73 results about "Spatial similarity" patented technology

The invention discloses a traffic flow prediction method and device based on global-local space-time dependency interaction. The method comprises the following steps: acquiring historical traffic flow data; performing data embedding on the historical traffic flow data by using a plurality of embedding modes, and synthesizing the obtained plurality of embedding representations by using a learnable weight to obtain a comprehensive embedding representation of the historical traffic flow data; encoding the comprehensive embedded representation by using a preset local space self-attention mechanism, a similarity-based space self-attention mechanism and a global time self-attention mechanism to obtain a local space feature, a remote space similarity feature and a time feature; fusing the remote spatial similarity feature and the time feature to obtain a preliminary fusion feature, and fusing the preliminary fusion feature and the local spatial feature to obtain a final fusion feature; and the traffic flow is predicted according to the final fusion features, so that the traffic flow prediction accuracy is improved.

The invention provides a slope displacement monitoring data processing system based on an unmanned aerial vehicle laser radar, and relates to the technical field of data processing, and the system comprises the steps: carrying out the spatial interpolation processing of a topographic feature data set, and constructing a digital topographic surface model; the displacement field calculation module is used for performing iterative optimization based on a digital terrain surface model through spatial similarity analysis and fusion with a gradient descent algorithm, calculating a slope surface displacement vector field, identifying a potential sliding surface and a deformation abnormal region, and generating a displacement field calculation result; and the evaluation module is used for inputting a displacement field calculation result into a risk evaluation model and carrying out slope stability quantitative evaluation through a multi-source data fusion analysis platform. According to the invention, the practicability and operability of the monitoring result are improved.

The invention discloses a compliance evaluation method, system, medium and equipment for electric power inspection behaviors, and belongs to the technical field of operation and maintenance intelligentization of an electric power system, and the method comprises the steps: obtaining the track data of an inspector, and preset inspection standard data; calculating a time deviation score and a spatial similarity score according to the trajectory data and the inspection standard data; mapping the trajectory data into a three-dimensional grid, generating a space-time cube, extracting space-time features in the space-time cube through a preset convolutional neural network model, and outputting a space-time score; wherein the spatio-temporal characteristics comprise a motion characteristic, a behavior mode characteristic and a path time sequence mode characteristic; and performing weighted fusion on the time deviation score, the spatial similarity score and the space-time score to obtain an inspection behavior compliance evaluation result corresponding to the inspection personnel. Therefore, through implementation of the method, the problems of spatial-temporal feature splitting and insufficient single-dimension scoring precision during power inspection compliance evaluation in the prior art can be solved.

The invention discloses a landslide-debris flow disaster chain assessment method based on a graph neural network, and the method comprises the steps: generating a composite ground feature unit through the watershed segmentation of a composite curvature field and hydrological analysis, abstracting the composite ground feature unit as a geographic node, optimizing a multi-source environment factor through mutual information screening, constructing a directed weighted graph in combination with spatial similarity, and carrying out the reconstruction of a landslide-debris flow disaster chain. An improved GraphSAGE model which introduces neighbor weight, self-loop and residual error information and cancels random sampling is adopted, a sample set adaptive to a less-data area is constructed through a unit splitting-feature matching-terrain fitting mechanism, and model training and risk prediction are completed. The method solves the problems of single disaster assessment, strong sample dependence and high missed judgment rate in the traditional technology, has excellent performance indexes, effectively reduces the missed judgment risk of a high-risk area, and provides accurate and efficient technical support for disaster chain risk assessment of a small watershed in a small-data mountainous area.

The invention discloses a short-term power prediction method and system based on a wind power plant cluster. The method comprises the following steps: constructing a similarity distance matrix of a key meteorological element time-varying rule between different stations; calculating a Copula entropy between the historical power samples of each station, and constructing a spatial similarity matrix of station power according to each Copula entropy; performing normalization processing on the similarity distance matrix and the spatial similarity matrix, and summing the similarity distance matrix and the spatial similarity matrix after normalization processing to obtain a space-time similarity distance matrix; clustering the similarity distance matrix by adopting a hierarchical clustering algorithm, determining a partitioning scheme in combination with the geographic distribution of the new energy stations, and dividing each wind power plant in the target region into each region; and outputting a total power prediction result of the obtained region according to the LSTM time sequence prediction model. The overall precision and robustness of power prediction are improved, and a more reliable scheduling basis is provided for large-scale access of a new energy station to a power grid.

The invention discloses a cross-device multi-target tracking method and system. The method comprises the following steps: converting vehicle driving data collected by a camera and a radar into trajectory data under a global coordinate system; dividing all the sensing devices into a plurality of adjacent device pairs; checking the time dimension and the space dimension of the upstream track and the downstream track which are respectively formed by the adjacent equipment pair in sequence; and if the upstream track and the downstream track meet verification, splicing the track sequence according to a timestamp sequence to perform fusion of the global track, otherwise, temporarily storing the track as an isolated track independent of the global track. The system is used for implementing the method. The method has the advantages that feature extraction is not needed, the calculation complexity is low, and the real-time processing requirement of a large-scale sensing network can be met. Spatial similarity calculation methods are respectively designed for devices of the same type and different types, the cooperation requirements of mainstream sensing devices in an intelligent traffic scene are covered, and the compatibility is high.

The invention discloses a main earthquake group intelligent identification method considering a micro-earthquake contour coefficient and spectrogram clustering. The method comprises the following steps: 1, acquiring micro-earthquake monitoring data; 2, acquiring a spatial similarity matrix, an energy similarity matrix and a time similarity matrix, and weighting to form a comprehensive similarity matrix; 3, forming a feature matrix based on the comprehensive similarity matrix, and mapping data points in the feature matrix into two-dimensional space coordinates; 4, clustering data points in the two-dimensional space coordinates by adopting a DBSCAN algorithm, and obtaining a neighborhood radius and a minimum sample number optimization combination based on a micro-seismic contour coefficient; and 5, under the optimization combination of the neighborhood radius and the minimum sample number, clustering data points in the two-dimensional space coordinates by adopting a DBSCAN algorithm to obtain a plurality of micro-seismic event optimization clusters. And 6, screening out a micro-seismic event cluster with the highest comprehensive feature score as a main seismic group. The method is reasonable in design, and the main earthquake group is selected by fusing evaluation indexes of space density, energy characteristics and time characteristics.

The invention relates to the field of image sensors, and provides a sudoku difference readout CMOS image sensor based on spatial similarity, and the sensor comprises a key global module which is used for providing a time sequence control signal, a ramp signal and a bias voltage for a key column level module; the key column level module comprises a successive approximation type analog-to-digital converter and a monoclinic type analog-to-digital converter and is used for reading and processing the voltage value of each pixel in the pixel array according to the signals; wherein the pixel array is divided into a plurality of Sudoku units, and each Sudoku unit comprises a central pixel and eight peripheral pixels surrounding the central pixel; the successive approximation analog-to-digital converter is used for quantizing and reading out a central pixel; and the monoclinic analog-to-digital converter is used for quantizing and reading the difference between the peripheral pixels and the central pixel. The problem that extra power consumption and energy efficiency depend on image boundaries due to prediction errors in the prior art is solved, and a low-power-consumption and high-energy-efficiency CMOS image sensor without introducing extra power consumption overhead is achieved.

The invention provides a surface water body dynamic monitoring method based on multi-source remote sensing and spatial similarity reconstruction, and relates to the technical field of remote sensing detection. The method comprises the following steps: acquiring a to-be-reconstructed water body distribution diagram sequence collected by an optical remote sensor; searching a reference image with the smallest spatial structure difference with the to-be-reconstructed water body distribution map in the reference image set, and taking the reference image as an optimal reference water body map of the to-be-reconstructed water body distribution map; and carrying out pixel replacement on a missing region in each to-be-reconstructed water body distribution diagram by utilizing a pixel category in the optimal reference water body diagram, and finally obtaining a reconstructed water body distribution diagram sequence. According to the method, pixel-level completion is carried out on a to-be-reconstructed water body distribution diagram acquired by an optical remote sensor by using a pre-constructed missing-free reference image set, so that the integrity of a time sequence water body information image acquired by the optical remote sensor within a period of time is ensured, and the continuity of water body information in time sequence is further ensured; the monitoring requirements of large-range and high-dynamic change water bodies can be met.

The application provides a method, device and equipment for identifying a relationship between trajectories. The method of the application connects the starting points of two target trajectories and the ending points of the two target trajectories to form a closed trajectory line containing the two target trajectories. The total area and total perimeter of the closed area surrounded by the closed trajectory line are determined, the similarity distance between the two target trajectories is determined according to the ratio of the total area to the total perimeter, the average time difference between the two target trajectories is determined according to the time stamps of the trajectory points in the trajectory point sequences of the two target trajectories, and whether the two target trajectories have a relationship is determined according to the similarity distance and the average time difference, the spatial similarity and the time similarity. The method can accurately identify whether the two trajectories have a relationship, has low computational complexity, and improves the efficiency of the relationship analysis.

The present invention relates to the field of spatial positioning data analysis and application technology, and discloses a geographic named entity matching method and electronic device that integrates spatial semantics. The method comprises: inputting target query address information into a multi-target fusion matching model based on spatial semantics, the multi-target fusion matching model being used to generate a similarity score between the target query address information and candidate address information; the multi-target fusion matching model comprising an encoder and a similarity calculation module; the encoder training process comprising: setting multiple decoders corresponding to an encoding network; adjusting the parameters of the encoding network based on the outputs of the multiple decoders to obtain the encoder; the multiple decoders comprising at least two of the following: a geographic named entity feature segmentation decoder, a geographic named entity matching decoder, and a geographic named entity spatial similarity score decoder. This provides a new geographic named entity matching method that integrates spatial semantics.

The application discloses a dynamic prototype multi-model medical image classification method based on feature credibility evaluation, and relates to the technical field of medical image processing.The method effectively solves the problems in traditional medical image subtype classification, such as lack of high-quality labeled data, high uncertainty of pseudo-labels, and difficulty of static prototypes in adapting to dynamic changes of lesions, and the like.Through construction of a multi-structure feature extraction network and completion of hierarchical feature fusion, the method combines feature cross learning, local attention modeling and expanded convolution to supplement context information, and strengthens semantic consistency and structural continuity of lesion region features.Meanwhile, the method constructs a spatial similarity graph through cosine similarity, and models and fuses a spatial uncertainty graph with any and cognitive uncertainty based on a Dirichlet distribution, obtains a reliable evidence graph through exponential fusion, and generates a pseudo-label with sample-level confidence, so that effective supervision information is accurately screened from a feature level, cumulative deviation of false pseudo-labels is greatly reduced, and stability of a semi-supervised learning process is improved.

This invention provides a flexible production line control method and system based on a large vertical domain model. The method includes: constructing a pruning index combining temporal stability and spatial similarity for the original large vertical domain model to evaluate the redundancy of the activation tensors in the intermediate layers, and performing structured pruning accordingly to obtain a sparse structure model; performing mixed-precision quantization on the pruned model: allocating quantization bits according to the sensitivity of each network layer weight to task loss, and calculating scaling factors and zeros based on the range of non-zero weights to represent sparse weights; calculating the output deviation between the quantized pruned model and the original model under the same input, and training a feedforward network with the former's output as input to fit and compensate for the output deviation; combining the quantized pruned model and the feedforward network to form a composite control model; after deployment, adding the output command of the quantized pruned model to the compensation vector during inference to generate control commands.

The application discloses a multi-source sensing fusion expressway slope geological disaster real-time early warning method and concretely relates to the technical field of safety early warning monitoring, and is used for solving the problem of false consistency of multi-source data leading to missed reports in the early stage of deep concealed disasters of the existing fusion system; abnormal correlation marks are generated by analyzing energy conversion efficiency differential entropy mutation through collecting time series data of slope multi-source geological parameters; a cooperative evolution matrix is constructed under the mark state; the space-time overlapping area of the zero point of the second derivative of the main deformation parameter and the extreme value point of the frequency domain of the auxiliary parameter in the matrix is searched, and the phase change critical state is compared by comparing the geological history envelope line mark; the hidden variable decoupling is carried out on the critical state matrix, and the concealed disaster risk index is generated based on the spatial similarity of the main feature vector and the disaster mode vector; when the risk index continuously exceeds the dynamic critical value and the frequency domain characteristics show non-steady-state evolution, an early warning signal is generated, the deep disaster critical state is effectively identified, and the missed report caused by the safety illusion is avoided.

The invention discloses a tunnel AI event detection system and method based on spatio-temporal feature fusion, and the system achieves the cross-point event merging and repeated filtering through constructing the differentiated feature vectors of congestion, parking and spilled objects and carrying out the weighted calculation of the comprehensive similarity through the three elements of time, space and features, and supports the dynamic adjustment of the spatial similarity parameters. And a filtering rule of crossing three points and low feature similarity is designed for a light object. The method is suitable for real-time detection and repeated alarm filtering of events such as congestion, parking and object throwing in the expressway tunnel, realizes real-time supervision of the events in the tunnel, then effectively analyzes and timely issues the event information, realizes real-time and accurate detection of various events affecting traffic safety in the expressway tunnel, and improves the traffic safety of the expressway tunnel. Therefore, effective services are provided for drivers and conductors and expressway managers. The tunnel traffic safety is ensured; and the traffic service capability is improved.

The invention provides a multimedia data security measurement method based on multi-dimensional association, and the method comprises the steps: dividing a plurality of features of a single image in multimedia data into a plurality of clustering groups based on spatial similarity through a spectral clustering algorithm for the multimedia data; and judging whether the multimedia data is safe or not by combining the content safety score, the privacy information safety score, the semantic safety score and the formalized safety score, thereby realizing optimization of safety evaluation and calculation efficiency.

The present application relates to remote sensing image processing technical field, specifically a kind of tensor singular spectrum analysis method for hyperspectral image three-dimensional feature extraction, including based on spatial self-similarity adaptive embedding, decomposition and low rank representation based on t-SVD, feature image and classification, compared with prior art, through adaptive embedding and t-SVD process, a new adaptive embedding operation, utilize the spatial similarity feature of HSI, jointly utilize target pixel and non-local similar pixel, combine with re-projection operation, keep target inter-class difference while enhanced intra-class similarity, by designing a trajectory tensor, combined with t-SVD, jointly represent the global low rank feature of HSI, the arrangement of similar pixel in trajectory tensor makes it have low rank feature, and further extract low rank feature by truncated t-SVD, realize the feature extraction of three-dimensional hyperspectral image, and then improve the class separability in hyperspectral image classification.

The embodiment of the invention provides a method and device for automatically generating a Chinese address element knowledge graph of a generative large language model, and the method comprises the steps: constructing a segmentation frame of Chinese address elements based on the semantic features of the address elements, and segmenting a complete address into an address element sequence based on the segmentation frame; the method comprises the following steps: defining address elements based on national standards, and constructing an ontology framework of a Chinese address element knowledge graph with a hierarchical structure and semantic constraints based on the definition of the address elements; based on the ontology framework, performing text classification on the address element sequence by adopting a GPT-based light supervision classification method; according to the ontology framework and the text classification, constructing a Chinese address element knowledge graph based on a Neo4j graph database; and adopting a semantic-spatial similarity calculation method to optimize the synonymous and synonymous entities in the Chinese address element knowledge graph.

Industrial equipment and its progressive distillation unsupervised anomaly detection method, device and medium, relate to the technical field of equipment anomaly detection. The method comprises: acquiring monitoring time series data and preprocessing. The time neighbor set of each sample is calculated in the time dimension. The spatial similarity is measured in the spatial dimension, and the BallTree index structure is constructed through the hierarchical spatial division strategy, and the spatial neighbor set is searched. The current sample is normalized by the spatiotemporal weighted fusion strategy. After normalization, the adaptive threshold unsupervised anomaly detection teacher model is input for training. The student network is trained in stages by using the progressive distillation strategy, the teacher model is used to guide the student model learning, and the distillation student model is obtained. The data to be detected is input into the anomaly detection model, the reconstruction error between the input sample and the reconstructed sample is calculated, and the adaptive threshold judgment mechanism is combined to obtain the anomaly detection result.

An embodiment of the present application provides a data processing method, apparatus, electronic device, and storage medium, the method comprising: acquiring a frame image of video data, and determining a feature vector and a frame image combination of a target object within a detection frame of the frame image; determining a feature map based on the similarity between target objects in the frame image combination, wherein the similarity includes feature similarity and / or spatial similarity; determining a classification result of each target object based on the feature map of each target object, so as to determine a movement trajectory of the target object in the video data; and improving the accuracy of movement trajectory recognition.

The application relates to a power transmission line fault maintenance method and system under severe weather. The method comprises the following steps: performing baseline correction on a traveling wave current signal to identify a wave peak interval corresponding to the traveling wave, determining the symmetry deviation of the wave peak interval according to the difference between the trend item components of two sequences, screening a plurality of suspected fault traveling waves according to the fitting goodness of each wave peak interval, the shape similarity between the two sequences and the symmetry deviation, determining the spatial similarity according to the similarity between each suspected fault traveling wave and a plurality of suspected fault traveling waves at the opposite end, determining the incident waves at both ends of the power transmission line according to the energy intensity of the wave peak interval and the spatial similarity, correcting the initial speed of the incident wave according to the preset corresponding relationship and the main frequency of each incident wave, and positioning the fault point according to the corrected speed and the arrival time of the corresponding incident wave. The method improves the fault positioning accuracy and reliability, and reduces the operation and maintenance difficulty of the power transmission line.

The invention discloses a data linkage analysis method and system for a test operation scene, and belongs to the technical field of data visualization and intelligent monitoring. The field type of each data field is determined by identifying the field type of collected data, extracting the multi-dimensional statistical characteristics of each field and classifying the multi-dimensional statistical characteristics; obtaining a value score of each field type, screening out a plurality of field types meeting a test analysis target value score threshold value requirement, and carrying out priority ranking on the screened field types; constructing a search tree according to the multiple field types subjected to priority ranking and the value scores of the multiple field types, and generating multiple analysis paths by traversing the search tree; and obtaining a statistical feature vector of a multi-field type combination corresponding to each analysis path, and obtaining an optimal recommendation chart type corresponding to each analysis path and a binding analysis scheme thereof by determining a spatial similarity between the statistical feature vector and each chart attribute vector in a chart type library. According to the method, the incidence relation between the key parameters can be captured in time.

The invention relates to the technical field of artificial intelligence and clean room intelligent operation and maintenance, in particular to a clean room intelligent operation and maintenance method and system based on artificial intelligence, and the method comprises the steps: collecting a business data source, an Internet of Things data source and spatial topology configuration data; based on a production scheduling plan, a personnel authority and standard operation program, an equipment ledger and maintenance record and spatial configuration data, constructing an ideal reference space-time tensor organized according to a preset time granularity and a spatial partition; performing parameterized disturbance by using an abnormal event operator to generate an abnormal simulation state set; differentiating the real acquisition data and the abnormal simulation state with the ideal reference to obtain a real residual matrix set and a theoretical residual matrix set; a diagnosis result is generated based on the time sequence and space similarity, and an operation and maintenance work order or a to-be-observed record is generated according to conditions; and collecting execution and re-checking results, updating disturbance parameters and distribution thresholds, and realizing accurate identification and disposal of personnel violation, equipment degradation and logistics retention.

The present application relates to the field of seabed terrain reconstruction, and provides a multi-beam sonar seabed terrain reconstruction method, system, device, product and medium, comprising obtaining an approximation coefficient and an approximation coefficient variance, thereby obtaining a state estimation point, obtaining an initial filtering data point according to the state estimation point, and performing adaptive filtering on the filtering data point to obtain a filtering data point; calculating attention weights and spatial similarity parameters of multi-source data, and then calculating spatial similarity; calculating a message vector according to the spatial similarity, obtaining a final node representation based on the message vector, calculating a data fidelity term, a smoothing constraint term and a terrain regularization term, and obtaining fused terrain data; inputting the fused terrain data into an encoder, decoding through a decoder, performing dense skip connection, and obtaining decoding output; calculating a terrain reconstruction total loss, adjusting model parameters according to the terrain reconstruction total loss, outputting reconstructed terrain data, and completing the reconstruction of the seabed terrain.

This application discloses a target matching method, a target matching device, and a storage medium. The target matching method includes: acquiring a first image and a second image of the same monitored area captured by different cameras; obtaining a first target set from the first image and a second target set from the second image; calculating the spatial similarity distance between each target pair in the first target set and the second target set, wherein each target pair includes one target in the first target set and one target in the second target set; and matching the two targets in corresponding target pairs whose spatial similarity distance is less than a first preset distance. Through the above method, this application can match targets in different images by calculating the spatial similarity distance between two targets and comparing it with a preset distance, thereby achieving the fusion of the same target and improving the accuracy of target fusion.

The invention discloses an urban road waterlogging risk sensing equipment layout optimization method based on a clustering algorithm, and belongs to the field of monitoring point optimization layout, and the method comprises the steps: constructing a road-pipe network double-drainage model; constructing a space similarity index matrix and a time similarity index matrix of road nodes in the road-pipe network double-drainage model; performing primary classification on the road nodes by using the spatial similarity index matrix to obtain an initial clustering result; according to the initial clustering result, carrying out secondary clustering by using the time similarity index matrix to obtain a time-space similarity cluster, and according to the time-space similarity cluster, determining the installation number of the road risk sensing equipment in the target area; and calculating the drainage risk and road node traffic connectivity of each road in the road-pipe network double drainage model, and determining the installation position of the waterlogging risk sensing equipment. The technical problem that the layout of the waterlogging risk sensing equipment is different from the actual waterlogging risk area is solved.

The application provides a workpiece defect detection method based on a self-supervised twin comparison network, first, subgraphs are generated by dividing defect-free workpiece images through a sliding window, and a self-supervised similarity matrix label is calculated based on the spatial overlap of the subgraphs and the original graph; meanwhile, the subgraphs and the original graph are respectively subjected to enhancement processing; the enhanced subgraphs and the original graph are input into a twin feature extraction network, first, iterative optimization is performed through an affine adaptive architecture containing a FastMatch layer and an STNs layer: the FastMatch layer matches the features of the subgraphs and the original graph and outputs the highest similarity feature pair, and the STNs layer generates an affine matrix based on a full connection network to calibrate the perspective of the subgraphs; then, mutual convolution operation is performed on the optimized feature vectors to generate an initial similarity matrix; subsequently, a spatial similarity distribution graph is generated through a convolution network, and network model training is completed; during detection, the image to be detected and the reference image are input into the trained model after being aligned in perspective, and the existence of defects is determined based on the bidirectional similarity matching result and a threshold.

The embodiment of the invention provides a scene positioning method, a scene positioning model training method and a scene positioning model-based information processing method, which are applied to the technical field of deep learning, and the scene positioning method comprises the following steps: obtaining a scene image of a target scene; inputting the scene image into a scene positioning model to obtain target address information of the target scene, the scene positioning model being obtained by training based on a first feature similarity and a second feature similarity, the first feature similarity being a feature similarity between a sample image of the sample scene and the sample address information, and the second feature similarity being a feature similarity between the sample image of the sample scene and the sample address information; the second feature similarity is the geographic space similarity between the sample images of the sample scene. According to the method, the geographic information contained in the sample image is utilized, it is guaranteed that close image features in the geographic space have high similarity, far image features in the geographic space have low similarity, the scene positioning model obtained through training is distributed more evenly in the multi-modal feature space of the image and address information, and the accuracy of scene positioning is improved.

The invention relates to the technical field of data processing, and discloses a grid downscaling random forest model construction method based on a spatial similarity theory, and the method comprises the steps: obtaining original sample grid data of a to-be-trained region and reference sample grid data obtained after downscaling the original sample grid data; determining original characteristic variables in the original sample grid data; performing feature screening on the original feature variables based on the reference sample grid data to obtain target feature variables; and inputting the target characteristic variable into the random forest model, and training the random forest model by taking the reference sample grid data as a label until the training is completed. By screening the original feature variables, it is ensured that the variables of the input model can reflect key factors for driving target spatial variation, the most important information for downscaling is reserved while the number of features is effectively reduced, and not only is the refinement of the grid downscaling ensured, but also the efficiency of the grid downscaling is improved.

The invention discloses an airport scene space similarity measurement method and system. The method comprises the following steps: step 1, airport scene taxiway representation: generating vector representation of taxiway road sections and probability distribution between the road sections; 2, airport scene space similarity measurement: calculating airport scene space traffic similarity and space function similarity by utilizing probability distribution between road sections and cosine similarity expressed by taxiway road section vectors; and step 3, airport scene space similarity analysis: based on the calculation results of the space traffic similarity and the space function similarity, revealing the conventional running direction of the airport scene taxiway, and identifying road sections with similar functions. The method can reveal the spatial correlation characteristics of the road section, can be applied to complex airport scene operation mode mining and congestion prevention and control, provides a theoretical basis for airport operation management and optimization decision making, and fills the blank in the field of airport scene spatial similarity measurement.