Adjustment Model and Algorithm Based on Ellipsoid Uncertainty

In surveying adjustment models,there is usually some uncertain additional information or prior information on parameters,which can constrain the parameters,and guarantee the uniqueness and stability of parameter solution.In this paper,we firstly use ellipsoidal sets to describe uncertainty,and establish a new adjustment model with ellipsoidal uncertainty.Furthermore,we give a new adjustment criterion based on minimization trace of an outer ellipsoid with two ellipsoid intersections,and analyze the propagation law of uncertainty.Correspondingly,we give a new algorithm for the adjustment model with ellipsoid uncertainty.Finally,we give three examples to test and verify the effectiveness of our algorithm,and illustrate the relation between our result and the weighted mixed estimation.

Abnormal Behavior Detection and Recognition Method Based on Improved ResNet Model

The core technology in an intelligent video surveillance system is that detecting and recognizing abnormal behaviors timely and accurately.The key breakthrough point in recognizing abnormal behaviors is how to obtain the effective features of the picture,so as to solve the problem of recognizing them.In response to this difficulty,this paper introduces an adjustable jump link coefficients model based on the residual network.The effective coefficients for each layer of the network can be set after using this model to further improving the recognition accuracy of abnormal behavior.A convolution kernel of 1×1 size is added to reduce the number of parameters for the purpose of improving the speed of the model in this paper.In order to reduce the noise of the data edge,and at the same time,improve the accuracy of the data and speed up the training,a BN(Batch Normalization)layer is added before the activation function in this network.This paper trains this network model on the public ImageNet dataset,and then uses the transfer learning method to recognize these abnormal behaviors of human in the UTI behavior dataset processed by the YOLO_v3 target detection network.Under the same experimental conditions,compared with the original ResNet-50 model,the improved model in this paper has a 2.8%higher accuracy in recognition of abnormal behaviors on the public UTI dataset.

Linear-regression models and algorithms based on the Total-Least-Squares principle

In classical regression analysis, the error of independent variable is usually not taken into account in regression analysis. This paper presents two solution methods for the case that both the independent and the dependent variables have errors. These methods are derived from the condition-adjustment and indirect-adjustment models based on the Total-Least-Squares principle. The equivalence of these two methods is also proven in theory.

A structural topological optimization method for multi-displacement constraints and any initial topology configuration

In density-based topological design, one expects that the final result consists of elements either black （solid material） or white （void）, without any grey areas. Moreover, one also expects that the optimal topology can be obtained by starting from any initial topology configuration. An improved structural topological optimization method for multidisplacement constraints is proposed in this paper. In the proposed method, the whole optimization process is divided into two optimization adjustment phases and a phase transferring step. Firstly, an optimization model is built to deal with the varied displacement limits, design space adjustments, and reasonable relations between the element stiffness matrix and mass and its element topology variable. Secondly, a procedure is proposed to solve the optimization problem formulated in the first optimization adjustment phase, by starting with a small design space and advancing to a larger deign space. The design space adjustments are automatic when the design domain needs expansions, in which the convergence of the proposed method will not be affected. The final topology obtained by the proposed procedure in the first optimization phase, can approach to the vicinity of the optimum topology. Then, a heuristic algorithm is given to improve the efficiency and make the designed structural topology black/white in both the phase transferring step and the second optimization adjustment phase. And the optimum topology can finally be obtained by the second phase optimization adjustments. Two examples are presented to show that the topologies obtained by the proposed method are of very good 0/1 design distribution property, and the computational efficiency is enhanced by reducing the element number of the design structural finite model during two optimization adjustment phases. And the examples also show that this method is robust and practicable.

BDS satellite clock offset prediction based on a semiparametric adjustment model considering model errors

In view of the influence of model errors in conventional BeiDou prediction models for clock offsets,a semiparametric adjustment model for BeiDou Navigation Satellite System(BDS)clock offset prediction that considers model errors is proposed in this paper.First,the model errors of the conventional BeiDou clock offset prediction model are analyzed.Additionally,the relationship among the polynomial model,polynomial model with additional periodic term correction,and its periodic correction terms is explored in detail.Second,considering the model errors,combined with the physical relationship between phase,frequency,frequency drift,and its period in the clock sequence,the conventional clock offset prediction model is improved.Using kernel estimation and comprehensive least squares,the corresponding parameter solutions of the prediction model and the estimation of its model error are derived,and the dynamic error correction of the clock sequence model is realized.Finally,the BDS satellite precision clock data provided by the IGS Center of Wuhan University with a sampling interval of 5 min are used to compare the proposed prediction method with commonly used methods.Experimental results show that the proposed prediction method can better correct the model errors of BDS satellite clock offsets,and it can effectively overcome the inaccuracies of clock offset correction.The average forecast accuracies of the BeiDou satellites at 6,12,and 24 h are 27.13%,37.71%,and 45.08%higher than those of the conventional BeiDou clock offset forecast models;the average model improvement rates are 16.92%,20.96%,and 28.48%,respectively.In addition,the proposed method enhances the existing BDS satellite prediction method for clock offsets to a certain extent.

The optimization method of industrial robot arm structure based on green manufacturing technology

In order to realize the optimal design of the industrial robot arm structure,an optimization method of the industrial robot arm structure based on green manufacturing technology is proposed.The stability of arm structure parameter acquisition can be controlled.The quantitative adjustment model of structural optimization parameters is constructed.The differential fusion control of the arm structure is realized.This paper analyzes the structure parameter law of the robot arm.We use dynamic parameter prediction and output torque parameter compensation method to control the arm structure.According to the adaptive iterative processing results,the arm structure parameter identification is realized.According to the identification results,the cutting parameter optimization method is adopted for the analytical control of the arm structure,and finally the optimized design of the industrial robot arm structure is realized through the green manufacturing technology.The simulation test results show that for the accuracy of the industrial robot arm structure design,this method is better,the output stability is higher,and the arm motion trajectory has a low deviation from the actual motion trajectory,which improves the optimization control and design capabilities of the industrial robot arm structure.

Effect of adjusted mesoscale drag model on flue gas desulfurization in powder-particle spouted beds

An energy minimum multiscale model was adjusted to simulate the mesoscale structure of the flue gas desulfurization process in a powder-particle spouted bed and verified experimentally.The obtained results revealed that the spout morphology simulated by the adjusted mesoscale drag model was unstable and discontinuous bubbling spout unlike the stable continuous spout obtained using the Gidaspow model.In addition,more thorough gas radial mixing was achieved using the adjusted mesoscale drag model.The mass fraction of water in the gas mixture at the outlet determined by the heterogeneous drag model was 1.5 times higher than that obtained by the homogeneous drag model during the simulation of water vaporization.For the desulfurization reaction,the experimental desulfurization efficiency was 75.03%,while the desulfurization efficiencies obtained by the Gidaspow and adjusted mesoscale drag models were 47.63%and 75.08%,respectively,indicating much higher accuracy of the latter technique.

Investigation and Research on Auto-Aiming of Self-Propelled Gun

This paper analyses the automatic adjusting of the aim angle because of self-propelledgun’s platform sloping with ground plane caused by self-propelled gun movement, sets up adjustingmethod and model, and presents the corresponding algorithm.