Axiomatic characterizations of dual concept lattices

Formal concept analysis is an algebraic model based on a Galois connection. It is used for symbolic knowledge exploration from an elementary form of a formal context. This paper mainly presents a general framework for concept lattice in which axiomatic approaches are used. The relationship between concept lattice and dual concept lattice is first studied. Based on set-theoretic operators, generalized concept systems are established. And properties of them are examined. By using axiomatic approaches, a pair of dual concept lattices is characterized by different sets of axioms. The connections between 0-1 binary relations and generalized concept systems are examined. And generalized dual concept systems can be constructed by a pair of dual set-theoretic operators. Axiomatic characterizations of the generalized concept systems guarantee the existence of a binary relation producing a formal context.     

Modelling framework for artificial hybrid dynamical systems

Many current industry branches use hybrid approaches to solve complex application problems. Over the last decades, different tools for the simulation of such hybrid systems (e.g. Hysdel and YAMLIP) as well as the identification of hybrid systems (e.g. HIT, MLP and OAF NN) have been developed. The framework presented in this work facilitates the integration of artificial feed-forward neural networks in the modelling process of hybrid dynamical systems (HDS). Additionally, the framework provides a structured language for characterising these feed-forward networks itself. Therefore, an interdisciplinary exchange in the field of neural networks and its integration into hybrid dynamical systems is enabled. Focusing on hybrid systems with autonomous events, two different approaches, namely the artificial hybrid model and the artificial hybrid dynamics, are introduced. Challenges of the modelling process of HDS are reflected and advantages as well as disadvantages are discussed. The case study includes two common examples of HDS and analyses the simulation results and examines limitations of the modelling framework.     

Critical heuristics of social systems design

Health care planning, city and regional planning, energy and transportation planning, environmental design and other areas of social systems design are becoming issues of increasing concern to policy makers. Faced with complex steering problems that may have far-reaching societal implications, they except help from applied disciplines such as Operational Research, systems science, technology assessment, program evaluation, cost-effectiveness analysis, and similar approaches to scientifically informed planning. But these approaches offer little help in critically reflecting on the normative implications of the problem definitions and solution proposals they inspire.Critical Heuristics seeks to provide both the involved planners and affected citizens with a conceptual framework for identifying and discussing the normative implications of problem definitions, systems designs, program evaluations, etc. The paper introduces some key concepts of Critical Heuristics and briefly discusses their significance to ‘rational’ planning.     

Linear time-periodic dynamical systems: an H2 analysis and a model reduction framework

Linear time-periodic (LTP) dynamical systems frequently appear in the modelling of phenomena related to fluid dynamics, electronic circuits and structural mechanics via linearization centred around known periodic orbits of nonlinear models. Such LTP systems can reach orders that make repeated simulation or other necessary analysis prohibitive, motivating the need for model reduction. We develop here an algorithmic framework for constructing reduced models that retains the LTP structure of the original LTP system. Our approach generalizes optimal approaches that have been established previously for linear time-invariant (LTI) model reduction problems. We employ an extension of the usual H₂ Hardy space defined for the LTI setting to time-periodic systems and within this broader framework develop an a posteriori error bound expressible in terms of related LTI systems. Optimization of this bound motivates our algorithm. We illustrate the success of our method on three numerical examples.     

Multiscale recurrence analysis of long-term nonlinear and nonstationary time series

Recurrence analysis is an effective tool to characterize and quantify the dynamics of complex systems, e.g., laminar, divergent or nonlinear transition behaviors. However, recurrence computation is highly expensive as the size of time series increases. Few, if any, previous approaches have been capable of quantifying the recurrence properties from a long-term time series, while which is often collected in the real-time monitoring of complex systems. This paper presents a novel multiscale framework to explore recurrence dynamics in complex systems and resolve computational issues for a large-scale dataset. As opposed to the traditional single-scale recurrence analysis, we characterize and quantify recurrence dynamics in multiple wavelet scales, which captures not only nonlinear but also nonstationary behaviors in a long-term time series. The proposed multiscale recurrence approach was utilized to identify heart failure subjects from the 24-h time series of heart rate variability (HRV). It was shown to identify the conditions of congestive heart failure with an average sensitivity of 92.1% and specificity of 94.7%. The proposed multiscale recurrence framework can be potentially extended to other nonlinear dynamic methods that are computationally expensive for large-scale datasets.     

Preconditioned Dynamic Iteration for Coupled Differential-Algebraic Systems

The network approach to the modelling of complex technical systems results frequently in a set of differential-algebraic systems that are connected by coupling conditions. A common approach to the numerical solution of such coupled problems is based on the coupling of standard time integration methods for the subsystems. As a unified framework for the convergence analysis of such multi-rate, multi-method or dynamic iteration approaches we study in the present paper the convergence of a dynamic iteration method with a (small) finite number of iteration steps in each window. Preconditioning is used to guarantee stability of the coupled numerical methods. The theoretical results are applied to quasilinear problems from electrical circuit simulation and to index-3 systems arising in multibody dynamics.     

A variational approach to estimate incompressible fluid flows

A variational approach is used to recover fluid motion governed by Stokes and Navier–Stokes equations. Unlike previous approaches where optical flow method is used to track rigid body motion, this new framework aims at investigating incompressible flows using optical flow techniques. We formulate a minimization problem and determine conditions under which unique solution exists. Numerical results using finite element method not only support theoretical results but also show that Stokes flow forced by a potential are recovered almost exactly.     

A novel approach for studies of multispectral bioluminescence tomography

Bioluminescence tomography (BLT) is a promising new area in biomedical imaging. The goal of BLT is to provide quantitative reconstruction of bioluminescent source distribution within a small animal from optical signals on the animal’s body surface. The multispectral version of BLT takes advantage of the measurement information in different spectrum bands. In this paper, we propose a novel approach for the multispectral BLT. The new feature of the mathematical framework is to use numerical prediction results based on two related but distinct boundary value problems. This mathematical framework includes the conventional framework in the study of multispectral BLT. For the new framework introduced here, we establish the solution existence, uniqueness and continuous dependence on data, and characterize the limiting behaviors when the regularization parameter approaches zero or when the penalty parameter approaches infinity. We study two kinds of numerical schemes for multispectral BLT and derive error estimates for the numerical solutions. We also present numerical examples to show the performance of the numerical methods.     

Control of a novel fractional hyperchaotic system using a located control method

Fractional order dynamics and chaotics systems have been recently combined, yielding interesting behaviours. In this paper, a novel integer order hyperchaotic system is considered. Then, a fractional order hyperchaotic representation of said system is proposed using a natural fractionalization. Two different linear control methodologies to deal with the complexity which introduce such systems are proposed. Those methods are able to modify the hyperchaotic behaviour of the system and force it to move towards a fixed point; i.e. steady state. These approaches give a general framework for taming such complex systems using simple linear controllers. The main tools for analysing the controlled system are Matignon stability criterion and RouthHurwitz test. Using a reliable numerical simulation, the designed system is simulated to verify the theoretical analysis.     

Theoretical and numerical analysis on multispectral bioluminescence tomography

Recently, molecular imaging has been rapidly developed to studyphysiological and pathological processes in vivo at the cellularand molecular levels. Among molecular imaging modalities, opticalimaging has attracted a major attention for its unique advantages.In this paper, we establish a mathematical framework for multispectralbioluminescence tomography (BLT) that allows simultaneous studiesof multiple optical reporters. We show solution existence, uniquenessand continuous dependence on data as well as the limiting behaviourswhen the regularization parameter approaches zero or when thepenalty parameter approaches infinity. Then, we propose twonumerical schemes for multispectral BLT and derive error estimatesfor the corresponding solutions.     

Stability analysis for networked control systems with sampling,transmission protocols and input delays

In this paper, the stability problem is investigated for networked control systems. Input delays and multiple communication imperfections containing time-varying transmission intervals and transmission protocols are considered. A unified framework based on the hybrid systems with memory is proposed to model the whole networked control system. Hybrid systems with memory are used to model hybrid systems affected by delays and permit multiple jumps at a jumping instant. The stability analysis depends on the Lyapunov–Krasovskii functional approaches for hybrid systems with memory and the proposed stability theorem does not need strict decrease of the Lyapunov–Krasovskii functional during jumps. Based on the developed stability theorems, stability conditions for networked control systems are established. An explicit formula is given to compute the maximal allowable transmission interval. In the special case that the networked control system contains linear dynamics, an explicit Lyapunov functional is constructed and stability conditions in terms of linear matrix inequalities (LMI) are proposed. Finally, an example of a chemical batch reactor is given to illustrate the effectiveness of the proposed results.     

An efficient evolutionary multi-objective framework for MEMS design optimisation: validation, comparison and analysis

The application of multi objective evolutionary algorithms (MOEA) in the design optimisation of microelectromechanical systems (MEMS) is of particular interest in this research. MOEA is a class of soft computing techniques of biologically inspired stochastic algorithms, which have proved to outperform their conventional counterparts in many design optimisation tasks. MEMS designers can utilise a variety of multi-disciplinary design tools that explore a complex design search space, however, still follow the traditional trial and error approaches. The paper proposes a novel framework, which couples both modelling and analysis tools to the most referenced MOEAs (NSGA-II and MOGA-II). The framework is validated and evaluated through a number of case studies of increasing complexity. The research presented in this paper unprecedentedly attempts to compare the performances of the mentioned algorithms in the application domain. The comparative study shows significant insights into the behaviour of both of the algorithms in the design optimisation of MEMS. The paper provides extended discussions and analysis of the results showing, overall, that MOGA-II outperforms NSGA-II, for the selected case studies.     

QML-Morven: A novel framework for learning qualitative differential equation models using both symbolic and evolutionary approaches

In this paper, a novel qualitative differential equation model learning (QML) framework named QML-Morven is presented. QML-Morven employs both symbolic and evolutionary approaches as its learning strategies to deal with models of different complexity. Based on this framework, a series of experiments were designed and carried out to: (1) investigate factors that influence the learning precision and minimum data requirement for successful learning; (2) address the scalability issue of QML systems.     

Two derivative-free projection approaches for systems of large-scale nonlinear monotone equations

This study proposes two derivative-free approaches for solving systems of large-scale nonlinear equations, where the underlying functions of the systems are continuous and satisfy a monotonicity condition. First, the framework generates a specific direction then employs a backtracking line search along this direction to construct a new point. If the new point solves the problem, the process will be stopped. Under other circumstances, the projection technique constructs an appropriate hyperplane strictly separating the current iterate from the solutions of the problem. Then the projection of the new point onto the hyperplane will determine the next iterate. Thanks to the low memory requirement of derivative-free conjugate gradient approaches, this work takes advantages of two new derivative-free conjugate gradient directions. Under appropriate conditions, the global convergence result of the recommended procedures is established. Preliminary numerical results indicate that the proposed approaches are interesting and remarkably promising.     

Mixing methodologies for information systems development and strategy: A higher education case study

Although informed approaches from an interpretivist perspective have been widely promoted over the last two decades information systems development is largely informed by a functionalist perspective, into which human-centred or interpretivist issues are incorporated as they are seen to be required. By treating such developments as critically informed social systems, this paper offers an alternative viewpoint. Firstly, a theoretical justification for such an approach is offered, drawing on developments in Critical Management Science, themselves based on a strand of Critical Social Theory. Secondly, an action research-based intervention is detailed, demonstrating the practical applicability of such an approach, an applicability that has hitherto been questioned. The outcome is a synthesised approach to operational and strategic information systems development, within a critical framework, which, it is argued, offers a greater potential for success than the currently promoted approaches.     

屏蔽系统寿命数据的统计分析综述(英文)

系统竞争失效数据在工程应用中广泛存在.屏蔽数据作为它的一种特殊数据形式在工程中有着重要的作用.本文首先介绍了屏蔽数据的形式及其与常规竞争失效数据的区别,然后对串联系统或并联系统,阐述了两种分析屏蔽数据的方法(极大似然法和贝叶斯方法),最后用这两种方法分析了一个实际例子.     

On the usage of qualitative reasoning as an approach towards enterprise modelling

This paper analyzes the computational requirements of enterprise modelling, presents a reasoning framework, and reports about a prototype implementation. Enterprise modelling studies complex systems which are normally only partially known, and focuses on studying the qualitative properties of the underlying system, and thus differs from traditional quantitative approaches. Various modelling approaches from computer science and management science are discussed with respect to their suitability for enterprise modelling. Qualitative reasoning is identified as a promising basis to develop a computational framework for enterprise modelling. A rules-constraints-method of reasoning with imprecise knowledge, which is essentially an interval-based version of difference equations, is proposed as a powerful and versatile tool for enterprise modelling. As an illustrative example, a model of a business organization, derived from the concept of a financial statement, is developed and simulated.     

A transdisciplinary approach for formalized integrated planning and decision-making in complex systems

Multi-sectoral systems, such as cities or regional economies, face strategic challenges of optimal development due to the complexity of interacting perspectives, interests, and preferences of decision-makers and stakeholders. In coping with these challenges, integrated approaches in strategic planning and decision support promise to generate more efficient and effective results than sectoral approaches. In this article, we follow the concept of operational research (OR) by applying a formalized approach to cross-sectoral integrated planning and decision-making in complex, i.e., multi-sectoral systems. The presented Transdisciplinary Integrated Planning and Synthesis (TIPS) approach relies on (soft) OR methods combined in a multi-methodological framework that is adapted to the cognitive skills and habits of the stakeholders and experts involved in mutual and joint learning processes (transdisciplinarity). This methodological contribution structures, formalizes, and empirically illustrates cross-sectoral integrated strategic planning step by step, and evaluates the TIPS methodology against requirements for integrated approaches derived from literature.     

组合系统的行为化方法

在行为框架下研究线性微分组合系统的建模问题 .建立了 n个子系统串、并联组合系统的行为化模型 ,给出了其能控性、能观性的判别条件 .分析过程表明用行为化方法对组合系统的研究有着比传统方法更便捷、适用的模型类更广等优点 .     

LAND USE OPTIMIZATION USING SELF-ORGANIZING ALGORITHMS

ABSTRACT. In many spatial systems the interaction between various regions decreases dramatically with distance. This suggests that local trade-offs may be more important than global ones in land use planning and that a decentralized, parallel optimization of the individual regions may be an attractive supplement to more centralized optimization approaches. In this paper, we solve a forest planning problem using a series of decentralized approaches. The approaches can be characterized as self-organizing algorithms and are modeled in the framework of a cellular automaton. We compare our results with those obtained by more centralized approaches, viz. a large sample approach, simulated annealing, and a genetic algorithm. We find that the self-organizing algorithms generally converge much faster to solutions which are at least as good as those obtained by simulated annealing and the genetic algorithm.