102 research outputs found

    4DRVO-Net: Deep 4D Radar-Visual Odometry Using Multi-Modal and Multi-Scale Adaptive Fusion

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    Four-dimensional (4D) radar--visual odometry (4DRVO) integrates complementary information from 4D radar and cameras, making it an attractive solution for achieving accurate and robust pose estimation. However, 4DRVO may exhibit significant tracking errors owing to three main factors: 1) sparsity of 4D radar point clouds; 2) inaccurate data association and insufficient feature interaction between the 4D radar and camera; and 3) disturbances caused by dynamic objects in the environment, affecting odometry estimation. In this paper, we present 4DRVO-Net, which is a method for 4D radar--visual odometry. This method leverages the feature pyramid, pose warping, and cost volume (PWC) network architecture to progressively estimate and refine poses. Specifically, we propose a multi-scale feature extraction network called Radar-PointNet++ that fully considers rich 4D radar point information, enabling fine-grained learning for sparse 4D radar point clouds. To effectively integrate the two modalities, we design an adaptive 4D radar--camera fusion module (A-RCFM) that automatically selects image features based on 4D radar point features, facilitating multi-scale cross-modal feature interaction and adaptive multi-modal feature fusion. In addition, we introduce a velocity-guided point-confidence estimation module to measure local motion patterns, reduce the influence of dynamic objects and outliers, and provide continuous updates during pose refinement. We demonstrate the excellent performance of our method and the effectiveness of each module design on both the VoD and in-house datasets. Our method outperforms all learning-based and geometry-based methods for most sequences in the VoD dataset. Furthermore, it has exhibited promising performance that closely approaches that of the 64-line LiDAR odometry results of A-LOAM without mapping optimization.Comment: 14 pages,12 figure

    The 3D Qp Model of the China Seismic Experiment Site (CSES-Q1.0) and Its Tectonic Implications

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    The Chuan-Dian region is located in the southeastern part of the geologically complex and seismically active Tibetan Plateau. Since 2008, the Chuan-Dian region has experienced several major earthquakes, including the Wenchuan MS 8.0, Lushan MS 7.0, and Jiuzhaigou MS7.0, making it one of the areas with the most severe earthquake disasters. The China Seismic Experimental Site (CSES) under construction in this area will deepen the understanding of the preparation and generation of earthquakes and the disaster mechanisms, which can further enhance the defense capability against earthquake risks. To build a world-class seismic experimental field, it is necessary to establish high-precision medium structure models. Currently, several institutions have established high-resolution three-dimensional (3D) velocity models in the CSES, but there is still a lack of high-resolution 3D attenuation (∝1/Q) structure models. Using the local seismic tomography method, we obtain the highest resolution 3D Qp model in the CSES to date. Combining the existing velocity models in the CSES with other geophysical and geochemical observations by predecessors, this study find that the Qp value anomalies along large fault zones and some basin areas are low, reflecting the high degree of medium fragmentation in these areas, with thick sedimentary layers or rich in fluids. The high attenuation anomaly of the upper crust dipping westward in the Tengchong volcanic characterizes the possible upward flow of deep-seated magma from west to east. This study also find that most of earthquakes above magnitude 6 occurred in low attenuation zones or the boundary areas of high-low attenuation anomalies. The source areas of the 2008 Wenchuan MS 8.0 earthquake and the 2013 Lushan MS 7.0 earthquake were separated by a low attenuation area, and there is still a risk of major earthquakes in the future. The 3D attenuation model constructed in this study will provide a high-resolution reference model for seismological and earthquake disaster research in the CSES

    Improving the Classification Accuracy for Near-Infrared Spectroscopy of Chinese Salvia miltiorrhiza

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    In order to improve the classification accuracy of Chinese Salvia miltiorrhiza using near-infrared spectroscopy, a novel local variable selection strategy is thus proposed. Combining the strengths of the local algorithm and interval partial least squares, the spectra data have firstly been divided into several pairs of classes in sample direction and equidistant subintervals in variable direction. Then, a local classification model has been built, and the most proper spectral region has been selected based on the new evaluation criterion considering both classification error rate and best predictive ability under the leave-one-out cross validation scheme for each pair of classes. Finally, each observation can be assigned to belong to the class according to the statistical analysis of classification results of the local classification model built on selected variables. The performance of the proposed method was demonstrated through near-infrared spectra of cultivated or wild Salvia miltiorrhiza, which are collected from 8 geographical origins in 5 provinces of China. For comparison, soft independent modelling of class analogy and partial least squares discriminant analysis methods are, respectively, employed as the classification model. Experimental results showed that classification performance of the classification model with local variable selection was obvious better than that without variable selection

    A methodological framework for identifying potential sources of soil heavy metal pollution based on machine learning: a case study in the Yangtze Delta, China

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    It is a great challenge to identify the many and varied sources of soil heavy metal pollution. Often little information is available regarding the anthropogenic factors and enterprises that could potentially pollute soils. In this study we use freely available geographical data from a search engine in conjunction with machine learning methodologies to identify and classify potentially polluting enterprises in the Yangtze Delta, China. The data were classified into 31 separate and five integrated industry types by five different machine learning approaches. Multinomial naive Bayesian methods achieved an accuracy of 86.5% and Kappa coefficient of 0.82 and were used to classify the geographic data from more than 250 000 enterprises. The relationship between the different industry classes and measurements of soil cadmium and mercury concentrations was explored using bivariate local Moran's I analysis. The analysis revealed areas where different industry classes had led to soil pollution. In the case of cadmium, elevated concentrations also occurred in some areas because of natural sources. This study provides a new approach to investigate the interaction between anthropogenic pollution and natural sources of soil heavy metals to inform pollution control and planning decisions regarding the location of industrial sites

    Abnormal focal segments in left uncinate fasciculus in adults with obsessive–compulsive disorder

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    BackgroundAlthough the specific role of the uncinate fasciculus (UF) in emotional processing in patients with obsessive–compulsive disorder (OCD) has been investigated, the exact focal abnormalities in the UF have not been identified. The aim of the current study was to identify focal abnormalities in the white matter (WM) microstructure of the UF and to determine the associations between clinical features and structural neural substrates.MethodsIn total, 71 drug-naïve patients with OCD and 81 age- and sex-matched healthy controls (HCs) were included. Automated fiber quantification (AFQ), a tract-based quantitative approach, was adopted to measure alterations in diffusion parameters, including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (AD), along the trajectory of the UF. Additionally, we utilized partial correlation analyses to explore the relationship between the altered diffusion parameters and clinical characteristics.ResultsOCD patients showed significantly higher FA and lower RD at the level of the temporal and insular portions in the left UF than HCs. In the insular segments of the left UF, increased FA was positively correlated with the Hamilton Anxiety Scale (HAMA) score, while decreased RD was negatively correlated with the duration of illness.ConclusionWe observed specific focal abnormalities in the left UF in adult patients with OCD. Correlations with measures of anxiety and duration of illness underscore the functional importance of the insular portion of left UF disturbance in OCD patients

    Biochar has no effect on soil respiration across Chinese agricultural soils

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    This work was supported by NSFC (41371298 and 41371300), Ministry of Science and Technology (2013GB23600666 and 2013BAD11B00), and Ministry of Education of China (20120097130003). The international cooperation was funded under a “111” project by the State Agency of Foreign Expert Affairs of China and jointly supported under a grant for Priority Disciplines in Higher Education by the Department of Education, Jiangsu Province, China; The work was also a contribution to the cooperation project of “Estimates of Future Agricultural GHG Emissions and Mitigation in China” under the UK-China Sustainable Agriculture Innovation Network (SAIN). Pete Smith contributed to this work under a UK BBSRC China Partnership Award. The authors are grateful to Yuming Liu, Bin Zhang, Xiao Li, Gang Wu, Jinjin Qu and Yinxin Ye and Dongqi Liu for their contribution to field experiments, and to Rongjun Bian and Qaiser Hussain for their participation in discussions of the data analysis and interpretation, and to Xinyan Yu and Jiafang Wang for their assistance in lab works.Peer reviewedPostprin

    Biochar-based fertilizer: Supercharging root membrane potential and biomass yield of rice

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    Biochar-based compound fertilizers (BCF) and amendments have proven to enhance crop yields and modify soil properties (pH, nutrients, organic matter, structure etc.) and are now in commercial production in China. While there is a good understanding of the changes in soil properties following biochar addition, the interactions within the rhizosphere remain largely unstudied, with benefits to yield observed beyond the changes in soil properties alone. We investigated the rhizosphere interactions following the addition of an activated wheat straw BCF at an application rates of 0.25% (g·g−1 soil), which could potentially explain the increase of plant biomass (by 67%), herbage N (by 40%) and P (by 46%) uptake in the rice plants grown in the BCF-treated soil, compared to the rice plants grown in the soil with conventional fertilizer alone. Examination of the roots revealed that micron and submicron-sized biochar were embedded in the plaque layer. BCF increased soil Eh by 85 mV and increased the potential difference between the rhizosphere soil and the root membrane by 65 mV. This increased potential difference lowered the free energy required for root nutrient accumulation, potentially explaining greater plant nutrient content and biomass. We also demonstrate an increased abundance of plant-growth promoting bacteria and fungi in the rhizosphere. We suggest that the redox properties of the biochar cause major changes in electron status of rhizosphere soils that drive the observed agronomic benefits
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