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Dataset results
180 results for “optimal control”
Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches
<p>Raw experimental data associated with the manuscript "Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches", by Sosa-Carrillo and colleagues, bioRxiv, 10.1101/2022.11.02.514931.<br> The code to analyze this raw data and generate the figures for the manuscript is available on GitLab at https://gitlab.inria.fr/InBio/Public/yeastcybersecretion.</p>
Dataset for article: Integer programming for optimal yaw control of wind farms
<div> <p>This is the dataset for the article "Integer programming for optimal yaw control of wind farms". We provide the integer programs (lp-files) and corresponding solver log files for each case of our series of experiments. Submission of manuscript: September 2024 (v1). Major revision of manuscript: February 2025 (v2). Minor revision of manuscript: April 2025 (v3).</p> </div>
Figure 1. A in Evaluate the efficiency of releasing two predatory species at their optimal temperature for controlling Tetranychus urticae (Acari: Tetranychidae) in a croton greenhouse
Figure 1. A timeline representing the release of predatory species P. persimilis and S. punctillum to control the twospotted mite T. urticae in a croton greenhouse.
Figure 3 in Evaluate the efficiency of releasing two predatory species at their optimal temperature for controlling Tetranychus urticae (Acari: Tetranychidae) in a croton greenhouse
Figure 3. The comparison between the population of T. urticae in predators' greenhouse and control greenhouse.
MR Gradient System Long-Term Stability Investigation and Protocol Optimization for Quality Control using Gradient Impulse Response Function (GIRF)
<p>The dataset of the abstract "MR Gradient System Long-Term Stability Investigation and Protocol Optimization for Quality Control using Gradient Impulse Response Function (GIRF)" for ISMRM 2022, London, UK. The data processing code with instructions could be found <a href="https://github.com/BRAIN-TO/girfISMRM2022">here</a>.</p> <p> </p> <p>Meas1.zip and Meas2.zip contain the first and the second measurements of the raw T2* decay signal acquired with the phantom-based method. Note that the coil dimension has been averaged to save data volume for demonstration purposes. This will lead to a lower SNR of the calculated output gradient and GIRF.</p> <p> </p> <p>CalculatedGIRF.zip provides the author's pre-calculated GIRFs using the data without coil averaging. This data is used for all the postprocessing (e.g. SNR and stability analysis, etc.) in the published abstract with the source code provided in the same Github repository.</p> <p> </p>
Dataset article "Demand-Response Control of Electric Storage Water Heaters Based on Dynamic Electricity Pricing and Comfort Optimization"
<p>"README-SupplementaryMaterial.txt" explains the information gathered in each csv files, and including the DHW consumption profiles generated, the hourly electricity pricing for 2022 (Spain), and the experimental data utilized for the validation of the model. </p>
User preference optimization for control of ankle exoskeletons using sample efficient active learning
<p>A major challenge to the widespread success of augmentative exoskeletons is accurately adjusting the controller to provide cooperative assistance with their wearer. Often, the controller parameters are ``tuned'' to optimize a physiological or biomechanical objective. However, these approaches are resource-intensive, while typically only enabling optimization of a single objective. In reality, the exoskeleton user experience is derived from many factors, including comfort and stability, among others. This work introduces an approach to conveniently tune four parameters of the exoskeleton controller that maximize user preference. We use an evolutionary algorithm to recommend potential parameters, which are ranked by a neural network that is pre-trained with previously collected preference data. The controller parameters that have the highest preference ranking are provided to the exoskeleton, and the wearer provides feedback as forced-choice comparisons. Our approach was able to converge on controller parameters preferred by the wearer compared to randomized parameters with an accuracy of 88% on average. The result indicates that the proposed algorithm was able to identify users' preferences while requiring less than 50 queries to users. This work demonstrates user preference can be used to tune high-dimensional controller spaces easily and accurately, which shows the potential of translating lower-limb wearable technologies into our daily lives.</p>
User preference optimization for control of ankle exoskeletons using sample efficient active learning
Open the record for dataset details and reuse information.
Reaching the limit in autonomous racing: Optimal control versus reinforcement learning
<p>A central question in robotics is how to design a control system for an agile mobile robot. This paper studies this question systematically, focusing on a challenging setting: autonomous drone racing. We show that a neural network controller trained with reinforcement learning (RL) outperformed optimal control (OC) methods in this setting. We then investigated which fundamental factors have contributed to the success of RL or have limited OC. Our study indicates that the fundamental advantage of RL over OC is not that it optimizes its objective better but that it optimizes a better objective. OC decomposes the problem into planning and control with an explicit intermediate representation, such as a trajectory, that serves as an interface. This decomposition limits the range of behaviors that can be expressed by the controller, leading to inferior control performance when facing unmodeled effects. In contrast, RL can directly optimize a task-level objective and can leverage domain randomization to cope with model uncertainty, allowing the discovery of more robust control responses. Our findings allowed us to push an agile drone to its maximum performance, achieving a peak acceleration greater than 12 times the gravitational acceleration and a peak velocity of 108 kilometers per hour. Our policy achieved superhuman control within minutes of training on a standard workstation. This work presents a milestone in agile robotics and sheds light on the role of RL and OC in robot control.</p>
Code and data for Nguyen Le et al. "Robust optimal control of interacting multi-qubit systems for quantum sensing"
<p>This is the code and simulation data for the paper "Robust optimal control of interacting multi-qubit systems for quantum sensing" by Nguyen Le et al.</p>
Optimal Control of Renewable Energy Communities with Controllable Assets: consumption and production profiles
<p>consumption and production profiles for cases I and II used for computing simulations in Optimal Control of Renewable Energy Communities with Controllable Assets</p>
Optical motion capturing of change of direction motions reconstructed with inverse kinematics and dynamics and optimal control simulation
<p>This is the data belonging to the publication "Change the direction: 3D optimal control simulation by directly tracking marker and ground reaction force data".</p> <p>This study investigated the feasibility and accuracy of reconstructing, especially change of direction motions with a 3D full-body musculoskeletal model by tracking marker and ground reaction force (GRF) data in optimal control simulations. We recorded in total 30 trials with optical motion capture. Using this data, we compared inverse methods (inverse kinematics and dynamics) to coordinate tracking simulations and marker tracking simulations.</p> <p>Please see the README and the publication for further details.</p>
Sensitivity-enhanced multidimensional solid-state NMR spectroscopy by optimal-control-based transverse mixing sequences
<p>The dataset here contains the raw data and pulse programs used for the publication "Sensitivity-enhanced multidimensional solid-state NMR spectroscopy by optimal-control-based transverse mixing sequences" submitted to JACS.</p> <p>All data is in a native Bruker TopSpin format. Data is organized in folders corresponding to Figures of the original publication. Detailed description is included in the file description.txt.</p> <p>We reccomend to visit our website optimal-nmr.net for additional information about optimal control methods applied to pulse sequence development for solid-state magic-angle-spinning NMR studies of proteins.</p>
Supplementary Data: Real-time optimal flood control decision making under uncertainty
<p>The files in this record contain data for real-time optimal flood control decision making and risk propagation under multiple uncertainties considered for publication in Water Resources Research.</p> <p>The files consist of:</p> <ul> <li>Pubugou Reservoir data;</li> <li>Source code and results of the Martingale Model of Forecast Evolution (MMFE);</li> <li>Source code and results of the SMAA-2 model;</li> <li>Source code and results of SMAA-TOPSIS model;</li> <li>Source code and results of the stochastic programming with recourse model.</li> </ul>
AEROARMS Control-based local optimization methods for planning
<p>This package allows to perform a Dynamic (Re-)Planning within the Set-Based Task-Priority Inverse-Kinematics (SBTP-IK) framework. It is ROS-based and it is provided to work with the Kinova Jaco2 7DOFs but it is can be easily adapted to another robot system changing the files describing the robot kinematics such as Denvait Hartenberg parameters.</p> <p>The proposed approach is based on merging control-based local optimization methods inside the planning algorithms. For the experiments, a fixed-based 7 DOFs manipulator has been considered in two different scenarios. In the first case, a static environment has been considered. In the second one, the user places an obstacle on the manipulator’s path in real-time. Thus, a re-planning results necessary to manage this change in the environment.</p> <p> </p>
CAABA/MECCA model output for evaluating optimized step size control in Rosenbrock solvers for stiff ODEs
<p>This dataset comprehend the simulation output obtained with the CAABA/MECCA model for the optimization of the step size control in the Rosenbrock integrators available from the Kinetic PreProcessor (KPP) version 2.2.3_rs4. This dataset is made avalaible for the peer-review of the manuscript describing the model and the different options of step size control that was implemented. </p>
Comparing and Combining Existing and Emerging Data Collection and Modeling Strategies in Support of Signal Control Optimization and Management (Project M2)
<p>For decades, traffic signal management agencies have used signal timing optimization tools combined with fine-tuning of signal timing based on field observations in their updates of time-of-day signal timing plans. These traditional signal optimization methods and tools use very limited amount of data and depend on default values in the signal timing optimization/simulation tools to estimate network performance under different signal optimization strategies. In recent years, new data collection technologies are emerging including high resolution controller data, more advanced detection technologies such as video image detection that are based on vehicle tracking and possible integration with microwave detectors, automatic-vehicle based identification technologies, third party crowdsourcing data, connected vehicles, and connected automated vehicles data. The objective of the proposed study is to propose methods and algorithms to combine data collected from existing and emerging sources with enhanced models and optimization algorithms to optimize and manage signal operations. The results from applying the developed methods and algorithms will be compared with traditional signal timing and optimization methods currently used by transportation agencies. </p>
Optimized structures for Optical control of ultrafast structural motion in a fluorescent protein
<p>QM-MM Optimized structures of the<strong> </strong>hydrogen bonding configuration in states A1, A2 and Transition State (TS) between them for rsKiiro protein on ground (s0) and first excited (s1) states. Structures were optimized at PBE0-D3/cc-pVDZ//Amber03 level.</p>
Automated control and optimization of laser-driven ion acceleration: Data
<p>This data repository contains python scripts that can recreate the plots from the article on 'Automated control and optimization of laser-driven ion acceleration'.</p>
Dataset for Optimal reactive nitrogen control pathways identified for cost-effective PM2.5 mitigation in Europe
<p>This folder includes six folders:</p> <p>The "Nr anthropogenic emissions" folder includes the anthropogenic Nr emissions used in the baseline simulations for January, April, July, and October 2015.</p> <p>The "Concentration" folder includes daily surface ammonia (NH<sub>3</sub>), nitric acid (HNO<sub>3</sub>), nitrate (NO<sub>3</sub><sup>-</sup>), ammonium (NH<sub>4</sub><sup>+</sup>), sulfate (SO<sub>4</sub><sup>2-</sup>), and fine particulate matter (PM<sub>2.5</sub>) concentrations for January, April, July, and October 2015 from WRF-Chem simulations (one BASE simulation and twelve sensitivity simulations as stated in Method).</p> <p>The "N-share" folder includes the N-share caused by anthropogenic Nr (NH<sub>3</sub> and NO<sub>x</sub>)/ NH<sub>3</sub>/NO<sub>x</sub> emissions in 2015.</p> <p>The "Health" folder includes the PM2.5-related premature deaths from WRF-Chem combined with GEMM simulations in 2015. </p> <p>The "Instant efficiency" folder includes the instant efficiency of Nr/ NH<sub>3</sub>/NO<sub>x</sub> emission controls from WRF-Chem simulations in 2015. </p> <p>The "G ratio" folder includes the G ratio from WRF-Chem simulations for the year 2015 and the indicator months (January, April, July, and October). </p> <p>For any questions, please contact Zehui Liu<br> Email: liuzh18@pku.edu.cn<br> </p>
ScienceDex guides
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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.