Assessing system architectures: the Canonical Decomposition Fuzzy Comparative methodology

The impacts of decisions made during the selection of the system architecture propagate throughout the entire system lifecycle. The challenge for system architects is to perform a realistic assessment of an inherently ambiguous system concept. Subject matter expert interpretations, intuition, and heuristics are performed quickly and guide system development in the right overall direction, but these methods are subjective and unrepeatable. Traditional analytical assessments dismiss complexity in a system by assuming severability between system components and are intolerant of ambiguity. To be defensible, a suitable methodology must be repeatable, analytically rigorous, and yet tolerant of ambiguity. The hypothesis for this research is that an architecture assessment methodology capable of achieving these objectives is possible by drawing on the strengths of existing approaches while addressing their collective weaknesses. The proposed methodology is the Canonical Decomposition Fuzzy Comparative approach. The theoretical foundations of this methodology are developed and tested through the assessment of three physical architectures for a peer-to-peer wireless network. An extensible modeling framework is established to decompose high-level system attributes into technical performance measures suitable for analysis via computational modeling. Canonical design primitives are used to assess antenna performance in the form of a comparative analysis between the baseline free space gain patterns and the installed gain patterns. Finally, a fuzzy inference system is used to interpret the comparative feature set and offer a numerical assessment. The results of this experiment support the hypothesis that the proposed methodology is well suited for exposing integration sensitivity and assessing coupled performance in physical architecture concepts --Abstract, page iii

Human reliability analysis: exploring the intellectual structure of a research field

Humans play a crucial role in modern socio-technical systems. Rooted in reliability engineering, the discipline of Human Reliability Analysis (HRA) has been broadly applied in a variety of domains in order to understand, manage and prevent the potential for human errors. This paper investigates the existing literature pertaining to HRA and aims to provide clarity in the research field by synthesizing the literature in a systematic way through systematic bibliometric analyses. The multi-method approach followed in this research combines factor analysis, multi-dimensional scaling, and bibliometric mapping to identify main HRA research areas. This document reviews over 1200 contributions, with the ultimate goal of identifying current research streams and outlining the potential for future research via a large-scale analysis of contributions indexed in Scopus database

Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future

Fourth Annual Workshop on Space Operations Applications and Research (SOAR 90)

The proceedings of the SOAR workshop are presented. The technical areas included are as follows: Automation and Robotics; Environmental Interactions; Human Factors; Intelligent Systems; and Life Sciences. NASA and Air Force programmatic overviews and panel sessions were also held in each technical area

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

An Overall Policy Decision-Support System For Educational Facilities Management: An Agent-Based Approach

Although K-12 public school facilities infrastructure investments are second only to highways, schools continue to suffer from an approximately $38 billion annual funding gap. Massive reductions in funding are forcing school districts to make tough decisions to optimize maintenance expenditures. Over the last three decades, a huge body of research has determined that the condition of school facilities do affect student health and performance, and some have further demonstrated that schools are overwhelmed by deteriorating facilities that threaten the health, safety, and learning opportunities of students. The currently available educational facility management approaches oversee the influence of the complex and mutual interactions between a school facility and its occupants. This thesis aimed to develop an overall decision support system for decision-makers that promotes efficient planning and management of educational infrastructure system by embracing a proactive management style rather than reactive. The proposed system consists of three main components: (1) an overall condition prediction model for educational facilities as a whole, (2) a tactical level Agent-based model (ABM) for classroom interaction simulation, and (3) a strategic level ABM for maintenance budget allocation. ABM was selected for its flexibility, natural representation of the problem, and suitability for modeling real-world complex systems with heterogenous agents. The first tool was accomplished through the development of a three-stage condition prediction methodology. The first stage aims to recognize the deterioration pattern of the educational facility as a whole by utilizing a Markov chain modeling approach. The second stage focuses on determining the overall useful service life of educational facilities. The third stage identifies the higher and lower limits of the educational facilities’ deterioration rate. The resulted model can help decision-makers plan and forecast their maintenance needs and better manage the available resources. The proposed methodology can be applied to any multi-component asset. The second tool, the tactical level decision support ABM, was developed to provide decision-makers with new insights into the effects of different maintenance polices on the educational system. The model simulates day-by-day classroom interactions and highlights the importance of preventive maintenance on the educational system’s major stakeholders (agents). The third decision support tool presented in this research is the strategic level model for testing the effects of different maintenance budget allocation strategies on the school district revenues, overall performance, enrollment size, and land values over years. ABM enhances the overall comprehension of the current situation and its complex relations, increases resource allocation efficiency, highlights the important factors affecting the system that are overlooked in traditional management styles, thereby improving the quality of educational outcomes. The main challenge in developing the proposed ABM was identifying and quantifying the main stakeholders’ complex interactions due to the uncertainties inherent in human behavior. This thesis demonstrated the need for a holistic bottom-top asset management modeling approach rather than asset-centric top-down approach. The case study results of this research confirmed that ABM has great potential as an asset management tool for decision-makers that can provide a comprehensive and holistic understanding of the system dynamics