Find research datasets worth reusing

opencc-by-4.0Mar 2022View details →

MR Gradient System Long-Term Stability Investigation and Protocol Optimization for Quality Control using Gradient Impulse Response Function (GIRF)

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>.   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.   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.

opencc-by-4.0Jun 2022View details →

Fiware-enabled tool for real-time control of the raw-water conveyance system of Athens

This database includes the data used to produce the results for the following article: Bellos, V., Kossieris, P., Efstratiadis, A., Papakonstantis, I., Papanicolaou, P., Dimas, P., Makropoulos, C. 2022. Fiware-enabled tool for real-time control of the raw-water conveyance system of Athens. Proceedings of the 39th IAHR World Congress, 19-24 June 2022, Granada, Spain (accepted paper for oral presentation, in press).

opencc-by-4.0Sep 2022View details →

Data for the publication "Control of electronic topology in a strongly correlated electron system"

Data sets of the figures in the publication "Control of electronic topology in a strongly correlated electron system"

opencc-by-4.0Apr 2010View details →

Figure 4. (a)Our Humanoid soccer robot, (b) Overview of the Control System-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller

Figure 4 shows the block diagram of the software which runs in the robot’s main processor. The program consists of 4 main blocks: • Hardware Interface: Contains all low level routines to access hardware of the robot including sensors and actuators. • Vision: Contains image processing algorithms such as recognition of landmarks and other object. Self localization is done using particle filtering. Particles are scored by comparing a simulated image from each particle with the current frame captured by camera. Using “Sampling-Importance Resampling” method, a new distribution of the particles is created after each step. Particles are also updated using a motion model. Final distribution of the particles converges to the real pose of the robot. • Planning: Planning system of the robot is based on a multi layer, and multi thread structure. The layers are named Strategy, Role, Behavior and Motion. Each layer contains a Scenario which runs in parallel with the scenarios in the other layers. A scenario in a higher level can terminate and change the scenario running in the lower level; however it is usually done in synchronization with the lower level scenario to avoid conflicts and instabilities. (Such as stopping the walking motion while one of the feet is still in the air). • Network: Mainly responsible for the wireless communication of the robot with the other robots or the referee box. This is done via WLAN. • Motion Control: manages all the actuators of the robot, and controls locomotion or any other action of the robot according to the requests from Cognition. • Sensor Control: manages other sensors, and interacts with the Sub-Controller.

opencc-by-4.0Apr 2010View details →

Figure 3. (a) Our Humanoid soccer robot, (b) Overview of the Control System-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller

The PERSIA Humanoid robot designed for has multipurpose capability. This robot equipped with main board for motion control, vision sensor, other balancing sensors, servo motors and etc. Figure 3 shows picture of the robot and overview of the Persia humanoid robot control system.

opencc-by-4.0Nov 2011View details →

Figure 7. The outcomes of the Monitoring and Control of the Greenhouse soil and climate Conditions for tomato crops-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

In the past generation greenhouses it was enough to have one cabled measurement point in the middle to provide the information to the greenhouse automation system. The system itself was usually simple without opportunities to control locally heating, lights, ventilation or some other activity, which was affecting the greenhouse interior climate. The optimal greenhouse climate and soil adjustment can enable us to improve productivity and to achieve remarkable energy savings. In this paper we proposed a multi-agent methodology for integrated management systems in greenhouses. In this regards wireless sensor networks play a vital role to monitor greenhouse and environment parameters. Each controlled process of the greenhouse environment is modeled as an autonomous agent with its own inputs, its own outputs and its own interactions with the other agents. Each agent acts autonomously, as it knows a priori the desired environmental set-points. In this way, any possible conflicting decisions of conventional environmental control methodologies are resolved through negotiations between the agents so that the possible optimal integrated solution is achieved. The developed system is simple, cost effective, and easily installable.

Figure 6. Structure of the MAS for integrated greenhouse management system.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The complete agents “environment” is shown in figure 6. Each agent receives the necessary environmental or plant condition measurements. While each agent keeps its autonomy and has its own “personal” goals, it interacts with the other agents through some specific communication language, in the “Agent discussion area”, where the overall goal is taken into account. This interaction takes place under the presence of information from the existing models and the target goals. At that point, each agent has decided on its best strategy.

Figure 2. Experimental wireless sensors setup in greenhouse-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

Figure 2 illustrates how the sensor nodes were deployed to the greenhouse block. The idea of the vertical deployment was to get a better understanding of the microclimate layers which typically exist in the greenhouse, and to figure out what kind of differences occur in the climate between lower and upper flora.

Figure 5. Multi-agent system information and knowledge scheme.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The negotiations between agents are subject to optimization based on “knowledge” that is derived from complete production models, yield models or even sparse models as expressed in fuzzy expert rules or practical rules of thumb. In addition, pest control and plant disease models provide additional information useful to the design of a successful strategy for optimal management [15] (illustrated in Figure 5).

Figure 1. Agent Architecture-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The multi agent method creates a non-structured environment using agents, these agents, in order to reach the global optimum, must be capable of performing transactions between each other, achieving several “deals”, which is performed though a common vocabulary, a finite set of information exchange, a finite set of possible actions, penalties, etc. The power of each agent depends on the degree of contribution of its represented process to the final output of the entire system. Each agent communicates with the environment and adapts to its own internal state as well as to the state of the entire system. In this systems, for the realization of the multi agent architecture the JADE 4.1.1 (Java Agent DEvelopment Framework) have been used [7].

Figure 4. The multi-agent overall environment for integrated management-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The value of the final product incorporates not only quantity issues but also quality issues, which are difficult to be measured or even estimated. The environment of each agent is defined by the same parameters that define the physical environment in addition to internal states reported by each agent.

opencc-by-4.0Oct 2013View details →

BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 3. Basic "Components" of Artificial and Biological (Brain-Controlled) Automation Systems

Although basing on different concepts concerning their details, artificial and biological (brain-controlled) automation systems show common points concerning their principal components (see Figure 3).

Figure 3. Photosynthetic activity in different wavelengths of light radiation. 5.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System

The greenhouse protects the plants from the extreme weather conditions. However, if the period of daylight prevents the photosynthetic activity, the plants do not grow. Horticultural lighting allows the grower to extend the growing season. It enables a year-round producing of plants or makes it possible for the grower to start sowing in early spring and continue season till the first frost. Plants need about 10-12 hours light to improve growth. When the plants are producing flowers or fruits the supplemental need of light per day increases up to 16 hours. Figure 3 shows the photosynthetic activity in different wavelengths of light radiation [16].

opencc-by-4.0Nov 2011View details →

Figure 1. The use of visual servo control for helicopter stabilization-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

Visual servoing is an approach to control motion of a helicopter using information feedback from a camera mounted on it. For their tremendous potential applications in various areas including environmental monitoring and anti-terrorism, unmanned small helicopters are being extensively studied in robotics and control in recent years. However, the research advance in dynamic control of small helicopters is limited due to highly coupled non- linear dynamics and the existence of various uncertain- ties. Many people studied controller design based on a Publisher Item Identifier. linearized or simplified model, but the controllers developed under linearized models cannot guarantee dynamic stability rigorously. Another effort is application of modern non-linear control theory to helicopter control because small helicopter are good test beds for sophisticated control techniques for their small size and highly coupled dynamics [4].

opencc-by-4.0Dec 2021View details →

Open-source quality control routine and multi-year power generation data of 175 PV systems

Description The repository contains an extensive dataset of PV power measurements and a python package (qcpv) for quality controlling PV power measurements. The dataset features four years (2014-2017) of power measurements of 175 rooftop mounted residential PV systems located in Utrecht, the Netherlands. The power measurements have a 1-min resolution. PV power measurements Three different versions of the power measurements are included in three data-subsets in the repository. Unfiltered power measurements are enclosed in unfiltered_pv_power_measurements.csv. Filtered power measurements are included as filtered_pv_power_measurements_sc.csv and filtered_pv_power_measurements_ac.csv. The former dataset contains the quality controlled power measurements after running single system filters only, the latter dataset considers the output after running both single and across system filters. The metadata of the PV systems is added in metadata.csv. This file holds for each PV system a unique ID, start and end time of registered power measurements, estimated DC and AC capacity, tilt and azimuth angle, annual yield and mapped grids of the system location (north, south, west and east boundary). Quality control routine An open-source quality control routine that can be applied to filter erroneous PV power measurements is added to the repository in the form of the Python package qcpv (qcpv.py). Sample code to call and run the functions in the qcpv package is available as example.py. Objective By publishing the dataset we provide access to high quality PV power measurements that can be used for research experiments on several topics related to PV power and the integration of PV in the electricity grid. By publishing the qcpv package we strive to set a next step into developing a standardized routine for quality control of PV power measurements. We hope to stimulate others to adopt and improve the routine of quality control and work towards a widely adopted standardized routine.  Data usage If you use the data and/or python package in a published work please cite: Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2022. Open-source quality control routine and multi-year power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy. Units Timestamps are in UTC (YYYY-MM-DD HH:MM:SS+00:00). Power measurements are in Watt. Installed capacities (DC and AC) are in Watt-peak. Additional information A detailed discussion of the data and qcpv package is presented in: Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2022. Open-source quality control routine and multi-year power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy. Corrections are discussed in: Visser, L., Elsinga, B., AlSkaif, T., van Sark, W., 2024. Erratum: Open-source quality control routine and multiyear power generation data of 175 PV systems. Journal of Renewable and Sustainable Energy. Acknowledgements  This work is part of the Energy Intranets (NEAT: ESI-BiDa 647.003.002) project, which is funded by the Dutch Research Council NWO in the framework of the Energy Systems Integration & Big Data programme. The authors would especially like to thank the PV owners who volunteered to take part in the measurement campaign.

opencc-by-4.0Apr 2024View details →

Dataset for "Adaptive connectivity control in networked multi-agent systems: A distributed approach"

<div> <div>Dataset accompanying the paper "Adaptive connectivity control in networked multi-agent systems: A distributed approach" by M. Krizmancic and S. Bogdan submitted to PLOS ONE journal on April 30, 2024.</div> <div> </div> <div> <div> <div>Contains:</div> <ul> <li>Vector images of the figures presented in the paper.</li> <li>Data files containing the values used to build the figures.</li> </ul> Detailed information and instructions are available in the README file within the dataset. </div> </div> </div>

Fig. 4 in Trunk injection of systemic insecticides to control stem and leaf gall wasps, Josephiella species (Hymenoptera: Agaonidae), on Chinese banyan (Rosales: Moraceae) in Hawaii

Fig. 4. Mean leaf gall infestation level on new shoots associated with chemical treatments and untreated control (1–5, where 1 = no infestation and 5 = severe infestation), where * indicates P ≤ 0.05 and ** indicates P ≤ 0.01 within each sampling month (Kruskal–Wallis test).

Fig. 5 in Trunk injection of systemic insecticides to control stem and leaf gall wasps, Josephiella species (Hymenoptera: Agaonidae), on Chinese banyan (Rosales: Moraceae) in Hawaii

Fig. 5. Mean ratings of tree health condition (A) and mean ratings of new shoot emergence (B) 14 mo afer treatment (rating of tree health condition: 1 = excellent, 2 = good, 3 = fair, 4 = poor, 5 = dead; rating of new shoots emergence: 1 = many, 2 = moderate, 3 = some, 4 = few, 5 = very few). Means with the same letter are not significantly different (Kruskal–Wallis test).

Fig. 2 in Trunk injection of systemic insecticides to control stem and leaf gall wasps, Josephiella species (Hymenoptera: Agaonidae), on Chinese banyan (Rosales: Moraceae) in Hawaii

Fig. 2. Mean stem gall infestation levels on new shoots associated with chemical treatments and untreated control (1–5, where 1 = no infestation and 5 = severe infestation), where * indicates P ≤ 0.05 and ** indicates P ≤ 0.01 within each sampling month (Kruskal–Wallis test).

Fig. 3 in Trunk injection of systemic insecticides to control stem and leaf gall wasps, Josephiella species (Hymenoptera: Agaonidae), on Chinese banyan (Rosales: Moraceae) in Hawaii

Fig. 3. Mean percentage (± SE) of leaves infested with leaf gall wasps associated with chemical treatments and untreated control. Means with the same letter are not significantly different (ANOVA).