial exploded number of rule components, or a combinatorial number of possible outcomes, exceeding computational capacity of present day computing systems.

The invention relates to a road network trajectory clustering-based travel hotspot region mining method. According to the method, taxi trajectories are mapped into a road network, and a clustering method combining points of interest and trajectories collected in actual roads is adopted; on the basis of a density peak clustering algorithm, an OPAM algorithm which optimizes an initial center based on density peaks is provided, namely, a namely DP-OPAM algorithm is provided; according to the algorithm, the local density of data points and shortest distances from the points to points with higher density are adopted, and a decision graph is adopted to select the category of data points with higher density and closest distance, and the category is adopted as an initial clustering center; and onthe basis of the initial clustering center, an OPAM clustering algorithm additionally adopting inverse learning is used to obtain a clustering result. Compared with an original OPAM algorithm, the newalgorithm can not only automatically determine a clustering center, but also improve accuracy and shorten clustering time and realize the analysis of user travel hotspots.

This invention describes a race logic audit program, RaceCheck, which is unique from the prior arts. Specifically, RaceCheck can perform both static and dynamic race logic analysis, and it works with a plurality of hardware description languages (HDL), which include but not limited to: VHDL, Verilog, SystemVerilog, and SystemC. Furthermore, RaceCheck makes use of both structural and timing information of IC designs, as well as binary-decision diagram (BDD) and automatic test pattern generation (ATPG) techniques to derive accurate race logic analysis results with few false violations. Finally, RaceCheck can detect concurrent invocation races of the $random system function, concurrent invocation races of user-defined tasks and functions, zero-delay combination loops, and RaceCheck's dynamic analysis engine uses a HDL simulation kernel to perform timing-accurate race logic analysis. All these aforementioned features are unique for the invention and have not been reported in any prior arts.

A rule processing apparatus includes modules for defining / entering attributes, enumerations, and / or relationships; packaging the definitions in a reduced canonical form suitable for propositional logic manipulation using zero-suppressed binary decision diagrams (Zdd) to produce a prime Zdd; and / or (iii) executing the rule by applying a series of user inputs to the prime Zdd to determine a result that preferably includes conflict and selection advice to guide the user to satisfaction. Elective events, such as but not limited to the display of messages or the performance of calculations, may optionally be packaged along with the prime rule or components thereof, and presented during execution to help guide the end user to satisfaction or compliancy when choosing among possible selections. The apparatus automates determination of a complex rule having a combinatorial exploded number of rule components, or a combinatorial number of possible outcomes, exceeding computational capacity of present day computing systems.

The invention relates to a mass spatial data density clustering method based on an elastic distribution dataset. According to the method, first, automatic meshing and data distribution are performed according to distribution of data in space based on the design ideology of "RDD partition--intra-partition parallel computing--local result merging" targeting quick mining of an aggregation characteristic base of large-scale spatial data, so that data volumes in meshes are relatively balanced, and the purpose of balancing arithmetic node loads is achieved; second, a local density definition suitable for parallel computing is proposed, a computing mode of a cluster center is improved, and the defect that a cluster center object needs to be judged by drawing a decision graph through an original algorithm is overcome; and last, quick clustering processing of the large-scale spatial data is realized through clustering, merging and other optimization strategies in the meshes and between the meshes. Through the method, quick clustering of the large-scale spatial data can be effectively realized, and the method has high precision and better system processing performance compared with traditional density clustering methods.

The present invention provides collaborative filtering systems and methods employing default scores of decision graphs / trees to quickly create a top-n prediction list that can efficiently determine a user's interest in items. In one instance of the present invention, the list is refined by utilizing a variable maximum score algorithm and / or an invalidation list algorithm to insert items that score above an inclusion threshold set by a last item in the top-n prediction list. In another instance of the present invention, an invalidation list for a decision graph and / or decision tree is utilized to create a top-n prediction list. An algorithm employing default scores is then utilized to refine the top-n prediction list to insert items with default scores above an inclusion threshold set by a last item in the top-n prediction list.

A rule packaging system and method to define and / or package parameters, attributes, enumerations of a prime rule in a reduced canonical form suitable for propositional logic manipulation using, for example, zero-suppressed binary decision diagrams. The reduced form of the prime rule is subsequently used by applying a series of user inputs to determine a result that preferably includes conflict and selection advice to guide the user to satisfaction. Elective events, such as but not limited to the display of messages or the performance of calculations, may optionally be packaged along with the prime rule or components thereof, and presented during execution to help guide an end user to satisfaction or compliancy when choosing among possible configuration parameters. The apparatus automates determination of a complex rule having a combinatorial exploded number of rule components, or a combinatorial number of possible outcomes, exceeding computational capacity of present day computing systems.

Embodiments of the invention disclose a density peak-based big data mining method and apparatus. The method comprises a first stage of selecting initial clustering centers by using a density peak clustering algorithm, and a second stage of determining an initial clustering number and accelerating convergence of a conventional fuzzy C-mean algorithm. In the algorithm of the first stage, a decision graph is provided by using density peak clustering, the initial clustering centers are selected, and the initial clustering center number is namely a clustering center number after the selection is finished, so that the clustering number does not need to be manually specified. In the algorithm of the second stage, an iterative process of the conventional fuzzy C-mean algorithm is optimized, the influence of local density on the algorithm is considered, and a density weighting factor is added, so that the algorithm can obtain a global optimal solution in an accelerated way; and an oscillating factor is added, so that the algorithm convergence can be accelerated.

The invention discloses a multi-focus image fusion method based on a decision diagram and sparse representation. According to the method, a multi-focus image fusion framework which is different from the conventional multi-focus image fusion algorithm is put forward based on the characteristics of a human vision system, and analysis and research are performed for the transition region of multi-focus images so as to avoid the influence on the fusion result and enhance the quality of the fusion image. The implementation process comprises the steps that the decision diagram is generated on the basis of resolution analysis of the low-scale images of the multi-focus images, and a fusion result is acquired according to the decision diagram; the generated decision diagram has error due to the fact that judgment of the resolution of the transition region has deviation so that the transition region requires to be determined and processed by using the multi-focus image fusion algorithm based on sparse representation and the fusion result of the transition region can be acquired; and finally mean operation is performed on the fusion result based on the decision diagram and the fusion result of the transition region so that the final fusion image is acquired.

A method for verifying a design through symbolic simulation is disclosed. The method comprises creating one or more binary decision diagram variables for one or more inputs in a design containing one or more state variables and building a binary decision diagram for a first node of one or more nodes of the design. A binary decision diagram for the initial state function of one or more state variables of the design is generated and the design is subsequently initialized. Binary decisions diagrams for one or more constraints are synthesized. A set of constraint values is accumulated over time by combining the binary decision diagrams for the one or more constraints with a set of previously generated binary decision diagrams for a set of constraints previously used in one or more previous time-steps. A binary decision diagram for the next state function of the one or more state variables in the design is constructed in the presence of the constraints. The one or more state variables in the design are updated by propagating the binary decision diagram for the next state function to the one or more state variables and a set of binary decision diagrams for the one or more targets in the presence of the one or more constraints is calculated. The set of binary decision diagrams for one or more targets is constrained and the design is verified by determining whether the one or more targets were hit.

A method, apparatus and computer-readable medium for conjunctive binary decision diagram building and variable quantification using case-splitting are presented. A BDD building program builds a BDD for at least one node in a netlist graph representation of a circuit design. One or more variables are selected for case-splitting. The variable is set to a constant logical value and then the other. A BDD is built for each case. The program determines whether the variable is scheduled to be quantified out. If so, the program combines the BDDs for each case according to whether the quantification is existential or universal. If the variable is not scheduled to be quantified, the program combines the BDDs for each case so that the variable is introduced back into the resulting BDD, which has a reduced number of peak live nodes.

The invention relates to a fast multi-focus image fusion algorithm. The algorithm comprises the following steps of firstly, decomposing a source image by using a smoothing filter and constructing thedecision graph model of multi-focus image fusion; secondly, using the correlation between the neighborhood pixels of multi-focus images to provide a fast steering filter optimization decision graph sothat calculating complexity is greatly reduced; then, reconstructing two scales of images; and finally, using the fusion method of the invention and other triple existing fusion methods to fuse the multi-focus images. Compared with the other three kinds of fusion methods, by using the method of the invention, regardless of subjective visual and objective evaluation indexes, the texture information, the significance information and other details of the multi-focus images can be preserved.

A system, method and computer-usable medium for designing a firewall to protect a data processing system. A user first specifies a firewall decision diagram. The firewall decision diagram is then reduced and marked. Finally, a firewall is generated from the marked firewall decision diagram.

The invention discloses a contact network failure risk assessment method based on a binary decision graph algorithm, comprising the steps of: (1) generating a BDD structure, determining system boundaries, basic events and top events, establishing and normalizing a fault tree, and generating a BDD structure, wherein the corresponding BDD nodes may be directly created by ITE operations for basic events; and ITE operations may be performed on basic events or other intermediate events to obtain the BDD structure of the original intermediate event for intermediate events; (2) calculating the accident rate of the contact network failure risk, generating a Boolean logic expression by the fault tree, and generating a Boolean logic function corresponding to the fault tree top event, wherein when the true value is obtained, the probability of occurrence of the top event or any intermediate event may be obtained; and (3) measuring the event importance. The invention applies the BDD method to thefailure risk assessment of the contact network, which simplifies the calculation process, and solves the problems such as the combined explosion and the complicated solving process encountered by thecut set method in the contact network failure fault tree analysis.

The invention discloses an automatic identification method of microaneurysm in a fundus colorful image. The method comprises the following steps: firstly, carrying out preprocessing and shade correction on an image of a green channel, then, utilizing the local information of a sliding window to determine an adaptive threshold value to carry out binarization on the image, obtaining a decision graph through a double-window filter based on connectivity restriction, and carrying out region growing on the decision graph, wherein the result of the region growing is the accurate identification microaneurysm. By use of the whole method, the fine identification and segmentation of the microaneurysm can be realized, identification missing and misjudgement can be effectively avoided, the processes of feature extraction and classifier classification do not need to be adopted, and identification efficiency is effectively improved.

In one embodiment, a method includes accessing an inverted index of a searchable set of objects including key words. The inverted index includes multiple lists each corresponding to a particular key word and identifying a particular subset of the objects including the particular key word. The method includes generating a binary decision diagram (BDD) for each of one or more of the lists. The BDD corresponds to the particular key word of the list, and each decision node of the BDD represents an object in the searchable set of objects including the particular key word of the list. The method includes storing each of one or more of the lists as its BDD. Storage of the BDD facilitates more efficient storage of the inverted index.

The invention provides a feedback density peak value clustering method and a system thereof. A problem that one type is divided into a plurality of types when a plurality of density peak values are generated in one type in an original density peak value algorithm is solved. Simultaneously, accuracy of an original algorithm in a high dimension data set is increased. The method comprises the following steps of 1, using non-negative matrix factorization to carry out characteristic extraction on a data set; 2, according to an original density peak value clustering algorithm, drawing a decision graph and selecting a plurality of clustering centers; 3, using a ''nearest neighbor'' algorithm to distribute the rest of points and removing a noise point; 4, using a SVM to feed back a clustering result between two types; and 5, according to a feedback result, merging the types which can be merged. By using the method, robustness of a density peak value algorithm can be effectively increased, clusters with any shapes can be well discovered, high dimension data can be effectively processed, and a good clustering effect is possessed.

The invention provides a modeling method oriented to multi-agent synchronous gaming and an action prediction system. The modeling method comprises the steps that a state set and an action set of synchronous gaming problems are obtained; according to the characteristics of the synchronous gaming problems, gaming features and a feature coding method are designed; according to the gaming features andthe feature coding method, the state set and the action set are subjected to data preprocessing, and thus a basic feature graph and an action decision graph are obtained; a multi-scale feature fusionsynchronous gaming strategy model is established on the basis of a deep neural network; and according to the basic feature graph and the action decision graph, the synchronous gaming strategy model is trained layer by layer, thus the trained synchronous gaming strategy model is obtained, and the synchronous gaming strategy model has the high real-time performance while the gaming ability of the synchronous gaming strategy model is improved. The action prediction system realized based on the synchronous gaming strategy model is high in accuracy and real-time performance and has the good practical application value.

The invention relates to an image super-resolution and fusion method based on regional information enhancement and block self-attention, and belongs to the technical field of digital image processing.The method comprises a source image super-resolution branch and a fusion super-resolution branch. In a source image super-resolution branch, a feature extraction block is iteratively used to extracta source image feature map, and dense connections are used to fully utilize previous and subsequent feature map information. The output of each feature extraction block passes through a region information enhancement block to explore the region where each object in the source image is located, and the information assists in fusing the super-resolution branches to accurately predict the fusion decision graph. In the fusion super-resolution branch, two source images are spliced together to be input, and a fusion block based on a block self-attention mechanism is iteratively used in combination with region-enhanced source image information input in the source image super-resolution branch so as to better distinguish a focusing region from a non-focusing region. The last use of sub-pixel convolution of each branch produces a super-resolution source image and a fused image.

The present invention provides a method and mechanism for simulating complex digital circuits using hybrid control and data flow representations. Specifically, the invention provides a method of simulating a digital circuit in such a way that the simulation is stopped at desired functions for subsequent analysis. A hardware design code describing the digital circuit is converted to an assignment decision diagram (ADD) representation that is then annotated with one or more control nodes that are used for maintaining control flow through a simulator. In this way, one or more break points are created that allow the simulator to stop at associated points in the simulation.

The embodiment of the invention discloses a text clustering method, device and equipment based on density and a storage medium, and relates to the technical field of text data analysis. The method comprises the steps of receiving a target data set; determining a target distance formula; generating a distance matrix about the whole target data set; calculating the local density of each data point;separately extracting the minimum value of the distance between each data point and each data point in the sample point set, and recording the minimum value as the minimum point distance; establishinga clustering decision diagram according to the local density and the minimum point distance; determining the number of class clusters and a class cluster center in the clustering decision diagram; and dividing each data point into class clusters of the clustering decision diagram. According to the method, in the whole clustering process, the non-spherical data can be clustered only by calculatingthe distance between the sample points once without iterative calculation, the algorithm performance is greatly improved, the clustering decision diagram is used for scientifically selecting the number of the class clusters, and the situation that the number of the class clusters is manually set without basis is avoided.

A method for verifying a design through symbolic simulation is disclosed. The method comprises creating one or more binary decision diagram variables for one or more inputs in a design containing one or more state variables and building a binary decision diagram for a first node of one or more nodes of the design. A binary decision diagram for the initial state function of one or more state variables of the design is generated and the design is subsequently initialized. Binary decisions diagrams for one or more constraints are synthesized. A set of constraint values is accumulated over time by combining the binary decision diagrams for the one or more constraints with a set of previously generated binary decision diagrams for a set of constraints previously used in one or more previous time-steps. A binary decision diagram for the next state function of the one or more state variables in the design is constructed in the presence of the constraints. The one or more state variables in the design are updated by propagating the binary decision diagram for the next state function to the one or more state variables and a set of binary decision diagrams for the one or more targets in the presence of the one or more constraints is calculated. The set of binary decision diagrams for one or more targets is constrained and the design is verified by determining whether the one or more targets were hit.

The invention provides a network test method and system, a storage medium and electronic equipment, and the method comprises the steps: obtaining a target binary decision diagram matched with a flow table rule and a P4 program; generating and updating a probe according to the target binary decision diagram, the topology and the rule updating event; forwarding the probe, and writing an action corresponding to the flow table rule into the probe; and when the probe returns to the server, checking the probe to obtain a detection result of a rule error in the operation process. According to the network test method and system, the storage medium and the electronic equipment provided by the embodiment of the invention, the binary decision diagram can be used for representing the P4 program and the flow table rule, so that the low overhead of network resources is realized, and the full coverage of the flow table rule can be realized.

System and software for verifying that a model of an integrated circuit satisfies its specification includes performing a sequence of at least one sequential transformation on a sequential model of the integrated circuit to produce a simplified sequential model of the integrated circuit. Thereafter, the simplified sequential model is unfolded for N time steps to create a combinational representation of the design. A sequence of at least one combinational transformation algorithms is then performed on the unfolded design to produce a simplified unfolded model. Finally, an exhaustive search algorithm is performed on the simplified unfolded model. The sequence of sequential transformations may include a sequential redundancy removal (SRR) algorithm and / or another sequential algorithm such as a retiming transformation. The combinational transformations may include a combinational redundancy removal algorithm or a logic re-encoding algorithm. The exhaustive search includes performing an exhaustive satisfiability search by propagating a binary decision diagram (BDD) through the netlist.