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59 results about "Domain testing" patented technology
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Domain testing is one of the most widely practiced software testing techniques. It is a method of selecting a small number of test cases from a nearly infinite group of candidate test cases. Domain knowledge plays a very critical role while testing domain-specific work.
Mechanical equipment intelligent fault diagnosis method based on partial migration convolutional network
ActiveCN110866365AFault goodEasy to troubleshootMachine part testingCharacter and pattern recognitionData setDomain testing
The invention provides a mechanical equipment intelligent fault diagnosis method based on a partial migration convolutional network. The method comprises: collecting operation data of mechanical equipment under different operation working conditions; constituting data sets, taking part of data in the data set X as a source domain training sample set and a target domain test sample set; performingdata standardization on each piece of sample data; training two one-dimensional convolutional neural network models with the same structure and different initialization parameters by using the sourcedomain training sample set, and correcting the two trained convolutional neural network models based on the target domain test sample set to obtain a convolutional neural network mechanical equipmentfault diagnosis model; and performing fault diagnosis on the mechanical equipment based on the real-time operation data by using the fault diagnosis model to output a fault type. The method can be effectively used in more real mechanical fault diagnosis, that is to say, the label-free property of the target domain is considered, so that the trained diagnosis model can better diagnose faults of mechanical equipment.
Owner:BEIHANG UNIV +1
Double-frequency multichannel synchronization detection method for electric domain imaging
InactiveCN105137125AGuaranteed phase synchronizationImprove signal-to-noise ratioScanning probe microscopyDomain testingDomain imaging
The invention belongs to the technical field of ferroelectric or piezoelectric material electric domain testing, and relates to a double-frequency multichannel synchronization detection method for achieving electric domain imaging through an inverse piezoelectric effect. The method employs two phase locking amplifiers to achieve the real-time synchronous detection of out-plane and in-plane piezoelectric signals of a to-be-tested sample. Reference signals of the two phase locking amplifiers are provided by two independent frequency sources, and the frequency sources also provide AC signals which are the same as the reference signals, wherein the AC signals serve as excitation signals. The excitation signals provided by the two frequency sources are superposed through an adder and then serve as AC excitation signals located between a conductive probe and the to-be-tested sample. The method can detect the amplitude and phase signals of the out-plane and in-plane piezoelectric vibration of the to-be-tested sample through one-time scanning, achieves multichannel synchronous output in a real-time mode, and greatly improves the detection efficiency. Meanwhile, the method can improve the resolution of electric domain imaging, and effectively improves the detection accuracy.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA
Unsupervised cross-domain pedestrian re-identification method
PendingCN111881714AAvoid dependenceImprove recognition accuracyBiometric pattern recognitionNeural architecturesPattern recognitionDomain testing
The invention relates to an unsupervised cross-domain pedestrian re-identification method, and the method comprises the following steps: performing pre-training by using a labeled source domain training image to obtain a baseline network weight, and taking the baseline network weight as a baseline network initial weight of a multi-loss optimization learning training process; performing multi-lossoptimization learning training by using the label-free target domain training image, and performing multi-time multi-loss optimization learning training on the basis of the initial weight of the baseline network to obtain a baseline network after multi-loss optimization learning training; performing an unsupervised cross-domain pedestrian re-identification test by using the label-free target domain test image, and inputting the label-free target domain test image into the baseline network subjected to multi-loss optimization learning training for testing to obtain an identification result. According to the method, the natural similarity in the target domain image is concerned, complete dependence on a pseudo label is avoided, and compared with other methods in the same field, the method has higher recognition accuracy.
Owner:BEIJING JIAOTONG UNIV
Mechanical fault diagnosis method based on multi-sensor information fusion migration network
ActiveCN112161784AImprove classification accuracyImproved Smart Fault Diagnosis performanceMachine part testingMachine learningData setDomain testing
The invention discloses a mechanical fault diagnosis method based on a multi-sensor information fusion migration network, and the method comprises the steps of firstly collecting the multi-sensor data, obtaining a plurality of source domain data sets and target domain data sets, and then constructing a multi-sensor information fusion migration network diagnosis model, wherein the model is providedwith a feature sharing layer and M convolutional neural networks; constructing a loss function of each convolutional neural network; training the multi-sensor information fusion migration network diagnosis model, and based on the target domain training data of the M source domain data sets and the target domain data sets, in each iteration, sequentially training the first network to the M-th network according to the sequence of the source domain sensors until the number of iterations or the classification precision is reached; and finally, inputting the target domain test data of the target domain data sets into the model, and obtaining a final classification diagnosis result through model and loss function processing and weighted average of M outputs. The method can effectively improve the mechanical fault diagnosis precision.
Owner:SOUTH CHINA UNIV OF TECH
SAR image target detection method based on unsupervised domain adaptive CNN
ActiveCN112395987AImprove discrimination abilityImprove accuracyImage enhancementImage analysisLocation detectionDomain testing
The invention discloses an SAR image target detection method based on an unsupervised domain adaptive CNN, and provides the SAR image target detection method based on the unsupervised domain adaptiveCNN for SAR image target detection mainly aiming at the defects in the prior art. The method comprises the following steps: (1) generating a source domain data set; (2) generating a target domain training set and a target domain test set; (3) constructing a multi-layer feature domain adaptive network; (4) training a cyclic consistency generative adversarial network; (5) training a multi-layer feature domain adaptive network; (6) training the Faster R-CNN by using an iterative pseudo-marking method; and (7) performing position detection on the test SAR image in the target domain test set. By means of knowledge of marked source domain data, the method has the advantages that the accuracy is high, and marked SAR images do not need to be used for training target detection in a target domain.
Owner:XIDIAN UNIV
Lithium ion battery SOC estimation method based on deep-transfer learning
ActiveCN112540298AReduce training timeReduce collection workElectrical testingPhysical realisationDomain modelDomain testing
The invention relates to a lithium ion battery SOC estimation method based on deep-transfer learning. The method comprises the steps of obtaining a source domain training set, a target domain trainingset and a test set; constructing a lithium ion battery SOC estimation source domain model based on deep learning, training the lithium ion battery SOC estimation source domain model by using the source domain training set, and storing model training data parameters; constructing a lithium ion battery SOC estimation target domain model based on deep learning, transferring lithium ion battery SOC estimation source domain model training data parameters to the lithium ion battery SOC estimation target domain model by adopting a transfer learning method, and sharing model weight parameters to perform initialization setting; and importing the lithium ion battery target domain training set into the lithium ion battery SOC estimation target domain model to perform fine adjustment training processing, and further importing the target domain training set into a target domain test set to predict the SOC value of the lithium ion battery. According to the method, the training time of the SOC estimation model of the lithium ion battery is shortened, and a large amount of time and capital investment consumed in the experimental data collection process are reduced.
Owner:FUZHOU UNIV
Telecommunication value-added service integrated measuring system and measuring method thereof
InactiveCN1592232AAddressing the lack of objective means of testingSupervisory/monitoring/testing arrangementsTransmission monitoringCombined testTest script
A comprehensive test system and a method for a value-added telecommunication service belongs to test field. The system includes a physical test platform, platform control module, a test script code / decode module, an applied module and a test control module. The tested system and its system exchange are connected by E1 / T1 and matched with signaling. After finishing writing a test script in terms of test rule in X a script decoder, the script is decoded. A system software is loaded to the script decoding to generate a test order set to begin executing the test order. The software decides if the test is passed and displays realtime test result and stores the test data for off-line analysis.
Owner:ZTE CORP
Unsupervised domain adaptive fault diagnosis method
ActiveCN110940523AGood intra-class compactnessGood separability between classesMachine part testingCharacter and pattern recognitionData packDomain testing
The invention discloses an unsupervised domain adaptive fault diagnosis method, which comprises the steps of obtaining bearing source vibration data, and dividing the bearing source vibration data into a training sample and a test sample; constructing a CACD-1 DCNN model, training the model, determining model parameters, and performing the fault diagnosis; the source vibration data comprises target data without labels and source domain data with labels; the acquired bearing source vibration data is acquired through a sensor; an objective function of the model provided by the invention comprises cross entropy classification loss of a source domain, based on center discrimination loss and related alignment loss based on source domain and target domain features, the last two losses are executed in the last full connection layer, representation learned in a source domain can be applied to a target domain after model training, it can be guaranteed that the extracted domain invariant features have better intra-class compactness and inter-class separability, and meanwhile the extracted features can effectively improve the performance of cross-domain testing.
Owner:YANCHENG INST OF TECH
Domain adaptive Faster R-CNN (Recurrent Convolutional Neural Network) semi-supervised SAR (Synthetic Aperture Radar) detection method
ActiveCN112115916AReduce dependenceRich dataScene recognitionNeural architecturesDomain testingAlgorithm
The invention discloses a semi-supervised SAR (Synthetic Aperture Radar) detection method based on domain-adaptive Faster R-CNN (Recurrent Convolutional Neural Network), which solves the problem thatthe SAR target detection performance is reduced under a small number of marked images. The method comprises the following steps: obtaining a source domain containing a label and target domain data ofa small number of labels; training an original Faster R-CNN by using the source domain data; constructing a domain adaptive Faster R-CNN, initializing the domain adaptive Faster R-CNN, and performingtraining by utilizing source domain and target domain data to obtain a trained model; and inputting the target domain test data into the trained model to obtain a detection result of the test data. According to the method, the domain adaptation Faster R-CNN is constructed, the domain adaptation and decoder module is additionally arranged, SAR target detection is assisted by the optical remote sensing image, dependence on the SAR image with the label is reduced, global information of target domain data is learned through the decoder module, and the detection performance is further improved. Themethod is applied to SAR image target detection.
Owner:XIDIAN UNIV
Unsupervised cross-domain action recognition method based on channel fusion and classifier confrontation
PendingCN111797814ASpace invariantFast convergenceCharacter and pattern recognitionNeural architecturesData setDomain testing
The invention discloses an unsupervised cross-domain action recognition method (CAFCCN) based on channel fusion and classifier confrontation. Efficient action recognition of a target domain test set based on a source domain labeled data set and a target domain unlabeled training set is achieved. The method comprises the following specific steps of: (1) selecting an action recognition model; (2) optimizing a double-flow deep network structure; (3) constructing an objective function based on the double-flow network; (4) building an unsupervised cross-domain action recognition model based on thedouble-flow network; and (5) constructing a data set. The method has the advantages that unlabeled data sets of other training sets can be subjected to efficient action recognition on the basis of theknown data set, and the problem of unlabeled data of the training set of the target data set can be effectively solved. By applying the confrontation method, confusion of categories and domains can be achieved at the same time, domain-level and class-level invariant features are obtained, the convergence speed of the method is high, and efficient recognition of actions can be achieved.
Owner:TIANJIN UNIVERSITY OF TECHNOLOGY
Homogeneous domain testing device and testing method
InactiveCN103185841AEasy to operateElectrical testingMeasurement leads/probesDomain testingComputational physics
Owner:HONG FU JIN PRECISION IND (SHENZHEN) CO LTD +1
Cheating recording detecting neural network model optimization method and system
InactiveCN110223676AImprove generalization abilitySolve the problem of poor identification effectSpeech recognitionDomain testingFeature extraction
The embodiment of the invention provides a cheating recording detecting neural network model optimization method. The cheating recording detecting neural network model optimization method comprises the steps that a cheating recording detecting neural network model is constructed based on a feature extractor, a cheating detector and a domain predictor; source domain data and target domain data areinput into the feature extractor; the output of the feature extractor is input into the cheating detector and the domain predictor, the neural network model is detected by training cheating recording,and the loss function value of the cheating detector and the loss function value of the domain predictor are lowered; and adversarial training is conducted on the feature extractor based on the lowered loss function value of the domain predictor, and thus the deep feature output to the cheating detector by the feature extractor is feature with non-change of domain and cheating detecting distinction. The embodiment of the invention further provides a cheating recording detecting neural network model optimization system. According to the embodiment, the optimized model has no ability of distinguishing domain prediction in recording attacking detecting, and the generalization performance of cross domain testing is improved.
Owner:AISPEECH CO LTD
Method for predicting residual life of rotating machinery under multiple working conditions based on dynamic domain adaptation network
ActiveCN112765890AImprove forecast accuracyOvercome the problem of not considering the influence of conditional distribution on model prediction accuracyCharacter and pattern recognitionDesign optimisation/simulationPredictive learningDomain testing
The invention discloses a method for predicting the residual life of a rotating machinery under multiple working conditions based on a dynamic domain adaptation network. The method comprises the following steps: 1, generating a source domain sample set and a target domain sample set; 2, preprocessing vibration signals in the source domain sample set and the target domain sample set; 3, generating a target domain training set and a target domain test set; 4, selecting a source domain training set by adopting a reverse verification technology; 5, constructing a dynamic domain adaptive neural network which structurally comprises a feature extractor, a prediction learning module, a marginal distribution adaptive module and a conditional distribution adaptive module; 6, training the dynamic domain adaptive neural network to obtain a trained dynamic domain adaptive neural network model; and 7, predicting the residual life of a target domain test set by using the model. According to the method, the generalization ability and the prediction precision of the residual life prediction model are improved under the condition of multiple working conditions.
Owner:XIDIAN UNIV
PTP clock synchronization precision test method for linux system
The invention discloses a PTP clock synchronization precision test method for a linux system, and belongs to the field of network communication and testing. A test scene comprises a master clock device and four slave clock devices in the same PTP domain, the master clock device and the slave clock devices perform network communication through a common switch, and PTP protocol interface communication is adopted. PTP test software is operated on the master clock device and the slave clock devices respectively to perform PTP clock synchronization, the master clock device sends a synchronization message to determine a synchronization relationship between the master clock device and the slave clock devices, then the master clock device sends a timestamp following message, the slave clock devices record a time difference between the master clock device and the slave clock devices, and the slave clock devices record a synchronization error of set time. Whether precision requirements are met or not is determined. The test method is mainly based on a synchronization precision test on software, the slave clock devices do not support hardware synchronization, there is no requirement on hardware, test scene building cost is low, and the test method is easy to implement.
Owner:HEBEI HANGUANG HEAVY IND
Deep channel whole-domain testing method
ActiveCN109682624ARealize fully automatic controlReduce pressure errorStructural/machines measurementFrame basedDomain testing
Owner:SHANGHAI ELECTRICAL HYDRAULICS & PNEUMATICS
Domain generalization and domain adaptive learning method based on data expansion consistency
PendingCN111382871ASimple structureImprove domain generalizationMachine learningNeural learning methodsData expansionAdaptive learning
The invention belongs to the field of artificial intelligence machine learning, and discloses a domain generalization and domain self-adaption method based on data expansion consistency, which comprises the following steps: S1, according to task requirements, designing a prediction model p theta (y | x) based on a deep neural network, theta being a model parameter, and model output being conditional probability distribution of marking y under the condition of a given sample x; S2, constructing a data expander according to task characteristics, converting the sample, and keeping the core content of the sample unchanged, so as to keep the real mark of the converted sample unchanged; S3, constructing a multi-task loss function consisting of supervised loss and data expansion consistency lossby utilizing the original training sample and the expanded sample, and training to obtain p theta * (y | x); and S4, applying the trained model p theta * (y | x) to a target field test sample for prediction. The domain generalization and domain adaptive learning method is simple, universal and good in performance, and the technical problem of domain offset can be better solved.
Owner:NAT INNOVATION INST OF DEFENSE TECH PLA ACAD OF MILITARY SCI
WIFI radio frequency test system and method
The invention relates to a WIFI radio frequency test system and method, and belongs to the field of automatic test technology, the test system comprises a control server, a shielding box, a parameter test device, an attenuator and a high frequency probe; the parameter test device is used for measuring an emission index of an emission signal of a tested product and outputting a second signal; the control server is provided with factory test software; the input end of the attenuator is connected with the high-frequency probe through a radio frequency cable; the high-frequency probe is arranged in the shielding box and is used for being connected with an RF head of the tested product; the shielding box is used for bearing the tested product; and based on the test system, the invention provides the WIFI radio frequency test method. Compared with the prior art, the method and the device have the effect of improving the test efficiency under the condition of ensuring relatively low cost.
Owner:SHENZHEN WELLTEST TECH CO LTD
Vulnerability type guiding fuzzy testing method and system based on byte sensitive energy distribution
PendingCN114756471AReduce couplingImprove scalabilitySoftware testing/debuggingPlatform integrity maintainanceDomain testingAlgorithm
The invention discloses a vulnerability type guiding fuzz testing method and system based on byte sensitive energy distribution, and belongs to the technical field of software fuzz testing. The test method comprises the following steps: marking line number information of different types of vulnerabilities through a static analysis tool; compiling an instrumentation program to realize statistics of different types of vulnerability feature information during operation; constructing and maintaining seed queues of a plurality of specific vulnerability types based on the vulnerability types, and customizing different energy distribution modes for each seed queue according to the vulnerability types; energy distribution is further refined to a byte level, and weights are distributed for variation byte positions according to potential performance scores of seeds on specific types of vulnerabilities before and after variation. By means of the byte-level energy distribution algorithm matched with the vulnerability characteristics, the vulnerability mining efficiency of the fuzzy test tool on different types of vulnerabilities is greatly improved.
Owner:尚蝉(浙江)科技有限公司
Test result classification model training method and device and test result classification method and device
PendingCN113515625AImprove classification accuracyOvercoming technical issues with poor classification performanceSoftware testing/debuggingNeural architecturesData setDomain testing
The invention provides a test result classification model training method which is applied to the financial field, the artificial intelligence field or other fields. The test result classification model training method comprises the steps that a plurality of log texts are obtained, and each log text comprises a case of automatic test execution failure; a training sample data set is generated according to the multiple log texts, and the training sample data set comprises a word vector matrix and label information of the word vector matrix; and the to-be-trained test result classification model is trained by using the training sample data set to obtain a test result classification model for classifying the automatic test results. The invention further provides a test result classification method, a test result classification model training device, a test result classification device, electronic equipment, a computer readable storage medium and a computer program product.
Owner:INDUSTRIAL AND COMMERCIAL BANK OF CHINA
Robots, social robot systems, focusing software development for social robot systems, testing and uses thereof
ActiveUS10978205B1Improve performanceProgramme-controlled manipulatorData processing applicationsRobotic systemsDomain testing
A method and system for improving the software programming of a robot system, comprising monitoring of plurality of human user-robot interactive pairs' (HURIP) interactions. System comprises each of said plurality of HURIPs as using ‘front-end’ semi-autonomous robot component linked by wireless two-way communications to a ‘back-end’ cloud-based computerized component. Monitoring comprises review of robot sensor-gathered data and data from camera and audio data from homes of users during user-robot interactions. Analysis of said monitoring by authorized observers such as psychologist, parent, teacher, system administrator, software programmer(s), enables identification of areas for software improvement. Improved software is tested, wherein testing comprises at least similar monitoring of HURIPs, and wherein said testing comprises social robots comprising said updated software. Cycles of such monitoring of HURIP interactions, analyzing data derived from said monitoring, focus for improvement derived thereof and followed-up in coding updates, testing of updates comprising use within monitored HURIP interactions, such cycles are applied in herein disclosed method to manufacture progressively improved code for and uses of social robot system.
Owner:BEECHAM JAMES E
Pedestrian re-identification method based on two-way mutual promotion deentanglement learning
PendingCN113449676AAvoid burdenAvoid mixingCharacter and pattern recognitionNeural learning methodsData setDomain testing
The invention discloses a pedestrian re-identification method based on two-way mutual promotion deentanglement learning, and belongs to the field of computer vision. The method comprises the steps of obtaining a content encoder with extraction domain invariant features through a training process, and performing re-identification on pedestrians in a target domain test sample by using the content encoder in a test process. Compared with a traditional pedestrian re-identification method, the method of the invention is simple and effective and has higher practical value. And the invention shows more excellent performance on different data sets.
Owner:凌坤(南通)智能科技有限公司
Test method
ActiveCN111090575AImplement batch testingImprove test efficiencySoftware testing/debuggingTest efficiencyDomain testing
The invention provides a test method, and belongs to the technical field of computers. The test method comprises the steps of obtaining information of a to-be-tested system call interface; generatinga test template for testing the system call interface according to the information of the system call interface and a set generation rule, the test template which comprises a test instruction for performing a call test on the system call interface; and sending the test template to at least one to-be-tested device through the test server, and indicating each to-be-tested device to perform a call test on a local system call interface according to the test instruction in the test template. Therefore, on one hand, test instructions of all to-be-tested system call interfaces can be generated systematically; and on the other hand, for a plurality of to-be-tested devices needing to be tested, the same test template can be used for testing a plurality of local system call interfaces, batch testingof the system call interfaces of the to-be-tested devices is achieved, and therefore the testing efficiency of the system call interfaces is effectively improved.
Owner:亿度慧达教育科技(北京)有限公司
Multi-source distillation-migration mechanical fault intelligent diagnosis method based on high-order moment matching
ActiveCN112860183AFix performance degradationOvercoming the Negative Transfer ProblemInput/output to record carriersManufacturing computing systemsData setDomain testing
The invention discloses a multi-source distillation-migration mechanical fault intelligent diagnosis method based on high-order moment matching, and the method comprises the steps: building a multi-source data set through the operation data collected from a plurality of mechanical devices, carrying out the preprocessing, and dividing the multi-source data set into a source domain data set, a target domain training data set, and a target domain test data set; constructing a multi-source distillation-transfer learning network model based on high-order moment matching, and performing high-order moment matching, maximum classifier difference and multi-source distillation training by using the source domain data set and the target domain training data set; and taking the target domain test data set as test input, and synthesizing outputs of the plurality of classifiers by using an adaptive weighting strategy to complete cross-domain fault diagnosis. According to the method, features of a source domain and a target domain are aligned at domain and category levels by utilizing multi-source data, the classification capability of the model on target samples is improved through multi-source distillation, and adaptive weighting is provided to integrate diagnosis results, so that the problem that the performance of a traditional method is reduced in cross-domain diagnosis is solved, and the performance of a deep model is greatly improved.
Owner:XI AN JIAOTONG UNIV
New fault diagnosis method for rotating machinery based on deep confrontation convolutional neural network
PendingCN114358124AEfficient extractionAvoid artificial feature extractionCharacter and pattern recognitionNeural architecturesData setDomain testing
The invention discloses a new fault diagnosis method for a rotating machine based on a deep confrontation convolutional neural network. The method comprises the following steps: constructing a source domain sample data set and a target domain sample data set; constructing a deep adversarial convolutional neural network for identifying known faults and new faults, wherein the deep adversarial convolutional neural network comprises a feature extractor G, a label classifier CF, a domain discriminator D and a non-adversarial domain discriminator; in the training stage, data of a source domain and a target domain are mapped into a high-dimensional feature space through a feature extraction module, and data feature distribution is obtained; a weighted discrimination mechanism is designed, the similarity between target domain sample data and source domain data is evaluated, and the mobility of the data is discriminated; and inputting target domain test data into the trained network for testing, judging whether the data belongs to a new fault category or not through a calculated weight value, and outputting a final classification diagnosis result. Through weighted adversarial training and target domain test sample weight threshold selection, the constructed network is enabled to be suitable for known fault and new fault detection under variable working conditions.
Owner:SOUTH CHINA UNIV OF TECH
Cooperative robot reachable domain test system and method based on LABVIEW
ActiveCN111941463AImprove test efficiencyProgramme-controlled manipulatorTotal factory controlDomain testingSimulation
One technical scheme of the invention is to provide a cooperative robot reachable domain test system based on LABVIEW. Another technical scheme of the invention is to provide a cooperative robot reachable domain test method based on the LABVIEW. The cooperative robot reachable domain test method based on the LABVIEW provided by the invention comprises the steps of based on a semi-physical simulation technology and a robot control technology, using a robot reverse kinematics function for calculating whether a space coordinate point in a range is reachable or not, and verifying the reachable domain of a robot according to the actual running state and the position arrival condition of the robot; besides, through a mode of forming a coordinate point set, realizing automatic traversal of coordinate points and recording of reachable points by using the semi-physical simulation technology, and detecting the reachable problem of the robot between two points and the reachable problem of a single point within a certain range through directly calling the robot reverse kinematics function, so that the test efficiency is improved; and finally, calculating the space reachable coverage rate of the robot according to the reachable point recording of the reachable domain test.
Owner:SHANGHAI ROBOT IND TECH RES INST CO LTD +1
Performance test method and device, equipment and storage medium
PendingCN114371968AGuaranteed accuracySave human resourcesFunctional testingFaulty hardware testing methodsDomain testingBusiness data
The invention provides a performance testing method and device, equipment and a storage medium, and relates to the field of software testing. After the test equipment receives a service request sent by the server, executing a target service, and then obtaining corresponding service data when the target service is executed; and determining the performance of the server based on the business data and a preset business data base value. According to the method, the business data base value can be preset, and after the business data is acquired, the test equipment can automatically analyze the acquired business data based on the preset business data base value so as to determine the performance of the server, so that a large amount of human resources can be saved, and the accuracy of the test result can be ensured.
Owner:CHINA TELECOM CORP LTD
A method for automated testing of web browser behavior for new top-level domain names
Owner:深圳网基科技有限公司
An image intelligent cropping method and system based on confrontational domain adaptation
ActiveCN113205522BReduce feature differencesReduce the differenceImage enhancementImage analysisFeature extractionDomain testing
The invention provides an image intelligent cropping method and system based on confrontational domain adaptation, which belongs to the field of computer vision. The method specifically includes: inputting a target domain image to be cropped in a target application scene to a trained feature extractor to obtain global features ;According to the preset clipping method, the global features are resampled; the regional features are input to the aesthetic classifier for aesthetic scoring, and the clipping results are screened; the training process of the feature extractor is: the domain adaptation loss gradient calculated based on the target domain samples is reversed After transfer, it is transmitted to the feature extractor, and the domain adaptation loss gradient calculated based on the source domain samples is kept unchanged and transmitted to the feature extractor. The feature extractor learns the ability to align the global features of the source domain and target domain samples; and adjusts itself according to the aesthetic loss. Parameters, learn the ability of aesthetic analysis; the training of aesthetic classifier is to adjust its own parameters according to the aesthetic loss. The invention solves the problem that the performance of the existing intelligent clipping method drops significantly during the cross-domain test.
Owner:HUAZHONG UNIV OF SCI & TECH
Method, device and system for testing and comparing main domain and standby domain of recommendation platform
ActiveCN112583660AAvoid misjudgments as abnormal situationsAvoid the disadvantage of large judgment errorsDigital data information retrievalData switching networksDomain testingAlgorithm
The invention discloses a method for testing and comparing a main domain and a standby domain of a recommendation platform. The method comprises the following steps of: creating an input parameter object; calling interface services of a main domain and a standby domain respectively based on the parameter entry object to obtain main domain recommendation information and standby domain recommendation information respectively; sorting the main domain recommendation information and the standby domain recommendation information according to the commodity identifiers and grouping the information insequence; calculating error information of the commodity group; and when the error information of any commodity group exceeds a preset range, determining that the commodity group is an abnormal commodity group. According to the method, when test comparison is executed, a main domain and a standby domain are grouped based on a return result of the same input parameter to obtain corresponding commodity groups, error information between the corresponding main domain and standby domain commodity groups is compared, and when the error information exceeds a preset range, it is judged that the commodity groups are abnormal commodity groups. Return results of the main domain and the standby domain are allowed to have reasonable errors caused by different versions, and the defect of large judgmenterrors is effectively avoided.
Owner:GUANGZHOU PINWEI SOFTWARE
Automatic test case generation method and device, equipment, medium and program product
PendingCN114721966AImprove timelinessReduce workloadSoftware testing/debuggingDomain testingComputer science
The invention provides a test case automatic generation method and device, electronic equipment, a medium and a computer program product. The method and device can be applied to the technical field of artificial intelligence. The test case automatic generation method comprises the steps that m judgment conditions of a tested program statement are obtained, and m is an integer larger than or equal to 1; determining a test case of each judgment condition according to the m judgment conditions; according to the test cases, an effective test case group of the tested program statement is determined, and the effective test case group is a set of test cases capable of covering and testing the m judgment conditions; and calling a test case optimization model, and determining a case test value in the effective test case group.
Owner:INDUSTRIAL AND COMMERCIAL BANK OF CHINA
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