Scholarly Work - Electrical & Computer Engineering

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    Microfluidic production of silver nanoparticles demonstrates ability for on demand synthesis of a wide size distribution of particles
    (Springer Nature, 2024-02) Langguth, Katelyn J.; Maccagnano-Zachera, Sara; Heinemann, Joshua
    Silver nanoparticles (AgNP) can help prevent infection of virus and bacteria. The size and morphology of AgNP can be crucial to function, with smaller nanoparticles (< 20 nm) able to penetrate the cell wall. This is significant as oxidative stress and genotoxicity are associated with some sizes and coatings of AgNP, contraindicating the use of AgNP to reduce infection. We present evidence that a microfluidic chip can synthesize larger sizes and distributions of AgNP from the nano-to-micro size range. We show results from a microfluidic mixing chip that can produce a wide range of nano-to-micro size (~ 24–400 nm) AgNP. Synthesis is based on a modified Turkevich method, using a single-step AgNP synthesis on the microfluidic chip using two chemical components, trisodium citrate (NaCit) and AgNO3. To make AgNP more accessible, we describe the microfluidic chip and conditions capable of synthesis. We also describe how modification of flow rate and chemical reagent concentration change particle diameter during production. In our experiments, we found that AgNP production created a visible adsorption line in the microfluidic device, possibly owing to AgNP surface interaction at the polydimethylsiloxane (PDMS) interface. We characterize these particles with dynamic light scattering (DLS), transmission electron microscopy (TEM), and scanning electron microscopy (SEM). Based on optical light microscopy, we hypothesize that AgNP formation primarily occurs at the interface between the two chemical reagent streams. We also conclude that AgNP size increases could be due to interaction with the PDMS surface, which is known to be porous. Future work will help to understand how surface interaction may influence the formation of larger particles.
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    UAV-based hyperspectral imaging for river algae pigment estimation and development of a low-cost multispectral imager
    (SPIE, 2023-05) Logan, Riley D.; Hamp, Shannon M.; Torrey, Madison A.; de Lima, Rafa Feijó; Colman, Benjamin P.; Valett, H. M.; Shaw, Joseph A.
    Harmful and nuisance algal blooms are becoming a greater concern to public health, riparian ecosystems, and recreational uses of inland waterways. Algal bloom proliferation has increased in the Upper Clark Fork River in western Montana, USA, due to a combination of warming water temperatures, naturally high phosphorus levels, and an influx of contaminants through anthropogenic nitrogen enrichment along its banks. To improve understanding of bloom dynamics, such as algal biomass, a UAV-based hyperspectral imaging system was deployed to monitor several locations along the Upper Clark Fork River. Image data were collected across the spectral range of 400 - 1000 nm with 2.1 nm spectral resolution during two field sampling campaigns in 2021. Included are methods to estimate chlorophyll a standing crops using regression analysis of salient wavelength bands, before and after separating the pigments according to growth form. Estimates of total chlorophyll a standing crops generated through a brute-force analysis are compared to in-situ data, resulting in a maximum rsquared of 0.62 for estimating filamentous plus epiphytic chlorophyll a. Estimates of total and epilithic pigment standing crops are also included. The salient wavelengths bands used to estimate these pigments were then used as the basis for creating a low-cost imaging system for identifying algal blooms.
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    Fast charging of commercial lithium-ion battery without lithium plating
    (Elsevier BV, 2023-12) Thapa, Arun; Hedding, Noah; Gao, Hongwei
    Rapid charging of lithium-ion batteries (LIBs) enables the devices or systems powered by the batteries to provide services at faster rates or higher frequencies. However, fast charging of LIBs can cause lithium plating, resulting in rapid capacity degradation and even thermal runaway or fire in the batteries. Fast charging and lithium plating in a LIB are anode-centric events. Therefore, an anode-centric electrochemical model is critical for deriving a fast charging protocol for LIBs. In this work, we developed an electric circuit model for the negative electrode using tests conducted on laboratory three-electrode lithium-ion cells, used the model to estimate the fast charging current, and compared the fast charging current derived using the model to the fast charging current obtained from measurement. The fast charging current obtained using the model agrees well with the measured fast charging current. Furthermore, we implemented this fast charging protocol on commercial 18650 LIBs for 350 cycles using custom-built charging hardware and software and achieved an 80 % state of charge in 29 min with acceptable temperature rise. The cell aging analysis revealed no significant capacity degradation nor lithium plating on the anode surface, as the protocol explicitly imposes control to protect the battery from lithium plating.
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    Comparison of Supervised Learning and Changepoint Detection for Insect Detection in Lidar Data
    (MDPI AG, 2023-12) Vannoy, Trevor C.; Sweeney, Nathaniel B.; Shaw, Joseph A.; Whitaker, Bradley M.
    Concerns about decreases in insect population and biodiversity, in addition to the need for monitoring insects in agriculture and disease control, have led to an increased need for automated, non-invasive monitoring techniques. To this end, entomological lidar systems have been developed and successfully used for detecting and classifying insects. However, the data produced by these lidar systems create several problems from a data analysis standpoint: the data can contain millions of observations, very few observations contain insects, and the background environment is non-stationary. This study compares the insect-detection performance of various supervised machine learning and unsupervised changepoint detection algorithms and provides commentary on the relative strengths of each method. We found that the supervised methods generally perform better than the changepoint detection methods, at the cost of needing labeled data. The supervised learning method with the highest Matthew’s Correlation Coefficient score on the testing set correctly identified 99.5% of the insect-containing images and 83.7% of the non-insect images; similarly, the best changepoint detection method correctly identified 83.2% of the insect-containing images and 84.2% of the non-insect images. Our results show that both types of methods can reduce the need for manual data analysis.
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    Distribution System State Estimation Using Hybrid Traditional and Advanced Measurements for Grid Modernization
    (MDPI AG, 2023-06) Radhoush, Sepideh; Vannoy, Trevor; Liyanage, Kaveen; Whitaker, Bradley M.; Nehrir, Hashem
    Distribution System State Estimation (DSSE) techniques have been introduced to monitor and control Active Distribution Networks (ADNs). DSSE calculations are commonly performed using both conventional measurements and pseudo-measurements. Conventional measurements are typically asynchronous and have low update rates, thus leading to inaccurate DSSE results for dynamically changing ADNs. Because of this, smart measurement devices, which are synchronous at high frame rates, have recently been introduced to enhance the monitoring and control of ADNs in modern power networks. However, replacing all traditional measurement devices with smart measurements is not feasible over a short time. Thus, an essential part of the grid modernization process is to use both traditional and advanced measurements to improve DSSE results. In this paper, a new method is proposed to hybridize traditional and advanced measurements using an online machine learning model. In this work, we assume that an ADN has been monitored using traditional measurements and the Weighted Least Square (WLS) method to obtain DSSE results, and the voltage magnitude and phase angle at each bus are considered as state vectors. After a period of time, a network is modified by the installation of advanced measurement devices, such as Phasor Measurement Units (PMUs), to facilitate ADN monitoring and control with a desired performance. Our work proposes a method for taking advantage of all available measurements to improve DSSE results. First, a machine-learning-based regression model was trained from DSSE results obtained using only the traditional measurements available before the installation of smart measurement devices. After smart measurement devices were added to the network, the model predicted traditional measurements when those measurements were not available to enable synchronization between the traditional and smart sensors, despite their different refresh rates. We show that the regression model had improved performance under the condition that it continued to be updated regularly as more data were collected from the measurement devices. In this way, the training model became robust and improved the DSSE performance, even in the presence of more Distributed Generations (DGs). The results of the proposed method were compared to traditional measurements incorporated into the DSSE calculation using a sample-and-hold technique. We present the DSSE results in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values for all approaches. The effectiveness of the proposed method was validated using two case studies in the presence of DGs: one using a modified IEEE 33-bus distribution system that considered loads and DGs based on a Monte Carlo simulation and the other using a modified IEEE 69-bus system that considered actual data for loads and DGs. The DSSE results illustrate that the proposed method is better than the sample-and-hold method.
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    Agile collaboration: Citizen science as a transdisciplinary approach to heliophysics
    (Frontiers Media SA, 2023-04) Ledvina, Vincent; Brandt, Laura; MacDonald, Elizabeth; Frissell, Nathaniel; Anderson, Justin; Chen, Thomas Y.; French, Ryan J.; Mare, Francesca Di; Grover, Andrea; Sigsbee, Kristine; Gallardo-Lacourt, Bea; Lach, Donna; Shaw, Joseph A.; Hunnekuhl, Michael; Kosar, Burcu; Barkhouse, Wayne; Young, Tim; Kedhambadi, Chandresh; Ozturk, Dogacan S.; Claudepierre, Seth G.; Dong, Chuanfei; Witteman, Andy; Kuzub, Jeremy; Sinha, Gunjan
    Citizen science connects scientists with the public to enable discovery, engaging broad audiences across the world. There are many attributes that make citizen science an asset to the field of heliophysics, including agile collaboration. Agility is the extent to which a person, group of people, technology, or project can work efficiently, pivot, and adapt to adversity. Citizen scientists are agile; they are adaptable and responsive. Citizen science projects and their underlying technology platforms are also agile in the software development sense, by utilizing beta testing and short timeframes to pivot in response to community needs. As they capture scientifically valuable data, citizen scientists can bring expertise from other fields to scientific teams. The impact of citizen science projects and communities means citizen scientists are a bridge between scientists and the public, facilitating the exchange of information. These attributes of citizen scientists form the framework of agile collaboration. In this paper, we contextualize agile collaboration primarily for aurora chasers, a group of citizen scientists actively engaged in projects and independent data gathering. Nevertheless, these insights scale across other domains and projects. Citizen science is an emerging yet proven way of enhancing the current research landscape. To tackle the next-generation’s biggest research problems, agile collaboration with citizen scientists will become necessary.
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    Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network
    (MDPI AG, 2023-02) Radhoush, Sepideh; Vannoy, Trevor; Liyanage, Kaveen; Whitaker, Bradley M.; Nehrir, Hashem
    Distribution system state estimation (DSSE) has been introduced to monitor distribution grids; however, due to the incorporation of distributed generations (DGs), traditional DSSE methods are not able to reveal the operational conditions of active distribution networks (ADNs). DSSE calculation depends heavily on real measurements from measurement devices in distribution networks. However, the accuracy of real measurements and DSSE results can be significantly affected by false data injection attacks (FDIAs). Conventional FDIA detection techniques are often unable to identify FDIAs into measurement data. In this study, a novel deep neural network approach is proposed to simultaneously perform DSSE calculation (i.e., regression) and FDIA detection (i.e., binary classification) using real measurements. In the proposed work, the classification nodes in the DNN allow us to identify which measurements on which phasor measurement unit (PMU), if any, were affected. In the proposed approach, we aim to show that the proposed method can perform DSSE calculation and identify FDIAs from the available measurements simultaneously with high accuracy. We compare our proposed method to the traditional approach of detecting FDIAs and performing SE calculations separately; moreover, DSSE results are compared with the weighted least square (WLS) algorithm, which is a common model-based method. The proposed method achieves better DSSE performance than the WLS method and the separate DSSE/FDIA method in presence of erroneous measurements; our method also executes faster than the other methods. The effectiveness of the proposed method is validated using two FDIA schemes in two case studies: one using a modified IEEE 33-bus distribution system without DGs, and the other using a modified IEEE 69-bus system with DGs. The results illustrated that the accuracy and F1-score of the proposed method are better than when performing binary classification only. The proposed method successfully detected the FDIAs on each PMU measurement. Moreover, the results of DSSE calculation from the proposed method has a better performance compared to the regression-only method, and the WLS methods in the presence of bad data.
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    Validation of a microwave energy meter to non-lethally estimate energetic reserves in adult sturgeon
    (Oxford University Press, 2023-05) Daigle, Nicole J.; Djokic, Matea A.; Kappenman, Kevin M.; Gaylord, T Gibson; Quinn, Sierra; Verhille, Christine E.
    Whole-body (WB) energetic reserves influence fish survival, growth, and reproduction but are typically quantified using lethal methods (i.e. proximate analyses) or interpreted through body condition indices. Energetic reserves can impact population dynamics through influences on growth rates, age-at-first-reproductive-maturity, and spawning periodicity at the individual-fish level, especially in long-lived sturgeon species. Therefore, a non-lethal tool to track the energetic reserves of endangered sturgeon populations could inform adaptive management and further our understanding of the sturgeon’s biology. The Distell Fatmeter is a microwave energy meter that has been validated to non-lethally estimate energetic reserves in some fish species, but never successfully for sturgeon. Here, stepwise linear regressions were applied to test commonly monitored body metrics and Fatmeter measurements at nine different anatomical sites on captive adult pallid sturgeon (Scaphirhynchus albus; total length of 790–1015 mm; WB lipid of 13.9–33.3%) compared with WB lipid and energy content determined by proximate analyses. Fatmeter measurements alone explained approximately 70% of the variation in WB energetic reserves, which outperformed models considering body metrics alone by a margin of approximately 20%. The top-ranked models based on AICc score (second-order Akaike Information Criterion) included a combination of body metrics and Fatmeter measurements and accounted for up to 76% of the variation in WB lipid and energy. We recommend the incorporation of Fatmeter measurements at a single site located dorsally to the lateral scutes at the posterior end of the fish above the pelvic fins (U-P) into conservation monitoring programs for adult pallid sturgeon (total length [TL] ≥ 790 mm; fork length [FL] ≥ 715 mm) and the cautious application of Fatmeter measurements for sturgeon between 435 and 790 mm TL (375–715 mm FL). Measurements at this U-P site combined with body mass explained approximately 75% of the variation in WB lipid and energy.
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    Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network
    (MDPI AG, 2023-02) Radhoush, Sepideh; Vannoy, Trevor; Liyanage, Kaveen; Whitaker, Bradley M.; Nehrir, Hashem
    Distribution system state estimation (DSSE) has been introduced to monitor distribution grids; however, due to the incorporation of distributed generations (DGs), traditional DSSE methods are not able to reveal the operational conditions of active distribution networks (ADNs). DSSE calculation depends heavily on real measurements from measurement devices in distribution networks. However, the accuracy of real measurements and DSSE results can be significantly affected by false data injection attacks (FDIAs). Conventional FDIA detection techniques are often unable to identify FDIAs into measurement data. In this study, a novel deep neural network approach is proposed to simultaneously perform DSSE calculation (i.e., regression) and FDIA detection (i.e., binary classification) using real measurements. In the proposed work, the classification nodes in the DNN allow us to identify which measurements on which phasor measurement unit (PMU), if any, were affected. In the proposed approach, we aim to show that the proposed method can perform DSSE calculation and identify FDIAs from the available measurements simultaneously with high accuracy. We compare our proposed method to the traditional approach of detecting FDIAs and performing SE calculations separately; moreover, DSSE results are compared with the weighted least square (WLS) algorithm, which is a common model-based method. The proposed method achieves better DSSE performance than the WLS method and the separate DSSE/FDIA method in presence of erroneous measurements; our method also executes faster than the other methods. The effectiveness of the proposed method is validated using two FDIA schemes in two case studies: one using a modified IEEE 33-bus distribution system without DGs, and the other using a modified IEEE 69-bus system with DGs. The results illustrated that the accuracy and F1-score of the proposed method are better than when performing binary classification only. The proposed method successfully detected the FDIAs on each PMU measurement. Moreover, the results of DSSE calculation from the proposed method has a better performance compared to the regression-only method, and the WLS methods in the presence of bad data.
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    Distribution System State Estimation and False Data Injection Attack Detection with a Multi-Output Deep Neural Network
    (MDPI AG, 2023-02) Radhoush, Sepideh; Vannoy, Trevor; Liyanage, Kaveen; Whitaker, Bradley M.; Nehrir, Hashem
    Distribution system state estimation (DSSE) has been introduced to monitor distribution grids; however, due to the incorporation of distributed generations (DGs), traditional DSSE methods are not able to reveal the operational conditions of active distribution networks (ADNs). DSSE calculation depends heavily on real measurements from measurement devices in distribution networks. However, the accuracy of real measurements and DSSE results can be significantly affected by false data injection attacks (FDIAs). Conventional FDIA detection techniques are often unable to identify FDIAs into measurement data. In this study, a novel deep neural network approach is proposed to simultaneously perform DSSE calculation (i.e., regression) and FDIA detection (i.e., binary classification) using real measurements. In the proposed work, the classification nodes in the DNN allow us to identify which measurements on which phasor measurement unit (PMU), if any, were affected. In the proposed approach, we aim to show that the proposed method can perform DSSE calculation and identify FDIAs from the available measurements simultaneously with high accuracy. We compare our proposed method to the traditional approach of detecting FDIAs and performing SE calculations separately; moreover, DSSE results are compared with the weighted least square (WLS) algorithm, which is a common model-based method. The proposed method achieves better DSSE performance than the WLS method and the separate DSSE/FDIA method in presence of erroneous measurements; our method also executes faster than the other methods. The effectiveness of the proposed method is validated using two FDIA schemes in two case studies: one using a modified IEEE 33-bus distribution system without DGs, and the other using a modified IEEE 69-bus system with DGs. The results illustrated that the accuracy and F1-score of the proposed method are better than when performing binary classification only. The proposed method successfully detected the FDIAs on each PMU measurement. Moreover, the results of DSSE calculation from the proposed method has a better performance compared to the regression-only method, and the WLS methods in the presence of bad data.
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    A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges
    (MDPI, 2022-02) Radhoush, Sepideh; Bahramipanah, Maryam; Nehrir, Hashem; Shahooei, Zagros
    This paper provides a comprehensive review of distribution system state estimation in terms of basic definition, different methods, and their application. In the last few years, the operation of distribution networks has been influenced by the installation of distributed generations. In order to control and manage an active distribution network’s performance, distribution system state estimation methods are introduced. A transmission system state estimation cannot be used directly in distribution networks since transmission and distribution networks are different due to topology configuration, the number of buses, line parameters, and the number of measurement instruments. So, the proper state estimation algorithms should be proposed according to the main distribution network features. Accuracy, computational efficiency, and practical implications should be considered in the designing of distribution state estimation techniques since technical issues and wrong decisions could emerge in the control center by inaccurate distribution state estimation results. In this study, conventional techniques are reviewed and compared with data-driven methods in order to highlight the pros and cons of different techniques. Furthermore, the integrated distribution state estimation methods are compared with the distributed approaches, and the different criteria, including the level of area overlapping execution time and computing architecture, are elaborated. Moreover, mathematical problem formulation and different measuring methods are discussed.
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    Automatic detection of multilayer hexagonal boron nitride in optical images using deep learning-based computer vision
    (Springer Science and Business Media LLC, 2023-01) Ramezani, Fereshteh; Parvez, Sheikh; Fix, J. Pierce; Battaglin, Arthur; Whyte, Seamus; Borys, Nicholas J.; Whitaker, Bradley M.
    Computer vision algorithms can quickly analyze numerous images and identify useful information with high accuracy. Recently, computer vision has been used to identify 2D materials in microscope images. 2D materials have important fundamental properties allowing for their use in many potential applications, including many in quantum information science and engineering. One such material is hexagonal boron nitride (hBN), an isomorph of graphene with a very indistinguishable layered structure. In order to use these materials for research and product development, the most effective method is mechanical exfoliation where single-layer 2D crystallites must be prepared through an exfoliation procedure and then identified using reflected light optical microscopy. Performing these searches manually is a time-consuming and tedious task. Deploying deep learning-based computer vision algorithms for 2D material search can automate the flake detection task with minimal need for human intervention. In this work, we have implemented a new deep learning pipeline to classify crystallites of hBN based on coarse thickness classifications in reflected-light optical micrographs. We have used DetectoRS as the object detector and trained it on 177 images containing hexagonal boron nitride (hBN) flakes of varying thickness. The trained model achieved a high detection accuracy for the rare category of thin flakes (<50 atomic layers thick). Further analysis shows that our proposed pipeline could be generalized to various microscope settings and is robust against changes in color or substrate background.
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    Planetary boundary layer height retrieval from a diode-laser-based high spectral resolution lidar
    (SPIE-Intl Soc Optical Eng, 2022-04) Colberg, Luke; Cruikshank, Owen; Repasky, Kevin S.
    The planetary boundary layer height (PBLH) is an essential parameter for weather forecasting and climate modeling. The primary methods for obtaining the PBLH include radiosonde measurements of atmospheric parameters and lidar measurements, which track aerosol layers in the lower atmosphere. Radiosondes provide the parameters to determine the PBLH but cannot monitor changes over a diurnal cycle. Lidar instruments can track the temporal variability of the PBLH and account for spatial variability when operated in a network configuration. The networkable micropulse DIAL (MPD) instruments for thermodynamic profiling are based on diode-laser technology that is eye-safe and cost-effective and has demonstrated long-term autonomous operation. We present a retrieval algorithm for determining the PBLH from the quantitative aerosol profiling capability of the high spectral resolution channel of the MPD. The PBLH is determined using a Haar wavelet transform (HWT) method that tracks aerosol layers in the lower atmosphere. The PBLH from the lidar is compared with the PBLH determined from potential temperature profiles from radiosondes. In many cases, good agreement among the PBLH retrievals was seen. However, the radiosonde retrieval often missed the lowest inversion layer when several layers were present, while the HWT could track the lowest layer.
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    Optical transmittance of 3D printing materials
    (Optica Publishing Group, 2021-07) Hamp, Shannon M.; Logan, Riley D.; Shaw, Joseph A.
    The increasing prevalence of three-dimensional (3D) printing of optical housings and mounts necessitates a better understanding of the optical properties of printing materials. This paper describes a method for using multithickness samples of 3D printing materials to measure transmittance spectra at wavelengths from 400 to 2400 nm [visible to short-wave infrared (IR)]. In this method, 3D samples with material thicknesses of 1, 2, 3, and 4 mm were positioned in front of a uniform light source with a spectrometer probe on the opposing side to measure the light transmittance. Transmission depended primarily on the thickness and color of the sample, and multiple scattering prevented the use of a simple exponential model to relate transmittance, extinction, and thickness. A Solidworks file and a 3D printer file are included with the paper to enable measurements of additional materials with the same method.
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    Multi-curvature micropatterns unveil distinct calcium and mitochondrial dynamics in neuronal networks
    (Royal Society of Chemistry, 2021-01) Khan, Hammad; Beck, Connor; Kunze, Anja
    Soft-embossed highly-parallelized multi-curvature micropatterns model the impact of different curvatures (k = 0.003–0.2 μm−1) inspired by the human cerebral tissue folds on changes in spontaneous neuronal calcium signals and mitochondrial transport.
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    MicroPulse DIAL (MPD) – a diode-laser-based lidar architecture for quantitative atmospheric profiling
    (Copernicus GmbH, 2021-06) Spuler, Scott M.; Hayman, Matthew; Stillwell, Robert A.; Carnes, Joshua; Bernatsky, Todd; Repasky, Kevin S.
    Continuous water vapor and temperature profiles are critically needed for improved understanding of the lower atmosphere and potential advances in weather forecasting skill. Ground-based, national-scale profiling networks are part of a suite of instruments to provide such observations; however, the technological method must be cost-effective and quantitative. We have been developing an active remote sensing technology based on a diode-laser-based lidar technology to address this observational need. Narrowband, high-spectral-fidelity diode lasers enable accurate and calibration-free measurements requiring a minimal set of assumptions based on direct absorption (Beer–Lambert law) and a ratio of two signals. These well-proven quantitative methods are known as differential absorption lidar (DIAL) and high-spectral-resolution lidar (HSRL). This diode-laser-based architecture, characterized by less powerful laser transmitters than those historically used for atmospheric studies, can be made eye-safe and robust. Nevertheless, it also requires solar background suppression techniques such as narrow-field-of-view receivers with an ultra-narrow bandpass to observe individual photons backscattered from the atmosphere. We discuss this diode-laser-based lidar architecture's latest generation and analyze how it addresses a national-scale profiling network's need to provide continuous thermodynamic observations. The work presented focuses on general architecture changes that pertain to both the water vapor and the temperature profiling capabilities of the MicroPulse DIAL (MPD). However, the specific subcomponent testing and instrument validation presented are for the water vapor measurements only. A fiber-coupled seed laser transmitter optimization is performed and shown to meet all of the requirements for the DIAL technique. Further improvements – such as a fiber-coupled near-range receiver, the ability to perform quality control via automatic receiver scanning, advanced multi-channel scalar capabilities, and advanced processing techniques – are discussed. These new developments increase narrowband DIAL technology readiness and are shown to allow higher-quality water vapor measurements closer to the surface via preliminary intercomparisons within the MPD network itself and with radiosondes.
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    A Novel Agent-Based Power Management Scheme for Smart Multiple-Microgrid Distribution Systems
    (MDPI AG, 2022-02) Shahooei, Zagros; Martin, Lane; Nehrir, Hashem; Bahramipanah, Maryam
    In this work, a novel agent-based day-ahead power management scheme is proposed for multiple-microgrid distribution systems with the intent of reducing operational costs and improving system resilience. The proposed power sharing algorithm executes within each microgrid (MG) locally, and the neighboring MGs cooperate via a multi-agent system cooperation scheme, established to model the communication among the agents. The power management for each agent is modeled as a multi-objective optimization problem (MOP) including two objectives: maximizing load coverage and minimizing the operating costs. The proposed MOP is solved using the Nondominated Sorting Genetic Algorithm (NSGA-II), where a set of Pareto optimal solutions is obtained for each agent through the NSGA-II. The final solution is obtained using an Analytical Hierarchical Process. The effectiveness of the proposed scheme is evaluated using a benchmark 4-MG distribution system. It is shown that the proposed power management scheme and the cooperation of agents lead to a higher overall system resilience and lower operation costs during extreme events.
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    Generalized Nighttime Radiative Deficits
    (Elsevier BV, 2021-10) Howell, John C.; Yizhaq, Tomer; Drechsler, Nadav; Zamir, Yuval; Beysens, Daniel; Shaw, Joseph A.
    We derive a general, tilt-dependent, nighttime, radiative deficit model with an eye towards improved dew collection. The model incorporates atmospheric/environmental incoming radiation, a linear precipitable water vapor transmittance function dependent on local meteo data and the influence of near-horizon obstacles. A brief discussion of cloud cover is given. The model is then used more specifically to predict radiative deficits for an ideal blackbody emitter in an environment with an isotropic temperature. Knowing the tilt angle, near-horizon obstacles and local meteo-data, it is then possible to estimate the radiative deficit of a given emitter. We consider errors resulting from the assumption that the ground and obstacles are at the same temperature as the air. We also analyze the errors arising from the linear precipitable water vapor transmittance function by comparing the results against high-resolution, full-spectrum Modtran® data [1]. We show that for typical tilt angles, the isotropic temperature model is a reasonable approximation as long as the above-horizon environmental heating is small. We believe these results will be broadly valuable for the field of radiative cooling where a general radiative treatment has yet to be made and in particular the field of dew water harvesting.
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    Trutinor: A Conceptual Study for a Next-Generation Earth Radiant Energy Instrument
    (MDPI, 2020-10) Young, Cindy L.; Lukashin, Constantine; Taylor, Patrick C.; Swanson, Rand; Kirk, William S.; Cooney, Michael; Swartz, William H.; Goldberg, Arnold; Stone, Thomas; Jackson, Trevor; Doelling, David R.; Shaw, Joseph A.; Buleri, Christine
    Uninterrupted and overlapping satellite instrument measurements of Earth’s radiation budget from space are required to sufficiently monitor the planet’s changing climate, detect trends in key climate variables, constrain climate models, and quantify climate feedbacks. The Clouds and Earth’s Radiant Energy System (CERES) instruments are currently making these vital measurements for the scientific community and society, but with modern technologies, there are more efficient and cost-effective alternatives to the CERES implementation. We present a compact radiometer concept, Trutinor (meaning “balance” in Latin), with two broadband channels, shortwave (0.2–3 μm) and longwave (5–50 μm), capable of continuing the CERES record by flying in formation with an existing imager on another satellite platform. The instrument uses a three-mirror off-axis anastigmat telescope as the front optics to image these broadband radiances onto a microbolometer array coated with gold black, providing the required performance across the full spectral range. Each pixel of the sensor has a field of view of 0.6°, which was chosen so the shortwave band can be efficiently calibrated using the Moon as an on-orbit light source with the same angular extent, thereby reducing mass and improving measurement accuracy, towards the goal of a gap-tolerant observing system. The longwave band will utilize compact blackbodies with phase-change cells for an absolute calibration reference, establishing a clear path for SI-traceability. Trutinor’s instrument breadboard has been designed and is currently being built and tested.
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    Reduced-cost hyperspectral convolutional neural networks
    (2020-09) Morales, Giorgio; Sheppard, John W.; Scherrer, Bryan; Shaw, Joseph A.
    Hyperspectral imaging provides a useful tool for extracting complex information when visual spectral bands are not enough to solve certain tasks. However, processing hyperspectral images (HSIs) is usually computationally expensive due to the great amount of both spatial and spectral data they incorporate. We present a low-cost convolutional neural network designed for HSI classification. Its architecture consists of two parts: a series of densely connected three-dimensional (3-D) convolutions used as a feature extractor, and a series of two-dimensional (2-D) separable convolutions used as a spatial encoder. We show that this design involves fewer trainable parameters compared to other approaches, yet without detriment to its performance. What is more, we achieve comparable state-of-the-art results testing our architecture on four public remote sensing datasets: Indian Pines, Pavia University, Salinas, and EuroSAT; and a dataset of Kochia leaves [Bassia scoparia] with three different levels of herbicide resistance. The source code and datasets are available online.
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