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.

Development of Fixture Layout Optimization for Thin-Walled Parts:A Review

An increasing number of researchers have researched fixture layout optimization for thin-walled part assembly during the past decades.However,few papers systematically review these researches.By analyzing existing literature,this paper summarizes the process of fixture layout optimization and the methods applied.The process of optimization is made up of optimization objective setting,assembly variation/deformation modeling,and fixture layout optimization.This paper makes a review of the fixture layout for thin-walled parts according to these three steps.First,two different kinds of optimization objectives are introduced.Researchers usually consider in-plane variations or out-of-plane deformations when designing objectives.Then,modeling methods for assembly variation and deformation are divided into two categories:Mechanism-based and data-based methods.Several common methods are discussed respectively.After that,optimization algorithms are reviewed systematically.There are two kinds of optimization algorithms:Traditional nonlinear programming and heuristic algorithms.Finally,discussions on the current situation are provided.The research direction of fixture layout optimization in the future is discussed from three aspects:Objective setting,improving modeling accuracy and optimization algorithms.Also,a new research point for fixture layout optimization is discussed.This paper systematically reviews the research on fixture layout optimization for thin-walled parts,and provides a reference for future research in this field.

Web Layout Design of Large Cavity Structures Based on Topology Optimization

Large cavity structures are widely employed in aerospace engineering, such as thin-walled cylinders, blades andwings. Enhancing performance of aerial vehicles while reducing manufacturing costs and fuel consumptionhas become a focal point for contemporary researchers. Therefore, this paper aims to investigate the topologyoptimization of large cavity structures as a means to enhance their performance, safety, and efficiency. By usingthe variable density method, lightweight design is achieved without compromising structural strength. Theoptimization model considers both concentrated and distributed loads, and utilizes techniques like sensitivityfiltering and projection to obtain a robust optimized configuration. The mechanical properties are checked bycomparing the stress distribution and displacement of the unoptimized and optimized structures under the sameload. The results confirm that the optimized structures exhibit improved mechanical properties, thus offering keyinsights for engineering lightweight, high-strength large cavity structures.

Optimizing wind farm layout for enhanced electricity extraction using a new hybrid PSO-ANN method

With the growing need for renewable energy,wind farms are playing an important role in generating clean power from wind resources.The best wind turbine architecture in a wind farm has a major influence on the energy extraction efficiency.This paper describes a unique strategy for optimizing wind turbine locations on a wind farm that combines the capabilities of particle swarm optimization(PSO)and artificial neural networks(ANNs).The PSO method was used to explore the solution space and develop preliminary turbine layouts,and the ANN model was used to fine-tune the placements based on the predicted energy generation.The proposed hybrid technique seeks to increase energy output while considering site-specific wind patterns and topographical limits.The efficacy and superiority of the hybrid PSO-ANN methodology are proved through comprehensive simulations and comparisons with existing approaches,giving exciting prospects for developing more efficient and sustainable wind farms.The integration of ANNs and PSO in our methodology is of paramount importance because it leverages the complementary strengths of both techniques.Furthermore,this novel methodology harnesses historical data through ANNs to identify optimal turbine positions that align with the wind speed and direction and enhance energy extraction efficiency.A notable increase in power generation is observed across various scenarios.The percentage increase in the power generation ranged from approximately 7.7%to 11.1%.Owing to its versatility and adaptability to site-specific conditions,the hybrid model offers promising prospects for advancing the field of wind farm layout optimization and contributing to a greener and more sustainable energy future.

GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

With the increasing demand for electrical services,wind farm layout optimization has been one of the biggest challenges that we have to deal with.Despite the promising performance of the heuristic algorithm on the route network design problem,the expressive capability and search performance of the algorithm on multi-objective problems remain unexplored.In this paper,the wind farm layout optimization problem is defined.Then,a multi-objective algorithm based on Graph Neural Network(GNN)and Variable Neighborhood Search(VNS)algorithm is proposed.GNN provides the basis representations for the following search algorithm so that the expressiveness and search accuracy of the algorithm can be improved.The multi-objective VNS algorithm is put forward by combining it with the multi-objective optimization algorithm to solve the problem with multiple objectives.The proposed algorithm is applied to the 18-node simulation example to evaluate the feasibility and practicality of the developed optimization strategy.The experiment on the simulation example shows that the proposed algorithm yields a reduction of 6.1% in Point of Common Coupling(PCC)over the current state-of-the-art algorithm,which means that the proposed algorithm designs a layout that improves the quality of the power supply by 6.1%at the same cost.The ablation experiments show that the proposed algorithm improves the power quality by more than 8.6% and 7.8% compared to both the original VNS algorithm and the multi-objective VNS algorithm.

Optimizing Memory Access Efficiency in CUDA Kernel via Data Layout Technique

Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous vehicles, and exascale computing. Despite these advancements, efficiently programming GPUs remains a daunting challenge, often relying on trial-and-error optimization methods. This paper introduces an optimization technique for CUDA programs through a novel Data Layout strategy, aimed at restructuring memory data arrangement to significantly enhance data access locality. Focusing on the dynamic programming algorithm for chained matrix multiplication—a critical operation across various domains including artificial intelligence (AI), high-performance computing (HPC), and the Internet of Things (IoT)—this technique facilitates more localized access. We specifically illustrate the importance of efficient matrix multiplication in these areas, underscoring the technique’s broader applicability and its potential to address some of the most pressing computational challenges in GPU-accelerated applications. Our findings reveal a remarkable reduction in memory consumption and a substantial 50% decrease in execution time for CUDA programs utilizing this technique, thereby setting a new benchmark for optimization in GPU computing.

Application of a Parallel Adaptive Cuckoo Search Algorithm in the Rectangle Layout Problem

The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution.It has good performance in global optimization fields such as maximization.In this paper,a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search(CS)algorithm.This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal.This paper compares the optimization performance of Parallel Adaptive Cuckoo Search(PACS)with CS,Parallel Cuckoo Search(PCS),Particle Swarm Optimization(PSO),Sine Cosine Algorithm(SCA),Grey Wolf Optimizer(GWO),Whale Optimization Algorithm(WOA),Differential Evolution(DE)and Artificial Bee Colony(ABC)algorithms by using the CEC-2013 test function.The results show that PACS algorithmoutperforms other algorithms in 20 of 28 test functions.Due to the superior performance of PACS algorithm,this paper uses it to solve the problem of the rectangular layout.Experimental results show that this scheme has a significant effect,and the material utilization rate is improved from89.5%to 97.8%after optimization.

A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization

Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in downstream.Wind farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind farm.Nevertheless,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion efficiency.Conventional heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind scenarios.Thus,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO problems.CGPSO is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art algorithms.The experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and robustness.To be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search local.It improves the solution quality.The parameter and search pattern of chaotic local search are also analyzed for WFLO problems.

Complexation with pre-formed“empty”V-type starch for encapsulation of aroma compounds

Aroma compounds are low-molecular-weight organic volatile molecules and are broadly utilized in the food industry.However,due to their high volatility and evaporative losses during processing and storage,the stabilization of these volatile ingredients using encapsulation is a commonly investigated practice.Complexation of aroma compounds using starch inclusion complex could be a potential approach due to the hydrophobicity of the left-handed single helical structure.In the present study,we used starch of three different V-type structures,namely V,V,and V,to encapsulate six different aroma compounds,including1-decanol(DN),cis-3-hexen-1-ol(HN),4-allylanisole(AN),γ-decalactone(DA),trans-cinnamaldehyde(CA),and citral(CT).The formed inclusion complexes samples were characterized using complementary techniques,including X-ray diffraction(XRD)and differential scanning calorimetry(DSC).The results showed that upon complexation with aroma compounds,all V-subtypes retained their original crystalline structures.However,different trends of crystallinity were observed for each type of the prepared inclusion complexes.Additionally,among three V-type starches,V-type starch formed inclusion complexes with aroma compounds most efficiently and promoted the formation of FormⅡcomplex.This study suggested that the structure of aroma compounds and the type of V starch could both affect the complexation properties.

Semantic Document Layout Analysis of Handwritten Manuscripts

A document layout can be more informative than merely a document’s visual and structural appearance.Thus,document layout analysis(DLA)is considered a necessary prerequisite for advanced processing and detailed document image analysis to be further used in several applications and different objectives.This research extends the traditional approaches of DLA and introduces the concept of semantic document layout analysis(SDLA)by proposing a novel framework for semantic layout analysis and characterization of handwritten manuscripts.The proposed SDLA approach enables the derivation of implicit information and semantic characteristics,which can be effectively utilized in dozens of practical applications for various purposes,in a way bridging the semantic gap and providingmore understandable high-level document image analysis and more invariant characterization via absolute and relative labeling.This approach is validated and evaluated on a large dataset ofArabic handwrittenmanuscripts comprising complex layouts.The experimental work shows promising results in terms of accurate and effective semantic characteristic-based clustering and retrieval of handwritten manuscripts.It also indicates the expected efficacy of using the capabilities of the proposed approach in automating and facilitating many functional,reallife tasks such as effort estimation and pricing of transcription or typing of such complex manuscripts.

A Spacecra Equipment Layout Optimization Method for Diverse and Competitive Design

The spacecraequipment layout optimization design(SELOD)problems with complicated performance con-straints and diversity are studied in this paper.The previous literature uses the gradient-based algorithm to obtain optimized non-overlap layout schemes from randomly initialized cases eectively.However,these local optimal solutions are too dicult to jump out of their current relative geometry relationships,signi􀀀cantly limiting their further improvement in performance indicators.Therefore,considering the geometric diversity of layout schemes is put forward to alleviate this limitation.First,similarity measures,including modi􀀀ed cosine similarity and gaussian kernel function similarity,are introduced into the layout optimization process.Then the optimization produces a set of feasible layout candidates with the most remarkable dierence in geometric distribution and the most representative schemes are sampled.Finally,these feasible geometric solutions are used as initial solutions to optimize the physical performance indicators of the spacecra,and diversi􀀀ed layout schemes of spacecraequipment are generated for the engineering practice.The validity and eectiveness of the proposed methodology are demonstrated by two SELOD applications.

Advanced surface coil layout with intrinsic noise cancellation properties for surface-NMR applications

A recent study demonstrated that in small-scale prepolarized surface nuclear magnetic resonance(SNMR-PP)measurements with a footprint of a few square meters,customized PP switch-off ramps can serve as an efficient excitation mechanism,eliminating the requirement for a conventional oscillating excitation pulse.This approach enables the detection of short relaxation signals from the unsaturated soil zone and can,therefore,be used to directly provide soil moisture and pore geometry information.Because ultimately such small-scale SNMR-PP setups are intended for a mobile application,it is necessary to develop strategies that allow for speedy measurement progress and do not require noise cancellation protocols based on reference stations.Hence,we developed a new concentric figure-of-eight(cFOE)loop layout that combines the direction independence of a circular loop with the intrinsic noise cancellation properties of a classical FOE-loop.This approach significantly decreases the measurement time because suitable signal-to-noise ratios are reached much faster compared to a classical circular loop and will bring us one step further toward fast and non-invasive soil moisture mapping applications.

The Evolution of Landscape Layout Concept of Lingnan Taoist Zuting Temples in the Qing Dynasty

The replacement of Lingnan Taoist Zuting Temples from Lingbao Sect to Quanzhen Sect in the Qing Dynasty has led to the change of the layout of Taoist temples on Luofu Mountain. Through literature review, field research, image comparison and other research methods, the paper explores how the layout of the Taoist temples on Luofu Mountain evolved from the Four Taoist Temples in the Gehong period to the Five Taoist Temples in the Qing Dynasty under the influence of different Taoist ideologies, aiming to explore the cultural implications of the evolution of the layout of Lingnan Taoism Zuting Temple in the Qing Dynasty.

HSCA-Net: A Hybrid Spatial-Channel Attention Network in Multiscale Feature Pyramid for Document Layout Analysis

Document images often contain various page components and complex logical structures,which make document layout analysis task challenging.For most deep learning-based document layout analysis methods,convolutional neural networks(CNNs)are adopted as the feature extraction networks.In this paper,a hybrid spatial-channel attention network(HSCA-Net)is proposed to improve feature extraction capability by introducing attention mechanism to explore more salient properties within document pages.The HSCA-Net consists of spatial attention module(SAM),channel attention module(CAM),and designed lateral attention connection.CAM adaptively adjusts channel feature responses by emphasizing selective information,which depends on the contribution of the features of each channel.SAM guides CNNs to focus on the informative contents and capture global context information among page objects.The lateral attention connection incorporates SAM and CAM into multiscale feature pyramid network,and thus retains original feature information.The effectiveness and adaptability of HSCA-Net are evaluated through multiple experiments on publicly available datasets such as PubLayNet,ICDAR-POD,and Article Regions.Experimental results demonstrate that HSCA-Net achieves state-of-the-art performance on document layout analysis task.

我国城市幼儿园空间布局适配性的现状、成因与优化建议

供需适配的幼儿园空间布局是促进学前教育资源均衡配置的关键举措,能有效推动学前教育高质量发展。本研究以我国中部地区某省会城市主城区759所幼儿园为例,运用地理信息分析工具中的核密度估计、最近邻指数等方法,分别从“有园上”“入园近”“上好园”“园幼共生”四个维度对幼儿园空间布局是否存在错配的核心议题进行深入剖析。研究发现:该市部分幼儿园空间分布存在欠适配现象,且形成了空间与结构错配的问题,其主要原因是资源投入层面的财政失衡、需求提出层面的逐优心理和城市规划层面的建设滞后。本文建议通过构建数字化管理平台、优化供需对接机制、加强配套园治理、加大治薄扶弱补缺的力度等措施,推动幼儿园空间布局从错配到适配的转换,从而促进区域学前教育公共服务一体化和高质量发展。

基于组合圆锥构型的水下声学定位系统布站优化

在水下目标定位中,布站构型的共面约束会导致目标单点定位的几何精度因子(GDOP)理论最小值不能达到。为改善水下布站的观测矩阵,将单圆锥扩展为组合圆锥构型布站模式。然而,传统的优化算法易陷入局部最优且存在收敛效率低等问题。针对以上问题,提出了一种改进的自适应粒子群算法。首先提出了一种有共面约束的水下声学定位系统组合圆锥构型,并证明了所提出的组合圆锥构型具有可叠加性、可旋转性以及GDOP旋转不变性;其次,针对布站优化指标单一的问题,构建了一种基于水下目标全局GDOP的布站优化准则;在此基础上提出了一种基于自适应粒子群的优化算法,并应用于组合圆锥构型下的水下声学定位系统布站优化。所提出的自适应粒子群算法能增强布站的全局最优性,提高算法收敛效率和水下目标的定位精度。

“十三五”国家重点研发计划“林业资源培育及高效利用技术创新”重点专项布局与实施成效分析

“十三五”国家科技计划改革构建了全新的国家重点研发计划,设置“林业资源培育及高效利用技术创新”重点专项对林业科技创新进行支持。在揭示林业资源培育及高效利用调控机制、提升主要人工林单位生产力和提高资源利用效率等方面进行布局,按照基础研究、关键技术研究和技术集成示范进行“产业链布局创新链、一体化组织实施”。截止到2022年,专项已经执行结束并顺利完成项目综合绩效评价。以“十三五”国家重点研发计划“林业资源培育及高效利用技术创新”重点专项为主题,总结并分析专项实施背景、立项情况并介绍专项实施成效,为今后林业科技资源配置和组织管理提供参考。

城市洪涝应急响应能力评价及优化研究

在气候变化和全球化的背景下,极端降水事件及其导致的洪涝灾害频发。为准确评估暴雨内涝影响下的城市应急服务水平,基于InfoWorks ICM构建了城区内涝模型,利用ArcGIS中的最快路径法评估了医疗、公安、消防的空间可达性,利用基尼系数评估了公共应急服务相对幼儿园分布的合理性,采用最大覆盖算法对医疗、公安、消防的分布进行了优化,并基于研究区的情况进行了分析。结果显示:研究区的医疗、公安、消防相对幼儿园的基尼系数随着降雨重现期的增加而增大;对研究区的医疗、公安、消防分布优化前,这些部门的分布不尽合理,其15 min内的应急服务可达性不能满足应急救援要求,研究区应急响应资源相对幼儿园缺失严重,相对来说,医疗应急响应缺失最为严重;对研究区的医疗、公安、消防分布优化后,这些部门相对于幼儿园的基尼系数普遍在0.400以下,医疗和公安的应急服务即使在百年一遇洪涝加晚高峰情景下仍可以在15 min内覆盖40%的研究区面积,消防可覆盖70%的研究区面积,医疗、公安、消防的分布相对合理。本研究为中国城市洪涝灾害应急响应能力评价提供参考。

基于SLP与NSGA-Ⅱ的装配式建筑施工现场临时设施布置多目标优化研究

装配式建筑在现代建筑领域中越来越受关注,有效的临时设施布局对于提高装配效率和降低安全风险至关重要。针对装配式建筑临时设施布局问题,提出了一种基于改进的NSGA-Ⅱ算法的多目标优化方法。首先建立以塔式起重机吊装时间、塔式起重机水平吊装路径长度及与密切程度相关的安全风险的多目标优化数学模型,再采用以SLP法与随机法结合生成初始种群的改进NSGA-Ⅱ算法对模型进行求解。结果表明,所提出的方法能有效减少塔式起重机的吊装时间、缩小吊装范围,并降低与密切程度相关的安全风险。

大城市工业用地布局演变中的城市问题研究——以西安市为例

改革开放以来在我国经济转型过程中大城市工业用地的规模和布局发生了巨大变化,并影响城市的空间形态。以西安市为例,基于多源数据,采用定量与定性相结合方法,研究发现1974—2019年西安市工业用地面积呈先持续增长后快速减少变化趋势,空间布局在园区化集聚的过程中具有显著的工业用地郊区化与城市中心用地去工业化特征,布局模式则经历了由改革开放初期“组团布局+扇形扩展”向增量规划时期“圈层扩展+跳跃扩张+轴向集聚”至存量规划时期“外围园区化集聚”转变。帮助企业纾困、城市增长、生活居住条件改善与土地经济价值是推动工业用地布局演变的主要驱动力。工业用地郊区化与城市中心去工业化加剧了职住分离、交通拥堵等城市问题,不利于城市健康可持续发展。