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.

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.

Optimal Tracking Control for a Class of Unknown Discrete-time Systems with Actuator Saturation via Data-based ADP Algorithm

为有致动器浸透和未知动力学的分离时间的系统的一个班的一个新奇最佳的追踪控制方法在这份报纸被建议。计划基于反复的适应动态编程(自动数据处理) 算法。以便实现控制计划，一个 data-based 标识符首先为未知系统动力学被构造。由介绍 M 网络，稳定的控制的明确的公式被完成。以便消除致动器浸透的效果， nonquadratic 表演功能被介绍，然后一个反复的自动数据处理算法被建立与集中分析完成最佳的追踪控制解决方案。为实现最佳的控制方法，神经网络被用来建立 data-based 标识符，计算性能索引功能，近似最佳的控制政策并且分别地解决稳定的控制。模拟例子被提供验证介绍最佳的追踪的控制计划的有效性。

Sampled-data-based LQ control of stochastic linear continuous-time systems

Sampled-data (SD) based linear quadratic (LQ) control problem of stochastic linear continuous-time (LCT) systems is discussed. Two types of systems are involved. One is time-invariant and the other is time-varying. In addition to stability analysis of the closed-loop systems, the index difference between SD-based LQ control and conventional LQ control is investigated. It is shown that when sample time ?T is small, so is the index difference. In addition, the upper bounds of the differences are also presented, which are O(?T2) and O(?T), respectively.

Data-based intelligent modeling and control for nonlinear systems

With the ever increasing complexity of industrial systems,model-based control has encountered difficulties and is facing problems,while the interest in data-based control has been booming.This paper gives an overview of data-based control,which divides it into two subfields,intelligent modeling and direct controller design.In the two subfields,some important methods concerning data-based control are intensively investigated.Within the framework of data-based modeling,main modeling technologies and control strategies are discussed,and then fundamental concepts and various algorithms are presented for the design of a data-based controller.Finally,some remaining challenges are suggested.

外篇:日本的漫画发展逻辑——日本同人志、Data-base消费论对独立漫画的启示

在漫画市场极度发达的日本,与动漫画相关的书本大致分为两类,一种是"商业志",就是一般在市场流通、在书店能够买得到的、通常有国际书号的出版物,另一种是"同人志",泛指一些同好们按照版权原作,进行二次创作,自费出版,在同人志贩卖会交易的作品。因此,日本的分类与概念,重点不在销售量以及销售量决定的主流或非主流,反而是销售渠道;无关"商业市场逻辑"与"独立文化"的对立,反而是消费模式的变迁。在日本语境,"独立漫画"勉强可翻译成"次文化漫画"(sabu-karu komikkuサブカルミック),其实也是商业出版。所以备受漫画学者关注的是第二种现象,同人志的出现。

Data-Based Predictive Control for Networked Nonlinear Systems with Packet Dropout and Measurement Noise

In this paper, the data-based control problem is investigated for a class of networked nonlinear systems with measurement noise as well as packet dropouts in the feedback and forward channels. The measurement noise and the number of consecutive packet dropouts in both channels are assumed to be random but bounded. A data-based networked predictive control method is proposed, in which a sequence of control increment predictions are calculated in the controller based on the measured output error, and based on the control increment predictions received by the actuator, a proper control action is obtained and applied to the plant according to the real-time number of consecutive packet dropouts at each sampling instant. Then the stability analysis is performed for the networked closedloop system. Finally, the effectiveness of the proposed method is illustrated by a numerical example.

Dynamic prediction of building subsidence deformation with data-based mechanistic self-memory model

This paper describes a building subsidence deformation prediction model with the self-memorization principle.According to the non-linear specificity and monotonic growth characteristics of the time series of building subsidence deformation,a data-based mechanistic self-memory model considering randomness and dynamic features of building subsidence deformation is established based on the dynamic data retrieved method and the self-memorization equation.This model first deduces the differential equation of the building subsidence deformation system using the dynamic retrieved method,which treats the monitored time series data as particular solutions of the nonlinear dynamic system.Then,the differential equation is evolved into a difference-integral equation by the self-memory function to establish the self-memory model of dynamic system for predicting nonlinear building subsidence deformation.As the memory coefficients of the proposed model are calculated with historical data,which contain useful information for the prediction and overcome the shortcomings of the average prediction,the model can predict extreme values of a system and provide higher fitting precision and prediction accuracy than deterministic or random statistical prediction methods.The model was applied to subsidence deformation prediction of a building in Xi'an.It was shown that the model is valid and feasible in predicting building subsidence deformation with good accuracy.

大数据驱动的企业与用户互动创新:理论框架与前沿课题

大数据驱动的企业与用户互动创新已成为企业洞察市场和用户动态、提升创新敏捷性和适应性的重要模式,其关键因素和作用机制日益成为创新、营销和信息系统等领域的热点议题。结合系统性综述设计和关键词共现分析,梳理大数据情境下企业与用户互动创新的研究洞见。针对情境和工具如何影响领先用户与普通用户关系及二者的相互作用机制两个研究缺口,提出大数据驱动的企业与用户互动创新“条件—路径—绩效”理论框架。依据该理论框架,结合文献和案例证据,重点阐述在什么情境和工具条件下企业与领先用户、普通用户以何种互动创新路径影响创新产品和大数据合作资产,由此形成理论创新。分析结果表明,互动创新路径选择本质是在异质性情境和工具条件下,企业在创新新颖性—泛用性以及数据分析成本—收益之间的动态权衡;两类创新路径的动态变化由互动创新工具成熟度、创新主导权的变更所触发。最后,进一步提炼出大数据驱动的企业与用户互动创新理论研究的三类前沿课题,并讨论其理论和实践意义。

数字化转型背景下高校图书馆数字基座的构建探索:以复旦大学图书馆为例

高校图书馆数字基座是一种支撑图书馆数字化转型的数字基础设施,其核心结构由数据中心、业务中心、基础服务中心、物联中心和AI服务中心组成,为业务转型和服务创新提供数据能力、通用业务能力和数字技术应用能力的支撑。本文以复旦大学图书馆数字基座的建设和成效为例,探索高校图书馆数字基座的建设思路、策略和方法,提出数字基座的建设需要在数字化转型战略指引下,适度超前规划并完善总体信息技术架构;围绕用户需求,从平台工具、数据资源、运营机制、场景创新、标准规范等方面进行系统性的构建和持续演进。

大数据驱动的产品创新商务智能——基于SSP框架的分析

人工智能改变产品创新主体,引发产品创新战略与流程变革。本文依据大数据合作资产理论,从企业与用户互动的适应性创新视角,通过逻辑推演分析提出大数据驱动的产品创新商务智能主体—战略—流程框架,以探讨人工智能影响制造产品创新的新机制。研究表明:首先,人与人工智能双向卷入形成要素组合与知识积累的双向反馈式增强机制,促使创新主体发生结构性改变,形成产品创新战略与流程变革的新基础。其次,企业人工智能认知战略与竞争战略、组织文化、商业模式和生态系统的匹配,共同影响人工智能在创新流程中的一体化应用。最后,模糊前端的全流程扩散、需求—解决方案对的非线性迭代和智能制造的生成式自测构成大数据驱动的产品创新流程的新特征。研究表明,主体—战略—流程框架适合分析大数据驱动的产品创新商务智能的新机制,可为人工智能与产品创新的关系研究提供新视角。

铜陵特色文化资源挖掘利用的研究——以铜陵市图书馆“铜文化”专题数据库建设为例

铜文化作为铜陵特色文化,有着极高的文化价值和经济价值。为进一步挖掘、整合与利用铜文化资源,强化其在公共文化服务体系中的地位和作用,促进铜文化事业和产业发展,铜陵市图书馆着力打造“铜文化”专题数据库。文章介绍了数据库建设概况与意义,梳理了数据库资源收集方式与主要内容,总结了数据库建设工作中遇到的问题并提出了四点举措建议,以期数据库建设工作顺利推进并实现预期目标。

复杂系统测试性设计与故障诊断策略研究进展

测试性设计是提高系统可靠性、安全性、维修性、保障性的重要前沿技术,决定了系统故障检测率和隔离率,直接影响系统的维护(测试)成本。系统测试性设计包含结构化设计、模型化设计、数据驱动设计等多种设计策略。其中,数据驱动设计于近年逐渐兴起并成为重要发展方向之一,该类方法通过对系统测试与故障之间的关系进行建模,依据测试结果进行故障推理,形成故障诊断方案。首先,简要回顾了系统测试性设计的发展历程;其次,重点介绍了测试性设计的研究进展,分析总结了结构化、模型化、数据驱动3类测试方案;然后,介绍了测试性诊断策略构建,根据测试方案中的建模方法确定诊断策略的构建技术,并总结归纳了每类技术的研究特点和适用性;最后,探讨了当前复杂系统测试性设计面临的挑战性问题和可能的未来研究方向。

中东石油平台项目法兰管理特殊注意事项

中东石油平台项目,由于中东油品硫化氢含量高,硫化氢属于有毒有害气体,泄露的话对人体危害大,所以中东项目管线系统密封性要求高,特别是一些含硫化氢等酸性流体的系统,对于法兰密封的要求就更高,对于法兰类型的选择,相应特殊工艺对法兰管理方法和要求也提出相对于常规油气项目的一些特殊要求。为了保证法兰密封效果,就需要对法兰材料选择,法兰管理方式,螺栓摩擦系数等参数正确选择。

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。

一种近实时全球电离层数据同化和预报系统的构建与实现

电离层天气变化正成为目前空间天气预报最重要的内容之一,建立一个可靠的、精确的电离层特征参量现报和预报系统对空间科学研究及军民用无线电信息系统保障均具有重要价值。基于国际GNSS服务组织(International GNSS Service,IGS)的地基GNSS和全球电离层无线电观测站(Global Ionospheric Radio Observatory,GIRO)数字测高仪的实时数据,以国际参考电离层(International Reference Ionosphere,IRI)模型为背景模型,采用高斯-马尔可夫-限带卡尔曼滤波同化技术,结合超大规模矩阵稀疏存储与处理方法,在云计算平台上构建完成了近实时全球电离层数据同化和预报系统(near-Real-Time Global Ionospheric Data AssiMilation and forecasting system,RT-GIDAM)。该系统具备了全球电离层TEC和电子密度的近实时(延时约5 min)、较高空间(5°×2.5°)和时间分辨率(15 min)的同化和预报功能,可为空间物理研究及相关无线电系统应用提供数据支撑。