9 results about "Multidimensional scaling analysis" patented technology

The invention discloses a project cost prediction method fusing MDS-LOF and GBRT. The method comprises the steps of S1, selection and primary processing of project cost data; s2, performing project feature analysis and reserving core data; s3, performing dimension reduction processing by using a multi-dimensional scaling analysis (MDS) method; s4, using a local outlier factor (LOF) algorithm to identify abnormal values and removing the abnormal values; s5, training is carried out by using the GBRT prediction model and the processed data, and a trained model is obtained; and S6, outputting and verifying the trained prediction model. According to the invention, by constructing the adaptive prediction model, the influence caused by sudden policy regulation and control is weakened, so that the project cost prediction result is more in line with the market law of the building construction cost, and scientific basis and reference value are provided for project early-stage decision making and cost prediction of investment subjects such as enterprises and governments in a complex policy environment period.

This invention discloses a positioning method based on single-station ranging with bias reduction using multidimensional scaling analysis. The method first establishes a multidimensional similarity pseudolinear model based on the geometric relationship between the observation station and the target in the measured values; then, it sets an optimization function; and finally, it solves the model using Newton's iterative algorithm. This invention provides a general positioning method based on single-station ranging, achieving higher positioning accuracy compared to traditional pseudolinear and maximum likelihood methods.

The invention provides a UUV cluster positioning method based on merging enhanced multi-dimensional scaling analysis, and relates to the technical field of cluster positioning, and the method comprises the steps: obtaining coordinate information of anchor nodes and an observation distance matrix of a node group, and constructing different reference groups based on reference points; for the same reference group, combining the reference group with nodes which do not belong to the reference group in the node group to obtain sub-combinations, calculating relative coordinates of the nodes in each sub-combination by using an MDS algorithm, and then performing rigid body registration to obtain global relative coordinates corresponding to the reference group; performing alignment processing on the global relative coordinates based on the coordinate information of the anchor nodes to obtain an absolute coordinate set corresponding to the reference group; and performing optimization based on each absolute coordinate set to obtain a positioning result of the to-be-positioned node. According to the method, the position information of the reference point is fully utilized, the ranging error and the channel noise are remarkably suppressed, the positioning effect is improved, and the problem of performance degradation of the traditional MDS in a low-connectivity scene is solved.

Described herein are devices, systems, methods, and processes for improving the accuracy of access point (AP) location in a Wi-Fi network using client device data and AP ranging measurements. APs may be deployed across a specific area. The APs can range to one another and form a matrix of measurements. Techniques such as semidefinite programming or multidimensional scaling (MDS) can be employed to transform these AP-to-AP ranges into a set of coordinates. Client devices in the area may also range to the APs. The client devices may provide their location measurement report (LMR) feedback and geo-position estimation to the network. The client devices-provided data, along with the AP-to-AP matrices, may be returned to a location server. The location server can use the data to refine the accuracy of the AP-to-AP graph and ascertain the most probable geo-position of the APs.

A structure estimation method (S1) comprising: a step (S12) for generating a recurrence plot from information about the structures of spatially adjacent moieties of a molecule by representing the primary sequence of the molecule on the time axis; a step (S13) for generating a distance matrix that provides the distance between any two vertices of the recurrence plot; a step (S14) for reconstructing time-series data by using multidimensional scaling with respect to the distance matrix; and a step (S15) for reconstructing the three-dimensional structure of the molecule from the reconstructed time-series data, wherein the distance matrix has, as a component, a post-transformation distance obtained by transforming a Jaccard distance related to the recurrence plot, and the post-transformation distance is obtained as a solution or an approximate solution of an equation in which said post-transformation distance is defined as a variable.

This invention provides a UUV cluster localization method based on merged enhanced multidimensional scale analysis (MDS), belonging to the field of cluster localization technology. The method includes: acquiring the coordinate information of anchor nodes and the observation distance matrix of the node group; constructing different reference groups based on reference points; for the same reference group, combining the reference group with nodes in the node group that do not belong to the reference group to obtain sub-groups; calculating the relative coordinates of nodes within each sub-group using the MDS algorithm, and then performing rigid body registration to obtain the global relative coordinates corresponding to the reference group; aligning the global relative coordinates based on the coordinate information of the anchor nodes to obtain the absolute coordinate set corresponding to the reference group; and optimizing based on each absolute coordinate set to obtain the localization result of the node to be located. This method fully utilizes the positional information of the reference points, significantly suppresses ranging errors and channel noise, improves the localization effect, and solves the performance degradation problem of traditional MDS in low-connectivity scenarios.

The invention discloses a method for analyzing raw material-structure-strength correlation of a geopolymer material based on deconvolution quantification and dimension reduction, and belongs to the technical field of comprehensive utilization of solid wastes. The method comprises the following steps: obtaining component content data and element proportion data in a solid waste raw material under different raw material ratio conditions, mechanical strength data of a geopolymer material, and oxygen bond energy spectrum peak information, silicon spectrum peak data and aluminum spectrum peak data of the geopolymer material; the proportion data of the oxygen bond type, the silicon type and the aluminum type of the geopolymer material are obtained through deconvolution calculation; performing standardized conversion on the obtained data to obtain dimensionless standardized data; and carrying out correlation analysis and non-metric multi-dimensional scaling analysis on the obtained dimensionless standardized data, and analyzing the incidence relation among the raw material components, the microstructure and the mechanical strength of the geopolymer material. According to the method, the incidence relation between different raw material components of the geopolymer material prepared from the complex solid waste and the mechanical strength of the microstructure can be accurately analyzed.

This invention discloses a method, system, platform, and storage medium for intelligent identification and processing of adverse geological bodies in tunnels based on an improved leech optimization algorithm fused with near-infrared spectroscopy. The invention deeply integrates the improved leech optimization algorithm with near-infrared spectroscopy detection technology to construct a complete intelligent identification system for adverse geological bodies in tunnels. The optimization algorithm is used to globally and collaboratively optimize key model parameters throughout the spectral data processing process, improving the dimensionality reduction effect of Multidimensional Scaling Analysis (MDS), the outlier removal capability of Fuzzy C-means Clustering (FCM), and the classification accuracy of Least Squares Support Vector Machine (LSSVM). Ultimately, in complex tunnel construction environments, it achieves rapid and accurate identification and real-time early warning of four types of adverse geological bodies: cavities, faults, fracture zones, and water-rich areas, improving the reliability of geological prediction and the safety of tunnel construction operations.

The invention discloses a deposition path process fingerprint decoding method and system based on a complex source-sink system, and relates to the technical field of tectonic sedimentology and data modeling crossing. Target strata geochemical characteristic data of a research area are collected and arranged, and a large deposition geochemical data set of the research area is established; and establishing standard stratum characteristics and sedimentary geochemical characteristics thereof in combination with a typical stratum profile of the basin. According to the method, the multi-source supply and re-cycle contribution proportion on the geologic source-sink system deposition path is quantitatively recovered through establishment of target layer series multi-dimensional scaling analysis, homogenization degree analysis, fingerprint decoding parameter PCA data statistics and the like, the defects that a traditional source-sink analysis method depends on prior end member selection and is easily influenced by personal errors are overcome, and the method is suitable for large-scale analysis of the geologic source-sink system deposition path. The inverse Monte Carlo model is utilized to carry out iterative simulation for multiple times, the simulation effect is gradually optimized, the accuracy and reliability of the deposition path and process description result are ensured, and a more scientific and accurate technical means is provided for geological research and resource exploration.