Conservation conditions of chainsaws and electric chainsaws and safety of the farmers who operate these machines

The increasing use of chainsaws in rural areas has demanded the health of rural producers who operate these machines. Thus, the objective of this work was to evaluate the conservation conditions of chainsaws with a 2-stroke Otto Cycle engine and check if they meet NR12 Annex V, as well as confirm whether users meet NR31 in terms of training for use. With the aid of a questionnaire and visits to farms, 103 chainsaws were verified in six municipalities in the central region of the State of Rio Grande do Sul. After organizing the data in an electronic spreadsheet, descriptive statistics and canonical correlation were performed. The questions were divided into four groups, namely: operational, mandatory machine safety equipment, cutting set, and engine. The conservation condition of the machines was seen as worrisome. This is because it was found that 66.01% of machines did not have a saber guard and 49.51% of these were worn out. In addition, 97.08% of the producers did not take a chainsaw operation course, and 85.44% reported not using Personal Protective Equipment (PPE), therefore, in disagreement with the NR6, NR12 Annex V, and NR31 standards. It was clear the need for the operators to carry out training on the safe use and handling of chainsaws.

Facial Emotions Recognition in Machine Learning

Facial expression recognition (FER) is a field where many researchers have tried to create a model able to recognize emotions from a face. With many applications such as interfaces between human and machine, safety or medical, this field has continued to develop with the increase of processing power. This paper contains a broad description on the psychological aspects of the FER and provides a description on the datasets and algorithms that make the neural networks possible. Then a literature review is performed on the recent studies in the facial emotion recognition detailing the methods and algorithms used to improve the capabilities of systems using machine learning. Each interesting aspect of the studies are discussed to highlight the novelty and related concepts and strategies that make the recognition attain a good accuracy. In addition, challenges related to machine learning were discussed, such as overfitting, possible causes and solutions and challenges related to the dataset such as expression unrelated discrepancy such as head orientation, illumination, dataset class bias. Those aspects are discussed in detail, as a review was performed with the difficulties that come with using deep neural networks serving as a guideline to the advancement domain. Finally, those challenges offer an insight in what possible future directions can be taken to develop better FER systems.

Exoskeleton cloud-brain platform and its application in safety assessment

Purpose This paper aims at the problem of attaching the data of doctors, patients and the real-time sensor data of the exoskeleton to the cloud in intelligent rehabilitation applications. This study designed the exoskeleton cloud-brain platform and validated its safety assessment. Design/methodology/approach According to the dimension of data and the transmission speed, this paper implements a three-layer cloud-brain platform of exoskeleton based on Alibaba Cloud's Lambda-like architecture. At the same time, given the human–machine safety status detection problem of the exoskeleton, this paper built a personalized machine-learning safety detection module for users with the multi-dimensional sensor data cloned by the cloud-brain platform. This module includes an abnormality detection model, prediction model and state classification model of the human–machine state. Findings These functions of the exoskeleton cloud-brain and the algorithms based on it were validated by the experiments, they meet the needs of use. Originality/value This thesis innovatively proposes a cloud-brain platform for exoskeletons, beginning the digitalization and intelligence of the exoskeletal rehabilitation process and laying the foundation for future intelligent assistance systems.

Risk Assessment Proposal In HydrauliC Presses With Fuzzy Logic

The aim of this study was to evaluate the safety level of industrial machines, in particular hydraulic press. The dissertation used hydraulic presses as the object of study. The research instruments used were machine safety analyzes based on normative items pre-established in ABNT NBR: 12100, possible accidents that these machineries can cause. The results show that hydraulic presses cause many accidents, in some situations dying. Through Annex B of ABNT NBR 14153: 2013, there are 4 risk categories for machinery, the greater the degree of risk, the more unsafe the machine is. The appraiser's experience is very important to analyze the machine and reach the level of risk level before and after the adjustment. Finally, it appears that the machine analyzed in this study was at risk level 3, after analysis and adaptations the same machine was at risk level 1, totally acceptable to maintain the operator's safety level.

Supervised learning-based reconstruction of magnet errors in circular accelerators

Magnetic field errors and misalignments cause optics perturbations, which can lead to machine safety issues and performance degradation. The correlation between magnetic errors and deviations of the measured optics functions from design can be used in order to build supervised learning models able to predict magnetic errors directly from a selection of measured optics observables. Extending the knowledge of errors in individual magnets offers potential improvements of beam control by including this information into optics models and corrections computation. Besides, we also present a technique for denoising and reconstruction of measurements data, based on autoencoder neural networks and linear regression. We investigate the usefulness of supervised machine learning algorithms for beam optics studies in a circular accelerator such as the LHC, for which the presented method has been applied in simulated environment, as well as on experimental data.

Performance Level (PL) Part I - Basic Parameter of the Machine Safety Function (SF)

The paper discusses the concept of the safety function for the machine control circuit, which is one of the basic concepts that allow to design machines in accordance with the safety requirements. The basics of the architecture of systems implementing safety functions in machines were also discussed. Using the example of the fundamental emergency stop circuit, the basics of the reliability parameters for electromechanical components are explained. The concept of forced contact guidance is also explained. The basic formulas are given and the basis for further clarification of the performance level (PL) is prepared.

Machine Safety in Terms of Electrical Engineering – in the Light of Machinery and the LV Directives

Selected issues related to machine safety in terms of electrical engineering have been discussed in the paper. The author has focused on the relationship between machine directive and low voltage directive, since it proves somewhat difficult to interpret. This is due to the fact that basic requirements for machinery cover all safety issues (including electrical ones), while electrical devices and equipment during design, manufacture and marketing are subjected to low voltage directive (if they are designed for use within certain L V. limits).