22 results about "Greedy selection" patented technology

The invention relates to the field of structural health monitoring, in particular to a three-axis sensor number and position optimization method for structural response reconstruction. Aiming at the information coupling characteristics of the three-axis sensor in all directions, constructing a redundancy evaluation index based on the difference degree of a three-axis information matrix, screening the minimum initial measuring point set, removing measuring points with repeated information contribution, and forming a non-redundancy candidate measuring point set; based on a non-redundant candidate measuring point set, a forward greedy selection algorithm is adopted to carry out iterative optimization on the number and positions of sensors, unmeasured redundant measuring points enabling marginal gains to be maximum are selected to be added into an optimization set, it is ensured that redundancy is effectively removed in the initial stage of iteration, the redundancy is moderately relaxed in the later stage, and dependence on prior knowledge of a target structure is reduced; an objective function value changing along with the number of the sensors is used as an uncertainty curve, and the optimal critical value of the number of the sensors is determined by combining an inflection point criterion and a platform criterion.

The invention discloses an image segmentation optimization method, system, equipment and medium, and relates to the technical field of computer optimization algorithms, and the method comprises the steps: constructing a two-dimensional histogram for an original gray level image, employing a two-dimensional Renyi entropy as a target function for evaluating the segmentation quality of the two-dimensional histogram, finding a group of optimal threshold combinations, and carrying out the segmentation of the two-dimensional histogram through the optimal threshold combinations; the problem of searching the optimal threshold value combination is converted into an optimization problem to be solved, the found optimal threshold value combination is used for segmenting the original image, and a final segmentation result image is generated; when an optimization problem is solved, operating individuals in the population by adopting a differential variation strategy so as to generate a first group of candidate solutions, and executing a Rime optimization mechanism and a randomized step size covariance matrix adaptive mechanism in parallel on each individual in the population so as to generate a second group of candidate solutions; and updating the population according to a greedy selection mechanism. The algorithm premature convergence can be effectively prevented, and the method can be applied to complex optimization tasks such as image segmentation and engineering design.

The present application relates to the technical field of large model, and more particularly to a data set semantic consistency generation and quality stratification method for large model training, which comprises the following steps: step one: performing semantic coding on candidate samples containing synthetic samples and original samples to obtain unit direction vectors, determining radial coordinates according to the semantic sparsity of the nearest neighbor original samples, and dividing radial shell layers in the Poincare ball hyperbolic space; step two: establishing a support cone for the original sample, determining the synthetic samples falling into the inner support cone and anchored with the shell layer as the semantic consistent samples together with the original sample, and determining the quality level according to the radial shell layer number; step three: constructing a core matrix in each quality level to perform a determinant point process greedy selection, and outputting the selected samples and quality level of the data set. The present application suppresses the semantic drift and redundancy of synthetic samples, and improves the semantic consistency and coverage balance of data.

The invention discloses a full-coverage path planning method. The method comprises the following steps: determining a grid map of an area to be subjected to path planning, and determining a transition probability matrix corresponding to the grid map; an initial population corresponding to the grid map is determined according to the transition probability matrix and preset selection strategies, and the preset selection strategies comprise a greedy selection strategy and a fuzzy selection strategy; determining a local distance contribution value of a target path in the initial population, executing a multi-modal disturbance operation on the target path according to the local distance contribution value, and generating a filial generation population corresponding to the initial population; and performing iterative optimization according to the initial population and the offspring population until an optimal path corresponding to the grid map is determined. According to the invention, technical problems of path redundancy, slow convergence speed and poor adaptability to a complex environment existing in a path planning algorithm in a complex obstacle scene in the prior art are solved.

The application discloses a kind of solid crystal machine temperature sensitive point selection method, device, equipment and storage medium, belong to equipment manufacturing technical field.The method comprises: obtaining the temperature data of first preset quantity temperature measuring points of each position point of solid crystal machine;The position point of solid crystal machine includes second preset quantity position points;Using improved AP clustering algorithm, in combination with multiple preset cluster numbers, the temperature data of each position point obtained is clustered, and the clustering result corresponding to multiple preset cluster numbers of each position point is obtained;According to multidimensional index, the clustering result corresponding to multiple preset cluster numbers of each position point is analyzed, and target cluster number is determined;Based on the clustering result corresponding to multiple preset cluster numbers of each position point, in combination with target cluster number, determine candidate point set;Temperature sensitive point set is determined from candidate point set using greedy selection algorithm.The effectiveness and accuracy of temperature sensitive point selection can be improved using the technical scheme provided by the application.

The application discloses an open source dialogue model-oriented automatic jailbreaking prompt word generation and attack method and system. The application first screens a prompt word set from an original prompt word set; secondly, based on an attack question, the prompt word with the optimal attack efficiency is screened out from the original open source prompt word set, multi-path parallel testing is carried out by using a proxy model, and the attack success rate and the prompt word length of different prompt words in the prompt word set are evaluated in real time through a dynamic evaluation mechanism based on a greedy selection strategy, the final attack prompt word is output to a target model, and after being spliced with the attack question, the attack prompt word is returned and an analysis report is output; finally, the prompt word in the analysis report is adjusted by using an automatic mutation and expert modification strategy, and the prompt word is reconstructed and iterated according to the feedback result of each round of attack, so that a new prompt word is generated. The application combines offline evolution and online decision-making to automatically generate a high-success-rate jailbreaking prompt word, controls the length and overhead, and optimizes mutation by using semantic rewriting and logic skeleton extraction.

The invention belongs to the technical field of swarm intelligence optimization algorithms, and particularly relates to a multi-strategy fusion improved star-graffiti optimization method, which comprises the following specific steps of: generating an initial population by adopting a strategy of combining Logistic chaotic mapping and mirror reflection learning, and enhancing the uniformity and quality of population distribution; in the iteration process, a nonlinear decline function and a periodic fluctuation function are used for adjusting key control parameters respectively, and dynamic balance of global exploration and local development is achieved; introducing a mode search operator based on a dynamic step size in the middle stage of iteration, and carrying out refined mining on an optimal solution region through direction search and disturbance optimization; high-quality gene transfer is accelerated through an information sharing mechanism based on a cross strategy and greedy selection. Through multi-strategy fusion improvement, the risk that the algorithm falls into local optimum is reduced, and the problem that the global search capability of an original algorithm is limited is solved.

The invention relates to the technical field of laser cutting, in particular to a parameter self-adaption method and system for laser cutting, and the method comprises the steps: constructing a solution space containing laser power and cutting speed, and initializing a plurality of nectar sources of an artificial bee colony algorithm; the follow-up error in the laser cutting process is collected in real time, the risk index of the current working condition is calculated according to the Rayleigh length, the size of the follow-up error and the change rate, and then the search step length and the fitness weight are determined; generating a new solution in the neighborhood of the nectar source by using the search step size, and calculating a fitness function value of the new solution according to the fitness weight; and the nectar sources are updated according to a greedy selection strategy, the nectar source with the highest fitness function value is selected as the optimal control parameter after iteration is completed, and the laser power and the cutting speed are adjusted according to the optimal control parameter. According to the technical scheme, the stability of a cutting energy field can be maintained, and the quality defects of slag adhering, incomplete cutting and the like are effectively overcome.

The application discloses a vehicle computing task scheduling method based on vehicle-edge-cloud cooperation and belongs to the technical field of intelligent transportation systems and Internet of Vehicles. The method is aimed at the problems of limited computing resources of intelligent networked vehicles, high task delay and large energy consumption. An improved greedy algorithm is used to optimize the task execution order, and local greedy selection is performed according to the data volume, computation volume and maximum tolerable delay of the task to output an optimal platform allocation label. Then, a feedforward neural network is trained using the labels. The trained neural network can quickly predict an optimal platform vehicle-edge-cloud cooperative allocation scheme according to the input task characteristics in a real-time scenario. Through the fusion of the algorithm and the neural network, the application realizes efficient online real-time scheduling, significantly improves the task scheduling efficiency and system reliability, has excellent real-time performance and scalability, effectively reduces the task execution delay and energy consumption, and is suitable for efficient scheduling and resource allocation of large-scale computing tasks in the Internet of Vehicles.

This invention proposes a deep learning-based method for joint generation of beam training and measurement patterns, belonging to the field of communication technology. The method includes: acquiring a full codebook beam measurement quality dataset; performing self-supervised pre-training on the dataset using a high-ratio random masking strategy to obtain a general beam prediction model; inputting complete beam measurement data into the model and calculating importance scores through backpropagation; iteratively selecting the beam measurement points with the highest importance scores using a greedy selection strategy based on a given beam training overhead constraint; updating the importance scores of the remaining beams after each round of selection by penalizing them based on the spatial correlation between the selected beams and the remaining beams; stopping the iteration and outputting the final optimal beam measurement pattern when the number of beam measurement points in the optimal pattern set reaches the beam training overhead constraint, guiding the base station to send the corresponding reference signal. It possesses strong generalization capabilities and can flexibly adapt to various measurement configurations and scenario changes.

This invention provides a method for inverting and measuring the complex permittivity based on the MHHO-DE hybrid optimization algorithm, belonging to the field of electromagnetic property measurement technology. The method includes: constructing an initial Harris Hawk population of the material to be inverted within a search space, the initial Harris Hawk population comprising multiple Harris Hawk individuals, the position vectors of which represent the complex permittivity; updating the position vectors of the multiple Harris Hawk individuals based on the current optimal solution among them using a multi-strategy improved Harris Hawk optimization algorithm, forming a first optimal individual population; selecting a second optimal individual population from the first optimal individual population through a greedy selection mechanism based on mutation and crossover operations of a differential evolution algorithm; and determining the global optimal solution from the second optimal individual population, which is used to determine the complex permittivity of the material to be inverted. This method solves the technical problems of easily getting trapped in local optima and generating multivalued solutions in related technologies.

The application provides a phased array antenna beam broadening method, electronic equipment and a readable storage medium. The method comprises the following steps: constructing an optimization fitness function suitable for a phased array antenna, wherein the optimization fitness function comprises a normalized weighted combination of a beam width term for guiding an optimized half-power beam width to approximate a target beam width and a sidelobe level term for guiding an optimized sidelobe level to approximate a sidelobe suppression reference level; setting array physical parameters and beam optimization target parameters; initializing a meta-heuristic algorithm and generating an initial population, each individual in the initial population corresponding to a phase configuration scheme of all elements in the phased array antenna; starting an iterative optimization cycle, and in the case that the fitness value of each individual in the current population does not satisfy an iteration end condition, sequentially performing elite population screening, algorithm parameter updating, mutation operation, crossover operation and greedy selection operation to obtain a population of the next iteration.

The invention discloses a large model context generation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a to-be-queried text segment vector and a candidate text segment vector set, calculating a redundancy weight coefficient according to a budget constraint and a text segment length average value in the candidate text segment vector set, constructing a target gain function according to a similarity weight coefficient and the redundancy weight coefficient, initializing a target text segment vector set into a null set, and obtaining a target text segment vector set. And sequentially selecting the text segment vectors which enable the target gain function to be maximum from the candidate text segment vector set by utilizing a greedy algorithm, adding the text segment vectors into a target text segment vector set to obtain a target text segment vector set after greedy selection, and generating a context corresponding to the to-be-queried text segment vector according to the target text segment vector set after greedy selection. By adopting the scheme of the invention, the redundancy between the selected text segments can be reduced, and the diversity and the utilization rate of evidences can be improved, so that the quality and the consistency of the generated context can be ensured.

The invention discloses a pathological image classification method and system based on class incremental learning in the technical field of medical images.The method comprises the steps that a greedy selection algorithm based on the maximum mean value difference is adopted, a playback sample set is constructed from samples in the current stage, and when a next incremental task is entered, the playback sample set is replayed; new data of the stage and a playback sample stored in the previous stage are input into the model together for joint training, in the training process, class balance loss of prior regularization is introduced, meanwhile, knowledge distillation is conducted on model output of the current stage and model output of the previous stage, a gradient calibration mechanism is introduced into a full-connection classification layer, and therefore the model output of the current stage and the model output of the previous stage are obtained. Dynamically adjusting the gradient change of the new and old categories; the method can effectively solve the problem that the performance of the pathological image is reduced due to domain difference and class imbalance in the class incremental learning process, maintains the classification accuracy of the existing disease classes, has the capability of quickly adapting to new disease classes, and has important clinical diagnosis application value.

The invention discloses a microbial solidified soil constitutive model parameter identification method, and belongs to the field of computational geotechnical engineering and intelligent optimization algorithms. The method comprises the following steps: constructing a microbial solidified soil constitutive model containing a mobile normal consolidation line MNCL, constructing an error objective function, performing parameter inversion by adopting an improved radial movement algorithm, obtaining an optimal to-be-identified parameter vector, and completing identification of to-be-identified parameters. And a greedy selection mechanism is introduced into an improved radial movement algorithm, so that the problem of convergence oscillation caused by easy acceptance of an inferior solution in particle updating is solved, and the optimization stability and precision are remarkably improved. And a parameter identification problem. In addition, a layered decoupling strategy is provided for multi-confining-pressure data, and confining-pressure sensitive parameters and intrinsic parameters of materials are recognized through the recognition method. According to the method, high-precision and automatic identification of the complex mechanical behavior model parameters of the microorganism solidified soil is realized, and the defects of a traditional method are overcome.

This invention discloses a relay resource allocation method and system based on a quantum flying fox optimization mechanism, relating to the field of wireless communication technology. It aims to address the problems of uneven energy consumption, short network lifetime, and poor usability in existing cooperative multi-user relay networks under high-dimensional relay nodes. The key technical points of this invention include: Step 1: Establishing a wireless sensor network system model with the goal of maximizing network throughput; Step 2: Setting the operating parameters of the cooperative multi-user relay network and initializing the network state; Step 3: Initializing the quantum flying fox population, constructing an fitness function based on the objective function, and determining the globally optimal and worst quantum positions; Step 4: Updating the quantum rotation angle of each quantum flying fox using hopping and exploration strategies to generate the quantum positions of the quantum flying foxes; Step 5: Updating the next generation of quantum positions, the globally optimal quantum position, and the worst quantum position using a greedy selection mechanism; and finally, solving for the relay resource allocation result.

The invention discloses an underwater acoustic communication codebook generation method, device and equipment and a readable storage medium, and the method comprises the steps: normalizing the characteristics of candidate signal segments through preprocessing, and weakening the inherent correlation influence caused by a stable line spectrum and a similar envelope in real ship noise; a similarity evaluation index between candidate signal segments is defined, and similarity misjudgment caused by similar segment characteristics is avoided; based on the similarity, a simulated annealing optimization algorithm is adopted to screen a target signal segment from a candidate signal set, the algorithm does not adopt a simple random or greedy selection mode, and low-cross-correlation segments are screened in a targeted mode through heuristic search logic, so that the defect that random or greedy selection is easy to select high-cross-correlation segments is effectively avoided, and the selection accuracy of the target signal segment is improved. Further, the codebook separability is improved, and the problem that the codebook separability is poor due to the fact that real ship noise contains stable line spectrums and similar envelopes is solved; meanwhile, in the screening process of the method, a low-correlation starting point set is obtained through farthest point sampling, and a traditional exhaustion or global optimization mode does not need to be adopted.

This invention discloses a multi-target adaptive optimization cooperative tracking planning method based on a dynamic hybrid mutation sparrow search algorithm. It aims to solve the problems of slow convergence speed, susceptibility to local optima, and difficulty in balancing tracking accuracy, resource consumption, and redundancy in multi-radar cooperative tracking resource allocation optimization. This method constructs a multi-factor fitness function that integrates tracking accuracy, spatiotemporal redundancy, resource consumption, and decision consistency. It initializes the sparrow population and assigns roles, executes a position update strategy, calculates the fitness change rate and population diversity to dynamically select either Cauchy mutation or elite back-learning strategies, updates the population through greedy selection, and finally outputs the optimal tracking scheme. This invention effectively balances the algorithm's global search and local exploitation capabilities, improving tracking quality and resource utilization efficiency.

The application provides an intelligent adaptive traffic signal control method combining reinforcement learning and model predictive control, and belongs to the technical field of intelligent traffic.The application uses a Q-Learning algorithm to continuously optimize an MFD combination selection strategy through online learning, so that the system can dynamically adapt to different traffic flow modes and changes.The Q-Learning algorithm combines an epsilon-greedy selection strategy, gradually improves the control effect through a reward mechanism, and effectively improves the timing efficiency of the boundary signal light.In the signal optimization of the internal area, the application adopts a Cyclic MaxPressure algorithm to dynamically adjust the signal timing according to the real-time traffic flow distribution, so that the signal timing can quickly respond to the randomness and complexity of the traffic flow, thereby effectively relieving the congestion at the local intersection.

The invention discloses a vehicle computing task scheduling method based on vehicle-edge-cloud cooperation, and belongs to the technical field of intelligent traffic systems and Internet of Vehicles. The method aims at solving the problems of limited computing resources, high task delay and high energy consumption of intelligent network connection vehicles. Optimizing a task execution sequence by utilizing an improved greedy algorithm, performing local greedy selection according to the data volume, the calculation amount and the maximum tolerance delay of the task, and outputting an optimal platform distribution label; training a feedforward neural network by using the labels; the trained neural network can quickly predict an optimal platform vehicle-mounted-edge-cloud collaborative allocation scheme according to input task features in a real-time scene; through fusion of the algorithm and the neural network, efficient online real-time scheduling is realized, the task scheduling efficiency and the system reliability are remarkably improved, the real-time performance and the expansibility are excellent, the task execution delay and the energy consumption are effectively reduced, and the method is suitable for efficient scheduling and resource allocation of large-scale computing tasks in the Internet of Vehicles.

The application provides an unmanned tractor path planning method based on an improved PSO fusion DWA optimization algorithm, comprising: grid map initialization; forming a preliminary global path by using an improved PSO algorithm; performing local fine optimization on the preliminary global path by one-dimensional neighborhood disturbance and greedy selection; performing discrete path continuous, redundant node elimination and smoothing processing on the preliminary global path by a greedy optimization method; completely retaining a path point sequence of the final optimized global path as a local target point of the DWA algorithm, guiding local path planning and real-time obstacle avoidance, performing velocity sampling and trajectory prediction based on a kinematics model, a velocity constraint and the local target point, and selecting an optimal predicted trajectory and a velocity space by an improved evaluation function in the DWA optimization algorithm. The application effectively solves the problems of poor smoothness, long planning time, many inflection points, discontinuous path curvature and inability to achieve global optimization of the existing planning method.

The present application relates to the field of structural health monitoring, in particular to a triaxial sensor number and position optimization method for structural response reconstruction. In view of the information coupling characteristics among the directions of the triaxial sensor, a redundancy evaluation index based on the difference degree of the triaxial information matrix is constructed, the minimum initial measurement point set is screened, the measurement points with repeated information contribution are removed, and a non-redundant candidate measurement point set is formed; based on the non-redundant candidate measurement point set, a forward greedy selection algorithm is used to iteratively optimize the sensor number and position, the unmeasured redundant measurement points with the maximum marginal gain are selected to join the optimization set, the redundancy is effectively removed in the early stage of iteration, and the redundancy degree is moderately relaxed in the later stage, thereby reducing the dependence on the prior knowledge of the target structure; the target function value changing with the sensor number is used as an uncertainty curve, and the inflection point criterion and the platform criterion are combined to determine the optimal critical value of the sensor number.