3292results about How to "Increase credibility" patented technology

The invention provides a private domain live broadcast data storage and visitor authentication method and system based on a block chain, and relates to the technical field of data storage and access control, and the method comprises the steps: collecting multi-mode biological characteristic data of a visitor, carrying out the feature extraction and fusion through a deep neural network of a space-time attention mechanism, and carrying out the authentication of the visitor; authenticity verification is carried out in combination with an adversarial generative network, a dynamic identity token is generated, and a hash value of the dynamic identity token is written into the block chain. The method comprises the following steps: performing semantic analysis on live broadcast data flow through a self-adaptive content perception model, constructing a double-layer access control matrix according to a data sensitivity level and a credibility score, performing self-adaptive fragmentation and hierarchical encryption based on federated learning, and then storing. After an access request is subjected to identity verification, an optimal edge node is selected according to access authority and network performance parameters to construct multi-path transmission, and a transmission strategy is generated based on a service quality score and a data sensitivity level. And when the service quality is lower than a threshold value, triggering the smart contract to perform fault migration.

The invention provides a power plant intelligent early warning method and system based on big data, and relates to the technical field of power systems, and the method comprises the steps: generating a high-dimensional feature map, generating a multi-modal feature tensor through adaptive chaotic mapping, constructing a feature relation network, generating a time sequence multi-dimensional feature vector, and reconstructing a multi-dimensional state vector based on topological entropy. In combination with a topological data analysis method, carrying out high-dimensional topological space mapping on the multi-dimensional state vector, identifying an operation mode of the power plant equipment, calculating a fuzzy region and a mode abrupt change point of the operation state of the equipment, and obtaining a dynamic evaluation result of the health state of the equipment; a multi-level recurrent neural network is combined, a prediction result is obtained through multi-scale time sequence prediction, uncertainty corresponding to the prediction result is quantitatively solved based on a quantum probability graph model, quantum probability distribution of an early warning signal is generated, a real-time operation strategy of equipment is adjusted, and an intelligent early warning signal of the power plant equipment is generated.

The invention provides a satellite measurement and control data anomaly detection and intelligent diagnosis early warning method and system, and relates to the technical field of satellite measurement and control, and the method comprises the steps: obtaining multi-source heterogeneous data in a satellite measurement and control system, and carrying out the preprocessing of the multi-source heterogeneous data; constructing a multi-level space-time fusion network model, inputting the preprocessed multi-source heterogeneous data into the multi-level space-time fusion network model, and extracting high-dimensional space-time features; based on the extracted high-dimensional spatial-temporal features, designing a dynamic adaptive anomaly detection algorithm, and performing anomaly detection on the satellite measurement and control data; according to an anomaly detection result, performing intelligent diagnosis by using a pre-constructed multi-level satellite system knowledge graph; based on the intelligent diagnosis result, multi-level early warning information is generated; a distributed edge computing architecture is designed, and partial data processing and anomaly detection tasks are deployed to a ground station and a satellite end; and presenting the generated multi-level early warning information through a visual interface, and automatically triggering a corresponding emergency plan according to the early warning level.

The invention discloses a method for inverting key parameters in an underground water pollutant biodegradation numerical model based on a neural network algorithm. Carrying out preprocessing and refined dimension reduction on the data, and processing high-dimensional multi-scale data; building a multilayer neural network architecture under a physical information neural network framework, embedding a related reaction equation of a pollutant natural attenuation process, and coupling a biochemical reaction mechanism and a data driving model; a neural network model is trained, errors in the training process are calculated, and the accuracy of an inversion result is improved by using a sub-when optimization algorithm; integrating observation data and a model prediction result, determining final parameter setting of a numerical model, and carrying out underground water damage real object quantification; comparative verification of a neural network parameter inversion model is carried out, and feasibility and accuracy of the parameter inversion method are ensured. According to the method, the problem that the key parameters of the biodegradation numerical model are difficult to accurately invert in the real object quantification process of underground water pollution under complex geological conditions is solved.

The invention provides an unstructured file parallel synchronization method and system, and relates to the technical field of file synchronization, and the method comprises the steps: carrying out the intelligent partitioning of a target file through a pre-training file partitioning model, and generating a double-layer cascade index number and a data hash value; and then, according to the data transmission priority model, transmitting the data sub-blocks to a target end database through a self-adaptive parallel transmission channel, and performing asynchronous verification. And finally, recombining the data sub-blocks based on the double-layer cascade index number and the data block association graph by utilizing a pre-training file recombining model, generating a synchronous target file, and submitting confirmation information to the block chain network. According to the method, the unstructured file synchronization efficiency can be improved, the data integrity and security are guaranteed, and the network transmission pressure and the storage cost are reduced.

The invention provides a community management method and system based on an unmanned aerial vehicle, electronic equipment and a storage medium, and relates to the field of community management. The method comprises the steps of obtaining an initial inspection task of an unmanned aerial vehicle cluster and planning an inspection route, and controlling the unmanned aerial vehicle cluster to execute inspection operation to obtain multi-source inspection data. And constructing a community portrait based on the inspection data, identifying community management hidden dangers through a classification reasoning model, and generating an early warning event and a processing task. A processing task is decomposed into hierarchical sub-tasks by utilizing an ontology reasoning framework, a task execution main body is selected, and a task set is generated. And a multi-Agent reinforcement learning framework is adopted to carry out space-time collaborative arrangement on the task set to form an overall collaborative scheme, the overall collaborative scheme is converted into an interpretable scheduling decision instruction, and finally the unmanned aerial vehicle is controlled to execute the task. According to the technical scheme, the emergency response speed of security and protection and the capability of coping with complex conditions are improved.

The invention discloses a block chain-based digital right encryption management method and system, and relates to the technical field of digital right security management. According to the method, a unique identifier is generated for digital asset information through a Hash algorithm, data privacy is ensured by using asymmetric encryption, and asset uniqueness identification and information encryption protection are realized; according to the method, encrypted asset information is subjected to uplink evidence storage through an intelligent contract, node verification is carried out by adopting a consensus algorithm, tampering resistance, authenticity and transparency of the asset information are analyzed, authenticity of digital assets and safety and reliability of storage are guaranteed, legality of ownership transfer is verified through a consensus mechanism when the digital assets are transacted or transferred, and security and reliability of the digital assets are guaranteed. It is ensured that the transaction meets the preset compliance standard, the asset state on the block chain is updated in real time, and the security and compliance of the transaction are improved; the asset ownership history and the transaction path are tracked and recorded through the traceability mechanism of the block chain, and the traceability and transparency of the asset circulation process are realized.

The invention provides a space competition situation threat level evaluation method and system based on credibility weighting, and relates to the technical field of information, and the method comprises the steps: constructing a threat feature vector based on historical orbit data and an operation state, and obtaining an initial target threat probability vector through a deep learning model; then, a credibility weighting coefficient is calculated based on the multi-source situation awareness data, and an initial threat probability vector is corrected in combination with a feature weighting coefficient; and finally, classifying target threat levels by adopting an integrated learning method, determining a target priority in combination with the target orbit parameters and an ant colony algorithm, and outputting a threat target sequence and a situation threat assessment report. By fusing the multi-source situation awareness data and the credibility weighting mechanism, the threat level of the space target can be evaluated more accurately, and the reliability and efficiency of space situation awareness are effectively improved.

The invention relates to the technical field of law enforcement intelligent alarm, in particular to a multi-mode intelligent analysis method and system based on law enforcement supervision. The method comprises the following steps: acquiring a law enforcement scene video file from a law enforcement supervision recorder platform, and performing audio and video segmentation and law enforcement behavior information identification analysis to obtain law enforcement scene audio behavior information data and law enforcement scene image behavior information data; performing timestamp and spatial position extraction processing on the law enforcement scene video file, performing multi-modal time-space synchronization association analysis, and performing law enforcement behavior sequence connection processing and event behavior intention recognition analysis at the same time to obtain law enforcement scene event behavior intention information data; and obtaining a preset law enforcement event behavior knowledge graph, and carrying out behavior normative score calculation and nonstandard behavior intelligent alarm optimization analysis to generate a law enforcement scene event nonstandard behavior optimization suggestion report. According to the invention, the accuracy of law enforcement nonstandard behavior supervision alarm can be improved.

The invention proposes a GIS equipment partial discharge mode recognition method based on deep learning, and relates to the technical field of GIS partial discharge recognition, and the method comprises the steps: collecting a partial discharge signal of GIS equipment through an ultrahigh frequency sensor, and carrying out the signal conditioning; performing digital sampling and processing on the conditioned partial discharge signal to generate a phase-resolved pulse sequence (PRPS) map, and performing intelligent noise reduction processing on the PRPS map, the PRPS map including three-dimensional information of phase, amplitude and frequency; inputting the PRPS atlas into a preset neural network model, and extracting atlas features; and identifying and classifying the partial discharge type based on the extracted map features, and outputting an identification result. According to the method, automatic acquisition, intelligent processing and accurate identification of the partial discharge signals are realized, and compared with a traditional manual identification method, the identification efficiency and accuracy are greatly improved, and reliable technical support is provided for state monitoring and fault diagnosis of GIS equipment.

The invention provides an AI-based automatic identity card image cutting and OCR recognition method and system, and relates to the technical field of image processing, and the method comprises the steps: collecting a multi-angle depth image and a visible light image, reconstructing a three-dimensional model of a certificate through a three-dimensional point cloud registration network, and achieving the perspective correction of an image through the combination of attention posture estimation and projection matrix optimization; a double-flow memory neural network is adopted to carry out information area positioning, a feature extraction backbone network constructs a multi-scale feature pyramid, and a dynamic memory updating network carries out feature matching and dictionary updating based on a time sequence attention module and comparative learning; and performing character recognition on the segmented information sub-images through a multi-branch feature enhancement network, improving the recognition precision in combination with a self-attention enhancement unit and a context reasoning unit, and dynamically optimizing network parameters by using a reinforcement learning controller until a preset recognition credibility threshold value is reached.

The invention relates to the technical field of geological disaster intelligent monitoring, in particular to a geological disaster intelligent monitoring system and method based on multiple sensors. The method comprises the following steps: covering a monitoring area through multiple sensors, and collecting and summarizing environment data; separating effective signals and noise by adopting a space-time adaptive decomposition method, completing data denoising by combining a signal-to-noise ratio dynamic threshold and a local weighted regression smoothing trend residual error, then aligning time and dimensions through linear interpolation and standardization, and integrating multi-sensor data based on an adaptive weighted fusion method; the fused data is subjected to generation and review of a to-be-reviewed mark, so that the data integrity is ensured; key features are extracted, a mixed model based on LSTM and a random forest is constructed to evaluate disaster risks, and a risk grade division rule is set; and finally, outputting fusion data and an analysis result, and generating an early warning signal and a decision suggestion for potential geological disasters. According to the invention, accurate monitoring and risk assessment of geological environment changes are realized.

The invention relates to the technical field of airport baggage management, in particular to an airport baggage tracking system and method based on a block chain, and the method comprises the steps: baggage information collection and identification: collecting basic baggage information through an intelligent tag, encrypting the basic baggage information to generate a unique identifier, and recording the unique identifier in the block chain; luggage state updating: collecting state change information by using the Internet of Things equipment, and writing the state change information into the block chain after verification of the smart contract; anomaly detection and prediction: simulating and analyzing a luggage state by means of a generative adversarial network, and triggering an alarm and updating the block chain if an anomaly is found; path optimization: adopting a reinforcement learning algorithm to optimize a transportation path according to a real-time state and historical data and recording the transportation path; and performing anomaly analysis and response, analyzing path anomaly based on the graph convolutional network, and processing and recording according to a strategy. The data security and transparency are guaranteed by using the block chain technology, the luggage management efficiency is improved by combining various advanced algorithms, the abnormal risk is reduced, the service experience is optimized, and the method is suitable for the field of airport luggage management.

The invention relates to the technical field of machine learning, in particular to a supervised time sequence water level data generation method and system and a storage medium. The method comprises the following steps: acquiring multi-source hydrological monitoring data; performing abnormal value identification and data preprocessing according to the hydrological monitoring data to generate standardized hydrological basic data; constructing a data generation model of the conditional generative adversarial network based on the standardized hydrological basic data; hydrological variable driven condition feature regulation and control are carried out on the data generation model, parameter optimization of the generation network is carried out, and hydrological adaptability enhanced condition input feature data are generated; and injecting the condition input characteristic data into the data generation model to generate initial water level time sequence data. According to the method, the rare extreme hydrological events are oversampled by using a hierarchical importance sampling and self-service method (Bootstrap), so that the performance of the generative model in rare event prediction is remarkably enhanced.

The invention discloses a deep interpretable skin disease identification method based on small sample learning, which belongs to the field of image processing, and comprises the following steps of: respectively inputting similar or heterogeneous skin disease images into a double-branch small sample contrast learning network, segmenting a lesion area of the skin disease image to generate lesion segmentation masks, extracting shape information in the lesion segmentation masks, and identifying the shape information of the lesion segmentation masks; positioning a minimum square frame containing a lesion area, and performing clipping and zooming to obtain a lesion image; extracting the depth features of the branches corresponding to the lesion images of the two branches, calculating the contrast loss of the depth features of the double branches to optimize network parameters, and extracting multi-scale texture information at the same time; metadata containing shape information and texture information and depth features of an image are subjected to multi-modal fusion for skin disease identification, and through combination of a multi-stage deep learning network and medical diagnosis logic, features such as form and texture of a lesion area are extracted and fused step by step. The classification accuracy of skin disease identification and the interpretability of the model are improved.

The invention provides a wind power plant monitoring image automatic analysis and fault prediction method, system and device and a medium, and the method comprises the following steps: 1, collecting image data, sound data, vibration data, SCADA system data and environment data of a wind power plant, and forming multi-modal data; step 2, carrying out preprocessing on the obtained multi-modal data; 3, extracting features from the preprocessed multi-modal data, and fusing the features to obtain fused features; step 4, performing fan fault diagnosis and prediction based on the fused features to obtain a fault diagnosis and prediction result; according to the invention, end-to-end training of fault type identification and severity evaluation is realized, the accuracy of diagnosis and prediction is improved, and the workload of manual processing is greatly reduced.

The invention provides a digital asset security verification and information monitoring method and system, and the method comprises the steps: carrying out the classification of digital assets through employing a multi-dimensional classification model according to the type attributes of the digital assets, obtaining the classification labels and feature vector representations of the digital assets, and building a digital asset feature database; obtaining transaction historical data of the digital assets from the digital asset association map, discovering association rules and abnormal modes of digital asset transactions by using a time sequence mode mining technology, and forming a transaction verification rule base; according to the data structure of the algorithm adaptation layer, an intelligent data analysis and conversion technology is adopted, heterogeneous digital asset data is mapped to a unified feature space, standardized digital asset data is generated, and a digital asset feature database is formed; and performing deep verification analysis by using the digital asset feature database to obtain security attribute information of the digital assets, and performing dynamic assessment on the security level of the digital assets by using a multi-factor risk assessment model to generate the security rating of the digital assets.

The invention discloses a monitoring point distribution method and device based on industrial park gas monitoring and a medium, and relates to the technical field of carbon monitoring, triangular mesh division is carried out based on DT triangular mesh division, and according to the difference of monitored areas, the DT triangular mesh division method is used for setting point distribution of the monitored areas, so that the monitoring point distribution accuracy is improved. At least one monitoring point is selected in each grid for carbon dioxide concentration monitoring, point distribution setting can be carried out in different area ranges of prevailing wind directions in different states, the optimal monitoring result can be obtained through the least carbon dioxide gas monitoring points, the accuracy and credibility of the final data result are improved, and the method is suitable for popularization and application. The problems that the current industrial park monitoring coverage range is not wide, the device cost is high, and improvement of the carbon monitoring device distribution position affects the accuracy of the carbon ground monitoring technology are solved.

The invention relates to the technical field of data processing, in particular to a real estate surveying and mapping data processing method and system based on intelligent data. The method comprises the following steps: acquiring multi-source real estate surveying and mapping data; performing space-time synchronization on the multi-source real estate surveying and mapping data to generate multi-source real estate surveying and mapping space alignment data; real estate space feature extraction is carried out on the multi-source real estate surveying and mapping space alignment data to obtain real estate space feature data; performing cross-domain feature fusion on the multi-source real estate surveying and mapping data based on the real estate spatial feature data to generate real estate surveying and mapping fusion spatial feature data; public surveying and mapping point confirmation is carried out on the multi-source real estate surveying and mapping data through the real estate surveying and mapping fusion spatial feature data, and real estate surveying and mapping public surveying and mapping point data are obtained. Through multi-source data space-time synchronization, feature fusion, distortion correction, high-dimensional space reconstruction, semantic tag association and chain storage, the efficiency and accuracy of real estate surveying and mapping data processing are improved.

The invention discloses a photoelectric multi-mode decision fusion method and system for mental disorder recognition, relates to the technical field of mental disorder recognition, and performs decision fusion based on an improved machine learning algorithm by integrating photoelectric multi-mode data and collecting and analyzing various data in real time. Through effective fusion of result data of different modal recognition models, the accuracy and stability of mental disorder recognition are improved, meanwhile, the misjudgment rate of a single modal method is reduced, and the overall diagnosis efficiency is improved. The invention not only provides an innovative method, but also designs a corresponding fusion system, and provides a comprehensive solution for early recognition and diagnosis of mental disorders.

The invention relates to the technical field of data processing, and discloses an intelligent question answering method and device fusing a knowledge graph and a large model, and a storage medium, and the method comprises the steps: obtaining a to-be-answered original question; performing entity extraction on the original problem to generate an entity information set, and performing problem rewriting to generate similar problems of the original problem; traversing each piece of entity information in the entity information set, and searching and screening in a preset knowledge graph based on a search strategy fusing similar problems to obtain a knowledge path; obtaining knowledge according to the knowledge path, and according to the task instruction and the original question, performing logical reasoning by combining an answer generation large model with the obtained knowledge to generate a prediction answer of the original question; and carrying out authenticity detection on the predicted answers, filtering the predicted answers, and generating target answers. According to the method, the possibility that a large model generates unreal or irrelevant information is reduced, the calculation cost is reduced, the illusion problem of generating answers by the large model is relieved, and the credibility and the accuracy of the answers are improved.

The invention discloses a network threat deduction system and method based on Transform and a graph attention network model. The system mainly comprises four modules including a threat intelligence extraction module, a network threat entity construction module, a network threat deduction module and a deduction result application module. The method comprises the following steps: preprocessing and mining network security threat intelligence data by utilizing a graph attention network (GAT) model, constructing a complete network threat intelligence knowledge graph based on a data association relationship, and determining a vulnerability utilization weight in the graph; modeling is carried out on a reasoned path sequence based on a Transform model, a next reasoning sequence is predicted in a generative mode, and rapid and efficient network threat intelligence reasoning is achieved. According to the method, the complexity of attack deduction calculation is reduced, the deduction efficiency is improved, and security risk investigation and early warning can be carried out for a large-scale network environment.

The invention discloses a park data exchange intelligent management system based on block chain and computing network fusion. The system comprises a directory chain subsystem, a data probe subsystem, a computing network fusion scheduling subsystem, an auditing subsystem, an intelligent contract subsystem, a computing network cooperative processing subsystem, a security sand box subsystem and a trusted exchange subsystem. The directory chain subsystem is responsible for receiving data directory information and carrying out uplink evidence storage to ensure that the data directory cannot be tampered; the auditing subsystem completes data access permission auditing according to the computing power distribution scheme and the access strategy; the intelligent contract subsystem generates an intelligent contract containing data processing logic and a computing power scheduling strategy, and triggers computing power resource scheduling; the computing network cooperative processing subsystem coordinates edges and cloud computing resources to complete cooperative computing; the security sandbox system protects data privacy through multi-stage isolation and encryption; and the trusted exchange subsystem safely pushes an encryption processing result and releases computing power resources after the encryption processing result is completed, so that the security and efficiency of data processing are improved.

The invention provides a green power consumption classification accounting and right tracing method and system of a multi-dimensional account, and relates to the technical field of power, and the method comprises the steps: generating green power transaction matching data through collecting power utilization data and power generation plan data in real time; constructing a distributed account book node network, verifying the matched data, and generating a green power transaction block; and constructing a green power right traceability model based on the transaction block, calculating a green power consumption weight coefficient of the power consumption enterprise, and generating a green power consumption level and a right certificate. According to the invention, precise accounting and right traceability of green power consumption are realized, and the energy management efficiency is improved.

The invention discloses a corrugated pipe inner wall multi-type flaw detection method based on deep learning, and the method comprises the following steps: S1, collecting an initial corrugated pipe inner wall image and corrugated pipe inner wall images in different illumination environments, and generating an original image; s2, preprocessing the original image to generate a preprocessed image; s3, extracting surface geometric overall features of the inner wall of the corrugated pipe from the preprocessed image through a pre-trained ResNet101 network; s4, inputting the preprocessed image and the surface geometric overall features into an improved StyleGAN3 network to generate an enhanced image; s5, inputting the enhanced image into the multi-scale adaptive deep network, and outputting a defect detection result; and S6, performing post-processing on the flaw detection result, and outputting a final flaw detection result. According to the method, deep learning and geometric characteristic embedding technologies are adopted, so that the defects of the inner wall of the corrugated pipe are accurately detected, and the method has the advantages of high precision, high robustness and comprehensive adaptability.

The invention relates to the technical field of genomics, in particular to a gene detection data cleaning method and system based on artificial intelligence. The method comprises the following steps: acquiring gene detection data, and performing preliminary filtering on the gene detection data based on removal of low-quality reads to obtain preliminary filtering data; performing linker sequence identification and removal on the preliminarily filtered data, and performing sequencing error correction to generate gene sequence data; performing GC content analysis and repetitive sequence identification on the gene sequence data to obtain sequence feature data; performing dynamic quality threshold adjustment according to the sequence feature data, and performing secondary filtering on the gene sequence data to generate high-quality sequence data; and performing sequence pattern recognition on the high-quality sequence data to obtain sequence pattern feature data. According to the method, deep analysis is carried out by utilizing sequence characteristics such as GC content and nucleotide distribution, and the recognition capability on sequence complexity and variation modes is improved.

The invention provides a privacy computing security system based on a domestic cryptographic algorithm. The privacy computing security system comprises a key management module, a data encryption module, a homomorphic encryption module, a differential privacy module, an intelligent threat detection module and an access control module. The key management module generates a key by adopting an anti-quantum computing algorithm based on a learning and sampling problem LWE, and performs distributed storage and management by utilizing a block chain technology; the data encryption module performs data encryption and decryption by using an SM4 algorithm; the homomorphic encryption module executes arithmetic and logic operations in a data encryption state; the differential privacy module dynamically protects data privacy; the intelligent threat detection module monitors and detects abnormal activities in real time based on a deep learning model; and the access control module manages user access authority. According to the method, multiple security technologies are integrated, so that the anti-quantum computing capability, data privacy protection and computing efficiency are improved, and the method is suitable for data processing scenes needing high security and privacy protection.

The invention relates to the technical field of short message channel testing, and discloses a short message channel testing configuration method, device and equipment and a storage medium, and the method comprises the following steps: firstly verifying user login information and distributing authority, then extracting and screening channel information from a database based on the authority, and presenting a result in a paging mode. A user can input channel configuration information, and the database is updated after verification. The system then generates an association configuration with the engine user based on the updated channel information. And establishing a TCP / IP Socket communication session and acquiring a session identifier according to the channel selected by the user and the associated configuration. And the system receives the test short message content input by the user, packages the test short message content and the session identifier into a sending request, and sends and records a result through the communication session. According to the method, the channel configuration information can be managed in a centralized manner, a visual user interface is provided, flexible query and screening functions are supported, and an automatic test process can be realized.

The invention relates to the field of intelligent auditing data, in particular to a standard intelligent auditing and compliance checking method and system, a terminal and a medium. Dividing the to-be-audited document into a plurality of document elements based on the to-be-audited document and a preset classification rule; analyzing the to-be-audited document based on the plurality of document elements and a preset auditing direction corresponding to each document element; and generating an auditing report and an auditing manuscript based on the auditing result of each document element. According to the method, the document auditing efficiency and accuracy are improved.

The invention relates to the technical field of intelligent evaluation and early warning methods, in particular to a nuclear radiation dose space distribution intelligent evaluation and early warning method and system, and the method comprises the following steps: obtaining multi-source monitoring data which comprises gamma radiation dose, alpha radiation dose and beta radiation dose measuring instrument data; performing fusion and enhancement processing on the multi-source monitoring data to obtain an enhanced radiation monitoring signal; based on the enhanced radiation monitoring signal, high-precision prediction of nuclear radiation dose spatial distribution is carried out through a deep probabilistic neural network model; according to the high-precision prediction result, in combination with a nuclear accident severity level standard, a radiation alert evaluation result and early warning information are generated, short-term radiation fluctuation can be captured, a long-term change trend can be identified, and the understanding ability of the system to a complex radiation field is greatly improved.