Improving model performance in mapping cropland soil organic matter using time-series remote sensing data

Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management policies.As a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different scales.However,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance.To overcome this limitation,this study systematically assessed a framework of“information extractionfeature selection-model averaging”for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in 2021.The results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R^(2):0.48 to 0.53)without having obviously negative impacts on uncertainty.Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R^(2)of random forest from 0.44 to 0.48 and the R^(2)of extreme gradient boosting from 0.37to 0.43.Forward recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(<200),whereas Boruta is recommended when there are many environmental covariates(>500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty.When the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final predictions.Given the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales,so this approach can provide more reliable references for soil conservation policy-making.

Kinematic-mapping-model-guided analysis and optimization of 2-PSS&1-RR circular-rail parallel mechanism for fully steerable phased array antennas

This paper presents a systematic methodology for analyzing and optimizing an innovative antenna mount designed for phased array antennas, implemented through a novel 2-PSS&1-RR circular-rail parallel mechanism. Initially, a comparative motion analysis between the 3D model of the mount and its full-scale prototype is conducted to validate effectiveness. Given the inherent complexity, a kinematic mapping model is established between the mount and the crank-slider linkage, providing a guiding framework for subsequent analysis and optimization. Guided by this model, feasible inverse and forward solutions are derived, enabling precise identification of stiffness singularities. The concept of singularity distance is thus introduced to reflect the structural stiffness of the mount. Subsequently, also guided by the mapping model, a heuristic algorithm incorporating two backtracking procedures is developed to reduce the mount's mass. Additionally, a parametric finite-element model is employed to explore the relation between singularity distance and structural stiffness. The results indicate a significant reduction(about 16%) in the antenna mount's mass through the developed algorithm, while highlighting the singularity distance as an effective stiffness indicator for this type of antenna mount.

Estimation and mapping of water erosion and soil loss:Application of Gavrilovic erosion potential model(EPM)using GIS and remote sensing in the Assif el mal Watershed,Western high Atlas

Water erosion is a serious problem that leads to soil degradation,loss,and the destruction of structures.Assessing the risk of erosion and determining the affected areas has become crucial in order to understand the main factors influencing its evolution and to minimize its impacts.This study focuses on evaluating the risk of erosion in the Assif el mal watershed,which is located in the High Atlas Mountains.The Erosion Potential Model(EPM)is used to estimate soil losses depending on various parameters such as lithology,hydrology,topography,and morphometry.Geographic information systems and remote sensing techniques are employed to map areas with high erosive potential and their relationship with the distribution of factors involved.Different digital elevation models are also used in this study to highlight the impact of data quality on the accuracy of the results.The findings reveal that approximately 59%of the total area in the Assif el mal basin has low to very low potential for soil losses,while 22%is moderately affected and 19.9%is at high to very high risk.It is therefore crucial to implement soil conservation measures to mitigate and prevent erosion risks.

Intrusion Detection Model Using Chaotic MAP for Network Coding Enabled Mobile Small Cells

Wireless Network security management is difficult because of the ever-increasing number of wireless network malfunctions,vulnerabilities,and assaults.Complex security systems,such as Intrusion Detection Systems(IDS),are essential due to the limitations of simpler security measures,such as cryptography and firewalls.Due to their compact nature and low energy reserves,wireless networks present a significant challenge for security procedures.The features of small cells can cause threats to the network.Network Coding(NC)enabled small cells are vulnerable to various types of attacks.Avoiding attacks and performing secure“peer”to“peer”data transmission is a challenging task in small cells.Due to the low power and memory requirements of the proposed model,it is well suited to use with constrained small cells.An attacker cannot change the contents of data and generate a new Hashed Homomorphic Message Authentication Code(HHMAC)hash between transmissions since the HMAC function is generated using the shared secret.In this research,a chaotic sequence mapping based low overhead 1D Improved Logistic Map is used to secure“peer”to“peer”data transmission model using lightweight H-MAC(1D-LM-P2P-LHHMAC)is proposed with accurate intrusion detection.The proposed model is evaluated with the traditional models by considering various evaluation metrics like Vector Set Generation Accuracy Levels,Key Pair Generation Time Levels,Chaotic Map Accuracy Levels,Intrusion Detection Accuracy Levels,and the results represent that the proposed model performance in chaotic map accuracy level is 98%and intrusion detection is 98.2%.The proposed model is compared with the traditional models and the results represent that the proposed model secure data transmission levels are high.

General mapping of one-dimensional non-Hermitian mosaic models to non-mosaic counterparts:Mobility edges and Lyapunov exponents

We establish a general mapping from one-dimensional non-Hermitian mosaic models to their non-mosaic counterparts.This mapping can give rise to mobility edges and even Lyapunov exponents in the mosaic models if critical points of localization or Lyapunov exponents of localized states in the corresponding non-mosaic models have already been analytically solved.To demonstrate the validity of this mapping,we apply it to two non-Hermitian localization models:an Aubry-Andre-like model with nonreciprocal hopping and complex quasiperiodic potentials,and the Ganeshan-Pixley-Das Sarma model with nonreciprocal hopping.We successfully obtain the mobility edges and Lyapunov exponents in their mosaic models.This general mapping may catalyze further studies on mobility edges,Lyapunov exponents,and other significant quantities pertaining to localization in non-Hermitian mosaic models.

Imaging simulation and analysis of attitude jitter effect on topographic mapping for lunar orbiter stereo optical cameras

The geometric accuracy of topographic mapping with high-resolution remote sensing images is inevita-bly affected by the orbiter attitude jitter.Therefore,it is necessary to conduct preliminary research on the stereo mapping camera equipped on lunar orbiter before launching.In this work,an imaging simulation method consid-ering the attitude jitter is presented.The impact analysis of different attitude jitter on terrain undulation is conduct-ed by simulating jitter at three attitude angles,respectively.The proposed simulation method is based on the rigor-ous sensor model,using the lunar digital elevation model(DEM)and orthoimage as reference data.The orbit and attitude of the lunar stereo mapping camera are simulated while considering the attitude jitter.Two-dimensional simulated stereo images are generated according to the position and attitude of the orbiter in a given orbit.Experi-mental analyses were conducted by the DEM with the simulated stereo image.The simulation imaging results demonstrate that the proposed method can ensure imaging efficiency without losing the accuracy of topographic mapping.The effect of attitude jitter on the stereo mapping accuracy of the simulated images was analyzed through a DEM comparison.

Research on Urban Building Parametric Modelling Method Based on CityEngine

With the advancement of technology and the development of cities,urban planning and management methods are also constantly improving.From paper-based assignments to modern digitization,new technologies have enabled more efficient design and management for cities.3D modeling can used to simulate the urban environment,which can assist in urban planning and management.However,large-scale modeling cannot be achieved through existing modeling methods,and there are still some shortcomings in the maintenance of the model.Therefore,this article proposes a Computer Generated Architecture(CGA)parametric 3D modeling method based on CityEngine.Research on expanding and customizing modeling rules to create indoor and outdoor modeling rule templates for buildings and methods for generating urban 3D models have been carried out.The results have shown that the completed model can be displayed on different platforms thanks to parameterized modeling.The model can be modified easily and directly applied to the analysis and decision-making of urban planning schemes.

DP-Modeler精细化建模在水利工程测量中的应用

倾斜摄影测量生产的倾斜模型已能够应用在多数水利工程项目中,但现有倾斜模型生产的原理都是基于影像特征点提取和匹配来实现自动建模,自动化程度高,导致真三维模型建模效果不佳,因此需要对倾斜模型进行修饰以及单体化建模来弥补这种不足。基于生产需要,利用DP-Modeler软件,从建模原理与过程、模型修饰、单体化重构三方面来探讨一种更加有效的工作方法及流程,使得水利工程上的三维模型更加精细化、可利用化。此方法切合实际,可操作性强,对同类水利工程的精细化三维模型建设具有一定的参考借鉴意义。

基于MapleSim-MATLAB双向耦合技术的液压支架孪生体构建

为解决目前综采工作面存在难以全面掌握装备运行状态监测的问题,以液压支架孪生体的建模为切入点,提出基于MapleSim-MATLAB双向耦合技术的孪生体构建,仿真显示了液压支架升架运动过程中零部件的相对运动及装配关系,并通过实验证明孪生体和物理体交互效果良好。结果表明:以液压支架连杆和底座倾角为例,物理体与孪生体位姿误差率不超过3‰,其拟合度接近1,孪生体和物理体的相互映射,证明该方法能够完成对设备位姿的监测。

基于Z-map模型的球头铣刀铣削力建模与仿真

对球头铣刀铣削力建模进行了研究。在Z-map仿真模型的基础上,提出了识别加工中参与切削的切削刃单元的方法,通过考虑刀具偏心和刀具变形对瞬时切削厚度的影响,推导出了瞬时切削厚度的表达式,由铣削力和切削负载之间的关系,建立了球头铣刀三轴铣削力仿真模型。铣削力仿真计算与铣削实验的对比表明在考虑刀具偏心和刀具变形后所建立的铣削力模型能够对铣削力进行准确的预测。

Z-Map模型精简技术的研究

在数控编程技术向着智能化发展的情况下,留量模型在数控加工系统中得到了广泛的应用。文章指出了留量模型在数控编程技术中的重要性,分析了Z-Map模型作为加工用几何模型的优缺点,针对Z-Map模型数据量大的不足提出了一种在Z向误差控制下对Z-Map模型进行精简的算法,建立了一种数据压缩的精简留量模型。文章最后探讨了精简留量模型在刀具轨迹的仿真、生成和优化中的应用情况。

基于Z-map理论的喷枪轨迹生成方法

喷漆机器人作为一种先进的涂装生产设备已广泛应用于产品内外表面的喷涂,由于现代工业产品外形相当复杂,如何实现涂层均匀化变得极为重要。基于Z-map理论,在自由曲面CAD模型的基础上,将自由曲面三角网格化得到多面体模型,然后利用Z-map方法实现了等距喷涂情况下喷涂节点的坐标及其法向量的提取,将喷涂节点插补优化后连接为喷枪的喷涂轨迹,从而实现了自动生成自由曲面上喷枪轨迹。最后,以一汽车CAD模型为例生成了机器人喷涂轨迹。

Ontology mapping based on hidden Markov model

The existing ontology mapping methods mainly consider the structure of the ontology and the mapping precision is lower to some extent. According to statistical theory, a method which is based on the hidden Markov model is presented to establish ontology mapping. This method considers concepts as models, and attributes, relations, hierarchies, siblings and rules of the concepts as the states of the HMM, respectively. The models corresponding to the concepts are built by virtue of learning many training instances. On the basis of the best state sequence that is decided by the Viterbi algorithm and corresponding to the instance, mapping between the concepts can be established by maximum likelihood estimation. Experimental results show that this method can improve the precision of heterogeneous ontology mapping effectively.

GPS probe map matching algorithm based on spatial data model

To improve the performance of the traditional map matching algorithms in freeway traffic state monitoring systems using the low logging frequency GPS （global positioning system） probe data, a map matching algorithm based on the Oracle spatial data model is proposed. The algorithm uses the Oracle road network data model to analyze the spatial relationships between massive GPS positioning points and freeway networks, builds an N-shortest path algorithm to find reasonable candidate routes between GPS positioning points efficiently, and uses the fuzzy logic inference system to determine the final matched traveling route. According to the implementation with field data from Los Angeles, the computation speed of the algorithm is about 135 GPS positioning points per second and the accuracy is 98.9%. The results demonstrate the effectiveness and accuracy of the proposed algorithm for mapping massive GPS positioning data onto freeway networks with complex geometric characteristics.

A Flexible Multi-Layer Map Model Designed for Lane-Level Route Planning in Autonomous Vehicles

An increasing number of drivers are relying on digital map navigation systems in vehicles or mobile phones to select optimal driving routes in order to save time and improve safety. In the near future, digital map navigation systems are expected to play more important roles in transportation systems. In order to extend current navigation systems to more applications, two fundamental problems must be resolved: the lane-level map model and lane-level route planning. This study proposes solutions to both problems. The current limitation of the lane-level map model is not its accuracy but its flexibility;this study proposes a novel seven-layer map structure, called as Tsinghua map model, which is able to support autonomous driving in a flexible and efficient way. For lane-level route planning, we propose a hierarchical route-searching algorithm to accelerate the planning process, even in the presence of complicated lane networks. In addition, we model the travel costs allocated for lane-level road networks by analyzing vehicle maneuvers in traversing lanes, changing lanes, and turning at intersections. Tests were performed on both a grid network and a real lane-level road network to demonstrate the validity and efficiency of the proposed algorithm.

Modelling and mapping soil erosion potential in China

Soil erosion is an important environmental threat in China.However,quantitative estimates of soil erosion in China have rarely been reported in the literature.In this study,soil loss potential in China was estimated by integrating satellite images,field samples,and ground observations based on the Revised Universal Soil Loss Equation(RUSLE).The rainfall erosivity factor was estimated from merged rainfall data using Collocated CoKriging(ColCOK)and downscaled by geographically weighted regression(GWR).The Random Forest(RF)regression approach was used as a tool for understanding and predicting the relationship between the soil erodibility factor and a set of environment factors.Our results show that the average erosion rate in China is 1.44 t ha^(–1) yr^(–1).More than 60%of the territory in China is influenced by soil erosion limitedly,with an average potential erosion rate less than 0.1 t ha^(–1) yr^(–1).Other unused land and other forested woodlands showed the highest erosion risk.Our estimates are comparable to those of runoff plot studies.Our results provide a useful tool for soil loss assessments and ecological environment protections.

Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree,random forest and information value models

This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.

The homotopic mapping method for sea-air oscillator model of interdecadal climate fluctuations

The E1 Nifio/La Nifia Southern Oscillation （ENSO） is an interannual phenomenon involved in the tropical Pacific ocean-atmosphere interactions. In this paper, a coupled system of sea-air oscillator model is studied. The aim is to create an asymptotic solving method of nonlinear equation for the ENSO model. And based on a class of oscillators of ENSO model, employing the method of homotopic mapping, the approximate solution of corresponding problem is studied. It is proven from the results that the homotopic method can be used for analysing the sea surface temperature anomaly in the equatorial eastern Pacific and the thermocline depth anomaly of the atmosphere-ocean oscillation for ENSO model.

Feedback control scheme of traffic jams based on the coupled map car-following model

Based on the pioneering work of Konishi et al. [Phys. Rev. E （1999） 60 4000], a new feedback control scheme is presented to suppress traffic jams based on the coupled map car-following model under the open boundary condition. The effect of the safe headway on the traffic system is considered. According to the control theory, the condition under which traffic jams can be suppressed is analyzed. The results are compared with the previous results concerning congestion control. The simulations show that the suppression performance of our scheme on traffic jams is better than those of the previous schemes, although all the schemes can suppress traffic jams. The simulation results are consistent with theoretical analyses.

A new coupled map car-following model considering drivers' steady desired speed

Based on the pioneering work of Konishi et al., in consideration of the influence of drivers＇ steady desired speed ef/ect on the traffic flow, we develop a new coupled map car-following model in the real world. By use of the control theory, the stability condition of our model is derived. The validity of the present theoretical scheme is verified via numerical simulation, confirming the correctness of our theoretical analysis.