title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð² Ð°ÐºÑÑÑÐ¸ÑÐµÑÐºÐ¾Ð¼ Ð¼Ð¾Ð½Ð¸ÑÐ¾ÑÐ¸Ð½Ð³Ðµ Ð¿ÑÐ¾Ð¼ÑÑÐ»ÐµÐ½Ð½ÑÑ Ð¾Ð±ÑÐµÐºÑÐ¾Ð²
title: Convolutional neural networks application in acoustic monitoring of industrial objects
Creators: name: ÐÐ°Ð»Ð°ÑÐ¾Ð², ÐÐ»ÐµÐ³
name: ÐÐ¾ÑÐµÑÐ¸Ð½, ÐÑÑÐµÑÐ»Ð°Ð²
name: Ð¡ÑÐ°ÑÐ¾ÑÑÐµÐ½ÐºÐ¾, ÐÐ½Ð°ÑÑÐ°ÑÐ¸Ñ
name: Ð¡ÑÐ°ÑÐ¾ÑÑÐµÐ½ÐºÐ¾, ÐÐ¼Ð¸ÑÑÐ¸Ð¹
name: "Ð¡Ð¸ÑÑÐµÐ¼Ð½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· Ð² Ð¿ÑÐ¾ÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ð¸ Ð¸ ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ð¸", Ð¼ÐµÐ¶Ð´ÑÐ½Ð°ÑÐ¾Ð´Ð½Ð°Ñ Ð½Ð°ÑÑÐ½Ð¾-Ð¿ÑÐ°ÐºÑÐ¸ÑÐµÑÐºÐ°Ñ ÐºÐ¾Ð½ÑÐµÑÐµÐ½ÑÐ¸Ñ (27; 2023; Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³)
Publisher: name: ÐÐÐÐÐ¢ÐÐ¥-ÐÐ ÐÐ¡Ð¡
Year: 2024
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÐ¸ÑÑÐµÐ¼Ð° Ð¼Ð¾Ð½Ð¸ÑÐ¾ÑÐ¸Ð½Ð³Ð°
ÑÐ°ÑÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð½ÑÐ¹ Ð°ÐºÑÑÑÐ¸ÑÐµÑÐºÐ¸Ð¹ ÑÐµÐ½ÑÐ¾Ñ DAS
Ð·Ð°Ð´Ð°ÑÐ° ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸
monitoring system
distributed acoustic sensor DAS
classification analysis

description: Ð ÑÑÐ°ÑÑÐµ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÐµÑÑÑ Ð·Ð°Ð´Ð°ÑÐ° Ð¸Ð´ÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐ¾Ð±ÑÑÐ¸Ð¹, Ð¿ÑÐ¾Ð¸ÑÑÐ¾Ð´ÑÑÐ¸Ñ ÑÑÐ´Ð¾Ð¼ Ñ ÑÐ°ÑÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð½ÑÐ¼ Ð°ÐºÑÑÑÐ¸ÑÐµÑÐºÐ¸Ð¼ ÑÐµÐ½ÑÐ¾ÑÐ¾Ð¼ Â«ÐÑÐ½Ð°Ð¹Â», ÑÐ»ÑÐ¶Ð°ÑÐ¸Ð¼ Ð´Ð»Ñ Ð¾ÑÑÐ°Ð½Ñ Ð¿ÐµÑÐ¸Ð¼ÐµÑÑÐ°. ÐÑÑÐ¾Ð´Ð½ÑÐ¹ ÑÐ¸Ð³Ð½Ð°Ð», Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½ÑÐ¹ Ð¾Ñ 50 ÑÑÑÑÑ ÑÐ¾ÑÐµÑÐ½ÑÑ Ð¼Ð¸ÐºÑÐ¾ÑÐ¾Ð½Ð¾Ð², Ð¸Ð¼ÐµÐµÑ ÑÐ°ÑÑÐ¾ÑÑ Ð´Ð¸ÑÐºÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð¿Ð¾ÑÑÐ´ÐºÐ° 1 ÐºÐÑ, ÑÑÐ¾ ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²ÑÐµÑ Ð¾Ð±ÑÐµÐ¼Ñ Ð¿Ð¾ÑÐ¾ÐºÑ Ð´Ð°Ð½Ð½ÑÑ Ð¾ÐºÐ¾Ð»Ð¾ 1 ÐÐ±Ð¸Ñ Ð² ÑÐµÐºÑÐ½Ð´Ñ. ÐÐ»Ñ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ ÑÐ°ÐºÐ¾Ð³Ð¾ Ð¾Ð±ÑÐµÐ¼Ð° Ð´Ð°Ð½Ð½ÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½ÑÐµÑÑÑ Ð¼ÐµÑÐ¾Ð´ ÑÐ¶Ð°ÑÐ¸Ñ ÑÐ¸Ð³Ð½Ð°Ð»Ð° Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸ÐµÐ¼ ÑÐ°ÑÑÐ¾ÑÐ½ÑÑ ÑÐ¸Ð»ÑÑÑÐ¾Ð². ÐÐ° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½Ð¾Ð³Ð¾ ÑÐ¸Ð³Ð½Ð°Ð»Ð° ÑÑÑÐ¾Ð¸ÑÑÑ Â«Ð²Ð¾Ð´Ð¾Ð¿Ð°Ð´Â» (Ð´Ð²Ð¸Ð¶ÑÑÐµÐµÑÑ ÑÐ²ÐµÑÐ½Ð¾Ðµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ðµ). ÐÐ¾ÐºÐ°Ð·Ð°Ð½Ð¾, ÑÑÐ¾ Ð·Ð°Ð´Ð°ÑÐ° Ð¸Ð´ÐµÐ½ÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐ¾Ð±ÑÑÐ¸Ð¹ Ð¼Ð¾Ð¶ÐµÑ Ð±ÑÑÑ ÑÐµÑÐµÐ½Ð° ÑÑÐ°Ð½Ð´Ð°ÑÑÐ½ÑÐ¼Ð¸ Ð¼ÐµÑÐ¾Ð´Ð°Ð¼Ð¸ ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ð³Ð¾ Ð·ÑÐµÐ½Ð¸Ñ, ÑÐ°ÐºÐ¸Ð¼Ð¸ ÐºÐ°Ðº ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸. Ð ÑÐ°ÑÑÐ½Ð¾ÑÑÐ¸, ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð° Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ, Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑÐ°Ñ Ð¾ÑÐ»Ð¸ÑÐ¸ÑÑ ÑÐ°ÐºÐ¸Ðµ ÑÐ¸Ð¿Ñ Ð°ÐºÑÑÑÐ¸ÑÐµÑÐºÐ¸Ñ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð², ÐºÐ°Ðº ÑÐ°Ð³Ð¸ ÑÐµÐ»Ð¾Ð²ÐµÐºÐ°, ÑÑÑÐ½Ð°Ñ ÐºÐ¾Ð¿ÐºÐ° Ð¸ ÑÐ°Ð³Ð¸ Ð¶Ð¸Ð²Ð¾ÑÐ½ÑÑ Ð¸ Ñ. Ð¿.
description: The article shows the problem of identifying events occurring near the distributed acoustic sensor âDanubeâ which serves to protect the perimeter. The initial signal received from 50 thousand point microphones has a sampling frequency of about 1 kHz which corresponds to a total data flow of about 1 Gbit per second. To process such a big volume of data, a signal compression method using frequency filters is used. Based on the received signal, a âwaterfallâ (a moving colour image) is constructed. It is shown that the problem of event identification can be solved by standard computer vision methods, such as convolutional neural networks (CNN). In particular, a neural network has been developed that allows distinguishing such types of structure-borne signals as human steps, hand digging and animal steps, etc.