46 results about "Constraint graph" patented technology

A method and system for solving an optimization problem comprising a plurality of dynamic constraints. A genetic algorithm is used to iteratively generate potential solutions to the problem. A constraint graph is used to model the plurality of dynamic constraints, and any potential solution that does not correspond to a connected subgraph of the constraint graph is infeasible and discarded. Real-time changes in dynamic constraints are incorporated by modification of the constraint graph between iterations of the genetic algorithm. An exemplary embodiment comprising the scheduling of air missions is presented.

A method allocates resources in an orthogonal frequency-division multiple access (OFDMA) network including a set of base stations (BSs), and a set of mobile stations (MSs) for each BS. Each cell includes a center and edge zone. A node weighted constraint graph is constructed for the network. Maximal independent sets in the graph are searched as sub-channels are allocated to the MSs in edge zones. Remaining bandwidth is allocated to the MSs in the center zones. Power is assigned to the sub-channels so that inter-cell interference is minimized and traffic load is maximized.

An initial layout of at least a portion of a given layer of an integrated circuit design is decomposed into multiple sub-layouts by splitting each of a plurality of shapes of the initial layout into multiple segments, constructing a constraint graph to represent relationships between the segments, reducing the constraint graph to a stitch graph, determining at least one cut line of the stitch graph, and generating a decomposed layout based on the determined cut line. The decomposed layout in an illustrative embodiment includes first and second sub-layouts comprising respective disjoint subsets of the segments, with each of the sub-layouts of the decomposed layout being associated with a different pattern mask of a double patterning lithography process. The layout decomposition process advantageously minimizes the number of stitches between the sub-layouts without introducing excessive computational complexity.

The invention relates to an OCT (Optical Coherence Tomography) image layer segmentation method based on a neural network and a constraint graph search algorithm, and is designed for accurately segmenting a retina layer and new vessels. The OCT image layer segmentation method based on the neural network and the constraint graph search algorithm comprises the following steps that: obtaining OCT image features to train a neural network classifier; obtaining a final SF1 through a multi-resolution map search algorithm SF1; extracting 24 features of an OCT image, and using the neural network classifier to find initial surfaces S1, S2,...S8; according to initial boundaries S2 to S8, using the constraint graph search algorithm to find accurate SF2 to SF8 in sequence; and segmenting the new vessels and hydrops between SF7 and SF8. The OCT image layer segmentation method based on the neural network and the constraint graph search algorithm has the advantages of being simple in operation and accurate in detection results. The existing problems of low identification rate, poor segmentation effect and the like of a lesion OCT image segmentation algorithm.

The invention discloses an image classification method based on a high-constraint high-dispersion principal component analysis network. The image classification method comprises the following steps of a convolution and nonlinear variation step: in a convolution layer, obtaining a plurality of convolution kernels used for extracting the characteristics of each stage, and in a nonlinear transformation layer, utilizing the convolution kernels to carry out nonlinear transformation on an input image to obtain a characteristic pattern; a characteristic pooling step: in a characteristic pooling layer, importing a multiscale characteristic analysis formula, and outputting characteristics after a value meets a condition that a high-dispersion distribution formula and the optimal value of the scale zooming factor [Sigma] of the high-dispersion distribution formula are optimal is deduced; an integration step: unfolding the characteristics into vectors, and utilizing the vectors to form a characteristic matrix; and an image classification step: inputting the characteristics into a linear support vector machine to finish an image classification task. The image classification method is simple and efficient and exhibits self-adaption and expansibility, and only the structural parameter of the network needs to be input.

The invention discloses an image saliency detection method and device based on correlation constraint graph sorting. The method comprises the steps of performing super-pixel segmentation on a to-be-detected image, building a closed-loop graph model, and then calculating prior information of each super-pixel node; extracting information of color, texture, position and the like of an input image; obtaining a foreground probability value of each super-pixel node; taking a set of the nodes with the foreground probability values greater than a first preset threshold value as a foreground seed pointset ind_fore; taking a set of the nodes with the foreground probability values smaller than a second preset threshold value as a background seed point set ind_back, wherein the first preset thresholdvalue is greater than the second preset threshold value; and performing calculation by using a correlation constraint graph sorting model to obtain a foreground probability S_f of each super-pixel node, and using a foreground probability value S_f as a final saliency estimation value S_final. By applying the image saliency detection method and device, a saliency detection result can be more accurate.

A method for reconstructing—three-dimensional (3D) lines in a 3D world coordinate system from two-dimensional (2D) lines in a single image of scene detects and clusters the 2D lines using vanishing points. A constraint graph of vertices and edges is generated, wherein the vertices represent the 2D lines, and the edges represents constraints on the 2D lines, then identifying the 3D lines that satisfy the constraints and reconstructing the 3D lines using the identified constraints.

A control for defining data for setting the times for controlled events in a glass forming machine which is controlled by a programmable sequencer which defines the time of a machine cycle. The control includes a computerized model of a mathematical representation of a network constraint diagram of the unwrapped bottle forming process and a computer for analyzing the computerized model as a constrained optimization problem for determining, with the following data inputs: 1. the motion durations, 2. the submotion durations, 3. the machine cycle time, 4. the event time in an unwrapped bottle forming process for each displacement to begin and for each valve to be turned "on" and "off", and 5. thermal forming process duration "N". define the fastest machine cycle time for a feasible schedule and the event time in the unwrapped bottle forming process for each displacement to begin and for each valve to be turned "on" and "off".

The invention provides an RSSI area location method based on a wireless sensor network. The RSSI area location method based on the wireless sensor network includes the following steps that firstly, the network is initialized and a system area location graph, a topological constraint graph and an illegal path graph are built; secondly, an unknown node periodically broadcasts a message including the identity of the node and a timestamp; thirdly, after beacon nodes receive the message of the same unknown node, and the RSSI value is calculated out according to a formula and sent to a center node, wherein the sent message also includes the ID and a timestamp; fourthly, the center node carries out judgment through a time threshold value algorithm according to the timestamp; fifthly, the center node judges and corrects the area of the unknown node through a network topology restriction mechanism; sixthly, a location result of the unknown nodes is output. According to the RSSI area location method based on the wireless sensor network, the location algorithm including three core mechanisms is designed, the three core mechanisms are well improved, and the overall algorithm has the good location effect.

The invention discloses a hierarchical heat driving floor planning and layout method which comprises the following steps of: firstly, arranging a plurality of modules according to a power sequence, and forming a module sequence; secondly, grouping the module sequence from front to back according to an area sum, dividing into a plurality of module layer groups with approximately equal area sum; thirdly, establishing an analysis heat mold for calculating the average temperature of the module groups; fourthly, acquiring a power distribution constraint graph of each module group; and fifthly, sequentially arranging the module groups above a heat dissipating device at the bottom layer according to the power density, the sequence from big to small and the respective power distribution constraint graph. In the method, a three-dimensional layout problem is converted to a two-dimensional layout problem, thereby avoiding putting a high-power density device in a pile of vertical regions, realizing power density balanced distribution in vertical and horizontal directions, efficiently reducing the hot spot amount and optimizing the chip area and the interconnecting wire length.

The invention provides a self-adaptive multi-constraint path searching method based on feedback. The self-adaptive multi-constraint path searching method based on the feedback comprises the following steps: when searching a first path, which meets a first additive constraint and does not meet a second additive constrain, from a first node to a second node based on a single-source shortest path algorithm, searching a second path, which meets the second additive constraint, from the second node to the first node; and when determining the second path does not meet the first additive constraint, determining a measuring function for searching a third path from the first node to the second node based on the first additive constraint of the first path, the second additive constraint of the second path, the preset first additive constraint and the preset second additive constraint to search the third path based on the single-source shortest path algorithm so as to determine whether the third path meets the first additive constraint and the second additive constraint at the same time. Therefore, the searching success rate can be effectively improved.

The invention provides a circuit schematic diagram generation method. The method comprises the steps that a hierarchy value of a netlist device is calculated to determine a column of the netlist device; a sorting value of the netlist device is calculated to generate a pseudo device according to the communication line or the feedback line; only signal connection between adjacent devices is needed,wiring algorithm is simplified, the netlist devices and the pseudo devices are sorted according to the sorting values to generate a sorting graph; a constraint graph is generated according to the sorting graph; wire bending is less, forward traversal is performed on constraint graph, a numerical value of the first longitudinal position is calculated and the constraint graph is reversely traversed,the numerical value of the first longitudinal position is adjusted to generate the numerical value of the second longitudinal position, the signal connecting lines are distributed to the longitudinalline rails, the numerical value of the transverse position is calculated, the signal connecting lines are connected to generate the circuit schematic diagram, device layout does not need to be basedon grids, and the graphical display effect is better. The invention also provides a circuit schematic diagram generation system for realizing the circuit schematic diagram generation method.

The invention provides an image matching method based on deep planar constraint graph cut optimization. The image matching method includes selecting an image pair to be matched of any view, dividing the areas under the view to obtain all deep planes under the view, establishing energy functions for left and right boundary pixel points for all deep planes, establishing grid charts for all deep planes by using the adjacent relations between the left and right boundary pixel points of the deep planes of the image pair to be matched under the view, solving the minimal cut of the grid chart of each deep plane to obtain the parallax value of the left and right boundary pixel points of all deep planes of the image pair under the view in each energy function scope, shielding to determine the final parallax value of all deep planes of the image pair to be matched under the view according to the parallax result and generating the matching result according to the parallax value. The method overcomes the excess smoothness, and shielding problems and substantially reduces computational complexity.

The invention discloses a balance improvement method used for a collaborative robot assembling line and based on a genetic algorithm. By virtue of the method, balance ratio of the collaborative robotassembling line can be the highest. The method comprises the following specific steps: (1) inputting related information of the collaborative robot assembling line; (2) analyzing a preferential constraint relation among all the working procedures on the collaborative robot assembling line, and drawing a working procedure constraint graph; (3) according to the working procedure constraint graph, generating initial population, and performing a gene crossover operation and a mutation operation on the initial population; (4) partitioning stations by utilizing a maximum allocation principle, calculating a balance ratio of each station, and eliminating an individual with low balance ratio; and (5) judging whether gene sequences of the population are completely the same or not, if the gene sequences of the population are not completely the same, repeating the steps (3) and (4) until the gene sequences of the population are completely the same, and then outputting the gene sequences of the population as well as a station partitioning method of the population.

The invention discloses a dynamic constraint graph search-based algorithm for acquiring physiological parameters in a retinal OCT image. The algorithm is characterized in that the algorithm comprisesthe following steps of: S01, image preprocessing: a three-dimensional retina OCT image with an optic nerve head adopted a center is subjected to filtering and de-noising processing; S02, retina layering: the first layer of a retina is roughly separated out, dynamic constraint parameters are obtained, the first layer of the retina is accurately separated out on the basis of the dynamic constraint parameters, alignment processing is performed on the three-dimensional image, the eleventh layer of the retina is accurately separated out, and the seventh layer and ninth layer of the retina are accurately separated out; S03, optic disc and optic cup region segmentation; S04, cribriform plate upper boundary segmentation; and S05, the acquisition of physiological parameter information in the retinaimage. With the algorithm of the present invention adopted, the retina OCT image with the optic nerve head adopted the center can be accurately segmented, and relevant physiological parameters can beobtained.

The invention provides a group activity recognition method based on a coherence constraint graph long-short-term memory network, and the method comprises the following steps: (1), enabling the CNN features of all persons to serve as the input of the coherence constraint graph long-short-term memory network, and jointly learning the individual motion states of all persons along with time under thecoherence constraint of a space-time context; (2) quantifying contributions of related motions by learning attention factors corresponding to different motions by utilizing an attention mechanism of global context coherence; (3) aggregating all single motion states weighted by different attention factors into a hidden representation of the whole activity by adopting aggregation LSTM in each time step, and inputting the hidden representation of each activity into a softmax classifier; and (4) averaging the output of the softmax classifier of each time step to obtain a probability class vector of the group activity so as to deduce the class of the group activity.

The invention discloses a graph optimization-based collaborative navigation method in a communication limited environment. The graph optimization-based collaborative navigation method comprises the following steps: S1, establishing a sensor observation model composed of an agent dead reckoning constraint and a UWB (Ultra Wideband) relative distance measurement constraint; s2, setting a connection form of graph vertexes and edges of the factor graph model, and constructing the factor graph model; s3, based on the factor graph model of each cooperative node, solving the objective function by using an LM algorithm to obtain an optimization result of the state variable of each node at the k moment; according to the collaborative navigation method, a factor graph model is constructed, a real-time established model of gyroscope zero offset is realized by utilizing an established factor graph constraint graph optimization model containing inertial dead reckoning, the gyroscope zero offset can be accurately estimated, and distributed collaborative navigation and positioning are carried out by utilizing relative distance measurement information between nodes. Navigation error divergence of the node in a complex denial environment is effectively inhibited, and the navigation positioning precision has no obvious change compared with continuous synchronous observation under the condition that anchor point observation is discontinuous and asynchronous.

An authorization system provides fine grain entitlements to a resource server by retrieving a dynamic constraint graph as a function of an authorization request, and traversing the dynamic constraint graph as a function of the contextual attributes of the request. The contextual attributes can include local contextual attributes specific to the user or user group, and global contextual attributes specific to the current environment of the authorization system. Traversing the dynamic constraint graph provides an access result that can provide fine grain entitlements for a request.

The present invention discloses a pointer analysis method based on an offline constraint graph. The method comprises two phases: an offline analysis phase and an online analysis phase. In the offline analysis phase, ADD firstly constructs an offline constraint graph, next defines and calculates an ancestor set and a descendant set for each node in the offline constraint graph, then calculates a domination tree corresponding to the offline constraint graph, and based on domination node information, calculates equivalent top-layer variable pair information of a pointer. In the online analysis phase, the ADD performs online cycle detection by using the ancestor set and the descendant set of the node on the one hand, and reduces spreading overheads of inter-node point information in an analysis process by using the equivalent top-layer variable pair information of the pointer on the other hand. Compared with the conventional LCD based on the contained pointer analysis method, the ADD is capable of improving pointer analysis efficiency without affecting precision of the point information.

The invention discloses a SPARQL statement reordering method and device, and a SPARQL reordering-based BIM model checking method and device. The checking efficiency of a BIM model can be improved. The method comprises the following steps: determining a target component according to a predicate of an SPARQL check statement generated based on a building rule description language (SNL) rule; grouping the multiple SPARQL check statements, dividing the SPARQL check statements related to the same target component into the same group, and obtaining SPARQL check statement fragments of different target components; traversing the SPARQL check statement fragment of each target component, if both the subject and the object in the currently traversed SPARQL check statement are components, connecting a directed edge from the SPARQL check statement fragment taking the subject as the target component to the SPARQL check statement fragment taking the object as the target component, and generating a component relation constraint graph after traversing is finished; according to the component relation constraint graph, reordering SPARQL check statement fragments of different target components, and obtaining SPARQL check statements corresponding to the SNL rules after reordering.

The invention discloses a genetic scheduling method based on combined production and equipment fault constraints, is used for a workshop flexible job scheduling technology, and relates to the technical field of automatic control. The problem of production scheduling of a processing production line is solved by constructing an operation constraint graph model, and meanwhile, operation is completed on a healthy machine to the greatest extent by introducing a machine failure rate-based wheel disc selection method, so that the scheduling delay during equipment failure is reduced, the fitness calculation of a production line genetic scheduling algorithm is optimized and combined. An experiment result shows that compared with a scheduling method based on a genetic algorithm, the intelligent scheduling algorithm has the advantages that better initialized population, convergence and robustness can be obtained, the problem of intelligent scheduling of the combined production line is solved, and meanwhile, the possibility of machine loss faults is considered; and thus, flexible job shop scheduling is realized.

The invention relates to a hydropower station equipment maintenance operation instruction automatic generation method based on a genetic algorithm. The method comprises the following steps: accordingto an original drawing, using professional software for modeling unit equipment, and forming a three-dimensional model base; establishing associated knowledge data of equipment maintenance by utilizing a coding technology and a digitization technology; utilizing the directed constraint graph and the constraint matrix to establish a geometric constraint relationship of the equipment parts; establishing an evaluation function of an equipment overhaul and disassembly sequence through three indexes of direction change, tool replacement and spatial movement distance; automatically solving a disassembly sequence based on a team genetic algorithm; and realizing multi-information presentation of the three-dimensional visual operation process of equipment maintenance by utilizing a 3D engine, visual angle adaptive control and a multimedia technology. According to the invention, the problems of limited expression, high understanding difficulty and low automation degree of a paper maintenance operation instruction book can be solved, the three-dimensional visual maintenance operation instruction book of different maintenance projects of equipment is realized, and maintenance guidance is quickly provided for a maintenance site.