Models and evaluation of human-machine systems

"September 1993.""Prepared for: International Atomic Energy Association [sic], Wagramerstrasse 5, P. 0. Box 100 A-1400 Vienna, Austria."Part of appendix A and bibliography missingIncludes bibliographical referencesThe field of human-machine systems and human-machine interfaces is very multidisciplinary. We have to navigate between the knowledge waves brought by several areas of the human learning: cognitive psychology, artificial intelligence, philosophy, linguistics, ergonomy, control systems engineering, neurophysiology, sociology, computer sciences, among others. At the present moment, all these disciplines seek to be close each other to generate synergy. It is necessary to homogenize the different nomenclatures and to make that each one can benefit from the results and advances found in the other. Accidents like TMI, Chernobyl, Challenger, Bhopal, and others demonstrated that the human beings shall deal with complex systems that are created by the technological evolution more carefully. The great American writer Allan Bloom died recently wrote in his book 'The Closing of the American Mind' (1987) about the universities curriculum that are commonly separated in tight departments. This was a necessity of the industrial revolution that put emphasis in practical courses in order to graduate specialists in many fields. However, due the great complexity of our technological world, we feel the necessity to integrate again those disciplines that one day were separated to make possible their fast development. This Report is a modest trial to do this integration in a holistic way, trying to capture the best tendencies in those areas of the human learning mentioned in the first lines above. I expect that it can be useful to those professionals who, like me, would desire to build better human-machine systems in order to avoid those accidents also mentioned above

Safe Operation of Nuclear Power Plants - Is Safety Culture an Adequate Management Method?

One of the characteristics of a good safety culture is a definable commitment to the improvement of safety behaviours and attitudes at all organisational levels. A second characteristic of an organisation with excellent safety culture is free and open communication. The general understanding has been that safety culture is a part of organisation culture. In addition to safety culture thinking, proactive programmes and displays of proactive work to improve safety are required. This work needs to include, at a minimum, actions aiming at reducing human errors, the development of human error prevention tools, improvements in training, and the development of working methods and the organisation’s activities. Safety depends not only on the technical systems, but also on the people and the organisation. There is a need for better methods and tools for organisational assessment and development. Today there is universal acceptance of the significant impact that management and organisational factors have over the safety significance of complex industrial installations such as nuclear power plants. Many events with significant economic and public impact had causes that have been traced to management deficiencies. The objective of this study is development of new methods to increase safety of nuclear power plant operation. The research has been limited to commercial nuclear power plants that are intended for electrical power generation in Finland. Their production activities, especially operation and maintenance, are primarily reviewed from a safety point of view, as well as human performance and organisational factors perspective. This defines the scope and focus of the study. The research includes studies related to knowledge management and tacit knowledge in the project management context and specific studies related to transfer of tacit knowledge in the maintenance organization and transfer of tacit knowledge between workers of old generation and young generation. The empirical results of the research are presented in research papers which are enclosed in this thesis

A review of the use of artificial intelligence methods in infrastructure systems

The artificial intelligence (AI) revolution offers significant opportunities to capitalise on the growth of digitalisation and has the potential to enable the ‘system of systems’ approach required in increasingly complex infrastructure systems. This paper reviews the extent to which research in economic infrastructure sectors has engaged with fields of AI, to investigate the specific AI methods chosen and the purposes to which they have been applied both within and across sectors. Machine learning is found to dominate the research in this field, with methods such as artificial neural networks, support vector machines, and random forests among the most popular. The automated reasoning technique of fuzzy logic has also seen widespread use, due to its ability to incorporate uncertainties in input variables. Across the infrastructure sectors of energy, water and wastewater, transport, and telecommunications, the main purposes to which AI has been applied are network provision, forecasting, routing, maintenance and security, and network quality management. The data-driven nature of AI offers significant flexibility, and work has been conducted across a range of network sizes and at different temporal and geographic scales. However, there remains a lack of integration of planning and policy concerns, such as stakeholder engagement and quantitative feasibility assessment, and the majority of research focuses on a specific type of infrastructure, with an absence of work beyond individual economic sectors. To enable solutions to be implemented into real-world infrastructure systems, research will need to move away from a siloed perspective and adopt a more interdisciplinary perspective that considers the increasing interconnectedness of these systems

Understanding the Application and Benefits of Learning-based Methods in Nuclear Science and Engineering

Known as the fourth industrial revolution, digitization is an ongoing trend in all fields, in which various industries are integrating information technologies to support and improve their businesses. Nuclear technology industries have also increased their interest in data-driven methods by leveraging the potential of pattern recognition to identify anomalies and to take actions more rapidly, in areas such as health and monitoring, radiation detection, and optimization. By acknowledging the practicality and popularity of these methods, it is imperative to understand the benefits and barriers of implementing such methodologies to create better research plans and identify project risks and opportunities. This dissertation discusses different technologies and their integration and challenges within the nuclear industry. It is recognized that concepts of complexity and emergent behavior, as well as the importance of such properties as a distinctive aspect of nuclear and radiological engineering problems in which holistic approaches are crucial to innovation. Overall, the development and application of learning-based methods can be promising in the nuclear industry and many related tasks as long as expert knowledge is considered in the desired application to ensure a robust application of such methods. Cross-discipline studies and the creation of benchmarks are highly suggested for future practices

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Multi-Agent Systems

A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains

A Risk-Based Model for Construction Inspection in Highways

The quality and durability of highway construction projects have been a major concern to highway agencies and contractors. Quality assurance (QA) of highway construction is defined as a tool or means by which the owner and contractors ensure that the roads are constructed in accordance with approved plans and specifications by the most economical, efficient, and safe method. To ensure the quality of highway construction projects, transportation agencies typically perform a series of tests for construction materials and inspect workmanship processes through their QA programs. Transportation agencies face the critical challenge of increased demand for highway system rehabilitation and construction work with limited inspection resources. These resources play a crucial role in asserting the quality of highway projects. The shortage of experienced QA inspection staff due to retirement or migration to the private sector has significantly impacted construction inspection capabilities. The objective of this dissertation is to develop a risk-based inspection (RBI) framework. This framework optimizes inspection and testing activities of highway construction projects based on criticality. It introduces a core list of QA inspection and testing activities for the rigid pavement, flexible pavement, bridge deck, and structural concrete. This list highly contributes to the QA of design service life and long-term performance of the highway. The prioritized list of activities may help transportation agencies allocate their limited resources to the most critical construction operations. Additionally, this dissertation provides a RBI model that serves as a risk assessment tools for highway construction quality levels and identifies causes of any quality shortfall. Bayesian belief network (BBN), fuzzy set (FS) theory, and Delphi techniques have been applied to develop the RBI model. Further, this dissertation discusses different strategies to alleviate the risk of highway construction inspection