A DATA DRIVEN BASED METHODOLOGY FOR STURCTURAL HEALTH MONITORING WITH DISTRIBUTED OPTICAL FIBRE SENSORS

Structural health monitoring (SHM) is a means for maintaining structural integrity, safety and reliability by analysing various structural responses (i.e., mechanical signals) to pinpoint the anomalies of the structures due to damage. It is not an easy task to filter the noise and fluctuation of mechanical signals to successful find the damage-induced anomalies, but it might be achieved by machine learning algorithms. However, the successful implementation of a machine learning requires a large amount of training data, which is always available. In this work, a novel machine learning (ML) model, combining k-nearest neighbors kernel (KNN) and deep neural network (DNN), was proposed that can be trained by insufficient/incomplete SHM data. In addition, the damage states can be identified by Kernel Principle Component Analysis (KPCA). To demonstrate the accuracy of this model, training and validation data were taken from the strains of the braided composite beam under progressive three-point bending. The strain signals were measured by embedded distributed optical fibre sensors (DOFS). The prediction of the proposed novel ML model demonstrates a good agreement with the experimental observations for validation, which provides a novel approach for sufficient/incomplete training data. © 2023 International Committee on Composite Materials. All rights reserved

Utilization of Machine Vision to Monitor the Dynamic Responses of Rice Leaf Morphology and Colour to Nitrogen, Phosphorus, and Potassium Deficiencies

Machine vision technology enables the continuous and nondestructive monitoring of leaf responses to different nutrient supplies and thereby contributes to the improvement of diagnostic effects. In this study, we analysed the temporal dynamics of rice leaf morphology and colour under different nitrogen (N), phosphorus (P), and potassium (K) treatments by continuous imaging and further evaluated the effectiveness of dynamic characteristics for identification. The top four leaves (the 1st incomplete leaf and the top three fully expanded leaves) were scanned every three days, and all images were processed in MATLAB to extract the morphological and colour characteristics for dynamic analysis. Subsequently, the mean impact value was applied to evaluate the effectiveness of dynamic indices for identification. According to the results, higher nutrient supply resulted in a faster leaf extension rate and a lower developing rate of chlorosis, and the influence of N deficiency on leaf growth was the greatest, followed by P deficiency and then K deficiency. Furthermore, the optimal indices for identification were mainly calculated from morphological characteristics of the 1st incomplete leaf and colour characteristics of the 3rd fully expanded leaf. Overall, dynamic analysis contributes not only to the exploration of the plant growth mechanism but also to the improvement of diagnostics

Dynamics of Paddy Field Patterns in Response to Urbanization: A Case Study of the Hang-Jia-Hu Plain

Urban land has increasingly expanded and encroached upon a significant number of paddy fields in Hang-Jia-Hu Plain, due to the rapid socio-economic development and agro-pedoclimatic conditions favorable to rice cultivation and human settlement. Although many studies have analyzed the characteristics of urban land expansion, relatively less attention has been paid to exploring the various urban expansion patterns and the impact of different urban expansion patterns on paddy field at a regional scale. This paper characterized the changing patterns of paddy fields in response to various urban expansion patterns in the Hang-Jia-Hu Plain integrating geographic information systems, gradient analyses and landscape metrics. Our results demonstrate that the amount of urban land expanded to nearly four times that of the initial area during 1980–2010 and that 88% of new urban land was developed on paddy fields. Of the total area of paddy fields, paddy fields of level I accounted for 96%. Moreover, various urban expansion styles differentially influenced the patterns of paddy fields. In autonomous expansion cities, sprawled urban land mainly occupied paddy fields in urban centers. However, the irregular expansion of passive expansion cities encroached on a number of paddy fields in the urban fringe where the landscape of urban patches and paddy fields was more complex and irregular in shape. Furthermore, the urbanization curve implies that future urbanization efforts will focus on the passive expansion cities, indicating that paddy fields still face the risk of disruption. We suggest that the boundary of urban development should be restricted, permanent paddy reserves should be delimited, and ecologically oriented management systems that target paddy field protection should be implemented to ensure the sustainable development of this region. This work improved the understanding of the urbanization process that governed paddy fields dynamics, and provides a scientific basis for decision-making processes to achieve regional sustainability

Discrimination of Settlement and Industrial Area Using Landscape Metrics in Rural Region

Detailed and precise information of land-use and land-cover (LULC) in rural area is essential for land-use planning, environment and energy management. The confusion in mapping residential and industrial areas brings problems in energy management, environmental management and sustainable land use development. However, they remain ambiguous in the former rural LULC mapping, and this insufficient supervision leads to inefficient land exploitation and a great waste of land resources. Hence, the extent and area of residential and industrial cover need to be revealed urgently. However, spectral and textural information is not sufficient for classification heterogeneity due to the similarity between different LULC types. Meanwhile, the contextual information about the relationship between a LULC feature and its surroundings still has potential in classification application. This paper attempts to discriminate settlement and industry area using landscape metrics. A feasible classification scheme integrating landscape metrics, chessboard segmentation and object-based image analysis (OBIA) is proposed. First LULC map is generated from GeoEye-1 image, which delineated distribution of different land-cover materials using traditional OBIA method with spectrum and texture information. Then, a chessboard segmentation of the whole LULC map is conducted to create landscape units in a uniform spatial area. Landscape characteristics in each square of chessboard are adopted in the classification algorithm subsequently. To analyze landscape unit scale effect, a variety of chessboard scales are tested, with overall accuracy ranging from 75% to 88%, and Kappa coefficient from 0.51 to 0.76. Optimal chessboard scale is obtained through accuracy assessment comparison. This classification scheme is then compared to two other approaches: a top-down hierarchical classification network using only spectral, textural and shape properties, and lacunarity based hierarchical classification. The distinction approach proposed is overwhelming by achieving the highest value in overall accuracy, Kappa coefficient and McNemar test. The results show that landscape properties from chessboard segment squares could provide valuable information in classification

Insights into the mechanism in electrochemical CO2 reduction over single-atom copper alloy catalysts: A DFT study

Summary: Copper single-atom alloy catalysts (M@Cu SAAs) have shown great promise for electrochemical CO2 reduction reaction (CO2RR). However, a clear understanding of the CO2RR process on M@Cu SAAs is still lacking. This study uses density functional theoretical (DFT) calculations to obtain a comprehensive mechanism and the origin of activity of M@Cu SAAs. The importance of the adsorption mode of M@Cu is revealed: key intermediates either adsorbed in the adjacent hollow site around Cu atoms (AD mode) or adsorbed directly on the top site of M (SE mode). AD mode generally exhibits finely tuned binding strengths of key intermediates, which significantly enhances the activity of the catalysts. Increasing the coverage of ∗CO on the M@Cu with SE mode leads to relocation of the active site, resulting in improved activity of C2 products. The insights gained in this work have significant implications for rational design strategy toward efficient CO2RR electrocatalysts

Spatiotemporal Variability of Soil Nitrogen in Relation to Environmental Factors in a Low Hilly Region of Southeastern China

Soil total nitrogen (TN) plays a major role in agriculture, geochemical cycles and terrestrial ecosystem functions. Knowledge regarding the TN distribution is crucial for the sustainable use of soil resources. This paper therefore aims to characterize the spatiotemporal distribution of soil TN and improve the current understanding of how various factors influence changes in TN. Natural characteristics and remote sensing (RS) variables were used in conjunction with the random forest (RF) model to map the TN distribution in a low hilly region of southeastern China in 1979, 2004 and 2014. The means and changes of TN in different geographic regions and farmland protection regions were also analyzed. The results showed that: (1) the TN showed an increasing trend in the early periods and exhibited a decreasing trend from 2004 to 2014; (2) the geographic and RS variables played more important roles in predicting TN distribution than did the other variables; and (3) changes in the fertilization and crop planting structure caused by soil testing and formulated fertilization techniques (STFFT—Soil Testing and Formulated Fertilization Techniques) as well as farmland protection policies influenced the spatiotemporal variability of TN. Evidently, more attention should be focused on improving the quality and soil fertility in the surrounding low mountainous areas

Mapping Impervious Surfaces in Town–Rural Transition Belts Using China’s GF-2 Imagery and Object-Based Deep CNNs

Impervious surfaces play an important role in urban planning and sustainable environmental management. High-spatial-resolution (HSR) images containing pure pixels have significant potential for the detailed delineation of land surfaces. However, due to high intraclass variability and low interclass distance, the mapping and monitoring of impervious surfaces in complex town⁻rural areas using HSR images remains a challenge. The fully convolutional network (FCN) model, a variant of convolution neural networks (CNNs), recently achieved state-of-the-art performance in HSR image classification applications. However, due to the inherent nature of FCN processing, it is challenging for an FCN to precisely capture the detailed information of classification targets. To solve this problem, we propose an object-based deep CNN framework that integrates object-based image analysis (OBIA) with deep CNNs to accurately extract and estimate impervious surfaces. Specifically, we also adopted two widely used transfer learning technologies to expedite the training of deep CNNs. Finally, we compare our approach with conventional OBIA classification and state-of-the-art FCN-based methods, such as FCN-8s and the U-Net methods. Both of these FCN-based methods are well designed for pixel-wise classification applications and have achieved great success. Our results show that the proposed approach effectively identified impervious surfaces, with 93.9% overall accuracy. Compared with the existing methods, i.e., OBIA, FCN-8s and U-Net methods, it shows that our method achieves obviously improvement in accuracy. Our findings also suggest that the classification performance of our proposed method is related to training strategy, indicating that significantly higher accuracy can be achieved through transfer learning by fine-tuning rather than feature extraction. Our approach for the automatic extraction and mapping of impervious surfaces also lays a solid foundation for intelligent monitoring and the management of land use and land cover

Nitrogen diagnosis based on dynamic characteristics of rice leaf image.

Digital image processing is widely used in the non-destructive diagnosis of plant nutrition. Previous plant nitrogen diagnostic studies have mostly focused on characteristics of the rice canopy or leaves at some specific points in time, with the long sampling intervals unable to provide detailed and specific "dynamic features." According to plant growth mechanisms, the dynamic changing rate in leaf shape and color differ between different nitrogen supplements. Therefore, the objective of this study was to diagnose nitrogen stress levels by analyzing the dynamic characteristics of rice leaves. Scanning technology was implemented to collect rice leaf images every 3 days, with the characteristics of the leaves from different leaf positions extracted utilizing MATLAB. Newly developed shape characteristics such as etiolation area (EA) and etiolation degree (ED), in addition to shape (area, perimeter) and color characteristics (green, normalized red index, etc.), were used to quantify the process of leaf change. These characteristics allowed sensitive indices to be established for further model validation. Our results indicate that the changing rates in dynamic characteristics, in particular the shape characteristics of the first incomplete leaf (FIL) and the characteristics of the 3rd leaf (leaf color and etiolation indices), expressed obvious distinctions among different nitrogen treatments. Consequently, we achieved acceptable diagnostic accuracy (training accuracy 77.3%, validation accuracy 64.4%) by using the FIL at six days after leaf emergence, and the new shape characteristics developed in this article (ED and EA) also showed good performance in nitrogen diagnosis. Based on the aforementioned results, dynamic analysis is valuable not only in further studies but also in practice

Assessing the Impacts of Chinese Sustainable Ground Transportation on the Dynamics of Urban Growth: A Case Study of the Hangzhou Bay Bridge

Although China has promoted the construction of Chinese Sustainable Ground Transportation (CSGT) to guide sustainable development, it may create substantial challenges, such as rapid urban growth and land limitations. This research assessed the effects of the Hangzhou Bay Bridge on impervious surface growth in Cixi County, Ningbo, Zhejiang Province, China. Changes in impervious surfaces were mapped based on Landsat images from 1995, 2002, and 2009 using a combination of multiple endmember spectral mixture analysis (MESMA) and landscape metrics. The results indicated that the area and density of impervious surfaces increased significantly during construction of the Hangzhou Bay Bridge (2002–2009). Additionally, the bridge and connected road networks promoted urban development along major roads, resulting in compact growth patterns of impervious surfaces in urbanized regions. Moreover, the Hangzhou Bay Bridge promoted the expansion and densification of impervious surfaces in Hangzhou Bay District, which surrounds the bridge. The bridge also accelerated socioeconomic growth in the area, promoting rapid urban growth in Cixi County between 2002 and 2009. Overall, the Hangzhou Bay Bridge is an important driver of urban growth in Cixi County, and policy suggestions for sustainable urban growth should be adopted in the future