Underwater image super-resolution using multi-stage information distillation networks

Recently, single image super-resolution (SISR) has been widely applied in the fields of underwater robot vision and obtained remarkable performance. However, most current methods generally suffered from the problem of a heavy burden on computational resources with large model sizes, which limited their real-world underwater robotic applications. In this paper, we introduce and tackle the super resolution (SR) problem for underwater robot vision and provide an efficient solution for near real-time applications. We present a novel lightweight multi-stage information distillation network, named MSIDN, for better balancing performance against applicability, which aggregates the local distilled features from different stages for more powerful feature representation. Moreover, a novel recursive residual feature distillation (RRFD) module is constructed to progressively extract useful features with a modest number of parameters in each stage. We also propose a channel interaction & distillation (CI&D) module that employs channel split operation on the preceding features to produce two-part features and utilizes the inter channel-wise interaction information between them to generate the distilled features, which can effectively extract the useful information of current stage without extra parameters. Besides, we present USR-2K dataset, a collection of over 1.6K samples for large-scale underwater image SR training, and a testset with an additional 400 samples for benchmark evaluation. Extensive experiments on several standard benchmark datasets show that the proposed MSIDN can provide state-of-the-art or even better performance in both quantitative and qualitative measurements.     

WavNet — Visual saliency detection using Discrete Wavelet Convolutional Neural Network

In the recent advancements in image and video analysis, the detection of salient regions in the image becomes the initial step. This plays a crucial role in deciding the performance of such algorithms. In this work, a Multi-Resolution Feature Extraction (MRFE) technique that makes use of Discrete Wavelet Convolutional Neural Network (DWCNN) for generating features is employed. An Enhanced Feature Extraction (EFE) module extracts additional features from the high level features of the DWCNN, which are used to frame both channel as well as spatial attention models for yielding contextual attention maps. A new hybrid loss function is also proposed, which is a combination of Balanced Cross Entropy (BCE) loss and Edge based Structural Similarity (ESSIM) loss that effectively identifies and segments the salient regions with clear boundaries. The method is tested exhaustively with five different benchmark datasets and is proved superior to the existing state-of-the-art methods with a minimum Mean Absolute error (MAE) of 0.03 and F-measure of 0.956.     

Portrait of Cancer Stem Cells on Colorectal Cancer: Molecular Biomarkers,Signaling Pathways and miRNAome

Colorectal cancer (CRC) is a leading cause of cancer death worldwide, and about 20% is metastatic at diagnosis and untreatable. Increasing evidence suggests that the heterogeneous nature of CRC is related to colorectal cancer stem cells (CCSCs), a small cells population with stemness behaviors and responsible for tumor progression, recurrence, and therapy resistance. Growing knowledge of stem cells (SCs) biology has rapidly improved uncovering the molecular mechanisms and possible crosstalk/feedback loops between signaling pathways that directly influence intestinal homeostasis and tumorigenesis. The generation of CCSCs is probably connected to genetic changes in members of signaling pathways, which control self-renewal and pluripotency in SCs and then establish function and phenotype of CCSCs. Particularly, various deregulated CCSC-related miRNAs have been reported to modulate stemness features, controlling CCSCs functions such as regulation of cell cycle genes expression, epithelial-mesenchymal transition, metastasization, and drug-resistance mechanisms. Primarily, CCSC-related miRNAs work by regulating mainly signal pathways known to be involved in CCSCs biology. This review intends to summarize the epigenetic findings linked to miRNAome in the maintenance and regulation of CCSCs, including their relationships with different signaling pathways, which should help to identify specific diagnostic, prognostic, and predictive biomarkers for CRC, but also develop innovative CCSCs-targeted therapies.     

基于卷积神经网络的安全防御模式研究

云计算、大数据、5G通信等技术的快速发展，有力的促进了计算机网络在多个领域的普及和使用，人们已经进入到了“互联网+”时代，实现了工作、生活和学习的数字化、智能化和共享化。计算机网络在为人们提供各种便利服务的同时也面临着许多的安全威胁，比如木马、病毒等，不法分子攻击网络数据中心，破坏用户服务终端及服务器，为人们带来严重的威胁。网络安全学者或企业机构也提出了许多安全防御技术，比如入侵检测技术、深度包过滤技术、防火墙技术、数据加密技术等，一定程度上提高了网络安全防御的能力，但是随着互联网数据流量的增多，需要引入更加先进的人工智能技术，以便能够快速的采集、分析网络数据流，确定数据流中是否存在病毒或木马，以便及时的对其进行查杀，进一步提高网络安全防御的主动性、积极性     

工艺参数对动态流量控制法挤压弯管应力的影响

采用有限元模拟和实验研究了挤压钛合金弯曲管件。通过实验验证了工件的形状和尺寸精度,并通过有限元模拟分析了工艺参数对挤出过程中变形体的平均压应力分布情况和挤出弯管件的曲率半径的影响规律。结果表明:有限元模拟中,弯管件的曲率半径误差为6.03%,弯管直径误差为3.82%;在靠近定径带处,平均压应力呈非均匀分布;在焊合腔内,靠近细分流孔区域的平均压应力小于靠近粗分流孔区域的平均压应力,平均压应力的大小顺序在通过粗、细分流孔前后相反;在模具结构固定不变时,弯管件的曲率半径随挤压速度的减小而增大,不随挤压温度的变化而变化。     

梯度铝蜂窝夹芯板的力学行为

梯度分层铝合金蜂窝板是一种有效的吸能结构,本工作在梯度铝蜂窝结构的基础上根据梯度率的概念,通过改变蜂窝芯层的胞壁长度,设计了4种质量相同、梯度率不同的铝蜂窝夹芯结构。通过准静态压缩实验,并结合非线性有限元模拟准静态及冲击态下梯度铝蜂窝夹芯结构的变形情况及其力学性能,分析对比了相同质量下梯度铝蜂窝夹芯结构在准静态下的变形模式以及冲击载荷下分层均质蜂窝结构和不同梯度率的分层梯度蜂窝结构的动态响应和能量吸收特性。结果表明:在准静态压缩过程中,铝蜂窝梯度夹芯板的变形具有明显的局部化特征,蜂窝芯的变形为低密度优先变形直至密实,层级之间的密实化应变差随芯层密度的增大而逐渐减小;在高速冲击下,梯度蜂窝板并非严格按照准静态过程中逐级变形直至密实,而是在锤头冲击惯性及芯层密度的相互作用下整体发生的线弹性变形、弹性屈曲、塑性坍塌及密实化;另外,在本工作所设计的梯度率中,当梯度率为γ₁=0.0276时,梯度蜂窝夹芯板的吸能性达到最好,相较于同等质量下的均质蜂窝夹芯板,能量吸收提高了10.63%。     

Electrochemical performance of in situ LiFePO4 modified by N-doped graphene for Li-ion batteries

LiFePO₄ modified by N-doped graphene (NG) with a three-dimensional conductive network structure was synthesized via a one-step in situ hydrothermal method. The effects of N amount of NG on the phase structure, morphology, and electrochemical properties of LiFePO₄ are investigated in this study. X-ray diffraction (XRD) results show that doping suitable N amounts in NG do not alter the crystal structure of LiFePO_4, and scanning electron microscopy (SEM) images show that NG can slightly reduce the particle size of LiFePO₄. The high-resolution transmission electron microscopy (HRTEM) results show that the LiFePO₄ particles are well covered and connected by NG. The electrochemical performance confirms that LiFePO₄ modified by 20% N-doped graphene (named LFP/NG-4) displays a perfect specific capacity of 166.6 mAh·g^?1 at a rate of 0.2C and can reach 125 mAh·g^?1 at a rate of 5 C. Electrochemical impedance spectroscopy (EIS) results illustrate that the charge transfer resistance value of the LFP/NG-4 composite is only 58.6 Ω, which is very low compared with LiFePO₄. Cyclic voltammetry (CV) tests indicate that the addition of 20% N-doped graphene can effectively reduce electrode polarization and improve reversibility. The LFP/NG-4 composite with a three-dimensional conductive network structure can be regarded as a promising cathode material for Li-ion batteries.     

The P-graph approach for systematic synthesis of wastewater treatment networks

Wastewater treatment consists of three or four sequential stages: preliminary, primary, secondary, and tertiary. Each stage can comprise multiple alternative technologies that can perform the same tasks with different efficiencies, operating times, and costs. Thus, we propose a systematic approach for designing wastewater treatment networks by utilizing principles of mathematical modeling and generating an exhaustive enumeration of all the possible technologies and their connections during the early stages of designing a treatment facility. Some of these structures are nonintuitive and include recycling, reprocessing, bypasses, and multiple technologies in parallel or series to remove the same contaminant. The nonintuitive structures with multiple technologies may provide a measure of resilience compared to typical heuristic designs. Thus, the combination of P-graph methodology and the sequence of treatment technologies predicted via the optimization algorithm from the maximal structure is based on holistic considerations and does not lead to suboptimal solutions.     

5G技术在医院智能服务中的应用

纵观当今的发展格局,是科学技术高速发展的时代。随着5G时代的来临,网络技术在各个领域中扮演着越来越重要的角色。特别是在医疗方面,为医院的日常工作奠定了坚实的基础。今天就5G网络技术在医院只能服务中的应用展开谈论。     

基于GA-BP的汽车风振噪声声品质预测模型

目前对于汽车风振噪声的优化研究主要以声压级（Sound pressure level，SPL）作为单一评价指标，既不能全面反映噪声的物理属性，也无法考虑人耳对噪声的主观认知过程。为准确评价风振噪声，引入声品质，运用大涡模拟（Large eddy simulation，LES）对风振噪声进行数值仿真，根据实车道路试验判断仿真的准确性；对仿真结果进行声品质客观评价与主观评价，综合声品质客观评价参数与声品质主观评价试验结果建立BP神经网络预测模型；利用遗传算法（Genetic algorithm，GA），进一步对BP神经网络的结构参数进行优化，建立GA-BP声品质预测模型。研究结果表明，GA-BP声品质预测模型在训练速度和预测精度上都优于BP神经网络预测模型。预测模型基于声品质主客观评价结果，其预测值可以代替传统的声压级评价指标，为风振噪声提供更为准确合理的评价。