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76 results for “health state”
Dataset of "Advanced machine learning techniques for State-of-Health estimation in lithium-ion batteries: A comparative study"
This research focuses on State-of-Health (SOH) estimation of lithium-ion (Li-ion) batteries to enhance lifespan and reliability. Using Samsung INR18650-35E cells, 600 cycles were analyzed with machine learning (ML) techniques, including Gaussian Process Regression (GPR), Support Vector Regression (SVR), Feed-Forward Neural Network (FFNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). Input features from charging and discharging cycles were selected with Pearson Correlation Analysis (PCA) and Exhaustive Search (ES) to optimize inputs for each ML method. Models were tested on datasets of varying sizes to evaluate performance and overfitting, including an experiment where SOH estimation of one battery was performed using training data from another. The findings highlight each model's strengths and limitations, guiding their application in battery health prediction.
Code and measurement data - State of charge and state of health diagnosis of batteries with voltage-controlled models
<p><strong>This dataset contains the research data (code and measurement data) of the journal article: <a href="https://doi.org/10.1016/j.jpowsour.2022.231828">J. A. Braun, R. Behmann, D. Schmider, W. G. Bessler, "State of charge and state of health diagnosis of batteries with voltage-controlled models", Journal of Power Sources 544 (2022), 231828</a>.</strong></p> <p> </p> <p><strong>Abstract:</strong><br> The accurate diagnosis of state of charge (SOC) and state of health (SOH) is of utmost importance for battery users and for battery manufacturers. State diagnosis is commonly based on measuring battery current and using it in Coulomb counters or as input for a current-controlled model. Here we introduce a new algorithm based on measuring battery voltage and using it as input for a voltage-controlled model. We demonstrate the algorithm using fresh and pre-aged lithium-ion battery single cells operated under well-defined laboratory conditions on full cycles, shallow cycles, and a dynamic battery electric vehicle load profile. We show that both SOC and SOH are accurately estimated using a simple equivalent circuit model. The new algorithm is self-calibrating, is robust with respect to cell aging, allows to estimate SOH from arbitrary load profiles, and is numerically simpler than state-of-the-art model-based methods.</p> <p> </p> <p><strong>Intellectual property information:</strong><br> The Matlab codes and the research data provided here are under <strong><a href="https://creativecommons.org/licenses/by-nc/4.0/legalcode">CC-BY-NC-4.0</a></strong> license. Please note that the algorithms themselves are subject to industrial property rights, including, but not necessarily limited to, German patent <strong><a href="https://patents.google.com/patent/DE102019127828B4/en">DE102019127828B4</a></strong> and international patent application <strong><a href="https://patents.google.com/patent/WO2021073690A2/en">WO2021073690A2</a></strong>. Any use of the codes and algorithms presented here is subject to these property rights.</p> <p> </p> <p><strong>Overview of files:</strong><br> <strong>SOC_SOH_simple_model.m:</strong> Matlab script performing SOC and SOH diagnosis with the voltage-controlled "simple" equivalent circuit model. The script also reproduces the figures shown in the manuscript.</p> <p><strong>SOC_SOH_simple_extended.m:</strong> Matlab script performing SOC and SOH diagnosis with the voltage-controlled "extended" equivalent circuit model. The script also creates figures of additional data not shown in the manuscript.</p> <p><strong>Experimental_data_fresh_cell.csv:</strong> Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (99 h total with 1 s resolution) of a fresh lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</p> <p><strong>Experimental_data_aged_cell.csv:</strong> Tabulated experimental data (time, current, voltage, temperature) of the long-term experiment (85 h total with 1 s resolution) of a pre-aged lithium-ion cell. The cell is initally completely discharged. The data consist of full cycling, shallow cycling, and WLTP cycling.</p> <p><strong>OCV_vs_SOC_curve.csv:</strong> Tabulated experimentally-derived open-circuit voltage (OCV) as function of state of charge (SOC). 1001 data points between SOC = 0 and SOC = 1 in increments of 0.001.</p> <p><strong>readme.txt:</strong> Overview of files with a short description.</p>
Dataset on adult preferences toward EQ-5D-Y health states in Slovenia
<p>The dataset includes preferences of adults in Slovenia toward EQ-5D-Y health states in Slovenia, taking on a perspective of a 10-year old child. The preferences were collected using a discrete-choice experiment. The additional file presents the DCE design and variables descriptions.</p>
Dataset on preferences of rheumatic patients towards EQ-5D-5L health states in Germany
<p>The dataset includes preferences of rheumatic patients towards EQ-5D-5L health states in Germany, adjacent codebooks.</p>
Dataset on preferences of patients with diabetes towards EQ-5D-5L health states in Germany
<p>The dataset includes data on preferences of patients with diabetes toward EQ-5D-5L health states in Germany and adjacent codebooks. </p>
Dataset on adult preferences toward EQ-5D-Y health states in Germany
<p>The dataset include adult preferences towards EQ-5D-Y health states in Germany and adjacent codebook.</p>
Dataset on preferences of rheumatic patients preferences towards EQ-5D-5L health states in Spain
<p>The dataset includes preferences of rheumatic patients towards EQ-5D-5L health states in Spain.</p>
Stated Choice Experiment to measure willingness to pay for stream health improvements in Macon County, NC, 2012-2013
We ran a survey with a random sample of 3500 residents in Macon County, NC. We drew the sample from a total of 26993 parcels identified in the Macon County tax data. We ran the survey between September 2012 and February 2013 and had a response rate of 40.8%. The survey included a stated choice experiment designed to measure willingness to pay for stream health improvements in the region. The survey also included a set of auxiliary questions to assess general preferences for land management and relevant environmental characteristics.
Significant changes in bacterial communities associated with Pocillopora ingestion by CoTS: an important factor affecting the coral's health state
Coral ingestion by crown-of-thorns starfish (CoTS) is an important cause of coral reef degradation, although the impacts of CoTS feeding on coral-associated communities are not well understood. Therefore, in this study, we analyzed the zooxanthellae density (ZD), bacterial community composition, and microbial-metagenomic functional capacities associated with Pocillopora corals in healthy portions and feeding scars, following CoTS feeding. The ZDs were significantly lower in the feeding scars, and the ZD-loss rate was 92.05% ± 2.12%. The relative abundances of bacterial communities associated with corals after CoTS feeding changed significantly and were almost completely reorganized at the phylum and genus levels. Analysis of the microbial metagenomic-functional capacities showed that numerous physiological functions of the coral-bacterial holobionts in the feeding scars were diminished, including amino acid metabolism, xenobiotic biodegradation and metabolism, lipid metabolism, membrane transport, signal transduction, and cell motility. Compared with the healthy portions of Pocillopora corals, the abundances of potentially pathogenic bacteria in the corals of feeding scars significantly increased, whereas those of beneficial bacteria significantly decreased. Our research showed that coral-zooxanthellae holobionts were destroyed directly by CoTS, and our findings imply that the large increase in potentially pathogenic bacteria in feeding scars could threaten the long-term health of Pocillopora corals.
Data for Predictors of young people's access to and utilization of sexual and reproductive health services (SRHS) in Enugu State, Nigeria
<p>Data of a manuscript titled "Predictors of young people’s use of sexual and reproductive health services in Nigeria: a mixed-method approach" submitted to BMC Public Health</p>
Figure 2. from: Unicorn–Open science for assessing environmental state, human health and regional economy - Research Ideas and Outcomes 2: e9232 (16 May 2016) https://doi.org/10.3897/rio.2.e9232
Figure 2. - Time line of the tasks in UNICORN-project
Figure 1. from: Unicorn–Open science for assessing environmental state, human health and regional economy - Research Ideas and Outcomes 2: e9232 (16 May 2016) https://doi.org/10.3897/rio.2.e9232
Figure 1. - Links and interactions between the work packages
Fig. 2 in Metazoan Parasites And Health State Of European Eel, Anguilla Anguilla (Anguilliformes, Anguillidae From Tonga Lake And El Mellah Lagoon In The Northeast Of Algeria
Fig. 2. Distribution of the swimbladder degenerative index (SDI) in eels from the two localities.
Fig. 1 in Metazoan Parasites And Health State Of European Eel, Anguilla Anguilla (Anguilliformes, Anguillidae From Tonga Lake And El Mellah Lagoon In The Northeast Of Algeria
Fig. 1. Northeast of Algeria; stars show localization of sampling sites of eels.
Fig. 2 in Spatial distribution of Culicidae (Diptera) larvae, and its implications for Public Health, in five areas of the Atlantic Forest biome, State of São Paulo, Brazil
Fig. 2. Spatial distribution of the species found in the Cruzeiro do Sul rural district.
Fig. 1 in Spatial distribution of Culicidae (Diptera) larvae, and its implications for Public Health, in five areas of the Atlantic Forest biome, State of São Paulo, Brazil
Fig. 1. Localization of the study areas in the Atlantic Forest biome, São Paulo state – Brazil.
Fig. 6 in Spatial distribution of Culicidae (Diptera) larvae, and its implications for Public Health, in five areas of the Atlantic Forest biome, State of São Paulo, Brazil
Fig. 6. Adjusted Semi-variograms of the Simpson diversity index.
Fig. 3 in Spatial distribution of Culicidae (Diptera) larvae, and its implications for Public Health, in five areas of the Atlantic Forest biome, State of São Paulo, Brazil
Fig. 3. Spatial distribution of the species found in the Vale das Cigarras rural districts.
Dataset on adult preferences toward EQ-5D-Y health states in Spain
<p>The dataset includes data on adult preferences toward EQ-5D-Y health states. </p>
Dataset on preferences of patients with diabetes towards EQ-5D-5L health states in Spain
<p>The dataset includes data of patients with diabetes preferences toward EQ-5D-5L health states.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.