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Cable fault reason determination system and method based on parameter sequential relationship
PendingCN119293679AHigh accuracy of fault identificationaccurate reference dataFault location by conductor typesElectric cablesMultiple fault
The embodiment of the invention discloses a cable fault reason determination system and method based on a parameter time sequence relation, and the method comprises the steps: obtaining a plurality of fault parameter values of a preset time length before a fault when the occurrence of the cable fault is monitored, determining a plurality of abnormal parameter values and non-abnormal parameter values in the fault parameter values according to a preset abnormal condition, combining the abnormal parameter values according to a time sequence relationship to obtain first time sequence evaluation information, and combining the non-abnormal parameter values according to the time sequence relationship to obtain second time sequence evaluation information, and carrying out matching processing on the basis of the first time sequence evaluation information and the second time sequence evaluation information and pre-stored historical fault parameter value combinations, and determining a historical fault reason correspondingly associated with the successfully matched pre-stored historical fault value combinations as the fault reason of the cable. According to the scheme, the fault reason can be determined through the parameter time sequence combination before the cable fault occurs, so that the cable fault identification accuracy is higher, and more accurate reference data is provided for subsequent maintenance work.
Owner:GUANGZHOU PANYU CABLE WORKS
A method for detecting prostate cancer lesions based on PET / CT images
The present invention discloses a prostate cancer lesion detection method based on PET / CT images, and relates to the technical field of medical image processing. By acquiring the PSMA PET / CT images of the user, the soft tissue window CT images and the lung window CT images are extracted according to the CT images; the CT images are segmented to obtain organ segmentation images; the PSMA PET images, the soft tissue window CT images, the lung window CT images, and the organ segmentation images are respectively input into the PET lesion detection model and the CT lesion detection model to obtain the PET lesion detection images and the CT lesion detection images; and the PSMA PET / CT lesion detection information is generated according to the two lesion detection images. The present invention combines the PSMA PET / CT images with the CT anatomical information, and can more accurately detect and segment lesions, provide more accurate reference data for clinicians, and provide better data support for the formulation of individualized treatment plans.
Owner:THE FIRST AFFILIATED HOSPITAL OF GUANGZHOU MEDICAL UNIV (GUANGZHOU RESPIRATORY CENT)
Explosion simulation device
ActiveCN222337756Uimprove securityIntuitive understanding of explosion impact forceEducational modelsElectrical appliancesClassical mechanicsSlide plate
The utility model discloses an explosion simulation device which comprises a simulation assembly, and the simulation assembly comprises an explosion-proof housing, an observation window, a camera, a guide rod, a limiting block, a sliding plate, an antiskid sleeve, a connecting rod, scale marks, a handle, a damper, a spring, a buffer pad and a pressure sensor. An observation window is fixedly connected to the inner side wall of the anti-explosion shell, a camera is installed on one side of the inner wall of the anti-explosion shell, and a guide rod is fixedly connected to the side, away from the camera, of the inner side wall of the anti-explosion shell. According to the explosion simulation device, an explosion simulation experiment is completed through the simulation assembly, the camera is used for shooting an explosion picture of an explosive, and the explosion picture is displayed through the touch screen, so that an observer is far away from an explosion site, and the safety of the explosion simulation device is improved; the explosion impact force of the explosive is displayed by using the scale marks or the numerical value of the pressure sensor, and accurate reference data is provided for comparing the explosion impact force of different explosives.
Owner:CSSC BLUESTAR (BEIJING) CULTURE DEV CO LTD
A wind tunnel test attack angle adjusting device and an attached layer thickness measuring and calculating method thereof
ActiveCN121540376Beffective destructionreduce thicknessAerodynamic testingUsing fluid meansControl powerAir velocity
This invention provides a wind tunnel test angle-of-attack adjustment device and its calculation method. The device includes an angle-of-attack mechanism, a suction mechanism, a front wind speed sensing component, and a rear wind speed sensing component. The suction mechanism includes a suction channel and a fan. Both the front and rear wind speed sensing components include a high-sensitivity sensor and a lifting drive component. The front wind speed sensing component also includes a low-sensitivity sensor, located downstream of the suction channel. The high-sensitivity and low-sensitivity sensors are connected to the fan and the lifting drive component via a control unit. The method includes selecting a working sensor based on the test angle of attack adjusted by the angle-of-attack mechanism; setting the target boundary layer thickness and the wind tunnel set flow rate; adjusting the real-time power of the fan; controlling the height of the working high-sensitivity sensor; and obtaining the flow rates of the high-sensitivity and low-sensitivity sensors; adjusting the real-time power of the fan based on the flow rates of the high-sensitivity and low-sensitivity sensors. This invention has advantages such as effectively controlling the boundary layer thickness and improving test accuracy.
Owner:HUNAN UNIV
A method for predicting a porous medium structure using machine learning
The present application relates to a kind of methods for predicting porous medium structure using machine learning, comprising: obtaining the image of porous medium prepared in different working conditions and corresponding parameter information;According to phase state characteristics, image is processed, and image dataset is obtained;From image dataset, optionally two images of different working conditions are selected, and with corresponding parameter information, a training sample is formed;Using several training samples, generative adversarial network model is trained, which uses unsupervised image-to-image conversion algorithm for training, for each input training sample, the reconstruction image of original image and predicted image are output, the model is verified by the output predicted image, and the prediction model is obtained after training;The image of porous medium prepared in known working condition and corresponding parameter information are input into prediction model together with the preset parameter information of target object, and the predicted image of target object is obtained, so that the structure prediction of target object can be efficiently and accurately obtained by the present application.
Owner:SOUTHEAST UNIV
Defect identification method for trenchless municipal pipeline
InactiveCN119103485AHigh precisioneasy to analyzePipeline systemsManufacturing computing systemsAgricultural engineeringStructural engineering
Owner:HEFEI DALI ENVIRONMENTAL TECH CO LTD