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A description of the Intelligent Data Reduction (IDARE) expert system and an IDARE user's manual are given. IDARE is a data reduction system with the addition of a user profile infrastructure. The system was tested on a nickel-cadmium battery testbed. Information is given on installing, loading, maintaining the IDARE system.
Spatial information intelligence is a global issue that will increasingly affect our ability to survive as a species. Collectively we must better appreciate the complex relationships that make life on Earth possible. Providing spatial information in its native context can accelerate our ability to process that information. To maximize this ability to process information, three basic elements are required: data delivery (server technology), data access (client technology), and data processing (information intelligence). NASA World Wind provides the open source client technology based on open standards. The possibilities for data processing and data sharing are enhanced by this inclusive infrastructure for geographic information. It is interesting that this open source and open standards approach, unfettered by proprietary constraints, provides for unlimited innovation with this technology.
The penetration of distributed energy resources (DER) is growing at much higher rates than predicted 20 years ago. Far from being used only in residential settings, DER are now installed on distribution and transmission circuits. In this position, they do not have the same properties as traditional generators and are more flexible in many cases. The growing penetration and range of uses for DER motivate the need to reliably and safely integrate them into the grid. Operators must be able to rely on them not only for normal operation, but also during abnormal conditions like black starts or adverse cyber scenarios. To that end, we study the communications, device interfaces, and potential consequences of DER operation under abnormal and adversarial conditions. The weaknesses of communications networks are studied based on the industrial protocols used, and the benefits of security features are examined. The device interfaces are found to be vulnerable to attack based on the requirements in the IEEE-1547 standard for DER interconnection and interoperability, which is expected to be adopted in the next ten years. In addition to exploring the requirements of the standard, we show that these vulnerabilities and others do exist and can be used maliciously in a modern storage system DER. Consequences of these vulnerabilities range from exacerbated grid instability, to simultaneous loss of large portions of DER penetration, to physical damage to inverters or DER themselves and other sensitive equipment. We tie these outcomes to specific attacker actions in an effort to give operators a better threat intelligence view that allows them to prioritize mitigations. Finally, we discuss mitigations that could prevent many of the adversarial scenarios described. Some solutions can be added to existing infrastructure, while others may require longer term planning for grid modernization with consideration for security.
Today we face an explosion of data generation, ranging from health monitoring to national security infrastructure systems. More and more systems are connected to the Internet that collects data at regular time intervals. These systems share data and use machine learning methods for intelligent decisions, which resulted in numerous real-world applications (e.g., autonomous vehicles, recommendation systems, and heart-rate monitoring) that have benefited from it. However, these approaches are prone to identity thief and other privacy related cyber-security attacks. So, how can data privacy be protected efficiently in these scenarios? More dedicated efforts are needed to propose the integration of privacy techniques into existing systems and develop more advanced privacy techniques to address the complex challenges of multi-system connectivity and data fusion. Therefore, we have introduced Privacy Algorithms in Systems (PAS) at CIKM which provides a venue to gather academic researchers and industry researchers/practitioners to present their research in an effort to advance the frontier of this critical direction of privacy algorithms in systems.
The Cybersecurity Using Generative AI-Based Offensive and Automated Testing (Cyber-GOAT) project assessed the ability of generative AI to be used to target energy infrastructure.
Future space mission concepts and designs pose many networking challenges for command, telemetry, and science data applications with diverse end-to-end data delivery needs. For future end-to-end architecture designs, a key challenge is meeting expected application quality of service requirements for multiple simultaneous mission data flows with options to use diverse onboard local data buses, commercial ground networks, and multiple satellite relay constellations in LEO, MEO, GEO, or even deep space relay links. Effectively utilizing a complex network topology requires orchestration and direction that spans the many discrete, individually addressable computer systems, which cause them to act in concert to achieve the overall network goals. The system must be intelligent enough to not only function under nominal conditions, but also adapt to unexpected situations, and reorganize or adapt to perform roles not originally intended for the system or explicitly programmed. This paper describes architecture features of cognitive networking within the future NASA space communications infrastructure, and interacting with the legacy systems and infrastructure in the meantime. The paper begins by discussing the need for increased automation, including inter-system collaboration. This discussion motivates the features of an architecture including cognitive networking for future missions and relays, interoperating with both existing endpoint-based networking models and emerging information-centric models. From this basis, we discuss progress on a proof-of-concept implementation of this architecture as a cognitive networking on-orbit application on the SCaN Testbed attached to the International Space Station.
The Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) was aimed to enhance the understanding of cloud and aerosol properties in the region surrounding La Jolla, California. To address challenges in data collection and processing from various instruments, an edge computing device known as Waggle Sage Node (WSN) was deployed at the Ellen Browning Scripps Memorial Pier. WSN is a distributed-sensing platform designed to collect and analyze environmental data at the edge. Sage is a multi-agency-supported project that designs and builds a new kind of national-scale reusable cyberinfrastructure to enable artificial intelligence (AI) at the edge based on the Waggle platform. Sponsors include the U.S. Department of Energy (DOE) Advanced Scientific Computing Research (ASCR), DOE National Nuclear Security Administration (NNSA), DOE Biological and Environmental Research (BER) through DOE Artificial Intelligence for Earth System Predictability (AI4ESP), Argonne Laboratory-Directed Research and Development (LDRD). Sage (https://sagecontinuum.org/) is funded as a National Science Foundation Mid-Scale Research Infrastructure (MSRI) project (https://www.nsf.gov/awardsearch/showAward?AWD_ID=1935984). This robust, multi-architecture edge computing platform facilitated environmental monitoring during the campaign. This report details the scientific objectives, deployment process, and key results of integrating Waggle into the EPCAPE field campaign.
The electrical distribution landscape is rapidly transforming due to the proliferation of distributed energy resources (DERs) such as solar panels, wind turbines, battery storage systems, combined heat and power units, and electric vehicles, introducing variability and uncontrollability that traditional grid operators are ill-equipped to manage. This transformation is further accelerated by advancements in Information and Communication Technology infrastructure that connects control centers with end devices, demanding automation and a deeper understanding of new technologies by utility personnel. Advanced grid control techniques using system-level optimization, Artificial Intelligence, and Machine Learning at the enterprise level and distributed level are evolving to address these issues. There is also an opportunity to utilize the enormous data created by these new DER technologies in the grid. Advanced Distribution Management Systems (ADMS) and Distributed Energy Resource Management Systems (DERMS) are critical in addressing these challenges by automating grid operations and enhancing reliability. Given the relatively recent development of ADMS and DERMS, and the still relatively low level of ADMS and DERMS deployment in the industry, there is a notable deficiency in the comprehensive understanding of the challenges and benefits associated with these new technologies, especially with their complementary natures and integration architectures. This paper aims to bridge the knowledge gap in ADMS and DERMS integration, presenting three distinct integration architectures currently available, and discussing the challenges and benefits of each architecture to guide utilities, industry professionals, and researchers in optimizing grid management and decision-making processes for a resilient and efficient energy future.
Connected and automated vehicles (CAVs) can plan and actuate control that explicitly considers performance, system safety, and actuation constraints in a manner more efficient than their human-driven counterparts. In particular, eco-driving is enabled through connected exchange of information from signalized corridors that share their upcoming signal phase and timing (SPaT). This is accomplished in the proposed control approach, which follows first principles to plan a free-flow acceleration-optimal trajectory through green traffic light intervals by Pontryagin's Minimum Principle in a feedback manner. Urban conditions are then imposed from exogeneous traffic comprised of a mixture of human-driven vehicles (HVs) - as well as other CAVs. As such, safe disturbance compensation is achieved by implementing a model predictive controller (MPC) to anticipate and avoid collisions by issuing braking commands as necessary. The control strategy is experimentally vetted through vehicle-in-the-loop (VIL) of a prototype CAV that is embedded into a virtual traffic corridor realized through microsimulation. Up to 36% fuel savings are measured with the proposed control approach over a human-modelled driver, and it was found connectivity in the automation approach improved fuel economy by up to 26% over automation without. Additionally, the passive energy benefits realizable for human drivers when driving behind downstream CAVs are measured, showing up to 22% fuel savings in a HV when driving behind a small penetration of connectivity-enabled automated vehicles.
Abstract Artificial intelligence (AI) and machine learning (ML) can improve manufacturing efficiency, productivity, and sustainability. However, using AI in manufacturing also presents several challenges, including issues with data acquisition and management, human resources, infrastructure, as well as security risks, trust, and implementation challenges. For example, getting the data needed to train AI models can be difficult for rare events or costly for large datasets that need labeling. AI models can also pose security risks when integrated into industrial control systems. In addition, some industry players may be hesitant to use AI due to a lack of trust or understanding of how it works. Despite these challenges, AI has the potential to be extremely helpful in manufacturing, particularly in applications such as predictive maintenance, quality assurance, and process optimization. It is important to consider the specific needs and capabilities of each manufacturing scenario when deciding whether and how to use AI in manufacturing. This review identifies current developments, challenges, and future directions in AI/ML relevant to manufacturing, with the goal of improving understanding of AI/ML technologies available for solving manufacturing problems, providing decision‐support for prioritizing and selecting appropriate AI/ML technologies, and identifying areas where further research can yield transformational returns for the industry. Early experience suggests that AI/ML can have significant cost and efficiency benefits in manufacturing, especially when combined with the ability to capture enormous amounts of data from manufacturing systems.
The US Western Interconnection is facing unprecedented challenges in the form of less predictable peak demand, increasingly diverse generating resources, and fast-growing loads due to the onset of artificial intelligence, hyperscale computing, and electrification. Projecting where future generation may be developed is critical to maintaining a robust and resilient electric grid under this mounting uncertainty and variability. Using an integrated multisectoral, multiscale modeling framework that links a human-Earth systems model, an hourly load model, a geospatial power plant siting model, and an hourly grid operations model, we evaluate the power plant landscape evolution under eight alternative futures between 2020 and 2055. These futures represent a wide but plausible range of atmospheric conditions, emissions constraints, and economic, technological, and population growth assumptions. We find that local-level development can vary substantially both by generation type and capacity buildout across these futures. Specific regions of the Western Interconnection are projected to see large amounts of capacity development regardless of the future scenario. We additionally determine that projected power plant locations are more heavily influenced by the cost to interconnect to the electric grid than the locational energy value.
Future space mission concepts and designs pose many networking challenges for command, telemetry, and science data applications with diverse end-to-end data delivery needs. For future end-to-end architecture designs, a key challenge is meeting expected application quality of service requirements for multiple simultaneous mission data flows with options to use diverse onboard local data buses, commercial ground networks, and multiple satellite relay constellations in LEO, GEO, MEO, or even deep space relay links. Effectively utilizing a complex network topology requires orchestration and direction that spans the many discrete, individually addressable computer systems, which cause them to act in concert to achieve the overall network goals. The system must be intelligent enough to not only function under nominal conditions, but also adapt to unexpected situations, and reorganize or adapt to perform roles not originally intended for the system or explicitly programmed. This paper describes an architecture enabling the development and deployment of cognitive networking capabilities into the envisioned future NASA space communications infrastructure. We begin by discussing the need for increased automation, including inter-system discovery and collaboration. This discussion frames the requirements for an architecture supporting cognitive networking for future missions and relays, including both existing endpoint-based networking models and emerging information-centric models. From this basis, we discuss progress on a proof-of-concept implementation of this architecture, and results of implementation and initial testing of a cognitive networking on-orbit application on the SCaN Testbed attached to the International Space Station.
The rapid expansion of artificial intelligence (AI) has triggered an unprecedented surge in electricity demand, with US data center energy use projected to double or triple 2023 levels by 2028. This exponential growth places strain on grid infrastructure, which can hinder timely construction of desired computing capacity. To bridge this supply-demand gap, utilities and AI developers are increasingly turning to demand flexibility, a strategy that incentivizes shifting or reducing power use during peak periods of grid stress. Data centers are uniquely equipped for flexible operations due to their digital workloads, built-in redundancy, and onsite energy assets. This article outlines four primary mechanisms to enable data center flexibility: computational load flexibility (shifting tasks temporally or geographically), flexible use of core facility infrastructure adjustments, energy storage utilization, and onsite electricity generation. To encourage adoption, utilities are deploying new tariff designs, including voluntary interruptible service riders, mandated flexibility requirements, and streamlined interconnection processes for flexible loads. For the highly capitalized and rapidly growing AI industry, the primary motivators for embracing these strategies are expediting facility interconnection, satisfying emerging regulatory mandates, and mitigating community resistance. While demand flexibility cannot substitute the long-term need for new bulk power generation, it serves as an essential, immediate solution for enabling near-term deployment. By transforming data centers from grid stressors into stabilizing assets, flexible operations can ensure reliable grid integration, ease market pressures, and support a resilient power system.
With the advancement of space hardware technologies such as smaller spacecraft, component and instrument miniaturization and high performance space processors, and with the advancement of software technologies in artificial intelligence, big data analysis and autonomous decision making, Earth Science is looking at novel ways to observe phenomena that previously could not have been studied or would have been too expensive to study with traditional missions. In particular, the New Observing Strategies (NOS) component of the NASA Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) Program aims at leveraging these novel technologies as well as low cost and easy access to space to acquire multi-temporal or simultaneous multi-angular, multi-locations, multi-resolution and multi-spectral observations that will provide better multi-source measurements and will build a more dynamic and comprehensive picture of Earth Science phenomena that need to be studied and analyzed. For applications such as water resources management, air quality monitoring, biodiversity studies or disaster management, NOS will integrate the use of small instruments, small spacecraft, constellations of spacecraft and networks of sensors to design new missions that will provide the necessary measurements to improve future forecast and science modeling systems.Measurement acquisition will therefore be approached as a system of systems rather than on a mission basis, and a system of this complexity should not be expected to work without full integration and experimental characterization. Although most of the individual technologies enabling to link and coordinate multi-source observations are more or less mature, a few technologies need to be developed and all of them need to be integrated and tested as a system. In order for this validation to occur, the AIST Program is developing the NOS Testbed that includes 3 main goals:1.Validate novel NOS technologies, independently and as a system2.Demonstrate novel distributed operations concepts3.Socialize new Distributed Spacecraft Mission (DSM) and SensorWeb (SW) technologies and concepts to the science community by significantly retiring the risk of integrating these new technologies.The NOS Testbed will consist of multiple sensing nodes, simulated or actual, representing space, air and/or ground measurements, that are interconnected by a communications fabric (infrastructure that permits nodes to transmit and receive data between one another and interact with each other). Each node will be supported by hardware capabilities required to perform nodes monitoring and command & control, as well as intelligent "onboard" computing. The nodes will work together in a collaborative manner to demonstrate optimal science capabilities. The testbed will enable to validate technologies such as inter-node communication models, techniques and protocols; inter-node coordination; real-time data fusion and understanding; planning; sensor re-targeting; etc. Additionally, the testbed will have the capability to interact with various mission design tools, OSSEs and one or several forecast models. More details about the NOS Testbed will be presented at the confererence.
This paper describes middleware systems developed at JPL, shows how leveraging middleware implementation strategies can facilitate building a mission operations system for managing multiple interacting missions on Mars, and proposes an approach to implementing demonstrations as part of a roadmap providing progressively more intelligent remote exploration.
The OWL GroundAware GA1360 2D radar system with advertised capability of advanced digital beam-forming radar technology, classification intelligence, reconfigurability, and easy integration with other security systems to bring 360° of real-time, all-weather situational awareness for the physical security of perimeters and other sensitive areas for critical infrastructure.
The network of more than 2.5 million miles of natural gas pipelines the U.S. are exposed to a range of vulnerabilities, ranging from extreme weather, to human behavior, and increasingly, to cyber threats. Advanced, data-driven analytics, including the use of machine learning and artificial intelligence, afford an opportunity to identify potential vulnerabilities, better understand risks, and help mitigate vulnerabilities in the network. At NETL, machine learning is being used to explore pipeline vulnerabilities and identify significant clusters of failure events, including failures due to weather, human behavior, and materials. These findings are being used to support the development of new technologies, including sensors, but can also be used to evaluate and inform mitigation strategies to reduce risks and vulnerabilities throughout the network.
Accelerators increasingly use artificial intelligence (AI) and machine learning (ML) software and workflows for a variety of tasks, from optimization to fault detection and recovery. Efficient and sustainable application of these technologies necessitates specialized and facility-specific infrastructure commitments. Accelerator facilities also introduce unique radiation and security hazards, placing additional demands on operational infrastructure. These needs further escalate the prioritization of effective collaboration models and associated funding mechanisms and legal frameworks.