3,064 research outputs found
Flight crew aiding for recovery from subsystem failures
Some of the conceptual issues associated with pilot aiding systems are discussed and an implementation of one component of such an aiding system is described. It is essential that the format and content of the information the aiding system presents to the crew be compatible with the crew's mental models of the task. It is proposed that in order to cooperate effectively, both the aiding system and the flight crew should have consistent information processing models, especially at the point of interface. A general information processing strategy, developed by Rasmussen, was selected to serve as the bridge between the human and aiding system's information processes. The development and implementation of a model-based situation assessment and response generation system for commercial transport aircraft are described. The current implementation is a prototype which concentrates on engine and control surface failure situations and consequent flight emergencies. The aiding system, termed Recovery Recommendation System (RECORS), uses a causal model of the relevant subset of the flight domain to simulate the effects of these failures and to generate appropriate responses, given the current aircraft state and the constraints of the current flight phase. Since detailed information about the aircraft state may not always be available, the model represents the domain at varying levels of abstraction and uses the less detailed abstraction levels to make inferences when exact information is not available. The structure of this model is described in detail
On morphological hierarchical representations for image processing and spatial data clustering
Hierarchical data representations in the context of classi cation and data
clustering were put forward during the fties. Recently, hierarchical image
representations have gained renewed interest for segmentation purposes. In this
paper, we briefly survey fundamental results on hierarchical clustering and
then detail recent paradigms developed for the hierarchical representation of
images in the framework of mathematical morphology: constrained connectivity
and ultrametric watersheds. Constrained connectivity can be viewed as a way to
constrain an initial hierarchy in such a way that a set of desired constraints
are satis ed. The framework of ultrametric watersheds provides a generic scheme
for computing any hierarchical connected clustering, in particular when such a
hierarchy is constrained. The suitability of this framework for solving
practical problems is illustrated with applications in remote sensing
Ultrametric embedding: application to data fingerprinting and to fast data clustering
We begin with pervasive ultrametricity due to high dimensionality and/or
spatial sparsity. How extent or degree of ultrametricity can be quantified
leads us to the discussion of varied practical cases when ultrametricity can be
partially or locally present in data. We show how the ultrametricity can be
assessed in text or document collections, and in time series signals. An aspect
of importance here is that to draw benefit from this perspective the data may
need to be recoded. Such data recoding can also be powerful in proximity
searching, as we will show, where the data is embedded globally and not locally
in an ultrametric space.Comment: 14 pages, 1 figure. New content and modified title compared to the 19
May 2006 versio
Human-Intelligence and Machine-Intelligence Decision Governance Formal Ontology
Since the beginning of the human race, decision making and rational thinking played a pivotal role for mankind to either exist and succeed or fail and become extinct. Self-awareness, cognitive thinking, creativity, and emotional magnitude allowed us to advance civilization and to take further steps toward achieving previously unreachable goals. From the invention of wheels to rockets and telegraph to satellite, all technological ventures went through many upgrades and updates. Recently, increasing computer CPU power and memory capacity contributed to smarter and faster computing appliances that, in turn, have accelerated the integration into and use of artificial intelligence (AI) in organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational systems including healthcare and medical diagnosis, automated stock trading, robotic production, telecommunications, space explorations, and homeland security. Self-driving cars and drones are just the latest extensions of AI. This thrust of AI into organizations and daily life rests on the AI community’s unstated assumption of its ability to completely replicate human learning and intelligence in AI. Unfortunately, even today the AI community is not close to completely coding and emulating human intelligence into machines. Despite the revolution of digital and technology in the applications level, there has been little to no research in addressing the question of decision making governance in human-intelligent and machine-intelligent (HI-MI) systems. There also exists no foundational, core reference, or domain ontologies for HI-MI decision governance systems. Further, in absence of an expert reference base or body of knowledge (BoK) integrated with an ontological framework, decision makers must rely on best practices or standards that differ from organization to organization and government to government, contributing to systems failure in complex mission critical situations. It is still debatable whether and when human or machine decision capacity should govern or when a joint human-intelligence and machine-intelligence (HI-MI) decision capacity is required in any given decision situation.
To address this deficiency, this research establishes a formal, top level foundational ontology of HI-MI decision governance in parallel with a grounded theory based body of knowledge which forms the theoretical foundation of a systemic HI-MI decision governance framework
Frustration in Biomolecules
Biomolecules are the prime information processing elements of living matter.
Most of these inanimate systems are polymers that compute their structures and
dynamics using as input seemingly random character strings of their sequence,
following which they coalesce and perform integrated cellular functions. In
large computational systems with a finite interaction-codes, the appearance of
conflicting goals is inevitable. Simple conflicting forces can lead to quite
complex structures and behaviors, leading to the concept of "frustration" in
condensed matter. We present here some basic ideas about frustration in
biomolecules and how the frustration concept leads to a better appreciation of
many aspects of the architecture of biomolecules, and how structure connects to
function. These ideas are simultaneously both seductively simple and perilously
subtle to grasp completely. The energy landscape theory of protein folding
provides a framework for quantifying frustration in large systems and has been
implemented at many levels of description. We first review the notion of
frustration from the areas of abstract logic and its uses in simple condensed
matter systems. We discuss then how the frustration concept applies
specifically to heteropolymers, testing folding landscape theory in computer
simulations of protein models and in experimentally accessible systems.
Studying the aspects of frustration averaged over many proteins provides ways
to infer energy functions useful for reliable structure prediction. We discuss
how frustration affects folding, how a large part of the biological functions
of proteins are related to subtle local frustration effects and how frustration
influences the appearance of metastable states, the nature of binding
processes, catalysis and allosteric transitions. We hope to illustrate how
Frustration is a fundamental concept in relating function to structural
biology.Comment: 97 pages, 30 figure
An Evaluation of Perceptual Classification led by Cognitive Models in Traffic Scenes
The objects extraction and recognition constitute the most important link in the image processing and understanding, and it cannot be achieved without a solid objects organization during the processing through the learning mechanisms. Most often, both the response time and the accuracy are undeniable criteria for applications in this field. Actually, a vision system need to take into consideration these criteria, either in the structural, the methodological or in the algorithmic aspect. Thus, we consider that the ontological study at the domain and task levels, in the vision systems, has become essential in order to provide a substantial assistance to the multitudes of applications in image processing. Concerning the domain knowledge, several patterns for structuring were proposed to improve the objects representation and organization, they often advocate the precision aspect on time and on effort devoted to the recognition. In practical terms, clustering methods only focus on the accuracy aspect within a category, without considering the recognition aspect [1]. Thus, we propose in this study a new procedure of object categorization, which uses, according to the expertise in the domain, a fit evaluation that is able to adjust the level of partitioning. As a result, this procedure will find a compromise between the accuracy on the categories and the reduction of the supplied effort in recognition. Â
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M2-Branes and Quiver Chern-Simons: A Taxonomic Study
We initiate a systematic investigation of the space of 2+1 dimensional quiver gauge theories, emphasising a succinct "forward algorithm". Few "order parametres" are introduced such as the number of terms in the superpotential and the number of gauge groups. Starting with two terms in the superpotential, we find a generating function, with interesting geometric interpretation, which counts the number of inequivalent theories for a given number of gauge groups and fields. We demonstratively list these theories for some low numbers thereof. Furthermore, we show how these theories arise from M2-branes probing toric Calabi-Yau 4-folds by explicitly obtaining the toric data of the vacuum moduli space. By observing equivalences of the vacua between markedly different theories, we see a new emergence of "toric duality"
Semantic networks
AbstractA semantic network is a graph of the structure of meaning. This article introduces semantic network systems and their importance in Artificial Intelligence, followed by I. the early background; II. a summary of the basic ideas and issues including link types, frame systems, case relations, link valence, abstraction, inheritance hierarchies and logic extensions; and III. a survey of ‘world-structuring’ systems including ontologies, causal link models, continuous models, relevance, formal dictionaries, semantic primitives and intersecting inference hierarchies. Speed and practical implementation are briefly discussed. The conclusion argues for a synthesis of relational graph theory, graph-grammar theory and order theory based on semantic primitives and multiple intersecting inference hierarchies
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