Bond Graph Representation of Standard Interconnection Model

International audienceThe study of the robustness of a system's parametric uncertainties is based on state representations which separate the nominal part of the system from the uncertain part. The most used form is the standard interconnection model. Recent works have been formulated so as to find this representation graphically by the bond graph approach. A new procedure is proposed in this paper to determine an uncertain model adapted to the study of robustness and for robust control. The advantage of this procedure is in simplifying the resulting graphical model

Prediction of Atomization Energy Using Graph Kernel and Active Learning

Data-driven prediction of molecular properties presents unique challenges to the design of machine learning methods concerning data structure/dimensionality, symmetry adaption, and confidence management. In this paper, we present a kernel-based pipeline that can learn and predict the atomization energy of molecules with high accuracy. The framework employs Gaussian process regression to perform predictions based on the similarity between molecules, which is computed using the marginalized graph kernel. To apply the marginalized graph kernel, a spatial adjacency rule is first employed to convert molecules into graphs whose vertices and edges are labeled by elements and interatomic distances, respectively. We then derive formulas for the efficient evaluation of the kernel. Specific functional components for the marginalized graph kernel are proposed, while the effect of the associated hyperparameters on accuracy and predictive confidence are examined. We show that the graph kernel is particularly suitable for predicting extensive properties because its convolutional structure coincides with that of the covariance formula between sums of random variables. Using an active learning procedure, we demonstrate that the proposed method can achieve a mean absolute error of 0.62 +- 0.01 kcal/mol using as few as 2000 training samples on the QM7 data set

Numerical Key Performance Indicators for the Validation of PHM Health Indicators with Application to a Hydraulic Actuation System

In order to perform Prognostic and Health Management (PHM) of a given system, it is necessary to define some relevant variables sensitive to the different degradation modes of the system. Those variables are named Health Indicators (HI) and they are the keystone of PHM. However, they are subject to a lot of uncertainties when computed in real time and the stochastic nature of PHM makes it hard to evaluate the efficiency of a HI set before the extraction algorithm is implemented. This document introduces Numerical Key Performance Indicators (NKPI) for the validation of HI computed only from data provided by numerical models in the upstream stages of a PHM system development process. In order to match as good as possible the reality, the multiple sources of uncertainties are quantified and propagated into the model. After having introduced the issue of uncertain systems modeling, the different NKPI are defined and eventually an application is performed on a hydraulic actuation system of an aircraft engine

Fault evaluation and adaptive threshold detection of helicopter pilot using bond graph method

Hitherto, in the field of aerospace science and industry, some acceptable results from control behavior of human operator (pilot), are caught using usual methods. However, very fewer research, has been done based on personal characteristics. The performed investigations, show that many of happened faults (especially in helicopter), would result to the loss of natural behavior of system, and eventually some fatal accidents. Therefore, development of tools of assessment of pilot in this dynamical system, is one of the vital necessities. The tools of management of system, should be such that, can show the fault. The object of this paper is assessment of the modelled pilot in a simulator, in presence of standard fly inputs. For this purpose the performance of the pilot for collective and cyclic control of the helicopter, is investigated. The used methods are based on mathematical models and by bond graph modeling method. The existence of uncertainties in the simulator system and the modeled pilot’s behavior, causes creation of fault threshold, for human behavior of pilot. By having these fault thresholds, the improper actions of the pilot, cause creation of behavioral reminders, which can perform the assessment of the pilot. By other speech, using fault detection and isolation (FDI), by bond graph method in state space, the assessment is performed and a non-zero reminder shows existence of fault in the system. Therefore, in this paper there is the novelties for modeling the pilot’s body performance and the helicopter’s systems integrally and designing a fault detection system for it that shows the source of the fault, and obviously it can be useful in aviation industry.Keywords: Helicopter Control, Fault Detection and Isolation, Human Fault, Bond Grap

Robust Diagnosis by Observer Using the Bond Graph Approach

In this chapter, we have proposed a Luenberger observer linear systems diagnostic technique using the bond graph. Indeed, an observer can reconstruct or estimate the current state of a real system using the available measurements, without prior knowledge of the initial conditions. In addition, it allows to estimate the nonmeasurable states of a system. The design of the observer is carried out using graphical methods from the structural properties of the model bond graph becomes simple and practical to build. We presented the bond graph approach for the construction of a full-order observer and proposed a new BG-based observer diagnostic method. Subsequently, we presented the uncertain parameter systems modeled by the bond graph approach, and we also proposed a new method for diagnosing systems with uncertain parameters by Luenberger observer. In the last part of this chapter, we developed and proposed an observer bench diagnosis technique (BG-DOS/BG-GOS) to detect and locate defects

A Global Workspace perspective on mental disorders

Recent developments in Global Workspace theory suggest that human consciousness can suffer interpenetrating dysfunctions of mutual and reciprocal interaction with embedding environments which will have early onset and often insidiously staged developmental progression, possibly according to a cancer model. A simple rate distortion argument implies that, if an external information source is pathogenic, then sufficient exposure to it is sure to write a sufficiently accurate image of it on mind and body in a punctuated manner so as to initiate or promote simililarly progressively punctuated developmental disorder. There can, thus, be no simple, reductionist brain chemical 'bug in the program' whose 'fix' can fully correct the problem. On the contrary, the growth of an individual over the life course, and the inevitable contact with a toxic physical, social, or cultural environment, can be expected to initiate developmental problems which will become more intrusive over time, most obviously according to some damage accumulation model, but likely according to far more subtle, highly punctuated, schemes analogous to tumorigenesis. The key intervention, at the population level, is clearly to limit such exposures, a question of proper environmental sanitation, in a large sense, a matter of social justice which has long been understood to be determined almost entirely by the interactions of cultural trajectory, group power relations, and economic structure, with public policy. Intervention at the individual level appears limited to triggering or extending periods of remission, as is the case with most cancers

Culture and generalized inattentional blindness

A recent mathematical treatment of Baars' Global Workspace consciousness model, much in the spirit of Dretske's communication theory analysis of high level mental function, is used to study the effects of embedding cultural heritage on a generalized form of inattentional blindness. Culture should express itself quite distinctly in this basic psychophysical phenomenon, acting across a variety of sensory and other modalities, because the limited syntactic and grammatical 'bandpass' of the topological rate distortion manifold characterizing conscious attention is itself strongly sculpted by the constraints of cultural context

Darwin's Rainbow: Evolutionary radiation and the spectrum of consciousness

Evolution is littered with paraphyletic convergences: many roads lead to functional Romes. We propose here another example - an equivalence class structure factoring the broad realm of possible realizations of the Baars Global Workspace consciousness model. The construction suggests many different physiological systems can support rapidly shifting, sometimes highly tunable, temporary assemblages of interacting unconscious cognitive modules. The discovery implies various animal taxa exhibiting behaviors we broadly recognize as conscious are, in fact, simply expressing different forms of the same underlying phenomenon. Mathematically, we find much slower, and even multiple simultaneous, versions of the basic structure can operate over very long timescales, a kind of paraconsciousness often ascribed to group phenomena. The variety of possibilities, a veritable rainbow, suggests minds today may be only a small surviving fraction of ancient evolutionary radiations - bush phylogenies of consciousness and paraconsciousness. Under this scenario, the resulting diversity was subsequently pruned by selection and chance extinction. Though few traces of the radiation may be found in the direct fossil record, exaptations and vestiges are scattered across the living mind. Humans, for instance, display an uncommonly profound synergism between individual consciousness and their embedding cultural heritages, enabling efficient Lamarkian adaptation

Entering the blackboard jungle: canonical dysfunction in conscious machines

The central paradigm of Artificial Intelligence is rapidly shifting toward biological models for both robotic devices and systems performing such critical tasks as network management and process control. Here we apply recent mathematical analysis of the necessary conditions for consciousness in humans in an attempt to gain some understanding of the likely canonical failure modes inherent to a broad class of global workspace/blackboard machines designed to emulate biological functions. Similar problems are likely to confront other possible architectures, although their mathematical description may be far less straightforward

Institutional Cognition

We generalize a recent mathematical analysis of Bernard Baars' model of human consciousness to explore analogous, but far more complicated, phenomena of institutional cognition. Individual consciousness is limited to a single, tunable, giant component of interacting cogntivie modules, instantiating a Global Workspace. Human institutions, by contrast, seem able to multitask, supporting several such giant components simultaneously, although their behavior remains constrained to a topology generated by cultural context and by the path-dependence inherent to organizational history. Surprisingly, such multitasking, while clearly limiting the phenomenon of inattentional blindness, does not eliminate it. This suggests that organizations (or machines) explicitly designed along these principles, while highly efficient at certain sets of tasks, would still be subject to analogs of the subtle failure patterns explored in Wallace (2005b, 2006). We compare and contrast our results with recent work on collective efficacy and collective consciousness