Noise attenuation against military noises has been measured in several cases under practical field conditions. Commercial and military versions of earmuff noise attenuation were measured against rifle noise. All the tested earmuffs attenuated the C-weighted peak level to less than 135 dB, which is less than the proposed recommendation value. Combat and shooting exercises create a risk of hearing damage, reaching a peak level of 180 dB. Measurements were done during attack exercises with blank and normal cartridges and during a defence exercise with normal cartridges. The noise exposure levels were relatively moderate (outside the ear 95-97 dB, in ear canal 82-85 dB) for military exercises. Peak levels of 110-120 dB for military trainers were measured in the ear canal during the conscript use of small-bore weapons. Combat vehicles and tanks are noisy, and for noise control during their use headgear with communication properties is worn. Noise inside such headgear was found to reach up to 120 dB, and the noise doses varied between 90 and 105 dB. Noise was also measured for aviation pilots in Finnish jet fighters. The cockpit values averaged 96 dB - 100 dB over the flight, whereas noise in the ear canal averaged 88 dB - 95 dB. The analyses indicated that radio noise is 4-10 dB higher inside the helmet than the background noise is, when measured as equivalent noise. The technicians on the ground were exposed to noise levels varying from 93 to 97 dB over the day. In practice, hearing protectors attenuate noise by 10-30 dB, depending on the frequency content of the noise sources. However, the difference when measured outside and inside hearing protectors varies by 5-10 dB because communication increases the noise level at the entrance of ear the canal. Currently the best protection for soldiers seems to be active noise cancellation ear muffs that are equipped for communication purposes and worn during the entire military exercise.

Download full-text PDF

Source
http://dx.doi.org/10.4103/1463-1741.31644DOI Listing

Publication Analysis

Top Keywords

noise
18
ear canal
16
hearing protectors
12
noise attenuation
8
peak level
8
normal cartridges
8
noise measured
8
military
6
measured
6
ear
6

Similar Publications

Diffusion models, variational autoencoders, and generative adversarial networks (GANs) are three common types of generative artificial intelligence models for image generation. Among these, GANs are the most frequently used for medical image generation and are often employed for data augmentation in various studies. However, due to the adversarial nature of GANs, where the generator and discriminator compete against each other, the training process can sometimes end with the model unable to generate meaningful images or even producing noise.

View Article and Find Full Text PDF

The disease affects the optic nerve and represents the principle reasons of irreversible vision loss, mostly asymptomatic and uncontrolled. Consequently, early and accurate diagnosis is critical to prevent or reduce its effect, however, conventional diagnostic techniques often fail to provide concrete results. In this regard, we present a new approach built on Generative Adversarial Networks (GAN) and MobileNetV2 pretrained architecture for diagnosing glaucoma.

View Article and Find Full Text PDF

A comprehensive dataset on lemon leaf disease can surely bring a lot of potentials into the development of agricultural research and the improvement of disease management strategies. This dataset was developed from 1354 raw images taken with professional agricultural specialist guidance from July to September 2024 in Charpolisha, Jamalpur, and further enhanced with augmented techniques, adding 9000 images. The augmentation process involves a set of techniques-flipping, rotation, zooming, shifting, adding noise, shearing, and brightening-to increase variety for different lemon leaf condition representations.

View Article and Find Full Text PDF

This dataset is generated from real-time simulations conducted in MATLAB/Simscape, focusing on the impact of smart noise signals on battery energy storage systems (BESS). Using Deep Reinforcement Learning (DRL) agent known as Proximal Policy Optimization (PPO), noise signals in the form of subtle millivolt and milliampere variations are strategically created to represent realistic cases of False Data Injection Attacks (FDIA). These signals are designed to disrupt the State of Charge (SoC) and State of Health (SoH) estimation blocks within Unscented Kalman Filters (UKF).

View Article and Find Full Text PDF

Emissions from airport sources degrade air quality impacting community health. While some airports assess air pollution, others assess broader environmental effects, including CO emissions and noise. Utilising a transition management approach, this paper examines Australian airport practices and develops key sustainable strategies to reduce environmental impacts.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!