Different innovative concepts are aiming to improve last-mile urban logistics and reduce traffic congestion. Congested metropolitan cities are implementing last-mile delivery robots to make the delivery cheaper and faster. A key factor for the success of Automated Delivery Robots (ADRs) in the last-mile is its ability to meet the fluctuating demand for robots at each micro-hub. Delivery companies rent robots from micro-hubs scattered around the city, use them for deliveries, and return them at micro-hubs. This paper studies the dynamic assignment of the robots to satisfy their demands between the micro-hubs. A Mixed-Integer Linear Programming (MILP) model is developed, which minimizes the total transportation costs by determining the optimum required fleet size. The result determines the number of robots required for each planning period to meet all the demands. It provides algorithms to operate and schedule the robot-sharing system in the last leg of the delivery in dense urban areas. / Includes bibliography. / Thesis (MS)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

The next-generation (6G) networks promise to provide extended 5G capabilities with enhanced performance at high data rates, low latency, low energy consumption, and rapid adaptation. 6G networks are also expected to support the unprecedented Internet of Everything (IoE) scenarios with highly diverse requirements. With the emerging applications of autonomous driving, virtual reality, and mobile computing, achieving better performance and fulfilling the diverse requirements of 6G networks are becoming increasingly difficult due to the rapid proliferation of wireless data and heterogeneous network structures. In this regard, learning-based algorithms are naturally powerful tools to deal with the numerous data and are expected to impact the evolution of communication networks. This thesis employed learning-based approaches to enhance the performance and fulfill the diverse requirements of the next-generation intelligent networks under various network structures. Specifically, we design the trajectory of the unmanned aerial vehicle (UAV) to provide energy-efficient, high data rate, and fair service for the Internet of things (IoT) networks by employing on/off-policy reinforcement learning (RL). Thereafter, we applied a deep RL-based approach for heterogeneous traffic offloading in the space-air-ground integrated network (SAGIN) to cover the co-existing requirements of ultra-reliable low-latency communication (URLLC) traffic and enhanced mobile broadband (eMBB) traffic. Precise traffic prediction can significantly improve the performance of 6G networks in terms of intelligent network operations, such as predictive network configuration control, traffic offloading, and communication resource allocation. Therefore, we investigate the wireless traffic prediction problem in edge networks by applying a federated meta-learning approach. Lastly, we design an importance-oriented clustering-based high quality of service (QoS) system with software-defined networking (SDN) by adopting unsupervised learning.

The purpose of this mixed-methods study was to investigate the relationship between teachers’ conceptions of mathematics and their use of intelligent tutoring systems for mathematics instruction. Intelligent tutoring systems are adaptive computer programs which administer mathematics instruction to students based on their cognitive state. A conception is a mixture of beliefs and knowledge. The participants in this study were 93 junior high school mathematics teachers from three school districts in the Midwest. Data were gathered using a two-part online survey. The first part of the survey contained questions about their use of intelligent tutoring systems, graphing calculators, Desmos and dynamic geometry software. The second part of the survey contained Likert questions from the teachers’ version of the Conceptions of Mathematics Inventory. Desmos is a website providing interactive classroom activities and a user-friendly graphing calculator. Dynamic geometry software is a class of interactive geometry programs. The quantitative analysis revealed no statistically significant interactions between teachers’ conception scores and intelligent tutoring system use, or between teachers’ conception scores and how intelligent tutoring systems were used. There were statistically significant interactions between teachers’ conception scores and their use of graphing calculators, Desmos, and dynamic geometry software. The qualitative analysis revealed that teachers used intelligent tutoring systems for differentiation. Teachers used graphing calculators, Desmos, and dynamic geometry software for visual, computational, and exploratory purposes. Teachers exclusively using intelligent tutoring systems to incorporate technology should also incorporate technology which promotes student exploration.

A project report submitted to the Faculty of Engineering, University of Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Engineering. Johannesburg 1997. / This project report describes the design of a fuzzy logic controller for automatic generation control (AGC) in Eskom in 1995 and the process of re-optimisation of the fuzzy logic controller in 1997. The main purpose of the AGC controller is to determine the shortfall or surplus generation of electricity for South Africa. The difficulties associated with optimising the original AGC controller, the design,implementation and optimisation of the fuzzy controller are described in detail. [Abbreviated Abstract. Open document to view full version] / AC2017

In this thesis, a 3-D vision system is established to conduct the real-time measurement task for intelligent-open-die forging center. This system consists of a CCD camera, a slit alaser projector and a Kawasaki JS 6-axis robot all interfaced to a PC for image processing. The linear structured light technique is employed for data acquisition. An optical filtering algorithm for laser stripe segmentation is developed to locate the stripe peak positions to sub-pixel accuracy. A new calibration method is proposed to determine the mapping correspondence from 2-D image pixels to 3-D reference points. A cylindrical scanning scheme is applied to gather data about the 3-D shape of parts, so that the overlap problem is easily avoided. Finally, the cross-section at any place along the height of a workpiece is reconstructed so as to detect shape error for adaptive control of automated open-die forging. This vision system is low-cost, accurate and reliable. A cylinder shape has been successfully reconstructed and measured with it. The experimental results illustrating its performance are reported and discussed. / Thesis / Master of Applied Science (MASc)

The material presented in this thesis uses concepts of the finite element and doublet panel methods to develop a structural-aerodynamic coupled mathematical model for the analysis of a morphing wing tip composed of smart materials. Much research is currently being performed within many facets of engineering on the use of smart or intelligent materials. Examples of the beneficial characteristics of smart materials might include altering a structure's mechanical properties, controlling its dynamic response(s) and sensing flaws that might progressively become detrimental to the structure. This thesis describes a bio-inspired adaptive structure that will be used in morphing an aircraft's wing tip. The actuation system is derived from individual flexible matrix composite tube actuators embedded in a matrix medium that when pressurized, radical structural shape change is possible. A driving force behind this research, as with any morphing wing related studies, is to expand the limitations of an aircraft's mission, usually constrained by the wing design. Rather than deploying current methods of achieving certain flight characteristics, changing the shape of a wing greatly increases the flight envelope. This thesis gives some insight as to the structural capability and limitations using current numerical methods to model a morphing wing in a flow. / Master of Science

La conteneurisation a révolutionné le commerce international au 20éme siècle. L’introduction du conteneur, comme moyen standard de transport de marchandise, a profondément impacté le commerce maritime et a radicalement changé le fonctionnement des ports. En effet, les ports n’ont cessé de se développer par la construction de nouveaux terminaux à conteneurs (TC) et également par l’amélioration de leurs services par la réduction des délais et des coûts de livraison. En parallèle, la gestion des risques dans les TC a reçu beaucoup d'attention ces dernières années en raison de l'augmentation des activités frauduleuses liées aux conteneurs. La communauté internationale a proposé plusieurs initiatives pour améliorer la sécurité du transport maritime. Cependant, l'évaluation des risques des conteneurs reste une tâche difficile, souvent due à des informations incomplètes ou ambiguës sur les conteneurs. D’autre part, la réduction de l’empreinte écologique sur l’environnement est devenue une préoccupation majeure en matière de transport conteneurisé. Face à cet enjeu écologique, les acteurs du dernier kilomètre de la chaîne logistique du conteneur doivent améliorer et optimiser leur infrastructure et penser des modes de transport terrestre de conteneurs ayant une empreinte environnementale moindre que les modes de transports classiques à gasoil, sans toutefois impacter les performances. Dans cette optique, le premier objectif de cette thèse se focalise autour l’amélioration de la performance du processus d’inspection des conteneurs. Ainsi, nous proposons une nouvelle approche adaptative de la priorisation d’inspection des conteneurs. Nous avons nommé cette approche APRICOIN (Adaptive PRIoritizing Container INspection). Cette approche exploite le concept du produit intelligent ainsi que de la logique floue et les techniques de fouille de données. Cette approche est basée sur trois étapes. La première étape vise l’amélioration du flux informationnel et en assurant sa véracité et ce moyennant les capacités du conteneur intelligent. Ainsi on propose un descriptif enrichi du conteneur (DEC). La deuxième étape consiste à prioriser l’inspection des conteneurs en lui attribuant un score de risque à l’aide de la logique floue. La dernière étape consiste à prioriser l’inspection des conteneurs et exploiter les résultats d’inspection afin d’ajuster la précision de l’algorithme APROCOIN et s’adapter aux nouveaux facteurs de risque moyennant les techniques de fouille de données. Afin de valider l’approche proposée, une étude de cas illustratif a été réalisée ainsi qu’une étude comparative avec d’autres approches alternatives. Le deuxième objectif de cette thèse se focalise sur le projet green Truck qui vise particulièrement les transporteurs routiers de conteneurs à courtes distances dans la zone portuaire en assurant une transition énergétique de la flotte de tracteurs gasoil vers des tracteurs électriques à batteries rechargeables. Le projet s’intéresse à une technique récente de rechargement des batteries, à savoir le « Biberonnage ». Cette technologie est inspirée du fonctionnement de certains bus électriques. Elle consiste à exploiter les courts temps d’attentes du véhicule, comme par exemple la monté et la descente des passagers pour le cas des bus, afin de recharger automatiquement la batterie du véhicule. Ainsi dans cette thèse, nous avons exploré différentes techniques et modes de rechargement rapide des batteries de tracteurs routiers électriques compte tenu des contraintes d’exploitation, et d’évalué la faisabilité économique de la mise en œuvre d’un système comprenant le véhicule et l’infrastructure électrique. L’outil de simulation Anylogic a permis de dimensionner les batteries et les bornes de recharge des véhicules électriques, en plus de fournir aux décideurs des résultats technico-économiques avec des représentations animées et graphiques en 3D. / Containerization revolutionized international trade in the 20th century. The introduction of the container, as a standard means of transporting goods, has had a profound impact on maritime trade and has radically changed the functioning of ports. Indeed, ports have continued to grow through the construction of new container terminals (CT) and also by improving their services by reducing delays and delivery costs. At the same time, risk management in CTs has received a lot of attention in recent years due to an increase in fraudulent container activities. The international community has proposed several initiatives to improve the security of maritime transport. However, the risk assessment of containers remains a difficult task, often due to incomplete or ambiguous container information. On the other hand, reducing the ecological footprint on the environment has become a major concern in containerized transport. Faced with this ecological challenge, the last-mile players in the container logistics chain must improve and optimize their infrastructure and think of new land transport modes of containers with a lower environmental footprint than conventional diesel transport modes, without having an impact on performance. With this in mind, the first objective of this thesis focuses on improving the performance of the container inspection process. Thus, we propose a new adaptive approach to container inspection prioritization. We named this approach APRICOIN (Adaptive PRIoritizing Container INspection). This approach exploits the concept of intelligent product as well as fuzzy logic and data mining techniques. This approach is based on three stages. The first step is to improve the information flow and to ensure this truthfulness by means of the capabilities of the intelligent container. Thus we propose an enriched description of the container (DEC). The second step is to prioritize container inspection by assigning a risk score using fuzzy logic. The final step is to prioritize the inspection of the containers and use the inspection results to adjust the accuracy of the APROCOIN algorithm and to adapt to the new risk factors using the data mining technique. In order to validate the proposed approach, an illustrative case study was conducted as well as a comparative study with other alternative approaches. The second objective of this thesis focuses on the green truck project, which is particularly aimed at short-haul container road hauliers in the port area by ensuring an energy transition from the fleet of diesel tractors to electric tractors with rechargeable batteries. It consists in exploiting the short wait times of the vehicle, in order to automatically recharge the vehicle's battery. Thus, in this thesis, we have explored the different techniques and methods of recharging batteries of electric road tractors taking into account operating constraints, and evaluate the economic feasibility of implementing such system. The Anylogic simulation was used to size the batteries and the charging stations of electric vehicles. In addition technical and economic results with 3D animated and graphic representations was provided.

Thesis (M. Sc.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 64-65).

This thesis describes research on methods for Ubiquitous/Pervasive Computing to better suit users in an Intelligent Environment. The approach is to create and equip a computing environment, such as our Active Office, with technologies that can identify user needs and meet these need in a timely, efficient and unobtrusive manner.¶ The critical issues in the Intelligent Environment are how to enable transparent, distributed computing to allow continued operation across changing circumstances and how to exploit the changing environment so that it is aware of the context of user location, the collection of nearby people and objects, accessible devices and changes to those objects over time.¶ Since the Intelligent Environment is an environment with rapid and rich computing processing, the distributed context processing architecture (DiCPA) was developed to manage and respond to rapidly changing aggregation of sensor data. This architecture is a scalable distributed context processing architecture that provides: 1. continued operation across changing circumstances for users, 2. the collection of nearby people and objects, 3. accessible devices and 4. the changes to those objects over time in the environment. The DiCPA approach focuses on how the Intelligent Environment provides context information for user location, user mobility and the user activity model. Users are assumed mobile within the Intelligent Environment and can rapidly change their access to relevant information and the availability of communications and computational resources.¶ Context-Aware Computing is a new approach in software engineering for Intelligent Environment. It is an approach in the design and construction of a context-aware application that exploits rapid changes in access to relevant information and the availability of communication and computing resources in the mobile computing environment. The goal of Context-Aware Computing is to make user interaction with the computer easier in the smart environment where technology is spread throughout (pervasive), computers are everywhere at the same time (ubiquitous) and technology is embedded (ambient) in the environment. Context-aware applications need not be difficult, tedious or require the acquisition of new skills on the part of the user. They should be safe, easy, simple to use and should enable new functionality without the need to learn new technology. They should provide relevant information and a simple way for a user to manage.¶ The Intelligent Environment requires a context-aware application to improve its efficiency and to increase productivity and enjoyment for the user. The context awareness mechanism has four fundamental cores i.e. identity (who), activity (what), location (where) and timestamp (when). Based on DiCPA architecture, the model of user location (where), user mobility (where), user activity (what) and Intelligent Environment response (what) were developed. Prototypes were also developed to proof the Context-Aware Computing concept in the Intelligent Environment.¶ An Intelligent Environment uses the multi-disciplinary area of Context-Aware Computing, which combines technology, computer systems, models and reasoning, social aspects, and user support. A “good quality” project for Context-Aware Computing requires core content and provides iterative evaluation processes, which has two types of iteration: design and product iteration of the evaluation. The aim of the development of an evaluation program in Context-Aware Computing is to determine what to test, how to test and the appropriate metrics to use. This work presents the metrics for a good quality project in the Context-Aware Computing area, which is followed by the evaluation of the prototypes of this work.

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