Inverse Static Analysis of Massive Parallel Arrays of Three-State Actuators via Artificial Intelligence

Massive parallel robots (MPRs) driven by discrete actuators are force regulated robots that undergo continuous motions despite being commanded through a finite number of states only. Designing a real-time control of such systems requires fast and efficient methods for solving their inverse static analysis (ISA), which is a challenging problem and the subject of this thesis. In particular, five Artificial intelligence methods are proposed to investigate the on-line computation and the generalization error of ISA problem of a class of MPRs featuring three-state force actuators and one degree of revolute motion.

New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^

Report on Workshop on High Performance Computing and Communications for Grand Challenge Applications : computer vision, speech and natural language processing, and artificial intelligence /

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Application of the Artificial Intelligence Algorithms for System Analysis of Multi Dimension Physiological Data for Developing Polyparametric Information System of Public Health Diagnostics

The polyparametric intelligence information system for diagnostics human functional state in medicine and public health is developed. The essence of the system consists in polyparametric describing of human functional state with the unified set of physiological parameters and using the polyparametric cognitive model developed as the tool for a system analysis of multitude data and diagnostics of a human functional state. The model is developed on the basis of general principles geometry and symmetry by algorithms of artificial intelligence systems. The architecture of the system is represented. The model allows analyzing traditional signs - absolute values of electrophysiological parameters and new signs generated by the model – relationships of ones. The classification of physiological multidimensional data is made with a transformer of the model. The results are presented to a physician in a form of visual graph – a pattern individual functional state. This graph allows performing clinical syndrome analysis. A level of human functional state is defined in the case of the developed standard (“ideal”) functional state. The complete formalization of results makes it possible to accumulate physiological data and to analyze them by mathematics methods.

Ontological Approach to Domain Knowledge Representation for Information Retrieval in Multiagent Systems

An ontological representation of buyer interests’ knowledge in process of e-commerce is proposed to use. It makes it more efficient to make a search of the most appropriate sellers via multiagent systems. An algorithm of a comparison of buyer ontology with one of e-shops (the taxonomies) and an e-commerce multiagent system are realised using ontology of information retrieval in distributed environment.

Application of Artificial Intelligence Methods to Computer Design of Inorganic Compounds

In this paper the main problems for computer design of materials, which would have predefined properties, with the use of artificial intelligence methods are presented. The DB on inorganic compound properties and the system of DBs on materials for electronics with completely assessed information: phase diagram DB of material systems with semiconducting phases and DB on acousto-optical, electro-optical, and nonlinear optical properties are considered. These DBs are a source of information for data analysis. Using the DBs and artificial intelligence methods we have predicted thousands of new compounds in ternary, quaternary and more complicated chemical systems and estimated some of their properties (crystal structure type, melting point, homogeneity region etc.). The comparison of our predictions with experimental data, obtained later, showed that the average reliability of predicted inorganic compounds exceeds 80%. The perspectives of computational material design with the use of artificial intelligence methods are considered.

Identificación de las relaciones reales y posibles entre la administración basada en modelos, la ciencia computacional de la organización y la ingeniería emergente en el contexto de la gestión organizacional

Las teorías administrativas se han basado, casi sin excepción, en los fundamentos y los modelos de la ciencia clásica (particularmente, en los modelos de la física newtoniana). Sin embargo, las organizaciones actualmente se enfrentan a un mundo globalizado, plagado de información (y no necesariamente conocimiento), hiperconectado, dinámico y cargado de incertidumbre, por lo que muchas de las teorías pueden mostrar limitaciones para las organizaciones. Y quizá no por la estructura, la lógica o el alcance de las mismas, sino por la falta de criterios que justifiquen su aplicación. En muchos casos, las organizaciones siguen utilizando la intuición, las suposiciones y las verdades a medias en la toma de decisiones. Este panorama pone de manifiesto dos hechos: de un lado, la necesidad de buscar un método que permita comprender las situaciones de cada organización para apoyar la toma de decisiones. De otro lado, la necesidad de potenciar la intuición con modelos y técnicas no tradicionales (usualmente provenientes o inspiradas por la ingeniería). Este trabajo busca anticipar los pilares de un posible método que permita apoyar la toma de decisiones por medio de la simulación de modelos computacionales, utilizando las posibles interacciones entre: la administración basada en modelos, la ciencia computacional de la organización y la ingeniería emergente.

An Artificial Intelligence Approach to Individuals' Age Assessment

A link between patterns of pelvic growth and human life history is supported by the finding that, cross-culturally, variation in maturation rates of female pelvis are correlated with variation in ages of menarche and first reproduction, i.e., it is well known that the human dimensions of the pelvic bones depend on the gender and vary with the age. Indeed, one feature in which humans appear to be unique is the prolonged growth of the pelvis after the age of sexual maturity. Both the total superoinferior length and mediolateral breadth of the pelvis continues to grow markedly after puberty, and do not reach adult proportions until the late teens years. This continuation of growth is accomplished by relatively late fusion of the separate centers of ossification that form the bones of the pelvis. Hence, in this work we will focus on the development of an intelligent decision support system to predict individual’s age based on a pelvis' dimensions criteria. Some basic image processing techniques were applied in order to extract the relevant features from pelvic X-rays, being the computational framework built on top of a Logic Programming approach to Knowledge Representation and Reasoning that caters for the handling of incomplete, unknown, or even self-contradictory information, complemented with a Case Base approach to computing.

Design and testing of innovative, artificial intelligence-based techniques to model and control the combustion process and to reduce the Co2 emissions in modern, high-performance, gasoline direct injection engines

This work deals with the development of calibration procedures and control systems to improve the performance and efficiency of modern spark ignition turbocharged engines. The algorithms developed are used to optimize and manage the spark advance and the air-to-fuel ratio to control the knock and the exhaust gas temperature at the turbine inlet. The described work falls within the activity that the research group started in the previous years with the industrial partner Ferrari S.p.a. . The first chapter deals with the development of a control-oriented engine simulator based on a neural network approach, with which the main combustion indexes can be simulated. The second chapter deals with the development of a procedure to calibrate offline the spark advance and the air-to-fuel ratio to run the engine under knock-limited conditions and with the maximum admissible exhaust gas temperature at the turbine inlet. This procedure is then converted into a model-based control system and validated with a Software in the Loop approach using the engine simulator developed in the first chapter. Finally, it is implemented in a rapid control prototyping hardware to manage the combustion in steady-state and transient operating conditions at the test bench. The third chapter deals with the study of an innovative and cheap sensor for the in-cylinder pressure measurement, which is a piezoelectric washer that can be installed between the spark plug and the engine head. The signal generated by this kind of sensor is studied, developing a specific algorithm to adjust the value of the knock index in real-time. Finally, with the engine simulator developed in the first chapter, it is demonstrated that the innovative sensor can be coupled with the control system described in the second chapter and that the performance obtained could be the same reachable with the standard in-cylinder pressure sensors.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

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 2003–09. 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.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

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 2003–09. 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.

Building intelligence open systems (BIOS)

Os edifícios estão a ser construídos com um número crescente de sistemas de automação e controlo não integrados entre si. Esta falta de integração resulta num caos tecnológico, o que cria dificuldades nas três fases da vida de um edifício, a fase de estudo, a de implementação e a de exploração. O desenvolvimento de Building Automation System (BAS) tem como objectivo assegurar condições de conforto, segurança e economia de energia. Em edifícios de grandes dimensões a energia pode representar uma percentagem significativa da factura energética anual. Um BAS integrado deverá contribuir para uma diminuição significativa dos custos de desenvolvimento, instalação e gestão do edifício, o que pode também contribuir para a redução de CO2. O objectivo da arquitectura proposta é contribuir para uma estratégia de integração que permita a gestão integrada dos diversos subsistemas do edifício (e.g. aquecimento, ventilação e ar condicionado (AVAC), iluminação, segurança, etc.). Para realizar este controlo integrado é necessário estabelecer uma estratégia de cooperação entre os subsistemas envolvidos. Um dos desafios para desenvolver um BAS com estas características consistirá em estabelecer a interoperabilidade entre os subsistemas como um dos principais objectivos a alcançar, dado que o fornecimento dos referidos subsistemas assenta normalmente numa filosofia multi-fornecedor, sendo desenvolvidos usando tecnologias heterogéneas. Desta forma, o presente trabalho consistiu no desenvolvimento de uma plataforma que se designou por Building Intelligence Open System (BIOS). Na implementação desta plataforma adoptou-se uma arquitectura orientada a serviços ou Service Oriented Architecture (SOA) constituída por quatro elementos fundamentais: um bus cooperativo, denominado BIOSbus, implementado usando Jini e JavaSpaces, onde todos os serviços serão ligados, disponibilizando um mecanismo de descoberta e um mecanismo que notificada as entidades interessadas sobre alterações do estado de determinado componente; serviços de comunicação que asseguram a abstracção do Hardware utilizado da automatização das diversas funcionalidades do edifício; serviços de abstracção de subsistemas no acesso ao bus; clientes, este podem ser nomeadamente uma interface gráfica onde é possível fazer a gestão integrada do edifício, cliente de coordenação que oferece a interoperabilidade entre subsistemas e os serviços de gestão energética que possibilita a activação de algoritmos de gestão racional de energia eléctrica.

Perspectives and progress of ecology and conservation science in the Azores: the possible contribution of Artificial Intelligence

EPIA 2013 - XVI Portuguese Conference on Artificial Intelligence Angra do Heroísmo, Azores, Portugal, 9 – 12 September.