Search

The acceptance check is a key step to decide whether an overhead transmission line can be put into use. However, for the acceptance check of towers, the manual means is still adopted mostly nowadays, which is dangerous and inefficient. The main challenges for intelligent check technique are the automatic tower segmentation and intelligent requirements of some detection items. Here, based on point clouds from lidars, some intelligent methods are proposed for tower related acceptance check items. The geometric structural changing rules for towers are found and analyzed to identify and segment towers from the scene point cloud automatically. A model‐constrained method is proposed for fine filtering of towers. With point cloud registration strategies, the tilt angle of a tower is calculated intelligently and the defect detection of a tower is conducted successfully. Experiments show that the results of the proposed methods are accurate, efficient and automatic, which have potential for real applications. [ABSTRACT FROM AUTHOR]

Purpose: Doramectin (DRM) is a kind of avermectin drugs, and it has been shown that DRM has anti-cancer effects. However, the molecular mechanism of DRM in programmed cell death (PCD) aspects is still unclear. The objective of this study was to confirm whether DRM induced PCD in glioma cells. Methods: In this experiment, the MTT assay and Ki-67 assay were used to detect in vitro cell viability and in vivo tumor proliferation. Then, the effect of DRM on PCD was analyzed by transcriptome comparison. Next, Endogenous apoptosis was detected by transmission electron microscopy (TEM), the DNA gel electrophoresis, JC-1 assay, western blotting and qRT-PCR. Meanwhile, necroptosis was detected by TEM, Hoechst 33342, FITC and PI staining assay, western blotting. Results: We found DRM induced apoptosis through Bcl-2/Bax/Caspase-3 pathway. And, DRM induced ROS overproduction, then ROS caused necroptosis through RIPK1/RIPK3/MLKL pathway, Mitochondria acted as a bridge between the two pathways. Conclusion: Our research provided new insight with the function of anti-cancer of DRM. These results demonstrated DRM may be used as potential therapeutic agents inducing apoptosis and necroptosis for cancer therapy. [ABSTRACT FROM AUTHOR]

The Monarch Butterfly Optimization (MBO) algorithm has been proved to be an efficient meta-heuristic to directly address continuous optimization problems. In the MBO algorithm, the migration operator cooperates with the butterfly adjusting operator to generate the entire offspring population. Since the individual iterations of the MBO algorithm are not self-learning, the cooperative intelligence mechanism is a random process. In this study, an improved MBO algorithm with a knowledge-driven learning mechanism (KDLMBO) is presented to enable the algorithm to evolve effectively with a self-learning capacity. The neighborhood information extracted from the candidate solutions is treated as the prior knowledge of the KDLMBO algorithm. The learning mechanism consists of the learning migration operator and the learning butterfly adjusting operator. Then, the self-learning collective intelligence is realized by the two cooperative operators in the iterative process of the algorithm. The experimental results demonstrate and validate the efficiency and significance of the proposed KDLMBO algorithm. [ABSTRACT FROM AUTHOR]

Mammography technique is commonly used for diagnosing breast cancer, but mammography images usually show low contrast which cause difficulties to clinical diagnosis. Therefore, improving the visual quality of mammography images is an important issue. This is a challenging problem, because every mammography image consists of rich textures, including bright areas, dark areas, and textural details. Inspired by bio-inspired neural network, this paper proposes a Bidirectional Spiking Cortical Model (Bi-SCM) from the perspective of neural information fusion to enhance the contrast of bright areas and dark areas adequately, as well as textural details. This goal is achieved by utilizing the Bi-SCM to first enhance a mammography image and its inverse separately. The enhanced results are fused by a new fusion algorithm based on non-subsampled contourlet transform (NSCT) to ensure that both of the contrast of bright areas and dark areas are adequately improved. The textual details are then enhanced by an unsharp masking method which consists of cubic filter and log-ratio operation. Sufficient experiments on mammography images are conducted to evaluate the proposed approach. Experimental results show that the proposed method outperforms state-of-the-art methods on both enhancing contrast and details. Besides, over-enhancement and noise sensitivity are also significantly suppressed. [ABSTRACT FROM AUTHOR]

Human-machine interactive systems show increasing demand for analysing fast moving objects in high-frame-rate videos. Robust foreground detection, which is able to reduce large amount of redundant background data from high-frame-rate video, becomes the essence to achieve ultra-high-speed human-machine interactions. This paper proposes a local spatial propagation based background model generation, a local linear illumination correction based background model update, and a regional central coordinates and edge keypoints constrained foreground region reselection. The three proposals make up a robust and hardware-friendly foreground detection method. Experimental results prove that the proposed hardware-friendly algorithm achieves high accuracy and robustness on various kinds of challenging cases. Meanwhile, the hardware implementation utilizes little hardware resources and achieves realtime processing of high-frame-rate (784 frame/second) video with the delay less than 1 ms/frame in image processing core. In addition, a practical system is implemented by combing a PC, a high-speed camera and a field programmable gate array (FPGA) for realworld applications. This work will significatively promote the development and application of high-speed human machine interaction. A demo of the proposed vision system working at 784 FPS is available at https://wcms.waseda.jp/em/5f84f75136a6. Note to Practitioners—This paper was motivated by the problem of high-frame-rate video contains large amount of redundant background pixels which makes ultra-high-speed human-machine interactions inaccessible. Existing approaches are mainly focused on designing complex background models, but processing speed, which is the most important issue for ultra-high-speed human-machine interactions, has received relatively little attention. This paper suggests a robust and hardware-friendly foreground detection algorithm which has been implemented as a hardware system by using an FPGA, a high-frame-rate camera, and a PC. We show that the hardware implementation utilizes less hardware resources and achieves real-time processing speed of 784 FPS with the delay less than 1 ms/frame in the image processing core. This work is a pioneering attempt of ultra-high-speed foreground detection, which will significatively speed up the wide applications of ultra-high-speed human machine interactions. [ABSTRACT FROM AUTHOR]

The invasive weed algorithm (IWO) is a meta-heuristic algorithm, which is an effective and promising optimiser to address the optimisation problems. In this study, a hybrid algorithm based on the self-adaptive invasive weed algorithm (IWO) and differential evolution algorithm (DE), named SIWODE, is proposed to address the continuous optimisation problems. In the proposed SIWODE, first, the two parameters are adaptively proposed to improve the convergence speed of the algorithm. Second, the crossover and mutation operations are introduced in SIWODE to improve the population diversity and increase the exploration capability during the iterative process. Furthermore, a local perturbation strategy is presented to improve exploitation ability during the late process. The exploration and exploitation ability of the algorithm is effectively balanced by cooperative mechanisms. The experiment results of SIWODE show that the SIWODE has the superior searching quality and stability than other mentioned approaches. [ABSTRACT FROM AUTHOR]

3D human pose estimation has many important applications in human-computer interaction and human action recognition. Simultaneously achieving real-time speed, varying human number, and high accuracy from a single RGB image is a challenging problem. To this end, this paper proposes a multi-task and multi-level neural network structure with physical constraint. The unique network structure estimates 3D human poses from single RGB image in an end-to-end way and achieves both high accuracy and high speed. Experimental results shows that the proposed system achieves 21 fps on RTX 2080 GPU with only 33 mm accuracy loss compared with conventional works. The mechanism of the network is also analyzed through network visualization. This work shows the possibility of estimating 3D human pose from a single RGB monocular camera with real-time speed. [ABSTRACT FROM AUTHOR]

3D human pose estimation has many important applications in human-computer interaction and human action recognition. Simultaneously achieving real-time speed, varying human number, and high accuracy from a single RGB image is a challenging problem. To this end, this paper proposes a multi-task and multi-level neural network structure with physical constraint. The unique network structure estimates 3D human poses from single RGB image in an end-to-end way and achieves both high accuracy and high speed. Experimental results shows that the proposed system achieves 21 fps on RTX 2080 GPU with only 33 mm accuracy loss compared with conventional works. The mechanism of the network is also analyzed through network visualization. This work shows the possibility of estimating 3D human pose from a single RGB monocular camera with real-time speed. [ABSTRACT FROM AUTHOR]

Toughening modification of poly(l‐lactide) (PLLA) with rubber particles is often realized at the cost of transparency, mechanical strength, and modulus because high rubber loadings are generally required for toughening. In this work, a promising strategy to simultaneously improve the transparency and stiffness–toughness performance of poly(butyl acrylate)‐poly(methyl methacrylate) (BAMMA) core‐shell rubber nanoparticles toughened PLLA blends by utilizing the stereocomplex (SC) crystallization between PLLA and poly(d‐lactide) (PDLA) is devised. The results reveal that the construction of SC crystallites in PLLA matrix via melt‐mixing PLLA/BAMMA blends with PDLA can prevent BAMMA nanoparticles from aggregation and promote them to form network‐like structure at lower contents. As a result, not only higher toughening efficiency with less rubber contents but also superior transparency is achieved in the PLLA/PDLA/BAMMA blends as compared with the PLLA/BAMMA ones where large aggregated BAMMA clusters are formed. Moreover, the outstanding reinforcement of SC crystallites network for PLLA can impart an enhanced tensile strength and modulus to PLLA/PDLA/BAMMA blends, thus improving the stiffness–toughness performance of PLLA/PDLA/BAMMA blends to a higher degree. This work demonstrates that SC crystallization is a promising solution to solve the contradiction between transparency and mechanical properties and then obtain superior comprehensive performances in rubber toughened PLLA blends. [ABSTRACT FROM AUTHOR]

Compared with the great successes achieved by supervised learning, e.g. convolutional neural network (CNN), unsupervised feature learning is still a highly-challenging task suffering from no training labels. Because of no training labels for reference, blindly reducing the gap between features and image semantics is the most challenging problem. This paper proposes a Self-Taught Encoder-Decoder Network (STED-Net), which consists of a representation sub-network and a classification sub-network, for unsupervised feature learning. On one hand, the representation sub-network maps images to feature representation. On the other hand, using the features generated by representation sub-network, classification sub-network simultaneously maps feature representation to class representation and estimates pseudo labels by clustering feature representation. By minimizing the distance between class representation and the estimated pseudo labels, STED-Net teaches the features to represent class information. Through the self-taught feature representation, the gap between features and image semantics is reduced, and the features are promoted to be more and more "class-aware". The whole learning process of the STED-Net does not refer to any ground-truth class labels. Experimental results on widely-used image classification datasets prove that STED-Net achieves state-of-the-art classification performance compared with existing supervised and unsupervised feature learning models. [ABSTRACT FROM AUTHOR]

In recent years, scheduling problems have attracted enormous attentions from practitioners and researches in manufacturing systems, for instance, the scheduling of computing resource in cloud infrastructure and cloud services. The scheduling problems in cloud services, big data and other service-oriented computing problems are regarded as non-separable problems. In this paper, a hybrid biogeography-based optimization with the enhanced mutation operator and CMA-ES (HBBO-CMA) is proposed to enhance the ability of exploitation on non-separable problems and alleviate the rotational variance. In the migration operator, the rotationally invariant migration operator is designed to reduce the dependence of BBO on the coordinate system and control the diversity of population. In the mutation operator, an enhanced mutation operator, which is sampled from the mean value and stand deviation of the variables of population, is employed to effectively escape the local optimum. Furthermore, the CMA-ES, which has outstanding performance on the non-separable problem, is applied to extend the exploitation of HBBO-CMA. Experimental results on CEC-2017 demonstrated the effectiveness of the proposed HBBO-CMA. [ABSTRACT FROM AUTHOR]

Establishing local visual correspondence between video frames is an important and challenging problem in many vision based applications. Local keypoint detection and description based pixel-level matching is a typical way for visual correspondence estimation. Unlike traditional local keypoint descriptor based methods, this paper proposes a comprehensive yet low-dimensional local feature descriptor based on superpixels generated by over segmentation. The proposed local feature descriptor extracts shape feature, texture feature, and color feature from superpixels by orientated center-boundary distance (OCBD), gray-level co-occurrence matrix (GLCM), and saturation histogram (SHIST), respectively. The types of features are more comprehensive than existing descriptors which extract only one specific kind of feature. Experimental results on the widely used Middlebury optical flow dataset prove that the proposed superpixel descriptor achieves triple accuracy compared with the state-of-the-art ORB descriptor which has the same dimension of features with the proposed one. In addition, since the dimension of the proposed superpixel descriptor is low, it is convenient for matching and memory-efficient for hardware implementation. [ABSTRACT FROM AUTHOR]

As multipath is one of the dominating error sources for high accuracy Global Navigation Satellite System (GNSS) applications, multipath mitigation approaches are employed to minimize this hazardous error in receivers. Binary offset carriermodulation (BOC), as a modernized signal structure, is adopted to achieve significant enhancement. However, because of its multi-peak autocorrelation function, conventional multipath mitigation techniques for binary phase shift keying (BPSK) signal would not be optimal. Currently, non-parametric and parametric approaches have been studied specifically aiming at multipath mitigation for BOC signals. Non-parametric techniques, such as Code Correlation ReferenceWaveforms (CCRW), usually have good feasibility with simple structures, but suffer from low universal applicability for different BOC signals. Parametric approaches can thoroughly eliminate multipath error by estimating multipath parameters. The problems with this category are at the high computation complexity and vulnerability to the noise. To tackle the problem, we present a practical parametric multipath estimation method in the frequency domain for BOC signals. The received signal is transferred to the frequency domain to separate out the multipath channel transfer function for multipath parameter estimation. During this process, we take the operations of segmentation and averaging to reduce both noise effect and computational load. The performance of the proposed method is evaluated and compared with the previous work in three scenarios. Results indicate that the proposed averaging-Fast Fourier Transform (averaging-FFT) method achieves good robustness in severe multipath environments with lower computational load for both low-order and high-order BOC signals. [ABSTRACT FROM AUTHOR]

Local structural information is crucially important for human visual system to perceive natural scenes. Recent years, a variety of local image structure description methods have been proposed for the artificial modeling of visual perception. Although existing local image structure descriptors have shown successful performances, one general limitation is their numerical instability caused by ignoring the information of the spatial correlation of local orientation. In this paper, we propose a local image structure descriptor by modeling the anisotropic mechanism in the primary visual cortex. In particular, the pixel-wise anisotropy values of a given image are calculated by pseudo-Wigner-Ville distribution (PWVD) and Rényi entropy. Then the excitatory/inhibitory interactions among visual neurons in the local receptive field are modeled by measuring the similarities between their anisotropies. By mapping visual neurons to image pixels, the correlation between a central pixel and its local neighbors can be represented by a binary pattern which is named as local anisotropic pattern (LAP). Experimental results on texture classification verified that the proposed LAP has satisfactory texture classification accuracy, rotation invariance, and noise robustness; experimental results on no-reference image quality assessment demonstrated that the proposed LAP achieves state-of-the-art performance in objective evaluation of the perceptual quality of natural image, and this reflects that LAP can accurately represent the degradation of local image structure. [ABSTRACT FROM AUTHOR]

Blind image quality assessment (BIQA), which aims to estimate the perceptual quality of images without any reference information, is a very important yet challenging task. Although human visual system is sensitive to degradations on both spatial contrast and spatial distribution, most of the existing structural degradation based BIQA models consider only one of them. This study introduces a novel BIQA model by taking into account degradations on both contrast and spatial distribution. First, the authors construct a multi‐threshold local tetra pattern (MTLTrP) instead of local binary pattern to measure the changes on spatial distribution. Second, Weber–Laplacian of Gaussian (WLOG) operator, which responds to intensity contrast in a small spatial neighbourhood, is proposed to extract local contrast features. Finally, the joint statistics of MTLTrP and WLOG are utilised for BIQA model learning. Experimental results on three large benchmark databases demonstrate that the proposed model outperforms state‐of‐the‐art BIQA models, as well as with several well‐known full reference quality assessment methods. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to review our experience in managing renal or adrenal tumors with level III or IV inferior vena cava thrombus by using deep hypothermic circulatory arrest (DHCA), and to evaluate survival outcomes.Methods: Between September 2004 and March 2016, we treated 33 patients with renal or adrenal malignancy tumor and thrombus extending into the inferior vena cava. Patients were identified according to radiographic records and operative findings. Clinicopathological and operative characteristics were recorded, and comparisons of clinical and operative characteristics through DHCA were performed. A Cox regression model was used to determine predictors of perioperative mortality.Results: Twenty-one out of 33 patients with level III (n = 15), level IV (n = 5), or level II (n = 1) renal or adrenal tumors were treated surgically through cardiopulmonary bypass (CPB) with DHCA, and 12 patients with level II or III tumors were treated surgically through normothermic CPB. Three complications were observed, and one death occurred perioperatively, owing to multiple organ failure. The overall perioperative mortality was 4.7%. There were significant differences in the clinicopathological characteristics, operative duration, estimated blood loss, transfusions and hospital stay depending on use of DHCA. Multivariate analysis indicated that the operative duration (OR, 3.78; P < 0.001), estimated blood loss (OR, 1.08; P = 0.02), and transfusion (OR, 2.13; P = 0.038) during/after surgery were positively associated with higher mortality and morbidity. DHCA failed to reach statistical significance (P = 0.378).Conclusions: Use of CPB and DHCA to treat renal or adrenal tumors allows for complete tumor resection, especially at the T4 stage. Although it can cause physical damage, this technique does not increase operative risk and is a relatively safe approach. [ABSTRACT FROM AUTHOR]