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At least 19 records

Towards a Semantically-Enabled Control Strategy for Building Simulations: Integration of Semantic Technologies and Model Predictive Control

State-of-the-art building simulation control methods incorporate physical constraints into their mathematical models, but omit implicit constraints associated with policies of operation and dependency relationships among rules representing those constraints. To overcome these shortcomings, there is a recent trend in enabling the control strategies with inference-based rule checking capabilities. One solution is to exploit semantic web technologies in building simulation control. Such approaches provide the tools for semantic modeling of domains, and the ability to deduce new information based on the models through use of Description Logic (DL). In a step toward enabling this capability, this paper presents a cross-disciplinary data-driven control strategy for building energy management simulation that integrates semantic modeling and formal rule checking mechanisms into a Model Predictive Control (MPC) formulation. The results show that MPC provides superior levels of performance when initial conditions and inputs are derived from inference-based rules.

Delgoshaei, Parastoo↗

Incorporating Semantic Knowledge into Dynamic Data Processing for Smart Power Grids

Semantic Web allows us to model and query time-invariant or slowly evolving knowledge using ontologies. Emerging applications in Cyber Physical Systems such as Smart Power Grids that require continuous information monitoring and integration present novel opportunities and challenges for Semantic Web technologies. Semantic Web is promising to model diverse Smart Grid domain knowledge for enhanced situation awareness and response by multi-disciplinary participants. However, current technology does pose a performance overhead for dynamic analysis of sensor measurements. In this paper, we combine semantic web and complex event processing for stream based semantic querying. We illustrate its adoption in the USC Campus Micro-Grid for detecting and enacting dynamic response strategies to peak power situations by diverse user roles. We also describe the semantic ontology and event query model that supports this. Further, we introduce and evaluate caching techniques to improve the response time for semantic event queries to meet our application needs and enable sustainable energy management.

Zhou, Qunzhi↗

Using Semantic Web Technologies to Develop Intrinsically Resilient Energy Control Systems

To preserve critical energy control functions while under attack, it is necessary to perform comprehensive analysis on root causes and impacts of cyber intrusions without sacrificing the availability of energy delivery. We propose to design an intrinsically resilient energy control system where we extensively utilize Semantic Web technologies, which play critical roles in knowledge representation and acquisition. While our ultimate goal is to ensure availability/resiliency of energy delivery functions and the capability to assess root causes and impacts of cyber intrusions, the focus of this paper is to demonstrate a proof of concept of how Semantic Web technologies can significantly contribute to resilient energy control systems.

cybersecurity↗

Nuclear Fuel Cycle Reasoner: PNNL FY12 Report

Building on previous internal investments and leveraging ongoing advancements in semantic technologies, PNNL implemented a formal reasoning framework and applied it to a specific challenge in nuclear nonproliferation. The Semantic Nonproliferation Analysis Platform (SNAP) was developed as a preliminary graphical user interface to demonstrate the potential power of the underlying semantic technologies to analyze and explore facts and relationships relating to the nuclear fuel cycle (NFC). In developing this proof of concept prototype, the utility and relevancy of semantic technologies to the Office of Defense Nuclear Nonproliferation Research and Development (DNN R&D) has been better understood.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

CHAMPION: Intelligent Hierarchical Reasoning Agents for Enhanced Decision Support

We describe the design and development of an advanced reasoning framework employing semantic technologies, organized within a hierarchy of computational reasoning agents that interpret domain specific information. Designed based on an inspirational metaphor of the pattern recognition functions performed by the human neocortex, the CHAMPION reasoning framework represents a new computational modeling approach that derives invariant knowledge representations through memory-prediction belief propagation processes that are driven by formal ontological language specification and semantic technologies. The CHAMPION framework shows promise for enhancing complex decision making in diverse problem domains including cyber security, nonproliferation and energy consumption analysis.

99 GENERAL AND MISCELLANEOUS↗

Nuclear Nonproliferation Ontology Assessment Team (Final Report)

The Ontology Assessment Team began in May 2009 and concluded in September 2011. During this two-year time frame, the Ontology Assessment team had two objectives: (1) Assessing the utility of knowledge representation and semantic technologies for addressing nuclear nonproliferation challenges; and (2) Developing ontological support tools that would provide a framework for integrating across the Simulation, Algorithm and Modeling (SAM) program. The SAM Program was going through a large assessment and strategic planning effort during this time and as a result, the relative importance of these two objectives changed, altering the focus of the Ontology Assessment Team. In the end, the team conducted an assessment of the state of art, created an annotated bibliography, and developed a series of ontological support tools, demonstrations and presentations. A total of more than 35 individuals from 12 different research institutions participated in the Ontology Assessment Team. These included subject matter experts in several nuclear nonproliferation-related domains as well as experts in semantic technologies. Despite the diverse backgrounds and perspectives, the Ontology Assessment team functioned very well together and aspects could serve as a model for future inter-laboratory collaborations and working groups. While the team encountered several challenges and learned many lessons along the way, the Ontology Assessment effort was ultimately a success that led to several multi-lab research projects and opened up a new area of scientific exploration within the Office of Nuclear Nonproliferation and Verification.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Order Theoretical Semantic Recommendation

Mathematical concepts of order and ordering relations play multiple roles in semantic technologies. Discrete totally ordered data characterize both input streams and top-k rank-ordered recommendations and query output, while temporal attributes establish numerical total orders, either over time points or in the more complex case of startend temporal intervals. But also of note are the fully partially ordered data, including both lattices and non-lattices, which actually dominate the semantic strcuture of ontological systems. Scalar semantic similarities over partially-ordered semantic data are traditionally used to return rank-ordered recommendations, but these require complementation with true metrics available over partially ordered sets. In this paper we report on our work in the foundations of partial order measurement in ontologies, with application to top-k semantic recommendation in workflows.

Joslyn, Cliff A.↗

A Linked Fusion of Things, Services, and Data to Support a Collaborative Data Management Facility

The purpose of this paper is to illustrate the use of semantic technologies and approaches to seamlessly link things, services, and data in the proposed design of a scientific offshore wind energy research for the U.S. Department of Energy Wind and Water Technology Office of the Office of Energy Efficiency and Renewable Energy (EERE). By adapting linked community best practices, we were able to design a collaborative facility supporting both operational staff and end users that incorporates off-the-shelf components and overcome traditional barriers between devices, resulting data, and processing services. This was made largely possible through complementary advances in the Internet of Things (IoT), semantic web, Linked Services, and Linked Data communities, which provide the foundation for our design.

Stephan, Eric G.↗

Nuclear Fuel Cycle Reasoner: PNNL FY13 Report

In Fiscal Year 2012 (FY12) PNNL implemented a formal reasoning framework and applied it to a specific challenge in nuclear nonproliferation. The Semantic Nonproliferation Analysis Platform (SNAP) was developed as a preliminary graphical user interface to demonstrate the potential power of the underlying semantic technologies to analyze and explore facts and relationships relating to the nuclear fuel cycle (NFC). In Fiscal Year 2013 (FY13) the SNAP demonstration was enhanced with respect to query and navigation usability issues.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

THE NEW ONLINE METADATA EDITOR FOR GENERATING STRUCTURED METADATA

Nobody is better suited to “describe” data than the scientist who created it. This “description” about a data is called Metadata. In general terms, Metadata represents the who, what, when, where, why and how of the dataset [1]. eXtensible Markup Language (XML) is the preferred output format for metadata, as it makes it portable and, more importantly, suitable for system discoverability. The newly developed ORNL Metadata Editor (OME) is a Web-based tool that allows users to create and maintain XML files containing key information, or metadata, about the research. Metadata include information about the specific projects, parameters, time periods, and locations associated with the data. Such information helps put the research findings in context. In addition, the metadata produced using OME will allow other researchers to find these data via Metadata clearinghouses like Mercury [2][4]. OME is part of ORNL’s Mercury software fleet [2][3]. It was jointly developed to support projects funded by the United States Geological Survey (USGS), U.S. Department of Energy (DOE), National Aeronautics and Space Administration (NASA) and National Oceanic and Atmospheric Administration (NOAA). OME’s architecture provides a customizable interface to support project-specific requirements. Using this new architecture, the ORNL team developed OME instances for USGS’s Core Science Analytics, Synthesis, and Libraries (CSAS&L), DOE’s Next Generation Ecosystem Experiments (NGEE) and Atmospheric Radiation Measurement (ARM) Program, and the international Surface Ocean Carbon Dioxide ATlas (SOCAT).Researchers simply use the ORNL Metadata Editor to enter relevant metadata into a Web-based form. From the information on the form, the Metadata Editor can create an XML file on the server that the editor is installed or to the user’s personal computer. Researchers can also use the ORNL Metadata Editor to modify existing XML metadata files.As an example, an NGEE Arctic scientist use OME to register their datasets to the NGEE data archive and allows the NGEE archive to publish these datasets via a data search portal (http://ngee.ornl.gov/data). These highly descriptive metadata created using OME allows the Archive to enable advanced data search options using keyword, geo-spatial, temporal and ontology filters. Similarly, ARM OME allows scientists or principal investigators (PIs) to submit their data products to the ARM data archive.How would OME help Big Data Centers like the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC)?The ORNL DAAC is one of NASA’s Earth Observing System Data and Information System (EOSDIS) data centers managed by the Earth Science Data and Information System (ESDIS) Project. The ORNL DAAC archives data produced by NASA's Terrestrial Ecology Program. The DAAC provides data and information relevant to biogeochemical dynamics, ecological data, and environmental processes, critical for understanding the dynamics relating to the biological, geological, and chemical components of the Earth's environment. Typically data produced, archived and analyzed is at a scale of multiple petabytes, which makes the discoverability of the data very challenging. Without proper metadata associated with the data, it is difficult to find the data you are looking for and equally difficult to use and understand the data.OME will allow data centers like the NGEE and ORNL DAAC to produce meaningful, high quality, standards-based, descriptive information about their data products in-turn helping with the data discoverability and interoperability. Useful Links:USGS OME: http://mercury.ornl.gov/OME/NGEE OME: http://ngee-arctic.ornl.gov/ngeemetadata/ARM OME: http://archive2.ornl.gov/armome/Contact: Ranjeet Devarakonda (devarakondar@ornl.gov)References:[1] Federal Geographic Data Committee. Content standard for digital geospatial metadata. Federal Geographic Data Committee, 1998.[2] Devarakonda, Ranjeet, et al. "Mercury: reusable metadata management, data discovery and access system." Earth Science Informatics 3.1-2 (2010): 87-94.[3] Wilson, B. E., Palanisamy, G., Devarakonda, R., Rhyne, B. T., Lindsley, C., & Green, J. (2010). Mercury Toolset for Spatiotemporal Metadata.[4] Pouchard, L. C., Branstetter, M. L., Cook, R. B., Devarakonda, R., Green, J., Palanisamy, G., ... & Noy, N. F. (2013). A Linked Science investigation: enhancing climate change data discovery with semantic technologies. Earth science informatics, 6(3), 175-185.

FGDC CSDGM↗

VizBrick: A GUI-based Interactive Tool for Authoring Semantic Metadata for Building Datasets

Brick ontology is a unified semantic metadata schema to address the stand-ardization problem of buildings' physical, logical, and virtual assets and the relationships between them. Creating a Brick model for a building dataset means that the dataset's contents are semantically described using the standard terms defined in the Brick ontology. It will enable the benefits of data standardization, without having to recollect or reorganize the data and opens the possibility of automation leveraging the machine readability of the semantic metadata. The problem is that authoring Brick models for building datasets often requires knowledge of semantic technology (e.g., on-tology declarations and RDF syntax) and leads to repeated manual trial and error processes, which can be time-consuming and challenging to do with-out an interactive visual representation of the data. We developed VizBrick, a tool with a graphical user interface that can assist users in creating Brick models visually and interactively without having to understand the Re-source Description Framework (RDF) syntax. VizBrick provides handy ca-pabilities such as keyword search for easy find of relevant brick concepts and relations to their data columns and automatic suggestions of concept mapping. In this demonstration, we present a use-case of VizBrick to show-case how a Brick model can be created for a real-world building dataset.

Lee, Sangkeun (Matt)↗

Framework for Building Collaborative Research Environment

Wide range of expertise and technologies are the key to solving some global problems. Semantic web technology can revolutionize the nature of how scientific knowledge is produced and shared. The semantic web is all about enabling machine-machine readability instead of a routine human-human interaction. Carefully structured data, as in machine readable data is the key to enabling these interactions. Drupal is an example of one such toolset that can render all the functionalities of Semantic Web technology right out of the box. Drupal’s content management system automatically stores the data in a structured format enabling it to be machine. Within this paper, we will discuss how Drupal promotes collaboration in a research setting such as Oak Ridge National Laboratory (ORNL) and Long Term Ecological Research Center (LTER) and how it is effectively using the Semantic Web in achieving this.

97 MATHEMATICS AND COMPUTING↗