Robustness Analysis Of Systems Research Articles

Dual Function Matrix Projective Synchronization for Fractional-order Hyperchaotic Systems

Abstract It is well known that the variability and complexity of projection proportionality factors of Dual Projective Synchronization (DPS) can effectively enhance signal confidentiality. However, in most literatures, the proportionality factors are some simple fixed constants, which can't ensure high security of information. For two pairs of fractional-order hyperchaotic systems (FOHS), how to expand the projection proportionality factors to increase its complexity? Then, our work will propose a new synchronization type, i.e., Dual Function Matrix Projective Synchronization (DFMPS) and realize the DFMPS for FOHS for the first time. Firstly, based on the traditional DPS, we generalize the proportionality factors to a general function matrix depending on time t, present the error functions and define the DFMPS. Then, for FOHS, the active controller and synchronization condition are designed and proved. At the same time, when the system is affected by parameter disturbance, the proposed active controller can eliminate the influence of parameter disturbance to the system's DFMPS, which indicates that the proposed control strategy has strong robustness. Finally, the DFMPS of two pairs of fractional-order hyperchaotic Chen and Rabinovich systems are realized, and synchronizing analysis and system robustness analysis are verified by numerical simulation. Particularly, the DFMPS can be degenerated to dual anti-synchronization, dual complete synchronization, DPS, modified DPS and dual matrix projective synchronization. This work extends the synchronization types for FOHS and offers a useful method to explore DFMPS for other fractional-order systems.

In-air Hand Gesture Signature Recognition: An iHGS Database Acquisition Protocol.

Background:With the advances in current technology, hand gesture recognition has gained considerable attention. It has been extended to recognize more distinctive movements, such as a signature, in human-computer interaction (HCI) which enables the computer to identify a person in a non-contact acquisition environment. This application is known as in-air hand gesture signature recognition. To our knowledge, there are no publicly accessible databases and no detailed descriptions of the acquisitional protocol in this domain. Methods:This paper aims to demonstrate the procedure for collecting the in-air hand gesture signature's database. This database is disseminated as a reference database in the relevant field for evaluation purposes. The database is constructed from the signatures of 100 volunteer participants, who contributed their signatures in two different sessions. Each session provided 10 genuine samples enrolled using a Microsoft Kinect sensor camera to generate a genuine dataset. In addition, a forgery dataset was also collected by imitating the genuine samples. For evaluation, each sample was preprocessed with hand localization and predictive hand segmentation algorithms to extract the hand region. Then, several vector-based features were extracted. Results:In this work, classification performance analysis and system robustness analysis were carried out. In the classification analysis, a multiclass Support Vector Machine (SVM) was employed to classify the samples and 97.43% accuracy was achieved; while the system robustness analysis demonstrated low error rates of 2.41% and 5.07% in random forgery and skilled forgery attacks, respectively. Conclusions:These findings indicate that hand gesture signature is not only feasible for human classification, but its properties are also robust against forgery attacks.

Climate change impact on water supply and hydropower generation potential in Northern Manitoba

Study regionLower Nelson River Basin, Manitoba, Canada Study focusHydroelectricity makes up almost 97% of electricity generated in Manitoba, of which over 70% of its generation capacity is installed along the Lower Nelson River (LNR). In this study, 19 climate projections representing ~ 87% of climatic variability over Hudson Bay Drainage Basin are applied to coupled hydrologic-operations models to estimate water supply and hydropower generation potential changes under future climates. New hydrological insights for the regionFuture inflow to the forebay of the main hydropower generating stations along LNR is expected to increase in spring and summer but decrease in winter and fall. Consequently, hydropower generation potential is projected to increase for spring, the historical flood season, which may lead to reduced reservoir inflow retention efficiency. In extremely dry climatic simulations, winter seasons see a reduction in reservoir inflow and hydropower generation potential, up to 35% and 37% in 2021–2050 and 2041–2070, respectively. Projected changes in reservoir inflow and hydropower generation potential continue to diverge over time, with dry scenarios becoming drier and wet becoming wetter, yielding high basin climate sensitivity and uncertainty with system supply and generation potential. Despite the presence of statistically significant individual trends and changes, there is a low agreement within the climate ensemble. Analysis of system robustness shows adjustment of the operations along LNR should be considered over time to better leverage changing seasonal water supply.

In-air Hand Gesture Signature Recognition: An iHGS Database Acquisition Protocol

Background With the advances in current technology, hand gesture recognition has gained considerable attention. It has been extended to recognize more distinctive movements, such as a signature, in human-computer interaction (HCI) which enables the computer to identify a person in a non-contact acquisition environment. This application is known as in-air hand gesture signature recognition. To our knowledge, there are no publicly accessible databases and no detailed descriptions of the acquisitional protocol in this domain. Methods This paper aims to demonstrate the procedure for collecting the in-air hand gesture signature’s database. This database is disseminated as a reference database in the relevant field for evaluation purposes. The database is constructed from the signatures of 100 volunteer participants, who contributed their signatures in two different sessions. Each session provided 10 genuine samples enrolled using a Microsoft Kinect sensor camera to generate a genuine dataset. In addition, a forgery dataset was also collected by imitating the genuine samples. For evaluation, each sample was preprocessed with hand localization and predictive hand segmentation algorithms to extract the hand region. Then, several vector-based features were extracted. Results In this work, classification performance analysis and system robustness analysis were carried out. In the classification analysis, a multiclass Support Vector Machine (SVM) was employed to classify the samples and 97.43% accuracy was achieved; while the system robustness analysis demonstrated low error rates of 2.41% and 5.07% in random forgery and skilled forgery attacks, respectively. Conclusions These findings indicate that hand gesture signature is not only feasible for human classification, but its properties are also robust against forgery attacks.

Randomized multi-scale kernels learning with sparsity constraint regularization for regression

This paper presents a simple multiple kernel learning framework for complicated data modeling, where randomized multi-scale Gaussian kernels are employed as base kernels and a [Formula: see text]-norm regularizer is integrated as a sparsity constraint for the solution. The randomly pre-chosen scales provide random basis functions with diversity approximation ability and lead to extremely low computational complexity in finding the optimal solution. The random parameter appearing in the probability distribution and the regularizing factor are decided by the training data with cross validation techniques and the combination weights are solved by a well-posed linear system. Comparison experiments on one function approximation and three real-world regression problems of six learning algorithms are carried out. The way that multi-scale kernels fit the objective function is illustrated, the sparsity and the system robustness analysis with respect to the regularizing factor are given.

Recruit Until It Fails

Distinguishing identities is useful for several applications such as automated grocery or personalized recommendations. Unfortunately, several recent proposals for identification systems are evaluated using poor recruitment practices. We discovered that 23 out of 30 surveyed systems used datasets with 20 participants or less. Those studies achieved an average classification accuracy of 93%. We show that the classifier performance is misleading when the participant count is small. This is because the finite precision of measurements creates upper limits on the number of users that can be distinguished. To demonstrate why classifier performance is misleading, we used publicly available datasets. The data was collected from human subjects. We created five systems with at least 20 participants each. In three cases we achieved accuracies greater than 90% by merely applying readily available machine learning software packages, often with default parameters. For datasets where we had sufficient participants, we evaluated how the performance degrades as the number of participants increases. One of the systems built suffered a drop in accuracy that was over 35% as the participant count increased from 20 to 250. We argue that data from small participant count datasets do not adequately explore variations. Systems trained on such limited data are likely to incorrectly identify users when the user base increases beyond what was tested. We conclude by explaining generalizable reasons for this issue and provide insights on how to conduct more robust system analysis and design.

On the Robustness of Complex Systems With Multipartitivity Structures Under Node Attacks

Complex systems in the real world inevitably suffer from unpredictable perturbations, which can trigger system disasters, wreaking significant economical losses. To exploit the robustness of complex systems in the face of disturbances is of great significance. One of the most useful methods for system robustness analysis comes from the field of complex networks characterized by percolation theories. Many percolation theories, therefore, have been developed by researchers to investigate the robustness of diverse complex networks. Nevertheless, extant percolation theories are primarily devised for multilayer or interdependent networks. Little endeavor is dedicated to systems with multipartitivity structures, that is, multipartite networks, which are an indispensable part of complex networks. This paper fills this research gap by theoretically examining the robustness of multipartite networks under random or target node attacks. The generic percolation theory for robustness analysis of multipartite networks is accordingly put forward. To validate the correctness of the proposed percolation theory, we carry out simulations on computer-generated multipartite networks with Poisson degree distributions. The results yielded by the proposed theory coincide well with the simulations. Both theoretical and simulation results suggest that complex systems with multipartitivity structures could be more robust than those with multilayer structures.

Evolutionary design of regulatory control. II. Robust error-correcting feedback increases genetic and phenotypic variability

As systems become more robust against perturbations, they can compensate for greater sloppiness in the performance of their components. That robust compensation reduces the force of natural selection on the system’s components, leading to component decay. The paradoxical coupling of robustness and decay predicts that robust systems evolve cheaper, lower performing components, which accumulate greater mutational genetic variability and which have greater phenotypic stochasticity in trait expression. Previous work noted the paradox of robustness. However, no general theory for the evolutionary dynamics of system robustness and component decay has been developed. This article takes a first step by linking engineering control theory with the genetic theory of evolutionary dynamics. Control theory emphasizes error-correcting feedback as the single greatest principle in robust system design. Linking control theory to evolution leads to a theory for the evolutionary dynamics of error-correcting feedback, a unifying approach for the evolutionary analysis of robust systems. This article shows how increasingly robust systems accumulate more genetic variability and greater stochasticity of expression in their components. The theory predicts different levels of variability between different regulatory control architectures and different levels of variability between different components within a particular regulatory control system. The theory also shows that increasing robustness reduces the frequency of system failures associated with disease and, simultaneously, causes a strong increase in the heritability of disease. Thus, robust error correction in biological regulatory control may partly explain the puzzlingly high heritability of disease and, more generally, the surprisingly high heritability of fitness.

Robustness analysis of systems' safety through a new notion of input‐to‐state safety

SummaryIn this paper, we propose a new robustness notion that is applicable for certifying systems' safety with respect to external disturbance signals. The proposed input‐to‐state safety notion allows us to certify systems' safety in the presence of the disturbances, which is analogous to the notion of input‐to‐state stability for analyzing systems' stability.

Robust properties of systems with negative degree of homogeneity with respect to delay

The paper considers robustness problems of a class of weighted homogeneous systems with negative homogeneity degree in relation to the delay. It is shown that in the case of global asymptotic stability of a nonlinear weighted homogeneous system with negative homogeneity degree, in the presence of a delay in the system, all the trajectories converge asymptotically to some compact set containing the origin. In the absence of delay, such systems reach their equilibrium position in a finite time. The robustness analysis also covers cases of variable and multiple delays. The presented analysis is based on the application of the Lyapunov methods for delayed systems (the Lyapunov-Razumikhin function method) and the theory of weighted homogeneous systems. Computer simulation was performed to verify the analysis of system robustness with a negative degree of homogeneity in relation to the delay. A stabilizing system that represents a double integrator is used as an example. This system is weighted homogeneous with a negative degree when using a nonlinear state feedback control law that ensures that the system achieves its equilibrium position for the desired finite time. During computer simulation, the state vector was available for measurement with some delays. The computer simulation has confirmed the effectiveness of the presented theoretical results.

Cross-Dependency Inference in Multi-Layered Networks: A Collaborative Filtering Perspective.

The increasingly connected world has catalyzed the fusion of networks from different domains, which facilitates the emergence of a new network model-multi-layered networks. Examples of such kind of network systems include critical infrastructure networks, biological systems, organization-level collaborations, cross-platform e-commerce, and so forth. One crucial structure that distances multi-layered network from other network models is its cross-layer dependency, which describes the associations between the nodes from different layers. Needless to say, the cross-layer dependency in the network plays an essential role in many data mining applications like system robustness analysis and complex network control. However, it remains a daunting task to know the exact dependency relationships due to noise, limited accessibility, and so forth. In this article, we tackle the cross-layer dependency inference problem by modeling it as a collective collaborative filtering problem. Based on this idea, we propose an effective algorithm Fascinate that can reveal unobserved dependencies with linear complexity. Moreover, we derive Fascinate-ZERO, an online variant of Fascinate that can respond to a newly added node timely by checking its neighborhood dependencies. We perform extensive evaluations on real datasets to substantiate the superiority of our proposed approaches.

Backstepping Adaptive Fuzzy Control of Servo System With LuGre Friction

Aiming at the control problem of servo system with friction nonlinearity, we introduced the improved LuGre friction model of system. We used adaptive fuzzy system to approximate the nonlinear part and unknown parameters in system online. The purpose is to avoid the complex calculation in deducing the adaptive law of each unknown parameter. By using backstepping approach and recursively selecting the Lyapunov function, introducing the virtual control quantity, we designed an adaptive fuzzy controller with state feedback. Then we analyzed the stability of adaptive fuzzy controller. We carried out the system response analysis and system robustness analysis. We used sinusoidal signal as simulation input signal. Two cases were considered in simulation test comparing backstepping control with conventional PID control. Simulation results show that the proposed backstepping control has better position tracking performance and robustness than conventional PID control.

Improved Backstepping Adaptive Control of Dual-motor Driving Servo System with Backlash Based on Fuzzy Parameter Approximation

Aiming at the control problem of dual-motor driving servo system with backlash nonlinearity, a model for the system is introduced. Two adaptive fuzzy systems were used to approximate the nonlinear part and unknown parameters in system online, to avoid the complex calculation in deducing the adaptive law of each unknown parameter. By using improved backstepping approach and recursively selecting the Lyapunov function, introducing the virtual control quantity and the integration of position tracking error, an adaptive fuzzy controller with state feedback was designed, and its stability was analyzed. System response analysis and system robustness analysis were considered in simulation test for comparing the improved backstepping control with conventional backstepping control. Simulation results show that the improved backstepping control has better position tracking performance and robustness than conventional backstepping control. Finally, experimental analysis also validates the effectiveness and efficiency of the proposed control strategy.

Time-delay Robustness Analysis for Systems with Negative Degree of Homogeneity

Time-delay robustness analysis for homogeneous systems with negative degree is presented in the paper. It is established that if a system is homogeneous with negative degree and asymptotically stable at its origin in the absence of delays, then stability property is preserved with respect to a compact set containing the origin for any delay value. It is shown that obtained results can also be used for locally homogeneous systems. A simulation example is included to support theoretical results.

Developing system robustness analysis for drought risk management: an application on a water supply reservoir

Abstract. Droughts will likely become more frequent, greater in magnitude and longer in duration in the future due to climate change. Already in the present climate, a variety of drought events may occur with different exceedance frequencies. These frequencies are becoming more uncertain due to climate change. Many methods in support of drought risk management focus on providing insight into changing drought frequencies, and use water supply reliability as a key decision criterion. In contrast, robustness analysis focuses on providing insight into the full range of drought events and their impact on a system's functionality. This method has been developed for flood risk systems, but applications on drought risk systems are lacking. This paper aims to develop robustness analysis for drought risk systems, and illustrates the approach through a case study with a water supply reservoir and its users. We explore drought characterization and the assessment of a system's ability to deal with drought events, by quantifying the severity and socio-economic impact of a variety of drought events, both frequent and rare ones. Furthermore, we show the effect of three common drought management strategies (increasing supply, reducing demand and implementing hedging rules) on the robustness of the coupled water supply and socio-economic system. The case is inspired by Oologah Lake, a multipurpose reservoir in Oklahoma, United States. Results demonstrate that although demand reduction and supply increase may have a comparable effect on the supply reliability, demand reduction may be preferred from a robustness perspective. To prepare drought management plans for dealing with current and future droughts, it is thus recommended to test how alternative drought strategies contribute to a system's robustness rather than relying solely on water reliability as the decision criterion.

Modelling and analysis of system robustness for mechanical product based on axiomatic design and fuzzy clustering algorithm

Robust performance is the most important concern in the design of any product, especially in system design stage that precedes parameter design, because it actually determines the attainable level of product robustness in the parameter design phase. In this article, a framework of modelling and analysis of system robustness is proposed, which includes system modelling, cluster analysis and design of experiments. In the process of system modelling, the metamodel of general design theory was utilized to describe the function–structure model of product design, and the customer needs are transformed into functional requirements. Based on the independent axiom and zigzag mapping mode of axiomatic design, the functional requirements are mapping to design parameters, and the design matrix is created, which is then converted into design structure matrix by identifying the relationship between functional requirements and the sensitivity of functional requirements to design parameters. The fuzzy clustering algorithm is utilized to cluster the design parameters and to group the system components into modules in design structure matrix, and the interface among modules can be identified and system robustness incidence matrix is developed. Then the incidence parameters are considered as controllable factors, and experimental design techniques are utilized to analyse the influence of incidence parameters on the design objectives, if any, that may result in a robust system. The proposed framework is illustrated with the trolley design of overhead travelling crane.

Conditions for the Schur stability of segments of polynomials of the same degree

One of the most interesting problems in the robustness analysis of systems is the investigation of conditions that guarantee the stability of segments of polynomials. In this paper, given two polynomials $$P_1(z)$$ and $$P_2(z)$$ of the same degree, we give conditions on the polynomial $$H(z)=z^{n}[P_1(z)P_2(\frac{1}{z})-P_1(\frac{1}{z})P_2(z)]$$ such that the Schur stability of the segment $$[P_1, P_2]$$ is implied by the Schur stability of both $$P_1(z)$$ and $$P_2(z)$$ .

The added value of system robustness analysis for flood risk management illustrated by a case on the IJssel River

Abstract. Decision makers in fluvial flood risk management increasingly acknowledge that they have to prepare for extreme events. Flood risk is the most common basis on which to compare flood risk-reducing strategies. To take uncertainties into account the criteria of robustness and flexibility are advocated as well. This paper discusses the added value of robustness as an additional decision criterion compared to single-value flood risk only. We do so by quantifying flood risk and system robustness for alternative system configurations of the IJssel River valley in the Netherlands. We found that robustness analysis has added value in three respects: (1) it does not require assumptions on current and future flood probabilities, since flood consequences are shown as a function of discharge; (2) it shows the sensitivity of the system to varying discharges; and (3) it supports a discussion on the acceptability of flood damage. We conclude that robustness analysis is a valuable addition to flood risk analysis in support of long-term decision-making on flood risk management.

Robust control of uncertain systems: Classical results and recent developments

This paper presents a survey of the most significant results on robust control theory. In particular, we study the modeling of uncertain systems, robust stability analysis for systems with unstructured uncertainty, robustness analysis for systems with structured uncertainty, and robust control system design including H∞ control methods. The paper also presents some more recent results on deterministic and probabilistic methods for systems with uncertainty.

Clearance of flight control laws via parameter-dependent Lyapunov functions

Lyapunov-based analysis is a widely used tool to assess robust stability of dynamic systems affected by parametric uncertainties. When the uncertain parameters are constant, it is well known that parameter-dependent Lyapunov functions allow one to reduce conservatism of sufficient conditions for robust stability. In this paper, we consider several different techniques for robustness analysis of systems with LFR uncertainty structure, based on parameter-dependent Lyapunov functions. Such techniques are applied to LFR systems derived from real-world flight control schemes, with the aim of finding the largest region in the uncertain parameter space and/or in the flight envelope, for which robust stability is guaranteed. The trade off between conservatism and computational burden is analyzed for each considered technique.