13results about How to "Increase the difference" patented technology

The application provides a pre-processing method of to-be-compensated data, a picture compensation method and related equipment. The pre-processing method of to-be-compensated data comprises the following steps: acquiring first luminance data of a plurality of sub-pixels in a display panel when a to-be-compensated picture with a display gray scale less than or equal to a preset gray scale; determining first part sub-pixels and second part sub-pixels from the plurality of sub-pixels, adjusting the first luminance data of the first part sub-pixels to second luminance data, and adjusting the first luminance data of the second part sub-pixels to third luminance data; wherein the second luminance data is greater than the first luminance data, and the third luminance data is less than the first luminance data. By adopting the scheme of the application, more reasonable compensation data can be obtained when Mura compensation is performed, and a better compensation effect can be achieved.

An image processing apparatus and air conditioner include: an acquisition unit configured to acquire low-light and infrared images; and a processing unit comprising: a low-light image enhancement algorithm model, which includes: a gradient reconstruction module comprising: a frequency domain processing branch configured to transform the gradient map of the low-light image from the spatial domain to the frequency domain to extract the phase spectrum and amplitude spectrum; and a spatial domain processing branch configured to fuse the gradient map of the low-light image with the infrared image in the channel dimension; the gradient reconstruction module adds the residual of the spatial domain processing branch to the amplitude spectrum to obtain an enhanced amplitude spectrum; an amplitude-enhanced gradient map of the low-light image is obtained through an inverse Fourier transform between the enhanced amplitude spectrum and the original phase spectrum; and an enhanced visible light image is generated through an encoder-decoder architecture based on the gradient features of the amplitude-enhanced gradient map of the low-light image and the image features of the low-light image; this application has high accuracy and is suitable for use in smart home appliances.

This invention belongs to the field of multimedia analysis technology and discloses a multimodal media tampering detection method, system, device, and medium based on multi-view contrastive learning. The method includes: acquiring a training dataset, which includes training image-training text pairs and corresponding tampering category labels; introducing a cross-encoder based on a visual-language model, setting several multilayer perceptron head structures, and designing three contrastive learning methods: noise enhancement, prototype-based, and multi-label tampering classification, to obtain an initial multi-view contrastive learning framework; training the initial multi-view contrastive learning framework based on the training dataset to obtain a trained multi-view contrastive learning framework; and performing a tampering detection task on the image-text pair data to be detected based on the trained multi-view contrastive learning framework. The technical solution of this invention can improve the accuracy and robustness of multi-label classification.

The application belongs to the technical field of image detection, and particularly relates to a visual detection method for intelligent segmentation of components on a circuit board. The method comprises the following steps: collecting original images of the circuit board and performing manual labeling and correction to construct a component segmentation training data set; training a semantic segmentation model based on a deep convolutional neural network, adopting a cross-entropy and Dice mixed loss and dynamically adjusting the class weight to alleviate the class imbalance; inputting a to-be-detected image into the model after preprocessing to obtain a semantic segmentation mask; performing morphological post-processing on the segmentation mask to eliminate false areas and regularize the contour; and finally outputting the class, contour and position information of each component. Through standardized data set construction, preprocessing enhancement, mixed loss function and post-processing optimization, the application effectively improves the precision and stability of component segmentation under complex circuit board images, and especially improves the segmentation effect of low-frequency key components.

This application discloses a semiconductor device, a memory, a printhead, and a consumable cartridge. The semiconductor device includes a semiconductor substrate and a transistor, an antifuse element, a resistor element, a first terminal, and a second terminal disposed on the semiconductor substrate. The transistor is connected to the first terminal. The antifuse element is connected between the transistor and the second terminal via a first wiring and a third wiring. The resistor element is connected in parallel with the antifuse element between the transistor and the second terminal via a second wiring and a fourth wiring. The first and third wirings are both referred to as antifuse wirings, and the second and fourth wirings are both referred to as resistor wirings. At least one antifuse wiring is arranged in a first stacked structure, and / or, at least one resistor wiring and its corresponding antifuse wiring are arranged in a second stacked structure. This application enables a larger wiring space for the resistor wirings, thereby increasing the resistance of the resistor wirings and improving data reading accuracy.

The utility model provides a kitchen waste back-mixing water washing device with a material blocking early warning function. The kitchen waste back-mixing water washing device comprises a flow meter arranged on a conveying pipeline and used for monitoring the flow of oil and water in the conveying pipeline; a first weight sensor, a second weight sensor and a supervision analysis unit connected with the flow meter, the first weight sensor and the second weight sensor are arranged at the joint of the first supporting seat and the back-mixing washing device and the joint of the second supporting seat and the back-mixing washing device respectively. The flow meter on the conveying pipeline is used for monitoring the flow of oil and water in the conveying pipeline; a first weight sensor and a second weight sensor are arranged at the joint of the first supporting seat and the back-mixing washing device and the joint of the second supporting seat and the back-mixing washing device respectively, the sensors are connected with a flow meter and a supervision and analysis unit, and when a filter screen is blocked, an alarm is given through signals transmitted by the sensors and the flow meter. The problem that in the prior art, blockage of a filter screen caused by flaky residues retained in a back-mixing washing device cannot be found in time is solved.

The present application relates to the technical field of oil drilling risk monitoring, well control safety early warning and artificial intelligence identification, and specifically discloses an early overflow intelligent identification method and system based on knowledge guidance and residual error enhancement, which comprises the following steps: first, obtaining original drilling monitoring data and completing preprocessing; then, performing feature enhancement through a knowledge-guided self-attention module, obtaining a knowledge bias term by combining a standard self-attention score with a trend consistency index, a pool volume correction factor and an inflection point index, and generating an enhanced feature matrix; subsequently, utilizing a prototype learning residual error enhancement module to construct a normal working condition feature prototype, matching an optimal prototype and calculating a feature difference value to obtain a residual error feature matrix; finally, inputting the residual error feature matrix into a classification layer to output an early overflow prediction probability; the present application can focus on weak but mechanism-compliant abnormal periods and reduce the overlap interference between normal working conditions and early overflows, thereby improving the identification accuracy and early warning timeliness.

The invention discloses a radar target high-resolution one-dimensional range profile recognition method, and relates to the field of radar signal processing. Comprising the following steps: S1, constructing a target high-resolution one-dimensional range profile training sample set X, a label matrix H and a label block diagonal matrix Q; s2, extracting features from the training sample set X and the tag block diagonal matrix Q, and optimizing to obtain a feature dictionary matrix D and a feature representation matrix Z; s3, constructing a linear classifier C by using the feature representation matrix Z and the label matrix H; s4, constructing a feature representation matrix Zt of the test sample set Xt according to the feature dictionary matrix D; and S5, performing target classification by using the feature representation matrix Zt of the test sample set Xt and the linear classifier C. The problem that a traditional target recognition method is poor in training effect is solved.

The application is suitable for the field of image processing, and provides a no-reference color image quality evaluation method based on color and structure distortion, comprising the following steps: performing a blocking operation on a color image to form a color distortion image block data set, and forming a training set and a test set; a group of two-dimensional Scharr detection operators are constructed, and convolution processing is performed on the input image block by using the two-dimensional Scharr detection operators to obtain gradient feature information of the distortion image block; the obtained image block data set is converted into an HSV color space; a no-reference image quality evaluation convolutional neural network model based on color and structure distortion is constructed according to the existing image block data set; and the divided training set is input into the constructed no-reference image quality evaluation convolutional neural network model to obtain a trained no-reference image quality evaluation model. The application has reasonable design, the simulated quality score and the real quality score have strong correlation and small error, and is worth promoting.

Embodiments of this application disclose an RC oscillator. The RC oscillator may amplify a difference between a first voltage and a second voltage by using a first amplifier and a second amplifier. The first amplifier may include a first amplification circuit and a second amplification circuit. The first amplification circuit and the second amplification circuit may share a same voltage-current conversion circuit. The RC oscillator disclosed in the embodiments of this application can not only avoid noise introduced by the first amplifier, but also reduce internal noise of the RC oscillator. This reduces a jitter of a clock signal.

The application discloses a kind of based on optimization Makishima-Mackenzie formula's fiber glass modulus prediction method.The application first establishes an oxide dissociation energy optimization model based on differentiating weight coefficient, by introducing independent optimization coefficient, the difference of different oxides in glass network structure to modulus contribution can be optimized, compared with the prediction error of traditional MM formula on high modulus glass modulus is greatly reduced;At the same time, the clear physical image based on dissociation energy and component is retained, the internal relationship between glass microstructure and macro modulus can be revealed, and the "black box" problem of pure machine learning method is avoided.In addition, the modulus prediction can be completed only by oxide dissociation energy, molar volume and other basic parameters easy to obtain, the calculation efficiency is improved by more than 100 times, and high-performance computing resources are not needed, which significantly reduces the time cost and technical threshold of high-performance glass fiber research and development.

The application relates to a few-pole built-in rotor axial flux permanent magnet motor, which comprises a rotor, a stator and a rotating shaft, the rotor is fixed on the rotating shaft, the stator comprises a first stator and a second stator, the first stator and the second stator are symmetrically arranged on the two sides of the rotor, and the rotor and the rotating shaft rotate relative to the stator; the rotor is composed of a rotor frame, permanent magnets, pole shoes and pressing blocks, a pair of pole shoes cover a plurality of permanent magnets, the pole shoes are symmetrically arranged on the two sides of the permanent magnets, the pressing blocks are pressed on the two adjacent pole shoes, and the pole shoes and the rotor frame of the rotor are connected through a mortise and tenon structure. The application solves the problems of the insufficient strength and reliability of the existing high-speed few-pole double-stator inner rotor axial flux permanent magnet motor, the large eddy current loss of the permanent magnet and the small weak magnetic speed regulation range of the motor.

The invention discloses a switching converter and a control circuit and a control method thereof. The control circuit comprises an error amplifier used for providing an error amplification signal according to a feedback signal of the switching converter and a reference voltage; the signal processing unit is used for providing a peak value reference signal and a valley value reference signal according to the error amplification signal, and the comparison unit is used for providing a first comparison result of the sampling signal and the peak value reference signal and providing a second comparison result of the sampling signal and the valley value reference signal; the logic driving circuit is used for driving the main power tube to be switched on / off according to the first comparison result, the second comparison result and the clock signal, and the signal processing unit further adjusts at least one of the peak reference signal and the valley reference signal according to the adjusting signal so as to adjust the main power tube to be switched off when the error amplification signal is smaller than a set value. The voltage difference value between the peak value reference signal and the valley value reference signal is larger than or equal to the voltage value represented by the adjusting signal, so that the time delay of the comparison unit is reduced.