246 research outputs found

    Integration of Artificial Neural Networks and Simulation Modeling in a Decision Support System

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    A simulation based decision support system is developed for AT&T Microelectronics in Orlando. This system uses simulation modeling to capture the complex nature of semiconductor test operations. Simulation, however, is not a tool for optimization by itself. Numerous executions of the simulation model must generally be performed to narrow in on a set of proper decision parameters. As a means of alleviating this shortcoming, artificial neural networks are used in conjunction with simulation modeling to aid management in the decision making process. The integration of simulation and neural networks in a comprehensive decision support system, in effect, learns the reverse of the simulation process. That is, given a set of goals defined for performance measures, the decision support system suggests proper values for decision parameters to achieve those goals

    Feedforward deep architectures for classification and synthesis

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    Cette thèse par article présente plusieurs contributions au domaine de l'apprentissage de représentations profondes, avec des applications aux problèmes de classification et de synthèse d'images naturelles. Plus spécifiquement, cette thèse présente plusieurs nouvelles techniques pour la construction et l'entraînment de réseaux neuronaux profonds, ainsi q'une étude empirique de la technique de «dropout», une des approches de régularisation les plus populaires des dernières années. Le premier article présente une nouvelle fonction d'activation linéaire par morceau, appellée «maxout», qui permet à chaque unité cachée d'un réseau de neurones d'apprendre sa propre fonction d'activation convexe. Nous démontrons une performance améliorée sur plusieurs tâches d'évaluation du domaine de reconnaissance d'objets, et nous examinons empiriquement les sources de cette amélioration, y compris une meilleure synergie avec la méthode de régularisation «dropout» récemment proposée. Le second article poursuit l'examen de la technique «dropout». Nous nous concentrons sur les réseaux avec fonctions d'activation rectifiées linéaires (ReLU) et répondons empiriquement à plusieurs questions concernant l'efficacité remarquable de «dropout» en tant que régularisateur, incluant les questions portant sur la méthode rapide de rééchelonnement au temps de l´évaluation et la moyenne géometrique que cette méthode approxime, l'interprétation d'ensemble comparée aux ensembles traditionnels, et l'importance d'employer des critères similaires au «bagging» pour l'optimisation. Le troisième article s'intéresse à un problème pratique de l'application à l'échelle industrielle de réseaux neuronaux profonds au problème de reconnaissance d'objets avec plusieurs etiquettes, nommément l'amélioration de la capacité d'un modèle à discriminer entre des étiquettes fréquemment confondues. Nous résolvons le problème en employant la prédiction du réseau des sous-composantes dédiées à chaque sous-ensemble de la partition. Finalement, le quatrième article s'attaque au problème de l'entraînment de modèles génératifs adversariaux (GAN) récemment proposé. Nous présentons une procédure d'entraînment améliorée employant un auto-encodeur débruitant, entraîné dans un espace caractéristiques abstrait appris par le discriminateur, pour guider le générateur à apprendre un encodage qui s'aligne de plus près aux données. Nous évaluons le modèle avec le score «Inception» récemment proposé.This thesis by articles makes several contributions to the field of deep learning, with applications to both classification and synthesis of natural images. Specifically, we introduce several new techniques for the construction and training of deep feedforward networks, and present an empirical investigation into dropout, one of the most popular regularization strategies of the last several years. In the first article, we present a novel piece-wise linear parameterization of neural networks, maxout, which allows each hidden unit of a neural network to effectively learn its own convex activation function. We demonstrate improvements on several object recognition benchmarks, and empirically investigate the source of these improvements, including an improved synergy with the recently proposed dropout regularization method. In the second article, we further interrogate the dropout algorithm in particular. Focusing on networks of the popular rectified linear units (ReLU), we empirically examine several questions regarding dropout’s remarkable effectiveness as a regularizer, including questions surrounding the fast test-time rescaling trick and the geometric mean it approximates, interpretations as an ensemble as compared with traditional ensembles, and the importance of using a bagging-like criterion for optimization. In the third article, we address a practical problem in industrial-scale application of deep networks for multi-label object recognition, namely improving an existing model’s ability to discriminate between frequently confused classes. We accomplish this by using the network’s own predictions to inform a partitioning of the label space, and augment the network with dedicated discriminative capacity addressing each of the partitions. Finally, in the fourth article, we tackle the problem of fitting implicit generative models of open domain collections of natural images using the recently introduced Generative Adversarial Networks (GAN) paradigm. We introduce an augmented training procedure which employs a denoising autoencoder, trained in a high-level feature space learned by the discriminator, to guide the generator towards feature encodings which more closely resemble the data. We quantitatively evaluate our findings using the recently proposed Inception score

    Adaptive Order Dispatching based on Reinforcement Learning: Application in a Complex Job Shop in the Semiconductor Industry

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    Heutige Produktionssysteme tendieren durch die Marktanforderungen getrieben zu immer kleineren Losgrößen, höherer Produktvielfalt und größerer Komplexität der Materialflusssysteme. Diese Entwicklungen stellen bestehende Produktionssteuerungsmethoden in Frage. Im Zuge der Digitalisierung bieten datenbasierte Algorithmen des maschinellen Lernens einen alternativen Ansatz zur Optimierung von Produktionsabläufen. Aktuelle Forschungsergebnisse zeigen eine hohe Leistungsfähigkeit von Verfahren des Reinforcement Learning (RL) in einem breiten Anwendungsspektrum. Im Bereich der Produktionssteuerung haben sich jedoch bisher nur wenige Autoren damit befasst. Eine umfassende Untersuchung verschiedener RL-Ansätze sowie eine Anwendung in der Praxis wurden noch nicht durchgeführt. Unter den Aufgaben der Produktionsplanung und -steuerung gewährleistet die Auftragssteuerung (order dispatching) eine hohe Leistungsfähigkeit und Flexibilität der Produktionsabläufe, um eine hohe Kapazitätsauslastung und kurze Durchlaufzeiten zu erreichen. Motiviert durch komplexe Werkstattfertigungssysteme, wie sie in der Halbleiterindustrie zu finden sind, schließt diese Arbeit die Forschungslücke und befasst sich mit der Anwendung von RL für eine adaptive Auftragssteuerung. Die Einbeziehung realer Systemdaten ermöglicht eine genauere Erfassung des Systemverhaltens als statische Heuristiken oder mathematische Optimierungsverfahren. Zusätzlich wird der manuelle Aufwand reduziert, indem auf die Inferenzfähigkeiten des RL zurückgegriffen wird. Die vorgestellte Methodik fokussiert die Modellierung und Implementierung von RL-Agenten als Dispatching-Entscheidungseinheit. Bekannte Herausforderungen der RL-Modellierung in Bezug auf Zustand, Aktion und Belohnungsfunktion werden untersucht. Die Modellierungsalternativen werden auf der Grundlage von zwei realen Produktionsszenarien eines Halbleiterherstellers analysiert. Die Ergebnisse zeigen, dass RL-Agenten adaptive Steuerungsstrategien erlernen können und bestehende regelbasierte Benchmarkheuristiken übertreffen. Die Erweiterung der Zustandsrepräsentation verbessert die Leistung deutlich, wenn ein Zusammenhang mit den Belohnungszielen besteht. Die Belohnung kann so gestaltet werden, dass sie die Optimierung mehrerer Zielgrößen ermöglicht. Schließlich erreichen spezifische RL-Agenten-Konfigurationen nicht nur eine hohe Leistung in einem Szenario, sondern weisen eine Robustheit bei sich ändernden Systemeigenschaften auf. Damit stellt die Forschungsarbeit einen wesentlichen Beitrag in Richtung selbstoptimierender und autonomer Produktionssysteme dar. Produktionsingenieure müssen das Potenzial datenbasierter, lernender Verfahren bewerten, um in Bezug auf Flexibilität wettbewerbsfähig zu bleiben und gleichzeitig den Aufwand für den Entwurf, den Betrieb und die Überwachung von Produktionssteuerungssystemen in einem vernünftigen Gleichgewicht zu halten

    MCMC-Interactive Variational Inference

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    Leveraging well-established MCMC strategies, we propose MCMC-interactive variational inference (MIVI) to not only estimate the posterior in a time constrained manner, but also facilitate the design of MCMC transitions. Constructing a variational distribution followed by a short Markov chain that has parameters to learn, MIVI takes advantage of the complementary properties of variational inference and MCMC to encourage mutual improvement. On one hand, with the variational distribution locating high posterior density regions, the Markov chain is optimized within the variational inference framework to efficiently target the posterior despite a small number of transitions. On the other hand, the optimized Markov chain with considerable flexibility guides the variational distribution towards the posterior and alleviates its underestimation of uncertainty. Furthermore, we prove the optimized Markov chain in MIVI admits extrapolation, which means its marginal distribution gets closer to the true posterior as the chain grows. Therefore, the Markov chain can be used separately as an efficient MCMC scheme. Experiments show that MIVI not only accurately and efficiently approximates the posteriors but also facilitates designs of stochastic gradient MCMC and Gibbs sampling transitions.Comment: 25 pages, 7 figures, 3 table

    Searching for New Physics using Classical and Quantum Machine Learning

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    The development of machine learning (ML) has provided the High Energy Physics (HEP) community with new methods of analysing collider and Monte-Carlo generated data. As experiments are upgraded to generate an increasing number of events, classical techniques can be supplemented with ML to increase our ability to find signs of New Physics in the high-dimensional event data. This thesis presents three methods of performing supervised and unsupervised searches using novel ML methods. The first depends on the use of an autoencoder to perform an unsupervised anomaly detection search. We demonstrate that this method allows you to carry out a data-driven, model-independent search for New Physics. Furthermore, we show that by extending the model with an adversary we can account for systematic errors that may arise from experiments. The second method develops a form of quantum machine learning to be applied to a supervised search. Using a variational quantum classifier (a neural network style model built from quantum information principles) we demonstrate a quantum advantage arises when compared to a classical network. Finally, we make use of the continuous-variable (CV) paradigm of quantum computing to build an unsupervised method of classifying events stored as graph data. Gaussian boson sampling provides an example of a quantum advantage unique to the CV method of quantum computing and allows our events to be used in an anomaly detector model built using the Q-means clustering algorithm

    Performance enhancement of active structures during service lives

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    This thesis describes a successful application of advanced computational methods to tasks in the field of active structural control. The control-task involves finding good control movements for a highly coupled, non-linear structure. It is demonstrated how these methods improve the accuracy of the analytical model. Also, stochastic search techniques are compared for the same task. Furthermore, the performance of the system can be enhanced during service life by storing, retrieving and adapting good solutions. The structure studied, a Tensegrity, is a special type of cable structure. Tensegrities stimulate the imagination of artists, researchers and engineers. Varying the amount of selftress changes structural shape as well as the load-bearing capacity. They offer unique applications, as deployable structures in the context of aerospace applications and more generally, as actively controlled structures. However, the non-linear behavior of tensegrities is difficult to model. Aspects of this work involve subjects such as tensegrity structures, active structural control, search algorithms and artificial intelligence. The focus of this thesis is on the last two subjects. This work demonstrates how advanced computing techniques can be used in order to increase solution quality. A hybrid approach, employing neural networks, increases the accuracy of the analytical model that is employed for simulating tensegrity structures. A comparison of three stochastic search techniques shows that computational time, first estimated to take centuries when adapting a "brute-force" approach, can be reduced to hours. Case-based reasoning (CBR) is used for a further tenfold decrease in computation time. The time needed to find good control solutions decreased from hours, when stochastic search is used, to minutes with CBR. CBR also provides possibilities for improving performance over service life. Successfully solved situations are stored as cases in a case-base. In new situations, a case close to the new situation is retrieved and then adapted. By storing additional cases, the system is able to retrieve better cases for adaptation. With increasing case-base size, adaptation time decreases. The combination of these techniques has much potential for improving the performance of complex structures during service lives. Results should contribute to the development of innovative structural solutions. Finally, it is expected that the findings in this thesis will become points of departure for subsequent studies
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