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185 results for “control loop”
Dataset for 'A deep learning-based quality control loop of the extrusion-based bioprinting process'
<p>Zip file containing the dataset for the paper 'A deep learning-based quality control loop of the extrusion-based bioprinting process' (available at <a href="https://ijb.whioce.com/index.php/int-j-bioprinting/article/view/620">https://ijb.whioce.com/index.php/int-j-bioprinting/article/view/620</a>). The dataset is composed of:</p> <ul> <li>A series of video folders, each having a unique ID, and containing the processed video frames.</li> <li>A master.csv file containing all the metadata for each video.</li> </ul> <p>The authors acknowledge the supported by the European Union’s Horizon 2020 research and innovation program under the project GIOTTO: “Giotto: Active ageing and osteoporosis: The next challenge for smart nanobiomaterials and 3D technologies,” grant agreement no. 814410.</p>
Figure 10b. The control loop's information displayed by the controller-Designing a Growing Functional Modules "Artificial Brain"
<p>Once performed the design and configuration, the controller and the local application are<br> executed by pressing the corresponding button. In case of a local simulation, two terminal windows<br> are generated. The first one, localized on the left side of the screen (figure 10.a), displays the<br> behavior of the simulation; the second one, localized on the right side of the screen (figure 10.b),<br> displays the behavior of the controller. Both application run concurrently and their respective<br> contents allow the user to observe and monitor the control session. The last text line of figure 10.b<br> displays the set of commands at cycle 201.</p>
Figure 1. The GFM controller and its control loop-Designing a Growing Functional Modules "Artificial Brain"
<p>The GFM control loop has many similarities to a standard one as shown in figure 1. The<br> controller, delimited by a dotted line, sends during each cycle an output command in order to trigger<br> some mechanical or virtual actuators and in return, receives a feedback composed of a sequence of<br> sensors' values. However, the concept of reference value is replaced by “Global Goals” which are<br> integrated to the controller.</p>
Contributions of Open-loop and Closed-loop Control in a Continuous Tracking Task Differ Depending on Attentional Demands during Practice
<p>We provide tracking data of the analysed segments after filtering, cognitive performance data and indivdual force levels. To this end, the data set contains three zip-files of numbers of csv-files and an additional csv-file. In each csv-file, comma delimits information within the rows. Participant numbers range between 1 and 21 where the first 11 numbers always refer to the participants of the single-task training group and the final 10 numbers refer to the participants of the dual-task training group.</p> <p>The <em>IndividualForceMaximum.csv</em> includes separate rows of participant’s information about training group, individual force maximum, and the 10% value of the force maximum, which was important for adapting the required force to the individual applicable leg force.</p> <p>The <em>CognitiveTaskData.zip </em>contains four csv-files. Two files are for the <em>TEST</em> sessions, which include either the <em>total</em> number of responses or the number of <em>correct</em> responses per trial for each participant within a row. The other two files are for the <em>TRAINING</em> data (either <em>total</em> number of responses or number of <em>correct</em> responses) of the cognitive task for the participants of the dual-task training group. The file includes 15 trials of the first and the last five training days (i.e., days 1 to 5, and days 18 to 22). For each participant the training data is presented in a row.</p> <p>The tracking data is split with respect to the training groups in either the single-task training group, i.e., <em>MotorTaskData_SingleTaskTrainingGroup.zip</em>, or the dual-task training group, i.e., <em>MotorTaskData_DualTaskTrainingGroup.zip</em>. Each of the zip-files includes <em>TESTING</em> and <em>TRAINING</em> data in csv-files for each participant <em>P</em> separately. We further separated the <em>TEMPLATE</em> data and the <em>PERFORMANCE</em> data, i.e., for instance template test file of P1 corresponds with performance test file of P1. Each trial is presented in a row, where the header includes the segment information (i.e., first segment = 1, second segment = 2, and third segment = 3) for each data point. <em>TEST </em>data include trials with the practiced second segment and the catch trials with another second segment. <em>TRAINING</em> data include the 15 trials of the first and the last five training days (i.e., days 1 to 5, and days 18 to 22).</p>
Closed Loop Glucose Control in Patients With Type 1 and Type 2 Diabetes
ClinicalTrials.gov study NCT05644730. IPD Sharing: NO. Countries: 1. Publications: 5.
Closed Loop Control in Adolescents Using Heart Rate as Exercise Indicator
ClinicalTrials.gov study NCT01945060. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Multi-center Trial in Adult and Pediatric Patients With Type 1 Diabetes Using Hybrid Closed Loop System and Control at Home
ClinicalTrials.gov study NCT02748018. IPD Sharing: NO. Countries: 9. Publications: 1.
Diabetes Closed-Loop Project 6 (DCLP6): Fully Automated Closed-Loop Control in Type 1 Diabetes Using Meal Anticipation
ClinicalTrials.gov study NCT04877730. IPD Sharing: YES. Countries: 1. Publications: 1.
Pilot Study of Closed-Loop Glucose Control in People With Type 1 Diabetes (BPK004)
ClinicalTrials.gov study NCT00977808. IPD Sharing: Not stated. Countries: 1. Publications: 1.
ECAP-controlled, Closed-loop Stimulation to Treat Chronic Trunk and/or Limb Pain (ECAP)
ClinicalTrials.gov study NCT04319887. IPD Sharing: NO. Countries: 1. Publications: 1.
Closed Loop Glucose Control in Patients With Type 2 Diabetes
ClinicalTrials.gov study NCT05386849. IPD Sharing: NO. Countries: 1. Publications: 5.
Optimizing Closed-Loop Control of Type 1 Diabetes Mellitus in Adolescents
ClinicalTrials.gov study NCT01890954. IPD Sharing: YES. Countries: 1. Publications: 1.
Closed-Loop Control in Young Children 5-8 Years Old Using DiAs Platform
ClinicalTrials.gov study NCT02750267. IPD Sharing: YES. Countries: 1. Publications: 1.
Fully Automated Closed Loop Control in Adolescents With Type 1 Diabetes
ClinicalTrials.gov study NCT04545567. IPD Sharing: YES. Countries: 1. Publications: 1.
Hybrid Closed-Loop Hotel Studies With Medtronic PID Controller
ClinicalTrials.gov study NCT02280863. IPD Sharing: YES. Countries: 1. Publications: 2.
Closed-loop Glucose Control for Automated Management of Type 1 Diabetes
ClinicalTrials.gov study NCT00811317. IPD Sharing: YES. Countries: 1. Publications: 3.
Effect of Closed Loop Glucose Control on Exercise-Associated Hypoglycemia
ClinicalTrials.gov study NCT00831389. IPD Sharing: NO. Countries: 1. Publications: 1.
Feasibility of Outpatient Closed Loop Control With the iLet Bionic Pancreas in Cystic Fibrosis Related Diabetes
ClinicalTrials.gov study NCT03258853. IPD Sharing: YES. Countries: 1. Publications: 0.
Feasibility Study of Closed Loop Control in Type 1 Diabetes Using Heart Rate Monitoring as an Exercise Marker
ClinicalTrials.gov study NCT01582139. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Diabetes Assistant (DiAs) Control-to-Range (CTR) Nocturnal Closed-Loop Camp Study
ClinicalTrials.gov study NCT01973413. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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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)
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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.