Coupled Complex Systems Research Articles

Robustness measurement of multiplex networks based on graph spectrum.

Networks can provide effective representations of the relationships between elements in complex systems through nodes and links. On this basis, relationships between multiple systems are often characterized as multilayer networks (or networks of networks). As a typical representative, a multiplex network is often used to describe a system in which there are many replaceable or dependent relationships among elements in different layers. This paper studies robustness measures for different types of multiplex networks by generalizing the natural connectivity calculated from the graph spectrum. Experiments on model and real multiplex networks show a close correlation between the robustness of multiplex networks consisting of connective or dependent layers and the natural connectivity of aggregated networks or intersections between layers. These indicators can effectively measure or estimate the robustness of multiplex networks according to the topology of each layer. Our findings shed new light on the design and protection of coupled complex systems.

Process Interactions Can Change Process Ranking in a Coupled Complex System Under Process Model and Parametric Uncertainty

AbstractFor a complex hydrologic system with multiple processes and process interactions, global sensitivity analysis is often used to identify important or influential parameters for model development and improvement. The identification is complicated by process model uncertainty, when a system process can be represented by multiple process models. This study develops a new total‐effect process sensitivity index to identify influential processes under model uncertainty. This is done by extending Sobol's total‐effect parameter sensitivity index for one system model to total‐effect process sensitivity index for multiple system models to account for uncertainty in process models and model parameters. The total‐effect process sensitivity index includes not only the first‐order process sensitivity index for measuring the importance of individual processes but also higher‐order indices that account for process interactions. The total‐effect process sensitivity index can identify an influential process that itself and its interactions with other processes influence a model output. The total‐effect process sensitivity index is applied to two numerical examples: (a) Sobol's G*‐functions with analytical solutions of first‐order and total‐effect process sensitivity indices, and (b) groundwater flow models with interactions between recharge, geology, and snowmelt processes. The second evaluation shows that, due to second‐order and higher‐order process interactions, the first‐order and total‐effect process sensitivity indices give different process ranking. It is thus necessary to estimate both first‐order and total‐effect process sensitivity indices to appreciate the difference between the first‐order impact of a process alone and the overall total‐effect impact of the process itself and its interactions with other processes on a model output.

Coupling quantitative vulnerability assessment and complex system governance for systems of systems

Coupling quantitative vulnerability assessment and complex system governance for systems of systems

Coupling Quantitative Vulnerability Assessment and Complex System Governance for Systems of Systems

Coupling Quantitative Vulnerability Assessment and Complex System Governance for Systems of Systems

A computational model for complex systems analysis: Causality estimation

Analysis of complex systems has migrated from theoretical analysis of model physical systems into the domain of real-world applications with far-reaching implications. This shift has been facilitated by the availability of measurement data, the capability to store and manage large-scale datasets, and significant increase in computational power. These developments have been matched by a concerted effort in the development of new methods of complex systems analysis. Each class of analytical methods are applicable only for certain system types and specific problems, such as estimating causality between coupled complex systems, are still unresolved even for simples cases involving only two connected systems. This study summarizes results for estimating causality using a novel mathematical computational model, System Structure, across several different types of coupled systems. System structure models a system as a dynamic network of connected components and provides a distribution-free and systematic quantification of inter-component dynamics as a basis for making several system-wide inferences including the inference of strength and direction of causal relations. System structure is a non-parametric scalable method that requires few non-restrictive a-priori assumptions, making it generally applicable across a diverse set of problems. The comparative study reported on here demonstrates the ability of system structure to correctly infer different types of causality across diverse system types.

Percolation on coupled networks with multiple effective dependency links.

The ubiquitous coupled relationship between network systems has become an essential paradigm to depict complex systems. A remarkable property in the coupled complex systems is that a functional node should have multiple external support associations in addition to maintaining the connectivity of the local network. In this paper, we develop a theoretical framework to study the structural robustness of the coupled network with multiple useful dependency links. It is defined that a functional node has the broadest connectivity within the internal network and requires at least M support link of the other network to function. In this model, we present exact analytical expressions for the process of cascading failures, the fraction of functional nodes in the stable state, and provide a calculation method of the critical threshold. The results indicate that the system undergoes an abrupt phase transition behavior after initial failure. Moreover, the minimum inner and inter-connectivity density to maintain system survival is graphically presented at different multiple effective dependency links. Furthermore, we find that the system needs more internal connection densities to avoid collapse when it requires more effective support links. These findings allow us to reveal the details of a more realistic coupled complex system and develop efficient approaches for designing resilient infrastructure.

Wave scattering properties of multiple weakly coupled complex systems.

The statistics of the scattering of waves inside single ray-chaotic enclosures have been successfully described by the random coupling model (RCM). We expand the RCM to systems consisting of multiple complex ray-chaotic enclosures with various coupling scenarios. The statistical properties of the model-generated quantities are tested against measured data of electrically large multicavity systems of various designs. The statistics of model-generated transimpedance and induced voltages on a load impedance agree well with the experimental results. The RCM coupled chaotic enclosure model is general and can be applied to other physical systems, including coupled quantum dots, disordered nanowires, and short-wavelength electromagnetic and acoustic propagation through rooms in buildings, aircraft, and ships.

Investigating Indian summer monsoon in coupled regional land–atmosphere downscaling experiments using RegCM4

The ability of Regional Climate Model (RCM: RegCM4) forced with two different Global Climate Model (GCM: CCSM4 and MIROC5) and three land-surface parameterization (LSP) (i.e., BATS, CLM4.5 and Subgrid-BATS) in simulating the Indian summer monsoon (ISM) is tested for the present climate (1975–2005). Thus six simulation combinations are assessed for seasonal mean temperature, precipitation, and low-level wind for ISM season (June, July, August, September: JJAS) over the COordinated Regional climate Downscaling EXperiment-South Asia (CORDEX-SA) domain. The simulations are evaluated in terms of Taylor’s metric (for precipitation, temperature, zonal wind, meridional wind and total cloud fraction), mean annual cycle, index of agreement, normalized root mean squared deviation and probability distribution function. The experiments simulated moderate events more accurately than high-intensity precipitation events compared to the corresponding observations. The inherent biases in the model simulations are attributed to the weaker meridional wind along with restrained vertical motion during ISM, especially with CCSM4 forcing. A careful analysis of tropospheric temperature gradient (TTG) suggests a weaker north–south (N–S) gradient due to the warmer atmospheric column in these experiments. On the contrary, the MIROC5_CLM4.5 experiment captures the magnitude and the temporal evolution of TTG better during ISM. It also represents the mean features of ISM better than other experiments. Also, the CLM4.5 LSP shows promising performance in ISM simulation when forced with MIROC5. It also provides further avenues for testing of the same combination under different frameworks, including the intended future climate study. This study emphasizes the importance of using appropriate GCM and LSP forcings to the RCM for simulating a coupled complex systems such as ISM.

Data-driven analysis and optimization of externally heat-integrated distillation columns (EHIDiC)

To present a data-driven framework for analyzing and optimizing a highly coupled complex system with multivariable in large search space, externally heat-integrated distillation columns (EHIDiC) for propylene/propane separation is studied. By analyzing a large dataset from Aspen Plus-MATLAB distributed computing, we not only make a wide range of global optimization but also achieve a further understanding of EHIDiC. First, each independent variable has a different degree of influence on the total annualized cost (TAC), in which the location of heat-integrated stages in the rectifier has the greatest impact. Second, the capital and operation costs of the compressor are the dominant parts of the TAC, which need to be considered carefully in design and optimization. Third, the EHIDiC scheme in lowest TAC performance prefers heat integrated between the bottom of the stripper and the top of the rectifier, while heat rejects from the bottom of the rectifier causes the highest TAC performance, regardless of heat distribution on the stripper. Fourth, compared with Genetic Algorithm optimization result, the data-driven analysis can discern more underlying scenarios with less computing time in much bigger search zones. At last, a more TAC saving EHIDiC scheme is constructed by shifting some separation work from the stripper to the rectifier.

Explosive synchronization of multi-layer frequency-weighted coupled complex systems**Project supported by the National Natural Science Foundation of China (Grant No. 61771299), the Key Laboratory of Speciality Fiber Optics and Optical Access Networks, Shanghai University, China (Grant No. SKLSFO2012-14), Funding of the Key Laboratory of Wireless Sensor Network and Communication, Shanghai Institute of Microsystem and Information Technology, China, and Funding of the

Synchronization is a phenomenon that is ubiquitous in engineering and natural ecosystems. The study of explosive synchronization on a single-layer network gives the critical transition coupling strength that causes explosive synchronization. However, no significant findings have been made on multi-layer complex networks. This paper proposes a frequency-weighted Kuramoto model on a two-layer network and the critical coupling strength of explosive synchronization is obtained by both theoretical analysis and numerical validation. It is found that the critical value is affected by the interaction strength between layers and the number of network oscillators. The explosive synchronization will be hindered by enhancing the interaction and promoted by increasing the number of network oscillators. Our results have importance across a range of engineering and biological research fields.

Sudden Unexpected Death and the Mammalian Dive Response: Catastrophic Failure of a Complex Tightly Coupled System.

In tightly coupled complex systems, when two or more factors or events interact in unanticipated ways, catastrophic failures of high-risk technical systems happen rarely, but quickly. Safety features are commonly built into complex systems to avoid disasters but are often part of the problem. The human body may be considered as a complex tightly coupled system at risk of rare catastrophic failure (sudden unexpected death, SUD) when certain factors or events interact. The mammalian dive response (MDR) is a built-in safety feature of the body that normally conserves oxygen during acute hypoxia. Activation of the MDR is the final pathway to sudden cardiac (SCD) in some cases of sudden infant death syndrome (SIDS), sudden unexpected death in epilepsy (SUDEP), and sudden cardiac death in water (SCDIW, fatal drowning). There is no single cause in any of these death scenarios, but an array of, unanticipated, often unknown, factors or events that activate or interact with the mammalian dive reflex. In any particular case, the relevant risk factors or events might include a combination of genetic, developmental, metabolic, disease, environmental, or operational influences. Determination of a single cause in any of these death scenarios is unlikely. The common thread among these seemingly different death scenarios is activation of the mammalian dive response. The human body is a complex tightly coupled system at risk of rare catastrophic failure when that “safety feature” is activated.

Probability distribution function of complex systems

Based on the probability distribution observed in some complex systems and an assumption that the entropies of complex systems satisfy a pseudo additivity, it is expounded that a new probability distribution function is suitable for not only a single complex system but also a coupling complex system, and consequently, a statistical theory of complex systems can be established in the extensive-like framework.

Phase-lag synchronization analysis in complex systems with directed inter-relations

In this work, we proposed a novel way to estimate phase-lag synchronization in coupled systems. This approach was applied into two systems: a directed-coupled Rossler-Lorenz system and a network of Izhikevich neurons. For the former case, the phase-lag synchronization revealed an increase in complexity for the Lorenz subsystem components, when the coupling is activated. The opposite behavior was observed when the Izhikevich network were organized in a hierarchical way. Our results point out to emergent synchronism related to causal interactions in coupled complex systems.

‘The intrusion therefore of cattle is by itself sufficient to produce the extirpation of the native race’: social ecological systems and ecocide in conflicts between Hunter–Gatherers and commercial stock farmers in Australia

ABSTRACTThis contribution attempts to discuss unintended, neglectful, or willful manipulations of Social Ecological Systems (SESs) by commercial stock farmers as part of an attempt to drive Aboriginal people from their lands into the hinterland thereby accepting or condoning their annihilation or demographic reduction. By displaying the different ways in which commercial stock farmers have engineered changes in the ecological system I will show that traditional definitions of the term genocide fall short of applied techniques of decimation which consist of a combination of micro-practices and quotidian low-level violence. It is doubtful that given the complexity and the resilience of SES, however, a group of commercial stock farmers is able to operate the system (of which, after all, they are an element) in a controlled way. I argue nevertheless that the term ecocide should be applied to the way stock farmers changed the habitat of the Indigenous population, because the settlers had agency without understanding the totality of their actions. SES as coupled complex systems are dependent on the existence of bio-diversity, the intensity of grazing,1 defoliation, habitat fragmentation and changes of the soil as a consequence of densification.2

Real-Time Implementation in DSPACE of Rotor Magnetic Field Control of Bearingless Induction Motor

A bearingless induction motor is a strong coupled complex system, the decoupling control of torque and radial suspension force is the premise of the stable operation of the bearingless induction motor. In this paper, the principle of bearingless induction motor is expounded. The magnetic mathematical models of bearingless induction motor are created. The rotor field oriented control strategy is designed. The control strategy is validated with Matlab/simulink. Real-time simulation model based on dSPACE is given and the platform based on dSPACE is constructed. The results show that this method has good decoupling control performance. The actual system development and experiment process confirms that the dSPACE real-time simulation platform can shorten the development time and facilitate the development process, and save costs during the design and test period.

Sliding Mode Control of Strip Rolling Mill Hydraulic AGC System

Hydraulic automatic gauge control (AGC) is a very important part of rolling mill for magnesium plate rolling. The control effect of automatic gauge control system influences the quality of the rolled product directly. AGC system is an essentially nonlinear, time delay, time-varying, large inertia, multivariable and coupling complex system, which increase the designing difficulty of control system. Based on the mathematical model of the automatic gauge control system, a simulation platform of rolling mill is set up, and a sliding mode variable structure controller is designed to realize position control for the hydraulic automatic gauge control system. The control effect of the proposed sliding mode controller is compared with that of a general PID controller whose parameters was adjusted by ZieglerNichols method. Simulation results show that the sliding mode controller has strong antiinterference ability and can give satisfactory control effect.

Comment on “Self-organized cooperative criticality in coupled complex systems” by Liu Lei and Hu Fei

Comment on “Self-organized cooperative criticality in coupled complex systems” by Liu Lei and Hu Fei

Mechanical Arm Active Disturbance Rejection Control Based on Artificial Bee Colony Algorithm

The mechanical arm is a multi-input multi-output, highly non-linear, strong coupling complex system. Active Disturbance Rejection Controller is of good performance and relatively simple algorithm. Artificial Bee Colony algorithm is a new swarm intelligence algorithm based on the behavior of honey bees. The linear ADRC was adopted to realize the moving control of the mechanical arm and the controller parameters were chosen and optimized by ABC algorithm. Simulation results are satisfied and well prove the feasibility and validity of the algorithm.

Self-organized cooperative criticality in coupled complex systems

We show that the coupled complex systems can evolve into a new kind of self-organized critical state where each subsystem is not critical; however, they cooperate to be critical. This criticality is different from the classical sandpile criticality where the single system itself evolves into a critical state. We also find that the outflows can be accumulated in the coupled systems. This will lead to the emergence of spatiotemporal intermittency in the critical state.

Research of High Speed Spindle Control Method Based on Neural Network

High speed spindle is a multivariable and strong coupling complex system. In order to improve its dynamic response characteristics and control effectively, based on vector control, the high speed spindle needs to be decoupling first. Then neural network theory and BP algorithm are adopted to form a speed controller to adjust the rotational speed. In this controller, gradient searching technique is applied and the proportional and integral parameters are used as weight of neural network which is employed to adjust the corresponding control parameters. Therefore, a new model of high speed spindle is set up and simulated. The result of simulation indicates that the way combine the decoupling method and the speed controller based on neural network can track the rotational speed more accurate and promote the robustness of high speed spindle.