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This paper describes a framework of incorporating smart sampling techniques in a probabilistic look-ahead contingency analysis application. The predictive probabilistic contingency analysis helps to reflect the impact of uncertainties caused by variable generation and load on potential violations of transmission limits.
This paper describes a probabilistic look-ahead contingency analysis application that incorporates smart sampling and high-performance computing (HPC) techniques. Smart sampling techniques are implemented to effectively represent the structure and statistical characteristics of uncertainty introduced by different sources in the power system. They can significantly reduce the data set size required for multiple look-ahead contingency analyses, and therefore reduce the time required to compute them. High-performance-computing (HPC) techniques are used to further reduce computational time. These two techniques enable a predictive capability that forecasts the impact of various uncertainties on potential transmission limit violations. The developed package has been tested with real world data from the Bonneville Power Administration. Case study results are presented to demonstrate the performance of the applications developed.
This work is a survey of current trends in applications of PMUs. PMUs have the potential to solve major problems in the areas of power system estimation, protection, and stability. A variety of methods are being used for these purposes, including statistical techniques, mathematical transformations, probability, and AI. The results produced by the techniques reviewed in this work are promising, but there is work to be performed in the context of implementation and standardization. As the smart grid initiative continues to advance, the number of intelligent devices monitoring the power grid continues to increase. PMUs are at the center of this initiative, and as a result, each year more PMUs are deployed across the grid. Since their introduction, myriad solutions based on PMU-technology have been suggested. The high sampling rates and synchronized measurements provided by PMUs are expected to drive significant advancements across multiple fields, such as the protection, estimation, and control of the power grid. This work offers a review of contemporary research trends and applications of PMU technology. Most solutions presented in this work were published in the last five years, and techniques showing potential for significant impact are highlighted in greater detail. Being a relatively new technology, there are several issues that must be addressed before PMU-based solutions can be successfully implemented. This survey found that key areas where improvements are needed include the establishment of PMU-observability, data processing algorithms, the handling of heterogeneous sampling rates, and the minimization of the investment in infrastructure for PMU communication. Solutions based on Bayesian estimation, as well as those having a distributed architectures, show great promise. The material presented in this document is tailored to both new researchers entering this field and experienced researchers wishing to become acquainted with emerging trends.
With the increasing penetration of distributed generators in the smart grids, having knowledge of rapid real-time electromechanical dynamic states has become crucial to system stability control. Conventional Supervisory Control and Data Acquisition (SCADA)-based dynamic state estimation (DSE) techniques are limited by the slow sampling rates, while the emerging phasor measurement units (PMUs) technology enables rapid real-time measurements at network nodes. Using generator bus terminal voltages, we propose a hybrid-learning DSE (HL-DSE) algorithm to estimate the synchronous machine rotor angle and speed in real time. The HL-DSE takes the power system model into account and trains neuroestimators with real-time data in an online manner. Compared with traditional DSE methods, the HL-DSE overcomes limitations by using a data-driven approach in conjunction with the physical power system model. The time efficiency, accuracy, convergence, and robustness of the proposed algorithm are tested under noises and fault conditions in both small- and large-scale test systems. Simulation results show that the proposed HL-DSE is much more computationally efficient than widely used Kalman filter (KF)-based methods while maintaining comparable accuracy and robustness. In particular, HL-DSE is over 100 times faster than square-root unscented KF (SR-UKF) and 80 times faster than extended KF (EKF). The advantages and challenges of the HL-DSE are also discussed.
Computer literature searches were carried out at Duke University and NASA Langley Research Center. The purpose is to enhance personal knowledge based on the technical problems of pattern recognition and image understanding which must be solved for the Mars Rover and Sample Return Mission. Intensive study effort of a large collection of relevant literature resulted in a compilation of all important documents in one place. Furthermore, the documents are being classified into: Mars Rover; computer vision (theory); imaging systems; pattern recognition methodologies; and other smart techniques (AI, neural networks, fuzzy logic, etc).
Fiber optic sensors provide the opportunity for fabricating materials with internal sensors which can serve as lifetime health monitors, analogous to a central nervous system. The embedded fiber optic sensors can be interrogated by various techniques to measure internal strain, temperature, pressure, acoustic waves and other parameters indicative of structural integrity. Experiments have been conducted with composite samples with embedded sensors to measure strain using optical time domain reflectometry, modal interference and an optical phase locked loop. Fiber optic sensors have been developed to detect acoustic emission and impact damage and have been demonstrated for cure monitoring. These sensors have the potential for lifetime monitoring of structural properties, providing real time nondestructive evaluation.
The emergence of connected and automated vehicles and smart cities technologies create the opportunity for new mobility modes and routing decision tools, among many others. To achieve maximum mobility and minimum energy consumption, it is critical to understand the energy cost of decisions and optimize accordingly. The Route Energy prediction model (RouteE) enables accurate estimation of energy consumption for a variety of vehicle types over trips or sub-trips where detailed drive cycle data are unavailable. Applications include vehicle route selection, energy accounting and optimization in transportation simulation, and corridor energy analyses, among others. The software is a Python package that includes a variety of pre-trained models from the National Renewable Energy Laboratory (NREL). However, RouteE also enables users to train custom models using their own data sets, making it a robust and valuable tool for both fast calculations and rigorous, data-rich research efforts. The pre-trained RouteE models are established using NREL’s Future Automotive Systems Technology Simulator paired with approximately 1 million miles of drive cycle data from the Transportation Secure Data Center, resulting in energy consumption behavior estimates over a representative sample of driving conditions for the United States. Validations have been performed using on-road fuel consumption data for conventional and electrified vehicle powertrains. Transferring the results of the on-road validation to a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology in a green-routing application would accurately select the route that consumes the least fuel 90% of the time. The novel machine learning techniques used in RouteE make it a flexible and robust tool for a variety of transportation applications.
The focus of the research is to build a compact, high performance lightstripe rangefinder using a Very Large Scale Integration (VLSI) smart photosensor array. Rangefinding, the measurement of the three-dimensional profile of an object or scene, is a critical component for many robotic applications, and therefore many techniques were developed. Of these, lightstripe rangefinding is one of the most widely used and reliable techniques available. Though practical, the speed of sampling range data by the conventional light stripe technique is severely limited. A conventional light stripe rangefinder operates in a step-and-repeat manner. A stripe source is projected on an object, a video image is acquired, range data is extracted from the image, the stripe is stepped, and the process repeats. Range acquisition is limited by the time needed to grab the video images, increasing linearly with the desired horizontal resolution. During the acquisition of a range image, the objects in the scene being scanned must be stationary. Thus, the long scene sampling time of step-and-repeat rangefinders limits their application. The fast range sensor proposed is based on the modification of this basic lightstripe ranging technique in a manner described by Sato and Kida. This technique does not require a sampling of images at various stripe positions to build a range map. Rather, an entire range image is acquired in parallel while the stripe source is swept continuously across the scene. Total time to acquire the range image data is independent of the range map resolution. The target rangefinding system will acquire 1,000 100 x 100 point range images per second with 0.5 percent range accuracy. It will be compact and rugged enough to be mounted on the end effector of a robot arm to aid in object manipulation and assembly tasks.
Currently, research on structural health monitoring systems is focused on direct integration of the system into a component or structure. The latter results in a so-called smart structure. One example of a smart structure is a component with integrated strain sensing for continuous load monitoring. Additive manufacturing, or 3D printing, now also enables such integration of functions inside components. As a proof-of-concept, the Fused Deposition Modeling (FDM) technique was used to integrate a strain sensing element inside polymer (ABS) tensile test samples. The strain sensing element consisted of a closed capillary filled with a fluid and connected to an externally mounted pressure sensor. The volumetric deformation of the integrated capillary resulted in pressure changes in the fluid. The obtained pressure measurements during tensile testing are reported in this paper and compared to state-of-the-art extensometer measurements. The sensitivity of the 3D printed pressure-based strain sensor is primarily a function of the compressibility of the capillary fluid. Air- and watertightness are of critical importance for the proper functioning of the 3D printed pressure-based strain sensor. Therefore, the best after-treatment procedure was selected on basis of a comparative analysis. The obtained pressure measurements are linear with respect to the extensometer readings, and the uncertainty on the strain measurement of a capillary filled with water (incompressible fluid) is ±3.1 µstrain, which is approximately three times less sensitive than conventional strain gauges (±1 µstrain), but 32 times more sensitive than the same sensor based on air (compressible fluid) (±101 µstrain).
Although Reinforcement Learning (RL) algorithms have made impressive progress in learning complex tasks over the past years, there are still prevailing short-comings and challenges. Specifically, the sample-inefficiency and limited adaptation across tasks often make classic RL techniques impractical for real-world applications despite the gained representational power when combining deep neural networks with RL, known as Deep Reinforcement Learning (DRL). Recently, a number of approaches to address those issues have emerged. Many of those solutions are based on smart DRL architectures that enhance single task algorithms with the capability to share knowledge between agents and across tasks by introducing Transfer Learning (TL) capabilities. Here this survey addresses strategies of knowledge transfer from simple parameter sharing to privacy preserving federated learning and aims at providing a general overview of the field of TL in the DRL domain, establishes a classification framework, and briefly describes representative works in the area.
Smart transportation infrastructure relies on networks of heterogeneous sensors - cameras, radars, and lidars - continuously monitoring traffic conditions. However, executing the initial spatial calibration of multiple sensors and the subsequent health monitoring presents significant operational challenges. Environmental factors, mechanical vibrations, and gradual drift cause spatial misalignment, degrading fusion performance and tracking accuracy. Traditional calibration approaches require manual intervention with specialized targets or survey equipment, resulting in service interruptions and high maintenance costs. This work presents an automated framework for initial calibration and continuous health monitoring without human intervention or service disruption. Our approach addresses two critical problems: (1) detecting when sensors become miscalibrated during operation, and (2) automatically re-establishing spatial alignment using only operational traffic data. The health monitoring component analyzes measurement innovations - differences between sensor observations and predicted object states - to detect systematic biases indicative of calibration drift. By computing bias magnitude, directional consistency, and rejection rates, the system identifies miscalibrations as small as 0.5 meters. Unlike traditional methods requiring known calibration targets, our diagnostic operates continuously on live traffic observations, enabling early detection before fusion quality degrades. The automatic recalibration algorithm leverages overlapping sensor fields-of-view and temporal correlation of vehicle observations. Using graph-based optimization, the system automatically discovers which sensor pairs observe common regions, estimates pairwise spatial transformations using RANSAC-based robust estimation, and jointly optimizes all sensor poses through bundle adjustment. The framework handles practical deployment challenges, including different sensor sampling rates (1-10 Hz), varying installation positions, unknown orientations, and limited overlap regions (>10%). When approximate sensor positions are available from installation surveys (+/-1m accuracy), the algorithm additionally estimates sensor orientations, refining both position and rotation to sub-meter and sub-degree accuracy. We validate the framework on multi-hour traffic datasets from six heterogeneous sensors with sampling rates ranging from 1 Hz to 10 Hz. Results demonstrate successful calibration even with sparse overlap (<20%) and automatic detection of miscalibrations exceeding 0.8 meters. This work enables a "deploy-and-forget" sensor infrastructure that maintains calibration autonomously, reducing maintenance costs while improving tracking accuracy. The techniques generalize beyond transportation to any multi-sensor monitoring application requiring robust spatial alignment, including smart cities, industrial monitoring, and surveillance systems.
In this research, a smart manufacturing approach was used to enhance the mechanical properties of aluminum (Al) for lightweight applications. The smart manufacturing involved cryomilling of Al powders with and without 5 wt.% magnesium (Mg) powders for varying durations followed by a high-pressure cold spray (HPCS) additive manufacturing process to prepare bulk components. The morphological changes, crystallite size, and composition of the cryomilled powders and cold sprayed (CS’ed) components were examined using scanning electron microscopy (SEM), x-ray diffraction (XRD), and transmission electron microscopy (TEM) techniques. The results showed that the crystallite size reduces with an increase in cryomilling time and the addition of Mg dopant. To test the mechanical properties of the bulk CS’ed components, microhardness tests were performed using a Vickers microhardness tester. Uniaxial tensile tests were also carried out to ascertain the material’s tensile properties. The mechanical testing results showed great improvement in the hardness and tensile strength of CS’ed Al–Mg samples as compared to pure Al samples. Subsequently, fractography analysis of the tensile failed samples was carried out to determine the nature of the failure. Here, the research article also discusses the inherent mechanisms for the improvement in mechanical properties of smart manufactured components as a result of Mg doping and cryomilling.
An experiment to release radioactive particles representative of small-scale venting from an underground nuclear test was conducted to gather data in support of treaty capability development activities. For this experiment, a CO2-driven “air cannon” was used to release lanthanum-140 at ambient temperatures. The radioisotope 140La was chosen as a representative fission fragment with a short half-life and prominent gamma-ray emissions; the choice was also influenced by the successful production and use of 140La with low levels of radioactive contaminants in the Defence Research and Development Canada Field Trials. The source was created through activation of high-purity lanthanum oxide at the TRIGA research reactor of Washington State University, Pullman, Washington. Witness plates and air samplers were laid out in an irregular grid covering the area for which the plume was anticipated to deposit based on climatological wind records. A vehicle-mounted spectrometer, and handheld and backpack instruments ranging from polyvinyl toluene to high purity germanium, were used to survey the plume. Additionally, three soil sampling techniques were investigated. The relative sensitivity and utility of sampling and survey methods are discussed in the context of On-Site Inspection.
The precipitation technique of Mn-IV/itaconic acid redox pair was chosen for grafting meth acrylic acid onto chitosan. This was done to maximize the graft yield and minimize the homopolymer formation to lower extent in addition to prepare water soluble chitosan instead of non-eco friendly acid soluble one. Evidence of grafting was confirmed instrumentally by (FTIR, SEM, and TGA), titrimetrically using carboxyl content and gravimetrically using dry weight method. The mechanism of grafting and factors affected grafting reaction were studied in details, and the optimum reaction conditions were obtained at [chitosan] 2 g, [MnO{sub 2}] 25.9 mmol/100 g sample, [itaconic acid],100 mmol/L, [MAA] 50% bows,[liquor ratio], 25, reaction time, 2 h, and [reaction temperature], 65 °C. It is also seen from the obtained results that, the resultant copolymer showed the following findings: (a) only nitric acid solutions are proved to be a superior solvent for the grafted product; (b) superabsorbent towards water (197 g H{sub 2}O/g polymer) and saline absorbency (60 g saline solution/g polymer) after cross-linked with epichlorohydrin, (c) higher water solubility (21%) at 48% graft yield and (d) 73.5% copper ion removal in comparison with 41% for chitosan.
We briefly review previous applications of machine learning (ML) in power grid analyses and introduce our ongoing effort toward developing a generative-adversarial (GA) model for fast and reliable grid contingency analyses. According to our review, the persisting limitation of traditional ML techniques in grid analyses is the need for an exhaustive amount of training data for model generalization and accurate predictions. GA models overcome this limitation by first learning true data distribution from a small training set, from which new samples assimilating true data are generated with some variations. Subsequently, GA models can transfer learn or super-generalize with increased accuracy, that is, accurately predict n - (k + 2) contingencies from a small n - k training set and generated n - (k + 1) data. The joint effort between Idaho National Lab and Florida State University strives to develop a zero-shot and deep learning-based contingency analysis tool, named Smart Contingency Analysis Neural Network (SCANN), by leveraging the aforementioned advantages of GA models. The basic architecture of SCANN stems from the Latent Encoding of Atypical Perturbations network combined with an adversarial network, and it is designed to generate imbalanced power flow data from learned true data distributions for prediction purposes. Here we also introduce the abstract concept of resilience-chaos plots, a new resilience characterization tool proposed to complement SCANN by aiding in the assessment of large amounts of high-order contingency predictions.
Biogeochemical interfaces such as bacteria-mineral, fungi-mineral, and water-mineral are hot spots for important processes and reactions in all environments. Studying these interfaces are useful over a range of spatial and temporal scales in order to understand elemental cycles, soil formation, restoration, and bioremediation. Advancement of micro- and nano-scale techniques lead to new discoveries about reactions and interactions at the heterogeneous interface of bacteria, fungi, and minerals. The current state-of-art of analytical techniques allows to study solid, liquid, and gas phases and their interactions with biological components and often permits simultaneous study of chemical and physical properties in a site-specific manner. Improved techniques not only increased spatial and temporal resolution with site specificity but also decreased sample preparation, artifact generation, sample size requirements and analytical time. There are limitations and challenges that still need to be overcome while studying the very heterogeneous microbe-mineral interface, but improved accessibility to beam time at the synchrotron facilities and instrument time at the user laboratories offer some solutions. This review chapter focuses on a subset of the available micro- and nano-scale techniques: electron and atomic force microscopy, and X-ray based spectroscopy of traditional and synchrotron-based applications.
Ferromagnetic shape memory alloys are smart materials because they exhibit temperature driven shape memory effect and magnetic field induced strain. Thus two types of energy, i.e. thermal and magnetic, are used to control their shape memory behaviour. Study of critical phenomenon in such materials has received increased experimental and theoretical attention for better understanding of the magnetic phase transition behavior as well as further development of ferromagnetic shape memory materials. In the present study we report the preparation and characterization of bulk Co{sub 45}Ni{sub 25}Ga{sub 30} alloy, prepared by a sequence of arc melting technique followed by homogenization at 1150 °C for 24 hours and ice-water quenching. Structural and magnetic properties of the alloys were studied by means of X-ray diffraction and vibrating sample magnetometer in an applied field range of ±18 kOe equipped with a high temperature oven. We have determined the critical temperature T{sub C} (∼375.5 K) and the critical exponents viz; β=0.40, γ=1.68 and δ=5.2. Asymptotic critical exponents β, γ, and δ obey Widom scaling relation, γ+β=βδ, and the magnetization data satisfy the scaling equation of state for second-order phase transition in the asymptotic critical region.
This invention is designed to ascertain the snow water equivalence (SWE) of snowpacks with better spatial and temporal resolutions than present techniques. The approach is ground-based, as opposed to some techniques that are air-based. In addition, the approach is compact, non-destructive, and can be communicated with remotely, and thus can be deployed in areas not possible with current methods. Presently there are two principal ground-based techniques for obtaining SWE measurements. The first is manual snow core measurements of the snowpack. This approach is labor-intensive, destructive, and has poor temporal resolution. The second approach is to deploy a large (e.g., 3x3 m) snowpillow, which requires significant infrastructure, is potentially hazardous [uses a approximately equal to 200-gallon (approximately equal to 760-L) antifreeze-filled bladder], and requires deployment in a large, flat area. High deployment costs necessitate few installations, thus yielding poor spatial resolution of data. Both approaches have limited usefulness in complex and/or avalanche-prone terrains. This approach is compact, non-destructive to the snowpack, provides high temporal resolution data, and due to potential low cost, can be deployed with high spatial resolution. The invention consists of three primary components: a robust wireless network and computing platform designed for harsh climates, new SWE sensing strategies, and algorithms for smart sampling, data logging, and SWE computation.