Coupled behavior informatics : modeling, analysis and learning

University of Technology, Sydney. Faculty of Engineering and Information Technology.Behavior refers to the action or property of an actor, entity or otherwise, to situations or stimuli in its environment. The in-depth analysis of behaviour has been increasingly recognized as a crucial means for understanding and disclosing interior driving forces and intrinsic cause-effects on business and social applications, including web community analysis, counterterrorism, fraud detection and customer relationship management, etc. Currently, behavior modeling and analysis have been extensively investigated by researchers in different disciplines, e.g. psychology, economics, mathematics, engineering and information science. From those diverse perspectives, there are widespread and long-standing explorations on behavior studies, such as behavior recognition, reasoning about action, interactive process modeling, multivariate time series analysis, and outlier mining of trading behaviors. All the above emerging methods however suffer from the following common issues and problems to different extents: (1) Existing behavior modelling approaches have too many styles and forms according to distinct situations, which is troublesome for cross-discipline researchers to follow. (2) Traditional behavior analysis relies on implicit behavior and explicit business appearance, often leading to ineffective and limited understanding on business and social activities. (3) Complex coupling relationships between behaviors are often ignored or only weakly addressed, which fails to provide a complete understanding of the underlying problems and their comprehensive solutions. (4) Current research usually overlooks the checking of behavior interactions, which weakens the soundness and robustness of models built for complex behavior applications. (5) Most of the classic mining and learning algorithms follow the fundamental assumption of independent and identical distribution (i.e. IIDness), but this is too strong to match the reality and complexities in practical applications. With the deepening and widening of social/business intelligences and their networking, the concept of behavior is in great demand to be consolidated and formalized to deeply scrutinize the native behavior intention, lifecycle and impact on complex problems and business issues. In the real-world applications, group behavior interactions (i.e. coupled behaviors) are widely seen in natural, social and artificial behavior-related problems. The verification of behavior modeling is further desired to assure the reliability and stability. In addition, complex behavior and social applications often exhibit strong explicit or implicit coupling relationships both between their entities and properties. They can not be abstracted or weakened to the extent of satisfying the IIDness assumption. These characteristics greatly challenge the current behavior-related analysis approaches. Moreover, it is also very difficult to model, analyze and check behaviors coupled with one another due to the complexity from data, domain, context and impact perspectives. Based on the above research limitations and challenges, this thesis reports state-of-the-art advances and our research innovations in modeling, analysing and learning coupled behaviors, which constitute the coupled behaviour informatics. Coupled behaviors are categorized as qualitative coupled behaviors and quantitative coupled behaviors, depending on whether the behaviour involved is qualified by actions or quantified by properties. In terms of the qualitative coupled behavior modeling and analysis, we propose an Ontology-based Qualitative Coupled Behavior Modeling and Checking (OntoB for short) system to explicitly represent and verify complex behaviour relationships, aggregations and constraints. The effectiveness of OntoB system in modeling multi-robot behaviors and their interactions in the Robocup soccer competition game has been demonstrated. With regard to the quantitative coupled behavior analysis and learning, we carry out explorations on three tasks below. They are under the non-IIDness assumption of entities or properties or both of them, which caters for the intrinsic essence of real-world problems and applications. For numerical coupled behavior analysis, we introduce a framework to address the comprehensive dependency among continuous properties. Substantial experiments show that the coupled representation can effectively model the global couplings of numerical properties and outperforms the traditional way. For categorical coupled behavior analysis, we present an efficient data-driven similarity learning approach that generates a coupled property similarity measure for nominal entities. Intensive empirical studies witness that the coupled property similarity can appropriately quantify the intrinsic and global interactions within and between categorical properties for especially large-scale behavior data. For coupled behavior ensemble learning, we explicate the couplings between methods and between entities in the application of clustering ensembles, and put forward a framework for coupled clustering ensembles (CCE). The CCE is experimentally exhibited to capture the implicit relationships of base clusterings and entities with higher clustering accuracy, stability and robustness, compared to existing techniques. All these models and frameworks are supported by statistical analysis. Finally, we provide a consolidated understanding of coupled behaviors by summarizing the qualitative and quantitative aspects, extract the multi-level couplings embedded in them, and then formalize a coupled behavior algebra at its preliminary stage. Many open research issues and opportunities related to our proposed approaches and this novel algebra are discussed accordingly. Under varying backgrounds and scenarios, our proposed systems, algorithms and frameworks for the coupled behavior informatics are evidenced to outperform state-of-the-art methods via theoretical analysis or empirical studies or both of them. All these outcomes have been accepted by top conferences, and the follow-up work has also been recognized. Therefore, coupled behavior informatics is a promising though wholly new research topic with lots of attractive opportunities for further exploration and development

Control of dynamical instability in semiconductor quantum nanostructures diode lasers: role of phase-amplitude coupling

We numerically investigate the complex nonlinear dynamics for two independently coupled laser systems consisting of (i) mutually delay-coupled edge emitting diode lasers and (ii) injection-locked quantum nano-structures lasers. A comparative study in dependence on the dynamical role of alpha parameter, which determine the phase-amplitude coupling of the optical field, in both the cases is probed. The variation of alpha lead to conspicuous changes in the dynamics of both the systems, which are characterized and investigated as a function of optical injection strength for the fixed coupled-cavity delay time. Our analysis is based on the observation that the cross-correlation and bifurcation measures unveil the signature of enhancement of amplitude-death islands in which the coupled lasers mutually stay in stable phase-locked states. In addition, we provide a qualitative understanding of the physical mechanisms underlying the observed dynamical behavior and its dependence on alpha. The amplitude death and the existence of multiple amplitude death islands could be implemented for applications including diode lasers stabilization.Comment: 9 Pages. arXiv admin note: text overlap with arXiv:1111.2439 by other author

Applying engineering feedback analysis tools to climate dynamics

The application of feedback analysis tools from engineering control theory to problems in climate dynamics is discussed through two examples. First, the feedback coupling between the thermohaline circulation and wind-driven circulation in the North Atlantic Ocean is analyzed with a relatively simple model, in order to better understand the coupled system dynamics. The simulation behavior is compared with analysis using root locus (in the linear regime) and describing functions (to predict limit cycle amplitude). The second example does not directly involve feedback, but rather uses simulation-based identification of low-order dynamics to understand parameter sensitivity in a model of El Nino/Southern Oscillation dynamics. The eigenvalue and eigenvector sensitivity can be used both to better understand physics and to tune more complex models. Finally, additional applications are discussed where control tools may be relevant to understand existing feedbacks in the climate system, or even to introduce new ones

Numerická analýza a simulace Rogowského cívky

This work illustrates an analysis of Rogowski coils for power applications, when operating under non ideal measurement conditions. The developed numerical model, validated by comparison with other methods and experiments, enables to investigate the effects of the geometrical and constructive parameters on the measurement behavior of the coil and we also study the behavior of Rogowski coils coupled with bar conductors under quasi-static conditions. Through a finite element (FEM) analysis, we estimate the current distribution across the bar and the flux linked by the transducer for various positions of the primary conductor and for various operating frequencies. Simulation and experimental results are reported in the text.Tato práce ilustruje analýzu rogowských cívek pro energetické aplikace při provozu v podmínkách bez ideálního měření. Vyvinutý numerický model, ověřený porovnáním s jinými metodami a experimenty, umožňuje zkoumat vliv geometrických a konstrukčních parametrů na chování měření cívky a také studujeme chování rogowských cívek spojených s tyčovými vodiči za kvazi-statických podmínek . Pomocí analýzy konečných prvků (FEM) odhadujeme rozložení proudu přes tyč a tok spojený snímačem pro různé polohy primárního vodiče a pro různé provozní frekvence. Simulační a experimentální výsledky jsou uvedeny v textu.410 - Katedra elektroenergetikydobř

Abrupt turn-on and hysteresis in a VCSEL with frequency-selective optical feedback

The emission characteristics of a vertical-cavity surface-emitting laser (VCSEL) operated in a single-transverse mode and coupled to an external cavity with a diffraction grating as a frequency-selective element are analyzed experimentally, numerically and analytically. The experiments yield a rather abrupt turn-on of the VCSEL to a high-amplitude emission state and hysteresis phenomena. The experimental results are explained by numerical simulations and analytical calculations demonstrating the possibility of bistability between lasing and non-lasing states close to threshold. Hence, the scheme might be useful in all-optical photonic switching applications. A detailed bifurcation analysis near threshold is given by superimposing the numerical results with analytical steady-state curves. The mode selection and switching behavior obtained in the simulations can be interpreted from the point of view of the preference of states with the minimal total losses

Cross-market behavior modeling

University of Technology Sydney. Faculty of Engineering and Information Technology.During the 2007 global financial crisis which was triggered by subprime borrowers in the US mortgage markets, strong market linkages were observed between different financial markets. The sharp fluctuations in the global stock market, commodity market and interest market illustrate some of the coupled behaviors that exist between various markets, namely the crisis effect is passed from one market to another through couplings. Here coupled behaviors refer to the activities (e.g. changes of market indexes) of financial markets which are associated with each other in terms of particular relationships. Therefore, a good understanding of coupled behaviors is of great importance in cross-market applications such as crisis detection and market trend forecasting. For instance, if the coupled behaviors are properly understood and modeled, investors can predict financial crisis and avoid the big loss, by detecting the changes of coupled relations between financial crisis period and non-crisis period. However, understanding and modeling coupled behaviors is quite challenging for following reasons: (1) The various coupled structures across financial markets (e.g. coupled relations between different types of markets, and coupled relations between the same type of market in different countries) bring challenges in terms of understanding and modeling them. (2) Various types of couplings. The typical forms are intra-coupling, inter-coupling and temporal-coupling. (3) The complex interactions between markets are driven by hidden features which cannot be observed directly from observation/data. (4) Different applications in cross-market analysis lead to the consideration of input factors/variables selection. All of these challenges the existing methods for cross-market analysis, which can be roughly categorized into two types: time series analysis represented by Logistic regression, Autoregressive Integrated Moving Average (ARIMA) and Generalized AutoRegressive Conditional Heteroscedasticity (GARCH) models. Model-based methods explore machine models such as Artificial Neural Networks (ANN) and Hidden Markov Models (HMM). The main limitations lie in their deficiencies: (1) Existing approaches are usually focused on the simple correlations of the cross-market, rather than coupled behaviors between markets. (2) State-of-the-art research work is usually built directly from the observation/data. Hidden features behind the observation/data are often ignored or only weakly addressed. (3) Some approaches follow assumptions that are too strong to match real financial markets. Based on the above research limitations and challenges, this thesis reports state-of-the-art advances and our research innovations in understanding and modeling complex coupled behaviors for the purpose of cross-market analysis. Chapter 3 presents a new approach, called Coupled Market Behavior Analysis (CMBA) for financial crisis detection. This caters for nonlinear couplings between major indicators selected from different markets, and it detects different coupled market behaviors at crisis and non-crisis periods. Chapter 4 seeks to overcome the limitations of most current methods which conduct financial crisis forecasting directly through observation and overlook the hidden interactions between markets. In this chapter, Coupled Market State Analysis (CMSA) is presented to build forecasters based on coupled market states instead of observation. Chapter 5 reports a new approach for market trend forecasting by analyzing its hidden coupling relationships with different types of related financial markets. Chapter 6 proposes Hierarchical Cross-market Behavior Analysis (HCBA) to forecast a stock market’s movements, by exploring the complex coupling relationships between variables of markets from a country (Layer-1 coupling) and couplings between markets from various countries (Layer-2 coupling). In addition, Chapter 7 designs a Coupled Temporal Deep Belief Network (CTDBN) which accommodates three different types of couplings across financial markets: interactions between homogeneous markets from various countries (intra-market coupling), interactions between heterogeneous markets (inter-market coupling) and interactions between current and past market behaviors (temporal coupling). With a deep-architecture model to capture the high-level coupled features, the proposed approach can infer market trends. In terms of cross-market applications (i.e. financial crisis detection and market trend forecasting), our proposed approaches and frameworks for modeling coupled behaviors across financial markets outperform state-of-the-art methods from both technical and business perspectives. All of these outcomes provide insightful knowledge for investors who naturally seek to make profits and avoid losses. Accordingly, cross-market behavior modeling is a promising research topic with lots of potential for further exploration and development

A multilayer anisotropic plate model with warping functions for the study of vibrations reformulated from Woodcock's work

In this paper, a plate model suitable for static and dynamic analysis of inhomogeneous anisotropic multilayered plates is described. This model takes transverse shear variation through the thickness of the plate into account by means of warping functions which are determined by enforcing kinematic and static assumptions at the layers interfaces. This model leads to a 10 x 10 behavior matrix in which membrane strains, bending curvatures, and transverse shear x and y-derivatives are coupled, and to a classical 2 x 2 shear behavior matrix. This model has been proven to be very efficient, especially when high ratios -up to 10E5- between the stiffnesses of layers are present. This work is related to Woodcock's model, so it can be seen as a reformulation of his work. However, it propose several enhancements: the displacement field is made explicit; it is reformulated with commonly used plate notations; laminate equations of motion are fully detailed; the place of this model relatively to other plate models is now easy to see and is discussed; the link between this formulation and the original one is completely written with all necessary proofs; misses and errors have been found in the energy coefficients of the original work, and then have been corrected; it is now easy to improve or to adapt the model for specific applications with the choice of refined or specific warping functions

Ductile damage parameters identification for cold metal forming applications

International audienceDuctile damage mechanics is essential to predict failure during cold metal forming applications. Several damage models can be found in the literature. These damage models are coupled with the mechanical behavior so as to model the progressive softening of the material due to damage growth. However, the identification of damage parameters remains an issue. In this paper, an inverse analysis approach is set-up to identify ductile damage parameters, based on different kind of mechanical tests and observables. The Lemaitre damage model is used and damage is coupled with the material behavior. The Efficient Global Optimization (EGO) method is used in a parallel environment. This global algorithm based on kriging meta-model enables the identification of a set of damage parameters based on experimental observables. Global and local observables are used to identify these parameters and a special attention is paid to the computation of the cost function. Finally, an identification procedure based on displacement field measurements is presented and applied for damage parameters identification