6 results about "Medoid" patented technology

The application discloses a transformer winding looseness fault diagnosis method based on an improved MPE and K-medoids algorithm, which is used for forming a transformer winding looseness MPE value criterion and realizing winding looseness fault diagnosis. The method steps are as follows: 1, measuring points are arranged on a transformer box body, vibration signals of each measuring point are acquired, and a vibration amplitude maximum measuring point D is selected as an optimal measuring point; 2, vibration signals of the transformer winding in different states are measured; 3, a particle swarm optimization algorithm is used to optimize parameters in a traditional MPE algorithm, so that the overall change of the MPE value is stable; 4, the MPE value of the measuring point D is calculated by using the optimized MPE algorithm; 5, the MPE values under two adjacent scale factors are selected as horizontal and vertical coordinates of a clustering coordinate system; 6, the K-medoids algorithm is used in the coordinate system to realize accurate classification of the transformer winding fault type; and 7, the MPE value criterion is summarized and formed, and a database is established. The method reduces the cross aliasing phenomenon of the traditional MPE algorithm, and realizes accurate judgment of the transformer fault type.

This invention discloses a method for evaluating the quality of marine controlled-source electromagnetic data, belonging to the field of data quality evaluation technology. The method includes: projecting the acquired marine controlled-source electromagnetic data onto a two-dimensional plane, whereby the real and imaginary parts of the data signal are projected onto the horizontal and vertical coordinates to obtain the data distribution; performing cluster statistics on the data distribution using the K-medoids clustering method to obtain cluster centers; and using the cohesion (CP) calculation formula to perform aggregation degree analysis on the data distributed according to the cluster centers to obtain the data quality assessment result. This invention can directly quantify and evaluate data based on the cohesion of the electromagnetic data distribution characteristics, without defining empirical functions, requiring no specific data volume, and is unaffected by fly-spot data, thus improving the applicability and accuracy of the evaluation method.

This invention discloses a method for detecting surface defects in aircraft electrical equipment based on a standard sample library. By establishing a structural functional region segmentation model, differentiated weights, sampling rules, and judgment thresholds are configured for regions with different risk levels, achieving accurate detection with high sensitivity in high-risk areas and low false detection rate in normal areas, thus meeting the safety requirements of aviation products. Compared with the basic PatchCore model, the method utilizes HRNet combined with CBAM to maintain high-resolution features while taking into account both local and global features, enabling better detection of minor defects. The method employs weighted K-medoids clustering to construct a memory library, which compresses the memory library size, improves detection efficiency, and avoids uneven feature distribution, thereby ensuring detection accuracy. Based on the anomaly heatmap obtained from PatchCore, the SAM model is used for defect segmentation, which can quantify the size of defects, determine their morphological characteristics, and assess their severity.

This invention provides a multi-source constrained loosely coupled asynchronous INS / UWB indoor positioning method and system, relating to the field of wireless indoor positioning technology. The method includes: acquiring distance observation sequences from UWB base stations and using the EM algorithm and K-Medoids clustering algorithm for optimization to obtain the LOS measurement values ​​of the UWB base stations; collecting acceleration and rotation speed information of mobile nodes, constructing an INS dynamic model and pre-integrating it to obtain the INS pre-integration result; calculating UWB position observation values ​​based on the LOS measurement values ​​of all valid base stations, and performing a validity check by combining the INS pre-integration result; if valid, fusing the UWB position observation values ​​and the INS pre-integration result using an improved adaptive extended Kalman filter to obtain the indoor positioning result; if invalid, using the INS pre-integration result as the indoor positioning result. This invention can ensure the stability and reliability of positioning accuracy in complex indoor environments.

This invention discloses a dynamic optimization and reconfiguration method for distribution networks considering the uncertainties of wind and solar power, comprising the following steps: Step 1: Characterizing the correlation between wind and solar power output using a Copula function, generating an initial set of correlated scenarios using Monte Carlo sampling, and selecting typical scenarios using a two-stage scenario reduction method; Step 2: Constructing source-load equivalent load curves, using dynamic time warping distance as a similarity measure, dividing the all-day equivalent load curves into time periods using a time-constrained k-medoids clustering algorithm, and determining the optimal number of segments using a loss function curve method; Step 3: Constructing a multi-objective function with system operating cost and voltage deviation as objectives, and using the analytic hierarchy process (AHP) for weight normalization to form a comprehensive optimization objective; Step 4: Solving the reconfiguration model using an improved snow ablation optimization algorithm to obtain the optimal network reconfiguration topology for each time period, and performing dynamic reconfiguration of the distribution network based on this topology.

The application provides a typical scene extraction method of a light storage system considering source-load causality, and belongs to the field of electric power. The historical data of light intensity and electrical load are input, and are sequentially subjected to discretization and standardization pretreatment; the pretreated data are used as initial input, the number of clustering clusters is set, and an iterative clustering process is cyclically executed until a convergence condition is met; the iterative clustering process comprises: performing initial clustering and assigning labels, dynamically updating feature weights based on mutual information and a maximum correlation minimum redundancy criterion, clustering by improving a k-medoids algorithm through a weighted Euclidean distance formula using the dynamic weights, and judging convergence according to a double criterion; and finally outputting the cluster centers as a typical scene set. The method can accurately capture the physical matching characteristics of light intensity and electrical load in time sequence fluctuation, overcome the defects of feature information redundancy and susceptibility to extreme outliers in clustering, and significantly improve the information structure restoration degree and intraday time sequence dynamic synchronism of the typical scene.