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104 results for “State estimation”
A Site Atmospheric State Best Estimate of Temperature for Lauder, New Zealand (1997-2012)
<p>A Site Atmospheric State Best Estimate (SASBE) of the temperature profile above the GCOS (Global Climate Observing System) Reference Upper-Air Network (GRUAN) site at Lauder, New Zealand, has been developed. Data from multiple sources are combined within the SASBE to generate a high temporal resolution data set that includes an estimate of the uncertainty on every value. The SASBE has been developed to enhance the value of measurements made at the distributed GRUAN site at Lauder and Invercargill (about 180 km apart), and to demonstrate a methodology which can be adapted to other distributed sites.</p> <p>Within GRUAN, a distributed site consists of a cluster of instruments at different locations.<br> The temperature SASBE combines measurements from radiosondes and automatic weather stations at Lauder and Invercargill, and ERA5 reanalysis, which is used to calculate a diurnal temperature cycle to which the SASBE converges in the absence of any measurements.<br> The SASBE provides hourly temperature profiles at 16 pressure levels between the surface and 10 hPa for the years 1997 to 2012. Every temperature value has an associated uncertainty which is calculated by propagating the measurement uncertainties, the ERA5 ensemble SDs, and the ERA5 representativeness uncertainty through the retrieval chain.</p> <p>This best-estimate temperature data product for Lauder is expected to be valuable for satellite and model validation as measurements of atmospheric essential climate variables are sparse in the Southern Hemisphere.</p> <p>A publication describing the data product is submitted to Earth System Science Data Discussions. The title of the publication is: Combining Data from the Distributed GRUAN Site<br> Lauder-Invercargill, New Zealand, to Provide a Site Atmospheric State Best Estimate of Temperature.</p> <p> </p> <p> </p>
Data for: Accurate state-of-charge estimation for sodium-ion batteries based on a low-complexity model with hierarchical learning
<p>The dataset accompanies the Journal of Energy Storage publication by Shuquan Wang et al. (2024), Accurate state-of-charge estimation for sodium-ion batteries based on a low-complexity model with hierarchical learning, DOI 10.1016/j.est.2024.112571. </p> <h2><strong>Experimental Description:</strong></h2> <p>The dataset comprises results from two experimental tests: pulse testing and driving cycle testing. These tests were conducted on two types of sodium-ion batteries—one with a capacity of 3.2 Ah (battery numbers: 1, 2, and 5) and another with a capacity of 10 Ah (battery numbers: 3, 4, and 6).</p> <h3><strong>Pulse Testing:</strong></h3> <p>The pulse tests were carried out using a battery test platform, consisting of an Arbin battery testing system, a temperature-controlled chamber, and a computer. The tests were performed on two 3.2 Ah and two 10 Ah sodium-ion batteries from Transimage and HiNa, respectively, with a nominal voltage of 3.0 V. The upper and lower cut-off voltages were set at 3.9 V and 1.5 V.</p> <p>Enhanced pulse tests were conducted at six different temperatures: -5 ℃, 5 °C, 15 ℃, 25 ℃, 35 ℃, and 45 ℃. The state-of-charge (SOC) was varied in 10% intervals, with pulse currents escalating incrementally from 0.25C to 3C at 0.25C intervals. Each pulse lasted for 5 seconds, followed by a 15-second rest. After completing each set of pulses, the current was increased, and the process was repeated with a two-minute pause between sets of pulses.</p> <h3><strong>Driving Cycle Testing:</strong></h3> <p>The driving cycle tests were designed to simulate real-world driving conditions using various standard test methods, including the Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), and Dynamic Stress Test (DST). These tests were performed in a temperature-controlled chamber using both the 3.2 Ah and 10 Ah sodium-ion batteries.</p> <p>As with the pulse tests, driving cycle tests were carried out at temperatures of -5 ℃, 5 °C, 15 ℃, 25 ℃, 35 ℃, and 45 ℃. Before each test, the batteries were charged with a 0.5C constant current-constant voltage (CC-CV) charging protocol up to 3.9 V, with a cut-off current of 0.02C. After a 30-minute rest, the driving cycle protocol was performed for seven iterations.</p> <h2><strong>File Naming Conventions:</strong></h2> <p>The dataset files are named based on the experimental conditions, as follows:</p> <ul> <li><strong>Pulse_data_tempX_batY</strong>: Data from the pulse tests, where X represents the testing temperature and Y denotes the battery number.</li> <li><strong>Driving_cycle_data_tempX_batY</strong>: Data from the driving cycle tests, where X represents the testing temperature and Y denotes the battery number.</li> </ul>
Estimates of high tide flooding on roadways within urban areas along the United States Atlantic coast
<p>Estimates of high tide flooding (HTF) on roadways in urban areas along the US Atlantic Coast. These estimates were calculated using NOAA HTF areal extent estimates, OpenStreetMap roadway data, and 2010 census-designated urban area and census block data. See the corresponding manuscript (Gold et al., 2021 - link coming soon) and <a href="https://github.com/acgold/HTF-on-roads">GitHub repository</a> for additional information about these data.</p>
Stroke data from: Robust dynamic brain coactivation states estimated in individuals
<p><span>A confluence of evidence indicates that brain functional connectivity (FC) is not static but rather dynamic. </span><span>Capturing transient </span><span>network interactions in the individual brain requires a technology that offers sufficient within-subject reliability. Here, we introduce an </span><span>individualized network-based dynamics analysis technique and demonstrate that it is reliable in detecting subject-specific brain states during both resting state and a cognitively challenging language task.</span> <span>Moreover, we evaluated the extent to which brain states showed hemispheric asymmetries and how various phenotypic factors such as handedness and gender might influence network dynamics. </span><span>W</span><span>e discovered a right-lateralized brain state that occurred more frequently in men than in women, and more frequently in right-handed versus left-handed individuals. Lastly, we demonstrated longitudinal brain state changes in 42 patients with subcortical stroke over 6 months. Taken together, this approach could quantify subject-specific dynamic brain states and has potential for use in both basic and clinical neuroscience research.</span></p>
Code and data from: A hierarchical approach for estimating state-specific mortality and state transition in dispersing animals with incomplete death records
<p>Unbiased mortality estimates are fundamental for testing ecological and evolutionary theory as well as for developing effective conservation actions. However, mortality estimates are often confounded by dispersal, especially in studies where dead-recovery is not possible. In such instances, missing individuals (i.e. individuals with unobserved time of death) may have died or permanently emigrated from a study area, making inferences about their fate difficult. Mortality before and during dispersal, as well as the decision to disperse, usually depend on a suite of individual, social, and environmental covariates, which in turn can be used to draw conclusions about the fate of missing individuals.<br>Here, we propose a Bayesian hierarchical model that takes into account time-varying covariates to estimate transitions between life-history states and mortality in each state using mark-resighting data with missing individuals. Specifically, our framework estimates mortality rates in two states (resident and dispersing state) by treating the fate of missing individuals as a latent (i.e. unobserved) variable that is statistically inferred based on information from individuals with a known fate and given the individual, social, and environmental conditions at the time of disappearance. Our model also estimates rates of state transition (i.e. emigration) to assess whether a missing individual was more likely to have died or survived due to unobserved emigration from the study area. <br>We used simulations to check the validity of our model and assessed its performance with data of varying degrees of uncertainty. Our modeling framework provided accurate mortality and emigration estimates for simulated data of different sample sizes, proportions of missing individuals, and resighting intervals. Variation in sample size appeared to affect the precision of estimated parameters the most.<br>Our approach offers a solution to estimating unbiased mortality of both resident and dispersing individuals as well as the probability of emigration using mark-resighting data with incomplete death records. Conditional on the availability of data on known-fate individuals and relevant time-varying covariates, our model can reconstruct the fate (death or emigration) of missing individuals. The modularity of our framework allows mortality analyses to be tailored to a variety of species-specific life histories.</p>
Dual-modal imaging of two-phase flows with electromagnetic flow tomography and electrical tomography -- experimental evaluation of the state estimation approach
<p>The supplementary files included are associated with our experimental research on two-phase flow estimation. This study experimentally investigates the feasibility of a state estimation approach for dynamic image reconstruction in dual-modal tomography of two-phase oil-water flows using electromagnetic flow tomography (EMFT) and electrical tomography (ET). By approximating the process with a convection-diffusion model, the extended Kalman filter and fixed-interval Kalman smoother are applied to reconstruct temporally evolving velocity and phase fraction distributions. The results demonstrate that the Kalman smoother-based reconstructions, along with uncertainty estimates, outperform conventional methods and provide feasible volumetric flow rate estimates for oil and water phases in a laboratory setup.</p>
eSEEd: emotional State Estimation based on Eye-tracking dataset
<p>We present eSEEd- emotional State Estimation based on Eye-tracking database. Eye movements of 48 participants were recorded as they watched 10 emotion evoking videos each of them followed by a neutral video. Participants rated five emotions (tenderness, anger, disgust, sadness, neutral) on a scale from 0 to 10, later translated in terms of emotional arousal and valence levels. Furthermore, each participant filled 3 self-assessment questionnaires. An extensive analysis of the participants' answers to the questionnaires self-assessment scores as well as their ratings during the experiments is presented. Moreover, eye and gaze features were extracted from the low level eye recorded metrics and their correlations with the participants' ratings are investigated. Finally, analysis and results are presented for machine learning approaches, for the classification of various arousal and valence levels based solely on eye and gaze features. The dataset is made publicly available and we encourage other researchers to use it for testing new methods and analytic pipelines for the estimation of an individual's affective state.<br><br>TO USE THIS DATASET PLEASE CITE:<br>Skaramagkas, V.; Ktistakis, E.; Manousos, D.; Kazantzaki, E.; Tachos, N.S.; Tripoliti, E.; Fotiadis, D.I.; Tsiknakis, M. eSEE-d: Emotional State Estimation Based on Eye-Tracking Dataset. <em>Brain Sci.</em> 2023, <em>13</em>, 589. https://doi.org/10.3390/brainsci13040589</p>
H2020 Platone Greek Demo State Estimation Results
<p>The dataset contains the results of the simulations of the State Estimation Tool for the Greek Demo of the Platone Project, for two separate instances during a day</p>
Stroke data from: Robust dynamic brain coactivation states estimated in individuals
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Code and data from: A hierarchical approach for estimating state-specific mortality and state transition in dispersing animals with incomplete death records
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Chronogram or phylogram for ancestral state estimation? Model-fit statistics indicate the branch lengths underlying a binary character’s evolution: R scripts and simulated trees
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Passive Non-Cooperative Intruder State Estimation and Optimal-Feedback Avoidance System for UAVs
<p>In recent years, numerous applications for unmanned aircraft systems (UAS) have emerged, such as manufacturing inspections and reconnaissance. Ensuring safety is crucial for integrating UAS into the National Airspace System (NAS); this integration is being conducted on the basis of a century of experience that has made manned aircraft operations incredibly safe.</p><p>A key challenge for unmanned flight is the inability to "detect-and-avoid" (DAA) obstacles. Various DAA systems have been proposed in recent years, each employing different sensor modalities. Cooperative systems enable air vehicles to exchange state information, while devices like the Automatic Dependent Surveillance-Broadcast (ADS-B) and Traffic Collision Avoidance System (TCAS) use satellite navigation sensors and transponders, respectively, to broadcast position data. Additionally, the Airborne Collision Avoidance System (ACAS) led to the creation of the ACAS-XU standard for unmanned aircraft.</p><p>The DAA capability for UAS must be extended to address non-cooperative intruders. This paper introduces an integrated vision-based passive collision alert system (PCAS) and guidance system that is designed to detect and optimally avoid collision with non-cooperative intruders. The system can adhere to recently-introduced regulations for safety zones and can be customized pre-flight. Hardware-in-the-loop (HITL) simulation demonstrates the feasibility for deployment on UAS in a plug-and-play fashion.</p>
Smart Battery Management System for Electric Vehicles: Selflearning Algorithms for Simultaneous State and Parameter Estimation, and Stress Detection
<p>The project proposes to develop parameter-varying SOH-coupled models for lithium-ion battery and self-learning algorithms to learn the model for simultaneous state and parameter estimation and fault detection. The traditional battery models use constant parameters, limiting their accuracy for predicting the state of the charge and health over the complete life-cycle. In practice, the battery parameters vary with the change in the state of charge and state of health. SOH-coupled models can be used to estimate the state of charge and health accurately. Further, obtaining the model parameters is also a challenging task for designing filters or observers for state estimation. A self-learning algorithm can eliminate the requirement of the model parameters. In this project, three SOH-coupled models are proposed and validated experimentally. The models are also used to design extended Kalman filters (EKF) for the state of charge, state of health, core and surface temperature, and internal resistance estimation. The results showed that the SOHcoupled models are more effective when compared to the uncoupled models in the literature. Further, it was found that EKFs based state estimation errors were within 1%. The self-learning algorithm using a two-layer neural network showed the ability to learn the models in real-time. However, the state estimation errors are higher for the self-learning scheme compared to the EKF based approaches. This is due to the limited measurement and online training schemes utilized to train neural networks. This requires further investigation in hyper-parameter tuning for implementation. Finally, a model-based fault detection scheme was proposed to detect internal thermal fault at its onset. The SOHcoupled model is reformulated to incorporate the internal resistance as a state. The EKF is used as a fault detection observer. The proposed fault detection scheme is validated using numerical simulation. It was observed that the fault detection scheme with SOH coupled electro-thermal-aging model could effectively detect a thermal fault at its incipient state.</p>
Data for climate-resilient snowpack estimation in the Western United States
<p>Generated and preprocessed files for the resilient snowpack estimation project. All preprocessed data were originally produced by the WUS-D3 project (https://dept.atmos.ucla.edu/alexhall/downscaling-cmip6) or PRISM (https://www.prism.oregonstate.edu/).</p>
ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - Corner Clamp Part 1
<p>@article{schieber2024asdf,<br> title={ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation},<br> author={Schieber, Hannah and Li, Shiyu and Corell, Niklas and Beckerle, Philipp and Kreimeier, Julian and Roth, Daniel},<br> journal={arXiv preprint arXiv:2403.16400},<br> year={2024}<br>}</p>
Estimating ancestral states of complex characters: A case study on the evolution of feathers
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Data from: United States cattle market location and annual market sales estimate data
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Estimated roadway segment traffic data by vehicle class for the United States: A machine learning approach
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Estimating spatio-temporal reproductive dynamics of fish populations with passive acoustic monitoring: A state-space model approach
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Estimates of surface layer net community production based on underway Lagrangian measurements of the dissolved O2/Ar ratio using Equilibrator Inlet Mass Spectrometry (EIMS), based on both steady-state and non-steady-state assumptions of the mixed-layer biological oxygen budget. Also included are estimates of the potential contribution of vertical fluxes: advection, eddy diffusion, and entrainment.
The ratio of dissolved oxygen to argon in surface seawater is frequently employed to estimate rates of net community production (NCP) in the oceanic mixed layer. The in situ O2/Ar-based method accounts for many physical factors that influence oxygen concentrations in the surface ocean, permitting isolation of the biological oxygen signal produced by the balance of photosynthesis and respiration. However, this technique traditionally relies upon several assumptions when calculating the mixed layer O2/Ar budget, most notably the absence of vertical fluxes of O2/Ar and the existence of a steady-state balance between net productivity and the air-sea gas exchange of biological oxygen. Employing a Lagrangian study design and leveraging data outputs from a regional physical oceanographic model, we conducted in situ measurements of O2/Ar in the California Current Ecosystem in spring 2016 and summer 2017 to evaluate these assumptions within a ‘worst-case’ field environment. Quantifying the magnitude of vertical fluxes and comparing NCP estimates obtained using steady-state versus non-steady-state assumptions, we find the importance of the non-steady-state term to be considerable, also observing significant potential effects from vertical flux terms, particularly advection. Additionally, we observe strong diel variability in O2/Ar and calculated NCP rates at multiple stations. Our results reemphasize the importance of accounting for vertical fluxes when interpreting O2/Ar-derived NCP data as well as the potentially large effect of non-steady-state conditions, including diel cycles in surface O2/Ar that can bias interpretation of NCP data based on local productivity and the time of day at which measurements were made.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.