A novel semisupervised support vector machine classifier based on active learning and context information

This paper proposes a novel semisupervised support vector machine classifier (Formula presented.) based on active learning (AL) and context information to solve the problem where the number of labeled samples is insufficient. Firstly, a new semisupervised learning method is designed using AL to select unlabeled samples as the semilabled samples, then the context information is exploited to further expand the selected samples and relabel them, along with the labeled samples train (Formula presented.) classifier. Next, a new query function is designed to enhance the reliability of the classification results by using the Euclidean distance between the samples. Finally, in order to enhance the robustness of the proposed algorithm, a fusion method is designed. Several experiments on change detection are performed by considering some real remote sensing images. The results show that the proposed algorithm in comparison with other algorithms can significantly improve the detection accuracy and achieve a fast convergence in addition to verify the effectiveness of the fusion method developed in this paper

Modified p-y curves for monopile foundation with different length-to-diameter ratio

The soil reaction of the monopile foundation subjected to lateral loading in offshore wind turbines is typically assessed relying on p-y curves advocated by API. However, this method is inadequate for gradually increasing monopile diameters and significantly underestimates the lateral soil confinement. In the present works, a 3D pile-soil interaction finite element model was first established, considering the soil suction and strain hardening characteristics for the normally consolidated clay in China’s sea. Modifications to the p-y curves in API were accomplished in the comparative process between the lateral soil resistance-displacement curves retrieved from the finite element model and the representative expression. Furthermore, the prediction accuracy for the corrected p-y curves has been proved by forecasting the monopile lateral bearing capacity with varying length-to-diameter ratios, which also demonstrates that the modified p-y curves could successfully reflect the lateral soil confinement of the normally consolidated clay and flexible piles. It also provides an approach to assess the deformation response and horizontal ultimate bearing capacity of monopiles with different length-to-diameter ratios

Attenuation of doxorubicin-induced cardiac injury by mitochondrial glutaredoxin 2

AbstractWhile the cardiotoxicity of doxorubicin (DOX) is known to be partly mediated through the generation of reactive oxygen species (ROS), the biochemical mechanisms by which ROS damage cardiomyocytes remain to be determined. This study investigates whether S-glutathionylation of mitochondrial proteins plays a role in DOX-induced myocardial injury using a line of transgenic mice expressing the human mitochondrial glutaredoxin 2 (Glrx2), a thiotransferase catalyzing the reduction as well as formation of protein–glutathione mixed disulfides, in cardiomyocytes. The total glutaredoxin (Glrx) activity was increased by 76% and 53 fold in homogenates of whole heart and isolated heart mitochondria of Glrx2 transgenic mice, respectively, compared to those of nontransgenic mice. The expression of other antioxidant enzymes, with the exception of glutaredoxin 1, was unaltered. Overexpression of Glrx2 completely prevents DOX-induced decreases in NAD- and FAD-linked state 3 respiration and respiratory control ratio (RCR) in heart mitochondria at days 1 and 5 of treatment. The extent of DOX-induced decline in left ventricular function and release of creatine kinase into circulation at day 5 of treatment was also greatly attenuated in Glrx2 transgenic mice. Further studies revealed that heart mitochondria overexpressing Glrx2 released less cytochrome c than did controls in response to treatment with tBid or a peptide encompassing the BH3 domain of Bid. Development of tolerance to DOX toxicity in transgenic mice is also associated with an increase in protein S-glutathionylation in heart mitochondria. Taken together, these results imply that S-glutathionylation of heart mitochondrial proteins plays a role in preventing DOX-induced cardiac injury

Attenuation of Doxorubicin-Induced Cardiac Injury by Mitochondrial Glutaredoxin 2

While the cardiotoxicity of doxorubicin (DOX) is known to be partly mediated through the generation of reactive oxygen species (ROS), the biochemical mechanisms by which ROS damage cardiomyocytes remain to be determined. This study investigates whether S-glutathionylation of mitochondrial proteins plays a role in DOX-induced myocardial injury using a line of transgenic mice expressing the human mitochondrial glutaredoxin 2 (Glrx2), a thiotransferase catalyzing the reduction as well as formation of protein–glutathione mixed disulfides, in cardiomyocytes. The total glutaredoxin (Glrx) activity was increased by 76% and 53 fold in homogenates of whole heart and isolated heart mitochondria of Glrx2 transgenic mice, respectively, compared to those of nontransgenic mice. The expression of other antioxidant enzymes, with the exception of glutaredoxin 1, was unaltered. Overexpression of Glrx2 completely prevents DOX-induced decreases in NAD- and FAD-linked state 3 respiration and respiratory control ratio (RCR) in heart mitochondria at days 1 and 5 of treatment. The extent of DOX-induced decline in left ventricular function and release of creatine kinase into circulation at day 5 of treatment was also greatly attenuated in Glrx2 transgenic mice. Further studies revealed that heart mitochondria overexpressing Glrx2 released less cytochrome c than did controls in response to treatment with tBid or a peptide encompassing the BH3 domain of Bid. Development of tolerance to DOX toxicity in transgenic mice is also associated with an increase in protein S-glutathionylation in heart mitochondria. Taken together, these results imply that S-glutathionylation of heart mitochondrial proteins plays a role in preventing DOX-induced cardiac injury

Prospective life cycle assessment targeting the dual-carbon strategy: Progress review and methodological framework

In order to achieve the “dual carbon” goals, it is of great significance to evaluate the potential environmental impact of emerging technologies and make decisions in the early stages of technological development to reduce energy consumption and environmental impact throughout the entire life cycle of these technologies. Life cycle assessment can analyze the potential environmental impact of a product or service throughout its life cycle. Prospective life cycle assessment has been proposed to evaluate emerging technologies that are uncommercialized. This study analyzed relevant definitions of life cycle assessment, reviewed articles on prospective life cycle assessment both in China and internationally, and used bibliometric methods to explore the status of prospective life cycle assessment research in terms of research trends, objects, and key fields. Considering the four challenges associated with the method, including low comparability, nonlinear scale amplification, limited data availability, and high uncertainty, a comprehensive framework for prospective life cycle assessment has been proposed. This article also discussed the difficulties and challenges of using prospective life cycle assessment in research in China and provides suggestions for future improvement and application prospects. This study provides a reference for the localization application of the method in China, help the development of carbon reduction technology innovation, and coordinate pollution reduction. It can also help us identify product and supply chain hotspots from a systematic perspective, adopt targeted adjustments, avoid the transfer of environmental pollution burden, and achieve the “dual carbon” goals with a lower cost and environmentally friendly path

Integrated GANs: Semi-Supervised SAR Target Recognition

With the advantage of working in all weathers and all days, synthetic aperture radar (SAR) imaging systems have a great application value. As an efficient image generation and recognition model, generative adversarial networks (GANs) have been applied to SAR image analysis and achieved promising performance. However, the cost of labeling a large number of SAR images limits the performance of the developed approaches and aggravates the mode collapsing problem. This paper presents a novel approach namely Integrated GANs (I-GAN), which consists of a conditional GANs, an unconditional GANs and a classifier, to achieve semi-supervised generation and recognition simultaneously. The unconditional GANs assist the conditional GANs to increase the diversity of the generated images. A co-training method for the conditional GANs and the classifier is proposed to enrich the training samples. Since our model is capable of representing training images with rich characteristics, the classifier can achieve better recognition accuracy. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset proves that our method achieves better results in accuracy when labeled samples are insufficient, compared against other state-of-the-art techniques

Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data

The recent emergence of high-resolution Synthetic Aperture Radar (SAR) images leads to massive amounts of data. In order to segment these big remotely sensed data in an acceptable time frame, more and more segmentation algorithms based on deep learning attempt to take superpixels as processing units. However, the over-segmented images become non-Euclidean structure data that traditional deep Convolutional Neural Networks (CNN) cannot directly process. Here, we propose a novel Attention Graph Convolution Network (AGCN) to perform superpixel-wise segmentation in big SAR imagery data. AGCN consists of an attention mechanism layer and Graph Convolution Networks (GCN). GCN can operate on graph-structure data by generalizing convolutions to the graph domain and have been successfully applied in tasks such as node classification. The attention mechanism layer is introduced to guide the graph convolution layers to focus on the most relevant nodes in order to make decisions by specifying different coefficients to different nodes in a neighbourhood. The attention layer is located before the convolution layers, and noisy information from the neighbouring nodes has less negative influence on the attention coefficients. Quantified experiments on two airborne SAR image datasets prove that the proposed method outperforms the other state-of-the-art segmentation approaches. Its computation time is also far less than the current mainstream pixel-level semantic segmentation networks

Weakly Supervised Segmentation of SAR Imagery Using Superpixel and Hierarchically Adversarial CRF

Synthetic aperture radar (SAR) image segmentation aims at generating homogeneous regions from a pixel-based image and is the basis of image interpretation. However, most of the existing segmentation methods usually neglect the appearance and spatial consistency during feature extraction and also require a large number of training data. In addition, pixel-based processing cannot meet the real time requirement. We hereby present a weakly supervised algorithm to perform the task of segmentation for high-resolution SAR images. For effective segmentation, the input image is first over-segmented into a set of primitive superpixels. This algorithm combines hierarchical conditional generative adversarial nets (CGAN) and conditional random fields (CRF). The CGAN-based networks can leverage abundant unlabeled data learning parameters, reducing their reliance on the labeled samples. In order to preserve neighborhood consistency in the feature extraction stage, the hierarchical CGAN is composed of two sub-networks, which are employed to extract the information of the central superpixels and the corresponding background superpixels, respectively. Afterwards, CRF is utilized to perform label optimization using the concatenated features. Quantified experiments on an airborne SAR image dataset prove that the proposed method can effectively learn feature representations and achieve competitive accuracy to the state-of-the-art segmentation approaches. More specifically, our algorithm has a higher Cohen’s kappa coefficient and overall accuracy. Its computation time is less than the current mainstream pixel-level semantic segmentation networks

BBox-Free SAR Ship Instance Segmentation Method Based on Gaussian Heatmap

Recently, deep learning methods have been widely adopted for ship detection in synthetic aperture radar (SAR) images. However, many of the existing methods miss adjacent ship instances when detecting densely arranged ship targets in inshore scenes. Besides, they suffer from the lack of precision in the instance indication information and the confusion of multiple instances by a single mask head. In this paper, we propose a novel center point prediction algorithm, which detects the center points by finding a long distance variation relationship between two points. The whole prediction process is anchor-free and does not require additional bounding box (BBox) predictions for non-maximum suppression (NMS). Therefore, our algorithm is BBox-free and NMS-free, solving the problem of low recall rates when conducting NMS for densely arranged targets. Furthermore, to tackle the deficiency of position indication information in localization tasks, we introduce a feature fusion module with feature decoupling (FD). This module uses classification branch to provide guidance information for localization branch, while suppressing the influence of the gradient flow mixing, effectively improving the algorithm’s segmentation performance of ship contours. Finally, through principal component analysis (PCA) of the Gaussian distribution covariance matrix, we propose a loss function based on the distance between centroids and the difference of angle, called centroid and angle constraint (CAC). CAC guides the network in learning the criterion that a single dynamic mask head is only valid for a single instance. Experiments conducted on PSeg-SSDD and HRSID demonstrate the effectiveness and robustness of our method

Airport Detection in SAR Images via Salient Line Segment Detector and Edge-Oriented Region Growing

Airport detection in synthetic aperture radar (SAR) images has attracted much concern in the field of remote sensing. Affected by other salient objects with geometrical features similar to those of airports, traditional methods often generate false detections. In order to produce the geometrical features of airports and suppress the influence of irrelevant objects, we propose a novel method for airport detection in SAR images. First, a salient line segment detector is constructed to extract salient line segments in the SAR images. Second, we obtain the airport support regions by grouping these line segments according to the commonality of these geometrical features. Finally, we design an edge-oriented region growing (EORG) algorithm, where growing seeds are selected from the airport support regions with the help of edge information in SAR images. Using EORG, the airport region can be mapped by performing region growing with these seeds. We implement experiments on real radar images to validate the effectiveness of our method. The experimental results demonstrate that our method can acquire more accurate locations and contours of airports than several state-of-the-art airport detection algorithms