Design for improving corrosion resistance of duplex stainless steels by wrapping inclusions with niobium armour.

Unavoidable nonmetallic inclusions generated in the steelmaking process are fatal defects that often cause serious corrosion failure of steel, leading to catastrophic accidents and huge economic losses. Over the past decades, extensive efforts have been made to address this difficult issue, but none of them have succeeded. Here, we propose a strategy of wrapping deleterious inclusions with corrosion-resistant niobium armour (Z phase). After systematic theoretical screening, we introduce minor Nb into duplex stainless steels (DSSs) to form inclusion@Z core-shell structures, thus isolating the inclusions from corrosive environments. Additionally, both the Z phase and its surrounding matrix possess excellent corrosion resistance. Thus, this strategy effectively prevents corrosion caused by inclusions, thereby doubly improving the corrosion resistance of DSSs. Our strategy overcomes the long-standing problem of "corrosion failure caused by inclusions", and it is verified as a universal technique in a series of DSSs and industrial production.

An Improved Method for Stable Feature Points Selection in Structure-from-Motion Considering Image Semantic and Structural Characteristics.

Feature matching plays a crucial role in the process of 3D reconstruction based on the structure from motion (SfM) technique. For a large collection of oblique images, feature matching is one of the most time-consuming steps, and the matching result directly affects the accuracy of subsequent tasks. Therefore, how to extract the reasonable feature points robustly and efficiently to improve the matching speed and quality has received extensive attention from scholars worldwide. Most studies perform quantitative feature point selection based on image Difference-of-Gaussian (DoG) pyramids in practice. However, the stability and spatial distribution of feature points are not considered enough, resulting in selected feature points that may not adequately reflect the scene structures and cannot guarantee the matching rate and the aerial triangulation accuracy. To address these issues, an improved method for stable feature point selection in SfM considering image semantic and structural characteristics is proposed. First, the visible-band difference vegetation index is used to identify the vegetation areas from oblique images, and the line feature in the image is extracted by the optimized line segment detector algorithm. Second, the feature point two-tuple classification model is established, in which the vegetation area recognition result is used as the semantic constraint, the line feature extraction result is used as the structural constraint, and the feature points are divided into three types. Finally, a progressive selection algorithm for feature points is proposed, in which feature points in the DoG pyramid are selected by classes and levels until the number of feature points is satisfied. Oblique images of a 40-km2 area in Dongying city, China, were used for validation. The experimental results show that compared to the state-of-the-art method, the method proposed in this paper not only effectively reduces the number of feature points but also better reflects the scene structure. At the same time, the average reprojection error of the aerial triangulation decrease by 20%, the feature point matching rate increase by 3%, the selected feature points are more stable and reasonable.

UAV-based hyperspectral analysis and spectral indices constructing for quantitatively monitoring leaf nitrogen content of winter wheat.

In flag leaf and flowering stages of winter wheat, unmanned aerial vehicle (UAV)-based and ground-measured hyperspectral data were collected simultaneously, and leaf nitrogen content (LNC) data were then measured in a laboratory. First, the accuracy of UAV-based hyperspectral data was analyzed using ground-measured hyperspectral data, and the analysis showed that the effectiveness and spectrum sampling precision of the UAV-based hyperspectral data are reliable. Hyperspectral characteristic analysis of winter wheat canopies of different LNCs was also conducted. Second, representative spectrum bands that are sensitive to the LNC of winter wheat were extracted through first-order differential spectral, continuum-removed reflectance, and band correlation prediction threshold methods. The optimal band combination that is sensitive to the LNC of winter wheat was obtained by comparing and analyzing the representative spectrum band results. Thus, several LNC spectral indices (LNCSI) were established through ratio, difference, and normalization methods, and linear regression statistical models for quantitatively simulating LNCs were established using the LNCSIs. Finally, comprehensive and comparative analyses of the LNCSIs and the inversion values of the LNC using the LNCSIs confirmed that the LNCSIs are effective in quantitatively inversing the LNC of winter wheat.

Relationship between Microstructure and Corrosion Behavior of Martensitic High Nitrogen Stainless Steel 30Cr15Mo1N at Different Austenitizing Temperatures.

The relationship between microstructure and corrosion behavior of martensitic high nitrogen stainless steel 30Cr15Mo1N at different austenitizing temperatures was investigated by microscopy observation, electrochemical measurement, X-ray photoelectron spectroscopy analysis and immersion testing. The results indicated that finer Cr-rich M2N dispersed more homogeneously than coarse M23C6, and the fractions of M23C6 and M2N both decreased with increasing austenitizing temperature. The Cr-depleted zone around M23C6 was wider and its minimum Cr concentration was lower than M2N. The metastable pits initiated preferentially around coarse M23C6 which induced severer Cr-depletion, and the pit growth followed the power law. The increasing of austenitizing temperature induced fewer metastable pit initiation sites, more uniform element distribution and higher contents of Cr, Mo and N in the matrix. In addition, the passive film thickened and Cr2O3, Cr3+ and CrN enriched with increasing austenitizing temperature, which enhanced the stability of the passive film and repassivation ability of pits. Therefore, as austenitizing temperature increased, the metastable and stable pitting potentials increased and pit growth rate decreased, revealing less susceptible metastable pit initiation, larger repassivation tendency and higher corrosion resistance. The determining factor of pitting potentials could be divided into three stages: dissolution of M23C6 (below 1000 °C), dissolution of M2N (from 1000 to 1050 °C) and existence of a few undissolved precipitates and non-metallic inclusions (above 1050 °C).

Information Extraction of High Resolution Remote Sensing Images Based on the Calculation of Optimal Segmentation Parameters.

Multi-scale image segmentation and the selection of optimal segmentation parameters are the key processes in the object-oriented information extraction of high-resolution remote sensing images. The accuracy of remote sensing special subject information depends on this extraction. On the basis of WorldView-2 high-resolution data, the optimal segmentation parameters methodof object-oriented image segmentation and high-resolution image information extraction, the following processes were conducted in this study. Firstly, the best combination of the bands and weights was determined for the information extraction of high-resolution remote sensing image. An improved weighted mean-variance method was proposed andused to calculatethe optimal segmentation scale. Thereafter, the best shape factor parameter and compact factor parameters were computed with the use of the control variables and the combination of the heterogeneity and homogeneity indexes. Different types of image segmentation parameters were obtained according to the surface features. The high-resolution remote sensing images were multi-scale segmented with the optimal segmentation parameters. Ahierarchical network structure was established by setting the information extraction rules to achieve object-oriented information extraction. This study presents an effective and practical method that can explain expert input judgment by reproducible quantitative measurements. Furthermore the results of this procedure may be incorporated into a classification scheme.