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Intelligent Electrochemical Sensors for Precise Identification of Volatile Organic Compounds Enabled by Neural Network Analysis
The volatile organic compounds (VOCs) in a wide spectrum of categories were identified as biomarkers in aquatic environments, playing an important role in marine and freshwater ecology and global atmospheric chemistry. VOCs released from biofuel have also attracted increasing attention. Although the importance has been recognized, the portable detection and analysis methods of VOC in aquatic systems have not yet been well developed and understood. Here, in this work, we innovatively proposed an intelligent electrochemical sensing approach to classify and quantify VOCs in solution. Utilizing the cyclic voltammetry (CV) method with an ionic liquid (IL)-based electrolyte, we analyzed 50 μL samples of various VOC analytes, including acetic acid (AC), acetone, dimethylformamide (DMF), dimethyl sulfoxide (DMSO), ethanol, formaldehyde, formic acid, methanol, methyl formate (MF), toluene, and a formaldehyde-methanol mixture, along with deionized water (DI water). The generated voltammograms were subsequently analyzed using our uniquely designed and optimized 1-D convolutional neural network (1D-CNN). This deep-learning algorithm achieved a 99.09% accuracy in VOC classification validated through fivefold cross-validation and demonstrated an impressive 94.4% test accuracy for methanol detection within a 10 μL error range. For quantification, the system accurately categorized methanol volumes ranging from 0 to 50 μL in 10 μL increments, achieving a 98.18% accuracy. A notable linear correlation (R2 = 95.56%) was found between max current density at the oxidation peak and methanol volume, with the limit of detection (LOD) at 9.3 μL. Such a sensing method exhibits potential for portability, high accuracy, and generalization in the classification and quantification, ultimately reshaping the realm of VOC analysis in solution.
Optimizing Intermittent Water Supply in Urban Pipe Distribution Networks
In many urban areas of the developing world, piped water is supplied only intermittently, as valves direct water to different parts of the water distribution system at different times. The flow is transient, and may transition between free-surface and pressurized, resulting in complex dynamical features with important consequences for water suppliers and users. Here, we develop a computational model of transition, transient pipe flow in a network, accounting for a wide variety of realistic boundary conditions. We validate the model against several published data sets and demonstrate its use on a real pipe network. The model is extended to consider several optimization problems motivated by realistic scenarios. Here, we demonstrate how to infer water flow in a small pipe network from a single pressure sensor, and show how to control water inflow to minimize damaging pressure transients.
On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments
This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at the High Luminosity Large Hadron Collider. We present our approach for developing a compact neuromorphic model that filters out the sensor data based on the particle's transverse momentum with the goal of reducing the amount of data being sent to the downstream electronics. The incoming charge waveforms are converted to streams of binary-valued events, which are then processed by the SNN. We present our insights on the various system design choices - from data encoding to optimal hyperparameters of the training algorithm - for an accurate and compact SNN optimized for hardware deployment. Our results show that an SNN trained with an evolutionary algorithm and an optimized set of hyperparameters obtains a signal efficiency of about 91% with nearly half as many parameters as a deep neural network.
A Comprehensive Analysis of Real-World Accelerometer Data Quality in a Global Smartphone-based Seismic Network
The proliferation of low-cost sensors in smartphones has facilitated numerous applications; however, large-scale deployments often encounter performance issues. Sensing heterogeneity, which refers to varying data quality due to factors such as device differences and user behaviors, presents a significant challenge. In this research, we perform an extensive analysis of 3-axis accelerometer data from the MyShake system, a global seismic network utilizing smartphones. We systematically evaluate the quality of approximately 22 million 3-axis acceleration waveforms from over 81 thousand smartphone devices worldwide, using metrics that represent sampling rate and noise level. We explore a broad range of factors influencing accelerometer data quality, including smartphone and accelerometer manufacturers, phone specifications (release year, RAM, battery), geolocation, and time. Our findings indicate that multiple factors affect data quality, with accelerometer model and smartphone specifications being the most critical. In addition, we examine the influence of data quality on earthquake parameter estimation and show that removing low-quality accelerometer data enhances the accuracy of earthquake magnitude estimation.
DIRECT SIMULTANEOUS MEASUREMENT OF PARTICULATE MATTER AND AMMONIA STORAGE ON COMBINED SELECTIVE CATALYTIC REDUCTION FILTER SYSTEMS USING RADIO FREQUENCY SENSORS
Tightening global emissions regulations are motivating interest in the development and implementation of Selective Catalytic Reduction + Filtration (SCRF) systems, which are designed to reduce the concentration of tailpipe particulate matter (PM) and NO x emissions. These systems allow designers to combine the NO x reduction capability of an SCR with the filtration capability of a particulate filter on a single unit. Practical implementation of these systems requires reliable measurement and diagnosis of their state – both with respect to trapped particulate matter as well as adsorbed ammonia. Currently, these systems rely on a variety of gas sensors, mounted upstream or downstream of the system, that only provide an indirect inference of the operation state. In this study, a single radio frequency (RF) sensor was used to perform simultaneous measurements of soot loading and ammonia inventory on an SCRF. Several SCRF core samples were tested at varying soot and ash loads in a catalyst reactor bench. Soot levels were measured by monitoring changes in the bulk dielectric properties within the catalyst using the sensor, while ammonia levels were determined by feeding selected regions of the RF spectrum into a pretrained generalized regression neural network model. Results show the RF sensor is able to directly measure the instantaneous ammonia inventory, while simultaneously providing soot loading measurements within 0.5 g/L. These results confirm that simultaneous measurements of both the PM and ammonia loading state of an SCRF are possible using a single RF sensor via analysis of specific features in the full RF spectrum. The results indicate significant potential to remove the control barriers typically associated with the implementation of advanced SCRF systems.
Resilient Distributed Estimation: Sensor Attacks
Here, this paper studies multi-agent distributed estimation under sensor attacks. Individual agents make sensor measurements of an unknown parameter belonging to a compact set, and, at every time step, a fraction of the agents’ sensor measurements may fall under attack and take arbitrary values. We present the Saturated Innovation Update (SIU) algorithm for distributed estimation resilient to sensor attacks. Under the iterative SIU algorithm, if less than one half of the agent sensors fall under attack, then, all of the agents’ estimates converge at a polynomial rate (with respect to the number of iterations) to the true parameter. The resilience of SIU to sensor attacks does not depend on the topology of the inter-agent communication network, as long as it remains connected. We demonstrate the performance of SIU with numerical examples.
A Medium-/Low-Voltage Joint State Estimator Through Linear Uncertainty Propagation
Traditionally, distribution system state estimations (DSSE) are challenged by the lack of measurements at both primary and secondary sides of the system. The widely available cable television (CATV) voltage sensors installed in low-voltage (LV) networks bring opportunities to achieve higher quality DSSE covering a broader area of the distribution network. This study proposes a medium-/low-voltage (MV/LV) joint distribution system state estimation approach using the untapped CATV measurements. It aims at addressing the need for system situational awareness at the grid edge while improving the estimation accuracy at both the primary and secondary sides compared to its disjointed counterpart. Linearized measurement functions and boundary condition uncertainty propagation rules are derived to ensure the computational efficiency and accuracy of the joint state estimator. Numerical experiments are conducted on an IEEE test feeder to demonstrate the efficacy of the proposed method and the value of CATV measurements.
Test and Validate Distributed Coaxial Cable Sensors for in situ Condition Monitoring of Coal-Fired Boiler Tubes
This project aims to test, validate, and advance the technology readiness level (from TRL5 to TRL7) of a novel low-cost distributed stainless-steel/ceramic coaxial cable sensing (SSC-CCS) technology for in situ monitoring of the boiler tube temperature in existing coal-fired power plants. The novel SSC-CCS sensing technology and associated condition-based monitoring (CBM) software to be demonstrated in this project will lead to an improved understanding of the boiler tube failure mechanisms and a prognostic system to improve the overall performance, reliability, and flexibility of the nation’s coal-fired power plant fleet. A boiler tube monitoring system with distributed coaxial cable temperature sensors and a sensor acquisition system was constructed. The high-temperature coaxial cable sensor with a length of 1.3m was made by using a quartz tube (1mm inner diameter (ID) and 6mm outer diameter (OD)) to concentrically separate a 304 stainless-steel (SS) rod (1mm OD) and SS tube (7.94mm OD and 6.16mm ID). The sensor acquisition system includes a vector network analyzer (VNA), a radio frequency (RF) power amplifier, multiple switches and a USB hub. The distributed stainless-steel quartz coaxial cable sensor (SSQ-CCS) had a linear response to temperature with a resolution uncertainty of σ = 0.77℃. To withstand the harsh conditions of 3,300 steam pressures and 800℃ high temperatures, the sensor was shielded by a protective tube made of the same material as the boiler tube. The protection tube had an OD of 1.5 inches and a thickness of 0.25 inches. In the laboratory tests, the sensor showed good sensitivity and fast response. The drift was bounded between +0.33% and -0.67% during a test at 600℃ for 350 hours, indicating good stability of the sensor. A field test was conducted where four sensors were welded on four superheat tubes (SH-Ts) at a coal-fired power station over 400 days. Conventional thermocouples were welded to the superheater tubes alongside the coaxial cable sensors for the purpose of comparison. Two sensors were capable of distributed sensing, with three multiplexed sensing sections. The other two sensors were single section. During the 400-day test period, the power plant experienced startups and shutdowns. At the steady state operations, the temperature of the boiler tube is about 600℃ (1112°F). The sensors recorded the entire coal-firing processes (start-up, steady state, and shut-down) and the glitch event. A GSM modem and a Watchdog were added to the system to ensure reliable data recording. The GSM modem sent daily messages to plant managers and Clemson team to inform the status of the sensor system. If the system was not normally working, the Watchdog would reboot the system automatically. The new coaxial cable based distributed sensing technology has been proven to be successful in both laboratory and field tests. A comprehensive four-stage multi-physics computational framework has been developed to assist the design, optimization, installation, and operation of SSQ-CCS. With the consideration of various operation conditions, we predict the distributions of flue gas temperatures within coal-fired boilers, the temperature correlation between the boiler tube and SSQ-CCS, and the safety of SSQ-CCS. A conditional-based monitoring system is implemented as well. The computational framework developed in this work can guide the future operation of coal-fired plants and other power plants for the safety prediction of boiler operations.
Secure Data Logging and Processing with Blockchain and Machine Learning (Final Report)
Secure Data Logging and Processing with Blockchain and Machine Learning (ML) research is focused on the development of a platform to securely log and process sensor data in fossil power plants. The platform integrates two emerging technologies, blockchain and ML, and incorporates several innovative mechanisms to ensure the integrity, reliability, and resiliency of power systems. The goal is to protect the power plant from various cyberattacks such as false data injection and denial of service attacks using these technologies. The research goal was enabled by the following Research Project Objectives: 1) Secure authentication and identity verification of sensor nodes, actuators, and other equipment within a network. 2) Development of mechanisms that ensure only data sent by legitimate sensors are accepted and stored in the data repository. 3) Development of data aggregation methodologies using ML / Deep Learning (DL) algorithms to minimize noise / faulty data. 4) Implementation of the blockchain technologies to provide data security using secured IOTA framework & nodes.
Investigating spatial variability of aerosol, cloud condensation nuclei, and ice nucleating particles in mountainous terrain
The ASR-supported Surface Atmosphere Integrated field Laboratory (SAIL) in the East River Watershed (ERW) of the Upper Colorado River Basin in southwestern Colorado ran from fall 2021 to spring 2023. Two monitoring sites were deployed in the East River Watershed as part of SAIL. The two sites were the Aerosol Observation System (AOS) located on Crested Butte Ski Mountain, and the ARM Mobile Facility (AMF-2), located at the Rocky Mountain Biological Laboratory in Gothic, Colorado. To gain a more comprehensive understanding of aerosols in complex, mountainous terrain, Handix Scientific deployed SAIL-Net, a distributed network of six measurement nodes spanning the domain of the SAIL research area from October 2021 to July 2023. Each node measured aerosol particles between 140 nm and 3.4 μm in diameter using a small particle counter (POPS, (Gao et al., 2016)), CNN using a miniature CCN counter (CloudPuck), and INP using the Time-Resolved Aerosol Filter Sampler (TRAPS, Creamean et al. (2018)). Our approach was similar to other studies that aimed to better characterize and understand aerosols and gas-phase pollutants using networks of lower-cost sensors (Caubel et al., 2019; Kelly et al., 2021; Asher et al., 2022). Such studies have identified neighborhood-level variations in pollutant concentrations (Schneider et al., 2017; Popoola et al., 2018; Caubel et al., 2019). Small-scale variations such as this are poorly represented in models and poorly measured by a single monitoring system (Caubel et al., 2019). Previous work has shown the representation error (the ability of measurements to represent a larger area) increases with complex orography, leading to decreases in model accuracy (Schutgens et al., 2017). The overall goal of SAIL-Net was to improve our understanding of the variability of aerosol in ERW, thus increasing our knowledge of aerosol-cloud interactions in this region and informing the usefulness of distributed networks of measurements for future studies.
Investigating Spatial Variability of Aerosol, Cloud Condensation Nuclei, and Ice Nucleating Particles in Mountainous Terrain Field Campaign Report
The U.S. Department of Energy Atmospheric System Research (ASR)-supported Surface Atmosphere Integrated Field Laboratory (SAIL) campaign in the East River Watershed (ERW) of the Upper Colorado River Basin in southwestern Colorado ran from fall 2021 to spring 2023. Two monitoring sites were deployed in the ERW as part of SAIL. The two sites were the Aerosol Observation System (AOS) located on Crested Butte Ski Mountain, and the second ARM Mobile Facility (AMF2), located at the Rocky Mountain Biological Laboratory in Gothic, Colorado. To gain a more comprehensive understanding of aerosols in complex, mountainous terrain, Handix Scientific deployed SAIL-Net, a distributed network of six measurement nodes spanning the domain of the SAIL research area from October 2021 to July 2023. Each node measured aerosol particles between 140 nm and 3.4 μm in diameter using a small portable optical particle spectrometer (POPS; Gao et al. 2016), cloud condensation nuclei (CNN) using a miniature CCN counter (CloudPuck), and ice nucleating particles (INP) using the time-resolved aerosol filter sampler (TRAPS; Creamean et al. 2018). Our approach was similar to other studies that aimed to better characterize and understand aerosols and gas-phase pollutants using networks of lower-cost sensors (Caubel et al. 2019, Kelly et al. 2021, Asher et al. 2022). Such studies have identified neighborhood-level variations in pollutant concentrations (Schneider et al. 2017, Popoola et al. 2018, Caubel et al. 2019). Small-scale variations such as this are poorly represented in models and poorly measured by a single monitoring system (Caubel et al. 2019). Previous work has shown the representation error (the ability of measurements to represent a larger area) increases with complex orography, leading to decreases in model accuracy (Schutgens et al. 2017). The overall goal of SAIL-Net was to improve our understanding of the variability of aerosol in the ERW, thus increasing our knowledge of aerosol-cloud interactions in this region and informing the usefulness of distributed networks of measurements for future studies. We met this goal by answering the following science questions: 1. What is the aerosol temporal variability, and how does aerosol inhomogeneity vary seasonally? Is there significant seasonal variability in sources, or are short-term meteorological conditions the most important determining factor in sources for cloud nuclei? 2. What is the aerosol spatial variability? What are the aerosol characteristics at cloud base, presumably the particles most representative of those acting as cloud nuclei? 3. How should measurement networks be designed to capture aerosol-cloud interactions, and what do they need to measure? Can a single measurement site accurately represent aerosol properties in regions of complex terrain? SAIL-Net consisted of six measurement nodes spread across the ERW near Crested Butte, Colorado. The primary objective in site placement was to select locations that captured the vertical variation in aerosol properties while also spanning the domain of the SAIL campaign. The elevation of the sites ranged from roughly 2750 m along the valley floor of the ERW to approximately 3500 m near the top of Crested Butte Mountain, which is one of the taller peaks in the ERW. The farthest distance between sites was 14 km, while the closest two sites were approximately 1 km apart. Two of the sites were collocated with the ARM SAIL sites; our instruments sat on top of one of the trailers at AOS and another one of our sites was located in a meadow just above AMF2.
Vehicular Re-Identification from Uncontrolled Multiple Views
Vehicle re-identification (re-ID) across disparate sensing modalities remains a fundamental challenge for transportation research. In this work, we introduce a deep multi-view vehicle re-ID framework that leverages Siamese networks to compare pairs of vehicle images and produce matching scores, enabling robust association across drastically different viewpoints such as those from UAVs, surveillance cameras, and ground sensors. The model exploits convolutional neural networks to learn features that remain discriminative under changes in angle, distance, and illumination, supporting more generalizable re-ID performance. As part of this effort, we also developed an automated pipeline to synchronize roadside and UAV video streams, producing a multi-perspective dataset that complements preexisting real collections and a synthetic dataset generated in this study. Together, these contributions advance the capability to re-identify vehicles across wide viewing baselines; establish a foundation for scalable, reproducible research in vehicle re-ID; and open pathways for future applications, such as inferring routine behaviors, movement patterns, and daily habits of the individual associated with the vehicle.
Journey over Destination: Dynamic Sensor Placement Enhances Generalization
Reconstructing complex, high-dimensional global fields from limited data points is a challenge across various scientific and industrial domains. This is particularly important for recovering spatio-temporal fields using sensor data from, for example, laboratory-based scientific experiments, weather forecasting, or drone surveys. Given the prohibitive costs of specialized sensors and the inaccessibility of
certain regions of the domain, achieving full field coverage is typically not feasible. Therefore, the development of machine learning algorithms trained to reconstruct fields given a limited dataset is of critical importance. In this study, we introduce a general
approach that employs moving sensors to enhance data exploitation during the training of an attention based neural network, thereby improving field reconstruction. The training of sensor locations is accomplished using an end-to-end workflow, ensuring
differentiability in the interpolation of field values associated to the sensors, and is simple to implement using differentiable programming. Additionally, we have incorporated a correction mechanism to prevent sensors from entering invalid regions within the domain. We evaluated our method using two distinct datasets; the results show that our approach enhances learning, as evidenced by improved test scores.
A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements
Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops a machine learning based approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurement. The machine learning model is developed by using random forest algorithm. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.
Master State Threat Identifier (masti)
The MSE cyber sensor offers a novel approach for scalable network monitoring using heterogeneous computing. Packet data is collected, analyzed, and stored by a stand alone cyber sensor. Machine learning algorithms are run to discover patterns in the communication without the need for deep packet inspection. Tuning of the detection algorithms happens automatically, without need for an engineer to manually create detection rules.
A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements
Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops an approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurements. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.
A Machine Learning-Based Method to Estimate Transformer Primary-Side Voltages with Limited Customer-Side AMI Measurements: Preprint
Distribution control applications such as volt/var optimization, network reconfiguration, and distribution automation require accurate knowledge of the distribution system state. The lack of sufficient sensors on the primary side of distribution networks often limits the accuracy of the control decisions by these applications. The deployment of advanced metering infrastructure (AMI) provides utilities an opportunity to translate the AMI data on the secondary onto the primary so that it can be used as pseudo-measurements to augment the limited existing measurements on the primary. This paper develops an approach for estimating service transformer primary-side voltages by using limited secondary-side AMI measurements. The estimated primary-side voltages can be used by utilities as pseudo-measurements for distribution control applications. The detailed secondary model topology, which is an essential input data for many existing algorithms, is not required for the proposed method. The performance of the proposed method is validated by using AMI measurements from the field and an actual distribution feeder model of San Diego Gas & Electric Company.