Tools and Algorithms for the Construction and Analysis of Systems

This book is Open Access under a CC BY licence. The LNCS 11427 and 11428 proceedings set constitutes the proceedings of the 25th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2019, which took place in Prague, Czech Republic, in April 2019, held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019. The total of 42 full and 8 short tool demo papers presented in these volumes was carefully reviewed and selected from 164 submissions. The papers are organized in topical sections as follows: Part I: SAT and SMT, SAT solving and theorem proving; verification and analysis; model checking; tool demo; and machine learning. Part II: concurrent and distributed systems; monitoring and runtime verification; hybrid and stochastic systems; synthesis; symbolic verification; and safety and fault-tolerant systems

Ein BIM Ontologie-basiertes Expertensystem für räumliche und zeitliche Programmierungen von Bauten

The effective realization of building construction is closely linked to the construction schedules that, if poorly designed, result in congested site areas, accidents and decline of productivity. In the past decade, many research efforts have been spent in BIM which represents the process of preparation and use of a computer-generated Building Information Model (BIM) even if an effective model to assist construction scheduling is still missing. This PhD thesis proposes an Expert-System able to identify the shortest completion sequence of a given Building Information Model, considering the on-site temporal-space allocation of workspaces. It is supported by an ontology-based system architecture integrated with a rule-based artificial intelligence. Four integrated ontologies, to formally represent construction site entities, constitute the system’s Knowledge-Base (KB): (1) scheduling ontology that maps the necessary components to specify the scheduling task (2) space ontology that contains workspaces requirements in terms of geometries, locations and interactions (3) products ontology that describes geometrical and topological information of the building objects (4) time ontology that describes temporal properties of site entities in their evolution across time. Such a KB was rendered into a Protégé’s script (ontology editing environment) in order to convert it in machine-readable language (i.e., Web Ontology Language –OWL). Furthermore, four automated Reasoning Mechanisms –scripts- were incorporated in the model architecture: (i) an algorithm to define the on-site workspaces configuration pattern, (ii) an algorithm to automatically model workspaces geometries, (iii) a workspaces conflicts checking process and (iv) a rule-engine to deduce the shortest construction sequence and solve the identified conflicts manipulating the KB itself. A validation test was conducted on a BIM-based project of an industrial building composed of 98 building items and 611 workspaces, allocated by means of (i) and modelled with (ii). A construction sequence of 36 construction days was suggested by the system. Moreover, 118 workspaces conflicts were identified (iii) and automatically solved by using the planning rules included in the rule-engine as it was visually verified simulating the sequence itself within a 4D-BIM environment. This prototype can be considered a precursor model in developing BIM-based intelligent systems architectures for spatial construction planning.Eine erfolgreiche Umsetzung eines Gebäudeprojektes ist von der Planung der Montage auf der Baustelle abhängig. Im letzten Jahrzehnt wurden zahlreiche wissenschaftliche Projekte zur Montageplanung unter Verwendung eines Computermodells im Rahmen des Building Information Modelling (BIM) durchgeführt. Momentan fehlt aber noch ein Modell, das auch den Prozess selber auf der Baustelle integriert. In der vorliegenden Arbeit wird ein Expertensystem mit dem Ziel der Findung einer optimalen Montagefolge vorgestellt. Das Expertensystem basiert auf BIM und berücksichtigt die räumliche und zeitliche Interaktion der Arbeitsabläufe auf der Baustelle. Die entwickelte Methode stützt sich auf einer Ontologie-basierten Architektur, die in einer Regel-basierten künstlichen Intelligenz integriert ist. Dabei wird ein neues Objekt in das BIM Modell eingefügt, das den Raumbedarf einer Montagetätigkeit beschreibt. Dies kann beispielsweise ein erforderlicher Freiraum für einen Mobilkran sein oder ein bei der Montage nicht betretbarer Sicherheitsbereich. Die Wissensbasis des Expertensystems besteht aus vier Ontologien, die nötig sind um das Wesen der Baustelle darzustellen: Ontologie der Montageabläufe, die den technischen Ablauf der Aktivitäten bestimmt; Ontologie der baulichen Räume, die den räumlichen Bedarf berücksichtigt; Ontologie der Elemente des Gebäudes, welche die geometrischen und funktionalen Gebäudeelemente beschreibt, um Arbeitsprozesse zu bestimmen; Ontologie der Zeit, welche die Reihenfolge der Bauelemente vorgibt. Die Wissensbasis ist mit einem Protégé-Skript als Ontologie-Editor entwickelt worden, für einen Compiler der Web Ontology Language (OWL). Danach wurde die Wissensbasis mit vier Algorithmen verknüpft: Ein Algorithmus, der den Arbeitsraum definiert; Ein Algorithmus, der die Geometrien der Arbeitsräume modelliert; Ein Kontrollprozess, der die Konfliktstellen des Arbeitsraum identifiziert; Ein Optimierungs-Prozess, der den kürzesten Arbeitsprozess ermittelt. Zur Validierung wurde ein Industriegebäude mit 98 Elementen verwendet. Das Expertensystem hatte 611 Arbeitsräume errechnet und eine geschätzte Bauzeit von 36 Tagen. Das Expertensystem identifizierte 118 Konfliktstellen und entwickelte jeweils Lösungen. Das Ergebnis wurde mit Hilfe einer 4D-BIM Umgebung visualisiert. Das vorgestellte Expertensystem ist ein Prototyp, der einen Beitrag zur Entwicklung automatischer und intelligenter Programmierungen für den Montageablauf unter Verwendung von BIM leistet

Deep Underground Science and Engineering Laboratory - Preliminary Design Report

The DUSEL Project has produced the Preliminary Design of the Deep Underground Science and Engineering Laboratory (DUSEL) at the rehabilitated former Homestake mine in South Dakota. The Facility design calls for, on the surface, two new buildings - one a visitor and education center, the other an experiment assembly hall - and multiple repurposed existing buildings. To support underground research activities, the design includes two laboratory modules and additional spaces at a level 4,850 feet underground for physics, biology, engineering, and Earth science experiments. On the same level, the design includes a Department of Energy-shepherded Large Cavity supporting the Long Baseline Neutrino Experiment. At the 7,400-feet level, the design incorporates one laboratory module and additional spaces for physics and Earth science efforts. With input from some 25 science and engineering collaborations, the Project has designed critical experimental space and infrastructure needs, including space for a suite of multidisciplinary experiments in a laboratory whose projected life span is at least 30 years. From these experiments, a critical suite of experiments is outlined, whose construction will be funded along with the facility. The Facility design permits expansion and evolution, as may be driven by future science requirements, and enables participation by other agencies. The design leverages South Dakota's substantial investment in facility infrastructure, risk retirement, and operation of its Sanford Laboratory at Homestake. The Project is planning education and outreach programs, and has initiated efforts to establish regional partnerships with underserved populations - regional American Indian and rural populations

Engineering Automation for Reliable Software Interim Progress Report (10/01/2000 - 09/30/2001)

Prepared for: U.S. Army Research Office P.O. Box 12211 Research Triangle Park, NC 27709-2211The objective of our effort is to develop a scientific basis for producing reliable software that is also flexible and cost effective for the DoD distributed software domain. This objective addresses the long term goals of increasing the quality of service provided by complex systems while reducing development risks, costs, and time. Our work focuses on "wrap and glue" technology based on a domain specific distributed prototype model. The key to making the proposed approach reliable, flexible, and cost-effective is the automatic generation of glue and wrappers based on a designer's specification. The "wrap and glue" approach allows system designers to concentrate on the difficult interoperability problems and defines solutions in terms of deeper and more difficult interoperability issues, while freeing designers from implementation details. Specific research areas for the proposed effort include technology enabling rapid prototyping, inference for design checking, automatic program generation, distributed real-time scheduling, wrapper and glue technology, and reliability assessment and improvement. The proposed technology will be integrated with past research results to enable a quantum leap forward in the state of the art for rapid prototyping.U. S. Army Research Office P.O. Box 12211 Research Triangle Park, NC 27709-22110473-MA-SPApproved for public release; distribution is unlimited

Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective

Data-driven decision making is becoming an integral part of manufacturing companies. Data is collected and commonly used to improve efficiency and produce high quality items for the customers. IoT-based and other forms of object tracking are an emerging tool for collecting movement data of objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over space and time. Movement data can provide valuable insights like process bottlenecks, resource utilization, effective working time etc. that can be used for decision making and improving efficiency. Turning movement data into valuable information for industrial management and decision making requires analysis methods. We refer to this process as movement analytics. The purpose of this document is to review the current state of work for movement analytics both in manufacturing and more broadly. We survey relevant work from both a theoretical perspective and an application perspective. From the theoretical perspective, we put an emphasis on useful methods from two research areas: machine learning, and logic-based knowledge representation. We also review their combinations in view of movement analytics, and we discuss promising areas for future development and application. Furthermore, we touch on constraint optimization. From an application perspective, we review applications of these methods to movement analytics in a general sense and across various industries. We also describe currently available commercial off-the-shelf products for tracking in manufacturing, and we overview main concepts of digital twins and their applications

Concept of a Robust & Training-free Probabilistic System for Real-time Intention Analysis in Teams

Die Arbeit beschäftigt sich mit der Analyse von Teamintentionen in Smart Environments (SE). Die fundamentale Aussage der Arbeit ist, dass die Entwicklung und Integration expliziter Modelle von Nutzeraufgaben einen wichtigen Beitrag zur Entwicklung mobiler und ubiquitärer Softwaresysteme liefern können. Die Arbeit sammelt Beschreibungen von menschlichem Verhalten sowohl in Gruppensituationen als auch Problemlösungssituationen. Sie untersucht, wie SE-Projekte die Aktivitäten eines Nutzers modellieren, und liefert ein Teamintentionsmodell zur Ableitung und Auswahl geplanten Teamaktivitäten mittels der Beobachtung mehrerer Nutzer durch verrauschte und heterogene Sensoren. Dazu wird ein auf hierarchischen dynamischen Bayes’schen Netzen basierender Ansatz gewählt

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

An Optimisation-based Framework for Complex Business Process: Healthcare Application

The Irish healthcare system is currently facing major pressures due to rising demand, caused by population growth, ageing and high expectations of service quality. This pressure on the Irish healthcare system creates a need for support from research institutions in dealing with decision areas such as resource allocation and performance measurement. While approaches such as modelling, simulation, multi-criteria decision analysis, performance management, and optimisation can – when applied skilfully – improve healthcare performance, they represent just one part of the solution. Accordingly, to achieve significant and sustainable performance, this research aims to develop a practical, yet effective, optimisation-based framework for managing complex processes in the healthcare domain. Through an extensive review of the literature on the aforementioned solution techniques, limitations of using each technique on its own are identified in order to define a practical integrated approach toward developing the proposed framework. During the framework validation phase, real-time strategies have to be optimised to solve Emergency Department performance issues in a major hospital. Results show a potential of significant reduction in patients average length of stay (i.e. 48% of average patient throughput time) whilst reducing the over-reliance on overstretched nursing resources, that resulted in an increase of staff utilisation between 7% and 10%. Given the high uncertainty in healthcare service demand, using the integrated framework allows decision makers to find optimal staff schedules that improve emergency department performance. The proposed optimum staff schedule reduces the average waiting time of patients by 57% and also contributes to reduce number of patients left without treatment to 8% instead of 17%. The developed framework has been implemented by the hospital partner with a high level of success