A new approach of drawing airport noise contours on computer based on Surfer.

J Environ Sci (China)

Department of Environmental Science, Zhejiang University, Hangzhou 310028, China.

Published: December 2004

Noise contours are used to describe the extent of airport noise pollution and to plan land use around airports. The L(WECPN) (weighted equivalent continuous perceive noise level) recommended by ICAO(International Civil Aviation Organization) is adopted as airport noise rating parameter in this paper. With the help of various mathematical models in the software Surfer, noise contours can be drawn automatically by the completed program in Visual C++ Code. Corrections for thrust, velocity, atmospheric temperature, humidity and lateral ground attenuation are also considered in the new method, which can improve the efficiency of drawing contours. An example of its use for drawing noise contours of an airport in Zhejiang Province of China is proposed and the predictions and the measurements show agreements well.

Download full-text PDF

Source

Publication Analysis

Top Keywords

noise contours
16
airport noise
12
surfer noise
8
noise
7
contours
5
approach drawing
4
airport
4
drawing airport
4
contours computer
4
computer based
4

Similar Publications

Image-guided mouse irradiation is essential to understand interventions involving radiation prior to human studies. Our objective is to employ Swin UNEt TRansformers (Swin UNETR) to segment native micro-CT and contrast-enhanced micro-CT scans and benchmark the results against 3D no-new-Net (nnU-Net). Swin UNETR reformulates mouse organ segmentation as a sequence-to-sequence prediction task using a hierarchical Swin Transformer encoder to extract features at five resolution levels, and it connects to a Fully Convolutional Neural Network (FCNN)-based decoder via skip connections.

View Article and Find Full Text PDF

Image segmentation is a crucial task in artificial intelligence fields such as computer vision and medical imaging. While convolutional neural networks (CNNs) have achieved notable success by learning representative features from large datasets, they often lack geometric priors and global object information, limiting their accuracy in complex scenarios. Variational methods like active contours provide geometric priors and theoretical interpretability but require manual initialization and are sensitive to hyper-parameters.

View Article and Find Full Text PDF

Straightness is the basic measurement parameter in machining, and the traditional straightness measurement methods such as light gap method, table method, et al., have extremely low measurement efficiency and cannot achieve online real-time high-precision detection. Our research group has proposed a machine vision online detection based on 10 industrial camera arrays, which can obtain the surface profile straight line of the sucker rod by collecting the edge profile image of the sucker rod and performing morphological transformation.

View Article and Find Full Text PDF

Detection and teeth segmentation from X-rays, aiding healthcare professionals in accurately determining the shape and growth trends of teeth. However, small dataset sizes due to patient privacy, high noise, and blurred boundaries between periodontal tissue and teeth pose challenges to the models' transportability and generalizability, making them prone to overfitting. To address these issues, we propose a novel model, named Grouped Attention and Cross-Layer Fusion Network (GCNet).

View Article and Find Full Text PDF

Background And Purpose: Segmentation imperfections (noise) in radiotherapy organ-at-risk segmentation naturally arise from specialist experience and image quality. Using clinical contours can result in sub-optimal convolutional neural network (CNN) training and performance, but manual curation is costly. We address the impact of simulated and clinical segmentation noise on CNN parotid gland (PG) segmentation performance and provide proof-of-concept for an easily implemented auto-curation countermeasure.

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!