Energy Efficiency

Energy efficiency is finally a common sense term. Nowadays almost everyone knows that using energy more efficiently saves money, reduces the emissions of greenhouse gasses and lowers dependence on imported fossil fuels. We are living in a fossil age at the peak of its strength. Competition for securing resources for fuelling economic development is increasing, price of fuels will increase while availability of would gradually decline. Small nations will be first to suffer if caught unprepared in the midst of the struggle for resources among the large players. Here it is where energy efficiency has a potential to lead toward the natural next step - transition away from imported fossil fuels! Someone said that the only thing more harmful then fossil fuel is fossilized thinking. It is our sincere hope that some of chapters in this book will influence you to take a fresh look at the transition to low carbon economy and the role that energy efficiency can play in that process

INTRUSION DETECTION OF A SIMULATED SCADA SYSTEM USING A DATA-DRIVEN MODELING APPROACH

Supervisory Control and Data Acquisition (SCADA) are large, geographically distributed systems that regulate help processes in industries such as nuclear power, transportation or manufacturing. SCADA is a combination of physical, sensing, and communications equipment that is used for monitoring, control and telemetry acquisition actions. Because SCADA often control the distribution of vital resources such as electricity and water, there is a need to protect these cyber-physical systems from those with possible malicious intent. To this end, an Intrusion Detection System (IDS) is utilized to monitor telemetry sources in order to detect unwanted activities and maintain overall system integrity. This dissertation presents the results in developing a behavior-based approach to intrusion detection using a simulated SCADA test bed. Empirical modeling techniques known as Auto Associative Kernel Regression (AAKR) and Auto Associative Multivariate State Estimation Technique (AAMSET) are used to learn the normal behavior of the test bed. The test bed was then subjected to repeated intrusion injection experiments using penetration testing software and exploit codes. Residuals generated from these experiments are then supplied to an anomaly detection algorithm known as the Sequential Probability Ratio Test (SPRT). This approach is considered novel in that the AAKR and AAMSET, combined with the SPRT, have not been utilized previously in industry for cybersecurity purposes. Also presented in this dissertation is a newly developed variable grouping algorithm that is based on the Auto Correlation Function (ACF) for a given set of input data. Variable grouping is needed for these modeling methods to arrive at a suitable set of predictors that return the lowest error in model performance. The developed behavior-based techniques were able to successfully detect many types of intrusions that include network reconnaissance, DoS, unauthorized access, and information theft. These methods would then be useful in detecting unwanted activities of intruders from both inside and outside of the monitored network. These developed methods would also serve to add an additional layer of security. When compared with two separate variable grouping methods, the newly developed grouping method presented in this dissertation was shown to extract similar groups or groups with lower average model prediction errors

Innovation-ICT-cybersecurity: The triad relationship and its impact on growth competitiveness

This study examines the global growth competitiveness of countries using the dynamics of growth, ICT, and innovation. It also introduces a new dynamic, cybersecurity, and argues that within a growth competitiveness framework, ICT, innovation, and cybersecurity mechanisms allow some countries to achieve higher ranks on the competitiveness ladder than others. Based on a theoretical framework that encompasses the economic growth model, the complementarity theory, and the international law theory, a model that integrates ICT, innovation, and cybersecurity, depicts the relationships amongst them and with growth competitiveness, and incorporates complementary factors with possible moderating effect is presented. The model proposed relationships are then tested using PLS-PM. The model proves to have adequate goodness-of-fit as well as predictive validity. Results support most hypotheses showing: (1) a positive relationship between ICT and innovation; (2) a positive relationship between each of innovation and ICT with growth competitiveness; (3) a mediating effect of innovation has in the ICT – growth competitiveness relationship; (4) a positive relationship between ICT and innovation on one hand and cybersecurity on the other; (5) a mediating role of cybersecurity in the ICT – growth as well as the innovation – growth relationships; and the (6) moderating effect that human capital has in the above relationships. Cyber threats, however, do not have a moderator role in these relationships. These findings are interpreted in relation to the extant body of knowledge related to ICT, innovation, and cybersecurity. Moreover, the theoretical and the practical implications are discussed and the practical significance is shown. Finally, the study limitations are listed, the recommendations are presented, and the direction for future work is discussed

Performance Analysis Of Data-Driven Algorithms In Detecting Intrusions On Smart Grid

The traditional power grid is no longer a practical solution for power delivery due to several shortcomings, including chronic blackouts, energy storage issues, high cost of assets, and high carbon emissions. Therefore, there is a serious need for better, cheaper, and cleaner power grid technology that addresses the limitations of traditional power grids. A smart grid is a holistic solution to these issues that consists of a variety of operations and energy measures. This technology can deliver energy to end-users through a two-way flow of communication. It is expected to generate reliable, efficient, and clean power by integrating multiple technologies. It promises reliability, improved functionality, and economical means of power transmission and distribution. This technology also decreases greenhouse emissions by transferring clean, affordable, and efficient energy to users. Smart grid provides several benefits, such as increasing grid resilience, self-healing, and improving system performance. Despite these benefits, this network has been the target of a number of cyber-attacks that violate the availability, integrity, confidentiality, and accountability of the network. For instance, in 2021, a cyber-attack targeted a U.S. power system that shut down the power grid, leaving approximately 100,000 people without power. Another threat on U.S. Smart Grids happened in March 2018 which targeted multiple nuclear power plants and water equipment. These instances represent the obvious reasons why a high level of security approaches is needed in Smart Grids to detect and mitigate sophisticated cyber-attacks. For this purpose, the US National Electric Sector Cybersecurity Organization and the Department of Energy have joined their efforts with other federal agencies, including the Cybersecurity for Energy Delivery Systems and the Federal Energy Regulatory Commission, to investigate the security risks of smart grid networks. Their investigation shows that smart grid requires reliable solutions to defend and prevent cyber-attacks and vulnerability issues. This investigation also shows that with the emerging technologies, including 5G and 6G, smart grid may become more vulnerable to multistage cyber-attacks. A number of studies have been done to identify, detect, and investigate the vulnerabilities of smart grid networks. However, the existing techniques have fundamental limitations, such as low detection rates, high rates of false positives, high rates of misdetection, data poisoning, data quality and processing, lack of scalability, and issues regarding handling huge volumes of data. Therefore, these techniques cannot ensure safe, efficient, and dependable communication for smart grid networks. Therefore, the goal of this dissertation is to investigate the efficiency of machine learning in detecting cyber-attacks on smart grids. The proposed methods are based on supervised, unsupervised machine and deep learning, reinforcement learning, and online learning models. These models have to be trained, tested, and validated, using a reliable dataset. In this dissertation, CICDDoS 2019 was used to train, test, and validate the efficiency of the proposed models. The results show that, for supervised machine learning models, the ensemble models outperform other traditional models. Among the deep learning models, densely neural network family provides satisfactory results for detecting and classifying intrusions on smart grid. Among unsupervised models, variational auto-encoder, provides the highest performance compared to the other unsupervised models. In reinforcement learning, the proposed Capsule Q-learning provides higher detection and lower misdetection rates, compared to the other model in literature. In online learning, the Online Sequential Euclidean Distance Routing Capsule Network model provides significantly better results in detecting intrusion attacks on smart grid, compared to the other deep online models

Multimodal Robotic Health in Future Factories Through IIot, Data Analytics, and Virtual Commissioning

The manufacturing sector is continuously reinventing itself by embracing opportunities offered by the industrial internet of things and big data, among other advances. Modern manufacturing platforms are defined by the quest for ever increasing automation along all aspects of the production cycle. Furthermore, in the next decades, research and industry are expected to develop a large variety of autonomous robots for a large variety of tasks and environments enabling future factories. This continuing pressure towards automation dictates that emergent technologies are leveraged in a manner that suits this purpose. These challenges can be addressed through the advanced methods such as [1] large-scale simulation, [2] system health monitoring sensors and [3] advanced computational technologies to establish a life-like digital manufacturing platform and capture, represent, predict, and control the dynamics of a live manufacturing cell in a future factory. Autonomy is a desirable quality for robots in manufacturing, particularly when the robot needs to act in real-world environments together with other agents, and when the environment changes in unpredictable or uncertain way. This dissertation research will focus on experimentally collecting sensor signals from force sensors, motor voltages, robot monitors and thermal cameras to connect to such digital twin systems so that more accurate real-time plant descriptions can be collected and shared between stakeholders. Creating a future factory based on an Industrial Internet-of-Things (IIoT) platform, data-driven science and engineering solutions will help accelerating Smart Manufacturing Innovation. Besides, this study will examine the ways of sharing knowledge between robots, and between different subsystems of a single robot, and implement concepts for communicating knowledge that are machine logical and reliable. My work will focus on applying the proposed methodology on more diverse manufacturing tasks and materials flows, including collaboratively assembly jobs, visual inspection, and continuous movement tasks. These tasks will require higher-dimensional information such as, analog plant signals, and machine vision feedback to be fed into and train the digital twin

Prognostics and Health Management in Nuclear Power Plants: A Review of Technologies and Applications

This report reviews the current state of the art of prognostics and health management (PHM) for nuclear power systems and related technology currently applied in field or under development in other technological application areas, as well as key research needs and technical gaps for increased use of PHM in nuclear power systems. The historical approach to monitoring and maintenance in nuclear power plants (NPPs), including the Maintenance Rule for active components and Aging Management Plans for passive components, are reviewed. An outline is given for the technical and economic challenges that make PHM attractive for both legacy plants through Light Water Reactor Sustainability (LWRS) and new plant designs. There is a general introduction to PHM systems for monitoring, fault detection and diagnostics, and prognostics in other, non-nuclear fields. The state of the art for health monitoring in nuclear power systems is reviewed. A discussion of related technologies that support the application of PHM systems in NPPs, including digital instrumentation and control systems, wired and wireless sensor technology, and PHM software architectures is provided. Appropriate codes and standards for PHM are discussed, along with a description of the ongoing work in developing additional necessary standards. Finally, an outline of key research needs and opportunities that must be addressed in order to support the application of PHM in legacy and new NPPs is presented

An Approach to Guide Users Towards Less Revealing Internet Browsers

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed

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

Air Force Institute of Technology Research Report 2020

This Research Report presents the FY20 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document

The Politics of Uncertainty

"Why is uncertainty so important to politics today? To explore the underlying reasons, issues and challenges, this book’s chapters address finance and banking, insurance, technology regulation and critical infrastructures, as well as climate change, infectious disease responses, natural disasters, migration, crime and security and spirituality and religion. The book argues that uncertainties must be understood as complex constructions of knowledge, materiality, experience, embodiment and practice. Examining in particular how uncertainties are experienced in contexts of marginalisation and precarity, this book shows how sustainability and development are not just technical issues, but depend on deeply political values and choices. What burgeoning uncertainties require lies less in escalating efforts at control, but more in a new – more collective, mutualistic and convivial – politics of responsibility and care. If hopes of much-needed progressive transformation are to be realised, then currently-blinkered understandings of uncertainty need to be met with renewed democratic struggle. Written in an accessible style and illustrated by multiple case studies from across the world, this book will appeal to a wide cross-disciplinary audience in fields ranging from economics to law to science studies to sociology to anthropology and geography, as well as professionals working in risk management, disaster risk reduction, emergencies and wider public policy fields.