A Microscopic Simulation Laboratory for Evaluation of Off-street Parking Systems

The parking industry produces an enormous amount of data every day that, properly analyzed, will change the way the industry operates. The collected data form patterns that, in most cases, would allow parking operators and property owners to better understand how to maximize revenue and decrease operating expenses and support the decisions such as how to set specific parking policies (e.g. electrical charging only parking space) to achieve the sustainable and eco-friendly parking. However, there lacks an intelligent tool to assess the layout design and operational performance of parking lots to reduce the externalities and increase the revenue. To address this issue, this research presents a comprehensive agent-based framework for microscopic off-street parking system simulation. A rule-based parking simulation logic programming model is formulated. The proposed simulation model can effectively capture the behaviors of drivers and pedestrians as well as spatial and temporal interactions of traffic dynamics in the parking system. A methodology for data collection, processing, and extraction of user behaviors in the parking system is also developed. A Long-Short Term Memory (LSTM) neural network is used to predict the arrival and departure of the vehicles. The proposed simulator is implemented in Java and a Software as a Service (SaaS) graphic user interface is designed to analyze and visualize the simulation results. This study finds the active capacity of the parking system, which is defined as the largest number of actively moving vehicles in the parking system under the facility layout. In the system application of the real world testbed, the numerical tests show (a) the smart check-in device has marginal benefits in vehicle waiting time; (b) the flexible pricing policy may increase the average daily revenue if the elasticity of the price is not involved; (c) the number of electrical charging only spots has a negative impact on the performance of the parking facility; and (d) the rear-in only policy may increase the duration of parking maneuvers and reduce the efficiency during the arrival rush hour. Application of the developed simulation system using a real-world case demonstrates its capability of providing informative quantitative measures to support decisions in designing, maintaining, and operating smart parking facilities

Metodología dirigida por modelos para las pruebas de un sistema distribuido multiagente de fabricación

Las presiones del mercado han empujado a las empresas de fabricación a reducir costes a la vez que mejoran sus productos, especializándose en las actividades sobre las que pueden añadir valor y colaborando con especialistas de las otras áreas para el resto. Estos sistemas distribuidos de fabricación conllevan nuevos retos, dado que es difícil integrar los distintos sistemas de información y organizarlos de forma coherente. Esto ha llevado a los investigadores a proponer una variedad de abstracciones, arquitecturas y especificaciones que tratan de atacar esta complejidad. Entre ellas, los sistemas de fabricación holónicos han recibido una atención especial: ven las empresas como redes de holones, entidades que a la vez están formados y forman parte de varios otros holones. Hasta ahora, los holones se han implementado para control de fabricación como agentes inteligentes autoconscientes, pero su curva de aprendizaje y las dificultades a la hora de integrarlos con sistemas tradicionales han dificultado su adopción en la industria. Por otro lado, su comportamiento emergente puede que no sea deseable si se necesita que las tareas cumplan ciertas garantías, como ocurren en las relaciones de negocio a negocio o de negocio a cliente y en las operaciones de alto nivel de gestión de planta. Esta tesis propone una visión más flexible del concepto de holón, permitiendo que se sitúe en un espectro más amplio de niveles de inteligencia, y defiende que sea mejor implementar los holones de negocio como servicios, componentes software que pueden ser reutilizados a través de tecnologías estándar desde cualquier parte de la organización. Estos servicios suelen organizarse como catálogos coherentes, conocidos como Arquitecturas Orientadas a Servicios (‘Service Oriented Architectures’ o SOA). Una iniciativa SOA exitosa puede reportar importantes beneficios, pero no es una tarea trivial. Por este motivo, se han propuesto muchas metodologías SOA en la literatura, pero ninguna de ellas cubre explícitamente la necesidad de probar los servicios. Considerando que la meta de las SOA es incrementar la reutilización del software en la organización, es una carencia importante: tener servicios de alta calidad es crucial para una SOA exitosa. Por este motivo, el objetivo principal de la presente Tesis es definir una metodología extendida que ayude a los usuarios a probar los servicios que implementan a sus holones de negocio. Tras considerar las opciones disponibles, se tomó la metodología dirigida por modelos SODM como punto de partida y se reescribió en su mayor parte con el framework Epsilon de código abierto, permitiendo a los usuarios que modelen su conocimiento parcial sobre el rendimiento esperado de los servicios. Este conocimiento parcial es aprovechado por varios nuevos algoritmos de inferencia de requisitos de rendimiento, que extraen los requisitos específicos de cada servicio. Aunque el algoritmo de inferencia de peticiones por segundo es sencillo, el algoritmo de inferencia de tiempos límite pasó por numerosas revisiones hasta obtener el nivel deseado de funcionalidad y rendimiento. Tras una primera formulación basada en programación lineal, se reemplazó con un algoritmo sencillo ad-hoc que recorría el grafo y después con un algoritmo incremental mucho más rápido y avanzado. El algoritmo incremental produce resultados equivalentes y tarda mucho menos, incluso con modelos grandes. Para sacar más partidos de los modelos, esta Tesis también propone un enfoque general para generar artefactos de prueba para múltiples tecnologías a partir de los modelos anotados por los algoritmos. Para evaluar la viabilidad de este enfoque, se implementó para dos posibles usos: reutilizar pruebas unitarias escritas en Java como pruebas de rendimiento, y generar proyectos completos de prueba de rendimiento usando el framework The Grinder para cualquier Servicio Web que esté descrito usando el estándar Web Services Description Language. La metodología completa es finalmente aplicada con éxito a un caso de estudio basado en un área de fabricación de losas cerámicas rectificadas de un grupo de empresas español. En este caso de estudio se parte de una descripción de alto nivel del negocio y se termina con la implementación de parte de uno de los holones y la generación de pruebas de rendimiento para uno de sus Servicios Web. Con su soporte para tanto diseñar como implementar pruebas de rendimiento de los servicios, se puede concluir que SODM+T ayuda a que los usuarios tengan una mayor confianza en sus implementaciones de los holones de negocio observados en sus empresas

Distributed Planning for Self-Organizing Production Systems

Für automatisierte Produktionsanlagen gibt es einen fundamentalen Tradeoff zwischen Effizienz und Flexibilität. In den meisten Fällen sind die Abläufe nicht nur durch den physischen Aufbau der Produktionsanlage, sondern auch durch die spezielle zugeschnittene Programmierung der Anlagensteuerung fest vorgegeben. Änderungen müssen aufwändig in einer Vielzahl von Systemen nachgezogen werden. Das macht die Herstellung kleiner Stückzahlen unrentabel. In dieser Dissertation wird ein Ansatz entwickelt, um eine automatische Anpassung des Verhaltens von Produktionsanlagen an wechselnde Aufträge und Rahmenbedingungen zu erreichen. Dabei kommt das Prinzip der Selbstorganisation durch verteilte Planung zum Einsatz. Die aufeinander aufbauenden Ergebnisse der Dissertation sind wie folgt: 1. Es wird ein Modell von Produktionsanlagen entwickelt, dass nahtlos von der detaillierten Betrachtung physikalischer Produktionsprozesse bis hin zu Lieferbeziehungen zwischen Unternehmen skaliert. Im Vergleich zu existierenden Modellen von Produktionsanlagen werden weniger limitierende Annahmen gestellt. In diesem Sinne ist der Modellierungsansatz ein Kandidat für eine häufig geforderte "Theorie der Produktion". 2. Für die so modellierten Szenarien wird ein Algorithmus zur Optimierung der nebenläufigen Abläufe entwickelt. Der Algorithmus verbindet Techniken für die kombinatorische und die kontinuierliche Optimierung: Je nach Detailgrad und Ausgestaltung des modellierten Szenarios kann der identische Algorithmus kombinatorische Fertigungsfeinplanung (Scheduling) vornehmen, weltweite Lieferbeziehungen unter Einbezug von Unsicherheiten und Risiko optimieren und physikalische Prozesse prädiktiv regeln. Dafür werden Techniken der Monte-Carlo Baumsuche (die auch bei Deepminds Alpha Go zum Einsatz kommen) weiterentwickelt. Durch Ausnutzung zusätzlicher Struktur in den Modellen skaliert der Ansatz auch auf große Szenarien. 3. Der Planungsalgorithmus wird auf die verteilte Optimierung durch unabhängige Agenten übertragen. Dafür wird die sogenannte "Nutzen-Propagation" als Koordinations-Mechanismus entwickelt. Diese ist von der Belief-Propagation zur Inferenz in Probabilistischen Graphischen Modellen inspiriert. Jeder teilnehmende Agent hat einen lokalen Handlungsraum, in dem er den Systemzustand beobachten und handelnd eingreifen kann. Die Agenten sind an der Maximierung der Gesamtwohlfahrt über alle Agenten hinweg interessiert. Die dafür notwendige Kooperation entsteht über den Austausch von Nachrichten zwischen benachbarten Agenten. Die Nachrichten beschreiben den erwarteten Nutzen für ein angenommenes Verhalten im Handlungsraum beider Agenten. 4. Es wird eine Beschreibung der wiederverwendbaren Fähigkeiten von Maschinen und Anlagen auf Basis formaler Beschreibungslogiken entwickelt. Ausgehend von den beschriebenen Fähigkeiten, sowie der vorliegenden Aufträge mit ihren notwendigen Produktionsschritten, werden ausführbare Aktionen abgeleitet. Die ausführbaren Aktionen, mit wohldefinierten Vorbedingungen und Effekten, kapseln benötigte Parametrierungen, programmierte Abläufe und die Synchronisation von Maschinen zur Laufzeit. Die Ergebnisse zusammenfassend werden Grundlagen für flexible automatisierte Produktionssysteme geschaffen -- in einer Werkshalle, aber auch über Standorte und Organisationen verteilt -- welche die ihnen innewohnenden Freiheitsgrade durch Planung zur Laufzeit und agentenbasierte Koordination gezielt einsetzen können. Der Bezug zur Praxis wird durch Anwendungsbeispiele hergestellt. Die Machbarkeit des Ansatzes wurde mit realen Maschinen im Rahmen des EU-Projekts SkillPro und in einer Simulationsumgebung mit weiteren Szenarien demonstriert

Toxicity in Evolving Twitter Topics - Employing a novel Dynamic Topic volution Model (DyTEM) onTwitter data

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceThis thesis presents an extensive investigation into the evolution of topics and their association with speech toxicity on Twitter, based on a large corpus of tweets, providing crucial insights for monitoring online discourse and potentially informing interventions to combat toxic behavior in digital communities. A Dynamic Topic Evolution Model (DyTEM) is introduced, constructed by combining static Topic Modelling techniques and sentence embeddings through the state-of-the-art sentence transformer, sBERT. The DyTEM, tested and validated on a substantial sample of tweets, is represented as a directed graph, encapsulating the inherent dynamism of Twitter discussions. For validating the consistency of DyTEM and providing guidance for hyperparameter selection, a novel, hashtag-based validation method is proposed. The analysis identifies and scrutinizes five distinct Topic Transition Types: Topic Stagnation, Topic Merge, Topic Split, Topic Disappearance, and Topic Emergence. A speech toxicity classification model is employed to delve into the toxicity dynamics within topic evolution. A standout finding of this study is the positive correlation between topic popularity and its toxicity, implying that trending or viral topics tend to contain more inflammatory speech. This insight, along with the methodologies introduced in this study, contributes significantly to the broader understanding of digital discourse dynamics and could guide future strategies aimed at fostering healthier and more constructive online spaces

TOWARD INTELLIGENT WELDING BY BUILDING ITS DIGITAL TWIN

To meet the increasing requirements for production on individualization, efficiency and quality, traditional manufacturing processes are evolving to smart manufacturing with the support from the information technology advancements including cyber-physical systems (CPS), Internet of Things (IoT), big industrial data, and artificial intelligence (AI). The pre-requirement for integrating with these advanced information technologies is to digitalize manufacturing processes such that they can be analyzed, controlled, and interacted with other digitalized components. Digital twin is developed as a general framework to do that by building the digital replicas for the physical entities. This work takes welding manufacturing as the case study to accelerate its transition to intelligent welding by building its digital twin and contributes to digital twin in the following two aspects (1) increasing the information analysis and reasoning ability by integrating deep learning; (2) enhancing the human user operative ability to physical welding manufacturing via digital twins by integrating human-robot interaction (HRI). Firstly, a digital twin of pulsed gas tungsten arc welding (GTAW-P) is developed by integrating deep learning to offer the strong feature extraction and analysis ability. In such a system, the direct information including weld pool images, arc images, welding current and arc voltage is collected by cameras and arc sensors. The undirect information determining the welding quality, i.e., weld joint top-side bead width (TSBW) and back-side bead width (BSBW), is computed by a traditional image processing method and a deep convolutional neural network (CNN) respectively. Based on that, the weld joint geometrical size is controlled to meet the quality requirement in various welding conditions. In the meantime, this developed digital twin is visualized to offer a graphical user interface (GUI) to human users for their effective and intuitive perception to physical welding processes. Secondly, in order to enhance the human operative ability to the physical welding processes via digital twins, HRI is integrated taking virtual reality (VR) as the interface which could transmit the information bidirectionally i.e., transmitting the human commends to welding robots and visualizing the digital twin to human users. Six welders, skilled and unskilled, tested this system by completing the same welding job but demonstrate different patterns and resulted welding qualities. To differentiate their skill levels (skilled or unskilled) from their demonstrated operations, a data-driven approach, FFT-PCA-SVM as a combination of fast Fourier transform (FFT), principal component analysis (PCA), and support vector machine (SVM) is developed and demonstrates the 94.44% classification accuracy. The robots can also work as an assistant to help the human welders to complete the welding tasks by recognizing and executing the intended welding operations. This is done by a developed human intention recognition algorithm based on hidden Markov model (HMM) and the welding experiments show that developed robot-assisted welding can help to improve welding quality. To further take the advantages of the robots i.e., movement accuracy and stability, the role of the robot upgrades to be a collaborator from an assistant to complete a subtask independently i.e., torch weaving and automatic seam tracking in weaving GTAW. The other subtask i.e., welding torch moving along the weld seam is completed by the human users who can adjust the travel speed to control the heat input and ensure the good welding quality. By doing that, the advantages of humans (intelligence) and robots (accuracy and stability) are combined together under this human-robot collaboration framework. The developed digital twin for welding manufacturing helps to promote the next-generation intelligent welding and can be applied in other similar manufacturing processes easily after small modifications including painting, spraying and additive manufacturing

Modelling the Structure and Dynamics of Science Using Books

Scientific research is a major driving force in a knowledge based economy. Income, health and wellbeing depend on scientific progress. The better we understand the inner workings of the scientific enterprise, the better we can prompt, manage, steer, and utilize scientific progress. Diverse indicators and approaches exist to evaluate and monitor research activities, from calculating the reputation of a researcher, institution, or country to analyzing and visualizing global brain circulation. However, there are very few predictive models of science that are used by key decision makers in academia, industry, or government interested to improve the quality and impact of scholarly efforts. We present a novel 'bibliographic bibliometric' analysis which we apply to a large collection of books relevant for the modelling of science. We explain the data collection together with the results of the data analyses and visualizations. In the final section we discuss how the analysis of books that describe different modelling approaches can inform the design of new models of science.Comment: data and large scale maps http://cns.iu.edu/2015-ModSci.html, Ginda, Michael, Andrea Scharnhorst, and Katy B\"orner. "Modelling Science". In Theories of Informetrics: A Festschrift in Honor of Blaise Cronin, edited by Sugimoto, Cassidy. Munich: De Gruyter Sau

The Application of Driver Models in the Safety Assessment of Autonomous Vehicles: A Survey

Driver models play a vital role in developing and verifying autonomous vehicles (AVs). Previously, they are mainly applied in traffic flow simulation to model realistic driver behavior. With the development of AVs, driver models attract much attention again due to their potential contributions to AV certification. The simulation-based testing method is considered an effective measure to accelerate AV testing due to its safe and efficient characteristics. Nonetheless, realistic driver models are prerequisites for valid simulation results. Additionally, an AV is assumed to be at least as safe as a careful and competent driver. Therefore, driver models are inevitable for AV safety assessment. However, no comparison or discussion of driver models is available regarding their utility to AVs in the last five years despite their necessities in the release of AVs. This motivates us to present a comprehensive survey of driver models in the paper and compare their applicability. Requirements for driver models in terms of their application to AV safety assessment are discussed. A summary of driver models for simulation-based testing and AV certification is provided. Evaluation metrics are defined to compare their strength and weakness. Finally, an architecture for a careful and competent driver model is proposed. Challenges and future work are elaborated. This study gives related researchers especially regulators an overview and helps them to define appropriate driver models for AVs

Study applying simulation to improve a real production process in the context of Industry 4.0

During the thesis development, simulation theories and techniques will be applied to a part of the production process of an Italian manufacturing company. A simulation model of the steaming and washing phases will be developed to outline the automated and manual procedures that are performed in the AS-IS state. Several what-if scenarios will be then envisioned and simulated to analyze how the production activities could be re-engineered in the light of the new technological advancements, such as the introduction of full traceability

Inductive Pattern Formation

With the extended computational limits of algorithmic recursion, scientific investigation is transitioning away from computationally decidable problems and beginning to address computationally undecidable complexity. The analysis of deductive inference in structure-property models are yielding to the synthesis of inductive inference in process-structure simulations. Process-structure modeling has examined external order parameters of inductive pattern formation, but investigation of the internal order parameters of self-organization have been hampered by the lack of a mathematical formalism with the ability to quantitatively define a specific configuration of points. This investigation addressed this issue of quantitative synthesis. Local space was developed by the Poincare inflation of a set of points to construct neighborhood intersections, defining topological distance and introducing situated Boolean topology as a local replacement for point-set topology. Parallel development of the local semi-metric topological space, the local semi-metric probability space, and the local metric space of a set of points provides a triangulation of connectivity measures to define the quantitative architectural identity of a configuration and structure independent axes of a structural configuration space. The recursive sequence of intersections constructs a probabilistic discrete spacetime model of interacting fields to define the internal order parameters of self-organization, with order parameters external to the configuration modeled by adjusting the morphological parameters of individual neighborhoods and the interplay of excitatory and inhibitory point sets. The evolutionary trajectory of a configuration maps the development of specific hierarchical structure that is emergent from a specific set of initial conditions, with nested boundaries signaling the nonlinear properties of local causative configurations. This exploration of architectural configuration space concluded with initial process-structure-property models of deductive and inductive inference spaces. In the computationally undecidable problem of human niche construction, an adaptive-inductive pattern formation model with predictive control organized the bipartite recursion between an information structure and its physical expression as hierarchical ensembles of artificial neural network-like structures. The union of architectural identity and bipartite recursion generates a predictive structural model of an evolutionary design process, offering an alternative to the limitations of cognitive descriptive modeling. The low computational complexity of these models enable them to be embedded in physical constructions to create the artificial life forms of a real-time autonomously adaptive human habitat