This paper analyzes human motion, more specifically the human gait in the sagittal plane. A video camera is used to acquire images of a walking person, fitted with a set of white light-emitting diodes (LEDs). The acquired trajectories of the light points are then used to specify joint trajectories in a biped robot. To analyze the stability of the human gait, a system was also developed to acquire the center of pressure (CoP). This system uses eight force sensors, four under each foot. The influence of the human torso angle on the CoP position during walking was confirmed. Some experiments were carried out on a biped robot, and the results show that the acquired human gait can be used in a biped robot, after scale conversion.

The real-time balance control of an eight-link biped robot using a zero moment point (ZMP) dynamic model is difficult due to the processing time of the corresponding equations. To overcome this limitation, two alternative intelligent computing control techniques were compared: one based on support vector regression (SVR) and another based on a first-order Takagi-Sugeno-Kang (TSK)-type neural-fuzzy (NF) network. Both methods use the ZMP error and its variation as inputs and the output is the correction of the robot's torso necessary for its sagittal balance. The SVR and the NF were trained based on simulation data and their performance was verified with a real biped robot. Two performance indexes are proposed to evaluate and compare the online performance of the two control methods. The ZMP is calculated by reading four force sensors placed under each robot's foot. The gait implemented in this biped is similar to a human gait that was acquired and adapted to the robot's size. Some experiments are presented and the results show that the implemented gait combined either with the SVR controller or with the TSK NF network controller can be used to control this biped robot. The SVR and the NF controllers exhibit similar stability, but the SVR controller runs about 50 times faster.

The study of power system of relays requires some previous experience in this field. Laboratories focusing on teaching and researching the area of power-system protection have therefore been amply reported. However, these facilities require the actual presence of the students in the laboratories. To overcome this problem, a power-system-relaying remote laboratory has been developed. To implement this laboratory, a testing system of the relay operating characteristic, together with Matlab-based software, was developed. This remote laboratory enables real power-system-relaying experiments through the Internet. In fact, the user can remotely test a real relay disoperation. This system will therefore allow proficient analysis of sensitivities to relay settings and network configurations.

Flexible manufacturing cells (FMC) are capable of producing a broad variety of products and changing their characteristics quickly and frequently. This flexibility provides for more efficient use of resources but makes the control of these systems more difficult. This paper presents several software applications developed for industrial robots and computer numerical control (CNC) machines. The objective of this software is to allow these equipments to be integrated, in an easy and efficient way, in modern FMC. For the industrial robots, we developed the "winRS232ROBOTcontrol" and "winEthernetROBOTcontrol" software. For the CNC machines, the software that was developed was "winMILLcontrol" for the mill and "winTURNcontrol" for the lathe. With this software, industrial robots and CNC machines can be integrated in modern FMC, in an easy and efficient way. An FMC was developed, with industrial characteristics, with the objective of showing the potentialities of the developed software. Thus, the proposed software in this paper was tested and validated in the developed FMC, and the experimental results show the viability and success of this software in the coordination of the different sectors in the FMC and in the improvement of their performance

Solar activity has been subject to increasingly more research in the last decades. Its influence on life on Earth is now better understood. Solar winds impact the earth’s magnetic field and atmosphere. They can disrupt satellite communication and navigation tools and even electrical power grids and several other infrastructure crucial for our technology-based society. Coronal mass ejections (CMEs), solar energetic particles, and flares are the main causes of problems that affect the systems mentioned. It is possible to predict some of those by monitoring the sun and analyzing the images obtained in different spectra, thus identifying solar phenomena related to its activity, such as filaments, pores, and sunspots. Several studies have already been carried out on the subject of automation of the mentioned analysis, most of which use neural networks and other machine learning approaches. In this work, we develop a method for sunspot detection based on the YOLOv5 network, applying it to a dataset of images from the Geophysical and Astronomical Observatory of the University of Coimbra (OGAUC), which has one of the oldest and more complete datasets of sun images in the world. Our method reaches mAP@.5 over 90% with YOLOv5s, which is higher than other methods previously applied for the same dataset. This shows that CNN models can be used in spectroheliographs for detecting and tracking sunspots.