Optimization Design of the Multi-Layer Cross-Sectional Layout of An Umbilical Based on the GA-GLM

Marine umbilical is one of the key equipment for subsea oil and gas exploitation,which is usually integrated by a great number of different functional components with multi-layers.The layout of these components directly affects manufacturing,operation and storage performances of the umbilical.For the multi-layer cross-sectional layout design of the umbilical,a quantifiable multi-objective optimization model is established according to the operation and storage requirements.Considering the manufacturing factors,the multi-layering strategy based on contact point identification is introduced for a great number of functional components.Then,the GA-GLM global optimization algorithm is proposed combining the genetic algorithm and the generalized multiplier method,and the selection operator of the genetic algorithm is improved based on the steepest descent method.Genetic algorithm is used to find the optimal solution in the global space,which can converge from any initial layout to the feasible layout solution.The feasible layout solution is taken as the initial value of the generalized multiplier method for fast and accurate solution.Finally,taking umbilicals with a great number of components as examples,the results show that the cross-sectional performance of the umbilical obtained by optimization algorithm is better and the solution efficiency is higher.Meanwhile,the multi-layering strategy is effective and feasible.The design method proposed in this paper can quickly obtain the optimal multi-layer cross-sectional layout,which replaces the manual design,and provides useful reference and guidance for the umbilical industry.

Multi-layer perceptron-based data-driven multiscale modelling of granular materials with a novel Frobenius norm-based internal variable

One objective of developing machine learning(ML)-based material models is to integrate them with well-established numerical methods to solve boundary value problems(BVPs).In the family of ML models,recurrent neural networks(RNNs)have been extensively applied to capture history-dependent constitutive responses of granular materials,but these multiple-step-based neural networks are neither sufficiently efficient nor aligned with the standard finite element method(FEM).Single-step-based neural networks like the multi-layer perceptron(MLP)are an alternative to bypass the above issues but have to introduce some internal variables to encode complex loading histories.In this work,one novel Frobenius norm-based internal variable,together with the Fourier layer and residual architectureenhanced MLP model,is crafted to replicate the history-dependent constitutive features of representative volume element(RVE)for granular materials.The obtained ML models are then seamlessly embedded into the FEM to solve the BVP of a biaxial compression case and a rigid strip footing case.The obtained solutions are comparable to results from the FEM-DEM multiscale modelling but achieve significantly improved efficiency.The results demonstrate the applicability of the proposed internal variable in enabling MLP to capture highly nonlinear constitutive responses of granular materials.

Target Controllability of Multi-Layer Networks With High-Dimensional Nodes

This paper studies the target controllability of multilayer complex networked systems,in which the nodes are highdimensional linear time invariant(LTI)dynamical systems,and the network topology is directed and weighted.The influence of inter-layer couplings on the target controllability of multi-layer networks is discussed.It is found that even if there exists a layer which is not target controllable,the entire multi-layer network can still be target controllable due to the inter-layer couplings.For the multi-layer networks with general structure,a necessary and sufficient condition for target controllability is given by establishing the relationship between uncontrollable subspace and output matrix.By the derived condition,it can be found that the system may be target controllable even if it is not state controllable.On this basis,two corollaries are derived,which clarify the relationship between target controllability,state controllability and output controllability.For the multi-layer networks where the inter-layer couplings are directed chains and directed stars,sufficient conditions for target controllability of networked systems are given,respectively.These conditions are easier to verify than the classic criterion.

A flexible ultra-broadband multi-layered absorber working at 2 GHz-40 GHz printed by resistive ink

A flexible extra broadband metamaterial absorber(MMA)stacked with five layers working at 2 GHz–40 GHz is investigated.Each layer is composed of polyvinyl chloride(PVC),polyimide(PI),and a frequency selective surface(FSS),which is printed on PI using conductive ink.To investigate this absorber,both one-dimensional analogous circuit analysis and three-dimensional full-wave simulation based on a physical model are provided.Various crucial electromagnetic properties,such as absorption,effective impedance,complex permittivity and permeability,electric current distribution and magnetic field distribution at resonant peak points,are studied in detail.Analysis shows that the working frequency of this absorber covers entire S,C,X,Ku,K and Ka bands with a minimum thickness of 0.098λ_(max)(λ_(max) is the maximum wavelength in the absorption band),and the fractional bandwidth(FBW)reaches 181.1%.Moreover,the reflection coefficient is less than-10 dB at 1.998 GHz–40.056 GHz at normal incidence,and the absorptivity of the plane wave is greater than 80%when the incident angle is smaller than 50°.Furthermore,the proposed absorber is experimentally validated,and the experimental results show good agreement with the simulation results,which demonstrates the potential applicability of this absorber at 2 GHz–40 GHz.

Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

Fetal health care is vital in ensuring the health of pregnant women and the fetus.Regular check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential problems.To know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,etc.Still,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during pregnancy.Several researchers have proposed various methods for classifying the status of fetus growth.Manual processing of CTG data is time-consuming and unreliable.So,automated tools should be used to classify fetal health.This study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal health.Various strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also illustrated.This study easily provides valuable interpretations for healthcare professionals in the decision-making process.

Multi-layer network embedding on scc-based network with motif

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new solutions.It is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based networks.In previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node classification.However,the content of semantic information is quite complex.Although graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of loss.Therefore,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology network.The Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node classification.We verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

Target layer state estimation in multi-layer complex dynamical networks considering nonlinear node dynamics

In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied.A suitable functional state observer is constructed with the limited measurement.The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem.Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained.Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states.Thus, it can greatly reduce the placement of observers and computational cost.Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.

Dynamic interwell connectivity analysis of multi-layer waterflooding reservoirs based on an improved graph neural network

The analysis of interwell connectivity plays an important role in the formulation of oilfield development plans and the description of residual oil distribution. In fact, sandstone reservoirs in China's onshore oilfields generally have the characteristics of thin and many layers, so multi-layer joint production is usually adopted. It remains a challenge to ensure the accuracy of splitting and dynamic connectivity in each layer of the injection-production wells with limited field data. The three-dimensional well pattern of multi-layer reservoir and the relationship between injection-production wells can be equivalent to a directional heterogeneous graph. In this paper, an improved graph neural network is proposed to construct an interacting process mimics the real interwell flow regularity. In detail, this method is used to split injection and production rates by combining permeability, porosity and effective thickness, and to invert the dynamic connectivity in each layer of the injection-production wells by attention mechanism.Based on the material balance and physical information, the overall connectivity from the injection wells,through the water injection layers to the production layers and the output of final production wells is established. Meanwhile, the change of well pattern caused by perforation, plugging and switching of wells at different times is achieved by updated graph structure in spatial and temporal ways. The effectiveness of the method is verified by a combination of reservoir numerical simulation examples and field example. The method corresponds to the actual situation of the reservoir, has wide adaptability and low cost, has good practical value, and provides a reference for adjusting the injection-production relationship of the reservoir and the development of the remaining oil.

Multi-layer phenomena in petawatt laser-driven acceleration of heavy ions

Laser-accelerated high-flux-intensity heavy-ion beams are important for new types of accelerators.A particle-in-cell program(Smilei) is employed to simulate the entire process of Station of Extreme Light(SEL) 100 PW laser-accelerated heavy particles using different nanoscale short targets with a thickness of 100 nm Cr, Fe, Ag, Ta, Au, Pb, Th and U, as well as 200 nm thick Al and Ca. An obvious stratification is observed in the simulation. The layering phenomenon is a hybrid acceleration mechanism reflecting target normal sheath acceleration and radiation pressure acceleration, and this phenomenon is understood from the simulated energy spectrum,ionization and spatial electric field distribution. According to the stratification, it is suggested that high-quality heavy-ion beams could be expected for fusion reactions to synthesize superheavy nuclei. Two plasma clusters in the stratification are observed simultaneously, which suggest new techniques for plasma experiments as well as thinner metal targets in the precision machining process.

A Well Productivity Model for Multi-Layered Marine and Continental Transitional Reservoirs with Complex Fracture Networks

Using the typical characteristics of multi-layered marine and continental transitional gas reservoirs as a basis,a model is developed to predict the related well production rate.This model relies on the fractal theory of tortuous capillary bundles and can take into account multiple gas flow mechanisms at the micrometer and nanometer scales,as well as the flow characteristics in different types of thin layers(tight sandstone gas,shale gas,and coalbed gas).Moreover,a source-sink function concept and a pressure drop superposition principle are utilized to introduce a coupled flow model in the reservoir.A semi-analytical solution for the production rate is obtained using a matrix iteration method.A specific well is selected for fitting dynamic production data,and the calculation results show that the tight sandstone has the highest gas production per unit thickness compared with the other types of reservoirs.Moreover,desorption and diffusion of coalbed gas and shale gas can significantly contribute to gas production,and the daily production of these two gases decreases rapidly with decreasing reservoir pressure.Interestingly,the gas production from fractures exhibits an approximately U-shaped distribution,indicating the need to optimize the spacing between clusters during hydraulic fracturing to reduce the area of overlapping fracture control.The coal matrix water saturation significantly affects the coalbed gas production,with higher water saturation leading to lower production.

The role of polyurethane foam compressible layer in the mechanical behaviour of multi-layer yielding supports for deep soft rock tunnels

The polyurethane foam(PU)compressible layer is a viable solution to the problem of damage to the secondary lining in squeezing tunnels.Nevertheless,the mechanical behaviour of the multi-layer yielding supports has not been thoroughly investigated.To fill this gap,large-scale model tests were conducted in this study.The synergistic load-bearing mechanics were analyzed using the convergenceconfinement method.Two types of multi-layer yielding supports with different thicknesses(2.5 cm,3.75 cm and 5 cm)of PU compressible layers were investigated respectively.Digital image correlation(DIC)analysis and acoustic emission(AE)techniques were used for detecting the deformation fields and damage evolution of the multi-layer yielding supports in real-time.Results indicated that the loaddisplacement relationship of the multi-layer yielding supports could be divided into the crack initiation,crack propagation,strain-hardening,and failure stages.Compared with those of the stiff support,the toughness,deformability and ultimate load of the yielding supports were increased by an average of 225%,61%and 32%,respectively.Additionally,the PU compressible layer is positioned between two primary linings to allow the yielding support to have greater mechanical properties.The analysis of the synergistic bearing effect suggested that the thickness of PU compressible layer and its location significantly affect the mechanical properties of the yielding supports.The use of yielding supports with a compressible layer positioned between the primary and secondary linings is recommended to mitigate the effects of high geo-stress in squeezing tunnels.

Multi-Layer Feature Extraction with Deformable Convolution for Fabric Defect Detection

In the textile industry,the presence of defects on the surface of fabric is an essential factor in determining fabric quality.Therefore,identifying fabric defects forms a crucial part of the fabric production process.Traditional fabric defect detection algorithms can only detect specific materials and specific fabric defect types;in addition,their detection efficiency is low,and their detection results are relatively poor.Deep learning-based methods have many advantages in the field of fabric defect detection,however,such methods are less effective in identifying multiscale fabric defects and defects with complex shapes.Therefore,we propose an effective algorithm,namely multilayer feature extraction combined with deformable convolution(MFDC),for fabric defect detection.In MFDC,multi-layer feature extraction is used to fuse the underlying location features with high-level classification features through a horizontally connected top-down architecture to improve the detection of multi-scale fabric defects.On this basis,a deformable convolution is added to solve the problem of the algorithm’s weak detection ability of irregularly shaped fabric defects.In this approach,Roi Align and Cascade-RCNN are integrated to enhance the adaptability of the algorithm in materials with complex patterned backgrounds.The experimental results show that the MFDC algorithm can achieve good detection results for both multi-scale fabric defects and defects with complex shapes,at the expense of a small increase in detection time.

Multi-Layer Tree Hierarchical Architecture Based on Web Service

To solve the problem of the information share and services integration in population information system, we propose a multi-layer tree hierarchical architecture. The command （Web Service Call） is recursively muhicast from top layer of tree to bottom layer of tree and statistical data are gatbered from bottom layer to top layer. We implemented the architecture by using Web Services technology. In our implementation, client program is the requestor of Web Services, and all leaf nodes of the last layer are only the provider of Web Services. For those nodes of intermediate layers, every node is not only the provider of Web Services, but also the dispatcher of Web Services. We take population census as an cxample to describe the working flow of the architecture.

Web Enabled Multi-Layer Geo-Database for Moinabad Mandal in Rangareddy District, Telangana

The importance of Web-based GIS applications for government is to analyze the information they produce and to establish their impact on efficiency, effectiveness and transparency. The potential of Remote Sensing and GIS provides cost and time-effective resource database. An attempt was made to develop a Web enabled Geo database for the Moinabad Mandal, Ranga Reddy District, Telangana. Further this database was used to develop a web enabled multi-layer geo database using ArcGIS Server. It enables the users to know the information and take better decisions, better planning in different rural development sectors.

基于Web of Science对土壤胶体影响重金属行为研究的计量分析

为全面了解土壤胶体影响重金属行为方向的研究现状和前沿动态,基于Web of Science(WoS)核心合集数据库,利用WoS自带分析工具、HistCite引文图谱分析软件、VOSviewer和Citespace可视化分析软件对1990—2021年间土壤胶体影响重金属行为的文献进行了计量分析。结果表明,在世界范围内该方向的发文量逐年稳步增长,我国相关研究起步较晚,但近些年呈现迅猛发展的势头。目前土壤胶体影响重金属行为研究发文量最多的国家和研究机构分别是美国和中国科学院,发文量最高的期刊为Environmental Science&Technology,主要研究学科为环境科学与生态学的交叉学科。关键词聚类分析显示“土壤胶体颗粒粒径分级与重金属的形态分布”、“土壤胶体的释放、沉积及对重金属的吸附作用”和“土壤胶体颗粒的迁移机制与迁移模型研究”为主要的研究主题,人工纳米颗粒在土壤中的行为、迁移转化以及生物有效性是现阶段的研究热点。利用场流分离技术结合单粒子电感耦合等离子体质谱等技术,探讨土壤胶体与人工纳米颗粒之间发生的复杂相互作用及其对人工纳米颗粒迁移归趋与环境命运的影响,是未来的主要研究方向。

基于Stacking融合模型的Web攻击检测方法

随着计算机技术与互联网技术的飞速发展,Web应用在人们的生产与生活中扮演着越来越重要的角色。但是在人们的日常生活与工作中带来了更多便捷的同时,却也带来了严重的安全隐患。在开发Web应用的过程中,大量不规范的新技术应用引入了很多的网站漏洞。攻击者可以利用Web应用开发过程中的漏洞发起攻击,当Web应用受到攻击时会造成严重的数据泄露和财产损失等安全问题,因此Web安全问题一直受到学术界和工业界的关注。超文本传输协议(HTTP)是一种在Web应用中广泛使用的应用层协议。随着HTTP协议的大量使用,在HTTP请求数据中包含了大量的实际入侵,针对HTTP请求数据进行Web攻击检测的研究也开始逐渐被研究人员所重视。本文提出了一种基于Stacking融合模型的Web攻击检测方法,针对每一条文本格式的HTTP请求数据,首先进行格式化处理得到既定的格式,结合使用Word2Vec方法和TextCNN模型将其转换成向量化表示形式;然后利用Stacking模型融合方法,将不同的子模型(使用配置不同尺寸过滤器的Text-CNN模型搭配不同的检测算法)进行融合搭建出Web攻击检测模型,与融合之前单独的子模型相比在准确率、召回率、F1值上都有所提升。本文所提出的Web攻击检测模型在公开数据集和真实环境数据上都取得了更加稳定的检测性能。

多维深度导向的Java Web模糊测试方法

随着Java Web的广泛应用,其安全问题日益突出。模糊测试作为一种有效的漏洞挖掘方法,目前已经被用于Java漏洞的检测。然而,由于Java Web应用代码规模庞大、业务逻辑复杂,现有的漏洞挖掘工具在模糊测试中存在随机性高、代码检测深度低的问题,导致漏洞挖掘的准确率较低。因此,文章提出基于多维深度导向的Java Web模糊测试方法。该方法使用Jimple作为待测Java Web应用字节码的三地址码中间表示,并生成代码对应的函数间调用图和函数内控制流图,在此基础上分析每个基本块的多维深度。同时,根据多维深度和模糊测试执行时间优化模糊测试指导策略,设计相应的输入结构解析策略、能量分配策略和变异算法调度策略,提升模糊测试的准确性。实验结果表明,相较于现有的模糊测试工具Peach和Kelinci,该方法能够在性能消耗较低的情况下取得更好的漏洞检测效果。

基于博弈的Web应用程序中访问控制漏洞检测方法

针对工业互联网中程序的访问控制策略隐藏在源码中难以提取,以及用户的访问操作难以触发所有访问路径而导致逻辑漏洞的通用化检测难以实现的问题,将博弈思想应用于访问控制逻辑漏洞检测中,通过分析不同参与者在Web应用程序中对资源页面的博弈结果来识别漏洞,使得不同用户的访问逻辑能被有针对性地获取。实验结果表明,所提方法在开源的11个程序中检测出31个漏洞,其中8个为未公开的漏洞,漏洞检测覆盖率均超过90%。

中国地震科学实验场三维断层模型Web展示原型系统

活动断层的三维结构和模型是地震危险性研究的基础参数和重要数据。随着对接近真实形态的活动断层三维模型的应用需求不断增加,对这类模型数据的共享及研究的需求也日益增加。数据共享课题的一个重要议题是如何构建基于Web的三维断层模型展示平台,提高数据共享水平和用户体验。中国地震科学实验场(川滇地区)三维断层模型Web展示原型系统①是基于Arc GIS 10.6企业版的Web App Builder工具构建的一个实验性系统。该系统实现了基于Web GIS的三维展示系统的基本功能,并成功测试了更适用于地震行业标准的用户交互方式、基本功能、场景数据迁移方案和三维场景设计方案。场景数据从三维断层建模平台到GIS平台的迁移,是实现基于Web GIS三维展示系统的基础。场景中展示的三维数据采用与二维数据标准一致且视觉上融合度高的可视化方案。实践结果表明,三维数据从建模平台迁出后,应结合数据可视化方案,在属性层面上与二维数据相融合,以保证二维和三维数据在视觉上较好地融合。

基于主动探测的Web容器探测识别方法

随着工业互联网的飞速发展,各类Web容器的广泛使用呈现不断增长的趋势,然而,这也使得Web容器资产管理的问题变得更加复杂.随之而来的是诸多网络安全风险和潜在隐患,对于这些挑战,提升网络安全防御水平显得尤为迫切.为了解决这一问题,本文引入了一种新的基于主动探测的Web容器探测识别方法.在探测阶段,采用了一种先进的Web容器探针构建方法,通过此方法构建了Web容器探针.这个探针在识别阶段发挥关键作用,借助一种基于负载内容的Web容器识别方法,通过协议解码技术,实现了对Web容器的高度准确的识别.通过结合这两种先进的识别方法,成功识别了4种不同类型的Web容器,并且提升了精度,能够精确地区分这些Web容器的各个版本,总计实现了10个版本的准确识别.通过这种先进的主动探测方法,企业可以更好地了解和管理其Web容器资产,降低网络安全风险,并确保网络系统的稳定性和安全性.