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.

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。

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.

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。

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。

早发型2型糖尿病患者子女颈动脉全息血管硬度及影响因素分析

目的评估早发型2型糖尿病患者子女颈动脉弹性功能,并分析影响其弹性的因素。资料与方法前瞻性分析2020年10月—2021年8月河南省人民医院2型糖尿病患者子女63例,根据其父母糖尿病发病年龄(<40岁为早发型,≥40岁为晚发型)分为父母早发糖尿病子女组(早发组,32例)和父母晚发糖尿病子女组(晚发组,31例)。同期选择32例年龄、性别及体重指数相匹配的健康志愿者作为对照组。应用全息血管内-中膜厚度测量和全息血管硬度分析技术测量受试者颈总动脉内-中膜厚度、血管壁位移、血管收缩期管径、硬度指数和脉搏波传导速度,比较上述参数的组间差异。结果早发组颈动脉内-中膜厚度、脉搏波传导速度、硬度指数高于晚发组及对照组(t=0.054~1.228,P均<0.05),血管壁位移低于晚发组及对照组(t=0.048、0.109,P<0.05)。结论2型糖尿病患者子女颈动脉僵硬度明显高于正常对照组,且早发组的颈动脉弹性下降较晚发组更明显。

ELOVL3在预测肝细胞癌预后中的作用

目的探索精准预测肝细胞癌患者预后的分子标志物。方法在TCGA数据库下载肝细胞癌(LIHC)样本的mRNA表达数据及相应的临床预后数据,并且收集从2017年1月1日至2022年1月1日在新疆维吾尔自治区人民医院接受肝切除手术的91例肝细胞癌(HCC)患者的临床病理资料和随访资料。用R软件筛查差异表达基因(DEGs)后,进一步进行Cox单变量回归分析和LASSO回归分析筛出与HCC患者生存相关的风险基因;用R软件的ggplot2分析包和免疫组织化学染色法分析ELOVL3在不同肝组织上的表达情况,Survival分析包实现Kaplan-Meier生存分析。结果初步筛查得到752个DEGs,包括表达上调的247个基因和505个下调基因。进一步通过Cox回归分析得出175个与HCC患者的生存显著相关的基因集,LASSO回归分析,挑出14个与HCC患者生存密切相关的风险基因。最终通过预实验和文献调研将目标基因确定为ELOVL3。TCGA-LIHC的分析结果显示:ELOVL3在癌组织中的表达量明显高于正常肝组织(P<0.001),而且该基因在癌组织中的表达量也高于互相匹配的癌旁组织(P<0.001)。免疫组化染色结果提示:ELOVL3在59例(64.8%)患者的肝癌组织中表达阳性,而32例(35.2%)患者的肝癌组织中没有表达;在互相匹配的癌旁组织中表达均为阴性。生存分析结果显示ELOVL3阳性患者的5年生存率明显比ELOVL3阴性患者低(r=0.028);ELOVL3高表达的患者总体生存率相对于ELOVL3低表达的患者低,而且表达量越高,预后越差(HR=1.7,P=0.008)。结论ELOVL3的表达与HCC患者的不良预后相关,而且具有做预测HCC预后的新型分子标志物的潜力。

油气储层勘探建模技术新进展及未来展望

油气储层建模利用地质统计学等方法,综合测井、地质、地震等多学科信息,是油气田开发研究的利器,油藏地质模型可以将油藏各种地质特征在三维空间的变化及分布定量表征出来,是油气藏的类型、几何形态、规模、油藏内部结构、储层参数及流体分布的高度概括,储层地质模型是油藏地质模型的核心,可以对储层的沉积特征、非均质性、物性及流体等特征进行综合表征。但在勘探阶段,面对大尺度沉积体系和稀疏井网条件下的储层展布规律表征的建模难点为:①地质知识的量化表达问题,包括地质专家的经验认识如何数字化表征。②稀疏井网条件下无法直接用钻井资料对地质体的发育规模、展布方向和结构特征准确定量描述及构建地质模式,大尺度空间中复杂沉积体系无法用简单数学函数表征。③传统地质统计学等方法在勘探模型构建中如何实现地震、测井、地质、油藏等多维度数据的融合问题。因此,基于确定性建模和传统地质统计学等随机建模的储层建模理论和技术遇到极大挑战。笔者在系统剖析传统储层建模技术流程和方法的基础上,通过构建涵盖地质、测井、地震、分析化验等信息的多学科地学大数据知识库,开展多维数据凝聚层次聚类的沉积相模式库表征和基于生成式网络的智能建模,提出了多学科协同的油气储层勘探建模技术对策及技术体系,实现了构造、沉积及储层之间匹配关系的定量表征。该技术体系在东营凹陷北部陡坡带、洼陷带勘探部署中开展系统应用,构建融合古地貌、古物源、搬运通道、测井及地震属性等多信息的岩相、物性及油气运聚的地质模型,基于模型新范式指导部署井位,支撑了陆相断陷盆地复杂砂砾岩体、页岩油等勘探实践。笔者通过深度剖析东营凹陷北部陡坡带勘探建模实践难点及精度问题,进一步探讨了未来油气储层勘探建模技术发展趋势和应用前景。

可撤销属性加密的区块链数据访问控制方法

针对区块链数据共享中存在的粗粒度访问控制问题,提出一种基于属性撤销密文策略属性基加密的区块链数据访问控制方法。在现有方案基础上进行改造,引入预解密过程,结合属性撤销列表实现属性实时撤销;基于非对称群下的DBDH困难问题假设进行安全性证明;基于超级账本Fabric进行系统设计,结合星际文件系统采用链上链下存储方式解决区块链容量不足和系统效率问题。实验结果表明,所提方案撤销属性时无需更新密钥密文重复上链,仅需要6次Pairing操作进行预解密和解密,且在大规模属性集下,预解密时间和解密时间平均保持在百毫秒左右的常量级上,实现区块链数据高效、细粒度的访问控制。

数据要素市场化配置对数实融合的影响研究——基于数据交易平台设立的准自然实验

数字经济与实体经济深度融合将推动全国统一大市场建设和双循环新发展格局的构建,成为经济增长的新动能。基于数据交易平台设立的准自然实验,采用耦合评价模型测度2010—2021年全国280个地级市的数字经济与实体经济融合水平,以多期双重差分法实证检验以数据交易平台设立为表征的数据要素市场化配置对数实融合的影响。研究发现:数据要素市场化配置显著促进了数字经济与实体经济的融合,该结论经一系列稳健性检验依旧成立;机制分析表明,数据要素市场化配置通过成本效应、要素配置效应、技术创新效应等路径影响数字经济与实体经济的融合;异质性分析表明,数据要素市场化配置对数实融合的提升效应在产权保护程度较高、行政等级较高及市场化程度较高的城市表现更为明显;拓展性分析发现,数据要素市场化配置对数字经济与实体经济融合的影响存在空间溢出效应。因而应加强数据交易平台建设,多维度探索数实融合路径,挖掘试点政策潜力,加强区域主体的交流协作,优化数据要素市场化配置,更好推动数字经济与实体经济的融合发展。