48results about How to "Fewer calculation parameters" patented technology

The invention discloses a soil deformation prediction method, system and device and a readable storage medium. The method comprises the steps: obtaining geometric design data of a target tunnel which passes through a large-thickness collapsible loess stratum, a soil internal friction angle of the large-thickness collapsible loess stratum, and a collapsibility coefficient which passes through the stratum; according to the geometric design data of the target tunnel, the friction angle in the soil body and the collapsibility coefficient of the penetrating stratum, the width of a settling tank at any vertical depth from the tunnel vault to the earth surface and the stratum loss rate after the target tunnel is excavated are calculated; according to geometric design data of the target tunnel, the stratum loss rate after excavation of the target tunnel and the width of a settling tank at the vertical depth in the large-thickness collapsible loess stratum, vertical displacement and horizontal displacement at any point between the earth surface of the large-thickness collapsible loess stratum and the arch crown of the target tunnel are calculated; a soil deformation prediction result is obtained; the method has the characteristics of clear meaning, simple formula, few calculation parameters and better accuracy.

The invention discloses a vehicle detection method in a video stream, comprising the following steps: acquiring a video frame, using a trained convolutional neural network to identify the obtained video frame, and judging whether there is a vehicle in the video according to the result, and the vehicle The specific position of the vehicle; the position of the vehicle in the image is obtained by the vehicle detection, and its value in the next frame is predicted by the Kalman filter as the vehicle tracking result; the results of the vehicle detection and the results of the vehicle tracking are correlated, according to the correlation results Modify filter parameters and update filter group information. The invention designs a convolutional neural network with less network layers and a small structure for the vehicle detection task, which has fast detection speed and high precision. The invention uses a Kalman tracking algorithm to predict the position of the detection frame, and the tracking speed is fast and stable. The invention uses a data association strategy to combine a detection algorithm with a tracking algorithm, embodies the correlation of vehicles between frames, and can reduce the missed detection rate of vehicle objects.

The invention relates to a photovoltaic power generation fault diagnosis system based on a photovoltaic physical model. The photovoltaic power generation fault diagnosis system is connected with a photovoltaic system to be monitored. The photovoltaic power generation fault diagnosis system comprises an environmental parameter collection device, a photovoltaic system parameter collection device, a signal converter and a fault diagnosis subsystem. When the photovoltaic system operates, the environmental parameter collection device collects photovoltaic system environmental parameters every a preset time, and the photovoltaic system environmental parameters are transmitted to the fault diagnosis subsystem after being converted by the signal converter; meanwhile, the photovoltaic system parameter collection device collects photovoltaic system parameters every a preset time, and the photovoltaic system parameters are transmitted to the fault diagnosis subsystem after being converted by the signal converter; the fault diagnosis subsystem carries out calculation and fault condition judgment according to the acquired parameters and outputs results. Compared with the prior art, the photovoltaic power generation fault diagnosis system has the advantages of being small in number of needed calculating parameters, simple in wire layout and reconstruction, low in cost and the like, and achieves safe and reliable monitoring and fault diagnosis on the photovoltaic system.

The invention discloses an image defogging method based on blue channel correction, which is applied to the technical field of computer vision and image processing, and aims at solving the problems ofpoor defogging effect and large algorithm limitation of the existing histogram equalization algorithm, multi-scale Retinex (MSR) algorithm, dark channel prior defogging algorithm and the like. The method comprises the following steps: firstly, extracting information of red, green and blue channels, combining and sorting the information to obtain depth and transmissivity information, then respectively calculating the red, green and blue channels of an image to obtain a defogged image of each channel, and finally combining the images to obtain a final defogged image. The method has the advantages of few calculation parameters, simple and rapid calculation, good effect and no color cast, and is very suitable for processing outdoor fog images.

The invention provides a method for measuring the salt expansion of saline soil foundation. The method for measuring the salt expansion of the saline soil foundation can determine the salt expansion of the saline soil foundation in a winter temperature reduction period scientifically and reasonably, and can meet practical engineering requirements. The method for measuring the salt expansion of thesaline soil foundation comprises the following steps that through field investigation or data query, the salt expansion deformation calculating depth l<0> of the saline soil foundation, the temperature T<i1> of an I layer of soil before winter temperature reduction and the lowest temperature T<i2> of the I layer of the soil during winter temperature reduction period are determined; soil samples of saline soil are collected on site, and a salt expansion test, a moisture content test, a soil particle specific gravity test and a gravity test are carried out indoors, the salt expansion index C<si> of the I layer of the soil, the salt expansion force sigma<Ti> of the I layer of the soil after the temperature drops from T<i1> to T<i2>, the initial void ratio e<0i> of the I layer of the soil at the temperature of T<i1> and the effective self-weight stress sigma<y0i> are determined; and the salt expansion of the saline soil foundation is determined through the following formula, and please see the specification for the formula.

The invention discloses an image defogging method based on blue channel correction, which is applied in the technical field of computer vision and image processing, and aims at the existing histogram equalization algorithm, multi-scale Retinex (MSR) algorithm and dark channel prior defogging method and other algorithms, the problem of poor defogging effect and large algorithm limitations; the present invention firstly extracts the information of the red, green and blue channels, and combines them to obtain the depth and transmittance information, and then converts the image red, The green and blue channels are calculated separately to obtain the defogged image of each channel, and the final combined image is obtained to obtain the final defogged image; it has fewer calculation parameters, simple and fast calculation, good effect, and no color cast, which is very suitable for outdoor fog images Advantages of processing.

The present application relates to a machine learning-based spectrum sensing method, device, computer equipment and storage medium in the NOMA system. The method includes: when it is necessary to use the target frequency band to send a message, sending a sensing application of the target frequency band to the corresponding base station, so that the base station issues a spectrum sensing task for the target frequency band to each user within the coverage area; Spectrum observation energy signals collected by secondary users in the sensing time slot; according to the location information of each user, the spectrum observation energy values ​​corresponding to each spectrum observation energy signal are filled into the spectrum energy observation matrix, and the obtained energy observation matrix is ​​pre-formed. Processing, the processed energy observation matrix is ​​converted into an image, and the obtained grayscale image is input to the spectrum sensing model based on the Shuffle-Dense neural network for identification, and the current channel state of the target frequency band is output, which improves the accuracy of spectrum sensing.

The invention discloses a method for quickly determining a third-order passive intermodulation product level of a rectangular waveguide flange, and belongs to the passive intermodulation field of microwave components. The method includes: Step 1: the rectangular waveguide flange contact surface is equivalent to a metal-insulation layer-metal structure, and the potential difference between the twosides of the rectangular waveguide flange contact surface is determined; Step 2: The third-order nonlinear current generated by the rectangular waveguide flange contact surface is determined by the rectangular waveguide size, the flange contact area, and the potential difference between the two sides of the contact surface; Step 3: the level of the third-order passive intermodulation products of the rectangular waveguide flange is determined by using the carrier frequency, the rectangular waveguide size, and the third-order nonlinear current.. The invention can obtain the third-order passive intermodulation product level of the rectangular waveguide flange by simple calculation without obtaining the passive intermodulation low-order product and the surface topography parameter measurementin advance, thereby greatly improving the rectangular waveguide method. The efficiency of the passive passive intermodulation problem is reduced and the cost is reduced.