On integration and adaptation in complex service systems

The services sector employs a large and growing proportion of workers in the industrialized nations, and it is increasingly dependent on information and communication technologies. While the interdependences, similarities and complementarities of manufacturing and services are significant, there are considerable differences between goods and services, including the shift in focus from mass production to mass customization （whereby a service is produced and delivered in response to a customer＇s stated or imputed needs）. In general, services can be considered to be knowledge-intensive agents or components which work together as providers and consumers to create or co-produce value. Like manufacturing systems, an efficient service system must be an integrated system of systems, leading to greater connectivity and interdependence. Integration must occur over the physical, temporal, organizational and functional dimensions, and must include methods concerned with the component, the management, and the system. Moreover, an effective service system must also be an adaptable system, leading to greater value and responsiveness. Adaptation must occur over the dimensions of monitoring, feedback, cybernetics and learning, and must include methods concerned with space, time, and system. In sum, service systems are indeed complex, especially due to the uncertainties associated with the human-centered aspects of such systems. Moreover, the system complexities can only be dealt with methods that enhance system integration and adaptation. The paper concludes with several insights, including a plea to shift the current misplaced focus on developing a science or discipline for services to further developing a systems engineering approach to services, an approach based on the integration and adaptation of a host of sciences or disciplines （e.g., physics, mathematics, statistics, psychology, sociology, etc.）. In fact, what is required is a services-related transdisciplinary - beyond a single disciplinary - ontology or taxon     

Expert discovery and knowledge mining in complex multi-agent systems

Complex problem solving requires diverse expertise and multiple techniques. In order to solve such problems, complex multi-agent systems that include both of human experts and autonomous agents are required in many application domains. Most complex multi-agent systems work in open domains and include various heterogeneous agents. Due to the heterogeneity of agents and dynamic features of working environments, expertise and capabilities of agents might not be well estimated and presented in these systems. Therefore, how to discover useful knowledge from human and autonomous experts, make more accurate estimation for experts＇ capabilities and find out suitable expert（s） to solve incoming problems （＂Expert Mining＂） are important research issues in the area of multi-agent system. In this paper, we introduce an ontology-based approach for knowledge and expert mining in hybrid multi-agent systems. In this research, ontologies are hired to describe knowledge of the system. Knowledge and expert mining processes are executed as the system handles incoming problems. In this approach, we embed more self-learning and self-adjusting abilities in multi-agent systems, so as to help in discovering knowledge of heterogeneous experts of multi-agent systems.     

Energy flows in complex ecological systems: a review

Energy flow drives the complex systems to evolve. The allometric scaling as the universal energy flow pattern has been found in different scales of ecological systems. It reflects the general power law relationship between flow and store. The underlying mechanisms of energy flow patterns are explained as the branching transportation networks which can be regarded as the result of systematic optimization of a biological target under constraints. Energy flows in the ecological system may be modelled by the food web model and population dynamics on the network. This paper reviews the latest progress on the energy flow patterns, explanatory models for the allometric scaling and modelling approach of flow and network evolution dynamics in ecology. Furthermore, the possibility of generalizing these flow patterns, modelling approaches to other complex systems is discussed. This research is supported by Guozhi Xu Post Doctoral Research Foundation and the National Natural Science Foundation of China under Grant No. 60574068.     

基于模糊逻辑的一类复杂系统全息控制设计

针对一类结构上具有相似性的非线性不确定复杂系统,利用模糊逻辑系统可以充分逼近连续函数的性质,研究这类复杂系统的鲁棒镇定问题,设计出了系统的全息鲁棒控制器和模糊逻辑系统参数估计的自适应律。为了简化控制器结构,在构造模糊逻辑系统时对被控系统的不确定性进行了归一化处理。在较弱的假设条件下,证明了这种控制器使被控系统的状态及参数估计误差一致终极有界,同时给出了误差的严密的数学分析。仿真实例表明所提出的方法是有效的。     

Stability conditions of complex switched systems with unstable subsystems

New stability conditions for complex switched systems are presented. We propose the concepts of attractive region and semi-attractive region, which are used as a tool for analyzing the stability of switched systems with unstable subsystems. Based on attractive region the sufficient conditions with less conservative for stability of switched systems have been established, there is no limitation for all members of the system set to be stable. Since our results have considered and utilized the decreasing span of oscillating solutions of the switched systems, they are more practical than the other presented ones of stability of switched systems, and need not resort to multiple Lyapunov functions.     

基于贝叶斯网络的复杂系统故障预测

针对复杂电子系统信号具有不确定性的特点,提出一种基于贝叶斯网络的故障预测模型。该模型通过对连续的信号特征进行量化处理,利用专家知识结合信号建立贝叶斯网络结构;对不同样本采用不同算法来进行网络学习,采用概率推理定量估计信号的区间预测概率,从而建立一个可推理的预测模型。将该方法应用于电源系统进行故障预测,针对不同数据样本进行实验,结果验证具有较高的区间预测率,为复杂系统的故障预测提供了新手段。     

A complex systems science perspective on wireless networks

The paper targets a future world where all wireless networks are self-organising entities and in which the predominant mode of spectrum access is dynamic. The paper explores whether the behaviour of a collection of autonomous self-organising wireless systems can be treated as a complex system and whether complex systems science can shed light on the design and deployment of these networks. The authors focus on networks that self-organise from a frequency perspective to understand the behaviour of a collection of wireless self-organising nodes. Each autonomous network is modelled as a cell in a lattice and follows a simple set of self-organisation rules. Two scenarios are considered, one in which each cell is based on cellular automata and which provides an abstracted view of interference and a second in which each cell uses a self-organising technique which more accurately accounts for interference. The authors use excess entropy to measure complexity and in combination with entropy gain an understanding of the structure emerging in the lattice for the self-organising networks. The authors show that the self-organising systems presented here do exhibit complex behaviour. Finally, the authors look at the robustness of these complex systems and show that they are robust against changes in the environment.     

复杂系统使用任务完成概率模型

研究了任务期间允许换件修理时复杂系统的使用任务完成能力。对武器系统的使用任务进行分类。利用马尔可夫过程分析部件系统在特定维修保障策略下的状态转移过程,以部件系统固定任务时间内在正常状态停留时间的分布函数作为其任务完成概率模型,并通过全概率分解对任务完成概率进行求解。通过分析任务期间部件系统、装备、武器系统三者之间的任务可靠性关系进而得到复杂系统的任务完成概率。最后,运用数值计算的方法绘制了复杂系统任务完成概率随任务时间和工作量的变化曲线,讨论并分析了任务时间对任务完成概率的影响。     

复杂系统的模糊故障诊断方法研究

复杂系统的故障具有模糊性与精确性的复杂特性,精确的推理方法难以有效地对复杂特性的故障进行诊断。提出模糊产生式规则与Petri网推理技术相结合的故障诊断模型,以研究模糊故障的推理诊断方法。以某发射装置控制系统为研究对象,给出一个复杂特征故障的诊断实例。研究结果表明,模糊产生式规则Petri网模型方法可以有效地对复杂系统的模糊性与精确性双重特征的故障进行推理诊断。     

The game theoretical approach for multi-phase complex systems in chemical engineering

This paper explores the application of noncooperative game theory together with the concept of Nash equilibrium to the investigation of some basic problems on multi-scale structure, especially the meso-scale structure in the multi-phase complex systems in chemical engineering. The basis of this work is the energy-minimization-multi-scale(EMMS) model proposed by Li and Kwauk(1994) and Li, et al.(2013) which identifies the multi-scale structure as a result of ‘compromise-in-competition between dominant mechanisms' and tries to solve a multi-objective optimization problem. However,the existing methods often integrate it into a problem of single objective optimization, which does not clearly reflect the ‘compromise-in-competition' mechanism and causes heavy computation burden as well as uncertainty in choosing suitable weighting factors. This paper will formulate the compromise in competition mechanism in EMMS model as a noncooperative game with constraints, and will describe the desired stable system state as a generalized Nash equilibrium. Then the authors will investigate the game theoretical approach for two typical systems in chemical engineering, the gas-solid fluidization(GSF) system and turbulent flow in pipe. Two different cases for generalized Nash equilibrium in such systems will be well defined and distinguished. The generalize Nash equilibrium will be solved accurately for the GSF system and a feasible method will be given for turbulent flow in pipe. These results coincide with the existing computational results and show the feasibility of this approach, which overcomes the disadvantages of the existing methods and provides deep insight into the mechanisms of multi-scale structure in the multi-phase complex systems in chemical engineering.     

模型驱动的复杂人机系统过程建模仿真方法

复杂人机系统涉及复杂的技术系统过程、认知决策过程以及人机协同过程,是当前系统工程研究最具挑战的领域之一。建模仿真在很多情况下是分析复杂人机系统过程性能效能的唯一可行选择。分析了复杂人机系统过程建模以及系统仿真模型表示的主要方法。结合平台级作战过程建模应用问题,给出了一种物理域、认知域、模型框架三分的复杂人机系统过程建模框架。在模型驱动方法的指导下,综合运用多方法建模、领域特定建模等方法讨论了复杂人机系统过程仿真模型设计问题。对其他领域的复杂人机系统过程建模仿真问题研究具有示范意义。     

面向虚拟采办的复杂异构系统统一建模方法

针对集团企业大型装备虚拟采办应用中,存在多层次、多方位的模型异构性,以及已有大量专业沉淀,需要提高模型的可重用性和可组合性水平的问题,本文基于一种复杂异构系统统一建模的理论框架,给出复杂异构系统统一建模方法的形式化定义,从可信度分析的角度探讨统一建模方法的自洽性,并从工程化的角度提出统一建模方法的实现方式及其文本描述规范。最后,以某装备虚拟采办为背景,对本文所提出的统一建模方法进行应用验证。结果表明,所提出的统一建模方法可有效支持在集团企业开展大型装备的虚拟采办。     

基于经济相关性的复杂系统维修优化模型研究

针对存在经济相关性的复杂系统,建立了以系统预防维修费用率最小化为目标、系统可靠度为约束的优化模型。该模型分析了复杂系统中存在的维修经济相关性,并考虑了实际预防维修中的不完备性,为此引入了役龄回退因子对预防维修活动前后系统性能的动态变化进行了描述。最后给出了优化模型的量子遗传算法,并通过实例对模型的客观有效性进行了验证。     

基于多模型结构的非线性鲁棒自适应跟踪控制

针对复杂的非线性系统,提出一种基于多模型结构的鲁棒自适应控制方法,使得系统可以在不同的运行环境下跟踪给定的信号.由多个线性模型和一个模糊模型及其相应的控制器构成基本的多模型控制系统,再引入动态结构自适应神经网络以保证系统的稳定性及抑制由频繁切换引起的噪声.最后,对某小型飞机进行全包络飞行跟踪控制的仿真,验证所提控制方法是有效的.     

综合集成与复杂系统专辑序

系统科学和系统工程近几年来的一个重要进展就是研究复杂系统以及随之而来解决复杂系统的新理论和新方法 .综合集成方法无疑是解决复杂系统的一个非常重要的方法 .综合集成就是将数据各种信息、模型和专家的经验与计算机有机结合起来 .钱学森教授提出的综合集成研讨厅更是将机器体系、专家体系和知识体系有机结合起来而构成的一个高度智能化的人机结合系统 .在国家自然科学基金会的支持下我们开展了“支持宏观经济决策的人机结合综合集成体系研究”( 79990 580 )的重大项目的研究 ,以戴汝为院士为首的课题组成员从 1 999年夏立题以来 ,已经…     

复杂网络信息系统安全资源优化配置研究

安全资源的优化配置对于实现复杂网络信息系统安全风险管理具有非常重要的作用.建立了基于攻击传播性及分层防护的复杂网络信息系统安全资源分配模型.以该模型为基础,实现了单层防护以及双层防护方式下的安全资源分配过程.通过仿真实验的验证,在安全风险评估过程中考虑攻击传播性,有助于更加准确地评估整个组织中的安全风险.同时,采用分层防护方式能够在固有投资条件下更加有效地降低复杂网络信息系统的安全风险.     

复杂体系效能测度建模研究

根据复杂体系的概念及其所呈现出的多种特性,提出了复杂体系“两层四级”的效能测度建模框架,并针对复杂体系内部的相互位置层次及其功能关联定义了度量实体、层级映射、系统关联性和软构层等相关概念,清晰地阐释了复杂体系效能测度建模时体系层次映射的实质;最后,进行了复杂体系效能测度的通用建模设计,从而为复杂体系的效能评估以及体系改进和优化提供一定的理论指导.     

基于蚁群算法的复杂系统脆性研究

针对复杂系统的研究,提出了一种复杂系统脆性分析的方法。建立了复杂系统脆性的赋权图模型,以图的边描述子系统之间的脆性联系,边的权值描述子系统之间的脆性联系程度。而且定义了系统最大崩溃路径,并且提出了一种求解系统最大崩溃路径的蚁群算法。为了进一步说明问题,又以24-Bus IEEE测试系统为例验证了新算法,并对算法中参数的选取进行了讨论。     

基于GOOPN++的复杂关联系统可靠性分析模型

提出了一种可用于复杂关联系统可靠性分析的增强的扩展面向对象Petri网模型(GOOPN ),给出了该模型形式化描述与运行规则。该模型引入了抽象库所、抽象变迁、消息颜色以及对象间的消息传递函数等新的概念,更好地体现了面向对象的思想,具有很好的模块性、继承性和可维护性,能够反映复杂关联系统的动态柔性特点。最后给出了一个用于装备保障能力评估的GOOPN 模型。