34 results about "Gaussian density" patented technology

The invention relates to the field of intelligent charging, and particularly discloses an intelligent charging system and a working method thereof, which extract correlation characteristics among charging data such as voltage, current, temperature, pressure and the like of a battery monomer which are acquired in real time through a characteristic encoder, and meanwhile, store the correlation characteristics among the charging data, such as the voltage, the current, the temperature, the pressure and the like, of the battery monomer. In addition, a convolutional neural network model based on a deep learning technology is adopted to mine high-dimensional implicit features of the electrolyte surface image, and a Gaussian density map is applied to fuse the charging feature vector and the surface feature vector, so that the classification result is more accurate. Therefore, a more accurate control type of the charging current can be obtained to judge whether the charging current needs to be increased, decreased or maintained, so that the charging effect can be better.

The invention relates to the field of intelligent manufacturing design, and particularly discloses a technological parameter optimization method and system for an aluminum-zirconium-carbon sliding plate and electronic equipment. According to the method, a context-based encoder model is adopted to carry out context encoding on physical performance parameters of the aluminum-zirconium-carbon slide plate prepared after a certain component in a preparation formula of the aluminum-zirconium-carbon slide plate is changed so as to obtain global physical parameter associated information, and distribution of data in a high-dimensional feature space is further corrected by utilizing a Gaussian density map; in this way, the parametric feature vectors are properly fused, so that the feature distributions can converge on the profile relative to each other. Meanwhile, on this basis, an image of a microstructure is introduced as a medium to improve the incidence relation among the parameters, and the accuracy of a decoding regression result is higher based on responsiveness estimation of a Gaussian density map, so that the optimal granularity and the optimal content of the silicon powder are determined through an artificial intelligence algorithm, and the accuracy of the decoding regression result is improved. Therefore, the finally sintered aluminum-zirconium-carbon material has better physical and chemical properties.

A method for characterizing jitter of an internal clock signal of a circuit may include generating a series of samples of the internal clock signal by a reference clock signal, comparing the word formed by the N most recent samples of the series to an N-bit pattern, where N is an integer greater than, or equal to 2, and incrementing a first counter if the word complies with the pattern. The method may also include incrementing a second counter when the count of the first counter reaches a first threshold X1, and incrementing a third counter when the count of the first counter reaches a second threshold different from the first. The method may include calculating an average p and a standard deviation σ of a Gaussian density curve as a function of the counts reached in the second and third counters.

The invention discloses a dense crowd counting method and system based on hidden density distribution and a terminal. The method comprises the steps: obtaining a self-adaptive hidden Gaussian densitygraph through a Gaussian network according to a crowd point graph; according to the counting loss item, the smooth item and the Bayesian item, guiding the hidden Gaussian density graph optimization, so as to enable the generation quality to be higher; taking the hidden Gaussian density map as a training target, combining an adversarial loss function and a Bayesian loss function, and outputting thedense crowd image as a predicted density distribution map; summing the predicted density distribution maps to obtain the predicted density people number. The density predictor, the hidden Gaussian density generator and the discriminator are alternately trained and cooperatively optimized. According to the method, the precision is improved to a large extent, the robustness is good, and the application value is high due to the fact that the parameter quantity and the operand of the inference stage are not increased.

The invention relates to the field of intelligent charging of electric vehicles, and particularly discloses a charging alarm system for an electric vehicle and a working method thereof, and the system employs a convolutional neural network model of a deep learning technology to extract high-dimensional implicit features of a magnetic field distribution diagram based on the magnetic field distribution of a transmitting coil and a receiving coil. And high-dimensional correlation feature information between the magnetic field distribution of the transmitting coil and the magnetic field distribution of the receiving coil is fused by using a Gaussian density map, so that the classification result is more accurate. In this way, the type of the foreign matter can be accurately recognized, and therefore different alarm levels are set based on different types.

The invention provides an electric power system noise self-adaptive robust state estimation method. The method comprises the following steps that 1, L measuring fracture surfaces are obtained, wherein the L is an integer; 2, measuring error estimation is conducted on each measuring fracture surface to obtain the error vector; 3, the statistical learning method is used for carrying out estimation to obtain the parameters of a general Gaussian density model GGD on the basis of the error vector obtained through the estimation of the L measuring fracture surfaces to obtain the distribution type of measured noise, and the best corresponding robust state estimation model is selected according to the distribution type of the measured noise. According to the electric power system noise self-adaptive robust state estimation method, due to the fact that the distribution rules of the noise can be obtained through the statistical learning process, and the distribution rules of the noise are matched with the robust state estimation method in an on-line mode, so that the self-adaptive effect of various kinds of noise types is achieved, namely, the best estimation result which is closer to the state variable true value can be obtained in any noise distribution type.

The invention discloses a visual vibration amplification method, a visual vibration detection method and a visual vibration detection system based on morphological component analysis, and belongs to the technical field of computer vision. The method comprises: acquiring a video file comprising a target object, and determining a region of interest of the target object in each frame of image in the video file; adopting a quality factor adjustable wavelet dictionary and a discrete sine and cosine dictionary to respectively represent a structure component and a texture component of each frame of video; adopting a threshold selection algorithm based on generalized Gaussian density distribution to determine thresholds of the structural components; separating an image structure component and a texture component by adopting a self-adaptive MCA; according to the separated structure components, in combination with the Euler visual angle principle, achieving amplification of micro-vibration signals, reconstructing an amplified video, meanwhile, extracting visual vibration signals for the structure components, and achieving measurement of multiple vibration frequencies.

The invention discloses an interactive yield optimization method and system based on a recurrent neural network, belongs to the technical field of chemical reaction yield optimization, and aims to solve the technical problem of how to obtain a relatively high chemical reaction yield on the premise of reducing the experiment cost. Comprising the following steps: acquiring various experimental condition parameters; simulating a current chemical reaction through a mixed Gaussian density function to obtain a reaction yield; constructing a chemical reaction model based on the recurrent neural network model; and initializing a historical data set, training the chemical reaction model by taking the encoded experimental condition parameters and the corresponding chemical reaction yield as current experimental condition parameters and corresponding reaction yield, outputting experimental condition parameters of the next round as target experimental parameters, and performing a chemical experiment based on the target experimental condition parameters. The obtained reaction yield is used as a target reaction yield; and under the condition that the target reaction yield reaches a threshold value, carrying out multiple rounds of training on the chemical reaction model.

The invention relates to the field of medical data processing, and particularly discloses a data processing system for early warning of osteofascial compartment syndrome through forearm skin data. A convolutional neural network model and an encoder model comprising a one-dimensional convolutional layer and a full-connection layer are adopted to carry out implicit feature extraction on a surface image, skin tension data and skin hardness data of the skin of a forearm; and the tension feature vector, the hardness feature vector and the surface feature map are fused by using a Gaussian density map and Gaussian responsiveness distribution so as to effectively unify the tension feature and the hardness feature on the basis of high-dimensional feature distribution of the surface image feature. Therefore, whether the front arm of the patient has the osteofascia space syndrome or not can be early warned in time by processing the medical data.

The embodiment of the invention provides a lithium ion battery SOC estimation method and device. The method comprises the following steps: determining a mapping relationship between each model parameter and an SOC value in a battery equivalent circuit model; running at least two Kalman filters in parallel, and establishing a battery discrete state space model in combination with the battery equivalent circuit model and the mapping relationship between each model parameter and the SOC value; and estimating the SOC value of the battery discrete state space model through Gaussian and Kalman filtering algorithms. According to the lithium ion battery SOC estimation method provided by the embodiment of the invention, a plurality of Kalman filters can be combined in the form of different weight coefficient proportions, and finally, the SOC value of the battery discrete state space model is estimated through Gaussian and Kalman filter algorithms to obtain the optimal SOC estimation value, so that the SOC estimation accuracy of the battery discrete state space model is improved. Equivalently, a plurality of Gaussian density functions are adopted to accurately describe the process noise andthe measurement noise in the whole process according to a certain weight coefficient, so that the accuracy of the SOC estimation result is greatly improved, and the universality is high.

The invention discloses a livestock inventory method, device and equipment, and a storage medium. The livestock inventory method comprises the steps of obtaining a image for inventory of a place wherelivestock is located; marking coordinate points at the geometric center point and a plurality of feature points of each target in the image for inventory; based on the coordinate points, establishingan elliptical Gaussian model taking the geometric center point as a Gaussian kernel, and obtaining a Gaussian density map; and inputting the for inventory image and the corresponding Gaussian densitymap into a deep learning neural network model, and performing regression training and integral processing to obtain the number of livestock. The embodiment of the invention can effectively improve the accuracy of the statistical result of the live pig number.

The invention relates to the field of wearable device interaction, and particularly discloses an interaction control system of a wearable device and a working method of the interaction control system. A convolutional neural network model based on a deep learning technology is adopted to mine correlation features between data items and between time points, wherein the data items and the time points are collected by a plurality of sensors pasted on an arm. Meanwhile, local correlation features in the signal oscillogram of the bio-electricity signals captured by the myoelectricity sensor within the preset time period are extracted, and a Gaussian density map based on likelihood maximization is further adopted to improve the fusion degree in a high-dimensional space, so that the classification result is more accurate. In this way, the willingness expressed by the human body action can be more accurately obtained from the obtained data, and then the experience of a user when the user controls the wearable device is improved.

The invention relates to the field of power supply control, and particularly discloses a power supply control system of terminal equipment with an NFC function and a working method of the power supply control system, high-dimensional implicit mode information between working states of an SIM card and working states of NFC is mined through a deep learning technology based on a neural network model, and in the process, the working states of the SIM card and the working states of the NFC card are subjected to the deep learning technology. And a Gaussian density map is adopted to accurately express respective information and classification characteristics of associated information of the two, so that whether the power supply power of the power supply needs to be adjusted or not is judged. Through the mode, the SIM card and the NFC card can be ensured to work normally under the condition of no unnecessary power fluctuation.