A novel methodology is presented for organizing swarms of robots into a formation utilizing artificial potential fields generated from normal and sigmoid functions. These functions construct the surface which swarm members travel on, controlling the overall swarm geometry and the individual member spacing. Nonlinear limiting functions are defined to provide tighter swarm control by modifying and adjusting a set of control variables forcing the swarm to behave according to set constraints, formation and member spacing. The swarm function and limiting functions are combined to control swarm formation, orientation, and swarm movement as a whole. Parameters are chosen based on desired formation as well as user defined constraints. This approach compared to others, is simple, computationally efficient, scales well to different swarm sizes, to heterogeneous systems, and to both centralized and decentralized swarm models. Simulation results are presented for a swarm of four and ten particles following circle, ellipse and wedge formations. Experimental results are also included with a swarm of four unmanned ground vehicles (UGV) as well as UGV swarm and unmanned aerial vehicle (UAV) coordination.

This paper presents an intelligent, hybrid system for stamping process planning in progressive die design. The system combines the flexibility of blackboard architecture with case-based reasoning. The hybrid system has the advantage that it can use past knowledge and experience for case-based reasoning when it exists, and other reasoning approaches when it doesn’t exist. A prototype system has been implemented in CLIPS and interfaced with Solid Edge CAD system. An example is included to demonstrate the approach. / Singapore-MIT Alliance (SMA)

The new approach for sensor diagnostics is presented. The approach, Enhanced Autoassociative Neural Networks (E-AANN), adds enhancement to Autoassociative Neural Networks (AANN) developed by Kramer in 1992. This enhancement allows AANN to identify faulty sensors. E-AANN uses a secondary optimization process to identify and reconstruct sensor faults. Two common types of sensor faults are investigated, drift error and shift or offset error. In the case of drift error, the sensor error occurs gradually while in the case of shift error, the sensor error occurs abruptly. EAANN catches these error types. A chiller model provided synthetic data to test the diagnostic approach under various noise level conditions. The results show that sensor faults can be detected and corrected in noisy situations with the E-AANN method described. In high noisy situations (10% to 20% noise level), E-AANN performance degrades. E-AANN performance in simple dynamic systems was also investigated. The results show that in simple dynamic situations, E-AANN identifies faulty sensors.

The new approach for sensor diagnostics is presented. The approach, Enhanced Autoassociative Neural Networks (E-AANN), adds enhancement to Autoassociative Neural Networks (AANN) developed by Kramer in 1992. This enhancement allows AANN to identify faulty sensors. E-AANN uses a secondary optimization process to identify and reconstruct sensor faults. Two common types of sensor faults are investigated, drift error and shift or offset error. In the case of drift error, the sensor error occurs gradually while in the case of shift error, the sensor error occurs abruptly. EAANN catches these error types. A chiller model provided synthetic data to test the diagnostic approach under various noise level conditions. The results show that sensor faults can be detected and corrected in noisy situations with the E-AANN method described. In high noisy situations (10% to 20% noise level), E-AANN performance degrades. E-AANN performance in simple dynamic systems was also investigated. The results show that in simple dynamic situations, E-AANN identifies faulty sensors.

Η Μονάδα Εντατικής Θεραπείας – Μ.Ε.Θ. (Intensive Care Unit – ICU) προϋποθέτει ευρύ φάσμα γνώσεων από έναν επαγγελματία υγείας (νοσηλευτή ή ιατρό), που εργάζεται στον χώρο. Σε καθημερινή βάση έρχεται αντιμέτωπος µε απειλητικές καταστάσεις για τη ζωή του ασθενούς και η αντιμετώπιση των διαταραχών της οξεοβασικής ισορροπίας είναι το στοίχημα, που πρέπει να κερδηθεί. Η ορθή ερμηνεία της ανάλυσης των αερίων του αρτηριακού αίματος από έναν επαγγελματία υγείας αποτελεί το βασικό συστατικό για την προαγωγή της υγείας ενός ασθενή στη Μ.Ε.Θ. Όμως παρά την αλματώδη τεχνολογική εξέλιξη του εργαστηριακού τομέα διαπιστώνουμε μια πολύχρονη στασιμότητα στον διαγνωστικό τομέα. Αυτό έχει ως αποτέλεσμα την επιπλέον επιβάρυνση της υγείας του ασθενή, αλλά και επιπρόσθετο φορτίο στο οικονομικό σκέλος. Στη παρούσα διπλωματική εργασία περιγράφεται η δημιουργία ενός ευφυούς συστήματος υποστήριξης αποφάσεων, με σκοπό να αποτελέσει σύμβουλο λήψης αποφάσεων από μη εξειδικευμένους επαγγελματίες υγείας, όταν αυτοί αντιμετωπίζουν προβλήματα οξεοβασικών διαταραχών στις ΜΕΘ, αλλά και να συμβάλλει στην αποτελεσματικότερη και ταχύτερη υποστήριξη του νοσηλευτικού και ιατρικού προσωπικού γενικά. Επίσης στόχος της διπλωματικής αυτής εργασίας είναι να αξιολογηθούν και να συγκριθούν οι μέθοδοι, διά των οποίων δημιουργήθηκε το ευφυές σύστημα. Για να γίνει αυτό δημιουργήσαμε 4 διαφορετικά συστήματα. Στη δημιουργία του πρώτου συστήματος χρησιμοποιήθηκαν κανόνες ασαφούς λογικής(FuzzyClips) και στα επόμενα τρία, μέθοδοι εξόρυξης γνώσης με μηχανική μάθηση. Στο τέλος έγινε η σύγκριση αποτελεσματικότητάς τους, με βάση διεθνώς χρησιμοποιούμενες μετρικές.Τα συστήματα με τη βοήθεια μεθόδων εξόρυξης γνώσης του WEKA παρουσίασαν και τη μεγαλύτερη αποτελεσματικότητα. Τα συστήματα αυτά δεν έχουν σκοπό την αντικατάσταση ενός επαγγελματία υγείας. Έχουν ως κύριο στόχο να λειτουργήσουν επικουρικά, στην καλύτερη, ταχύτερη και πιο αξιόπιστη διάγνωση οξεοβασικών διαταραχών των ασθενών, που νοσηλεύονται σε Μονάδα Εντατικής Θεραπείας ενός νοσοκομείου, αλλά και να χρησιμοποιηθεί ως εργαλείο με εκπαιδευτικό χαρακτήρα σε αρχάριους επαγγελματίες υγείας . / -

Ένα εκπαιδευτικό λογισμικό κατασκευάζεται, προκειμένου με τη χρήση του να εκπληρωθούν συγκεκριμένοι μαθησιακοί στόχοι. Μπορεί να χρησιμοποιηθεί ως συμπληρωματικό μέσο διδασκαλίας από τον εκπαιδευτή ή ως υποστηρικτικό μέσο αυτοδιδασκαλίας από τον εκπαιδευόμενο. Αποτελεί μέσο αξιολόγησης ή αυτοαξιολόγησης του εκπαιδευόμενου, χωρίς βέβαια αυτό να αποτελεί κύριο σκοπό για την κατασκευή του. Οι σύγχρονες τεχνολογίες εκπαιδευτικού λογισμικού, που βασίζονται στις τεχνολογίες δικτύων υπολογιστών και των συστημάτων υπερμέσων, προσφέρουν την δυνατότητα να εξηγούνται, με παραστατικό τρόπο και πολλαπλά μέσα παρουσίασης, τα γνωστικά αντικείμενα, να διευκολύνεται η επικοινωνία και η συνεργασία μεταξύ εκπαιδευόμενων και εκπαιδευτών, να καταργείται η αποκλειστική χρήση μιας πηγής μαθησιακού υλικού, η οποία πολλές φορές περιέχει ξεπερασμένες πληροφορίες και, ως συνεπακόλουθο όλων αυτών, να μπορεί να αναπτύσσεται η κριτική σκέψη του υποκειμένου στην εκπαίδευση. Στην εργασία αυτή παρουσιάζουμε την συμβολή ενός τέτοιου προηγμένου συστήματος στην δημιουργία ενός μαθήματος για την Ακτινοπροστασία στους χώρους Υγείας. Για το σκοπό αυτό χρησιμοποιήσαμε μια εκπαιδευτική πλατφόρμα ικανή να παρουσιάζει προσαρμοστικά το περιεχόμενο, να προτείνει μαθησιακές δραστηριότητες ανάλογα με τον εκπαιδευόμενο, να προσφέρει διαφορετικούς τρόπους επικοινωνίας και συνεργασίας ανάλογα με το επίπεδο και τη διάθεση του μαθητή. Περιγράφουμε τους λόγους για τους οποίους τέτοια συστήματα μπορούν να προσφέρουν στην Ιατρική εκπαίδευση, καθώς και το πόσο απαραίτητο είναι το μάθημα της ακτινοπροστασίας για τα επαγγέλματα Υγείας. Παραθέτουμε την λειτουργικότητα των εργαλείων, τα οποία έχουν στη διάθεση εκπαιδευτές και εκπαιδευόμενοι, και τέλος αναφέρουμε τις τροποποιήσεις που κάναμε προκειμένου το σύστημα να διαθέτει ένα προσαρμοστικό τρόπο αξιολόγησης. Δίνουμε τα αποτελέσματα μιας πρώιμης αξιολόγησης του συστήματος-μαθήματος, από φοιτητές της Νοσηλευτικής του Τεχνολογικού Ινστιτούτου της Πάτρας. Τέλος αναφέρουμε μια συνοπτική περιγραφή της αρχιτεκτονικής και του τρόπου υλοποίησης του συστήματος. Η παρούσα εργασία αποτελεί ένα πρότυπο τόσο του πώς μια εκπαιδευτική πλατφόρμα μπορεί να χρησιμοποιηθεί για εκπαίδευση στους χώρους Υγείας, όσο και του πώς μια προϋπάρχουσα τέτοια εκπαιδευτική πλατφόρμα μπορεί να βελτιωθεί χρησιμοποιώντας τεχνολογίες τεχνητής νοημοσύνης. / -

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2017-02-21T14:53:51Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) main_abntex.pdf: 4558296 bytes, checksum: 6f077e7cc7e54787fdfdb3b25b18eabb (MD5) / Made available in DSpace on 2017-02-21T14:53:51Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) main_abntex.pdf: 4558296 bytes, checksum: 6f077e7cc7e54787fdfdb3b25b18eabb (MD5) Previous issue date: 2016-09-26 / A previsão de séries temporais é uma tarefa importante no campo da aprendizado de máquina, possuindo diversas aplicações em mercado de ações, hidrologia, meteorologia, entre outros. A análise da dependência existente nas observações adjacentes da série é necessária para que seja possível prever valores futuros com alguma precisão. Modelos dinâmicos são utilizados para realizar mapeamentos de uma série temporal, se aproximando do mecanismo gerador da série e sendo capazes de realizar previsões. No entanto, o mecanismo gerador de uma série temporal pode produzir padrões lineares e não-lineares que precisam ser devidamente mapeados. Modelos lineares como o auto-regressivo integrado de média móvel (ARIMA) são capazes de mapear padrões lineares, porém não são indicados quando existem padrões não-lineares na série. Já os modelos não-lineares como as redes neurais artificais (RNA) mapeiam padrões não-lineares, mas podem apresentar desempenho reduzido na presença de padrões lineares em relação aos modelos lineares. Fatores como a definição do número de elementos de entrada da RNA, número de amostras de treinamento podem afetar o desempenho. Abordagens híbridas presentes na literatura realizam o mapeamento dos padrões lineares e não-lineares simultaneamente ou aplicando duas ou mais fases nas previsões. Seguindo a suposição de que os modelos são bem ajustados, a diferença entre o valor previsto e a série real demonstra um comportamento de ruído branco, ou seja, considera-se que a diferença entre os valores (resíduo) é composta por choques aleatórios não correlacionados. Na abordagem de duas ou mais fases, o resíduo gerado pelo modelo aplicado na primeira fase é utilizado pelo segundo modelo. O problema do ajuste pode ser decorrente dos parâmetros mal ajustados e também da série temporal devido à possível necessidade de transformações. Tais abordagens geram previsões mais precisas quando comparadas às técnicas tradicionais. Nesta tese, são explorados sistemas evolucionários para a otimização de parâmetros de técnicas lineares e não-lineares visando o mapeamento dos padrões da série temporal. A abordagem proposta utiliza um preprocessamento automático através de um filtro de suavização exponencial para extrair uma série com distribuição normal. A diferença da série temporal e a série filtrada é mapeada por um sistema composto por um método auto-regressivo (AR) e máquina de vetor de suporte para regressão (SVR). Variações do algoritmo de otimização por enxame de partículas (PSO) e algoritmos genéticos são aplicados na otimização dos hiper-parâmetros do sistema. A previsão final é realizada através da soma das previsões de cada série. Para fins de avaliação do método proposto, experimentos foram realizados com bases de problemas reais utilizando métodos da literatura. Os resultados demonstram que o método obteve previsões precisas na maioria dos casos testados. O filtro de suavização exponencial utilizado supõe que a série possua nível constante (sem tendência). Séries que possuem tendências lineares foram devidamente tratadas, no entanto tendências exponenciais ou polinomiais apresentaram desempenho reduzido. O método proposto possui potencial para melhorias, aplicando métodos que realizem o mapeamento automático de tendências como a suavização exponencial dupla. Nesta tese o método aditivo foi utilizado para combinação de previsões, no entanto em algumas séries o modelo multiplicativo pode ser mais adequado, produzindo previsões mais precisas. / Time series forecasting is an important task in the field of machine learning and has many applications in stock market, hydrology, weather and so on. The analysis of the dependence between adjacent observations in the series is necessary in order to achieve better forecasts. Dynamic models are used to perform mappings in the time series by approximating to thedata generating process and being able to perform predictions. However, the data generating process of a time series may produce both linear and nonlinear patterns that need to be mapped. Linear models such as the autoregressive integrated moving average (ARIMA) are able to map linear patterns, although not indicated when nonlinear patterns are present in the series. Nonlinear models such as the artificial neural networks (ANNs) perform nonlinear mappings but demonstrate reduced performance in the presence of linear patterns in comparison to linear models. Hybrid approaches in the literature perform mappings of linear and nonlinear patterns simultaneously or applying two or more phases.Supposing that the models are adjusted to the data, the difference between the predicted value and the data presents a White noise behavior, thus it is considered that the difference of values (residual) is composed by uncorrelated random shocks. In two-phase approaches the residual produced by the linear model in the first phase is used in the nonlinear model. Also the parameters of the models have an important influence on their performance. Such approaches produce more accurate predictions when compared with traditional methods. In this thesis, we explore evolutionary system in the context of optimization of parameters for both linear and nonlinear methods, taking into consideration the patterns in a time series. In the proposed approach, an exponential smoothing filter is used to decompose a series with normal distribution which is applied to an ARIMA model and the residual series is applied to a system composed by an autoregressive (AR) and a support vector regression methods (SVR). Variations of particle swarm optimization (PSO) algorithm and genetic algorithm (GA) are employed in the optimization of hyper-parameters of the system. Experiments were conducted using data sets from real world problems comparing with methods in the literature. The results indicate that the method achieved accurate predictions in most cases. The exponential smoothing filter assumes that the given series has no trend patterns. Series with linear trend were detrended, however in series with exponential or polynomial trends the proposed method achieved reduced performance. The proposed method has potential to improvements by using methods that perform an automatic mapping of trend patterns (double exponential smoothing). In this work, the additive model is adopted, however in some series a multiplicative model could achieve better forecasts.

Identificar e localizar faltas em alimentadores de distribuição representa um passo importante para a melhoria da qualidade de energia, pois proporciona impactos diretos sobre o tempo de inspeção. Na verdade, a duração da inspeção implica consideravelmente no intervalo em que os consumidores estão sem energia elétrica, quando ocorre uma interrupção não programada. O objetivo deste trabalho é fornecer um sistema de detecção automática de curtos-circuitos, permitindo aos profissionais das companhias de distribuição acompanhar e monitorar de maneira on-line a ocorrência de possíveis faltas e transitórios eletromagnéticos observados na rede primária de distribuição. A abordagem de detecção utiliza um sistema híbrido que combina ferramentas inteligentes e convencionais para identificar e localizar faltas em redes primárias. Os resultados que foram compilados demonstram grande potencialidade de aplicação da proposta em sistemas de distribuição. / Efficient faults identification and location in power distribution lines constitute an important step for power quality improvement, since they provide direct impacts on the inspection time. In fact, the duration of inspection implies directly in the time interval where consumers are without power, considering here the occurrence of a non-programmed interruption. The objective of this work is to provide an automated fault detection system, allowing to the power companies engineers to online track and monitor the possible occurrence of faults and electromagnetic transients observed in the primary network for the distribution circuits. The detection approach uses a hybrid system, which combines a set of intelligent and conventional tools to identify and locate faults in the primary networks. Validation results show great application potential in distribution systems.

Driving a vehicle involves a series of events, which are related to and evolve with the mental state (such as sleepiness, mental load, and stress) of the driv- er. These states are also identified as causal factors of critical situations that can lead to road accidents and vehicle crashes. These driver impairments need to be detected and predicted in order to reduce critical situations and road accidents. In the past years, physiological signals have become conven- tional measures in driver impairment research. Physiological signals have been applied in various studies to identify different levels of mental load, sleepiness, and stress during driving. This licentiate thesis work has investigated several artificial intelligence algorithms for developing an intelligent system to monitor driver mental state using physiological signals. The research aims to measure sleepiness and mental load using Electroencephalography (EEG). EEG signals, if pro- cessed correctly and efficiently, have potential to facilitate advanced moni- toring of sleepiness, mental load, fatigue, stress etc. However, EEG signals can be contaminated with unwanted signals, i.e., artifacts. These artifacts can lead to serious misinterpretation. Therefore, this work investigates EEG arti- fact handling methods and propose an automated approach for EEG artifact handling. Furthermore, this research has also investigated how several other physiological parameters (Heart Rate (HR) and Heart Rate Variability (HRV) from the Electrocardiogram (ECG), Respiration Rate, Finger Tem- perature (FT), and Skin Conductance (SC)) to quantify drivers’ stress. Dif- ferent signal processing methods have been investigated to extract features from these physiological signals. These features have been extracted in the time domain, in the frequency domain as well as in the joint time-frequency domain using wavelet analysis. Furthermore, data level signal fusion has been proposed using Multivariate Multiscale Entropy (MMSE) analysis by combining five physiological sensor signals. Primarily Case-Based Reason- ing (CBR) has been applied for drivers’ mental state classification, but other Artificial intelligence (AI) techniques such as Fuzzy Logic, Support Vector Machine (SVM) and Artificial Neural Network (ANN) have been investigat- ed as well. For drivers’ stress classification, using the CBR and MMSE approach, the system has achieved 83.33% classification accuracy compared to a human expert. Moreover, three classification algorithms i.e., CBR, an ANN, and a SVM were compared to classify drivers’ stress. The results show that CBR has achieved 80% and 86% accuracy to classify stress using finger tempera- ture and heart rate variability respectively, while ANN and SVM reached an accuracy of less than 80%. / Vehicle Driver Monitoring

Orientador: Luiz Carlos Pereira da Silva / Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-18T12:45:34Z (GMT). No. of bitstreams: 1 Niro_Glauco_M.pdf: 13465948 bytes, checksum: 504eb63346ec29f71bef99cd507174e1 (MD5) Previous issue date: 2011 / Resumo: Os sistemas elétricos vêm sendo alvo de estudos que buscam torná-los mais inteligentes e seguros. Um novo paradigma surge com o conceito das redes inteligentes (smart grids), as quais incluem como uma das suas características principais a integração de equipamentos dos consumidores de forma ativa à operação do sistema elétrico. Um exemplo é o gerenciamento da demanda no horário de pico, aliviando a carga e aumentando a segurança do sistema, com o objetivo de obter benefícios para o sistema elétrico e também para os consumidores. Para que isso seja possível, são necessários alguns requisitos e avanços tecnológicos: existência de um portal de comunicação para interface entre o consumidor e seus aparelhos e a concessionária; equipamentos que permitam tal gerenciamento sem prejudicar e interferir na rotina dos consumidores; e mecanismos de compensação financeira para incentivar a participação dos consumidores. Nesta dissertação se propõe um estudo sobre um aparelho que apresenta potencial para esse tipo de gerenciamento, o refrigerador doméstico. Devido a sua inércia térmica e isolamento o refrigerador pode ser ligado e desligado durante algum tempo sem prejuízos para sua função principal. Os objetivos deste trabalho são: desenvolver um modelo adequado para a simulação computacional de um grupo de refrigeradores; implementar esquemas de gerenciamento que visem a redução de consumo no horário de pico; bem como analisar o efeito que estes procedimentos acarretariam em uma rede de distribuição de energia elétrica, levando em conta a redução do consumo no período de pico, a redução de perdas e a melhoria do perfil de tensão / Abstract: The electrical systems have been exposed to new researches and developments with the main objectives of a smarter and safer operation and planning. A new paradigm arises with the concept of smart grids, which include as one of its main characteristics the integration of consumer's appliances in the system operation in a more active way. An example is the demand-side management at peak time, relieving the load and increasing system security, in order to obtain benefits to the electrical system and also to consumers. In order to make this possible, some requirements and technological advances are necessary: the existence of a communication interface between the consumers and their appliances and the utility; smart appliances that allow such management without any damage or interference on the routine of the consumer; compensation mechanisms to encourage consumer participation. In this dissertation it is proposed a study about a device that has potential to this kind of management, the household refrigerator. Due to its thermal inertia and isolation, the refrigerator can be turned on and off for some time without degradation of its primary function. The objectives of this work are: development of an adequate model for the computational simulation of a group of refrigerators; implementation of management schemes to reduce the consumption at peak time; analysis of the impacts of such procedures to a distribution grid, taking into consideration the reduction of the consumption at the peak hours, reduction of losses and improvement on voltage profile / Mestrado / Energia Eletrica / Mestre em Engenharia Elétrica