Context-wise attention-guided network for single image deraining

In this paper, we propose a context-wise attention-guided network for single image deraining. Unlike most existing deraining methods, our network exploits underlying complementary information not only across multiple scales but also between levels. Specifically, our network architecture is designed to transmit the inter-level and inter-scale features. To extract guiding information and improve the discriminating ability of context-wise attention-guided network, we propose a net-context-wise attention module to generate attention maps. Following residual learning, the clean image is created by removing the predicted rain streak layer from the rainy input. Experimental results show our method has better performance on public datasets than some state-of-the-art methods.</p

Research on the integrated manipulator of point cloud measurement and precise cutting for waste nuclear tank

Purpose Nuclear waste tanks need to be cut into pieces before they can be safely disposed of, but the cutting process produces a large amount of aerosols with radiation, which is very harmful to the health of the operator. The purpose of this paper is to establish an intelligent strategy for an integrated robot designed for measurement and cutting, which can accurately identify and cut unknown nuclear waste tanks and realize autonomous precise processing. Design/methodology/approach A robot system integrating point cloud measurement and plasma cutting is designed in this paper. First, accurate calibration methods for the robot, tool and hand-eye system are established. Second, for eliminating the extremely scattered point cloud caused by metal surface refraction, an omnidirectional octree data structure with 26 vectors is proposed to extract the point cloud model more accurately. Then, a minimum bounding box is calculated for limiting the local area to be cut, the local three-dimensional shape of the nuclear tank is fitted within the bounding box, in which the cutting trajectories and normal vectors are planned accurately. Findings The cutting precision is verified by changing the tool into a dial indicator in the simulation and the experiment process. The octree data structure with omnidirectional pointing vectors can effectively improve the filtering accuracy of the scattered point cloud. The point cloud filter algorithm combined with the structure calibration methods for the integrated measurement and processing system can ensure the final machining accuracy of the robot. Originality/value Aiming at the problems of large measurement noise interference, complex transformations between coordinate systems and difficult accuracy guarantee, this paper proposes structure calibration, point cloud filtering and point cloud-based planning algorithm, which can greatly improve the reliability and accuracy of the system. Simulation and experiment verify the final cutting accuracy of the whole system.</p

Effect of Sintering Temperature on Surface Morphology and Roughness of 3D-printed Silicon Ceramic Cores

Single crystal superalloy hollow blade is an important part of aero-engine, and its inner cavity structure is prepared by ceramic core. With the increase of thrust-weight ratio of aero-engine, the core structure is more and more complex. Traditional preparation technology is difficult to meet the requirements of complex core preparation. Stereolithography 3D printing of ceramic cores provides a feasible scheme for the preparation of complex cores. In order to improve the surface roughness of stereolithography 3D printed ceramic cores caused by step effect, this study used silicon-based core paste with solid content of 63% (in volume), and the cores of the green bodies were sintered at 1100 degrees C to 1300 degrees C. Microstructure, element distribution, phase composition, surface morphology, and roughness of the silicon-based ceramic core were analyzed. It is found that printed surface of the core is smooth without obvious surface defects. Roughness of the printed surfaces of the sintered cores at 1100, 1200 and 1300 degrees C are 1.83, 1.24 and 1.44 mu m, respectively. Their surface of lamellar stacking direction has lamellar structure characteristics, and microcracks appear between lamellar, and surface roughness of core sintered above 1200. meets the requirements (R-a <= 2.0 mu m) of hollow blade. Sintering temperatures affect the liquid content, mullite production, mullite formation morphology, and glass phase distribution of cores during the sintering process, and the surface roughness of stereolithography 3D-printed silicon ceramic cores is positively affected. Stereolithography 3D printing ceramic core technology combined with sintering process can produce a silicon-based ceramic core which surface roughness meets the requirements of an advanced hollow blade

Reducing self-absorption effect by double-pulse combination in laser-induced breakdown spectroscopy

The method of double-pulse laser-induced breakdown spectroscopy is usually employed to enhance the spectral signal intensity. However, in this study, double-pulse laser-induced breakdown spectroscopy is adopted to investigate the effect of the self-absorption reduction of the spectrum. This research explored that the influence of the change of the gas environment generated by the first laser beam on the self-absorption effect of the plasma spectrum by the second laser beam. Especially despite the different combinations of laser energy, for the three elements of Cu, Mn and Ni, the weakest spectral self-absorption effect can be obtained when the double-pulse delays are around 80 mu s, 100 mu s, and 110 mu s, respectively. In addition, this paper also found that when the energy of the first laser beam is unchanged, the spectral self-absorption effect has a strong correlation with the double-pulse delay, and has a weak correlation with the change of the second laser energy

Visual Measurement Method of Gap Width of Split Type Ammunition Based on Improved Ostu-Sobel

在分体式炮弹产品的质量检测中，螺纹连接处间隙的高精度稳定测量是保证炮弹质量的重要指标之一。为了精确测量螺纹连接处间隙，利用机器视觉的方法，提出了一种基于改进Otsu-Sobel的分体式炮弹缝宽视觉测量方法。该方法根据图像特征生成自适应感兴趣区域，再通过单调化处理与Sobel算子确定缝隙边缘的粗定位区间，在局部利用图像梯度的离散度精确定位缝隙边缘。为解决机械安装、缝宽倒角等因素对精度的影响，利用了最小二乘拟合方法对测量结果进行修正。实验结果表明，该方法可精确检测0.1～0.7mm的缝宽，且其测量误差小于0.02mm。该方法解决了分体式炮弹螺纹连接处间隙精确测量的技术难题，可满足产品质量检测的需要。</p

Characterization of microstructural anisotropy using the mode-converted ultrasonic scattering in titanium alloy

The mode-converted (Longitudinal to Transverse, L-T) ultrasonic scattering was utilized to characterize the microstructural anisotropy on three surfaces of samples cut from the low-scattering and high-scattering regions of a raw titanium alloy Ti-6Al-4V billet, respectively. The L-T ultrasonic measurements were performed in two perpendicular directions using two focused transducers with a 15 MHz center frequency in a pitch-catch configuration. The root mean square (RMS) of ultrasonic scattering was calculated for each L-T measurement and a Gaussian function was used to fit each RMS to determine the RMS amplitude. The ratio of RMS amplitudes for L-T measurements performed in two perpendicular directions was calculated to characterize the microstructural anisotropy on the measured surface of a sample. The results show that the amplitude of L-T ultrasonic scattering is highly dependent on the microstructural anisotropy. The microstructural isotropy was considered on the x-y planes of all samples, while the high anisotropy was seen on the x-z and y-z planes of all low-scattering and high-scattering samples. In addition, the microstructural anisotropy measured on the x-z planes of the low-scattering and high-scattering samples gradually increases and decreases, respectively, from the outside diameter (OD) to the centerline (CL) of the billet. The anisotropy measured on the y-z planes of the low-scattering samples slightly decreases and then increases towards the center, while the anisotropy measured on the y-z planes of the high-scattering samples continuously increases towards the center. The variation of microstructural anisotropy in the titanium alloy Ti-6Al-4V billet with duplex microstructure was quantified with the L-T ultrasonic method and the results agree well with micrographs shown in Ref. [18]. The mode-converted ultrasonic scattering method provides a NDE method to characterize microstructural anisotropy, which can be used as an NDE tool for quality control.</p

Adaptive learning attention network for underwater image enhancement

Underwater images suffer from color casts and low illumination due to the scattering and absorption of light as it propagates in water. These problems can interfere with underwater vision tasks, such as recognition and detection. We propose an adaptive learning attention network for underwater image enhancement, named LANet, to solve these degradation issues. First, a multiscale fusion module is proposed to combine different spatial information. Second, we design a novel parallel attention module(PAM) to focus on the illuminated features and more significant color information coupled with the pixel and channel attention. Then, an adaptive learning module(ALM) can retain the shallow information and adaptively learn important feature information. Further, we utilize a multinomial loss function that is formed by mean absolute error and perceptual loss. Finally, we introduce an asynchronous training mode to promote the network&#39;s performance of multinomial loss function. Qualitative analysis and quantitative evaluations show the excellent performance of our method on different underwater datasets. The code is available at: https://github.com/LiuShiBen/LANet.</p

A transfer weighted extreme learning machine for imbalanced classification

Previous class imbalance learning methods are mostly grounded on the assumption that all training data have been labeled, however, is impractical in many real-world applications. The limited amount of labeled instances may produce a classifier with poor generalization. To address the issue, a transfer weighted extreme learning machine (TWELM) classifier is proposed, with the purpose of extracting knowledge from other domains to improve the classification performance of a classifier in a limited labeled target domain. To be specific, a well-tuned weighted extreme learning machine classifier is first learned from source data that has been completely labeled. Subsequently, another extreme learning machine classifier is obtained from the limited labeled target domain data to preserve the target domain structural knowledge and the decision boundary information. Finally, the target classifier is optimized by minimizing the outputs of the two classifiers on unlabeled target data. Experimental results on real-world data sets show that TWELM outperforms existing algorithms on classification accuracy and computation cost.</p

Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization

Deep learning is recognized to be capable of discovering deep features for representation learning and pattern recognition without requiring elegant feature engineering techniques by taking advantages of human ingenuity and prior knowledge. Thus it has triggered enormous research activities in machine learning and pattern recognition. One of the most important challenges of deep learning is to figure out relations between a feature and the depth of deep neural networks (deep nets for short) to reflect the necessity of depth. Our purpose is to quantify this feature-depth correspondence in feature extraction and generalization. We present the adaptivity of features to depths and vice-verse via showing a depth-parameter trade-off in extracting both single feature and composite features. Based on these results, we prove that implementing the classical empirical risk minimization on deep nets can achieve the optimal generalization performance for numerous learning tasks. Our theoretical results are verified by a series of numerical experiments including toy simulations and a real application of earthquake seismic intensity prediction.</p

Medical image fusion via discrete stationary wavelet transform and an enhanced radial basis function neural network

Medical image fusion of images obtained via different modes can expand the inherent information of original images, whereby the fused image has a superior ability to display details than the original sub images, to facilitate diagnosis and treatment selection. In medical image fusion, an inherent challenge is to effectively combine the most useful information and image details without information loss. Despite the many methods that have been proposed, the effective retention and presentation of information proves challenging. Therefore, we proposed and evaluated a novel image fusion method based on the discrete stationary wavelet transform (DSWT) and radial basis function neural network (RBFNN). First, we analyze the details or feature information of two images to be processed by DSWT by using two-level decomposition to separate each image into seven parts, comprising both high-frequency and low-frequency sub-bands. Considering the gradient and energy attributes of the target, we substituted the pending parts in the same position in the two images by using the proposed enhanced RBFNN. The input, hidden, and output layers of the neural network comprised 8, 40, and 1 neuron(s), respectively. From the seven neural networks, we obtained seven fused parts. Finally, through inverse wavelet transform, we obtained the final fused image. For the neural network training method, the hybrid adaptive gradient descent algorithm (AGDA) and gravitational search algorithm (GSA) were implemented. The final experimental results revealed that the novel method has significantly better performance than the current state-of-the-art methods. (C) 2022 Elsevier B.V. All rights reserved