849 resultados para Artificial intelligence
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A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 200309. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.
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A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 200309. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.
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In this paper we discuss interesting developments of expert systems for machine diagnosis and condition-based maintenance. We review some elements of condition-based maintenance and its applications, expert systems for machine diagnosis, and an example of machine diagnosis. In the last section we note some problems to be resolved so that expert systems for machine diagnosis may gain wider acceptance in the future.
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Control Centre operators are essential to assure a good performance of Power Systems. Operators actions are critical in dealing with incidents, especially severe faults, like blackouts. In this paper we present an Intelligent Tutoring approach for training Portuguese Control Centre operators in incident analysis and diagnosis, and service restoration of Power Systems, offering context awareness and an easy integration in the working environment.
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EPIA 2013 - XVI Portuguese Conference on Artificial Intelligence Angra do Herosmo, Azores, Portugal, 9 12 September.
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Decision making in any environmental domain is a complex and demanding activity, justifying the development of dedicated decision support systems. Every decision is confronted with a large variety and amount of constraints to satisfy as well as contradictory interests that must be sensibly accommodated. The first stage of a project evaluation is its submission to the relevant group of public (and private) agencies. The individual role of each agency is to verify, within its domain of competence, the fulfilment of the set of applicable regulations. The scope of the involved agencies is wide and ranges from evaluation abilities on the technical or economical domains to evaluation competences on the environmental or social areas. The second project evaluation stage involves the gathering of the recommendations of the individual agencies and their justified merge to produce the final conclusion. The incorporation and accommodation of the consulted agencies opinions is of extreme importance: opinions may not only differ, but can be interdependent, complementary, irreconcilable or, simply, independent. The definition of adequate methodologies to sensibly merge, whenever possible, the existing perspectives while preserving the overall legality of the system, will lead to the making of sound justified decisions. The proposed Environmental Decision Support System models the project evaluation activity and aims to assist developers in the selection of adequate locations for their projects, guaranteeing their compliance with the applicable regulations.
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A thesis submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems.
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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.
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Geographic information systems (GIS) and artificial intelligence (AI) techniques were used to develop an intelligent snow removal asset management system (SRAMS). The system has been evaluated through a case study examining snow removal from the roads in Black Hawk County, Iowa, for which the Iowa Department of Transportation (Iowa DOT) is responsible. The SRAMS is comprised of an expert system that contains the logical rules and expertise of the Iowa DOTs snow removal experts in Black Hawk County, and a geographic information system to access and manage road data. The system is implemented on a mid-range PC by integrating MapObjects 2.1 (a GIS package), Visual Rule Studio 2.2 (an AI shell), and Visual Basic 6.0 (a programming tool). The system could efficiently be used to generate prioritized snowplowing routes in visual format, to optimize the allocation of assets for plowing, and to track materials (e.g., salt and sand). A test of the system reveals an improvement in snowplowing time by 1.9 percent for moderate snowfall and 9.7 percent for snowstorm conditions over the current manual system.
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The objective of this work was to develop, validate, and compare 190 artificial intelligence-based models for predicting the body mass of chicks from 2 to 21 days of age subjected to different duration and intensities of thermal challenge. The experiment was conducted inside four climate-controlled wind tunnels using 210 chicks. A database containing 840 datasets (from 2 to 21-day-old chicks) - with the variables dry-bulb air temperature, duration of thermal stress (days), chick age (days), and the daily body mass of chicks - was used for network training, validation, and tests of models based on artificial neural networks (ANNs) and neuro-fuzzy networks (NFNs). The ANNs were most accurate in predicting the body mass of chicks from 2 to 21 days of age after they were subjected to the input variables, and they showed an R² of 0.9993 and a standard error of 4.62 g. The ANNs enable the simulation of different scenarios, which can assist in managerial decision-making, and they can be embedded in the heating control systems.
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La scoliose idiopathique de ladolescent (SIA) est une dformation tri-dimensionelle du rachis. Son traitement comprend lobservation, lutilisation de corsets pour limiter sa progression ou la chirurgie pour corriger la dformation squelettique et cesser sa progression. Le traitement chirurgical reste controvers au niveau des indications, mais aussi de la chirurgie entreprendre. Malgr la prsence de classifications pour guider le traitement de la SIA, une variabilit dans la stratgie opratoire intra et inter-observateur a t dcrite dans la littrature. Cette variabilit saccentue dautant plus avec lvolution des techniques chirurgicales et de linstrumentation disponible. Lavancement de la technologie et son intgration dans le milieu mdical a men lutilisation dalgorithmes dintelligence artificielle informatiques pour aider la classification et lvaluation tridimensionnelle de la scoliose. Certains algorithmes ont dmontr tre efficace pour diminuer la variabilit dans la classification de la scoliose et pour guider le traitement. Lobjectif gnral de cette thse est de dvelopper une application utilisant des outils dintelligence artificielle pour intgrer les donnes dun nouveau patient et les vidences disponibles dans la littrature pour guider le traitement chirurgical de la SIA. Pour cela une revue de la littrature sur les applications existantes dans lvaluation de la SIA fut entreprise pour rassembler les lments qui permettraient la mise en place dune application efficace et accepte dans le milieu clinique. Cette revue de la littrature nous a permis de raliser que lexistence de black box dans les applications dveloppes est une limitation pour lintgration clinique ou la justification base sur les vidence est essentielle. Dans une premire tude nous avons dvelopp un arbre dcisionnel de classification de la scoliose idiopathique bas sur la classification de Lenke qui est la plus communment utilise de nos jours mais a t critique pour sa complexit et la variabilit inter et intra-observateur. Cet arbre dcisionnel a dmontr quil permet daugmenter la prcision de classification proportionnellement au temps pass classifier et ce indpendamment du niveau de connaissance sur la SIA. Dans une deuxime tude, un algorithme de stratgies chirurgicales bas sur des rgles extraites de la littrature a t dvelopp pour guider les chirurgiens dans la slection de lapproche et les niveaux de fusion pour la SIA. Lorsque cet algorithme est appliqu une large base de donne de 1556 cas de SIA, il est capable de proposer une stratgie opratoire similaire celle dun chirurgien expert dans prt de 70% des cas. Cette tude a confirm la possibilit dextraire des stratgies opratoires valides laide dun arbre dcisionnel utilisant des rgles extraites de la littrature. Dans une troisime tude, la classification de 1776 patients avec la SIA laide dune carte de Kohonen, un type de rseaux de neurone a permis de dmontrer quil existe des scoliose typiques (scoliose courbes uniques ou double thoracique) pour lesquelles la variabilit dans le traitement chirurgical varie peu des recommandations par la classification de Lenke tandis que les scolioses a courbes multiples ou tangentielles deux groupes de courbes typiques taient celles avec le plus de variation dans la stratgie opratoire. Finalement, une plateforme logicielle a t dveloppe intgrant chacune des tudes ci-dessus. Cette interface logicielle permet lentre de donnes radiologiques pour un patient scoliotique, classifie la SIA laide de larbre dcisionnel de classification et suggre une approche chirurgicale base sur larbre dcisionnel de stratgies opratoires. Une analyse de la correction post-opratoire obtenue dmontre une tendance, bien que non-statistiquement significative, une meilleure balance chez les patients oprs suivant la stratgie recommande par la plateforme logicielle que ceux aillant un traitement diffrent. Les tudes exposes dans cette thse soulignent que lutilisation dalgorithmes dintelligence artificielle dans la classification et llaboration de stratgies opratoires de la SIA peuvent tre intgres dans une plateforme logicielle et pourraient assister les chirurgiens dans leur planification propratoire.
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Most Artificial Intelligence (AI) work can be characterized as either ``high-level'' (e.g., logical, symbolic) or ``low-level'' (e.g., connectionist networks, behavior-based robotics). Each approach suffers from particular drawbacks. High-level AI uses abstractions that often have no relation to the way real, biological brains work. Low-level AI, on the other hand, tends to lack the powerful abstractions that are needed to express complex structures and relationships. I have tried to combine the best features of both approaches, by building a set of programming abstractions defined in terms of simple, biologically plausible components. At the ``ground level'', I define a primitive, perceptron-like computational unit. I then show how more abstract computational units may be implemented in terms of the primitive units, and show the utility of the abstract units in sample networks. The new units make it possible to build networks using concepts such as long-term memories, short-term memories, and frames. As a demonstration of these abstractions, I have implemented a simulator for ``creatures'' controlled by a network of abstract units. The creatures exist in a simple 2D world, and exhibit behaviors such as catching mobile prey and sorting colored blocks into matching boxes. This program demonstrates that it is possible to build systems that can interact effectively with a dynamic physical environment, yet use symbolic representations to control aspects of their behavior.
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This report outlines the problem of intelligent failure recovery in a problem-solver for electrical design. We want our problem solver to learn as much as it can from its mistakes. Thus we cast the engineering design process on terms of Problem Solving by Debugging Almost-Right Plans, a paradigm for automatic problem solving based on the belief that creation and removal of "bugs" is an unavoidable part of the process of solving a complex problem. The process of localization and removal of bugs called for by the PSBDARP theory requires an approach to engineering analysis in which every result has a justification which describes the exact set of assumptions it depends upon. We have developed a program based on Analysis by Propagation of Constraints which can explain the basis of its deductions. In addition to being useful to a PSBDARP designer, these justifications are used in Dependency-Directed Backtracking to limit the combinatorial search in the analysis routines. Although the research we will describe is explicitly about electrical circuits, we believe that similar principles and methods are employed by other kinds of engineers, including computer programmers.
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Pearon's resource from the Brookshear Chapter
