Detection of K in soil using time-resolved laser-induced breakdown spectroscopy based on convolutional neural networks

One of the technical bottlenecks of traditional laser-induced breakdown spectroscopy(LIBS) is the difficulty in quantitative detection caused by the matrix effect. To troubleshoot this problem,this paper investigated a combination of time-resolved LIBS and convolutional neural networks(CNNs) to improve K determination in soil. The time-resolved LIBS contained the information of both wavelength and time dimension. The spectra of wavelength dimension showed the characteristic emission lines of elements, and those of time dimension presented the plasma decay trend. The one-dimensional data of LIBS intensity from the emission line at 766.49 nm were extracted and correlated with the K concentration, showing a poor correlation of R_c^2?=?0.0967, which is caused by the matrix effect of heterogeneous soil. For the wavelength dimension, the two-dimensional data of traditional integrated LIBS were extracted and analyzed by an artificial neural network(ANN), showing R_v^2?=?0.6318 and the root mean square error of validation(RMSEV)?=?0.6234. For the time dimension, the two-dimensional data of time-decay LIBS were extracted and analyzed by ANN, showing R_v^2?=?0.7366 and RMSEV?=?0.7855.These higher determination coefficients reveal that both the non-K emission lines of wavelength dimension and the spectral decay of time dimension could assist in quantitative detection of K.However, due to limited calibration samples, the two-dimensional models presented over-fitting.The three-dimensional data of time-resolved LIBS were analyzed by CNNs, which extracted and integrated the information of both the wavelength and time dimension, showing the R_v^2?=?0.9968 and RMSEV?=?0.0785. CNN analysis of time-resolved LIBS is capable of improving the determination of K in soil.

Design and parameters optimization of the curved sieve for an air suction jujube harvester

In order to improve the screening performance and cleaning effect of the jujube harvesting machinery cleaning device,a vibrating curved screen device was designed in this study.By analyzing the structure mechanism of the curved sieve body,it was obtained that the arc-shaped mesh hole spacing S was 15-25 mm and the curved mesh hole curvature U was 90°-150°.By exploring the movement state and stress of jujube and impurities on the curved sieve body,it was determined that the horizontal spacing L of the curved layer sieve was 30 mm and the vertical spacing H was 45-65 mm.Taking the vertical spacing H of the curved layer sieve,the curvature U of the curved mesh hole,and the spacing S of the curved mesh hole as the experimental factors,considering the screening efficiencyαand the impurity contentβof the jujube as the response values,the three-factor three-level quadratic regression orthogonal experiment was designed,establishing the regression mathematical model of each factor and response value,and the multiple target optimization algorithm of Design-expert software was used to optimize various factors.The results showed that the influence factors on the screening efficiency were in the descending order as:the arc screen spacing,the vertical spacing of the curved layer screen,and the curved screen hole curvature;The significant factors affecting the impurity content of jujube were in the descending order as:the arc screen spacing,the curved screen hole curvature,and the vertical spacing of the curved layer screen.The experimental results were verified by the optimized combination of parameters:when the vertical spacing H of the curved layer screen was 65 mm,the curved screen hole curvature U was 110°,and the arc screen spacing S was 23 mm,the average screening efficiencyαin the test was 91.09%.The relative error between the experimental verification value and the theoretical optimization value was 1.36%,which was less than 5%.The impurity content of jujubeβin the test was 1.02%.The relative error between the experimental verification value and the theoretical optimization value was 2.00%,which was also less than 5%.The test results can provide a reference for the research and optimization of the subsequent air-suction-type jujube harvester cleaning device.

A new pose estimation method for non-cooperative spacecraft based on point cloud

Purpose–On-orbitservice technologyis one of the key technologies of space manipulation activities such as spacecraft life extension,fault spacecraft capture,on-orbit debris removal and so on.It is known that the failure satellites,space debris and enemy spacecrafts in space are almost all non-cooperative targets.Relatively accurate pose estimation is critical to spatial operations,but also a recognized technical difficulty because of the undefined prior information of non-cooperative targets.With the rapid development of laser radar,the application of laser scanning equipment is increasing in the measurement of non-cooperative targets.It is necessary to research a new pose estimation method for non-cooperative targets based on 3D point cloud.The paper aims to discuss these issues.Design/methodology/approach–In this paper,a method based on the inherent characteristics of a spacecraft is proposed for estimating the pose(position and attitude)of the spatial non-cooperative target.First,we need to preprocess the obtained point cloud to reduce noise and improve the quality of data.Second,according to the features of the satellite,a recognition system used for non-cooperative measurement is designed.The components which are common in the configuration of satellite are chosen as the recognized object.Finally,based on the identified object,the ICP algorithm is used to calculate the pose between two frames of point cloud in different times to finish pose estimation.Findings–The new method enhances the matching speed and improves the accuracy of pose estimation compared with traditional methods by reducing the number of matching points.The recognition of components on non-cooperative spacecraft directly contributes to the space docking,on-orbit capture and relative navigation.Research limitations/implications–Limited to the measurement distance of the laser radar,this paper considers the pose estimation for non-cooperative spacecraft in the close range.Practical implications–The pose estimation method for non-cooperative spacecraft in this paper is mainly applied to close proximity space operations such as final rendezvous phase of spacecraft or ultra-close approaching phase of target capture.Thesystem can recognizecomponents needed to be captureand provide the relative pose of non-cooperative spacecraft.The method in this paper is more robust compared with the traditional single component recognition method and overall matching method when scanning of laser radar is not complete or the components are blocked.Originality/value–This paper introduces a new pose estimation method for non-cooperative spacecraft based on point cloud.The experimental results show that the proposed method can effectively identify the features of non-cooperative targets and track their position and attitude.The method is robust to the noise and greatly improves the speed of pose estimation while guarantee the accuracy.