RTDNet：面向高分辨率卫星影像的赤潮探测网络

目的 赤潮是一种常见的海洋生态灾害,严重威胁海洋生态系统安全。及时准确获取赤潮的发生和分布信息可以为赤潮的预警和防治提供有力支撑。然而,受混合像元和水环境要素影响,赤潮分布精细探测仍是挑战。针对赤潮边缘探测的难点,结合赤潮边缘高频特征学习与位置语义,提出了一种计算量小、精度高的网络模型RTD-Net （red tide detection network）。方法 针对赤潮边缘探测不准确的问题,设计了基于RIR （residual-in-residual）结构的网络,以提取赤潮边缘水体的高频特征;利用多感受野结构和坐标注意力机制捕获赤潮水体的位置语义信息,增强赤潮边缘水体的细节信息并抑制无用的特征。结果 在GF1-WFV （Gaofen1 wide field of view）赤潮数据集上的实验结果表明,所提出的RTDNet模型赤潮探测效果不仅优于支持向量机（support vector machine,SVM）、U-Net、Deep-Labv3+及HRNet （high-resolution network）等通用机器学习和深度学习模型,而且也优于赤潮指数法GF1_RI （Gaofen1 red tide index ）以及赤潮探测专用深度学习模型RDU-Net （red tide detection U-Net）,赤潮误提取、漏提取现象明显减少,F1分数在两幅测试图像上分别达到了0.905和0.898,相较于性能第2的模型DeepLabv3+提升了2%以上。而且,所提出的模型参数量小,仅有2.65 MB,约为DeepLabv3+的13%。结论 面向赤潮探测提出一种基于RIR结构的赤潮深度学习探测模型,通过融合多感受野结构和注意力机制提升了赤潮边缘探测的精度和稳定性,同时有效降低了计算量。本文方法展现了较好的应用效果,可适用于不同高分辨率卫星影像的赤潮探测。     

基于改进深度学习的风机油污识别

针对风机设备油液渗漏影响风机正常运行亟需解决的对风机设备油污的识别问题,提出了一种基于改进深度学习的风机油污检测方法。基于深度学习在目标检测中的应用特点,对目标检测网络YOLOv5n（You Only Look Once v5n）进行改进,将原网络中的非极大抑制（Non Maximum Suppression,NMS）替换为Soft-NMS,降低了网络的误检率,添加CA (Coordinate Attention)注意力机制,增强了模型对目标的定位能力,改进原网络损失函数为α-IoU（Alpha- Intersection over Union）损失函数,提高了边界框检测的准确度。实验结果表明:模型平均精度提升了8.1%,查全率提高了19.1%,网络推理速度提高了28.6%。改进后的模型能准确检测风机油污,有效解决了风机实际运行中油液渗漏所带来的问题。     

Advanced Authentication Mechanisms for Identity and Access Management in Cloud Computing

Identity management is based on the creation and management of user identities for granting access to the cloud resources based on the user attributes. The cloud identity and access management (IAM) grants the authorization to the end-users to perform different actions on the specified cloud resources. The authorizations in the IAM are grouped into roles instead of granting them directly to the end-users. Due to the multiplicity of cloud locations where data resides and due to the lack of a centralized user authority for granting or denying cloud user requests, there must be several security strategies and models to overcome these issues. Another major concern in IAM services is the excessive or the lack of access level to different users with previously granted authorizations. This paper proposes a comprehensive review of security services and threats. Based on the presented services and threats, advanced frameworks for IAM that provide authentication mechanisms in public and private cloud platforms. A threat model has been applied to validate the proposed authentication frameworks with different security threats. The proposed models proved high efficiency in protecting cloud platforms from insider attacks, single sign-on failure, brute force attacks, denial of service, user privacy threats, and data privacy threats.     

Core–Shell Structure and Interaction Mechanism of γ‐MnO2 Coated Sulfur for Improved Lithium‐Sulfur Batteries

Lithium‐sulfur batteries have attracted worldwide interest due to their high theoretical capacity of 1672 mAh g^?1 and low cost. However, the practical applications are hampered by capacity decay, mainly attributed to the polysulfide shuttle. Here, the authors have fabricated a solid core–shell γ‐MnO₂‐coated sulfur nanocomposite through the redox reaction between KMnO₄ and MnSO₄. The multifunctional MnO₂ shell facilitates electron and Li⁺ transport as well as efficiently prevents polysulfide dissolution via physical confinement and chemical interaction. Moreover, the γ‐MnO₂ crystallographic form also provides one‐dimensional (1D) tunnels for the Li⁺ incorporation to alleviate insoluble Li₂S₂/Li₂S deposition at high discharge rate. More importantly, the MnO₂ phase transformation to Mn₃O₄ occurs during the redox reaction between polysulfides and γ‐MnO₂ is first thoroughly investigated. The S@γ‐MnO₂ composite exhibits a good capacity retention of 82% after 300 cycles (0.5 C) and a fade rate of 0.07% per cycle over 600 cycles (1 C). The degradation mechanism can probably be elucidated that the decomposition of the surface Mn₃O₄ phase is the cause of polysulfide dissolution. The recent work thus sheds new light on the hitherto unknown surface interaction mechanism and the degradation mechanism of Li‐S cells.     

Sound Absorption Performance and Mechanism of Aluminum Foams with Double Main Pore-Porous Cell Wall Structure

To enhance the sound absorption performance of open-cell aluminum foam, the double main pores-porous cell walls (DMP-PCW) aluminum foams via infiltration casting of preforms mixed with two sizes of NaCl particles are prepared. The pore structure, sound absorption performance, and mechanism of DMP-PCW aluminum foam are investigated. The pore structure consists of double-sized main pores similar to the NaCl particles and the cell wall pores formed by the connections between NaCl particles. It is found that the static flow resistivity of DMP-PCW aluminum foam reaches an optimum value of 28105 Pa.s m⁻² when the volume proportion of small main pores increases, the size of cell wall pores decreases, and the number of cell wall pores per unit main pore surface area (NPPA) increases. At 800–6300 Hz, the average absorption coefficient is 0.89. In addition, the Wilson model predicts the sound absorption properties of DMP-PCW aluminum foam. The predicted values agree well with the measured values. The finite-element acoustic simulations and dynamic viscous-thermal permeability calculations reveal that the improved sound absorption performance of DMP-PCW aluminum foam is correlated to the enhanced sound transmission caused by increased NPPA and increased viscous-thermal loss due to the double main pore structure.     

Research Progress of Laminated Composite Ceramic Cutting Tools

The design of the lamination structure based on bionic shell pearl layer is a successful method for toughening ceramics. Lamination with strong bonding interfaces is used to improve the mechanical property and low fracture toughness of ceramic cutting tools. Based on the idea of demand–design–preparation–analysis–failure, the development and research progress of laminated ceramic tools are reviewed herein. The research status of design, interlayer diffusion reaction, residual stress, toughening mechanism, and crack propagation path of the biomimetic laminated ceramic composite tool materials is mainly introduced. The major topics of current research include the creation of material systems, the evolution of microstructure, and the assessment of macroscopic mechanical properties. The entire mechanical properties of laminated ceramic tools are significantly influenced by the multicomposition design of the ceramic material system and the optimization design of structural parameters of layer number and layer thickness ratio. However, the research on the practical cutting application of laminated ceramic tools is limited. Cutting tool wear characteristics vary between laminated and homogeneous ceramic tools. The development of useful laminated ceramic cutting tools can greatly benefit from the study on failure mechanisms of laminated ceramic tools.     

Erosion wear response of ramie-epoxy composites using artificial neural network integrated with Taguchi technique

This paper aims on evaluating the erosion wear behavior of epoxy composites reinforced with ramie fibers. The possibility of reinforcing ramie fiber to improvise the wear resistance of epoxy is investigated in this study. Composites are fabricated by reinforcing multiple layers of woven ramie fiber mats into epoxy resin using conventional wet lay-up technique and erosion wear trials are conducted using solid particle erosion test setup. Taguchi analysis is done to assess the relative significance of each of the factors influencing the erosion rate using L₁₆ orthogonal array. The analysis reveals that the impact velocity followed by impingement angle are the most significant control factors affecting the erosion wear rate of ramie-epoxy composites. Steady state erosion analysis is done to ascertain the effect of each of the significant factors while keeping other factors fixed. Further, an analytical and predictive model based on the principle of neural computation is used to predict the rate of erosion wear of the composites and the obtained results are compared with the experimental outcomes. The worn morphologies of the eroded surfaces of the composites are studied and analyzed to identify possible mechanisms causing wear.