5 results about "Logarithmic scale" patented technology

The invention relates to the field of robot balance control, and discloses a control parameter optimization method of a second-order balance robot. According to the method, for a second-order balance system formed by connecting a vehicle body and a swing rod in series, a dynamic model and a linearization discrete state space model are established, and a linear quadratic regulator control framework is constructed; taking diagonal elements of the state weight matrix as to-be-optimized parameters, mapping the to-be-optimized parameters to a logarithmic scale search space after left-right symmetry constraint dimensionality reduction processing, and performing optimization by adopting a particle swarm optimization algorithm; and a composite cost function including a core error term, a control saturation term and a control stability term is constructed based on vehicle body inclination angle response, swing rod inclination angle response, control input and closed-loop poles, and an optimal state weight matrix and a corresponding control gain are output to serve as optimal control parameters of the second-order balance robot. The second-order balance robot control parameter setting method can improve pertinence and stability of second-order balance robot control parameter setting.

The utility model discloses a rapid navigation plotting ruler, which relates to the technical field of navigation plotting and comprises a ship position line plotting ruler and a plotting measuring ruler. Wherein a fan-shaped pattern with a vertex angle of 90 degrees is arranged on a panel of the ship position line plotting ruler; first open grooves are formed in two radiuses of the fan-shaped pattern, an open hole is formed in the circle center of the fan-shaped pattern, and angle scales are arranged at the arc of the fan-shaped pattern; a speed measuring ruler pattern and a drawing ruler pattern are arranged on a panel of the drawing measuring ruler; a second open groove is formed in the drawing ruler pattern; logarithmic scale scales are arranged on the edge of the plotting measuring scale. According to the utility model, the technical problem that the existing navigation plotting process is inconvenient to operate is solved.

This invention relates to the field of UAV image processing technology, specifically to a method and system for diagnosing secondary degradation of alpine grasslands based on multi-source data. Existing technologies suffer from the following drawbacks: low image processing accuracy, large data processing errors, and a lack of spatiotemporal comparability. To address these shortcomings, this invention first acquires images using the hypotenuse target method and calculates the modulation transfer function characteristic parameters to construct a multi-scale Gaussian space. An adaptive threshold algorithm is used to determine and fix thresholds, achieving binarization of the multi-scale images. Connected components meeting certain conditions are extracted from the binary images at each scale, and their directional robust perimeters are calculated, forming a perimeter sequence. Based on the perimeter sequence, a logarithmic scale-perimeter curve is established, and the second derivative is calculated to determine the collapse scale and standard scale, calculating the geometric features of the connected components. Combining degradation judgment thresholds and proportional constraints, crack-type early degradation patches satisfying the condition with the maximum negative second-order curvature are identified, achieving automatic identification of secondary grassland degradation.

The invention discloses a model training method and device, electronic equipment and a computer program product. The method comprises the following steps: constructing a target model for monocular depth estimation; obtaining training data including a color image and a corresponding real depth map; processing the color image based on the target model to obtain a predicted depth map; a preset mixed loss function is adopted to calculate the loss between the predicted depth map and the real depth map, parameters of the target model are adjusted according to the loss, and the mixed loss function is the weighted sum of a scale-invariant loss item, a gradient loss item and a smooth loss item; the scale invariant loss item is obtained by predicting the difference between the depth map and the real depth map on the logarithmic scale, the gradient loss item is obtained by predicting the difference between the depth map and the real depth map on the gradient space, and the smooth loss item is obtained by performing weighted punishment on the gradient of the depth map through the gradient of the color image. According to the scheme, the quality of the depth map output by the obtained depth estimation model can be improved.

The application discloses a kind of based on double-flow self-adapting physical information fusion forging press energy consumption prediction system and method, comprising: based on physical principle to establish energy consumption mechanism model, and according to real-time state parameter calculation each component theoretical energy consumption value as mechanism data, mechanism data and real-time state parameter are selected for feature, respectively construct mechanism dataset and acquisition dataset, mechanism flow uses time series encoder based on mechanism dataset to carry out prediction, and introduce adaptive correction mechanism to compensate non-modeling energy consumption, while using logarithmic scale soft constraint to keep reasonable deviation of predicted value and theoretical value;Data flow uses time series encoder based on acquisition dataset to carry out prediction driven by pure data, and is trained by minimizing its prediction error, based on Monte Carlo Dropout quantification double-flow prediction uncertainty, generate dynamic fusion weight, and under the constraint of anti-degeneration regularization, the weighted fusion of two-flow predicted values is carried out, and the final comprehensive energy consumption predicted value is output.