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Supervisory Control and Data Acquisition (SCADA) System Cyber Security Analysis using a Live Virtual and Constructive (LVC) Testbed.
Abstract not provided.
Supervisory Command and Data Acquisition (SCADA) system Cyber Security Analysis using a Live Virtual and Constructive (LVC) Testbed.
Abstract not provided.
GRADIENT: Graph Analytic Approach for Discovering Irregular Events, Nascent and Temporal
Finding a time-ordered signature within large graphs is a computationally complex problem due to the combinatorial explosion of potential patterns. GRADIENT is designed to search and understand that problem space.
GridOPTICS Software System
GridOPTICS Software System (GOSS) is a middleware that facilitates creation of new, modular and flexible operational and planning platforms that can meet the challenges of the next generation power grid. GOSS enables Department of Energy, power system utilities, and vendors to build better tools faster. GOSS makes it possible to integrate Future Power Grid Initiative software products/prototypes into existing power grid software systems, including the PNNL PowerNet and EIOC environments. GOSS is designed to allow power grid applications developed for different underlying software platforms installed in different utilities to communicate with ease. This can be done in compliance with existing security and data sharing policies between the utilities. GOSS not only supports one-to-one data transfer between applications, but also publisher/subscriber scheme. To support interoperability requirements of future EMS, GOSS is designed for CIM compliance. In addition to this, it supports authentication and authorization capabilities to protect the system from cyber threats. In summary, the contributions of the GOSS middleware are as follows: A platform to support future EMS development. A middleware that promotes interoperability between power grid applications. A distributed architecture that separates data sources from power grid applications. Support for data exchange with either one-to-one or publisher/subscriber interfaces. An authentication and authorization scheme for limiting the access to data between utilities.
Systematic Approach to Nuclear Data Uncertainty Quantification for Nuclear Security Applications
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Scalable, Secure Analysis of Social Sciences Data on the Azure Platform
Human activity and interaction data is beginning to be collected at population scales through the pervasiveness of social media and willingness of people to volunteer information. This can allow social science researchers to understand and model human behavior with better accuracy and prediction power. Political and social scientists are starting to correlate such large scale social media datasets with events that impact society as evidence abound of the virtual and physical public spaces intersecting and influencing each other [1,2]. Managers of Cyber Physical Systems such as Smart Power Grid utilities are investigating the impact of consumer behavior on power consumption, and the possibility of influencing the usage profile [3]. Data collection is also made easier through technology such as mobile apps, social media sites and search engines that directly collect data, and sensors such smart meters and room occupancy sensors that indirectly measure human activity. These technology platforms also provide a convenient framework for “human sensors” to record and broadcast data for behavioral studies, as a form of crowd sourced citizen science. This has the added advantage of engaging the broader public in STEM activities and help influence public policy.
Monitoring Wildlife Interactions with Their Environment: An Interdisciplinary Approach
In a rapidly changing world, wildlife ecologists strive to correctly model and predict complex relationships between animals and their environment, which facilitates management decisions impacting public policy to conserve and protect delicate ecosystems. Recent advances in monitoring systems span scientific domains, including animal and weather monitoring devices and landscape classification mapping techniques. The current challenge is how to combine and use detailed output from various sources to address questions spanning multiple disciplines. WolfScout wildlife and weather tracking system is a software tool capable of filling this niche. WolfScout automates integration of the latest technological advances in wildlife GPS collars, weather stations, drought conditions, and severe weather reports, and animal demographic information. The WolfScout database stores a variety of classified landscape maps including natural and manmade features. Additionally, WolfScout’s spatial database management system allows users to calculate distances between animals’ location and landscape characteristics, which are linked to the best approximation of environmental conditions at the animal’s location during the interaction. Through a secure website, data are exported in formats compatible with multiple software programs including R and ArcGIS. The WolfScout design promotes interoperability in data, between researchers, and software applications while standardizing analyses of animal interactions with their environment.
The Connected Traveler
The Connected Traveler project is a multi-disciplinary undertaking that seeks to validate potential for transformative transportation system energy savings by incentivizing energy efficient travel behavior.
Scalable Fault Detection and Localization of Network Issues
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An Efficient Holographic Huygens? Metasurface based on Dielectric Resonant Meta-Atoms.
Subwavelength-thin metasurfaces have shown great promises for the control of optical wavefronts, thus opening new pathways for the development of efficient flat optics. In particular, Huygens’ metasurfaces based on all-dielectric resonant meta-atoms have already shown a huge potential for practical applications with their polarization insensitivity and high transmittance efficiency. Here, we experimentally demonstrate a holographic Huygens’ metasurface based on dielectric resonant meta-atoms capable of complex wavefront control at telecom wavelengths. Our metasurface produces a hologram image in the far-field with 82% transmittance efficiency and 40% imaging efficiency. Such efficient complex wavefront control shows that Huygens’ metasurfaces based on resonant dielectric meta-atoms are a big step towards practical applications of metasurfaces in wavefront design related technologies, including computer-generated holograms, ultra-thin optics, security and data storage devices.
Emulating Genome Security Risks in Realistic Genomics Data Ecosystems.
Abstract not provided.
Emulating Genome Security Risks in Realistic Genomics Data Ecosystems.
Abstract not provided.
Multi-Timescale Integrated Dynamic and Scheduling Model (MIDAS-Solar)
Solar photovoltaic (PV) installations have experienced unprecedented growth in the United States. PV will become not only an energy producer but also a necessary provider of ancillary services at multiple timescales. Conventional methods to simulate power system operations - such as long-term production simulation (which typically considers schedules from hours to minutes by using an optimization framework) and short-term transient studies (which simulate dynamics from seconds to sub-seconds using state variables and differential equations) are not sufficient for studying the multiple-timescale variation of solar generation and its impact on system reliability. Long-term system economics and short-term system dynamics are highly coupled, particularly when the penetration level of renewable generation is extremely high, because the uncertainty and variability of solar generation will impact both power systems steady-state and dynamic performance. This project helps meet and exceed the Solar Energy Technologies Office goal of systems integration by directly addressing this stability and reliability challenge for electric grid planning and operation. This will be accomplished by developing temporally comprehensive, closed-loop simulation models that seamlessly simulate power systems operations from economic scheduling (day-ahead to hours) to dynamic response analysis (seconds to sub-seconds). Both a multi-timescale grid model and an integrated PV model will be developed in this project to accurately study the impacts of PV variability and uncertainty on system reliability at multiple timescales. Using quasi-dynamic simulation methods and data-driven security assessment (DSA) criteria will allow the dynamic characteristics of PV to be fed forward into longer-timescale scheduling models for a complete understanding of the effect of short-term PV dynamics on bulk systems operations (e.g., reserve scheduling and deployment). Upon completion of the proposed model, this project will help operators accurately assess system reliability by deploying energy and reserve scheduling under critical contingency conditions and studying interactions among all types of essential reliability services provided by modern PV power plants.
Advanced data structures for monitoring national security cyber streams.
Abstract not provided.