13 research outputs found
Positioning of a 3-degrees-of-freedom robot with pneumatic actuators
Interest in the use of pneumatic actuators has become increasingly significant, especially because of the potential benefits of employing clean energies. There are previous works related to this project; some made abroad that innovates in control methods but only in specific applications, and others in Mexico that progressively increase the complexity of the system. In this paper the difficulty of a generic threedegrees-of-freedom (DoF) robot arm control is tested, searching for the most efficient method to reproduce the positioning of the final effector (a griper) accurately. Furthermore, the ambitious goal of the project is to adequately develop an algorithm capable of being properly adjusted to any multipurpose robot system with pneumatic actuators. It is carefully considered the various non-linearities of the system. This work relied mostly on intelligent control techniques and modern programmable devices, the intention is to achieve similar or better results than previous projects. The possible combination of intelligent and even classical control can reasonably achieve the estimated conclusion, the first being flexible for developing robust design for non-linear systems
26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15â20 July 2017
This work was produced as part of the activities of FAPESP Research,\ud
Disseminations and Innovation Center for Neuromathematics (grant\ud
2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud
FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud
supported by a CNPq fellowship (grant 306251/2014-0)
Artificial Intelligence - Emerging Trends and Applications
Artificial intelligence (AI) is taking an increasingly important role in our society. From cars, smartphones, airplanes, consumer applications, and even medical equipment, the impact of AI is changing the world around us. The ability of machines to demonstrate advanced cognitive skills in taking decisions, learn and perceive the environment, predict certain behavior, and process written or spoken languages, among other skills, makes this discipline of paramount importance in today's world. Although AI is changing the world for the better in many applications, it also comes with its challenges. This book encompasses many applications as well as new techniques, challenges, and opportunities in this fascinating area
Artificial Intelligence - Applications in Medicine and Biology
Artificial intelligence (AI) is taking on an increasingly important role in our society today. In the early days, machines fulfilled only manual activities. Nowadays, these machines extend their capabilities to cognitive tasks as well. And now AI is poised to make a huge contribution to medical and biological applications. From medical equipment to diagnosing and predicting disease to image and video processing, among others, AI has proven to be an area with great potential. The ability of AI to make informed decisions, learn and perceive the environment, and predict certain behavior, among its many other skills, makes this application of paramount importance in today's world. This book discusses and examines AI applications in medicine and biology as well as challenges and opportunities in this fascinating area
TDAH Repository
<p><span><span><span>The dataset provides data obtained with eye-tracking while 55 volunteers solved 4 distinct neuropsychological tests on a screen inside a closed room. Among the 55 volunteers, 22 were women and 33 were men, all with ages ranging between 9 and 50, and 5 of whom were diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) [1]. The eye-tracker used for the collection of the data was an EyeTribe, which has a sampling rate of 60 Hz and an average visual angle between 0.5 and 1, which correspond to an on-screen error between 0.5 and 1cm (0.1969 to 0.393 inches aprox) respectively, when the distance to the user is around 60cm (23.62 in) [2], which was the case during the collection of these data. The neuropsychological tests were implemented in a software named NEURO-INNOVA KIDS® [3], which are the following: a</span><span><span> domino test</span></span><span> adapted from the D-48 intelligence test [4], an adaptation of the MASMI test consisting of unfolded cubes [5], the figures series completion test adapted from [6], and the Poppelreuter </span><span><span>figures test </span></span><span>[7]. Before each of the tests, a calibration process was performed, ensuring that the visual angle error was less than or equal to 0.5 cm (0.1969 in), which is considered an acceptable calibration. The collective mean duration of the four administered tests amounted to 20 minutes. This dataset exhibits significant promise for potential utilization due to the extensive prevalence of these neuropsychological assessments among healthcare practitioners for evaluating diverse cognitive faculties in individuals. Moreover, it has been empirically established that poor performance on these tests is associated with attention deficits [8].</span></span></span></p>
Implementation of ANN-Based Auto-Adjustable for a Pneumatic Servo System Embedded on FPGA
Artificial intelligence techniques for pneumatic robot manipulators have become of deep interest in industrial applications, such as non-high voltage environments, clean operations, and high power-to-weight ratio tasks. The principal advantages of this type of actuator are the implementation of clean energies, low cost, and easy maintenance. The disadvantages of working with pneumatic actuators are that they have non-linear characteristics. This paper proposes an intelligent controller embedded in a programmable logic device to minimize the non-linearities of the air behavior into a 3-degrees-of-freedom robot with pneumatic actuators. In this case, the device is suitable due to several electric valves, direct current motors signals, automatic controllers, and several neural networks. For every degree of freedom, three neurons adjust the gains for each controller. The learning process is constantly tuning the gain value to reach the minimum of the mean square error. Results plot a more appropriate behavior for a transitive time when the neurons work with the automatic controllers with a minimum mean error of ±1.2 mm
Reduced Calibration Strategy Using a Basketball for RGB-D Cameras
RGB-D cameras produce depth and color information commonly used in the 3D reconstruction and vision computer areas. Different cameras with the same model usually produce images with different calibration errors. The color and depth layer usually requires calibration to minimize alignment errors, adjust precision, and improve data quality in general. Standard calibration protocols for RGB-D cameras require a controlled environment to allow operators to take many RGB and depth pair images as an input for calibration frameworks making the calibration protocol challenging to implement without ideal conditions and the operator experience. In this work, we proposed a novel strategy that simplifies the calibration protocol by requiring fewer images than other methods. Our strategy uses an ordinary object, a know-size basketball, as a ground truth sphere geometry during the calibration. Our experiments show comparable results requiring fewer images and non-ideal scene conditions than a reference method to align color and depth image layers