Data-Based Filters for Non-Gaussian Dynamic Systems With Unknown Output Noise Covariance

This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise.The challenge of designing a suitable filter in the presence of an unknown covariance matrix is addressed by focusing on the output data set of the system.Considering that data generated from a Gaussian distribution exhibit ellipsoidal scattering,we first propose the weighted sum of norms(SON)clustering method that prioritizes nearby points,reduces distant point influence,and lowers computational cost.Then,by introducing the weighted maximum likelihood,we propose a semi-definite program(SDP)to detect outliers and reduce their impacts on each cluster.Detecting these weights paves the way to obtain an appropriate covariance of the output noise.Next,two filtering approaches are presented:a cluster-based robust linear filter using the maximum a posterior(MAP)estimation and a clusterbased robust nonlinear filter assuming that output noise distribution stems from some Gaussian noise resources according to the ellipsoidal clusters.At last,simulation results demonstrate the effectiveness of our proposed filtering approaches.

Recent Progress on Data-Based Optimization for MineralProcessing Plants

In the globalized market environment, increasingly significant economic and environmental factors withincomplex industrial plants impose importance on the optimization of global production indices; such opti-mization includes improvements in production efficiency, product quality, and yield, along with reductionsof energy and resource usage. This paper briefly overviews recent progress in data-driven hybrid intelli-gence optimization methods and technologies in improving the performance of global production indicesin mineral processing. First, we provide the problem description. Next, we summarize recent progress indata-based optimization for mineral processing plants. This optimization consists of four layers： optimiza-tion of the target values for monthly global production indices, optimization of the target values for dailyglobal production indices, optimization of the target values for operational indices, and automation systemsfor unit processes. We briefly overview recent progress in each of the different layers. Finally, we point outopportunities for future works in data-based optimization for mineral processing plants.

Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks

In this paper, a data-based fault tolerant control(FTC) scheme is investigated for unknown continuous-time(CT)affine nonlinear systems with actuator faults. First, a neural network(NN) identifier based on particle swarm optimization(PSO) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network(PSOCNN) is employed to solve the Hamilton-Jacobi-Bellman equation(HJBE) more efficiently.Then, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method.

Data-Based Optimal Tracking of Autonomous Nonlinear Switching Systems

In this paper,a data-based scheme is proposed to solve the optimal tracking problem of autonomous nonlinear switching systems.The system state is forced to track the reference signal by minimizing the performance function.First,the problem is transformed to solve the corresponding Bellman optimality equation in terms of the Q-function(also named as action value function).Then,an iterative algorithm based on adaptive dynamic programming(ADP)is developed to find the optimal solution which is totally based on sampled data.The linear-in-parameter(LIP)neural network is taken as the value function approximator.Considering the presence of approximation error at each iteration step,the generated approximated value function sequence is proved to be boundedness around the exact optimal solution under some verifiable assumptions.Moreover,the effect that the learning process will be terminated after a finite number of iterations is investigated in this paper.A sufficient condition for asymptotically stability of the tracking error is derived.Finally,the effectiveness of the algorithm is demonstrated with three simulation examples.

Linked Data-based Slide Repository： The Episodic Slide Retrieval Using the Episodic Keyword Networks

This paper focuses on developing a system that allows presentation authors to effectively retrieve presentation slides for reuse from a large volume of existing presentation materials. We assume episodic memories of the authors can be used as contextual keywords in query expressions to efficiently dig out the expected slides for reuse rather than using only the part-of-slide-descriptions-based keyword queries. As a system, a new slide repository is proposed, composed of slide material collections, slide content data and pieces of information from authors＇ episodic memories related to each slide and presentation together with a slide retrieval application enabling authors to use the episodic memories as part of queries. The result of our experiment shows that the episodic memory-used queries can give more discoverability than the keyword-based queries. Additionally, an improvement model is discussed on the slide retrieval for further slide-finding efficiency by expanding the episodic memories model in the repository taking in the links with the author-and-slide-related data and events having been post on the private and social media sites.

The COVID-19 and the“European Solution”of Data-based Approach to Governance:Public Security or Data Protection?

To cope with the challenges of CoViD-19,europe has adopted relevant measures of a data-based approach to governance,on which scholars have huge differences,and the related researches are conducive to further discussion on the differences.By sorting out the challenges posed by the pandemic to public security and data protection in europe,we can summarize the“european Solution”of the data-based approach to governance,including legislation,instruments,supervision,international cooperation,and continuity.The“Solution”has curbed the spread of the pandemic to a certain extent.However,due to the influence of the traditional values of the EU,the“Solution”is too idealistic in the balance between public security and data protection,which intensifies the dilemma and causes many problems,such as ambiguous legislation,inadequate effectiveness and security of instruments,an arduous endeavor in inter national cooperation,and imperfect regulations on digital green certificates.Therefore,in a major public health crisis,there is still a long way to go in exploring a balance between public security and data protection.

数据采集系统实验教学设计与实践

“电气测量技术”课程传统实验教学在一定程度上忽视了培养学生分析、解决问题的能力。因此,以“数据采集系统”为项目主题,设计了驱动问题、学习目标以及评价方案;在项目实施中,应用开源软件和硬件搭建项目“脚手架”,融入设计思维理念,从而构建了基于项目式学习与设计思维的实验教学模式。实践结果表明:该教学模式有助于激发学生对课程实验的兴趣以及提升学生的工程实践能力。

基于置信规则库气象站故障检测专家系统

针对气象站故障检测的需求,研制了基于置信规则库的气象站故障检测专家系统。专家系统利用前期建立起来的气象站的气象数据库,根据当前实时气象数据,通过改进的置信规则库模型结合专家经验知识自动完成数据分析和处理、是否有故障判断并对故障程度进行等级划分。工作人员在进行现场故障检测之前,只需通过气象站传感器故障专家系统对异常传感器进行辅助诊断,确定故障等级后依据故障等级携带相应检修设备或需更换的零部件进入现场实施检查和维修。实验测试表明系统对各个设备的总体平均检测准确率从69.8%提高到85.7%,极大提升了检测的效率和准确性。

结合区块链和关系数据库管理的会计信息系统安全方案

会计信息系统(AIS)是企业资源规划(ERP)的核心模块,过去一直被设计为集中式系统,存在单点故障、可扩展性、安全性、灵活性等方面的缺陷。为此,提出了结合联盟区块链和关系数据库管理系统(RDMBS),支持事务防篡改和签名验证的分布式AIS系统。通过将区块链嵌入关系表中,在数据库中以防篡改的方式存储可信信息,并支持数据的溯源和验证。整个系统由多个经授权的参与方共同维护,支持AIS数据的安全整合,并通过签名方案明确数据所有权。为支持大规模查询,应用了基于动态布隆过滤树(DBFT)的优化算法,以最大限度降低查询和验证作业的总时间成本。实验评估表明,所提方法在AIS系统中成功实现了基于区块链的身份验证和篡改溯源,并显著提升了大规模分析查询和验证的效率。

气象雷达数据管理和个例资料整编设计

雷达气象资料是短临预报、灾害天气预警的重要资料。气象雷达数据管理和个例资料整编系统采用WinForm编程语言和MSSQLServer2008数据库开发技术建立了相应的南充雷达气象资料数据库。同时,系统可以有效实现基数据、产品数据、历史个例资料等气象雷达数据的整编、压缩、入库、查询、统计、下载等功能,减少人为操作,提高业务工作效率。同时为各级气象业务人员下载雷达资料和查询历史个例信息提供了便利,并能够满足业务应用中对雷达气象资料存储和检索的要求,达到了对台站雷达资料高效管理的目的。

占空比传输机制下基于协同预测的时变不确定系统递推滤波

以工业互联网为背景,研究占空比传输机制下一类时变不确定系统的滤波问题,结合协同预测方法设计了新颖的递推滤波算法,解决了占空比传输机制下滤波性能降低的问题。首先给出描述占空比传输机制的数学模型,然后提出结合协同预测方法的递推滤波方案,设计基于占空比机制的递推滤波算法,推导了滤波误差协方差矩阵的一个上界,随后分析这个上界的有界性,实现了在稀疏数据情形下提高滤波性能的目的。仿真结果验证了所提算法的高效性和有效性。

保供降碳目标下能源转型模式及转型时点分析

应对气候变化目标要求全球能源系统进行广泛而深刻的转型。由于资源禀赋、能源战略、技术水平等差异,各国能源转型进程呈现出明显的异质性特征。通过对主要国家能源转型特征与减煤路径的分析,归纳了4种典型模式:能效提高模式、增气减煤模式、煤炭洁净利用模式、新能源替代模式,不同模式在不同时间点上与经济社会发展交互影响形成了国际能源转型的内在驱动机制。采用面板数据模型识别了影响能源转型模式的重要因素,并利用历史趋势分析方法对我国化石能源消费“拐点”与减煤时间节点进行研究。研究表明,当人均GDP达到25500美元时,发达国家即出现化石能源减少拐点。国家创新指数和城镇化与煤炭消费占比呈负相关,第二产业增加值占比与煤炭消费占比呈正相关。我国预计减煤时间范围在2028-2035年,化石能源消费减少时点在2040年左右。考虑到我国以煤炭为主的能源资源禀赋,我国能源转型目标的实现须依赖于煤炭洁净利用为主的转型路径,大力发展低碳化现代煤基能源体系,改变传统煤炭利用理念与方式,为未来多能耦合的新型能源体系提供重要支撑。

学生“三证”自助填报系统的设计与实现

针对传统“三证”人工管理中存在的不足,设计并实现学生“三证”自助填报系统。系统以Java为核心语言,通过后端链接MySQL数据库,进行数据的采集和存储,通过SSM作为主要架构来构建主体。前端主要用HTML等动态网页技术来完成界面的设计和开发。在真实的环境下,对开发完成的系统进行了测试,结果表明系统满足了设计目标,具有较好的稳定性和兼容性,用户可以使用该系统更简单、实时、高效的完成学生“三证”自助填报。

一种三星探测数据融合的弹道估计算法

针对天基红外系统(Space Based Infrared System,SBIRS)三星探测弹道估计问题,提出GEO卫星与HEO卫星探测数据融合的估计算法。根据星座构成和探测体制,利用STK分析SBIRS对某一区域的覆盖能力,约43%的时间可以实现三星以上完全覆盖;建立三星探测数据融合算法模型,对导弹目标的运动状态进行实时估计,导弹运动建模采用当前统计模型,数据融合采用集中式结构,滤波算法采用无迹卡尔曼滤波。试验表明,与双星探测弹道估计误差相比,三星探测弹道估计误差显著减小。

基于AI大数据的无线基站节能系统的设计与应用

为应对2G/3G/4G无线基站在运营商日常运营中高能耗开销及不断上升的电费支出问题,文章提出了一种基于AI大数据技术的无线RAN基站节能系统。该系统旨在针对全网各种场景和不同设备,通过对现有网络配置的深入梳理与调整优化,以及节能功能的智能部署和优化,并对现网站点的建模寻找最优门限值,最后通过AI大数据的持续优化迭代,持续提升节能效益,增强系统的稳定性。在满足用户业务需求的前提下,不断优化无线基站的节能功能,从而显著降低设备功耗,大幅度降低运营商的节能支出。通过实验结果表明,该系统部署到运营商网络中的节能效果十分明显,相比部署该系统之前功耗降幅接近12%,远远高于传统节能手段的增益效果。

面向知识图谱问答的查询图生成方法

该文提出一种新的查询图生成方法用于知识图谱问答系统的问句解析。现有查询图生成工作覆盖的复杂问句类型有限,不能较好地处理答案为关系或涉及关系约束的问句,且未充分考虑路径结果间的组合与运算。因此,该文在查询图生成中应用节点操作的同时引入基于关系的操作,并考虑不同主路径之间的组合情况,显著提升对复杂问句的分析能力。并在此基础上,构建了中文知识图谱问答系统。此外,该文构建一份包含多种复杂类型问句的中文知识图谱问答数据集。该数据集和CCKS2019-CKBQA数据集合并后构成一个新的数据集CCKS2019-Comp,并用来测试本文方法的有效性。实验结果表明,该文方法在CCKS2019-CKBQA和CCKS2019-Comp测试集上平均F 1值分别达到73.8%和73.3%。该文的新构建数据和代码已开源①。

基于数字基座的高质量教育大数据体系构建

文中探讨了如何打造数字基座,构建高质量的教育大数据体系.首先,阐述了数字基座的重要性及其在大数据体系中的作用.其次,介绍了构建高质量教育大数据体系的关键要素,包括数据采集、数据处理、数据存储、数据安全等.最后,提出了具体的实施方案,分析了该领域的未来发展方向,以期为相关领域的研究提供参考.

数字孪生航道建设及航道智慧化指标体系研究

数字孪生航道是智慧航道发展到一定阶段后所呈现的新模式和新业态,是能反应物理实体航道运行状态和智能交互的新型航道基础设施。当前数字孪生航道缺乏必要的理论研究,水运行业对数字孪生航道的认识和理解不同。文章阐述了数字孪生航道“由实映虚”、“虚实交互”和“以虚控实”的内涵要义,根据数据流向提出数字孪生航道建设技术框架,并梳理出航道数据底座底板、模拟仿真引擎、业务知识模型和航道场景应用等建设内容。同时,结合我国智慧航道建设现状,针对我国智慧航道建设水平参差不齐,航道智慧化水平无法统一衡量,在发展环境、智能监测、智能管理和智能服务方面构建了由一、二、三级指标共同组成的内河航道智慧化指标评价体系,并提出了内河航道智慧化综合评价方法。研究成果对数字孪生航道建设、促进智慧航道发展具有重要促进作用。

火炮图元化显控系统研究与应用

显控系统是火炮信息系统的重要组成部分之一,负责武器系统的信息可视化和操控标准化,是实现火炮武器系统信息获取和人机交互的核心系统。图元化显控系统立足于火炮武器系统的研发、设计、使用和维护,基于图元化显控设计思想和层次化软件体系架构,在建立了图元和图元库的基础上,利用图元管理、DDS和数据字典技术,构建了一种高效开发和灵活配置的具有图元化、通用化、模块化和标准化等特征的人机交互显控平台,进而实现了人机席位浮动。该设计方法不但提高了软件可重用性、可移植性和开发效率,也符合未来火炮显控系统的发展思路和趋势。

基于大数据技术的铁塔基站管理系统设计与实现

大数据是一个数据类型和数据群体较为庞大的数据集,能从各种各样类型的数据中心快速获取有价值的数据信息,具有数据量大、类型多样、运算高效、处理速度快等特点。大数据技术应用于铁塔基站管理系统对于提升管理系统的运行质量以及效率具有重要意义。基于此,文章设计了基于大数据技术的铁塔基站管理系统,该系统可有效提升铁塔基站运行的安全稳定性。