System Theoretic Approach Research Articles

The Economic Philosophy of Micro-Credit System

This paper aims at locating the practice of micro-credit system into a larger theoretical canvas of economic theory and philosophy. A systems theoretic approach has been adopted. Emergence of the ‘excluded’ class has been explained with the help of the theory of feedback. Individuals have been assumed to be myopic, local optimizer and bounded rational. In this context, the empirical experiences have been viewed and assessed as to their outcomes.

Complexity and the Philosophy of Becoming

This paper introduces Deleuze’s philosophy of becoming in a system theoretic framework and proposes an alternative ontological foundation to the study of systems and complex systems in particular. A brief critique of systems theory and the difficulties apparent in it is proposed as an introduction to the discussion. Following is an overview aimed at providing access to the ‘big picture’ of Deleuze’s revolutionary philosophical system with emphasis on a system theoretic approach and terminology. The major concepts of Deleuze’s ontology—difference, virtuality, multiplicity, assemblages, quasi-causation, becoming (individuation), intensity and progressive determination are introduced and discussed. Deleuze’s work is a radical departure from the dogma of western philosophy that guides the foundations of science and systems theory. It replaces identity with difference and being with becoming; in other words, it provides systems theory with an ontological ground based on change, heterogeneity and the inexhaustible novelty-producing process that underlies all phenomena. The conceptual tools made available by this philosophy seem to capture the fundamental aspects of complexity and complex systems much better than the current conceptual system that is based on static transcendent ontological entities.

Dynamic System Theory for Lung Cancer Patient Breathing

Dynamic System Theory for Lung Cancer Patient Breathing

On robust and optimal imperfect information state equipartitioning for network systems

This paper addresses some fundamental questions towards robust and optimal imperfect information state equipartitioning by means of a computational framework of a system-theoretic approach called semistable Gaussian Linear-Quadratic Consensus (LQC), which is motivated by Optimal Semistable Control (OSC). OSC deals with an optimal regulation problem for dynamical systems with unknown, nonzero set-points. In this paper, we address stochastic OSC for robust and optimal information state equipartitioning under Gaussian white noise disturbance or measurement and random distribution of initial conditions. We develop a new framework for semistable Gaussian LQC and recast the proposed problem into an alternative, constrained optimization problem. To this end, necessary and sufficient conditions to connect semistability and optimal information state equipartitioning are derived in the paper and the existence of optimal solutions to this optimization problem has been proved for network systems. To efficiently solve the proposed constrained optimization problem, we propose a convergence guaranteed numerical algorithm. The rigorous convergence proof of this algorithm is presented and simulation results show the efficacy of the proposed approach.

Treating Macro Theory as Systems Theory: What Does it Matter?

Standard macro theories have the same analytical structure as their micro counterparts. Where micro theories work with equilibrium between supply and demand for particular products, macro theories work with equilibrium applied to aggregates of products. This common approach treats the micro-macro relationship as scalable, with macro variables being aggregations over micro variables. In contrast, we pursue a systems-theoretic approach to the micro-macro relationship. This relationship is not scalable and rather entails a disjunction between micro- and macro-levels of theory. While micro phenomena are still susceptible to choice-theoretic analysis, macro phenomena are products of ecological interaction and so entail emergent phenomena. Our alternative approach treats macro theory as a form of systems theory where the behavior of the system has properties that are not reducible to properties of the individual elements within that system. Besides sketching this alternative approach, we examine some of the different insights this approach offers into such topics as unemployment and stabilization.

Multi-frequency atomic force microscopy: A system-theoretic approach

Multi-frequency atomic force microscopy (MF-AFM), employing the detection and/or excitation of multiple cantilever frequencies, has shown great promise in increasing the compositional sensitivity, and the spatial and temporal resolution of imaging. The multitude of frequency components generated in MF-AFM encode information about the tip-sample nonlinearity. For quantitative interpretation of the observables in MF-AFM operation, we propose a two-pronged approach combining special-purpose cantilevers and a system-theoretic modeling paradigm. This provides an excellent framework to understand and leverage the nonlinear dynamics of the interaction of a multi-eigenmodal cantilever with the nonlinear force potentials on the sample surface, to develop novel imaging methods. We describe experimental techniques for accurate in-situ identification of the cantilever (sensor/actuator) transfer functions, which are crucial components to understand the generation of MF-AFM observables. The modeling framework is verified with experiments and is shown to be able to predict several key features of MF-AFM operation.

Starling Flock Networks Manage Uncertainty in Consensus at Low Cost

Flocks of starlings exhibit a remarkable ability to maintain cohesion as a group in highly uncertain environments and with limited, noisy information. Recent work demonstrated that individual starlings within large flocks respond to a fixed number of nearest neighbors, but until now it was not understood why this number is seven. We analyze robustness to uncertainty of consensus in empirical data from multiple starling flocks and show that the flock interaction networks with six or seven neighbors optimize the trade-off between group cohesion and individual effort. We can distinguish these numbers of neighbors from fewer or greater numbers using our systems-theoretic approach to measuring robustness of interaction networks as a function of the network structure, i.e., who is sensing whom. The metric quantifies the disagreement within the network due to disturbances and noise during consensus behavior and can be evaluated over a parameterized family of hypothesized sensing strategies (here the parameter is number of neighbors). We use this approach to further show that for the range of flocks studied the optimal number of neighbors does not depend on the number of birds within a flock; rather, it depends on the shape, notably the thickness, of the flock. The results suggest that robustness to uncertainty may have been a factor in the evolution of flocking for starlings. More generally, our results elucidate the role of the interaction network on uncertainty management in collective behavior, and motivate the application of our approach to other biological networks.

Measurement reduction for mutual coupling calibration in DOA estimation

Mutual coupling is an important source of error in antenna arrays that should be compensated for super resolution direction‐of‐arrival (DOA) algorithms, such as Multiple Signal Classification (MUSIC) algorithm. A crucial step in array calibration is the determination of the mutual coupling coefficients for the antenna array. In this paper, a system theoretic approach is presented for the mutual coupling characterization of antenna arrays. The comprehension and implementation of this approach is simple leading to further advantages in calibration measurement reduction. In this context, a measurement reduction method for antenna arrays with omni‐directional and identical elements is proposed which is based on the symmetry planes in the array geometry. The proposed method significantly decreases the number of measurements during the calibration process. This method is evaluated using different array types whose responses and the mutual coupling characteristics are obtained through numerical electromagnetic simulations. It is shown that a single calibration measurement is sufficient for uniform circular arrays. Certain important and interesting characteristics observed during the experiments are outlined.

Consensus Computation in Unreliable Networks: A System Theoretic Approach

This paper addresses the problem of ensuring trustworthy computation in a linear consensus network. A solution to this problem is relevant for several tasks in multi-agent systems including motion coordination, clock synchronization, and cooperative estimation. In a linear consensus network, we allow for the presence of misbehaving agents, whose behavior deviate from the nominal consensus evolution. We model misbehaviors as unknown and unmeasurable inputs affecting the network, and we cast the misbehavior detection and identification problem into an unknown-input system theoretic framework. We consider two extreme cases of misbehaving agents, namely faulty (non-colluding) and malicious (Byzantine) agents. First, we characterize the set of inputs that allow misbehaving agents to affect the consensus network while remaining undetected and/or unidentified from certain observing agents. Second, we provide worst-case bounds for the number of concurrent faulty or malicious agents that can be detected and identified. Precisely, the consensus network needs to be 2 <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> +1 (resp. <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> +1) connected for <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> malicious (resp. faulty) agents to be generically detectable and identifiable by every well behaving agent. Third, we quantify the effect of undetectable inputs on the final consensus value. Fourth, we design three algorithms to detect and identify misbehaving agents. The first and the second algorithm apply fault detection techniques, and affords complete detection and identification if global knowledge of the network is available to each agent, at a high computational cost. The third algorithm is designed to exploit the presence in the network of weakly interconnected subparts, and provides local detection and identification of misbehaving agents whose behavior deviates more than a threshold, which is quantified in terms of the interconnection structure.

On the Infeasibility of Entanglement Generation in Gaussian Quantum Systems via Classical Control

This paper uses a system theoretic approach to show that classical linear time invariant controllers cannot generate steady state entanglement in a bipartite Gaussian quantum system which is initialized in a Gaussian state. The paper also shows that the use of classical linear controllers cannot generate entanglement in a finite time from a bipartite system initialized in a separable Gaussian state. The approach reveals connections between system theoretic concepts and the well known physical principle that local operations and classical communications cannot generate entangled states starting from separable states.

MiR-206-mediated dynamic mechanism of the mammalian circadian clock.

BackgroundAs a group of highly conserved small non-coding RNAs with a length of 21~23 nucleotides, microRNAs (miRNAs) regulate the gene expression post-transcriptionally by base pairing with the partial or full complementary sequences in target mRNAs, thus resulting in the repression of mRNA translation and the acceleration of mRNA degradation. Recent work has revealed that miRNAs are essential for the development and functioning of the skeletal muscles where they are. In particular, miR-206 has not only been identified as the only miRNA expressed in skeletal muscles, but also exhibited crucial roles in regulation of the muscle development. Although miRNAs are known to regulate various biological processes ranging from development to cancer, much less is known about their role in the dynamic regulation of the mammalian circadian clock.ResultsA detailed dynamic model of miR-206-mediated mammalian circadian clock system was developed presently by using Hill-type terms, Michaelis-Menten type and mass action kinetics. Based on a system-theoretic approach, the model accurately predicts both the periodicity and the entrainment of the circadian clock. It also explores the dynamics properties of the oscillations mediated by miR-206 by means of sensitivity analysis and alterations of parameters. Our results show that miR-206 is an important regulator of the circadian clock in skeletal muscle, and thus by study of miR-206 the main features of its mediation on the clock may be captured. Simulations of these processes display that the amplitude and frequency of the oscillation can be significantly altered through the miR-206-mediated control.ConclusionsMiR-206 has a profound effect on the dynamic mechanism of the mammalian circadian clock, both by control of the amplitude and control or alteration of the frequency to affect the level of the gene expression and to interfere with the temporal sequence of the gene production or delivery. This undoubtedly uncovers a new mechanism for regulation of the circadian clock at a post-transcriptional level and provides important insights into the normal development as well as the pathological conditions of skeletal muscles, such as the aging, chronic disease and cancer.

Wave Propagation in Networks: a System Theoretic Approach

AbstractWe consider dynamical systems defined on graph structures. The dynamics on the edges are governed by partial differential equations that are interconnected at the graph vertices through algebraic conditions involving the boundary conditions of the PDEs. We show that a variety of such wave propagation problems on networks are solvable (forward in time) and energy passive or conservative — given that the governing PDEs are solvable on the separate edges. We treat these problems in the operator theoretic boundary control system framework.

System theoretic approach to sustainable development problems

This paper shows that the concepts and methodology contained in the system theory and operations research are suitable for application in the planning and control of the sustainable development. The sustainable development problems can be represented using the state space concepts, such as the transition of system, from the given initial state to the final state. It is shown that sustainable development represents a specific control problem. The peculiarity of the sustainable development is that the target is to keep the system in the prescribed feasible region of the state space. The analysis of planning and control problems of sustainable development has also shown that methods developed in the operations research area, such as multicriteria optimization, dynamic processes simulation, non-conventional treatment of uncertainty etc. are adequate, exact base, suitable for resolution of these problems.

A System Theory Proof of the Infeasibility of Entanglement Generation in Gaussian Quantum Systems via Classical Control

This paper uses a system theoretic approach to show that classical linear time invariant controllers cannot generate steady state entanglement in a bipartite Gaussian quantum system which is initialized in a Gaussian state. The paper also shows that the use of such controllers cannot generate entanglement in a finite time from a bipartite system initialized in a separable Gaussian state. The approach reveals connections between systems theoretic concepts and the well known physical principle that local operations and classical communications cannot generate entangled states starting from separable states.

Modeling discrete event scalable network systems

Scalability in simulation tools is one of the most important traits to measure performance of software. The reason is that today’s Internet is the main instance of a large-scale and highly complex system. Simulation of Internet-scale network systems has to be supported by any simulation tool. Despite this fact, many network simulators lacks support for building large models. In this work, in order to propose a new approach for scalability issue in network simulation tools, a network simulator is developed based on behavior of honeybees and high performance DEVS, modular and hierarchical system theoretic approach. A biologically-inspired discrete event modeling approach is described for studying networks’ scalability and performance traits. Since natural systems can offer important concepts for modeling network systems, key adaptive and emergent attributes of honeybees and their societal properties are incorporated into a set of simulation models that are developed using the discrete event system specification approach. Large-scale network models are simulated and evaluated to show the benefits of nature-inspired network models.

A streamwise constant model of turbulence in plane Couette flow

Streamwise and quasi-streamwise elongated structures have been shown to play a significant role in turbulent shear flows. We model the mean behaviour of fully turbulent plane Couette flow using a streamwise constant projection of the Navier–Stokes equations. This results in a two-dimensional three-velocity-component (2D/3C) model. We first use a steady-state version of the model to demonstrate that its nonlinear coupling provides the mathematical mechanism that shapes the turbulent velocity profile. Simulations of the 2D/3C model under small-amplitude Gaussian forcing of the cross-stream components are compared to direct numerical simulation (DNS) data. The results indicate that a streamwise constant projection of the Navier–Stokes equations captures salient features of fully turbulent plane Couette flow at low Reynolds numbers. A systems-theoretic approach is used to demonstrate the presence of large input–output amplification through the forced 2D/3C model. It is this amplification coupled with the appropriate nonlinearity that enables the 2D/3C model to generate turbulent behaviour under the small-amplitude forcing employed in this study.

A system-theoretic model for cooperation, interaction and allocation

A system-theoretic approach to cooperation, interaction and allocation is presented that simplifies, unifies and extends the results on classical cooperative games and their generalizations. In particular, a general Weber theory of linear values is obtained and a new theory for local cooperation and general interaction indices is established. The model is dynamic and based on the notion of states of cooperation that change under actions of agents. Careful distinction between “local” states of cooperation and general “system” states leads to a notion of entropy for arbitrary non-negative and efficient allocations and thus to a new information-theoretic criterion for fairness of allocation mechanisms. Shapley allocations, for instance, are exhibited as arising from random walks with maximal entropy. For a large class of cooperation systems, a characterization of game symmetries in terms of λ -values is given. A concept for cores and Weber sets is proposed and it is shown that a Weber set of a game with selection structure always contains the core.

Sampling From a System-Theoretic Viewpoint: Part I—Concepts and Tools

This paper is first in a series of papers studying a system-theoretic approach to the problem of reconstructing an analog signal from its samples. The idea, borrowed from earlier treatments in the control literature, is to address the problem as a hybrid model-matching problem in which performance is measured by system norms. In this paper we present the paradigm and revise underlying technical tools, such as the lifting technique and some topics of the operator theory. This material facilitates a systematic and unified treatment of a wide range of sampling and reconstruction problems, recovering many hitherto considered different solutions and leading to new results. Some of these applications are discussed in the second part.

Large-Scale Integrated Network System Simulation with DEVS-Suite

Formidable growth of Internet technologies has revealed challenging issues about its scale and performance evaluation. Modeling and simulation play a central role in the evaluation of the behavior and performance of the large-scale network systems. Large numbers of nodes affect simulation performance, simulation execution time and scalability in a weighty manner. Most of the existing simulators have numerous problems such as size, lack of system theoretic approach and complexity of modeled network. In this work, a scalable discrete-event modeling approach is described for studying networks’ scalability and performance traits. Key fundamental attributes of Internet and its protocols are incorporated into a set of simulation models developed using the Discrete Event System Specification (DEVS) approach. Large-scale network models are simulated and evaluated to show the benefits of the developed network models and approaches.

Toward a system-level understanding of microRNA pathway via mathematical modeling

The microRNA (miRNA) pathway plays multiple roles in regulating mechanisms controlling both physiological and pathological processes such as the cell proliferation and cancers. But little is known about the dynamic properties, key rate-limiting steps as well as the stochastic noise in this pathway. Presently, a system-theoretic approach was presented to analyze and quantitative modeling of a generic miRNA pathway, which can be implemented deterministically and stochastically. Our results show that the inferred dynamic properties obtained from the mathematical models of the miRNA pathway are well consistent with previous experimental observations. By sensitivity analysis, the key steps in this pathway were found to be the miRNA gene transcription, RISC decay and mRNA formation. In addition, the results of quantified noise strength along the pathway demonstrate that the pathway can reduce the ingress noise and reveal the noise robustness property. Our findings also present testable hypothesis for experimental biologists to further investigate miRNA's increasing functional roles in regulating various cellular processes.