Improvement of performance of ultra-high performance concrete based composite material added with nano materials

Ultra-high performance concrete (UHPC), a kind of composite material characterized by ultra high strength, high toughness and high durability. It has a wide application prospect in engineering practice. But there are some defects in concrete. How to improve strength and toughness of UHPC remains to be the target of researchers. To obtain UHPC with better performance, this study introduced nano-SiO2 and nano-CaCO3 into UHPC. Moreover, hydration heat analysis, X-Ray Diffraction (XRD), mercury intrusion porosimetry (MIP) and nanoindentation tests were used to explore hydration process and microstructure. Double-doped nanomaterials can further enhance various mechanical performances of materials. Nano-SiO2 can promote early progress of cement hydration due to its high reaction activity and C-S-H gel generates when it reacts with cement hydration product Ca(OH)2. Nano-CaCO3 mainly plays the role of crystal nucleus effect and filling effect. Under the combined action of the two, the composite structure is denser, which provides a way to improve the performance of UHPC in practical engineering

Structured covariance principal component analysis for real-time onsite feature extraction and dimensionality reduction in hyperspectral imaging

Presented in a 3-D structure called hypercube, hyperspectral imaging (HSI) suffers from large volume of data and high computational cost for data analysis. To overcome such drawbacks, principal component analysis (PCA) has been widely applied for feature extraction and dimensionality reduction. However, a severe bottleneck is how to compute the PCA covariance matrix efficiently and avoid computational difficulties, especially when the spatial dimension of the hypercube is large. In this paper, structured covariance PCA (SC-PCA) is proposed for fast computation of the covariance matrix. In line with how spectral data is acquired in either the push-broom or tunable filter way, different implementation schemes of SC-PCA are presented. As the proposed SC-PCA can determine the covariance matrix from partial covariance matrices in parallel even without deducting the mean vector in prior, it facilitates real-time data analysis whilst the hypercube is acquired. This has significantly reduced the scale of required memory and also allows efficient onsite feature extraction and data reduction to benefit subsequent tasks in coding/compression, transmission, and analytics of hyperspectral data

Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images

Land-use classification using remote sensing images covers a wide range of applications. With more detailed spatial and textural information provided in very high resolution (VHR) remote sensing images, a greater range of objects and spatial patterns can be observed than ever before. This offers us a new opportunity for advancing the performance of land-use classification. In this paper, we first introduce an effective midlevel visual elements-oriented land-use classification method based on “partlets,” which are a library of pretrained part detectors used for midlevel visual elements discovery. Taking advantage of midlevel visual elements rather than low-level image features, a partlets-based method represents images by computing their responses to a large number of part detectors. As the number of part detectors grows, a main obstacle to the broader application of this method is its computational cost. To address this problem, we next propose a novel framework to train coarse-to-fine shared intermediate representations, which are termed “sparselets,” from a large number of pretrained part detectors. This is achieved by building a single-hidden-layer autoencoder and a single-hidden-layer neural network with an L0-norm sparsity constraint, respectively. Comprehensive evaluations on a publicly available 21-class VHR land-use data set and comparisons with state-of-the-art approaches demonstrate the effectiveness and superiority of this paper

EACOFT: an energy-aware correlation filter for visual tracking.

Correlation filter based trackers attribute to its calculation in the frequency domain can efficiently locate targets in a relatively fast speed. This characteristic however also limits its generalization in some specific scenarios. The reasons that they still fail to achieve superior performance to state-of-the-art (SOTA) trackers are possibly due to two main aspects. The first is that while tracking the objects whose energy is lower than the background, the tracker may occur drift or even lose the target. The second is that the biased samples may be inevitably selected for model training, which can easily lead to inaccurate tracking. To tackle these shortcomings, a novel energy-aware correlation filter (EACOFT) based tracking method is proposed, in our approach the energy between the foreground and the background is adaptively balanced, which enables the target of interest always having a higher energy than its background. The samples’ qualities are also evaluated in real time, which ensures that the samples used for template training are always helpful with tracking. In addition, we also propose an optimal bottom-up and top-down combined strategy for template training, which plays an important role in improving both the effectiveness and robustness of tracking. As a result, our approach achieves a great improvement on the basis of the baseline tracker, especially under the background clutter and fast motion challenges. Extensive experiments over multiple tracking benchmarks demonstrate the superior performance of our proposed methodology in comparison to a number of the SOTA trackers

Fusion of block and keypoints based approaches for effective copy-move image forgery detection

Keypoint-based and block-based methods are two main categories of techniques for detecting copy-move forged images, one of the most common digital image forgery schemes. In general, block-based methods suffer from high computational cost due to the large number of image blocks used and fail to handle geometric transformations. On the contrary, keypoint-based approaches can overcome these two drawbacks yet are found difficult to deal with smooth regions. As a result, fusion of these two approaches is proposed for effective copy-move forgery detection. First, our scheme adaptively determines an appropriate initial size of regions to segment the image into non-overlapped regions. Feature points are extracted as keypoints using the scale invariant feature transform (SIFT) from the image. The ratio between the number of keypoints and the total number of pixels in that region is used to classify the region into smooth or non-smooth (keypoints) regions. Accordingly, block based approach using Zernike moments and keypoint based approach using SIFT along with filtering and post-processing are respectively applied to these two kinds of regions for effective forgery detection. Experimental results show that the proposed fusion scheme outperforms the keypoint-based method in reliability of detection and the block-based method in efficiency

STUDY ON EARTHQUAKE DESTRUCTION MODE OF THE LARGEST CANAL CROSSING HIGHWAY BRIDGE BASED ON IEM BOUNDARY IN SOUTH-TO-NORTH WATER DIVERSION

  To study the dynamic failure mechanism and damage development law of highway bridge structure under the boundary effect in the process of seismic dynamic duration, the Wenchang Highway Bridge with the largest canal crossing in the South-to-North Water Diversion is taken as an example for seismic design analysis. Based on the finite element and infinite element coupling theory, the infinite element method boundary is introduced, the concrete damage plasticity is introduced, and the half-space free field model is established to study the energy dispersion phenomenon of waves in the boundary and the absorption effect of the infinite element method boundary on wave energy is verified. Under different peak acceleration intensities, the seismic response analysis of the bridge structure was carried out. The results show that: Under the action of selected artificial waves, the damage location of the bridge mainly concentrated in the junction of the box girder supported by the pier, the bottom of the pier and the junction of the pier and beam. The damage tends to develop downward near the bottom of the box girder. The damage at both ends of the beam extends from both ends to the middle. And the bottom and top of the pier have penetrating damage. These are weak points in seismic design. At a horizontal peak acceleration of 0.6g, in addition to damage to the pier column, damage also occurred to the bottom of the box girder. Therefore, when the horizontal peak acceleration of the seismic wave is greater than 0.6g, the failure of the bottom of the box girder is paid attention to. Moreover, the IEM boundary has a good control effect on the far-field energy dissipation of the wave, which is simpler and more efficient than the viscous–spring boundary

The Role of Matrine and Mitogen-Ativated Protein Kinase/Extracellular Signal-Regulated Kinase Signal Transduction in the Inhibition of the Proliferation and Migration of Human Umbilical Veins Endothelial Cells Induced by Lung Cancer cells

Background and objective Matrine, one of the major alkaloid components of the traditional Chinese medicine Sophora roots, has a wide range of pharmacological effects including anti-inflammatory activities, growth inhibition and induction of cell differentiation and apoptosis. Motigen-activated protein kinase (MAPK)/extracellular signal-regulated kinase (ERK) has found to be a crucial signaling pathway in endothelial cells. The aim of this study is to investigate the role of Matrine and MAPK/ERK signal transduction in the inhibition of the proliferation and migration of human umbilical veins endothelial cells (HUVECs) induced by lung cancer cells. Methods HUVECs were cultured with A549CM. Mat or PD98059 (i.e PD), specific inhibitor of MAPK/ERK, was added into the A549CM. The proliferation of the HUVECs was measured by cell counting. The migration of the HUVECs was observed by wound healing assay. The expression levels of ERK and p-ERK protein were detected by Western Blot analysis. Results On 24 hours after intervention, the A549CM significantly stimulated the proliferation, migration and expression of p-ERK of HUVECs. Compared with the A549CM group, Mat significantly inhibited the proliferation, migration and p-ERK expression of HUVECs induced by A549CM. While PD only decreased the proliferation and p-ERK expression of HUVECs induced by A549CM. PD had no effect in the migration of HUVECs. Conclusion The results demonstrated that Mat and PD98059 can effectively decrease proliferation and expression of p-ERK of HUVECs induced by A549CM. Furthermore Mat can also inhibit migration of HUVECs induced by A549CM that did not changed by PD98059. These data implied that suppressing MAPK/ERK signal transduction may play the crucial role in resisting lung cacinoma angiogenesis with Mat

Multi-scale diff-changed feature fusion network for hyperspectral image change detection.

For hyperspectral images (HSI) change detection (CD), multi-scale features are usually used to construct the detection models. However, the existing studies only consider the multi-scale features containing changed and unchanged components, which is difficult to represent the subtle changes between bi-temporal HSIs in each scale. To address this problem, we propose a multi-scale diff-changed feature fusion network (MSDFFN) for HSI CD, which improves the ability of feature representation by learning the refined change components between bi-temporal HSIs under different scales. In this network, a temporal feature encoder-decoder sub-network, which combines a reduced inception module and a cross-layer attention module to highlight the significant features, is designed to extract the temporal features of HSIs. A bidirectional diff-changed feature representation module is proposed to learn the fine changed features of bi-temporal HSIs at various scales to enhance the discriminative performance of the subtle change. A multi-scale attention fusion module is developed to adaptively fuse the changed features of various scales. The proposed method can not only discover the subtle change of bi-temporal HSIs but also improve the discriminating power for HSI CD. Experimental results on three HSI datasets show that MSDFFN outperforms a few state-of-the-art methods

Unsupervised Change Detection in Hyperspectral Images using Principal Components Space Data Clustering

Change detection of hyperspectral images is a very important subject in the field of remote sensing application. Due to the large number of bands and the high correlation between adjacent bands in the hyperspectral image cube, information redundancy is a big problem, which increases the computational complexity and brings negative factor to detection performance. To address this problem, the principal component analysis (PCA) has been widely used for dimension reduction. It has the capability of projecting the original multi-dimensional hyperspectral data into new eigenvector space which allows it to extract light but representative information. The difference image of the PCA components is obtained by subtracting the two dimensionality-reduced images, on which the change detection is considered as a binary classification problem. The first several principal components of each pixel are taken as a feature vector for data classification using k-means clustering with k=2, where the two classes are changed pixels and unchanged pixels, respectively. The centroids of two clusters are determined by iteratively finding the minimum Euclidean distance between pixel's eigenvectors. Experiments on two publicly available datasets have been carried out and evaluated by overall accuracy. The results have validated the efficacy and efficiency of the proposed approach.</p

PSSA: PCA-domain superpixelwise singular spectral analysis for unsupervised hyperspectral image classification.

Although supervised classification of hyperspectral images (HSI) has achieved success in remote sensing, its applications in real scenarios are often constrained, mainly due to the insufficiently available or lack of labelled data. As a result, unsupervised HSI classification based on data clustering is highly desired, yet it generally suffers from high computational cost and low classification accuracy, especially in large datasets. To tackle these challenges, a novel unsupervised spatial-spectral HSI classification method is proposed. By combining the entropy rate superpixel segmentation (ERS), superpixel-based principal component analysis (PCA), and PCA-domain 2D singular spectral analysis (SSA), both the efficacy and efficiency of feature extraction are improved, followed by the anchor-based graph clustering (AGC) for effective classification. Experiments on three publicly available and five self-collected aerial HSI datasets have fully demonstrated the efficacy of the proposed PCA-domain superpixelwise SSA (PSSA) method, with a gain of 15–20% in terms of the overall accuracy, in comparison to a few state-of-the-art methods. In addition, as an extra outcome, the HSI dataset we acquired is provided freely online