115 results about "Mean-shift" patented technology

SOLUTION: Provided is a complex environment GNSS data quality-oriented high-precision data processing method which includes the steps of: preprocessing complex environment GNSS data (S1); searching for large errors in the complex environment GNSS data (S2); searching for the complex environment GNSS data (S21); identifying large errors in GNSS data (S22); processing large errors in the complex environment GNSS data (S3); removing the large errors in the complex environment GNSS data (S31); processing the GNSS data by an interpolation method (S32); analyzing and calculating ambiguities (S4); evaluating the quality of the complex environment GNSS data (S5); fusing multi-source data (S6); performing adaptive adjustment of parameters (S7); handling abnormal situations (S8); and performing real-time monitoring and correction of the complex environment GNSS data (S9).EFFECT: When processing GNSS observation values of complex environments such as a narrow-space environment, strong reflections, and multi-frequency multi-systems, the combination of the mean-shift and dispersion-broadening concepts represents a significant improvement in large-error search and data processing.SELECTED DRAWING: Figure 1

The invention discloses a federated learning client selection method based on reinforcement learning and a federated learning system. The method comprises the following steps: a client performs local detection training and collects loss, delay data and other related data; the central server divides the clients by using mean shift clustering based on data distribution of the clients, and regards each cluster as an independent agent; the intelligent agent dynamically selects a client based on the multi-dimensional state information, and optimization selection is carried out by adopting an exploration strategy; the client uploads model update after local training; and the central server carries out aggregation updating, and the intelligent agent optimizes client selection through a multi-target reward function according to a feedback adjustment strategy, maximizes a model convergence speed and balances communication and calculation overhead. By using the method and the system of the invention, under challenged environments of data heterogeneity, computing resource limitation, communication delay and the like, the client can be intelligently selected, the training efficiency of federated learning is optimized, and the performance and generalization ability of a global model are improved.

The invention provides a space-time intelligent ocean satellite internet of things perception information screening method and device, and belongs to the technical field of data processing. Marine environment data space-time prediction is carried out based on a block time sequence converter, marine hot spot area mining is carried out based on a density clustering algorithm of mean shift, and marine environment data comprises sensing data of sea surface temperature, salinity, wave height and the like; marine environment risk assessment is carried out based on a BP neural network, information screening feedback control is carried out based on multi-agent reinforcement learning, the efficient information processing requirement of a complex marine environment detection scene is met, and the low-redundancy and high-precision data acquisition and transmission adaptive feedback control effect is achieved. The method can be directly applied to an ocean observation and detection facility networking system in the ocean Internet of Things, can provide powerful support in the aspects of military application and civil application, and has wide and important application prospects and value.

The invention discloses a defect edge extraction method and device based on a terahertz image, terminal equipment and a storage medium, and belongs to the technical field of defect detection, and the method comprises the steps: obtaining an original terahertz image; performing enhancement processing on the original terahertz image to obtain an enhanced terahertz image; segmenting the enhanced terahertz image into a plurality of target areas through a mean shift algorithm; for each target area, through a maximum between-class variance method, calculating segmentation threshold values of a foreground and a background in the target area; according to the segmentation threshold, segmenting a foreground region from the target region as a defect region; and inputting the defect area into an active contour model, so that the active contour model generates a defect edge curve according to the defect area. The problems that in the prior art, a defect area needs to be specified manually in advance, and the defect edge extraction error is large can be solved.

The invention provides a water chiller predictive maintenance method and system based on digital twinning, and the method comprises the steps: obtaining the evaporation pressure, suction temperature, refrigerant flow and outlet water temperature parameters during the operation of a main module of a water chiller, and carrying out the synchronous collection through multiple sensors, thereby forming a time series data set; according to the time sequence data set, a sliding window method is adopted to extract the mean shift amount, variance volatility and cross correlation coefficient of evaporation pressure and suction temperature, and a collaborative drift mode reflecting micro leakage is obtained; according to the corrected drift rate and cross correlation coefficient, generating a feature vector of a collaborative change direction among the evaporation pressure, the suction temperature and the refrigerant flow; and by calculating the Euclidean distance between the feature vector of the cooperative change direction and the health baseline, whether the micro leakage reaches the early warning triggering degree is judged, and the strength of an early warning signal is obtained.

The invention discloses a tracking optimization method for detecting a low-speed small unmanned aerial vehicle target by a single-photon laser radar, belongs to the technical field of optical detection and target tracking, and aims to solve the problem of unstable target imaging and tracking caused by speckle noise interference in the prior art. The invention provides a speckle noise suppression method based on a vibration emission optical fiber and a space-time dynamic kernel density estimation algorithm, and the method is combined with an improved mean shift-Kalman filtering algorithm to achieve the tracking optimization of a low-speed small unmanned aerial vehicle target, and obtains an echo signal through a single-photon laser radar. Constructing a three-dimensional data matrix and reconstructing distance and intensity images through space-time filtering and kernel density estimation; and then, in combination with gray level histogram probability estimation, similarity measurement and Mean Shift iteration, target area tracking is realized, and finally, a target position is predicted and output by using Kalman filtering. The method is suitable for the fields of long-distance unmanned aerial vehicle detection and monitoring, low-altitude security defense early warning, civil airspace management and control, military anti-unmanned aerial vehicle systems and the like.

Provided are methods and systems for obtaining, by a computer system, a machine learning model, the machine learning model comprising at least one transformer block; generating, by the computer system, one or more estimator based on the at least one transformer block, wherein at least one estimator comprises a mean shift estimator; and wherein at least one estimator comprises a dispersion shift estimator; training, by the computer system, the one or more estimators to obfuscate input data for the machine learning model; and storing, by the computer system, the trained one or more estimators in memory.

The invention discloses an image recognition method and system for tumor three-dimensional positioning, and relates to the technical field of image recognition. The method comprises the steps of extracting modal features of a CT image and an MRI image, performing alignment in a manifold space, performing weighted fusion to obtain a fusion feature tensor, calculating spinor representation of each position through a spinor deformation field generation network SpinNet, and generating a deformation field by using a fractional order ordinary differential equation; mapping the MRI image to a CT image space through a deformation field to generate a registration fusion image, and performing tumor region segmentation to generate a tumor probability graph; and calculating a tumor center coordinate according to the tumor probability graph and the dynamic image sequence, fitting a motion track, and generating a three-dimensional recognition image. The accuracy and robustness of tumor segmentation are improved through multi-modal feature matching and non-exchangeable geometric modulation fractional order U-Net, precise modeling of tumor space-time motion is achieved in combination with hyperbolic space Mean Shift clustering and quaternion spline interpolation, and a solid technical basis is provided for dynamic navigation and motion compensation.

The invention particularly relates to a sea surface oil spill identification and oil-like film target distinguishing method based on an optical flow method. The method comprises the following steps: acquiring a plurality of frames of time sequence remote sensing images including a seawater oil spill area; based on the first coordinate corresponding to the suspected oil spill area of the previous frame of time sequence remote sensing image, using a maximum absolute value error criterion to obtain a second coordinate corresponding to the suspected oil spill area of the next frame of time sequence remote sensing image; calculating a moving vector between the first coordinate and the second coordinate to obtain an optical flow speed of the suspected oil spill area; performing adaptive clustering on each suspected oil spill area by adopting a mean shift algorithm, and outputting optical flow features of each suspected oil spill area; and analyzing the optical flow characteristics of each suspected oil spill area, and judging the actual seawater oil spill area. According to the method, the motion characteristics of the oil film and the oil film-like target in the time sequence remote sensing data are analyzed, the oil film and the oil film-like target are distinguished, false positive and false alarm during oil spill identification are effectively reduced, and the oil spill identification precision is improved.

The invention relates to the technical field of agricultural remote sensing classification, and provides an intelligent field extraction and fusion method for high-resolution satellite images. The method comprises the following steps: step 1, collecting high-resolution satellite images of different areas and preprocessing the high-resolution satellite images to obtain a high-resolution satellite image data set; 2, constructing a high-resolution image field intelligent extraction and fusion network, and training the high-resolution image field intelligent extraction and fusion network by using the high-resolution satellite image data set to obtain a high-resolution image field intelligent extraction and fusion model; wherein the high-resolution image field intelligent extraction fusion network comprises a cultivated land information intelligent extraction module, a cultivated land boundary multi-task deep learning module and an improved mean shift multi-scale segmentation module. According to the method, layered extraction of cultivated land information extraction, cultivated land block boundary determination and crop field block segmentation is carried out in the cultivated land fragmentation area by fusing multiple remote sensing methods, and the refined extraction requirement of precision agriculture can be met.

The invention discloses a two-stage parameter adaptive optimization honeycomb lung focus segmentation method, a storage medium and equipment, and relates to the technical field of honeycomb lung image processing methods. The method comprises the following steps: image preprocessing: acquiring a honeycomb lung CT image, preprocessing the CT image, and optimizing a preprocessing process of the CT image by adopting a mean shift algorithm; coarse segmentation: performing lung coarse segmentation on the preprocessed image by using a classification method based on an SNIC algorithm and a random forest and a superpixel classification method; and fine segmentation: performing fine segmentation on the coarse segmentation result and the original CT image based on an adaptive optimization network SRU-Net. The method can improve the perception capability of the model for focus boundary details, and improve the segmentation precision and stability.

The embodiment of the invention provides a dike abnormal target object detection method based on infrared and visible light image fusion, which is used for identifying a dike target object needing to be detected in a dike disaster scene, and comprises the following steps: obtaining an infrared image and a visible light image; registering the infrared image and the visible light image by adopting a feature matching algorithm; fusing the registered infrared image and visible light image by adopting an image fusion algorithm to obtain a fused image; and iteratively executing the following segmentation optimization operations until the calculated value of the target function of the mean shift algorithm is minimum to obtain the image segmentation result of the fused image: segmenting the fused image by adopting the mean shift algorithm to obtain the current segmentation result, determining the calculated value of the target function according to the current segmentation result, and obtaining the image segmentation result of the fused image. And optimizing hyper-parameters of the mean shift algorithm according to the calculated value of the target function. According to the scheme, the embankment target object with a small size can be accurately segmented, and the problem of excessive segmentation can be solved.

The invention discloses a product detection method and system based on machine vision, and the method comprises the steps: collecting original image data of a product through machine vision equipment, carrying out the iterative clustering of pixel points through a mean shift algorithm, and dividing an image region; extracting feature parameters such as geometric dimension, surface texture and color of a product, constructing a product feature correlation model in combination with the optimized conditional random field model, and analyzing a feature relationship between regions; and through feature matching verification, identifying normal and abnormal areas of the product, and generating a detection report containing abnormal information. The system comprises an image acquisition unit, a mean shift clustering processing unit, a product parameter extraction unit, a feature correlation modeling unit, a feature matching verification unit and a detection result generation unit which work cooperatively. According to the invention, the algorithm, the model and product parameters are deeply fused, and efficient and accurate detection of products is realized.

The invention discloses an intermittent sampling and forwarding interference suppression method based on phase coding pulse train signal processing, and relates to the field of data processing, and the method comprises the steps: processing a power distribution function of an RD frequency spectrum to obtain a distance-Doppler matrix only containing target information, eliminating a true target protection region, and obtaining a distance-Doppler matrix containing target information; a two-dimensional switching constant false alarm rate detection algorithm is adopted to detect the distance-Doppler matrix only containing the false target, and delay coordinates of the false target are extracted; the delay coordinates of the false targets are clustered through a mean shift algorithm, a window function is constructed according to the clustering center of the false targets, the two-dimensional S-CFAR detection result is processed, the false targets are removed, and a rough detection result of the true targets is obtained; doppler vector spaces of a false target and a true target are constructed, an oblique projection operator is obtained, Doppler filtering is carried out, and a one-dimensional pulse compression result only containing a final detection result of the true target is obtained. According to the invention, fine detection when clutter or interference power is relatively high can be realized.

The invention relates to the technical field of data processing, in particular to a data processing method for an AI data management platform. The method comprises the following steps: acquiring multiple pieces of historical data of each customer in a customer cluster, taking each customer as a historical data point, and clustering the multiple customers in the customer cluster by using mean shift clustering based on the multiple pieces of historical data corresponding to each historical data point to obtain multiple clustering clusters; and for any cluster, analyzing the direction consistency of historical data points in the neighborhood of the updated data points and the spatial distance between the historical data points and the center of the cluster, calculating the classification probability that the updated data points belong to the cluster, and classifying the updated data points according to the classification probability of the plurality of clusters corresponding to the updated data points. The sales data processing method and the sales data processing device have the effects of real-time performance and accuracy in sales data processing.

The invention provides a regional prediction method for periodic weighting of a large-dip-angle working face, and belongs to the technical field of coal mining. According to the method, a twinborn model corresponding to a physical entity is created by using a digital twinborn technology, and collected data are analyzed and predicted in combination with multiple algorithms in machine learning. Regional division is performed on a working face through a Mean Shift clustering algorithm, and an algorithm model is constructed for hydraulic support resistance data of different regions, so that prediction precision and reliability are improved. And meanwhile, the prediction results of the twinborn model and the algorithm model are subjected to consistency test, so that the accuracy and consistency of the prediction results are ensured. According to the regional prediction method for the periodic weighting of the large-dip-angle working face, dynamic monitoring and high-precision prediction of the periodic weighting under complex geological conditions are achieved through cooperation of digital twinning and multiple algorithms, and a reliable basis is provided for coal mine intelligent management, disaster early warning and safety decision making.

The invention discloses a textile image data enhancement method and system. The method comprises the steps of collecting a textile image set, clustering textile texture colors, removing fragments, searching an optimal segmentation threshold value, modifying contour details and globally enhancing. The invention belongs to the field of textile image data enhancement, and particularly relates to a textile image data enhancement method and system.According to the scheme, color domain recognition and mean shift iteration processing are carried out on textile image data, and same-color pattern areas are completely reserved; contour detail modification based on angular point response eliminates sawteeth but does not lose a pattern structure, and the enhancement effect is improved; through an evolution search strategy, four mechanisms of elite seeds, crossover, variation and opposite learning are introduced for cooperation, and local optimum is avoided; through dynamic weight and scale updating, early search space breadth and later focusing fine optimization are ensured; the requirements of the textile image in different application scenes can be better met; and thus, the textile image data enhancement effect is improved.

The present invention relates to the technical field of data processing, and particularly relates to a method and system for intelligent analysis of connector data. The method includes the steps of: processing the parameter items of each data point in the connector data set through a mean shift clustering algorithm to obtain the clustering center of the clustering cluster where each data point is located, and recording the data points within the same clustering cluster as similar data points; obtaining the neighborhood of the data point with the data point as the center; obtaining the central directivity of the data point through the numerical difference between each parameter item of the data point before and after each iteration and the corresponding parameter items of the clustering center; calculating the abnormality degree of the data point; and obtaining the analysis result of the connector data through the comparison result between the abnormality degree of the data point and a preset threshold, effectively improving the accuracy of the obtained analysis result of the connector.

The invention provides a digestive tract tumor detection method and system, relates to the field of medical image detection, and solves the technical problem of low accuracy of tumor boundary detection and automatic positioning in a digestive tract tumor image acquisition process in the prior art. The method comprises the following steps: taking a pan-tumor region image as a training sample of a binary decision tree algorithm, constructing an objective function based on the training sample to obtain a first objective function value, and obtaining a depth threshold value and an optimal classification point of the training sample; optimizing the optimal classification point based on the candidate feature sound to obtain a second objective function value; an unknown digestive tract tumor image is accessed, image points of the digestive tract tumor image are marked through a trained binary decision tree algorithm and a regression forest algorithm, the position distribution probability of the image points is generated, a position distribution thermodynamic diagram is generated based on the position distribution probability, and a tumor core area is obtained through mean shift. And completing the separation of the tumor region and the non-tumor region. The application is used in the digestive tract tumor detection process.

The invention discloses an airborne multispectral point cloud individual tree segmentation method for a dense forest scene, and belongs to the technical field of multispectral point cloud data processing. In order to solve the problem that a point cloud single tree segmentation method used in a dense scene is large in calculation amount and limited in segmentation precision, the method aims at the spectral feature F of a multi-spectral point cloud, the spectral feature F is mapped to a normalized embedding space by using a neural network to obtain a normalized embedding space feature after mapping, and based on the feature, the point cloud single tree segmentation method is based on the normalized embedding space feature. Performing coarse segmentation on all points by using a mean shift algorithm, then performing segmentation optimization based on a point cloud classification prediction result, firstly judging whether a point cluster contains non-tree class points, and rejecting the non-tree class points; and then judging whether the point cluster contains a plurality of tree categories, and if the point cluster contains the plurality of tree categories, further segmenting the point cluster according to the tree categories. On this basis, a vertex offset vector is obtained based on the F; the vertex offset vector is applied to gather the points of each tree to the vicinity of the vertex of the tree.

The application provides a kind of spatio-temporal intelligent marine satellite internet of things sensing information screening method and device, belong to data processing technical field, this method is based on convolution bidirectional long short-term memory neural network realizes marine environment time series data repair, based on block time series transformer carries out marine environment data space-time prediction and based on mean shift density clustering algorithm carries out marine hotspot area mining, the marine environment data includes sea surface temperature, salinity, wave height and other sensing data, based on BP neural network carries out marine environment risk assessment and multi-agent reinforcement learning carries out information screening feedback control, meet the complex marine environment detection scene efficient information processing demand, realize low redundancy, high-precision data acquisition transmission adaptive feedback control effect.The present application can be directly applied to marine internet of things in marine observation detection facility networking system, can provide strong support in military application and civil application, has wide and important application prospect and value.

The invention relates to the technical field of lunar orbit image analysis, in particular to an annular mountain detection method based on orbit camera images, a training data set is constructed based on the lunar orbit camera images, and the training data set comprises an annular mountain edge thermodynamic diagram label, an offset direction label and a radius label based on multi-interval coding; training a deep learning network by using the data set, and setting a network output layer into three branches which are respectively used for predicting an edge thermodynamic diagram, an offset direction and a radius multi-interval; inputting a to-be-detected image into the network, obtaining a prediction result, screening an effective edge pixel set based on the thermodynamic diagram, and performing direction and radius decoding on the effective edge pixel set to generate an annular mountain center candidate point; the mean shift clustering algorithm is adopted to perform instance separation on the candidate points to obtain a ring-shaped mountain detection result, and edge prediction and multi-interval coding are combined to improve the detection rate and positioning precision of weak-edge, small-scale and adjacent ring-shaped mountains.

The application provides a remote sensing target detection sample sampling method and device based on active learning. The method comprises the following steps: performing difference judgment on a plurality of first multi-level features and a plurality of second multi-level features based on a feature difference mechanism, and obtaining difference distance information; obtaining stability results based on a sample consistency mechanism and a second data set; performing weighted summation on the difference distance information and the stability results to obtain uncertainty results of the second data set; obtaining a target category and a target category quantity of a first data set, and obtaining a category sampling weight according to the target category and the target category quantity; generating a sampling score of each second sample in the second data set according to the category sampling weight and the uncertainty results; performing clustering processing on the second samples based on a Mean Shift algorithm to obtain clustering clusters; and taking the second samples with sampling scores meeting requirements in each clustering cluster as target remote sensing sampling samples. The time cost of target detection sample acquisition is reduced.

The present application relates to a kind of cluster collaborative control method based on continuous mean shift theory, belong to involve multi-agent distributed collaborative control field, including three parts: desired configuration figure input and contour fitting, target shape physical parameter determination, distributed configuration control algorithm design based on continuous mean shift theory.The present application adopts Bezier curve to express desired shape, has strong representation ability to complex non-convex shape, target shape data storage is less, reduces memory requirement, and continuous control law can avoid the oscillation possibly caused by grid-based discrete control law.The present application uses distributed computing mode, and multi-agent system can enter desired shape area according to local perception information and actively explore unknown shape area, effectively reduce the requirement to agent communication ability, increase engineering feasibility, have strong robustness and scale expandability.The present application has strong adaptive capacity, and does not need global label and initial position configuration.

The present invention discloses a method, system, device and storage medium for autonomous collaborative exploration of an unknown environment by multiple intelligent agents, which belongs to the field of intelligent unmanned technology. The method comprises the following steps: obtaining a first boundary point set obtained by exploration based on a fast random tree strategy and sending the first boundary point set to a mean shift clustering module, calculating the center point of the cluster, and using the set of center points as a second boundary point set; filtering the second boundary point set by using an information gain function and an obstacle boundary point filter; calculating the benefit of each intelligent agent after reaching the remaining boundary points, processing the benefit by using a discount strategy, and then adding a hysteresis gain to obtain the total benefit of each intelligent agent after reaching each boundary point, subtracting the cost from the total benefit with the Euclidean distance from the intelligent agent to the boundary point as the cost, obtaining the total benefit of each intelligent agent reaching each boundary point, allocating the boundary point corresponding to the maximum value of the total benefit of the intelligent agent to the intelligent agent, and performing autonomous collaborative exploration of the unknown environment after the allocation is completed. The present invention reduces the amount of calculation and improves the operation efficiency and robustness.

The invention relates to the technical field of physiological data processing, in particular to a patient physiological data monitoring method and system based on the Internet of Things. The method comprises the steps that multiple pieces of historical physiological data and physiological data of a target patient are acquired, all the historical physiological data are clustered through mean shift clustering, and multiple clusters are obtained; and calculating the classification possibility of the physiological data belonging to each cluster, and selecting the cluster corresponding to the maximum value in the normalized values of the classification possibility as the category of the physiological data so as to complete the classification of the physiological data. According to the scheme, classified monitoring of the physiological data, collected in real time, of the target patient can be timely and accurately carried out.

The invention discloses a model dynamic compression method and system based on a Mean Shift clustering and SVD decomposition algorithm, electronic equipment and a storage medium. The method comprises the steps that S1, a convolutional neural network model subjected to sparse training is acquired; s2, performing clustering and pruning on the convolutional neural network model after the sparse training by using a Mean Shift clustering algorithm; s3, carrying out Finning training on the convolutional neural network model after clustering and pruning; s4, pruning operation is carried out on the convolutional neural network model after finishing the Finishing training based on an SVD decomposition algorithm; s5, after pruning operation is completed, whether the parameters meet the threshold value requirement or not is judged till a final model is output. After the dynamic clipping scheme is added, the three problems which are originally left can be effectively solved: 1, the size of the model is effectively reduced, so that the model can be deployed in equipment with a relatively small memory; 2, the model parameters can be effectively cut according to the requirements of a hardware deployment platform; and 3, for the pruned model, the performance similar to that of the original model can be effectively ensured.

The invention discloses a multi-extended target joint tracking and classification method based on star convex RHM and LMB filters, and belongs to the field of radar target tracking. According to the method, an LMB parameter set is initialized by using target prior information, and then a sensor measurement set is divided through a mean shift algorithm; then, state prediction is achieved by combining survival target parameter updating and new target parameter sampling, and then measurement updating is completed through LMB-to-GLMB, GLMB updating and GLMB-to-LMB; and then trimming and fusing the LMB parameter set, estimating the number of targets, extracting state information, and circularly executing until observation is finished. According to the method, a star convex RHM modeling expansion state is adopted to reduce dimensions, the low detection probability / high clutter scene performance is improved based on an LMB framework, the tracking classification effect and the real-time performance are both considered, and the engineering application value is high.

The invention discloses a doubly-fed wind power plant equivalence method based on a mean shift algorithm, and the method is specifically implemented according to the following steps: building a doubly-fed wind power generation grid-connected system model, obtaining the power data during the normal operation and fault period of a unit, and determining a grouping index; the grouping index is preprocessed, then the grouping bandwidth is calculated, then data of each wind turbine generator set serves as an initial grouping center, and initial grouping of the wind turbine generator sets is completed through a mean shift algorithm; introducing a Devensenberg index (DBI) as an evaluation index, and optimizing the preliminary grouping result to obtain the number of equivalent units; and calculating equivalent unit parameters by adopting a capacity weighting method according to an optimized grouping result to obtain a wind power plant equivalent model. According to the method, grouping fits the real characteristics of the unit, and the data deviation of the grouped unit is reduced.

The invention relates to the technical field of data processing, in particular to a network risk assessment method for an industrial internet, which comprises the following steps: acquiring a network flow value and a network bandwidth value of the industrial internet in real time; determining a potential abnormal factor at the current moment; determining an algorithm bandwidth demand degree used for carrying out adaptive correction on a fixed bandwidth in a mean shift clustering algorithm at the current moment; determining the self-adaptive bandwidth at the current moment; and carrying out clustering by using a mean shift clustering algorithm so as to realize network risk assessment of the industrial internet. According to the invention, by dynamically adjusting the bandwidth, two extreme problems caused by a fixed bandwidth are avoided, so that the abnormal traffic is identified more accurately; according to the real-time network flow value, historical data and potential abnormal factors, the optimal bandwidth suitable for the current condition is calculated, it is ensured that clustering parameters are adjusted along with changes of the network state, and more flexible and agile risk detection is achieved.