31 results about "Fuzzy recognition" patented technology

The invention discloses a light guide plate defect detection method and system based on a neural network, and particularly relates to the technical field of machine vision detection, and the method comprises the following steps: aiming at the problem of image instability of a light guide plate in a dynamic transmission or rotation process, continuously collecting an image sequence and extracting time domain features; and performing interference judgment in combination with the inter-frame consistency prediction coefficient and a first threshold to realize accurate identification of the abnormal image frame. For an abnormal image frame, further correcting the recognition credibility of the abnormal image frame by adopting a confidence adjustment and fusion mode, and meanwhile, introducing a frequency domain transformation and image enhancement strategy to compensate detail loss caused by motion blur; according to the method, inter-frame consistency analysis, confidence fusion regulation and control and frequency domain fuzzy recognition and compensation mechanisms are introduced, abnormal judgment and image quality restoration of the light guide plate image in the dynamic scene are realized, the recognition accuracy and stability of the neural network model on the defect type, position and confidence are improved, and the false detection and omission ratio is effectively reduced.

The invention discloses a knowledge graph data intelligent question and answer method and system based on voice activation, and the method comprises the steps: obtaining a voice instruction inputted by a user side, and converting the voice instruction into a natural language text; semantic fuzzy recognition is conducted on the natural language text, and then whether semantic fuzzy exists in the natural language text or not is determined; if yes, generating a voice prompt problem by using a standard entity in the knowledge graph and a corresponding known attribute, obtaining a correction text fed back by a user based on the voice prompt problem, performing named entity recognition on the correction text, and extracting a main entity and a target keyword; if not, named entity recognition is carried out on the natural language text, and a main entity and a target keyword are extracted; calculating the semantic similarity between the known attribute of the standard entity corresponding to the main entity in the knowledge graph and the target keyword; and determining a user intention based on the semantic similarity, generating a query statement for executing a corresponding query operation in the knowledge graph database, and obtaining a corresponding query result.

The invention discloses a camouflage target segmentation method based on multi-scale cross-spectral feature interaction, and belongs to the technical field of computer vision. The implementation method comprises the following steps: 1, constructing a camouflage target detection network; 2, obtaining visible light and infrared dual-mode features for the network subjected to feature extraction; 3, performing multi-layer feature fusion on the visible light image features and the infrared image features; therefore, bimodal complementary information extraction is realized; 4, constructing an edge sensing network for performing detail processing on the edge of the image so as to obtain an edge prediction result; 5, training the camouflage target detection network to obtain training parameters of the camouflage target detection network; optimizing training parameters of the camouflage target detection network; 6, inputting the image pair into the training optimization camouflage target detection network to obtain a predicted camouflage target detection result; compared with the prior art, the accuracy of boundary fuzzy recognition and appearance similarity recognition of camouflage target detection is improved.

The invention discloses a tower crane bolt axial force intelligent ultrasonic detection method and system based on multi-modal data fusion. Firstly, a multi-stage influence factor system is constructed based on a fuzzy hierarchical comprehensive evaluation theory, and a high-risk region identification model is established; and calculating a regional risk value based on the model, and determining a monitoring point distribution scheme. A heterogeneous network composed of a fiber grating strain sensor and an ultrasonic sensor is arranged in a selected area, and strain and ultrasonic data are synchronously collected. And inputting the multi-modal data into a deep belief network for feature fusion, and realizing health state dynamic evaluation through a fuzzy recognition model. And based on an evaluation result, performing fatigue life prediction by adopting an improved rain flow counting method and an accumulative damage theory. And finally, data fusion display and intelligent early warning are realized through the remote monitoring platform. According to the invention, full-chain intelligent health management from risk identification, real-time monitoring to life prediction is realized.

The invention relates to the field of character and image recognition, in particular to a multi-mode intelligent dictation and correction system and method based on dynamic calibration, and the system comprises an acquisition module for obtaining a writing test image based on voice feedback and carrying out character recognition, a feature splitting module for constructing a feature observation group and labeling a structural unit, the layout analysis module selects a recombination structure by sliding a window frame and sets a fuzzy identification tag based on the prior probability of spatial distribution features, and the identification module compares contour features through region mapping to determine difference types. And the calibration module realizes context restoration and dynamic calibration by removing fuzzy blocks, performing mapping comparison with a sample database and replacing contour features. According to the method, the recognition accuracy of complex handwriting such as scribbling and stroke adhesion is improved, and on this basis, the local image content is calibrated through a replacement mechanism based on context restoration, so that the accuracy and reliability of the correction process are improved.

The application discloses a hybrid vehicle energy management device, which comprises a driving style recognition module, a typical road condition recognition module and an energy management adaptive module; the driving style recognition module is used for acquiring driving style reference data in a preset time period, inputting the driving style reference data into a driving style fuzzy recognition model, and acquiring a current driving style output by the driving style fuzzy recognition model; the typical road condition recognition module is used for acquiring driving characteristic parameters of a vehicle in a preset time period, calculating driving characteristic similarities corresponding to various typical road conditions according to the driving characteristic parameters and various corresponding driving characteristic parameters of the various typical road conditions, and taking a typical road condition corresponding to a maximum driving characteristic similarity as a current road condition; and the energy management adaptive module is used for acquiring the current driving style and the current road condition, formulating an electric quantity balance point according to the current driving style and the current road condition, and selecting a proper vehicle driving mode according to a relationship between a current residual electric quantity and the electric quantity balance point.

This invention relates to a DNN fuzzy recognition model training method. The method involves extracting features from input sample objects using an object-oriented DNN fuzzy recognition model, mapping these features to obtain the object's classification probability distribution, and mapping these classification probabilities to membership degrees of fuzzy sets in a fuzzy sample model library. The model is trained by calculating a loss function value based on the classification probability distribution using sample annotations. After one generation of training, the model is adjusted by calculating a loss function value based on the fuzzy set proximity using sample annotations. If the result of either loss function calculation does not meet the preset loss value requirement, the samples in the fuzzy sample model library are randomly shuffled and reordered, and the calculation is iteratively repeated until the results of both loss function calculations meet the preset loss value requirement. This invention also relates to a method and apparatus for identifying hydrological and meteorological elements using a model trained using the above method. This method can simulate crew members automatically identifying hydrological and meteorological elements, improving the accuracy of identification and significantly reducing the workload of crew members.

The invention relates to the related field of document data engines, in particular to a document data engine method and system, which realizes high-robustness field extraction capability by combining a template engine with an AI model, and adapts to multiple types of unstructured documents; field splitting / combination is intelligent, and manual intervention is reduced. Automatic mapping of standard fields and alignment of platform fields are achieved, the adaptation relation between an industry field standard library and multi-system fields is constructed, and the fields can be directly pushed to multiple service systems from original samples; field data quality and consistency guarantee capability are improved, and output fields are more accurate due to field-level difference comparison and format restoration functions; the AI completion and fuzzy recognition mechanism solves the problem that fields are not covered or uncertain in the traditional recognition technology. The system has four characteristics of standardization, rule driving, model assistance and dynamic updating, and the manual operation intensity and the business risk are remarkably reduced.

This application provides a digital grading method and apparatus for paper-based assignments. The grading method includes: performing adaptive partitioning on an assignment image containing student information to obtain a question area and an answer area; the answer area includes an objective filling area and a subjective writing area, wherein the subjective writing area includes a handwritten text area and a handwritten formula area; based on the content characteristics of the target area, calling a detection and recognition model that matches the content characteristics to obtain the recognition result of each answer area; for fuzzy recognition results of the subjective writing area, introducing the writing style characteristics of the current student information to correct the fuzzy recognition results, and updating the recognition results of the subjective writing area based on the correction results; comparing the final recognition results of each answer area with the assignment answers to generate and output the grading results. The grading method and apparatus of this application form a fully automated process from image input to grading result output, requiring no manual intervention and achieving effective automatic grading.

The invention relates to the technical field of self-healing and repairing assistance, and discloses a self-healing and repairing assistance system based on a four-start system, and the system comprises a four-dimensional data collection module which collects the initial four-dimensional data of a user; the four-dimensional data analysis module is used for carrying out independent scoring and correlation analysis on the initial four-dimensional data and determining the short plate dimension of the user; the self-healing repair scheme generation module is used for generating a self-healing repair scheme according to the short plate dimension and the intervention strategy library; the scheme feedback adjustment module is used for acquiring feedback data of the user for executing the self-healing repair scheme and dynamically adjusting the self-healing repair scheme; and the repair effect evaluation module is used for obtaining the final-period four-dimensional data of the user, comparing the final-period four-dimensional data with the initial four-dimensional data, and determining the repair evaluation effect of the self-healing repair scheme. The technical bottlenecks of multi-dimensional cutting, fuzzy recognition, strategy stiffness, fault assessment and the like in traditional growth intervention are solved, and the functions of active prevention, accurate repair and system collaboration are achieved.

Methods and systems are described herein for identifying the location and nature of any blur within one or more images received as a user communication and generating an appropriate correction. The system utilizes a first machine learning model, which is trained to identify blurred components of inputted images and determine whether the blurred components are located in portions of the inputted images comprising textual information. The system may apply a corrective action selected by the first machine learning model, which may comprise stitching blurred images together to a sharp product image and / or some other method appropriate for rectifying images received.

The invention discloses a hydraulic structure safety state evaluation method based on multi-level fuzzy recognition, and relates to the technical field of hydraulic engineering safety monitoring, and the method comprises the following steps: pre-constructing a multi-level evaluation index system which comprises a target layer, a criterion layer and an index layer; by introducing trapezoidal and triangular membership functions, the limitation that a traditional accurate mathematical method cannot describe the continuous safety level is broken through, and the evaluation result is made to better conform to the gradual change rule of underwater engineering damage development. Meanwhile, scattered local data is converted into a unified global safety level through fuzzy comprehensive operation, the data utilization rate and evaluation efficiency are greatly improved, a whole-process scientific support from risk identification, level judgment to maintenance scheme selection is provided for engineering managers, the probability of occurrence of safety accidents is remarkably reduced, and the engineering management efficiency is improved. Reliable technical guarantee is provided for safety management of the whole life cycle of the water conservancy project.

The application discloses a neurosurgery operation scene intelligent understanding method, which is realized through a trained neurosurgery operation scene intelligent understanding network; the network comprises a time coding layer and a space coding layer, and the output end of the space coding layer is connected with at least one fuzzy attention module; the time feature output by the time coding layer and the fuzzy space feature output by the fuzzy attention module sequentially pass through the cross-modal fusion operation of a decoding layer and the dimension reduction processing of a feedforward neural network, and output an operation stage recognition prediction result; the fuzzy attention module evaluates and quantifies the reliable degree of the space visual feature at each position in the feature sequence output by the space coding layer, dynamically adjusts the attention weight and gives the space visual feature to form a fuzzy space feature; the method effectively improves the recall rate and Jaccard coefficient while maintaining high accuracy and high precision, and proves that the method has the effect of significantly optimizing the fuzzy recognition and prediction accuracy.

This invention relates to the field of speech recognition technology, and discloses a speech recognition method, apparatus, device, storage medium, and computer program product. The method includes: constructing a hash mapping table; acquiring the speech to be recognized and recognizing it using a preset speech recognition model to obtain a conventional recognition result; performing precise and fuzzy recognition on the speech to be recognized based on the hash mapping table to obtain precise and fuzzy recognition results; and calculating the target speech recognition result based on the conventional, precise, and fuzzy recognition results. This invention solves the technical problem of poor speech recognition performance in specialized fields or specific industries by calculating the target speech recognition result using the conventional recognition result obtained from the preset speech recognition model and the precise and fuzzy recognition results obtained from the hash mapping table.

The invention relates to the technical field of voice recognition, in particular to an LED multi-dimensional information issuing method based on voice intention recognition, which comprises the following steps of: decoding an input voice signal to generate a decoded word graph, positioning a preset LED multi-dimensional information semantic slot position in the decoded word graph and extracting similar competition paths; and calculating to obtain a high-confidence ambiguity candidate set. When voice signals are processed, similar candidate word combinations can be extracted from an ambiguous recognition path by constructing a decoding word graph and positioning LED multi-dimensional information semantic slots, posterior probability differences are further calculated to form an ambiguous candidate set, and a clear distinguishing basis is introduced in a scene with similar recognition precision but semantic conflicts, so that the recognition accuracy of the voice signals is improved, and the recognition accuracy of the voice signals is improved. And fuzzy recognition caused by path divergence in the word graph is relieved. The clarified questions embedded with the competition words are synchronously presented in a voice and LED dual-channel form, so that the user feedback is more targeted, and the ambiguity clarification efficiency is improved.

This invention discloses a gas channeling early warning method for tight oil reservoirs, belonging to the field of intelligent early warning technology. The method includes acquiring static data, dynamic production data, and geological parameters of the target reservoir; identifying dominant channels and their distribution patterns in the horizontal and vertical directions based on the static data; calculating the gas channeling sensitivity index (GSI) based on the dynamic production data and geological parameters; and triggering a gas channeling early warning when the GSI exceeds a preset threshold. This invention establishes a comprehensive risk awareness foundation by integrating static geological, dynamic production, and engineering parameters. It innovatively adopts a two-level early warning logic combining "static fuzzy recognition evaluation" and "real-time early warning of the dynamic GSI index," achieving a seamless transition from potential risk screening to real-time anomaly capture. This enables precise location of high-risk layers at the fracturing cluster level, prediction of the probability of gas channeling in the next 7-15 days, and visual diagnosis of channeling directions, providing direct and quantitative decision-making basis for plugging agent selection and well location optimization.

This invention provides a short text clustering and fuzzy recognition algorithm for large-scale online subgraph sampling, comprising the following steps: Step S1, extraction and preprocessing of training samples; Step S2, construction of the neural network; Step S3, overall clustering prediction; Step S4, fuzzy sample recognition; Step S5, retraining of the neural network. This invention combines short text clustering with a large language model, which not only improves clustering accuracy but also enables the handling of clustering tasks with different themes and classification requirements, significantly reducing the manual cost of data annotation. Furthermore, this invention can annotate fuzzy samples for classification, using K-nearest neighbors combined with minimum spanning trees to assist subgraph sampling in the selection scheme. This utilizes sparse structure to reduce computational costs and exposes the fluctuations of boundary samples through spectral clustering, providing a more comprehensive perspective for fuzzy sample selection and improving the accuracy and interpretability of the clustering results.

The invention discloses a medical consumable abnormal data type-2 fuzzy screening method based on cross-domain collaboration and a heterogeneous map, and belongs to the technical field of medical information processing. According to the method, professional extraction is carried out on medical consumable data features through a medical consumable attribute feature cleaning and extracting system based on a language processing model. Introducing an abnormal collaborative reasoning model based on first-class transfer learning and deep domain confrontation, and carrying out preliminary study and judgment on the distribution of data features; and in combination with a built heterogeneous semantic knowledge graph information fusion framework based on a knowledge graph and a graph neural network, carrying out deep anomaly detection and correction attempt on a product generating primary anomaly. And after the repair is completed, performing deep discrimination on the corrected abnormal product through an identification and analysis model based on type-2 fuzziness. The method is suitable for multi-dimensional information fusion, knowledge enhancement modeling, multi-section uncertainty discrimination and high-precision and extensible medical consumable data anomaly intelligent screening application requirements in a medical consumable treatment scene.

The invention relates to the technical field of remote sensing image processing, in particular to a river surface feature recognition method based on multi-modal remote sensing data fusion. The method comprises the following steps: collecting a visible light image and laser point cloud data of a river channel region, generating a digital orthoimage map for the visible light image, and constructing a digital surface model after performing noise reduction processing on the point cloud data; normalizing the elevation value of the digital surface model, performing spatial registration with the digital orthoimage, and constructing four-channel fusion data containing three channels of red, green and blue and an elevation channel; and calculating a mean value and adding Gaussian noise disturbance to generate an initial weight of a fourth channel, and constructing an improved network adaptive to four-channel input through channel dimension splicing. The invention provides a four-channel adaptive DeepLabV3 + improved network, seamless adaptation from three channels to four channels is realized through a pre-training weight migration mechanism, and the problem of fuzzy recognition in a complex boundary region is solved.

The application relates to the technical field of data processing systems or methods specially applicable to supervision or prediction purposes, in particular to a fuzzy recognition reservoir flood control safety chain type discrimination system and method, which comprises a storage module, an upstream water quantity estimation module, a downstream water quantity difference value module and a reservoir redundancy analysis module, the upstream water quantity estimation module is configured with an estimation model, the downstream water quantity difference value module is configured with a calculation model, and the reservoir redundancy analysis module is configured with an analysis model. Through the upstream water quantity estimation module, the downstream water quantity difference value module and the reservoir redundancy analysis module, reservoir flood control safety early warning can be realized based on the change of upstream water quantity, the existing technology needs to be combined with multiple data information for estimation, meanwhile, the application can realize simple reservoir flood control early warning in chain type by relying on upstream water quantity data.

The present invention belongs to the field of artificial intelligence technology related to formation recognition, and specifically relates to a multi-agent formation recognition method based on interpolation and fuzzy recognition. The present invention includes a multi-agent formation entering a radar recognition area, the radar scanning and obtaining the coordinates of each agent in the recognition area to obtain a scattered point set; obtaining four vertices from the scattered point set; using a multi-point interpolation method, randomly adding interpolation points between the vertices and adding them to the scattered point set; constructing a formation based on the lines connecting the vertices; using a fuzzy recognition method to identify the constructed formation; multiple iterations, based on the last recognition result, predicting the next membership of each formation through a fitting function, and taking the formation corresponding to the maximum membership as the final recognition result. The present invention uses fuzzy recognition and uses membership functions as a measure of samples and templates, which can better reflect the overall characteristics of the pattern and has a strong ability to eliminate interference and noise in the sample.

The invention relates to the technical field of commodity data management, in particular to a commodity sorting method based on a type-2 fuzzy set and online data. Obtaining online data and offline data of a commodity, and performing data processing and structured integration to obtain commodity associated data; constructing a type-2 fuzzy recognition model to recognize the commodity associated data, and converting the input data into type-2 fuzzy data defined by a membership function; constructing a logic fuzzy rule base, and performing fuzzy logic operation on the type-2 fuzzy data to obtain commodity fuzzy performance data; performing order reduction calculation on the commodity fuzzy performance data to obtain a commodity first-order fuzzy set; performing defuzzification calculation on the commodity first-order fuzzy set to obtain a commodity recommendation score; and sorting the commodities according to the commodity recommendation scores. According to the invention, sorting guidance is carried out on the commodities through commodity recommendation scores.

The invention is suitable for the technical field of artificial intelligence, and discloses a power document intelligent understanding and semantic completion method, system and device based on retrieval enhancement generation and knowledge graph fusion and a medium. Marking a fuzzy recognition area and an information missing area; based on the fuzzy recognition region, the information missing region and the corresponding document context, retrieving and obtaining reference knowledge from an electric power professional knowledge graph; constructing a structured prompt, and generating an identification result of the fuzzy identification region and a semantic completion result of the information missing region by adopting a large language model; performing cross validation on the recognition result and the semantic completion result through a multi-source knowledge collaborative validation mechanism: if the cross validation conflicts, triggering an error correction closed-loop process; and when the cross verification is passed, outputting a final identification result. Knowledge-driven intelligent understanding and complementation are realized, and the terminology recognition accuracy and the information integrity are improved.

The invention relates to the technical field of robot navigation, in particular to an intelligent navigation method for a robot, and solves the problem of fuzzy recognition of irregular obstacles in a traditional method by acquiring an obstacle image in a moving track of the robot in real time, preprocessing the image, extracting motion features and classifying motion types. According to the method, differential judgment logic is adopted for the obstacles of different motion types, the passing mode of the regularly-moving obstacles is determined by integrating the relative motion distance and the detour difficulty, excessive conservative is avoided, and on the other hand, the passing mode of the irregularly-moving obstacles is determined by integrating the relative motion distance and the detour difficulty. An operation uncertainty characterization value is introduced to quantify the motion disorder degree, and by combining real-time relative distance judgment, the detour safety is ensured, so that efficiency reduction caused by excessive avoidance of a low-risk obstacle is prevented, and collision caused by forced detour of a high-risk obstacle is also avoided.

The application discloses a medical consumable abnormal data two-type fuzzy screening method based on cross-domain cooperation and heterogeneous graphs, and belongs to the technical field of medical information processing. The method extracts the professional characteristics of medical consumable data characteristics through a medical consumable attribute characteristic cleaning and extraction system based on a language processing model. An abnormal cooperative reasoning model based on one-class transfer learning and deep field confrontation is introduced to preliminarily analyze and judge the distribution of data characteristics. In combination with a heterogeneous semantic knowledge graph information fusion framework based on a knowledge graph and a graph neural network, a depth abnormality detection and correction attempt is performed on the products that produce primary abnormalities. After the repair is completed, a two-type fuzzy recognition and analysis model is used to deeply distinguish the corrected abnormal products. The method is suitable for the application requirements of multi-dimensional information fusion, knowledge enhanced modeling, multi-section uncertainty discrimination and responsible medical consumable management scenes in high-precision, scalable medical consumable data abnormality intelligent screening.

The invention provides a short text clustering and fuzzy recognition algorithm for large-scale network online sub-graph sampling. The algorithm comprises the following steps: S1, extracting and preprocessing a training sample; s2, establishing a neural network; s3, overall clustering prediction is carried out; step S4, fuzzy sample identification; and S5, re-training the neural network. According to the method, short text clustering is combined with a large language model, so that the clustering accuracy is improved, clustering tasks with different themes and different classification requirements can be processed, and the labor cost of data annotation is greatly reduced; and on the other hand, classified fuzzy samples can be labeled, K-nearest neighbor is combined with a minimum spanning tree to assist subgraph sampling in a screening scheme, the calculation cost is reduced by using a sparse structure, fluctuation of boundary samples can be exposed by means of spectral clustering, a more comprehensive fuzzy sample screening view angle is provided, and the screening accuracy is improved. And the accuracy and the interpretability of the clustering result are improved.

The invention provides a feeding in-place detection method and system based on current multi-order fuzzy recognition, and the method and system replace a physical sensor with current analysis, and achieve the in-place detection of the feeding in-place through the innovative combination of multi-state hierarchical control, fuzzy recognition and a dual-threshold strategy. The three technical bottlenecks of poor anti-interference performance, high transient misjudgment and weak adaptability in industrial feeding detection are overcome, and the cooperative rise of the detection precision, the system robustness and the economic benefit is realized at the same time.

The present application relates to the field of character image recognition, and more particularly to a multi-modal intelligent dictation and correction system and method based on dynamic calibration, which acquires handwriting images based on voice feedback through a set acquisition module and performs character recognition, a feature splitting module constructs a feature observation group and labels structural units, a layout analysis module sets fuzzy recognition labels based on the prior probability of spatial distribution characteristics by sliding window frame selection and restructuring, an identification module determines the difference type by comparing contour features through regional mapping, and a calibration module restores the context and dynamically calibrates by eliminating fuzzy blocks, mapping and comparing with a sample database, and replacing contour features. The present application improves the recognition accuracy of complex handwriting such as messy handwriting and stroke adhesion, and on this basis, the local image content is calibrated through a replacement mechanism based on context restoration, thereby improving the accuracy and reliability of the correction process.

The invention provides a voice instruction recognition method suitable for industrial control. The voice instruction recognition method comprises the steps that a voice instruction input by a user is converted into an original text; performing term standardization processing on the original text to obtain a standard text; performing static matching on the standard text by using the static matching list; if the static matching list is not hit, executing the following multi-stage dynamic matching: matching a standard text by using matching elements containing complete fields, and if the matching is not successful, matching the standard text by using at least two different matching elements containing incomplete fields; and determining the successfully matched content as the identified instruction. According to the invention, a dynamic matching mechanism is added on the basis of static matching when the voice instructions are recognized, and the multi-stage matching engine is adopted to cover the voice instructions with different completeness, so that the dependence of a traditional scheme on fixed sentence patterns is solved. According to the method, the detection effect is efficient and accurate, and the fuzzy recognition and accurate execution performance of the voice instruction in an industrial scene is improved.

The present application relates to a DNN fuzzy identification model training method, which extracts features of input sample objects through an object DNN fuzzy identification model, maps the features to obtain the classification probability distribution of the objects, maps the classification probability equivalence to the membership of the fuzzy set in the fuzzy sample model library, combines sample labeling to calculate the loss function value of the classification probability distribution to train the model, combines sample labeling to calculate the loss function value of the fuzzy set closeness to adjust the model after completing a generation of training, if the result calculated by any loss function does not reach the preset loss value requirement, randomly shuffles the samples in the fuzzy sample model library and reorders them, iteratively calculates, until the results calculated by the two loss functions both reach the preset loss value requirement. The present application also relates to a method and device for identifying hydro-meteorological elements by using the above-mentioned model trained by the method, which can simulate the automatic identification of hydro-meteorological elements by the crew, improve the accuracy of identification, and greatly reduce the workload of the crew.