Automated knowledge acquisition tool for identification of generic tasks

Recent research in the Knowledge Acquisition (KA) field, centers on defining a formal methodology for the KA process. This research includes the following goals: automating the KA process to decrease the KA time constraint; applying psychological techniques to extract the underlying structure of the expert\u27s knowledge; and defining expertise in terms of generic tasks to yield possible knowledge organizations and strategies for the implementation of the expert system. This thesis provides an overview of the benefits and concerns of an automated KA system, psychological scaling techniques as they apply to KA, and the relevance of generic tasks. A generic task defines a knowledge type and organization, and a control strategy that characterizes a component of an expert system. This thesis also includes the design and implementation of a Knowledge Acquisition Tool for Identification of Generic Tasks. This tool provides an interface to the expert for the initial KA encounter. Using psychological techniques, the tool extracts a list of the main concepts of expertise, and elicits a rating from the expert comparing the similarity of each of these concepts to generic task concepts. The results become inputs to a clustering technique that organize the concepts into the generic tasks. The result of any concepts that do not cluster could identify a previously undefined generic task. The implementation is in the C language, accessing the FASTCLUS procedure of the SAS software package for VAX hardware

Fingerprint-based localization in massive MIMO systems using machine learning and deep learning methods

À mesure que les réseaux de communication sans fil se développent vers la 5G, une énorme quantité de données sera produite et partagée sur la nouvelle plate-forme qui pourra être utilisée pour promouvoir de nouveaux services. Parmis ceux-ci, les informations de localisation des terminaux mobiles (MT) sont remarquablement utiles. Par exemple, les informations de localisation peuvent être utilisées dans différents cas de services d'enquête et d'information, de services communautaires, de suivi personnel, ainsi que de communications sensibles à la localisation. De nos jours, bien que le système de positionnement global (GPS) des MT offre la possibilité de localiser les MT, ses performances sont médiocres dans les zones urbaines où une ligne de vue directe (LoS) aux satellites est bloqué avec de nombreux immeubles de grande hauteur. En outre, le GPS a une consommation d'énergie élevée. Par conséquent, les techniques de localisation utilisant la télémétrie, qui sont basées sur les informations de signal radio reçues des MT tels que le temps d'arrivée (ToA), l'angle d'arrivée (AoA) et la réception de la force du signal (RSS), ne sont pas en mesure de fournir une localisation de précision satisfaisante. Par conséquent, il est particulièrement difficile de fournir des informations de localisation fiables des MT dans des environnements complexes avec diffusion et propagation par trajets multiples. Les méthodes d'apprentissage automatique basées sur les empreintes digitales (FP) sont largement utilisées pour la localisation dans des zones complexes en raison de leur haute fiabilité, rentabilité et précision et elles sont flexibles pour être utilisées dans de nombreux systèmes. Dans les réseaux 5G, en plus d'accueillir plus d'utilisateurs à des débits de données plus élevés avec une meilleure fiabilité tout en consommant moins d'énergie, une localisation de haute précision est également requise. Pour relever un tel défi, des systèmes massifs à entrées multiples et sorties multiples (MIMO) ont été introduits dans la 5G en tant que technologie puissante et potentielle pour non seulement améliorer l'efficacité spectrale et énergétique à l'aide d'un traitement relativement simple, mais également pour fournir les emplacements précis des MT à l'aide d'un très grand nombre d'antennes associées à des fréquences porteuses élevées. Il existe deux types de MIMO massifs (M-MIMO), soit distribué et colocalisé. Ici, nous visons à utiliser la méthode basée sur les FP dans les systèmes M-MIMO pour fournir un système de localisation précis et fiable dans un réseau sans fil 5G. Nous nous concentrons principalement sur les deux extrêmes du paradigme M-MIMO. Un grand réseau d'antennes colocalisé (c'est-à-dire un MIMO massif colocalisé) et un grand réseau d'antennes géographiquement distribué (c'est-à-dire un MIMO massif distribué). Ensuite, nous ex trayons les caractéristiques du signal et du canal à partir du signal reçu dans les systèmes M-MIMO sous forme d'empreintes digitales et proposons des modèles utilisant les FP basés sur le regroupement et la régression pour estimer l'emplacement des MT. Grâce à cette procédure, nous sommes en mesure d'améliorer les performances de localisation de manière significative et de réduire la complexité de calcul de la méthode basée sur les FP.As wireless communication networks are growing into 5G, an enormous amount of data will be produced and shared on the new platform, which can be employed in promoting new services. Location information of mobile terminals (MTs) is remarkably useful among them, which can be used in different use cases of inquiry and information services, community services, personal tracking, as well as location-aware communications. Nowadays, although the Global Positioning System (GPS) offers the possibility to localize MTs, it has poor performance in urban areas where a direct line-of-sight (LoS) to the satellites is blocked by many tall buildings. Besides, GPS has a high power consumption. Consequently, the ranging based localization techniques, which are based on radio signal information received from MTs such as time-of-arrival (ToA), angle-of-arrival (AoA), and received signal strength (RSS), are not able to provide satisfactory localization accuracy. Therefore, it is a notably challenging problem to provide precise and reliable location information of MTs in complex environments with rich scattering and multipath propagation. Fingerprinting (FP)-based machine learning methods are widely used for localization in complex areas due to their high reliability, cost-efficiency, and accuracy and they are flexible to be used in many systems. In 5G networks, besides accommodating more users at higher data rates with better reliability while consuming less power, high accuracy localization is also required in 5G networks. To meet such a challenge, massive multiple-input multiple-output (MIMO) systems have been introduced in 5G as a powerful and potential technology to not only improve spectral and energy efficiency using relatively simple processing but also provide an accurate locations of MTs using a very large number of antennas combined with high carrier frequencies. There are two types of massive MIMO (M-MIMO), distributed and collocated. Here, we aim to use the FP-based method in M-MIMO systems to provide an accurate and reliable localization system in a 5G wireless network. We mainly focus on the two extremes of the M-MIMO paradigm. A large collocated antenna array (i.e., collocated M-MIMO ) and a large geographically distributed antenna array (i.e., distributed M-MIMO). Then, we extract signal and channel features from the received signal in M-MIMO systems as fingerprints and propose FP-based models using clustering and regression to estimate MT's location. Through this procedure, we are able to improve localization performance significantly and reduce the computational complexity of the FP-based method

Entwicklung einer Methode zum Einsatz von Reinforcement Learning für die dynamische Fertigungsdurchlaufsteuerung

Ziel dieser Arbeit ist es, eine Methode zu entwickeln, mit der die Matrixproduktion im Falle einer Störung umgeplant werden kann. Zu diesem Zweck werden verschiedene Methoden der künstlichen Intelligenz in neuartiger Weise kombiniert. Die entwickelte Methode wird anhand eines theoretischen und einem realen Terminierungsfalles validiert

Advanced Storage and Retrieval Policies in Automated Warehouses

Warehouses are key components in supply chain. They facilitate the product flow from production to distribution. The performance of supply chains relies on the performance of warehouses and distribution centers. Being able to realize short order delivery lead times, in retail and ecommerce particularly, is important for warehouses. Efficient and responsive storage and retrieval operations can help in realizing a short order delivery lead time. Additionally, space scarcity has brought some companies to use high-density storage systems that increase space usage in the warehouse. In such storage systems, most of the available space is used for storing products, as little space is needed for transporting loads. However, the throughput capacity of high-density storage systems is typically low. New robotic and automated technologies help warehouses to increase their throughput and responsiveness. Warehouses adapting such technologies require customized storage and retrieval policies fit for automated operations. This thesis studies storage and retrieval policies in warehouses using several common and emerging automated technologies

Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm

Abstract The topology design of switched enterprise networks (SENs) is a hard constrained combinatorial optimization problem. The problem consists of deciding the number, types, and locations of the network active elements (hubs, switches, and routers), as well as the links and their capacities. Several conflicting objectives such as monetary cost, network delay, and maximum number of hops have to be optimized to achieve a desirable solution. Further, many of the desirable features of a network topology can best be expressed in linguistic terms, which is the basis of fuzzy logic. In this paper, we present an approach based on Simulated Evolution algorithm for the design of SEN topology. The overall cost function has been developed using fuzzy logic. Several variants of the algorithm are proposed and compared together via simulation and experimental results are provided. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Enterprise networks; Simulated evolution; Fuzzy logic; NP-hard; Multiobjective optimizatio

Topology design of switched enterprise networks using a fuzzy simulated evolution algorithm

Abstract The topology design of switched enterprise networks (SENs) is a hard constrained combinatorial optimization problem. The problem consists of deciding the number, types, and locations of the network active elements (hubs, switches, and routers), as well as the links and their capacities. Several conflicting objectives such as monetary cost, network delay, and maximum number of hops have to be optimized to achieve a desirable solution. Further, many of the desirable features of a network topology can best be expressed in linguistic terms, which is the basis of fuzzy logic. In this paper, we present an approach based on Simulated Evolution algorithm for the design of SEN topology. The overall cost function has been developed using fuzzy logic. Several variants of the algorithm are proposed and compared together via simulation and experimental results are provided. r 2002 Elsevier Science Ltd. All rights reserved. Keywords: Enterprise networks; Simulated evolution; Fuzzy logic; NP-hard; Multiobjective optimizatio