1,686 research outputs found
Leader-Follower Formation via Complex Laplacian
No full textThe paper introduces complex-valued Laplacians for graphs whose edges are attributed with complex weights and studies the leader-follower formation problem based on complex Laplacians. The main goal is to control the shape of a planar formation of point agents in the plane using simple and linear interaction rules related to complex Laplacians. We present a characterization of complex Laplacians that preserve a specific planar formation as an equilibrium solution for both single integrator kinematics and double integrator dynamics. Planar formations under study are subject to translation, rotation, and scaling in the plane, but can be determined by two co-leaders in leader-follower networks. Furthermore, when a complex Laplacian does not result in an asymptotically stable behavior of the multi-agent system, we show that a stabilizing matrix, which updates the complex weights, exists to asymptotically stabilize the system while preserving the equilibrium formation. Also, algorithms are provided to find stabilizing matrices. Finally, simulations are presented to illustrate our results
Estimation of the Image Interpretability of ZY-3 Sensor Corrected Panchromatic Nadir Data
No full textImage quality is important for taking full advantage of satellite data. As a common indicator, the National Imagery Interpretability Scale (NIIRS) is widely used for image quality assessment and provides a comprehensive representation of image quality from the perspective of interpretability. The ZY-3 (Ziyuan-3) satellite is the first civil high resolution mapping satellite in China, which was established in 2012. So far, there has been no reports on adopting NIIRS as the common indicator for the quality assessment of that satellite image data. This lack of a common quality indicator results in a gap between satellite data users around the world and those in China regarding the understanding of the quality and usability of ZY-3 data. To overcome the gap, using the general image-quality equation (GIQE), this study evaluates the ZY-3 sensor-corrected (SC) panchromatic nadir (NAD) data in terms of the NIIRS. In order to solve the uncertainty resulting from the exceeding of the ground sample distance (GSD) of ZY-3 data (2.1 m) in GIQE (less than 2.03 m), eight images are used to establish the relationship between the manually obtained NIIRS and the GIQE predicted NIIRS. An adjusted GIQE is based on the relationship and verified by another five images. Our study demonstrates that the method of using adjusted GIQE for calculating NIIRS can be used for the quality assessment of ZY-3 satellite images and reveals that the NIIRS value of ZY-3 SC NAD data is about 2.79
Super-Resolution Reconstruction of High-Resolution Satellite ZY-3 TLC Images
No full textSuper-resolution (SR) image reconstruction is a technique used to recover a high-resolution image using the cumulative information provided by several low-resolution images. With the help of SR techniques, satellite remotely sensed images can be combined to achieve a higher-resolution image, which is especially useful for a two- or three-line camera satellite, e.g., the ZY-3 high-resolution Three Line Camera (TLC) satellite. In this paper, we introduce the application of the SR reconstruction method, including motion estimation and the robust super-resolution technique, to ZY-3 TLC images. The results show that SR reconstruction can significantly improve both the resolution and image quality of ZY-3 TLC images
中国云南象牙参属(姜科)一新种——苍山象牙参
No full textRoscoea cangshanensis M. H. Luo, X. F. Gao & H. H. Lin, a new species of the Zingiberaceae from Yunnan, China, is described and illustrated. The new species is related to R. forrestii Cowley in habit, but differs in having 2-lobed labellum, each lobe 2-lobulate, base narrowed to a stalk with white lines, and narrower leaf blade, (2-)7-24X1.5-2.5 cm, with base narrow to petiole-like. The new species is also similar to R. debilis Gagnep. in having leaf base narrow to petiole-like, labellum obovate-cuneate, 2.5-3.5X2.5-3.0 cm, with white lines at throat, but differs in having bracts non-tubular, shorter, 5-15 mm long, concealed in leaf sheaths, corolla tubes longer, 10-12.5 cm long, labellum. 2-lobed with each lobe further 2-lobulate
Identification of canine transmissible venereal tumor cells using in situ PCR and the stable sequence of the long interspersed nuclear element
No full textFeO-rich silicates and Ca, Al-rich inclusions in Qingzhen and Yamato 691 (EH3) meteorites: Evidence for migration of mass in the solar nebula
No full textKELVIN-HELMHOLTZ INSTABILITY IN COLLISIONLESS SPACE PLASMAS
No full textPhysics, Fluids & PlasmasSCI(E)9ARTICLE2440-447
An Optimized Object-Based Random Forest Algorithm for Marsh Vegetation Mapping Using High-Spatial-Resolution GF-1 and ZY-3 Data
No full textDiscriminating marsh vegetation is critical for the rapid assessment and management of wetlands. The study area, Honghe National Nature Reserve (HNNR), a typical freshwater wetland, is located in Northeast China. This study optimized the parameters (mtry and ntrees) of an object-based random forest (RF) algorithm to improve the applicability of marsh vegetation classification. Multidimensional datasets were used as the input variables for model training, then variable selection was performed on the variables to eliminate redundancy, which improved classification efficiency and overall accuracy. Finally, the performance of a new generation of Chinese high-spatial-resolution Gaofen-1 (GF-1) and Ziyuan-3 (ZY-3) satellite images for marsh vegetation classification was evaluated using the improved object-based RF algorithm with accuracy assessment. The specific conclusions of this study are as follows: (1) Optimized object-based RF classifications consistently produced more than 70.26% overall accuracy for all scenarios of GF-1 and ZY-3 at the 95% confidence interval. The performance of ZY-3 imagery applied to marsh vegetation mapping is lower than that of GF-1 imagery due to the coarse spatial resolution. (2) Parameter optimization of the object-based RF algorithm effectively improved the stability and classification accuracy of the algorithm. After parameter adjustment, scenario 3 for GF-1 data had the highest classification accuracy of 84% (ZY-3 is 74.72%) at the 95% confidence interval. (3) The introduction of multidimensional datasets improved the overall accuracy of marsh vegetation mapping, but with many redundant variables. Using three variable selection algorithms to remove redundant variables from the multidimensional datasets effectively improved the classification efficiency and overall accuracy. The recursive feature elimination (RFE)-based variable selection algorithm had the best performance. (4) Optical spectral bands, spectral indices, mean value of green and NIR bands in textural information, DEM, TWI, compactness, max difference, and shape index are valuable variables for marsh vegetation mapping. (5) GF-1 and ZY-3 images had higher classification accuracy for forest, cropland, shrubs, and open water
Land Cover Extraction from High Resolution ZY-3 Satellite Imagery Using Ontology-Based Method
No full textThe rapid development and increasing availability of high-resolution satellite (HRS) images provides increased opportunities to monitor large scale land cover. However, inefficiency and excessive independence on expert knowledge limits the usage of HRS images on a large scale. As a knowledge organization and representation method, ontology can assist in improving the efficiency of automatic or semi-automatic land cover information extraction, especially for HRS images. This paper presents an ontology-based framework that was used to model the land cover extraction knowledge and interpret HRS remote sensing images at the regional level. The land cover ontology structure is explicitly defined, accounting for the spectral, textural, and shape features, and allowing for the automatic interpretation of the extracted results. With the help of regional prototypes for land cover class stored in Web Ontology Language (OWL) file, automated land cover extraction of the study area is then attempted. Experiments are conducted using ZY-3 (Ziyuan-3) imagery, which were acquired for the Jiangxia District, Wuhan, China, in the summers of 2012 and 2013.The experimental method provided good land cover extraction results as the overall accuracy reached 65.07%. Especially for bare surfaces, highways, ponds, and lakes, whose producer and user accuracies were both higher than 75%. The results highlight the capability of the ontology-based method to automatically extract land cover using ZY-3 HRS images
A lightweight physics-data-driven method for real-time prediction of subgrade settlements induced by shield tunneling
No full textReal-time prediction of subgrade settlement caused by shield tunneling is crucial in engineering applications. However, data-driven methods are prone to overfitting, while physical methods rely on certain assumptions, making it difficult to select satisfactory parameters. Although there are currently physics-data-driven methods, they typically require extensive iterative calculations with physical models, which makes them unavailable for real-time prediction. This paper introduces a lightweight physics-data-driven method for predicting subgrade settlement caused by shield tunneling. The core concept involves using a single calculation of the physical model to provide a weak constraint. A deep learning network is then designed to capture spatiotemporal correlations based on ConvLSTM. By iteratively incorporating real-time data, the learning of physical constraints is further enhanced. This method combines the predictive power of data-driven method with the reasonable constraints of physical laws, validated a good performance in a practical project. The results demonstrate that this method meets real-time prediction requirements in engineering, achieving an coefficient of determination of 0.980, a root mean square error of 0.22 mm, and a mean absolute error of 0.15 mm. Furthermore, it outperforms both physical and data-driven models and demonstrates good generalization performance. This study provides effective guidance for engineering practices
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