Find research datasets worth reusing

opencc-by-4.0Mar 2020View details →

From Boston to Eden - or how to get systems that are really autonomous and sufficiently intelligent to survive in their niche

<a href="https://www.researchgate.net/project/Theoretical-artificial-intelligence/update/5e5f931a3843b0499fec8f6f?_iepl%5BviewId%5D=FMNsczWoobvAHwiOMdftISgB&_iepl%5Bcontexts%5D%5B0%5D=projectUpdatesLog&_iepl%5BinteractionType%5D=projectUpdateDetailClickThrough">From Boston to Eden - or how to get systems that are really autonomous and sufficiently intelligent to survive in their niche</a> [lecture for the Dept. of AI, University of Groningen, Tuesday, March 3rd, 2020] As impressive as the robots of the Boston Dynamics company are (no AI involved) and as impressive the many results of deep learning are (no AI involved, either), the goal of creating autonomous, intelligent machines is as far away as it ever was.  In this presentation, I will give a brief overview of several deep-learning projects in our group. As a next step I will try to indicate  what may be missing, as regards 'real' AI. We may need a closer look at biological systems, i.e., the brain of animals. There exists a wide gap between the control systems at the low level of reflexive movement and the equilibria that need to be maintained ('Boston') versus the higher levels of processing, up to the levels of cognition and reasoning, which are very much upstairs ('Eden'). The missing middleware layer should not be underestimated: It contains the brain stem, up to the thalamus in animals and humans.  It corresponds to the 300-million year period before the 200 million years period where the neocortex was present.  What is this middleware doing? The conclusion may be that there is no autonomy without self protection, possible due to the presence of a separate and specialized valuation network that determines probability times utility (p*U), similar to what brain stem, midbrain and amygdala are doing in animals.

opencc-by-4.0Nov 2024View details →

Dataset underlying the manuscript: MAViS: Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays

Datasets underlying the manuscript. Information on how to run the scripts is detailed in the README file.

opencc-by-4.0Aug 2024View details →

Automated Classification of Conversation Valence and Arousal using Autonomic Nervous System Responses

This repository contains the supplementary file for our study "Automated Classification of Conversation Valence and Arousal using Autonomic Nervous System Responses". The MS Excel file contains all physiological features (individual features and synchrony features) for all valid dyads and all intervals together with self-report ratings of the conversation (Self-Assessment Manikin) and personality trait data (CES-D, BFNES, QCAE). Synchrony features were calculated using code from a previous Zenodo submission (https://zenodo.org/record/7140829).

opencc-by-4.0Nov 2022View details →

VHR images were produced in the tunnel tubes of EOAE by autonomous robotic systems (2022)

In the context of the EU-funded project PILOTING (No. 871542), several validation scenarios were scheduled in the three pre-determined pilot sites, i.e. refinery, viaduct, and tunnel, in order to evaluate the good operation of 9 different robotic systems and a versatile data platform. Under this frame, the current dataset was generated during the pilot demonstrations in Metsovo tunnels on 30/9/2022-08/10/2022. The aforementioned were collected by two of the robotic systems, e.g. the CART and the TT-DRONE, covering the execution of the two inspections; a) the general inspection, where the robot covers all the tubes, and b) the local inspection, which captured images in dedicated positions.   This particular dataset is comprised of Very High Resolution (VHR) photos, gathered in the tunnel facilities of EGNATIA ODOS AE (EOAE). During the image capturing, no artificial flashlight was used. The total number of photos acquired from the general inspection is 133 and from the local inspection 8. The data format received by the two robotic systems is in JPEG with the CART vehicle producing images with the approximate dimensions of 9504x6336 pixels, and the TT-DRONE with 6000x4000 pixels.

opengpl-2.0Mar 2023View details →

DeepScenario: An Open Driving Scenario Dataset for Autonomous Driving System Testing

With the rapid development of autonomous driving systems (ADSs), testing ADSs under various driving conditions has become a key method to ensure the successful deployment of ADS in the real-world. However, it is impossible to test all the scenarios due to the inherent complexity and uncertainty of ADSs and the driving tasks. Further, testing of ADSs is expensive regarding time and computational resources. Therefore, a large-scale driving scenario dataset consisting of various driving conditions is needed. To this end, we present an open driving scenario dataset DeepScenario, containing over 30K executable driving scenarios, which are collected by 2880 test executions of three driving scenario generation strategies. Each scenario in the dataset is labeled with six attributes characterizing test results. We further show the attribute statistics and distribution of driving scenarios. For example, there are 1050 collision scenarios, in 917 scenarios there were collisions with other vehicles, 105 and 28 with pedestrians and static obstacles, respectively. This dataset contains: <ol> <li><a href="https://github.com/Simula-COMPLEX/DeepScenario/tree/main/deepscenario-dataset">deepscenario-dataset</a> - DeepScenario dataset, which includes driving scenarios generated by executing three scenario generation strategies: Reinforcement Learning (RL)-based Strategy, Random-based Strategy, Greedy-based Strategy;</li> <li><a href="https://github.com/Simula-COMPLEX/DeepScenario/tree/main/deepscenario-toolset">deepscenario-toolset</a> - The toolset for DeepScenario dataset, including ScenarioCollector that can automatically collect driving scenarios, and ScenarioRunner that can support replaying driving scenarios. We also provide <a href="https://github.com/Simula-COMPLEX/DeepScenario/tree/main/deepscenario-toolset/lgsvl/scenariotoolset">source code</a> and <a href="https://github.com/Simula-COMPLEX/DeepScenario/tree/main/deepscenario-toolset#usage">usage examples</a> for the toolset. </li> </ol> More information about DeepScenario dataset is available in our Github repository: <a href="https://github.com/Simula-COMPLEX/DeepScenario">https://github.com/Simula-COMPLEX/DeepScenario</a>.

opencc-by-4.0Sep 2023View details →

Optimal planning of autonomous electric vehicles charging stations with photovoltaic generations and energy storage systems

This database contains technical information on the 69-bus electrical distribution system. This system was tested in a mixed integer linear programming model for allocating autonomous electric vehicle charging stations equipped with photovoltaic generation and energy storage systems. Additionally, this document contains data related to charging stations, energy storage systems, and operational scenarios applied to the case studies.

opencc-by-4.0Nov 2023View details →

Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses

This repository contains extracted data features and all questionnaires from our study "Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses".  Data_FinalFeatureSet.xlsx contains data for the 42 dyads who completed the study protocol. Rows represent individual participants, with the two participants in the same dyad always on consecutive rows. Columns consist of:<ul><li>Participant gender and age.</li><li>Group that dyads were assigned to. PosInit/NeutInit/NegInit represent positive, neutral or negative initial prompts. Devil1st/NoEmot1st represent which of the two secret prompts was presented first ("devil's advocate" or "no emotion").</li><li>A column stating which of the two participants was given the secret prompts (participant on left or right).</li><li>A column stating whether the participants had already known each other before the session (Y/N).</li><li>Extracted physiological features for 12 intervals: the first baseline (interval 1), 10 conversation intervals (intervals 2-11), and the second baseline (interval 12). Individual features are present for all individual participants while synchrony features exist for dyads (not individuals) and are thus present for only one row of a dyad.</li><li>Raw data from three personality questionnaires: the Brief Fear of Negative Evaluation Scale (BFNES), the Questionnaire of Cognitive and Affective Empathy (QCAE) and the Center for Epidemiologic Studies Depression Scale (CESD).</li><li>Self-reported results of the Self-Assessment Manikin (SAM) for the 10 conversation intervals, with the three columns in each interval corresponding to valence, arousal and balance.</li></ul>Note that one dyad's physiological data were corrupted and that dyad was not used for further analysis. Their demographics and questionnaire data are included, but no physiological features were calculated. Questionnaire files include the BFNES, QCAE and CESD as well as three versions of our modified SAM: one with no secret prompts, one with secret prompts for participants who saw the "devil's advocate" prompt first, and one with secret prompts for participants who saw the "no emotion" prompt first.

opencc-by-4.0Dec 2023View details →

Autonomous millimeter scale high throughput battery research system

In this study, we present data from high-throughput cyclic voltammetry (CV) test, derived by our digital workflow, Auto-MISCHBARES, complemented by XPS analysis. This research is part of the pre-print publication named "Autonomous millimeter scale high throughput battery research system", showcasing Cathode Electrolyte Interphase (CEI) investigation. The electrolyte used for this experiment is 1M of LiPF6 solution in an Ethylene Carbonate (EC): Ethyl Methyl Carbonate (EMC) mixture with a 3:7 weight ratio, along with LFP as our electrode material. The CV tests were conducted through a high-throughput sequential process for two cycles, each with varying stop potentials. After the experimentation phase, XPS analysis was applied to characterize the synthesized CEI.

opencc-by-4.0May 2022View details →

UF & UAB's Phase 2 Demonstration Study: Developing a Model to Support Transportation System Decisions considering the Experiences of Drivers of all Age Groups with Autonomous Vehicle Technology (Project A3)

Enclosed you will find the data collected during our STRIDE Phase II research project (A3) and a data dictionary.

opencc-by-4.0Oct 2022View details →

Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses

This repository contains supplementary files for our study "Automated Classification of Dyadic Conversation Scenarios using Autonomic Nervous System Responses". The two files are: -  ConversationClassification_FeatureTable.xlsx is an MS Excel file that contains all physiological features (individual features and synchrony features) for all valid dyads and all intervals. - ConversationClassification_SynchronyCalculation.zip contains the MATLAB 2021b code used to calculate four physiological synchrony metrics: dynamic time warping, nonlinear interdependence, coherence, and cross-correlation. It also includes some open-source code from other authors that is required for our synchrony calculation code to work. As inputs, the synchrony calculation functions accept 4-minute signal vectors from both participants in the dyad.

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.

Figure 9. Trajectory Algorithm Simulation-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

We have presented the system for a fully autonomous navigation of an UAV based on Omni directional vision system and image processing. we explain vision system configuration ,image processing and feature extraction methods and finaly suggest an algorithm based on potential field for navigation of an UAV.

Figure 8. Potential at every point; it is highest in the obstacles and lowest at the goal-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

The numerical potential field path planner is guaranteed to produce a path even if the start or goal is placed in an obstacle. If there is no possible way to get from the start to the goal without passing through an obstacle then the path planner will generate a path through the obstacle, although if there is any alternative then the path will do that instead. For this reason, it is important to make sure that there is some possible path, although there are ways around this restriction such as returning an error if the potential at the start point is too high. The path is found by moving to the neighboring square with the lowest potential, starting at any point in the space and stopping when the goal is reached.

Figure 7. Obstacle force (repulsive potential) and goal force obstacle force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

Figure 6. Goal force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

Since the motion trajectory of UAV is divided into several median points that the UAV should reach them one by one in a sequence the output obtained after the execution of AI will be a set of position and velocity vectors. So the task of the trajectory will be to guide the UAV through the obstacles to reach the destination. The routine used for this purpose is the potential field method (also an alternative new method is in progress which models the UAV motion through opponents same as the owing of a bulk of water through obstacles) [5]. In this method, different electrical charges are assigned to UAV, obstacles, and the destination. Then by calculating the potential field of this system of charges a path will be suggested for the UAV. At a higher level, predictions can be used to anticipate the position of the obstacles and make better decisions in order to reach the desired vector. In our path- planning algorithm, an articial potential field is set up in the space; that is, each point in the space is assigned a scalar value. The value at the goal point is set to be 0 and the value of the potential at all other points is positive.

Figure 5. Images binarized by SVM.-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

In instance, the image in Fig. 4 was binarized manually (however, the person in charge of color adjustment is professional) and by using SVM resulting in Fig.5

Figure 4. Images binarized by hand.-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System

In instance, the image in Fig. 4 was binarized manually (however, the person in charge of color adjustment is professional) and by using SVM resulting in Fig.5

Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System-Figure 3. The principle of SVM

Manual segmentation training was hard and very time consuming and rely to operator's accuracy, so we developed a color calibration algorithm using SVM(Support Vector Machine). In this subsection, we present a color recognition algorithm using the support vector machine (SVM).SVM is one of the classification algorithms which it has high generality since it can calculate a super plane that maximizes the margin of classes, Fig.3.[8] In our algorithm, the SVM is trained by the H'SY values of the classes and the mean of the obtained image H'SY values. After training, the obtained image is binarized by setting the maximum and minimum value in the distribution of each class as a threshold.