Earthmoving construction automation with military applications: Past, present and future

© ISARC 2018 - 35th International Symposium on Automation and Robotics in Construction and International AEC/FM Hackathon: The Future of Building Things. All rights reserved. Amongst increasing innovations in frontier engineering sciences, the advancements in Robotic and Autonomous Systems (RAS) has brought about a new horizon in construction applications. There is evidence of the increasing interest in RAS technologies in the civil construction sector being reflected in construction efforts of many military forces. In particular, Army or ground-based forces are frequently called upon to conduct construction tasks as part of military operations, tasks which could be partially or fully aided by the employment of RAS technologies. Along with recent advances in the Internet of Things (IoT) and cyber-physical system infrastructure, it is essential to examine the current maturity, technical feasibility, and affordability, as well as the challenges and future directions of the adoption and application of RAS to military construction. This paper presents a comprehensive survey and provides a contemporary and industry-independent overview on the state-of-the-art of earthmoving construction automation used in defence, spanning current world’s best practice through to that which is predicted over the coming years

An Integrated Smart City Platform

Smart Cities aim to create a higher quality of life for their citizens, improve business services and promote tourism experience. Fostering smart city innovation at local and regional level requires a set of mature technologies to discover, integrate and harmonize multiple data sources and the exposure of eective applications for end-users (citizens, administrators, tourists...). In this context, Semantic Web technologies and Linked Open Data principles provide a means for sharing knowledge about cities as physical, economical, social, and technical systems, enabling the development of smart city services. Despite the tremendous effort these communities have done so far, there exists a lack of comprehensive and effective platforms that handle the entire process of identication, ingestion, consumption and publication of data for Smart Cities. In this paper, a complete open-source platform to boost the integration, semantic enrichment, publication and exploitation of public data to foster smart cities in local and national administrations is proposed. Starting from mature software solutions, we propose a platform to facilitate the harmonization of datasets (open and private, static and dynamic on real time) of the same domain generated by dierent authorities. The platform provides a unied dataset oriented to smart cities that can be exploited to offer services to the citizens in a uniform way, to easily release open data, and to monitor services status of the city in real time by means of a suite of web applications

Active and Passive Helicopter Noise Reduction Using the AVINOR/HELINOIR Code Suite

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143085/1/1.C034519.pd

A probabilistic evaluation procedure for process model matching techniques

Process model matching refers to the automatic identification of corresponding activities between two process models. It represents the basis for many advanced process model analysis techniques such as the identification of similar process parts or process model search. A central problem is how to evaluate the performance of process model matching techniques. Current evaluation methods require a binary gold standard that clearly defines which correspondences are correct. The problem is that often not even humans can agree on a set of correct correspondences. Hence, evaluating the performance of matching techniques based on a binary gold standard does not take the true complexity of the matching problem into account and does not fairly assess the capabilities of a matching technique. In this paper, we propose a novel evaluation procedure for process model matching techniques. In particular, we build on the assessments of multiple annotators to define the notion of a non-binary gold standard. In this way, we avoid the problem of agreeing on a single set of correct correspondences. Based on this non-binary gold standard, we introduce probabilistic versions of precision, recall, and F-measure as well as a distance-based performance measure. We use a dataset from the Process Model Matching Contest 2015 and a total of 16 matching systems to assess and compare the insights that can be obtained by using our evaluation procedure. We find that our probabilistic evaluation procedure allows us to gain more detailed insights into the performance of matching systems than a traditional evaluation based on a binary gold standard

Proposal for a maintenance management system in industrial environments based on ISO 9001 and ISO 14001 standards

This paper presents an effort to improve the quality of processes and products by the definition of the foundations of Maintenance Management Systems based on ISO standards that could help companies to strength the organization of work. We applied the Design Science Research methodology. Finally developing two prototypes which were implanted in different companies. With information gathered along the process, we can offer a system's design which can be adopted by companies to help om maintenance management tasks and serves as a base to carry out internal audit task

Assessing the value of ontologically unpacking a conceptual model for human genomics

Although the knowledge about human genomics is available to all scientists, information about this scientific breakthrough can often be difficult to fully comprehend and share. A Conceptual Schema of the Human Genome was previously developed to assist in describing human genome-related knowledge, by representing a holistic view of the relevant concepts regarding its biology and underlying mechanisms. This model should become helpful for any researcher who works with human genomics data. We, therefore, perform the process of ontological unpacking on a portion of the model, to facilitate domain understanding and data exchange among heterogeneous systems. The ontological unpacking is a transformation of an input conceptual model into an enriched model based on a foundational ontology. The preliminary analysis and enrichment process are supported by the ontological conceptual modeling language OntoUML, which has been applied previously to complex models to gain ontological clarity. The value of the used method is first assessed from a theoretical point of view: the transformation results in significant, diverse modeling implications regarding the characterization of biological entities, the representation of their changes over time, and, more specifically, the description of chemical compounds. Since the ontological unpacking process is costly, an empirical evaluation is conducted to study the practical implications of applying it in a real learning setting. A particularly complex domain such as metabolic pathways is either described by adopting a traditional conceptual model or explained through an ontologically unpacked model obtained from a traditional model. Our research is evidence that including a strong ontological foundation in traditional conceptual models is useful. It contributes to designing models that convey biological domains better than the original models

An ontology-based approach to engineering ethicality requirements

In a world where Artificial Intelligence (AI) is pervasive, humans may feel threatened or at risk by giving up control to machines. In this context, ethicality becomes a major concern to prevent AI systems from being biased, making mistakes, or going rogue. Requirements Engineering (RE) is the research area that can exert a great impact in the development of ethical systems by design. However, proposing concepts, tools and techniques that support the incorporation of ethicality into the software development processes as explicit requirements remains a great challenge in the RE field. In this paper, we rely on Ontology-based Requirements Engineering (ObRE) as a method to elicit and analyze ethicality requirements (‘Ethicality requirements’ is adopted as a name for the class of requirements studied in this paper by analogy to other quality requirements studied in software engineering, such as usability, reliability, and portability, etc. The use of this term (as opposed to ‘ethical requirements’) highlights that they represent requirements for ethical systems, analogous to how ‘trustworthiness requirements’ represent requirements for trustworthy systems. To put simply: the predicates ‘ethical’ or ‘trustworthy’ are not meant to be predicated over the requirements themselves). ObRE applies ontological analysis to ontologically unpack terms and notions that are referred to in requirements elicitation. Moreover, this method instantiates the adopted ontology and uses it to guide the requirements analysis activity. In a previous paper, we presented a solution concerning two ethical principles, namely Beneficence and Non-maleficence. The present paper extends the previous work by targeting two other important ethicality principles, those of Explicability and Autonomy. For each of these new principles, we do ontological unpacking of the relevant concepts, and we present requirements elicitation and analysis guidelines, as well as examples in the context of a driverless car case. Furthermore, we validate our approach by analysing the requirements elicitation made for the driverless car case in contrast with a similar case, and by assessing our method’s coverage w.r.t European Union guidelines for Trustworthy AI.</p