# The Method of Restoring Lost Information from Sensors Based on Auto-Associative Neural Networks
## 2 Related Work Discussion
In such cases, it is advisable to consider an ANN use [27; 28; 29], which is capable of extracting more complex and abstract features from data, taking into account their dynamic features and providing more accurate information restoration in the sensor failure event. The ANN [27; 28; 29] used in the lost information restoration task, including multimedia data, the advantages of which include the ability to highly compress data samples, efficient use of memory, the ability to fill missing data and generate said data for better analysis of incomplete data, as well as lower training and clustering time compared with most clustering algorithms, becomes suitable for use on low-performance equipment with limited computing power and memory, which is relevant when operating complex dynamic objects. However, to improve the ANN's ability to extract and represent a lot of complex and abstract features from data, as well as to ensure a better ability of the model to restore and generate information, especially in cases with incomplete data or high dimensionality, it is advisable to modify the traditional algorithm for training it, which is based on gradient descent. Ref. [30] considers the training data restoration problem from overparameterized ANN models. The ANN used was trained on a specific training data set to implicitly take into account this data set features when reconstructing information. We explore various deep ANN structures, including fully connected and U-Net, with different activation functions and training loss function values. We demonstrate that the method significantly outperforms previous approaches for data restoration from autoencoders. This approach's disadvantage is the need to take into account the degraded training sample and formulate the original sample restoration as an optimization problem, which requires complex calculations and estimates of the unknown degradation operator, which can reduce the method's effectiveness in practical operating conditions of complex dynamic objects. The article [31] proposed a technique for the automatic interpretation of distributed fiber optic sensor (DFOS) data for pipeline corrosion monitoring using machine learning. The machine learning model detects corrosion in real time, and the corrosion quantification method is based on the model data. The approach's effectiveness is confirmed by laboratory tests that take into account pipeline diameter, DFOS spatial resolution, optical fiber type, and sensor installation methods. The results show high values of the F1 metric of 0.986 and the determination coefficient of 0.953. The key disadvantage of [31] is the high dependence on the original data quality, which can be eliminated based on an autoencoder to improve the data representation and reduce noise. The closest to the method proposed in this work for lost information restoration in the complex dynamic object sensor failure event under operating conditions using an ANN is the method developed by [PERSON] and [PERSON] in [9; 10; 11] for information restoration in the sensor failure event using an ANN, by compressing input data in its hidden layer, the shaft, and performing restoration in the output layer. One of the key disadvantages of this method is that the ANN structure choice is carried out based on the minimum error in the ANN information restoration from the neuron number in the \"shaft\", which may be insufficient for the effective extraction and representation of complex features in the data. Another disadvantage is the traditional ANN training method based on gradient descent without regularization, which increases the model's overtraining risk when it becomes strongly adapted to the training data and loses the ability to generalize new data.