40 results about "Selection algorithm" patented technology

This invention discloses a clutter discrimination and suppression method based on multiple echo features, comprising: acquiring radar front-end clutter and target echo detection video data and dividing it into several connected regions; extracting multi-dimensional features and labeling prior information for each connected region; using the Relief feature selection algorithm to calculate and filter the weights of the multi-dimensional features, removing redundant features to obtain the optimal feature vector; using this feature vector to train a support vector machine classifier, and obtaining the optimal parameter model through cross-validation; acquiring measured clutter data, extracting corresponding features and inputting them into the model; and determining whether to remove clutter and retain targets based on the output. This invention effectively filters out dynamic clutter spots, avoids false deletion of weak targets, significantly reduces the false alarm rate of the system, and alleviates the computational burden of subsequent track processing.

This invention relates to the field of information processing technology, and in particular to a gridded interpolation method, apparatus, electronic device, and storage medium that integrates KNN search. It employs a KD-tree with contiguous memory storage and aggregated leaf node storage, which helps reduce memory usage and tree depth. Combined with an improved fast selection algorithm and iterative construction, it can increase tree construction speed and mitigate the risk of recursive stack overflow. By integrating KNN search with heap sort, the nearest neighbor is directly obtained during traversal. Compared to the two-stage method of querying first and then sorting, this reduces intermediate data storage and secondary sorting overhead. This invention improves interpolation speed and reduces memory usage when processing gridded interpolation of millions of scattered points. In typical browser scenarios, it can achieve millisecond-level response times, making it suitable for real-time visualization and rapid analysis in fields such as meteorology, oceanography, and the environment.

A gas temperature field reconstruction method using thermocouple measurement correction is disclosed. First, an Inventor 3D model and an ANSYS temperature field model are established for the gas temperature field. Then, a mapping relationship is established between the control parameters of the gas temperature field and the boundary conditions of the ANSYS temperature field model. Based on the principle of maximizing the influence of fuzzy boundary conditions on the temperature field reconstruction results, experiments are designed for precise boundary conditions and experimental data are obtained. Next, given the boundary conditions of the ANSYS temperature field model, the model is run to obtain the temperature field reconstruction results. A model quality assessment algorithm is run to calculate the fitting degree between the measured curve and the simulation curve, obtaining the algorithm fitting parameters. The algorithm fitting parameters are selected to form the loss function of the gradient descent algorithm. The learning rate, termination condition, and initial iteration parameters are determined. The gradient descent algorithm is run until convergence. Finally, the final iteration parameters are taken as the correction result of the fuzzy boundary conditions of the ANSYS temperature field model. This invention improves the spatiotemporal resolution of the temperature field reconstruction results.

The application discloses a power grid scheduling method and system based on aggregation units, and belongs to the field of power system scheduling. The method is as follows: by acquiring the dynamic characteristic vectors of various distributed resources in a power grid, a clustering algorithm is used to aggregate the dynamic characteristic vectors into aggregation matching units, a fast greedy multi-start selection algorithm is used to generate and screen candidate combinations, a final candidate combination is obtained, a scheduling optimization model is established according to the final candidate combination, and the optimal power instructions of the aggregation matching units are obtained by solving, and then the accurate scheduling of the power grid is realized. Therefore, by implementing the application, the problems of the prior art, such as static solidification of the aggregation model, difficulty in real-time adaptation to resource dynamic characteristics, high scheduling cost and low efficiency, can be solved.

The application discloses a kind of FPGA-based timing generator resolution and linearity promotion method and system, belong to automatic test equipment technical field.For the problem that existing FPGA timing generator is difficult to consider high resolution, large dynamic range and high linearity, the application adopts multi-phase clock and carry chain coarse, fine delay line to form three-stage time interpolation architecture, to realize picosecond-level interpolation of large dynamic range with very few logic resources;At the same time, the actual physical delay is obtained by constructing a time measurement channel in the chip, and is transmitted to the host computer to execute the selection algorithm, and the target delay code with the smallest integral nonlinear error is selected to update the delay code table.The application effectively overcomes the nonlinear deviation caused by the underlying hardware wiring, significantly improves the accuracy of timing edge placement and the system test rate, and is mainly used for generating high-precision timing signals in digital integrated circuit automatic test equipment.

The application relates to the technical field of intelligent logistics, in particular to an order dispatching method and system for intelligent logistics. The method comprises the following steps: dividing a logistics area into multiple grids; in response to a new order event, generating a preset initial value of a profit pheromone; acquiring a transport capacity node density in each grid, and generating a congestion pheromone when the transport capacity node density exceeds a preset threshold; calculating a composite pheromone in a preset grid range for each transport capacity node; based on the composite pheromone, calculating a state transition probability of the transport capacity node moving to an adjacent grid or accepting an order by using a probability selection algorithm; and generating a dispatching instruction according to the state transition probability of each transport capacity node. By means of the double pheromone mechanism, the problems of global supply-demand imbalance and local transport capacity congestion in the traditional dispatching method are effectively solved, and the scheduling efficiency and response speed of the logistics system are significantly improved.

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 device has multiple event counters, each event counter maintaining its own count value based on monitoring events occurring during the execution of a sequence of instructions by the processing circuit. When at least a given condition exists, the selection circuit responds to a given instruction in a sequence in which the execution by the processing circuit causes several operations that depend on relevant instruction control information, one or more of which are associated with a given event monitored by a given event counter, by applying a selection algorithm to derive a selected update indicator from the instruction control information. The selection algorithm is such that the selected update indicator changes across multiple applications of the selection algorithm. The count control circuit is configured to adjust the count value of a given event counter by a default amount to consider the execution of a given instruction by the processing circuit when the value of the selected update indicator is a first value, or to adjust the count value of a given event counter by a non-default amount to consider the execution of a given instruction by the processing circuit when the value of the selected update indicator is a second value.

This invention discloses a dynamic clustering method for space-based distributed collaborative detection. First, based on dynamically changing mission requirements and target status, a dynamic clustering algorithm optimized for collaborative detection performance is designed to cluster satellites within the constellation. Then, using the comprehensive capabilities of nodes as a crucial basis for node star election and cluster structure maintenance, a multi-factor weighted dynamic edge node star selection algorithm is designed, ultimately achieving a hierarchical, ordered, distributed collaborative architecture of "constellation-cluster-single star". This invention can reduce the information propagation cost within the constellation and improve information collaboration and mission execution efficiency.

The present application relates to a method of selecting a numerical format, a data processing system and a computer readable storage medium. A computer-implemented method of selecting a numerical format for representing two or more values of a recurrent neural network (RNN) for use in configuring a hardware implementation of the RNN, the method comprising: receiving a representation of the RNN; implementing the representation of the RNN as a test neural network for operating on a test input sequence, each step of the test neural network comprising an instance of the two or more values of the RNN; operating the test neural network for a plurality of steps on the test input sequence and collecting statistics to provide to a numerical format selection algorithm; and applying a numerical format selection algorithm to the statistics in order to derive a common numerical format for the plurality of instances of the two or more values of the RNN.

The application discloses a kind of fusion big language model and symbolic reasoning engine problem generation method and system, problem generation method includes receiving input question described in natural language, input question is converted into structured intermediate language representation, then intermediate language representation is converted into formalized logic predicate, after detecting that formalized logic predicate meets grammar specification, output;Formalized logic predicate is constructed by logic geometry, new geometric point is added using geometric reasoning engine, new geometric point is selected by geometric point biased selection algorithm to construct new geometric problem, and candidate question is obtained by verifying and screening new geometric problem;Difficulty coefficient and difference score are calculated to candidate question, and generated question of different difficulty and structural difference is screened out.The application does not need to rely on artificial, by introducing symbolic reasoning engine as the constraint of generation process, eliminate wrong question generation risk, reduce the proportion of invalid question generation, realize question difficulty stratification and large-scale high-quality output.

The present application proposes a chest and abdominal respiration signal decoupling and reconstruction method based on millimeter wave radar. Using a millimeter wave multiple-input multiple-output radar device, the radar echo is first mapped to a three-dimensional space using 3D beamforming technology, mapping the one-dimensional time domain signal to a spatial energy heat map containing distance, azimuth, and elevation three-dimensional information. Then, through spatial filtering strategy, the physiological vibration source signal clusters of the subjects with coupling relationship are separated. When the physiological vibration source includes the chest reflection area of the first anatomical site and the abdominal reflection area of the second anatomical site, combined with the adaptive best point selection algorithm, according to the anatomical features of the human body, the chest reflection area and the abdominal reflection area are physically located and distinguished in the three-dimensional space, and the independent breathing waveform is extracted from the body surface reflection to perform high-precision spatial separation and anti-interference waveform extraction operation of chest and abdominal respiration movement. The present application can realize high-precision spatial separation and anti-interference waveform extraction of chest and abdominal respiration movement.

The application discloses a grounding fault line selection and travelling wave distance measurement integrated diagnosis system based on a power distribution network, which comprises a data acquisition module, a data processing module, an integrated fault diagnosis module, a communication transmission module and a display and early warning module, and relates to the technical field of power distribution network fault diagnosis. The integrated diagnosis system effectively integrates the functions of grounding fault line selection and travelling wave distance measurement, collects the electrical parameters of the power distribution network in real time through the data acquisition module, analyzes and processes the electrical parameters through the data processing module, accurately determines the position of the fault line and the fault point through the integrated fault diagnosis module, adopts a multi-criterion fusion line selection algorithm and an accurate travelling wave distance measurement algorithm, improves the accuracy and reliability of the grounding fault diagnosis, extracts the core features such as the amplitude, direction and mutation time of the travelling wave head through a mobile recognition window, effectively eliminates noise interference, and improves the recognition accuracy of the wave head.

The invention relates to a method for operating a vehicle function for at least partially automatic control of at least one lateral guidance of a motor vehicle (1), comprising: determining boundary information (20) that describes at least one lane boundary (6) of a lane (3) and / or at least one road boundary (7) of a road (2) that includes the lane (3); providing a high-resolution map (23) and a swarm data-based map (24), wherein a lane center (8, 13) of the lane (3) is shown in the respective map;Fusion of the determined boundary information (20) on the one hand with the high-resolution map (23) or the swarm data-based map (24) on the other hand to determine a target path information (25) that describes a target path (9) of the motor vehicle (1) on the lane (3), wherein a choice between the high-resolution map (23) and the swarm data-based map (24) for the fusion is made by applying a predefined selection algorithm (26); operation of the vehicle function, wherein a trajectory planning (27) of the vehicle function takes the determined target path information (25) into account.

The present invention discloses an indoor zoning positioning method for streamlining measurement information, belonging to the field of indoor positioning. First, the present invention combines the traditional fingerprint positioning method and the compressed sensing technology to construct a positioning system model; then, it uses the spatial position characteristics to complete the fingerprint division of the fuzzy area and effectively screen APs according to multiple scales; finally, it calculates the target position and sparsely reconstructs the position information of the point to be located. The present invention performs compressed sampling on the measurement information, and then restores a nearly complete signal value through a limited number of measurement values, avoiding the acquisition and storage of samples of the complete signal. At the same time, the reduction of the sample size reduces the overhead during online calculation. Overall, APs outside the area with less influence are filtered out effectively, and individually, an AP subset with intensive information in the area is selected by comprehensively considering the discrimination and measurability. The SBL algorithm is selected to handle the influence of high correlation between fingerprint information, and the sorting of the AP information sequence is introduced to enhance the weight of adjacent RPs, effectively improving the positioning accuracy.

The application provides an AP dynamic selection method and system considering time delay and energy consumption, equipment and medium. According to the method, a UAV takes the maximum service radius of an AP as a search range, traverses the AP in the search range to form a candidate AP subset, and calculates the channel gain of a link to form a channel subset, and takes the AP corresponding to the maximum value in the channel gain as a core AP of the current UAV. The core AP adopts a preset cell-free scalable AP selection algorithm considering time delay and energy consumption to obtain an AP meeting a condition in the candidate AP subset and add the AP to an AP subset. The flight trajectory of the UAV is monitored in real time, and the core AP is updated. The technical scheme provided by the application can reduce the calculation overhead of the UAV and the power consumption of a network system, and improve network scalability.

With the development of big data, the privacy security of users is attracting more and more attention. K-anonymity algorithm is an effective and low-complexity privacy protection algorithm, which can protect the privacy of users by anonymizing the data. However, the algorithm currently only focuses on improving user privacy and ignores data availability. In addition, due to the influence of quasi-identifier attributes on sensitive attributes, the availability of processed data is reduced in statistical analysis. Based on this, a new K-anonymity algorithm is proposed. First, an information loss function is designed based on the amount of information and the influence degree of each quasi-identifier attribute. Second, an initial centroid selection algorithm is designed to improve the 2-means clustering effect. Finally, an improved 2-means clustering and a greedy algorithm are designed to design a K-anonymity algorithm. The algorithm can solve the privacy security problem in the context of big data, while improving the availability of data.

This invention relates to the field of encryption machines and discloses a method, system, device, and medium for algorithm optimization. The method includes: creating a mutex lock and a function pointer to an algorithm, and creating corresponding threads based on different implementations of the algorithm; starting the threads and executing the algorithm implementation corresponding to each thread; preempting the mutex lock according to the execution sequence; obtaining the thread that has locked the mutex lock and is pointed to by the function pointer, thereby obtaining the optimal implementation of the algorithm. This disclosed method facilitates the selection of the optimal algorithm in the current execution environment, and therefore allows for dynamic selection of the algorithm implementation according to actual needs, making it more flexible in use and operation.

To provide efficient methods, systems, and devices for determining and selecting beams in a Unified Environment (UE). [Solution] In a wireless communication system, UE115-f determines the best beam using a beam selection procedure and beam selection algorithm based on measurements of one or more beam selection parameters for different layers of the beam, measures the beam selection parameters to select a first layer beam, measures the beam selection parameters of one or more second layer beams corresponding to the first layer beam, and, based on the beam selection parameter measurements, decides to backtrack and select a different first layer beam in order to continue the beam selection procedure, and applies dithering to the initially selected first layer beam to reduce the likelihood that the initially selected first layer beam will be selected again.

The application relates to the technical field of unmanned aerial vehicle trajectory prediction, and discloses a multi-source data driven unmanned aerial vehicle flight trajectory prediction method and system, multi-source data of an unmanned aerial vehicle flight state and an external environment are collected and preprocessed, abnormal data is detected, interpolation completion is carried out based on adjacent time points, a spatiotemporal graph sequence is formed based on a spatiotemporal decoupling graph construction method, an input sequence is intercepted through a trend smoothing adaptive window selection algorithm, an improved Graph Transformer model is input, double-branch coding is carried out to extract and fuse features, and then sparse self-attention decoding is carried out to predict a trajectory. The scheme effectively mines a spatiotemporal coupling relationship, reduces a calculation amount, and realizes high-precision and high-real-time prediction of an unmanned aerial vehicle trajectory in a complex environment.

This invention discloses a method, device, and medium for flight skill training based on embodied robots, relating to the field of flight training technology. The method includes: performing manipulation correction and convergence teaching analysis on embodied flight training time-series data; determining the current flight training stage through stage anchor point convergence to generate a training stage determination map; generating state change characterization indicators based on the training stage determination map and embodied flight training time-series data through continuous quantitative evaluation; identifying unstable intervals of control strategies during flight training based on the state change characterization indicators; and selecting and executing embodied manipulation based on the uncertain intervals of strategy switching using a state change continuous control strategy selection algorithm, generating a flight skill training evaluation report. This invention improves the stability, relevance, and reliability of flight skill training and evaluation by temporally correlating flight state data and embodied manipulation data.

The application discloses a method for expert review based on a consortium chain, and relates to the technical field of academic review, which comprises the following steps: S1: the central organization invites the review experts to join the network; the method for expert review based on the consortium chain can assign a unique identification to academic achievements through a smart contract, the smart contract calls an expert matching algorithm and an expert node random selection algorithm to match k expert nodes for the academic achievements, and sends the academic achievements to the expert nodes to wait for the expert nodes to return the review results; the expert nodes review the academic achievements, score the academic achievements from three aspects of innovation, integrity and writing level, call the smart contract to return the review scores and review opinions, the smart contract collects the expert review results according to the unique identification of the academic achievements, calls an academic achievement review algorithm to obtain the final review result, and automatically distributes the academic achievements to the experts in this field for review, so that the distribution is not needed manually, and thus the expert review process is optimized.

Embodiments of the present application provide a kind of sentiment recognition method and device of voice information.The method is to a few minority class voice feature set Random oversampling processing, and the minority class voice feature set after processing is merged with majority class voice feature set, obtains target voice feature set;According to inverse order selection algorithm, the voice feature in target voice feature set is selected, and the voice feature selected is constituted candidate feature set;The voice feature in candidate feature set is input into classifier, obtains the voice emotion category of voice information, and the embodiments of the present application avoid the decision rule that model learns from unbalanced data is too much inclined to majority class sample, still can select optimal candidate feature set by the correlation analysis between features, reduce the feature dimension of initial target voice feature set, improve the computing efficiency of model, and improve the detection performance of model.

This invention belongs to the field of deep learning security technology and discloses a backdoor attack method for video recognition systems based on motion perception and sparse keyframe injection. The invention utilizes a motion-perception-based trigger modulation mechanism, guiding the trigger mode with optical flow information and spatial residuals to dynamically align with video content in the spatiotemporal dimension. The sparse keyframe injection mechanism employs an adaptive keyframe selection algorithm to dynamically select frames for trigger injection based on video content features, thereby improving the stealth and robustness of the attack. The method of this invention has good versatility, is compatible with current mainstream image backdoor attack paradigms, and is applicable to various deep learning frameworks and video recognition models. It can effectively evade the identification of most existing backdoor detection mechanisms while maintaining a high attack success rate, providing a novel adversarial defense verification tool for video security systems and possessing broad application potential and research value.

The application provides a multi-cluster resource scheduling method and system based on graphical modeling, comprising: selecting an algorithm package based on the demand of cluster resource management and establishing a business analysis model in a graphical manner by using a data set constructed by taking each cluster as a node; constructing a DAG task flow according to the business analysis model, and performing task instantiation on the DAG task flow and distributing the task to a big data platform of each cluster; and scheduling cluster resources based on the health state of each node cluster and the task instantiation distributed to each cluster; and the technical scheme of the application uniformly coordinates and allocates resources of a multi-cluster environment, which are heterogeneous and geographically dispersed.

The application provides a method for online updating of parameters of a low-orbit satellite network video code rate selection algorithm, and relates to the technical field of communication and network.The technical scheme of the application comprises the following steps: obtaining the current video download progress of a low-orbit satellite network, determining a fine-grained throughput sampling sequence according to the current video download progress, detecting the network change state of the low-orbit satellite network according to the fine-grained throughput sampling sequence, determining the current video code rate selection algorithm parameters of the low-orbit satellite network according to the network change state, and online updating the current video code rate selection algorithm parameters according to a parameter configuration table.The technical scheme of the application can, when accurately identifying the fine-grained throughput sampling sequence of a low-orbit communication satellite and determining the network change state on the player side, online update the current video code rate selection algorithm parameters determined based on the network change state according to the parameter configuration table, improve the parameter accuracy, and improve the stability of the video user experience quality.

The application discloses a kind of infectious disease network key node identification method based on graph neural network, comprising the following steps: 1, by traditional SIR infectious disease model on the generated network simulates virus transmission process, constructs training data label;2, select the highest score of Kendall coefficient and the score obtained in step 1 is combined with feature, the selected network features are input into the attention model with multiple graph attention layers are introduced, in a variety of artificial networks are trained, to obtain the importance score ranking of each node more suitable;3, consider the "rich club" effect, the obtained ranking sequence is selected by the node selection algorithm based on distance 2-hop, select the node set that the influence range is further expanded.The application provides a new idea and tool for key node identification in complex network, and provides a scientific basis for network optimization and risk prevention.

The present application relates to a kind of interference communication networking method of combination networking strategy and frequency planning, including determining the strategy of constructing subnet according to networking principle, realize tentative networking;Through the frequency point selection algorithm based on greed, solve frequency point combination, verify whether it meets the quantity of tentative networking, according to the corresponding processing of verification result;Set networking evaluation index, sort each networking index score, remove networking requirement from small to large in turn, until the number of networking is in the most frequency point allocation range;According to the initial condition of frequency planning and networking strategy, the frequency interval is divided, and the reasonable allocation of communication equipment is realized according to the principle of selecting and verifying by three-order intermodulation interference verification algorithm.The present application can effectively solve the problems such as co-channel interference, self-interference and three-order intermodulation interference in complex communication networking by efficiently utilizing frequency band resources, combining the designed communication networking strategy and frequency planning method, and improves the flexibility and adaptability of communication networking in complex electromagnetic environment.

This invention provides a method and system for automated classification and dynamic label generation of hydrological data. The method includes: noise identification and data processing of an imbalanced dataset constructed from hydrological monitoring data; obtaining optimized samples by traversing all minority class samples using a local overlap index; reconstructing the distribution and oversampling the optimized samples using a diffusion probability model to form a balanced dataset; using a distance-weighted roulette wheel selection algorithm to probabilistically downsample the majority class samples to form a sampled balanced dataset; using the sampled balanced dataset as the source domain and the original hydrological dataset as the target domain; training a base classifier using a transfer learning algorithm; iteratively training a label classification model based on an improved adaptive boosting algorithm and the base classifier; and inputting the real-time hydrological data to be classified into the label classification model to generate hydrological status labels. This invention can significantly improve the accuracy of identifying early warning events.