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Machine Learning-based Intrusion Detection for Smart Grid Computing: A Survey
Machine learning (ML)-based intrusion detection system (IDS) approaches have been significantly applied and advanced the state-of-the-art system security and defense mechanisms. In smart grid computing environments, security threats have been significantly increased as shared networks are commonly used, along with the associated vulnerabilities. However, compared to other network environments, ML-based IDS research in a smart grid is relatively unexplored, although the smart grid environment is facing serious security threats due to its unique environmental vulnerabilities. In this article, we conducted an extensive survey on ML-based IDS in smart grids based on the following key aspects: (1) The applications of the ML-based IDS in transmission and distribution side power components of a smart power grid by addressing its security vulnerabilities; (2) dataset generation process and its usage in applying ML-based IDSs in the smart grid; (3) a wide range of ML-based IDSs used by the surveyed papers in the smart grid environment; (4) metrics, complexity analysis, and evaluation testbeds of the IDSs applied in the smart grid; and (5) lessons learned, insights, and future research directions.
Goal-driven scheduling model in edge computing for smart city applications
Not Available
Advanced Machine Learning and Computational Methods: SMART (Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications) Phase 2
For presentation at the 2023 FECM/Carbon Management Review Meeting, Pittsburgh, PA, August 28-September 1, 2023.
A modular radiative transfer program for gas filter correlation radiometry
The fundamentals of a computer program, simulated monochromatic atmospheric radiative transfer (SMART), which calculates atmospheric path transmission, solar radiation, and thermal radiation in the 4.6 micrometer spectral region, are described. A brief outline of atmospheric absorption properties and line by line transmission calculations is explained in conjunction with an outline of the SMART computational procedures. Program flexibility is demonstrated by simulating the response of a gas filter correlation radiometer as one example of an atmospheric infrared sensor. Program limitations, input data requirements, program listing, and comparison of SMART transmission calculations are presented.
Spaceborne Processor Array
A Spaceborne Processor Array in Multifunctional Structure (SPAMS) can lower the total mass of the electronic and structural overhead of spacecraft, resulting in reduced launch costs, while increasing the science return through dynamic onboard computing. SPAMS integrates the multifunctional structure (MFS) and the Gilgamesh Memory, Intelligence, and Network Device (MIND) multi-core in-memory computer architecture into a single-system super-architecture. This transforms every inch of a spacecraft into a sharable, interconnected, smart computing element to increase computing performance while simultaneously reducing mass. The MIND in-memory architecture provides a foundation for high-performance, low-power, and fault-tolerant computing. The MIND chip has an internal structure that includes memory, processing, and communication functionality. The Gilgamesh is a scalable system comprising multiple MIND chips interconnected to operate as a single, tightly coupled, parallel computer. The array of MIND components shares a global, virtual name space for program variables and tasks that are allocated at run time to the distributed physical memory and processing resources. Individual processor- memory nodes can be activated or powered down at run time to provide active power management and to configure around faults. A SPAMS system is comprised of a distributed Gilgamesh array built into MFS, interfaces into instrument and communication subsystems, a mass storage interface, and a radiation-hardened flight computer.
Multispectral data acquisition and classification - Computer modeling for smart sensor design
In this paper a model of the processes involved in multispectral remote sensing and data classification is developed as a tool for designing and evaluating smart sensors. The model has both stochastic and deterministic elements and accounts for solar radiation, atmospheric radiative transfer, surface reflectance, sensor spectral reponses, and classification algorithms. Preliminary results are presented which indicate the validity and usefulness of this approach. Future capabilities of smart sensors will ultimately be limited by the accuracy with which multispectral remote sensing processes and their error sources can be computationally modeled.
Packaged SMIP Smart Connector for Ectron Computers
The project provides appropriate software modules, which allow to represent a physical oven as a virtual oven model. For that a virtual oven model is developed, which defines oven parameters like temperature, power consumption but also the door status and the humidity. To connect a real oven to the virtual model for monitoring and control multiple sensors are used to collect data. Using a MQTT based data connection the measured parameters are send to the CESMII environment for further processing and monitoring. Within this project the CESMII model for the general oven was developed and provided. Further the sensors were connected to the actual oven. The sensor data is collected via means of a MQTT based data transfer protocol to a gateway module. The gateway module then translates and transfers the collected data to the actual CESMII oven model.
Smart Proxy Modeling Application of Artificial Intelligence & Machine Learning in Computational Fluid Dynamics
Smart proxy technology leverages the art of artificial intelligence and machine learning in order to build accurate and very fast proxy models for highly complex numerical simulation models. In this project, smart proxy technology is used to replicate the results of a highly complex single phase CFD simulation with reasonable degree of accuracy while reducing the computational cost associated with such CFD simulations. The CFD model under study simulates the combustion of natural gas under various conditions such as varying natural gas composition and flow rate, inlet air flow rate and temperature, and outlet pressure in a High-Pressure Combustion Facility (B6 Combustor) with more than 4 million simulation cells. Only eight CFD simulation runs were used to create a smart proxy model that replicates the detail distribution of Pressure, Temperature, Nitrogen, Oxygen and Carbon-dioxide concentration in the CFD simulation model in seconds with less than 10% error.
Computer Vision-Enabled Smart Traffic Monitoring for Sustainable Transportation Management
Transportation accounts for a significant portion of total global energy consumption. Excessive energy consumption usually occurs in urban traffic environments with congestion and travel delays. With the advancement of remote sensing and computer vision technologies, real-time traffic conditions can be monitored. Therefore, sustainable transportation management strategies can be developed to optimize the overall energy and environment performance and reduce congestion and emissions. This work presents a smart traffic monitoring system based on remote camera sensors. Real-time and historical traffic conditions at the US Department of Energy’s Oak Ridge National Laboratory (ORNL) were monitored and analyzed to develop optimal transportation management strategies for sustainability. Computer vision algorithms were developed and applied to process the real-time camera data to obtain complete traffic information across the ORNL campus. Weeks of historical data were collected and processed to analyze the traffic and identify bottlenecks. The proposed traffic monitoring and management approach can be applied and extended to benefit other campuses or urban areas.
Smart Phone Application to Compute Annual Solar Production of One Panel of a Plug-and-Play Solar Appliance (Cooperative Research and Development Final Report, CRADA Number CRD-19-00832)
The primary goal of the project is to develop a smart phone application (App) that will be available in both the Apple App Store and the Android store that displays results from the NREL's photovoltaic application, PVWatts Calculator, an application that estimates the energy production and cost of energy of grid-connected PV energy systems, and that will provide quick-response changes based on the orientation of the smart phone by the end user.
Computational Offload with BlueField Smart NICs
The recent introduction of a new generation of "smart NICs" have provided new accelerator platforms that include CPU cores or reconfigurable fabric in addition to traditional networking hardware and packet offloading capabilities. While there are currently several proposals for using these smartNICs for low-latency, in-line packet processing operations, there remains a gap in knowledge as to how they might be used as computational accelerators for traditional high-performance applications. This work aims to look at benchmarks and mini-applications to evaluate possible benefits of using a smartNIC as a compute accelerator for HPC applications. We investigate NVIDIA's current-generation BlueField-2 card, which includes eight Arm CPUs along with a small amount of storage, and we test the networking and data movement performance of these cards compared to a standard Intel server host. We then detail how two different applications, YASK and miniMD can be modified to make more efficient use of the BlueField-2 device with a focus on overlapping computation and communication for operations like neighbor building and halo exchanges. Our results show that while the overall compute performance of these devices is limited, using them with a modified miniMD algorithm allows for potential speedups of 5 to 20% over the host CPU baseline with no loss in simulation accuracy.
A Multifunctional Aerospace Smart Skin Emerges From Computational Models and Physical Experiments
No abstract available
Smart Test Machines
Vern Wedeven, president of Wedeven Associates, developed the WAM4, a computer-aided "smart" test machine for simulating stress on equipment, based on his bearing lubrication expertise gained while working for Lewis Research Center. During his NASA years from the 1970s into the early 1980s, Wedeven initiated an "Interdisciplinary Collaboration in Tribology," an effort that involved NASA, six universities, and several university professors. The NASA-sponsored work provided foundation for Wedeven in 1983 to form his own company. Several versions of the smart test machine, the WAM1, WAM2, and WAM3, have proceeded the current version, WAM4. This computer-controlled device can provide detailed glimpses at gear and bearing points of contact. WAM4 can yield a three-dimensional view of machinery as an operator adds "what-if" thermal and lubrication conditions, contact stress, and surface motion. Along with NASA, a number of firms, including Pratt & Whitney, Caterpillar Tractor, Exxon, and Chevron have approached Wedeven for help on resolving lubrication problems.
Network Security Challenges and Countermeasures for Software-Defined Smart Grids: A Survey
The rise of grid modernization has been prompted by the escalating demand for power, the deteriorating state of infrastructure, and the growing concern regarding the reliability of electric utilities. The smart grid encompasses recent advancements in electronics, technology, telecommunications, and computer capabilities. Smart grid telecommunication frameworks provide bidirectional communication to facilitate grid operations. Software-defined networking (SDN) is a proposed approach for monitoring and regulating telecommunication networks, which allows for enhanced visibility, control, and security in smart grid systems. Nevertheless, the integration of telecommunications infrastructure exposes smart grid networks to potential cyberattacks. Unauthorized individuals may exploit unauthorized access to intercept communications, introduce fabricated data into system measurements, overwhelm communication channels with false data packets, or attack centralized controllers to disable network control. An ongoing, thorough examination of cyber attacks and protection strategies for smart grid networks is essential due to the ever-changing nature of these threats. Previous surveys on smart grid security lack modern methodologies and, to the best of our knowledge, most, if not all, focus on only one sort of attack or protection. This survey examines the most recent security techniques, simultaneous multi-pronged cyber attacks, and defense utilities in order to address the challenges of future SDN smart grid research. The objective is to identify future research requirements, describe the existing security challenges, and highlight emerging threats and their potential impact on the deployment of software-defined smart grid (SD-SG).
A Cybersecurity Testbed for Smart Buildings
Smart buildings are equipped with a plethora of cyber-physical systems, such as Internet of Things (IoT) devices and building automation systems. These devices, especially in commercial buildings, use legacy communications and hardware that were not designed with cybersecurity in mind. With increasing cyber threats in recent years, smart buildings have become an increasing target for attacks, but not enough published data are available from these incidents to study or replicate the scenarios to defend buildings. As part of the U.S. Department of Energy-funded project focusing on developing the Building Intelligence with Layered Defense Using Security-Constrained Optimization and Security Risk Detection (BUILD-SOS) platform, we developed a cybersecurity test bed for smart buildings. This test bed includes a building simulation tool, virtual devices, emulated operational technology networks, and remote hardware-in-the-loop. Using this test bed, we performed different cyberattacks on the smart building model and collected both physical building data, to understand the impacts on the building, and network data, to aid in separating mechanical faults from cyberattacks during the detection. This test bed is a significant tool in protecting smart buildings from cyberattacks because they can aid in both cybersecurity analysis and the evaluation of cyberattack detection tools by testing the tools in a secure environment without impacting the building operations.
A Cybersecurity Testbed for Smart Buildings
Smart buildings are equipped with a plethora of cyber-physical systems, such as Internet of Things (IoT) devices and building automation systems. These devices, especially in commercial buildings, use legacy communications and hardware that were not designed with cybersecurity in mind. With increasing cyber threats in recent years, smart buildings have become an increasing target for attacks, but not enough published data are available from these incidents to study or replicate the scenarios to defend buildings. As part of the U.S. Department of Energy-funded project focusing on developing the Building Intelligence with Layered Defense Using Security-Constrained Optimization and Security Risk Detection (BUILD-SOS) platform, we developed a cybersecurity test bed for smart buildings. This test bed includes a building simulation tool, virtual devices, emulated operational technology networks, and remote hardware-in-the-loop. Using this test bed, we performed different cyberattacks on the smart building model and collected both physical building data, to understand the impacts on the building, and network data, to aid in separating mechanical faults from cyberattacks during the detection. This test bed is a significant tool in protecting smart buildings from cyberattacks because it can aid in both cybersecurity analysis and the evaluation of other cyberattack detection tools by testing the tools in a secure environment without impacting the building operations.
Local Chemical Enhancement and Gating of Organic Coordinated Ionic-Electronic Transport
Superior properties in organic mixed ionic-electronic conductors (OMIECs) over inorganic counterparts have inspired intense interest in biosensing, soft-robotics, neuromorphic computing, and smart medicine. However, slow ion transport relative to charge transport in these materials is a limiting factor. Here, it is demonstrated that hydrophilic molecules local to an interfacial OMIEC nanochannel can accelerate ion transport with ion mobilities surpassing electrophoretic transport by more than an order of magnitude. Furthermore, ion access to this interfacial channel can be gated through local surface energy. This mechanism is applied in a novel sensing device, which electronically detects and characterizes chemical reaction dynamics local to the buried channel. The ability to enhance ion transport at the nanoscale in OMIECs as well as govern ion transport through local chemical signaling enables new functionalities for printable, stretchable, and biocompatible mixed conduction devices.