Purpose A transmission tower usually experiences bolt loosening under long-term alternating cyclic load, which may lead to collapse of the tower in extreme operating conditions. The paper aims to propose a data-driven identification method for bolt looseness of complicated tower structures based on reduced-order models and numerical simulations to perceive and evaluate the health state of a tower in operation. Design/methodology/approach The equivalent stiffnesses of three types of bolt joints under various loosening scenarios are numerically determined by three-dimensional finite element (FE) simulations. The order of the FE model of a tower structure with bolt loosening is reduced by means of the component modal synthesis method, and the dynamic responses of the reducer-order model under calibration loads are simulated and used to create the dataset. An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed. Findings An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed and the applicability of the model is investigated. It is shown that the proposed method has a high identification accuracy and strong robustness to data noise and data missing. Meanwhile, the method is less dependent on the number and location of sensors and is easier to apply in real transmission lines. Originality/value This paper proposes a data-driven identification method for bolt looseness of a complicated tower structure based on reduced-order models and numerical simulations. Non-linear relationships between equivalent stiffness of bolted joints and bolt preload depicting looseness are obtained and reduced-order model of tower structure with bolt looseness is established. Finally, this paper investigates applicability of identification model for bolt looseness.
The application of multiple-input multiple-output (MIMO) over orthogonal time frequency space (OTFS) modulation is envisioned to provide high-data-rate wireless transmission in high-mobility environments. However, in these communication scenarios, the multiple-dimensional interference, which can generate from space, delay and Doppler domains, challenges the channel equalization and symbol detection at the MIMO-OTFS receiver. To tackle this issue, we propose a time-space domain channel equalizer, relying on the mathematical least squares minimum residual algorithm, to remove the channel distortion on data symbols. The proposed channel equalizer adopts a recursion method to achieve symbol estimates, which can realize fast convergence by leveraging the sparsity of MIMO-OTFS channel matrix. Instead of directly remapping the equalized OTFS symbols into data bits, we develop an enhanced data detection (EDD) scheme to iteratively demodulate the superposed multi-antenna signal. The EDD can not only realize the linear-complexity interference cancellation, but also efficiently reap the spatial and multi-path diversities of MIMO-OTFS channel. The simulations show the proposed channel equalization and EDD algorithms enable the MIMO-OTFS receiver to robustly demodulate multi-stream 256-ary quadrature amplitude modulation symbols, under a maximum velocity of 550 km/h at 5.9 GHz carrier frequency.
The extraction of rivers in cold and arid regions is of great significance for applications such as ecological environment monitoring, agricultural planning, and disaster warning. However, there are few related studies on river extraction in cold and arid regions, and it is still in its infancy. The accuracy of river extraction is low, and the details are blurred. The rapid development of deep learning has provided us with new ideas, but with lack of corresponding professional datasets, the accuracy of the current semantic segmentation network is not high. This study mainly presents the following. (1) According to the characteristics of cold and arid regions, a professional dataset was made to support the extraction of rivers from remote sensing images in these regions. (2) Combine transfer learning and deep learning, migrate the ResNet-101 network to the LinkNet network, and introduce the attention mechanism to obtain the AR-LinkNet network, which is used to improve the recognition accuracy of the network. (3) A channel attention module and a spatial attention module with residual structure are proposed to strengthen the effective features and improve the segmentation accuracy. (4) Combining dense atrous spatial pyramid pooling (DenseASPP) with AR-LinkNet network expands the network receptive field, which can extract more detailed information and increase the coherence of extracted rivers. (5) For the first time, the binary cross-entropy loss function combined with the Dice loss function is applied to river extraction as a new loss function, which accelerates the network convergence and improves the image quality. Validation on the dataset shows that, compared with typical semantic segmentation networks, the method performs better on evaluation metrics such as recall, intersection ratio, precision, and score, and the extracted rivers are clearer and more coherent.
Segmenting common objects that have variations in color, texture and shape is a challenging problem.In this paper, we propose a new model that efficiently segments common objects from multiple images.We first segment each original image into a number of local regions.Then, we construct a digraph based on local region similarities and saliency maps.Finally, we formulate the co-segmentation problem as the shortest path problem, and we use the dynamic programming method to solve the problem.The experimental results demonstrate that the proposed model can efficiently segment the common objects from a group of images with generally lower error rate than many existing and conventional co-segmentation methods.
Point cloud registration is a key task in many computational fields. Previous correspondence matching based methods require the inputs to have distinctive geometric structures to fit a 3D rigid transformation according to point-wise sparse feature matches. However, the accuracy of transformation heavily relies on the quality of extracted features, which are prone to errors with respect to partiality and noise. In addition, they can not utilize the geometric knowledge of all the overlapping regions. On the other hand, previous global feature based approaches can utilize the entire point cloud for the registration, however they ignore the negative effect of non-overlapping points when aggregating global features. In this paper, we present OMNet, a global feature based iterative network for partial-to-partial point cloud registration. We learn overlapping masks to reject non-overlapping regions, which converts the partial-to-partial registration to the registration of the same shape. Moreover, the previously used data is sampled only once from the CAD models for each object, resulting in the same point clouds for the source and reference. We propose a more practical manner of data generation where a CAD model is sampled twice for the source and reference, avoiding the previously prevalent over-fitting issue. Experimental results show that our method achieves state-of-the-art performance compared to traditional and deep learning based methods. Code is available at https://github.com/megvii-research/OMNet.
This paper proposes a method to segment object from the web images using logo detection. The method consists of three steps. In the first step, the logos are located from the original images by SIFT matching. Based on the logo location and the object shape model, the second step extracts the object boundary from the image. In the third step, we use the object boundary to model the object appearance, which is then used in the MRF based segmentation method to finally achieve the object segmentation. The key of our method is the object boundary extraction, which is achieved by searching a variation of the shape model that best fits the local edge of the image. Affine transform is used to consider the variations among the objects. Meanwhile, the Nelder-Mead simplex method with a simple initial rough search is used to run the boundary search. To verify the proposed method, we collect a LogoSeg dataset from the web such as Flickr and Google. The MOMI dataset is also used for the verification. The experimental results demonstrate that the proposed logo detection based segmentation method can improve the performance of the object segmentation.
Pb-free double perovskites, such as Cs2AgInCl6, have received considerable attention in optoelectronic applications as a more stable and less toxic substitute for Pb-based perovskites. However, Cs2AgInCl6 faces challenges in being excited by blue light, and its relatively large spectral line width restricts its development in the display field. This study introduces a novel structure wherein the surface of double perovskite Cs2Ag0.6Na0.4In0.8Bi0.2Cl6 is coated with KSCN, enabling efficient excitation by blue light and producing ultranarrow line width green light emission at low temperatures. Cs2Ag0.6Na0.4In0.8Bi0.2Cl6@KSCN can emit 558 nm green light when excited by blue light (450 nm) at 10–30 K, with a remarkable full width at half-maximum (fwhm) of 36 nm. The emission mechanism of blue light-excited narrow-spectrum green emission has been determined through temperature-dependent photoluminescence (PL) and first-principles calculations. Surface reconstruction with KSCN results in the emergence of new donor energy levels and the potential to be excited by blue light. Below 30 K, the excitons are compressed and concentrated at the bottom of the self-trapped exciton singlet state, directly completing radiative transitions to produce narrowband green emission. The achievement of narrowband green emission provides important guidance for optimizing the photoelectric performance of lead-free double perovskite materials.
Accurately predicting the interaction between G-protein-coupled receptors (GPCR) and drugs is of great significance for understanding protein functions and drug discovery and has become a hot spot in current research. To improve the accuracy of GPCR-drug interaction prediction, this paper proposes a new GPCR-Drug interaction prediction method based on multi-feature integration and feature augmentation from deep random forest: First, the sequence features of GPCR from amino acid composition and protein evolution are extracted respectively, and the characteristics of the drug molecule from the molecular fingerprint perspective are formulated; then, the extracted multiple features are combined to obtain the feature representation of the GPCR-Drug pair; finally, based on the proposed GPCR-Drug feature representation method, we use deep random forest to generate augmented features and construct cascaded predictions model. The cross-validation and independent test results on the standard data set verify the effectiveness and greater explainability of the proposed method.
The paper proposes a hybrid synthesis method for multi-exposure image fusion taken by hand-held cameras. Motions either due to the shaky cameras or caused by dynamic scenes should be compensated before any content fusion. The misalignment will cause blurring/ghosting artifacts in the fused result. The proposed method can deal with such motions and maintain the exposure information of each input effectively. In particular, the proposed method first applies optical flow for a coarse registration, which performs well with complex non-rigid motion but produces deformations at regions with missing correspondences. To correct such error registration, we segment images into superpixels and identify problematic alignments based on each superpixel, which is further aligned by PatchMatch. After that, the proposed method obtains a fully aligned image stack which facilitates a high-quality fusion that is free from blurring/ghosting artifacts. We compare our method with existing fusion algorithms on various challenging examples, including the static/dynamic, the indoor/outdoor and the daytime/nighttime scenes. Experiment results demonstrate the effectiveness and robustness.
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