知识和数据协同驱动的群体智能决策方法研究综述EI北大核心CSCD

群体智能(Collectire intelligence,CI)系统具有广泛的应用前景.当前的群体智能决策方法主要包括知识驱动、数据驱动两大类,但各自存在优缺点.本文指出,知识与数据协同驱动将为群体智能决策提供新解法.本文系统梳理了知识与数据协同驱动可能存在的不同方法路径,从知识与数据的架构级协同、算法级协同两个层面对典型方法进行了分类,同时将算法级协同方法进一步划分为算法的层次化协同和组件化协同,前者包含神经网络树、遗传模糊树、分层强化学习等层次化方法;后者进一步总结为知识增强的数据驱动、数据调优的知识驱动、知识与数据的互补结合等方法.最后,从理论发展与实际应用的需求出发,指出了知识与数据协同驱动的群体智能决策中未来几个重要的研究方向.     

元数据管理平台总体架构设计研究

元数据管理解决的问题就是如何把业务系统中的数据分门别类地进行管理,并建立数据与数据之间的关系,为数据仓库的数据质量监控提供基础素材.元数据管理指管理数据仓库系统的元数据,它贯穿数据仓库系统的各个环节,并实现系统的各个处理单元由元数据驱动.     

间歇过程最优迭代学习控制的发展:从基于模型到数据驱动

本文综述了间歇过程的基于模型的和数据驱动的最优迭代学习控制方法.基于模型的最优迭代学习控制方法需要已知被控对象精确的线性模型,其研究较为成熟和完善,有着系统的设计方法和分析工具.数据驱动的最优迭代学习控制系统设计和分析的关键是非线性重复系统的迭代动态线性化.本文简要综述了基于模型的最优迭代学习控制的研究进展,详细回顾了数据驱动的迭代动态线性化方法,包括其详细的推导过程和突出的特点.回顾和讨论了广义的数据驱动最优迭代学习控制方法,包括完整轨迹跟踪的数据驱动最优迭代学习控制方法,提出和讨论了多中间点跟踪的数据驱动最优点到点迭代学习控制方法,和终端输出跟踪的数据驱动最优终端迭代学习控制方法.进一步,迭代学习控制研究中的关键问题,如随机迭代变化初始条件、迭代变化参考轨迹、输入输出约束、高阶学习控制律、计算复杂性等.本文突出强调了基于模型的和数据驱动的最优迭代学习控制方法各自的特点与区别联系,以方便读者理解.最后,本文提出数据驱动的迭代学习控制方法已成为越来越复杂间歇过程控制发展的未来方向,一些开放的具有挑战性的问题还有待于进一步研究.     

介电弹性体驱动系统建模及控制方法综述

介电弹性体驱动系统是一种基于介电材料的形变特性来驱动机械系统的新型柔性驱动装置,利用介电弹性体驱动与控制机器人成为当前软体机器人领域的研究热点.鉴于此,围绕介电弹性体驱动系统的核心关键问题,即驱动系统的建模与控制方法进行全面回顾与展望.首先,阐述介电弹性体驱动系统的结构与驱动过程,对介电弹性体驱动原理进行详细介绍;然后,针对介电弹性体驱动系统所展现出的复杂非线性特性,从建模与控制两个方面展开综述,详细分析不同建模方法与控制策略的优势与局限性;最后,探讨介电弹性体建模和控制中存在的问题及未来的研究方向.     

数据驱动的智能交通虚拟仿真系统研究与设计

主要研究大数据环境下数据驱动的智能交通虚拟仿真系统体系结构及其关键技术。首先对数据驱动进行简要描述,并引出相关研究的现实意义。随后提出数据驱动的智能交通虚拟仿真系统体系结构,从面向智能交通虚拟仿真的数据处理、数据驱动技术、决策支持理论与方法、虚拟仿真环境等方面讨论了实现数据驱动的智能交通虚拟仿真所需解决的主要问题和关键技术,支持交通工程研究和应用。     

基于数据驱动的小子样复杂系统剩余使用寿命预测方法综述及展望

针对小子样背景下复杂系统剩余使用寿命(RUL)预测的工程需求,结合复杂系统失效的时间数据、监测数据特点和RUL预测的不确定性问题,综述了小子样数据驱动的复杂系统RUL的预测方法。在小子样数据驱动的寿命预测技术中,数据的真实性、连续性和完整性等问题成为制约RUL预测准确度的重要因素。深入分析了基于失效时间数据、性能退化数据和多源数据融合的RUL预测技术的基本研究方法和发展动态,最后探讨了RUL预测领域未来可能的研究方向。     

系统通信模型执行性研究

针对可执行体系结构中系统数据交换是如何最终落实到具体的通信网络或设备,并进行具体数据传输问题,提出了系统通信模型执行方法.该方法描述了系统通信模型具有的基本元素及相关属性、模型元素之间的关系和表示,阐述了系统通信模型与系统接口描述模型、系统数据交换描述以及系统状态转移/系统时序图之间的关系,说明了系统功能驱动系统数据交换的具体步骤以及系统通信模型执行的过程.系统通信模型的执行使系统体系结构模型在整体上形成一个内在的一致性执行体,充实并完善了可执行体系结构,为可执行体系结构的执行奠定基础.     

一种基于Petri网的分布组件协调模型

协调是分布组件系统中的基本问题之一.但是,协调问题至今仍未得到很好的解决.根据实际应用的要求,提出了Concerto协调模型.它以Petri网为数学理论基础,扩充了Petri网的语义,引入了控制缓存和数据缓存,分别反映了分布组件的控制依赖和数据依赖关系,统一了现有的控制驱动和数据驱动两类协调模型.对于Concerto模型的运行,提出了驱动模式、动作规则和Concerto引擎.驱动模式有4种:依赖操作时间的驱动、依赖最小时间的驱动、依赖最大时间的驱动和依赖平均时间的驱动.这些驱动模式在实时系统、流量控制和任     

基于GPRS的Modbus协议驱动程序开发

以供热管网动态平衡监控系统为背景,基于GPRS无线传输模块,开发了ICP-7017模块的Modbus协议驱动程序,详细介绍了程序设计方法、协议规范、接口编程和驱动技术.实际运行表明,该驱动程序有效地解决了组态软件、GPRS模块与ICP-7017模块之间数据接口问题.实现了供热管网数据远程采集和传榆.     

非线性多智能体系统的无模型自适应聚类一致性控制

针对一类模型未知的离散时间非线性多智能体系统聚类一致性问题,提出一种无模型自适应控制算法.首先,假设系统具有固定拓扑,利用伪偏导数概念得到系统的数据关系模型,在考虑多智能体之间耦合系数条件下给出聚类一致性误差,在此基础上设计一种数据驱动的聚类一致性跟踪控制协议;然后,采用压缩映射方法在理论上分析了跟踪误差的收敛性,结果表明所提出算法不需要智能体模型信息即可完成跟踪任务,是一种数据驱动的控制方法;最后,将结果拓展至随机切换拓扑结构的多智能体系统中,数值仿真结果验证了所提出算法的有效性.     

Prioritizing usability problems: data-driven and judgement-driven severity estimates

Software-ergonomic system analysis often reveals numerous usability problems. Given that system design suffers from limited resources, the prioritization of usability problems seems inevitable. Surprisingly enough, prioritization is not in the focus of scientific interest. Within this paper, approaches to prioritization relying on severity estimates will be presented. Two of the approaches, namely priorities based on data about the impact of a problem (data-driven) and priorities based on judgements of interest group members (judgement-driven) will be further explored. In the data-driven approach total problem-handling time caused by a usability problem is presented as a measure of severity. The major disadvantage of the data-driven approach is its costs. A possible alternative are severity estimates based on judgements by members of involved interest groups. The first of two studies shows how to obtain judgement driven severity estimates and reveals a fundamental lack of correspondence between data-driven and judgement-driven severity estimates. The second study supports the notion that the lack of correspondence may stem from a difference between assumptions of the data-driven approach and the naive judgement model of interest group members in the judgement-driven approach. A hypothetical model for severity estimates by interest group members is presented.     

Prioritizing usability problems: Data-driven and judgement-driven severity estimates

Software-ergonomic system analysis often reveals numerous usability problems. Given that system design suffers from limited resources, the prioritization of usability problems seems inevitable. Surprisingly enough, prioritization is not in the focus of scientific interest. Within this paper, approaches to prioritization relying on severity estimates will be presented. Two of the approaches, namely priorities based on data about the impact of a problem (data-driven) and priorities based on judgements of interest group members (judgement-driven) will be further explored. In the data-driven approach total problem-handling time caused by a usability problem is presented as a measure of severity. The major disadvantage of the data-driven approach is its costs. A possible alternative are severity estimates based on judgements by members of involved interest groups. The first of two studies shows how to obtain judgement driven severity estimates and reveals a fundamental lack of correspondence between data-driven and judgement-driven severity estimates. The second study supports the notion that the lack of correspondence may stem from a difference between assumptions of the data-driven approach and the naive judgement model of interest group members in the judgement-driven approach. A hypothetical model for severity estimates by interest group members is presented.     

Model-based Crowd Behaviours in Human-solution Space

Realistic crowd simulation has been pursued for decades, but it still necessitates tedious human labour and a lot of trial and error. The majority of currently used crowd modelling is either empirical (model-based) or data-driven (model-free). Model-based methods cannot fit observed data precisely, whereas model-free methods are limited by the availability/quality of data and are uninterpretable. In this paper, we aim at taking advantage of both model-based and data-driven approaches. In order to accomplish this, we propose a new simulation framework built on a physics-based model that is designed to be data-friendly. Both the general prior knowledge about crowds encoded by the physics-based model and the specific real-world crowd data at hand jointly influence the system dynamics. With a multi-granularity physics-based model, the framework combines microscopic and macroscopic motion control. Each simulation step is formulated as an energy optimization problem, where the minimizer is the desired crowd behaviour. In contrast to traditional optimization-based methods which seek the theoretical minimizer, we designed an acceleration-aware data-driven scheme to compute the minimizer from real-world data in order to achieve higher realism by parameterizing both velocity and acceleration. Experiments demonstrate that our method can produce crowd animations that are more realistically behaved in a variety of scales and scenarios when compared to the earlier methods.     

Alternative solutions to multi-variate control performance assessment problems

Performance assessment of multi-variate control with minimum variance control as the benchmark requires an interactor matrix to filter the closed-loop output. This is to transfer the coordinate of the original variables into a new one in order to identify the control invariant disturbance dynamics from the first few terms of the closed-loop output Markov parameters. There has been a great deal of interest to simplify this approach, in particular, to find methods that do not need the interactor matrix. With this motivation, this paper explores alternative solutions to multi-variate control performance assessment problems. In particular, we will consider two practical scenarios: (1) known time delays between each pair of inputs and outputs, (2) no a priori knowledge about the process model or time delays at all. Solutions to these two scenarios are proposed. Two data-driven algorithms based on subspace approach are derived for the calculation of performance measures. Several examples illustrate the feasibility of the proposed approaches.     

Prediction methods and applications in the science of science: A survey

Science of science has become a popular topic that attracts great attentions from the research community. The development of data analytics technologies and the readily available scholarly data enable the exploration of data-driven prediction, which plays a pivotal role in finding the trend of scientific impact. In this paper, we analyse methods and applications in data-driven prediction in the science of science, and discuss their significance. First, we introduce the background and review the current state of the science of science. Second, we review data-driven prediction based on paper citation count, and investigate research issues in this area. Then, we discuss methods to predict scholar impact, and we analyse different approaches to promote the scholarly collaboration in the collaboration network. This paper also discusses open issues and existing challenges, and suggests potential research directions.     

Addressing problems with replicability and validity of repository mining studies through a smart data platform

The usage of empirical methods has grown common in software engineering. This trend spawned hundreds of publications, whose results are helping to understand and improve the software development process. Due to the data-driven nature of this venue of investigation, we identified several problems within the current state-of-the-art that pose a threat to the replicability and validity of approaches. The heavy re-use of data sets in many studies may invalidate the results in case problems with the data itself are identified. Moreover, for many studies data and/or the implementations are not available, which hinders a replication of the results and, thereby, decreases the comparability between studies. Furthermore, many studies use small data sets, which comprise of less than 10 projects. This poses a threat especially to the external validity of these studies. Even if all information about the studies is available, the diversity of the used tooling can make their replication even then very hard. Within this paper, we discuss a potential solution to these problems through a cloud-based platform that integrates data collection and analytics. We created SmartSHARK, which implements our approach. Using SmartSHARK, we collected data from several projects and created different analytic examples. Within this article, we present SmartSHARK and discuss our experiences regarding the use of it and the mentioned problems. Additionally, we show how we have addressed the issues that we have identified during our work with SmartSHARK.     

A benchmark analysis of data-driven and geometric approaches for robot ego-motion estimation

In the last decades, ego-motion estimation or visual odometry (VO) has received a considerable amount of attention from the robotic research community, mainly due to its central importance in achieving robust localization and, as a consequence, autonomy. Different solutions have been explored, leading to a wide variety of approaches, mostly grounded on geometric methodologies and, more recently, on data-driven paradigms. To guide researchers and practitioners in choosing the best VO method, different benchmark studies have been published. However, the majority of them compare only a small subset of the most popular approaches and, usually, on specific data sets or configurations. In contrast, in this work, we aim to provide a complete and thorough study of the most popular and best-performing geometric and data-driven solutions for VO. In our investigation, we considered several scenarios and environments, comparing the estimation accuracies and the role of the hyper-parameters of the approaches selected, and analyzing the computational resources they require. Experiments and tests are performed on different data sets (both publicly available and self-collected) and two different computational boards. The experimental results show pros and cons of the tested approaches under different perspectives. The geometric simultaneous localization and mapping methods are confirmed to be the best performing, while data-driven approaches show robustness with respect to nonideal conditions present in more challenging scenarios.     

A constraint-based approach to shape management in multimedia databases

Shape management is an important functionality in multimedia databases. Shape information can be used in both image acquisition and image retrieval. Several approaches have been proposed to deal with shape representation and matching. Among them, the data-driven approach supports searches for shapes based on indexing techniques. Unfortunately, efficient data-driven approaches are often defined only for specific types of shape. This is not sufficient in contexts in which arbitrary shapes should be represented. Constraint databases use mathematical theories to finitely represent infinite sets of relational tuples. They have been proved to be very useful in modeling spatial objects. In this paper, we apply constraint-based data models to the problem of shape management in multimedia databases. We first present the constraint model and some constraint languages. Then, we show how constraints can be used to model general shapes. The use of a constraint language as an internal specification and execution language for querying shapes is also discussed. Finally, we show how a constraint database system can be used to efficiently retrieve shapes, retaining the advantages of the already defined approaches.     

A hybrid framework for process monitoring: Enhancing data-driven methodologies with state and parameter estimation

In this study we bridge traditional standalone data-driven and knowledge-driven process monitoring approaches by proposing a novel hybrid framework that exploits the advantages of both simultaneously. Namely, we design a process monitoring system based on a data-driven model that includes two different data types: i) “actual” data coming from sensor measurements, and ii) “virtual” data coming from a state estimator, based on a first-principles model of the system under investigation. We test the proposed approach on two simulated case studies: a continuous polycondensation process for the synthesis of poly-ethylene terephthalate, and a fed-batch fermentation process for the manufacturing of penicillin. The hybrid monitoring model shows superior fault detection and diagnosis performances with respect to conventional monitoring techniques, even when the first-principles model is relatively simple and process/model mismatch exists.     

Data-Driven Based Fault Prognosis for Industrial Systems: A Concise Overview

Fault prognosis is mainly referred to the estimation of the operating time before a failure occurs, which is vital for ensuring the stability, safety and long lifetime of degrading industrial systems. According to the results of fault prognosis, the maintenance strategy for underlying industrial systems can realize the conversion from passive maintenance to active maintenance. With the increased complexity and the improved automation level of industrial systems, fault prognosis techniques have become more and more indispensable. Particularly, the data-driven based prognosis approaches, which tend to find the hidden fault factors and determine the specific fault occurrence time of the system by analysing historical or real-time measurement data, gain great attention from different industrial sectors. In this context, the major task of this paper is to present a systematic overview of data-driven fault prognosis for industrial systems. Firstly, the characteristics of different prognosis methods are revealed with the data-based ones being highlighted. Moreover, based on the different data characteristics that exist in industrial systems, the corresponding fault prognosis methodologies are illustrated, with emphasis on analyses and comparisons of different prognosis methods. Finally, we reveal the current research trends and look forward to the future challenges in this field. This review is expected to serve as a tutorial and source of references for fault prognosis researchers.