# The Method of Restoring Lost Information from Sensors Based on Auto-Associative Neural Networks
###### Abstract
Nevertheless, concurrently tackling a diverse array of tasks presents inherent challenges: constraints stemming from limited computational resources, such as RAM capacity, processing speed, and result precision; complexities in formalizing traditional control and diagnostic algorithms, thereby impeding their practical application; the imperative to execute aforementioned algorithms in a low-level language like assembler; and complications in data restoration in the sensor malfunction event. Another alternative approach to information restoration is using the autoencoder (auto-associative neural network, ANN), which is characterized by compression and subsequent information restoration. Works like [13] show the ANN use possibility to restore measured information based on known object parameters. This approach's disadvantage is a high error of single failure restoration of the onboard system for monitoring and diagnosing sensors--the value restoration error from the sensors in the break event is 4.5 to 5.0%, and in the gradual failure case, it is up to 5.5%. Another approach to restoring lost information from an onboard system for monitoring and diagnosing sensors based on an ANN is to use a buffer (memory stack) to store the last valid value from sensor failures. This approach was considered in [14], where, in contrast to [9; 10], a method for determining the optimal size of the ANN shaft was studied. However, there was no engineering method for obtaining a working ANN for the lost information restoration process from the onboard system for monitoring and diagnosing sensors. The principal component method research makes it possible to build an optimal ANN structure, the use of which to solve this task reduces the lost information restoration error from sensors. ANN has become widespread in object-tracking systems [15]. Their use allows us to effectively solve object detecting and tracking problems in real time by automatically studying and extracting important features from input data, such as a video stream or an image. ANNs can display complex relationships in objects and their changes, providing high tracking accuracy [16]. The research results [16] are of great interest for the development of control and information processing systems for computer vision and image analysis. They are especially valuable for tasks that involve determining the spatial parameters of imaged objects, opening up new opportunities for improving system tracking and object recognition in real time. This is especially relevant in the autonomous systems technologies' development context [17] where accurate spatial determination of object characteristics is key to effective functioning. For example, the ANN application in real-time object tracking systems can improve autonomous vehicle navigation, in particular, helicopters. They can effectively respond to environmental changes and avoid obstacles. And the smooth functioning of their engines will lead to safe flight. Such innovations for tracking objects with the ANN open new horizons and promote automated system development, which is important in the modern world where artificial intelligence technologies are becoming an indispensable part of everyday life. It is also worth stating how useful the ANN is in face recognition tasks in video sequences [18]. The ANN application allows us to effectively solve challenges related to dynamic changes in position, lighting, and facial expressions in real time. ANNs can automatically train and adapt to face variations, using an encoder to compress information and a decoder to reproduce the original images. This contributes to increasing the face recognition accuracy in changing context and video sequence dynamics conditions. It makes ANNs effective tools for real-time face tracking and identification system development. For example, in [19], an ANN was tested based on the ORL database. It included face images with slight changes in lighting, scale, spatial position, orientation, and various emotional expressions with a high recognition accuracy of 96%.