This paper presents the results of a neural convolutional system for recognizing the wearing of a mask by people entering a building. The algorithm is provided with input data thanks to cameras placed in the humanoid robot COVIDguard. The data collected by the humanoid -the temperature of people entering the facility, the location of the person, the way the protective mask was applied -are stored in the cloud, which enables the application of advanced image recognition algorithms and, consequently, the tracking of people within the range of the robot's sensory systems by the administrator and the verification of the security level in the given premises. The paper presents the architecture of the intelligent COVIDguard platform, the structure of the sensory system and the results of the neural network learning.

Renewable energy sources are a growing branch of industry. One such source is wind farms, which have significantly increased their number over recent years. Alongside the increased number of turbines, maintenance problems are growing. There is a need for newer and less intrusive predictive maintenance methods. About 40% of all turbine failures are due to bearing failure. This paper presents a modified neural direct classifier method using raw accelerometer measurements as input. This proprietary platform allows for better damage prediction results than convolutional networks in vibration spectrum image analysis. It operates in real time and without signal processing methods converting the signal to a time–frequency spectrogram. Image processing methods can extract features from a set of preset features and based on their importance. The proposed method is not based on feature extraction from image data but on automatically finding a set of features from raw tabular data. This fact significantly reduces the computational cost of detection and improves the failure detection accuracy compared to the classical methods. The model achieved a precision of 99.32% on the validation set, and 96.3% during bench testing. These results were an improvement over the method that classifies time–frequency spectrograms of 97.76% for the validation set and 90.8% for the real-world tests, respectively.

This paper presents an artificial intelligence algorithm responsible for the autonomy of a platform. The proposed algorithm allows the platform to move from an initial position to a set one without human intervention and with understanding and response to the dynamic environment. The implementation of such a task is possible by using a combination of a camera identifying the environment with a laser LIDAR sensor and a vision system. The signals from the sensors are analysed through convolutional neural networks. Based on AI inference, the platform makes decisions, including determining the optimal path for itself. A transfer learning method will be used to teach the neural network. This article presents the results of learning the applied neural algorithm.

No abstract

The growing number of wind farms is creating an increased demand for their trouble-free operation. Damage to their components can be catastrophic. A particular component that can be subject to damage during long-term operation and is difficult to diagnose is the mechanical gearbox located in the turbine. Traditional approaches to the subject of damage detection require, in the final stage, the involvement of an expert. Therefore, the article proposes a method based on the Deep Learning solution. A transfer learning method and a pre-trained Inception V3 network were used. A gearbox with in three states of healthy, worn out but still working and damaged was analyzed. Signal spectrograms were created from accelerometric measurements and then used as input for the neural network. Various approaches to creating spectrogram images were tested. The InceptionV3 network was taught on images generated in grayscale, and RGB and HSV. Channel reduction in the form of using grayscale improved the speed of the algorithm at the expense of precision. The use of HSV scale, on the other hand, made it more precise in detecting a worn out state.