An Adaptive Gaze Tracking System in the Diverse Environment

oai:ojs.ijiis.org:article/124For understanding the learning effect, the gaze tracking is a more objective method to collect the data on eye movement and used to analyze the learning behaviors. In order to provide an analysis tool to apply in diverse environments, an adaptive gaze tracking system is proposed in this paper. This system can offer helpful suggestions for educators when improving the instruction. Additionally, the experimental results show that the estimation of gaze points was successful even when the distance and head rotation alter

Automatic Fruit Harvesting Device Based on Visual Feedback Control

With aging populations, and people′s demand for high-quality or high-unit-price fruits and vegetables, the corresponding development of automatic fruit harvesting has attracted significant attention. According to the required operating functions, based on the fruit planting environment and harvesting requirements, this study designed a harvesting mechanism to independently drive a gripper and scissor for individual tasks, which corresponded to forward or reverse rotation using a single motor. The study utilized a robotic arm in combination with the harvesting mechanism, supported by a single machine vision component, to recognize fruits by deep-learning neural networks based on a YOLOv3-tiny algorithm. The study completed the coordinate positioning of the fruit, using a two-dimensional visual sensing method (TVSM), which was used to achieve image depth measurement. Finally, impedance control, based on visual feedback from YOLOv3-tiny and the TVSM, was used to grip the fruits according to their size and rigidity, so as to avoid the fruits being gripped by excessive force; therefore, the apple harvesting task was completed with a 3.6 N contact force for an apple with a weight of 235 g and a diameter of 80 mm. During the cutting process, the contact point of the metal scissors of the motor-driven mechanism provided a shear force of 9.9 N, which was significantly smaller than the simulation result of 94 N using ADAMS and MATLAB software, even though the scissors were slightly blunt after many cuts. This study established an automatic fruit harvesting device based on visual feedback control, which can provide an automatic and convenient fruit harvest by reducing harvesting manpower

Automatic Fruit Harvesting Device Based on Visual Feedback Control

With aging populations, and people′s demand for high-quality or high-unit-price fruits and vegetables, the corresponding development of automatic fruit harvesting has attracted significant attention. According to the required operating functions, based on the fruit planting environment and harvesting requirements, this study designed a harvesting mechanism to independently drive a gripper and scissor for individual tasks, which corresponded to forward or reverse rotation using a single motor. The study utilized a robotic arm in combination with the harvesting mechanism, supported by a single machine vision component, to recognize fruits by deep-learning neural networks based on a YOLOv3-tiny algorithm. The study completed the coordinate positioning of the fruit, using a two-dimensional visual sensing method (TVSM), which was used to achieve image depth measurement. Finally, impedance control, based on visual feedback from YOLOv3-tiny and the TVSM, was used to grip the fruits according to their size and rigidity, so as to avoid the fruits being gripped by excessive force; therefore, the apple harvesting task was completed with a 3.6 N contact force for an apple with a weight of 235 g and a diameter of 80 mm. During the cutting process, the contact point of the metal scissors of the motor-driven mechanism provided a shear force of 9.9 N, which was significantly smaller than the simulation result of 94 N using ADAMS and MATLAB software, even though the scissors were slightly blunt after many cuts. This study established an automatic fruit harvesting device based on visual feedback control, which can provide an automatic and convenient fruit harvest by reducing harvesting manpower

Magnus-Forces Analysis of Pitched-Baseball Trajectories Using YOLOv3-Tiny Deep Learning Algorithm

This study analyzed the characteristics of pitched baseballs from TV broadcast videos to understand the effects of the Magnus force on a pitched-baseball trajectory using aerodynamic theory. Furthermore, an automatic measurement and analysis system for pitched-baseball trajectories, ball speeds, and spin rates was established, capturing the trajectory of the baseball thrown by the pitcher before the catcher catches it and analyzing its related dynamic parameters. The system consists of two parts: (1) capturing and detecting the pitched baseball in all frames of the video using the YOLOv3-tiny deep learning algorithm and automatically recording the coordinates of each detected baseball position; (2) automatically calculating the average speed and spin rate of the pitched baseball using aerodynamic theory. As the baseball thrown by the pitcher is fast, and live-action TV videos like sports and concerts are typically at least 24 fps or more, this study used YOLOv3-tiny algorithm to speed up the calculation. Finally, the system automatically presented pitching data on the screen, and the pitching information in the baseball game was easily obtained and recorded for further discussion. The system was tested using 30 videos of pitched baseballs and could effectively capture the baseball trajectories, throw points, catch points, and vertical displacements. Compared with the values from the TV broadcast, the average errors on the calculated ball speed and spin rate were 1.88% and 7.51%, respectively. Using the ratio of the spin rate and ball speed as a parameter to analyze the pitching state of the pitcher’s four-seam fastball in the Nippon Professional Baseball and Major League Baseball matches, it was observed that when this ratio increased, the Magnus displacement of the ball increased, thereby decreasing its late break. Therefore, the developed system provides scientific pitching data to improve the performance of baseball pitchers

Magnus-Forces Analysis of Pitched-Baseball Trajectories Using YOLOv3-Tiny Deep Learning Algorithm

This study analyzed the characteristics of pitched baseballs from TV broadcast videos to understand the effects of the Magnus force on a pitched-baseball trajectory using aerodynamic theory. Furthermore, an automatic measurement and analysis system for pitched-baseball trajectories, ball speeds, and spin rates was established, capturing the trajectory of the baseball thrown by the pitcher before the catcher catches it and analyzing its related dynamic parameters. The system consists of two parts: (1) capturing and detecting the pitched baseball in all frames of the video using the YOLOv3-tiny deep learning algorithm and automatically recording the coordinates of each detected baseball position; (2) automatically calculating the average speed and spin rate of the pitched baseball using aerodynamic theory. As the baseball thrown by the pitcher is fast, and live-action TV videos like sports and concerts are typically at least 24 fps or more, this study used YOLOv3-tiny algorithm to speed up the calculation. Finally, the system automatically presented pitching data on the screen, and the pitching information in the baseball game was easily obtained and recorded for further discussion. The system was tested using 30 videos of pitched baseballs and could effectively capture the baseball trajectories, throw points, catch points, and vertical displacements. Compared with the values from the TV broadcast, the average errors on the calculated ball speed and spin rate were 1.88% and 7.51%, respectively. Using the ratio of the spin rate and ball speed as a parameter to analyze the pitching state of the pitcher’s four-seam fastball in the Nippon Professional Baseball and Major League Baseball matches, it was observed that when this ratio increased, the Magnus displacement of the ball increased, thereby decreasing its late break. Therefore, the developed system provides scientific pitching data to improve the performance of baseball pitchers

Experimental and Simulated Investigations of Thin Polymer Substrates with an Indium Tin Oxide Coating under Fatigue Bending Loadings

Stress-induced failure is a critical concern that influences the mechanical reliability of an indium tin oxide (ITO) film deposited on a transparently flexible polyethylene terephthalate (PET) substrate. In this study, a cycling bending mechanism was proposed and used to experimentally investigate the influences of compressive and tensile stresses on the mechanical stability of an ITO film deposited on PET substrates. The sheet resistance of the ITO film, optical transmittance of the ITO-coated PET substrates, and failure scheme within the ITO film were measured to evaluate the mechanical stability of the concerned thin films. The results indicated that compressive and tensile stresses generated distinct failure schemes within an ITO film and both led to increased sheet resistance and optical transmittance. In addition, tensile stress increased the sheet resistance of an ITO film more easily than compressive stress did. However, the influences of both compressive and tensile stress on increased optical transmittance were demonstrated to be highly similar. Increasing the thickness of a PET substrate resulted in increased sheet resistance and optical transmittance regardless of the presence of compressive or tensile stress. Moreover, J-Integral, a method based on strain energy, was used to estimate the interfacial adhesion strength of the ITO-PET film through the simulation approach enabled by a finite element analysis