Precise integration method without inverse matrix calculation for structural dynamic equations

The precise integration method proposed for linear time-invariant homogeneous dynamic systems can provide accurate numerical results that approach an exact solution at integration points. However, difficulties arise when the algorithm is used for non-homogeneous dynamic systems due to the inverse matrix calculation required. In this paper, the structural dynamic equalibrium equations are converted into a special form, the inverse matrix calculation is replaced by the Crout decomposition method to solve the dynamic equilibrium equations, and the precise integration method without the inverse matrix calculation is obtained. The new algorithm enhances the present precise integration method by improving both the computational accuracy and efficiency. Two numerical examples are given to demonstrate the validity and efficiency of the proposed algorithm.

Research of Integration of Setting Calculation System

This paper expounds the main structure design and function implementation of a provincial power grid relay protection setting calculation integration system. In accordance with the actual features of the relay protection setting calculation work, setting calculation system is developed based on the project. For the development and research of integration of setting calculation system, to achieve a certain provincial power grid relay protection setting calculation and the standardization of the operation and management, network and intelligent, is of great significance.

Using Process Energy Integration Technology in the Energy-Saving Retrofit of Large-Scale Complex Chemical Process System

1 INTRODUCTIONIn recent years,the energy-saving retrofit of existing chemical process plants,especially exist-ing large-scale complex chemical process system(LCCPS),has been becoming necessary as theengineers’knowledge goes on and the market changes.

Guangdong Dapeng LNG cold energy utilization-C_2++ extraction process integration

This paper makes a study of some technical and engineering aspects by using C2+ hydrocarbon separation facility at Guangdong Dapeng liquefied natural gas (GDLNG) terminal. In the C2+ hydrocarbon extraction process,the cold energy contained in LNG will be utilized. In order to ensure the optimum operating conditions of the terminal and C2+ hydrocarbon extraction facility by optimizing the current operating processes of the terminal,the C2+ hydrocarbon extraction facility construction plan is proposed. We conducted numerous calculations and simulations using such specific analysis software as PRO II<version 7.0>. Additionally available flow data are used to verify the cyclic send-out rates from the terminal,thus establishing the current and future projected load factors. This study is intended to make sure that GDLNG can continue to supply gas via the pipeline system safely without interruptions and most significantly solves the effects of flow fluctuations at the terminal gasification send-out facility on the hydrocarbons extraction,ensuring optimum pipeline operations and ensuring safe and effective means for such C2+ hydrocarbons extraction process as well. At the same time,the terminal is also in the optimum operation condition. This is very significant to the terminal safety operation and the energy conservation and emission reduction.

Preserving Data Secrecy and Integrity for Cloud Storage Using Smart Contracts and Cryptographic Primitives

Cloud computing has emerged as a viable alternative to traditional computing infrastructures,offering various benefits.However,the adoption of cloud storage poses significant risks to data secrecy and integrity.This article presents an effective mechanism to preserve the secrecy and integrity of data stored on the public cloud by leveraging blockchain technology,smart contracts,and cryptographic primitives.The proposed approach utilizes a Solidity-based smart contract as an auditor for maintaining and verifying the integrity of outsourced data.To preserve data secrecy,symmetric encryption systems are employed to encrypt user data before outsourcing it.An extensive performance analysis is conducted to illustrate the efficiency of the proposed mechanism.Additionally,a rigorous assessment is conducted to ensure that the developed smart contract is free from vulnerabilities and to measure its associated running costs.The security analysis of the proposed system confirms that our approach can securely maintain the confidentiality and integrity of cloud storage,even in the presence of malicious entities.The proposed mechanism contributes to enhancing data security in cloud computing environments and can be used as a foundation for developing more secure cloud storage systems.

基于SpringCloud框架微服务架构的融合媒体云平台设计

设计基于SpringCloud框架微服务架构的融合媒体云平台,提高媒体业务流程效率。对SpringCloud的基本情况与相关组件进行概述,提出融合媒体云平台设计思路与原则,以此为基础构建有三层服务层的融合媒体云平台,设计融合媒体云平台的安全,基于“一次采集、多种生成、多元传播”的融合媒体云平台设计思路,最终构成融合媒体云平台的业务流程。

基于“Vue+Spring Cloud”的教学一体化平台设计与实现

当今社会,教育是推动社会进步的关键要素之一,而提高教育质量一直是教育界难以应对的挑战。文章旨在利用微服务思维建立一个稳定、功能完善且质量高的教学一体化平台。平台前端采用Vue框架,后端基于Spring Boot的Spring Cloud框架,将教学、学习和管理等功能整合到一个统一的系统中,为教育工作者和学生提供一个全新的教学环境。该平台力求满足学习者的学习需求,有效激发学习兴趣,帮助教师改善教学效果,协助课堂管理,增强教学互动性和趣味性。

Cloud Resource Integrated Prediction Model Based on Variational Modal Decomposition-Permutation Entropy and LSTM

Predicting the usage of container cloud resources has always been an important and challenging problem in improving the performance of cloud resource clusters.We proposed an integrated prediction method of stacking container cloud resources based on variational modal decomposition(VMD)-Permutation entropy(PE)and long short-term memory(LSTM)neural network to solve the prediction difficulties caused by the non-stationarity and volatility of resource data.The variational modal decomposition algorithm decomposes the time series data of cloud resources to obtain intrinsic mode function and residual components,which solves the signal decomposition algorithm’s end-effect and modal confusion problems.The permutation entropy is used to evaluate the complexity of the intrinsic mode function,and the reconstruction based on similar entropy and low complexity is used to reduce the difficulty of modeling.Finally,we use the LSTM and stacking fusion models to predict and superimpose;the stacking integration model integrates Gradient boosting regression(GBR),Kernel ridge regression(KRR),and Elastic net regression(ENet)as primary learners,and the secondary learner adopts the kernel ridge regression method with solid generalization ability.The Amazon public data set experiment shows that compared with Holt-winters,LSTM,and Neuralprophet models,we can see that the optimization range of multiple evaluation indicators is 0.338∼1.913,0.057∼0.940,0.000∼0.017 and 1.038∼8.481 in root means square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE)and variance(VAR),showing its stability and better prediction accuracy.

Blockchain and Data Integrity Authentication Technique for Secure Cloud Environment

Nowadays,numerous applications are associated with cloud and user data gets collected globally and stored in cloud units.In addition to shared data storage,cloud computing technique offers multiple advantages for the user through different distribution designs like hybrid cloud,public cloud,community cloud and private cloud.Though cloud-based computing solutions are highly con-venient to the users,it also brings a challenge i.e.,security of the data shared.Hence,in current research paper,blockchain with data integrity authentication technique is developed for an efficient and secure operation with user authentica-tion process.Blockchain technology is utilized in this study to enable efficient and secure operation which not only empowers cloud security but also avoids threats and attacks.Additionally,the data integrity authentication technique is also uti-lized to limit the unwanted access of data in cloud storage unit.The major objec-tive of the projected technique is to empower data security and user authentication in cloud computing environment.To improve the proposed authentication pro-cess,cuckoofilter and Merkle Hash Tree(MHT)are utilized.The proposed meth-odology was validated using few performance metrics such as processing time,uploading time,downloading time,authentication time,consensus time,waiting time,initialization time,in addition to storage overhead.The proposed method was compared with conventional cloud security techniques and the outcomes establish the supremacy of the proposed method.

Enhanced 3D Point Cloud Reconstruction for Light Field Microscopy Using U-Net-Based Convolutional Neural Networks

This article describes a novel approach for enhancing the three-dimensional(3D)point cloud reconstruction for light field microscopy(LFM)using U-net architecture-based fully convolutional neural network(CNN).Since the directional view of the LFM is limited,noise and artifacts make it difficult to reconstruct the exact shape of 3D point clouds.The existing methods suffer from these problems due to the self-occlusion of the model.This manuscript proposes a deep fusion learning(DL)method that combines a 3D CNN with a U-Net-based model as a feature extractor.The sub-aperture images obtained from the light field microscopy are aligned to form a light field data cube for preprocessing.A multi-stream 3D CNNs and U-net architecture are applied to obtain the depth feature fromthe directional sub-aperture LF data cube.For the enhancement of the depthmap,dual iteration-based weighted median filtering(WMF)is used to reduce surface noise and enhance the accuracy of the reconstruction.Generating a 3D point cloud involves combining two key elements:the enhanced depth map and the central view of the light field image.The proposed method is validated using synthesized Heidelberg Collaboratory for Image Processing(HCI)and real-world LFM datasets.The results are compared with different state-of-the-art methods.The structural similarity index(SSIM)gain for boxes,cotton,pillow,and pens are 0.9760,0.9806,0.9940,and 0.9907,respectively.Moreover,the discrete entropy(DE)value for LFM depth maps exhibited better performance than other existing methods.

“主动面向、科教融合、双轮驱动”——工程管理专业新工科改革与引领的探索实践

面对国家战略需求以及新一轮科技革命和产业变革所带来的严峻挑战,工程管理专业建设正面临转型升级的迫切需要。西安建筑科技大学工程管理专业始终坚持改革探索与自我革新,在新工科背景下,努力探索工程管理专业改造升级路径、人才培养模式、课程体系建设、创新体系建设、平台建设和机制体制设计;坚持“主动面向”,以国家重大战略需求和科技创新的双轮驱动为导向,拓展专业内涵,加速科教融合,积极探索和重构专业课程体系;创新“精课程、微课堂”的课程建设和教学模式;开发“工程管理云专业”平台,大力推动“2+X”创新体系建设,构建“师生成长共同体”。为探索和形成工程管理专业发展的新模式、新路径、新体系提供重要支撑和经验借鉴。

基于区块链且可验证的智能电网多维数据聚合与分享方案

针对如何支持轻量级多维数据聚合,实现系统整体过程中多维数据的双端完整性验证,以及处理云服务器集中化等问题,提出了一种基于区块链且可验证的智能电网多维数据聚合与分享方案。首先,为了满足智能电网对电量数据细粒度分析的需求,利用掩蔽值和霍纳法则实现了隐私保护多维数据聚合。在此基础上,针对现有数据聚合方案在云存储数据与第三方分享方面存在的数据完整性验证问题,借用基于RSA的乘法同态承诺方案和同态哈希函数的同态性设计了一种新的签名算法,使云服务器不仅可以验证聚合数据的完整性,还可以验证数据分享阶段的完整性,即实现了云存储数据的双端可验证性,并且可以抵抗内部攻击。同时,提出了一种基于联盟链多链的聚合数据分享体系结构,有效地避免单机处理瓶颈和易受攻击等集中化问题。理论分析证明了所提方案的安全性。性能实验表明,所提方案比已有方案具有更低的计算和通信成本。

计及氢气注入与压缩因子的综合能源系统能流计算

为实现“双碳”目标,综合能源系统(integrated energy systems, IES)成为了近几年的重要研究方向之一,然而传统的IES能流计算已经无法精确地反映电制气(power-to-gas, P2G)技术带来的氢气注入天然气网络后的混合燃气的参数变化对IES的影响。为此,在传统天然气系统稳态分析方法的基础上加入了SRK(Soawk-Redlich-Kwong)气体状态方程,将压缩因子作为状态变量,提出可以反映氢气注入天然气系统,对气体流量和混合燃气热值产生影响的稳态分析方法。以此为基础,提出了计及氢气注入与压缩因子的电-热-气IES能流分解求解计算方法。最后通过算例验证了所提方法可有效反映混合燃气的参数变化对IES的影响。

气象应用业务融入气象大数据云平台的设计

针对现有气象应用不能直接融入气象大数据平台的现状,该文采用“云+端”融入的方法,对气象应用的数据存储、产品加工、业务监控、应用接口以及应用端进行改造设计,使现有气象应用业务能够快速接入气象大数据云平台。该设计在湖北气象业务进行应用,避免了因气象业务智能化建设而造成的重复建设和资源浪费,有利于推动气象业务智能化发展,为今后气象应用改造提供了参考。

云存储环境下基于模格算法的数据存储完整度检测

在云存储环境下进行数据保存后,需要依托于数据持有性证明检测数据存储完整度,每次验证的计算较为庞大,导致完整度检测的时间较长.对此,提出存储环境下基于模格算法的数据存储完整度检测方法 .基于热点循迹的数据分块技术,得到待检测的存储数据子块.根据数据块数量搭建包含众多叶子节点的多分支路径树,并通过哈希值匹配实现验证数据块动态更新.考虑数据存储完整度检测的3种参与实体,建立包含6种算法的验证机制,指导完整度检测的具体流程.最后,利用模格算法实现数据存储完整度检测.实验结果表明:在数据量为1 024 MB时,采用所提方法进行数据存储完整度检测的时间开销仅为3.5 s,满足了云存储数据检测要求.

基于多能流耦合规律的综合能源系统潮流及流分析

从能量流和流两个角度分析综合能源系统的多能流耦合规律,建立系统能流稳态潮流模型和流模型,根据模型特点研究系统潮流和流分布计算方法,以算例验证计算方法的可行性并分析计算数据,验证流机理模型相比于黑箱模型在局部分析方面的优越性。以算例数据为例,分析其潮流及流分布特点,结果表明:算例系统中能源转换环节的地方损最大,为系统薄弱环节,值得着重改进;同时电、热网络也有部分管段损较大,优化局部网络时也应纳入考虑。

星地融合边云协同网络下的资源调度研究

通过边缘计算和云计算的优势互补,面向6G的星地融合网络能够实现资源的弹性分配和协同利用,满足数据处理的实时性和智能化要求。为了提升资源服务能力和用户体验质量,提出了星地融合边云协同网络架构。首先,阐述了三种不同边云协同模式下的资源调度方案,从时延、能耗和多目标优化的角度分析了不同场景下的资源调度策略。然后,对比了现有资源调度求解模型和算法的优势和局限性。最后,对基于边云协同的星地融合网络中的资源调度研究进行了总结与展望。

面向星地融合的6G云雾化自组网

低轨星座朝支持星上处理、功能可重构、星地协作和通信遥感导航控制等多任务一体化的方向演进,受限于星上信号处理和计算能力不足、星间链路容量不宽等瓶颈,迫切需要和具有强大云化处理与计算能力的陆地移动通信网络深度融合。为此,提出了面向星地融合的6G云雾化自组网的体系架构和协议体系,实现了低轨雾化卫星网络和陆地云化通信网络的协同融合,研究了基于星上雾化处理的通信遥感导航控制一体化波形和资源管理,以及云雾化协同的移动性管理和智能信息处理等关键技术,展望了未来的标准化工作和硬件试验。

一种面向多场景的轨迹相似度计算框架

轨迹相似度计算作为轨迹数据分析中的一个研究热点,主要用来衡量两个移动对象轨迹相似程度。已有的轨迹相似度计算工作主要根据不同类型的轨迹数据和应用场景提出了大量轨迹相似性度量方法,但随着轨迹数据采集手段的逐渐丰富和应用需求的发展,在轨迹相似性计算方面却缺少一个统一的框架以支持多样化轨迹数据下的相似轨迹分析。为此,本文在综合分析已有相似轨迹度量方法共性特征基础上,提出了一个兼容多种轨迹相似度度量方法的集成计算框架(Multiple Scenarios Similarity Trajectory Framework,MSSTJ),并通过一种统一的轨迹数据分区索引方法来优化不同相似轨迹度量方法的计算效率,从而使得用户可以通过参数化配置的简单方式来实现不同场景下多样化的轨迹相似度高效计算的需求。通过基于两类不同类型轨迹数据集的相关实验表明,MSSTJ可以帮助用户快速实现不同的轨迹相似度计算算法,并且实现的算法具有较好的性能表现。

气象大数据云平台算法集约化环境设计与应用

气象业务系统集约化发展和“云+端”业务技术体制改革是实现气象业务高质量发展的重要措施。2020年中国气象局提出构建以气象大数据云平台为云、气象业务系统为端的“云+端”业务技术体制,明确气象大数据云平台作为关键基础技术平台的定位。加工流水线作为气象算法的集约化环境,应用数算一体、高效任务调度、可视化流程编排、容器等技术,实现气象算法的统一管理与高效集约调度运行。2021年加工流水线业务运行,支撑全国202个业务系统的实时运行,业务系统性能提升1~10倍,集约化程度显著提高,对提升业务系统的运行效率、增强业务系统的协同性、加速“云+端”业务技术体制改革进程和推进气象业务集约发展发挥了重要支撑作用。