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33results about How to "Reduce training requirements" patented technology

Hyperspectral unknown category target detection method based on a probability model and deep learning

The invention belongs to the field of hyper-spectral intelligent perception, and discloses a hyper-spectral unknown category target detection method based on a probability model and deep learning, which comprises the following steps: S1, inputting hyper-spectral training data into a trained CNN classification model, and outputting activation vectors of all samples of each category; S2, accumulating and averaging the activation vectors of all the samples which belong to the same category and are classified correctly to obtain an average activation vector, and representing the center of the category by using the average activation vector; S3, fitting a Weibull model belonging to each category based on the activation vectors of all the samples in each category and the mean activation vector of the category; S4, inputting hyperspectral test data into the CNN model and the probability model to form a network based on the Weibull fitting result of each category, and calculating the probability belonging to an unknown category; The method is clear in structure and easy to implement, the training requirement of a neural network learning model is lowered, and the effect of unknown categorytarget detection can be obviously improved.
Owner:NAT UNIV OF DEFENSE TECH

A hybrid simulation training system for smart substation

The invention discloses an intelligent transformer station mixing simulation training system, and belongs to a training reaching system. The system provided by the invention comprises an electrical network digital simulation system, a primary equipment simulation device, and secondary equipment of an intelligent transformer station. The electrical network digital simulation system comprises an electrical network real-time simulation server, a faculty machine and an I / O interface; the primary equipment simulation device comprises an electronic mutual inductor simulation device, a low-voltage switch simulation device, a breaker simulation device and a knife simulation device; the electrical network digital simulation system is connected with the primary equipment simulation device; and the primary equipment simulation device is connected with the secondary equipment. According to the invention, an integrated training platform is established for professional training of an intelligent electrical network, so that full-scope full-process and full-scene simulation training for transformer station operation personnel can be realized; an open principle is observed in terms of a network structure and hardware configuration, so that expansion capability and maintainability of the system are higher; and the network configuration and functions exhibit certain foresight, so that the needs for further development and training are satisfied, and a good base is laid for later function upgrading.
Owner:STATE GRID CORP OF CHINA +1

Probe card maintenance and correction method

The invention discloses a probe card maintenance and correction method. The maintenance and correction method comprises the following steps: step 1, correcting a reference point by adopting a standard probe card and a standard calibration sheet; step 2, placing the standard calibration sheet at a first reference point position, performing magnification projection with a first magnification factor on each needle position of the standard calibration sheet to form magnified needle positions on a projection surface, and marking each magnified needle position; step 3, placing a to-be-corrected probe card at a second reference point position, and projecting the tip of the probe of the to-be-corrected probe card by adopting a second magnification factor to form a magnified tip; and step 4, correcting the probe of the to-be-corrected probe card according to the deviation value of each amplified tip and the corresponding amplified probe position so that the corrected amplified tip is matched with the position of the corresponding amplified probe position. According to the invention, the probe correction difficulty of the probe card can be reduced, the operation window is improved, and the training requirement and the operation difficulty of maintenance personnel are reduced.
Owner:HUA HONG SEMICON WUXI LTD

Network communication equipment maintenance system

The invention discloses a network communication equipment maintenance system and an application method thereof. The network communication equipment maintenance system comprises a maintenance workbench used for fixing a detected object, a test case used for detection, and an instrument truck which is connected with the to-be-detected object and is provided with a plurality of instruments for carrying out equipment fault detection; the test case is communicated with each instrument through a network; and the to-be-detected equipment is in communication connection with the test instruments and the test case through a cable and/or an adapter plate. According to the network communication equipment maintenance system and the application method thereof provided by the invention, required and/or common detection instruments of different equipment are integrated, different fault detection requirements of different equipment and the same equipment are integrated through the matched chassis so as to form platform-based and modular equipment maintenance platforms, maintenance platforms of different equipment can be flexibly and rapidly built, the integration level is high, meanwhile, the cost is low, the automation degree is high, and the personnel training requirement is low.
Owner:MIANYANG NETOP TELECOM EQUIP

Hyperspectral unknown target detection method based on probabilistic model and deep learning

The invention belongs to the field of hyper-spectral intelligent perception, and discloses a hyper-spectral unknown category target detection method based on a probability model and deep learning, which comprises the following steps: S1, inputting hyper-spectral training data into a trained CNN classification model, and outputting activation vectors of all samples of each category; S2, accumulating and averaging the activation vectors of all the samples which belong to the same category and are classified correctly to obtain an average activation vector, and representing the center of the category by using the average activation vector; S3, fitting a Weibull model belonging to each category based on the activation vectors of all the samples in each category and the mean activation vector of the category; S4, inputting hyperspectral test data into the CNN model and the probability model to form a network based on the Weibull fitting result of each category, and calculating the probability belonging to an unknown category; The method is clear in structure and easy to implement, the training requirement of a neural network learning model is lowered, and the effect of unknown categorytarget detection can be obviously improved.
Owner:NAT UNIV OF DEFENSE TECH
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