Psycho-acoustic evaluation of the indoor noise in cabins of a naval vessel using a back-propagation neural network algorithm

HyungsukHan,

doi:10.3744/jnaoe.2012.4.4.374

Cited by 3 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ere is a continuous cycle between the process of information forward propagation and the process of error backpropagation, which will stop when the squared error of the network reaches minimum [14]. Standard backpropagation algorithm is widely used [15][16][17]. However, there are some shortcomings in the standard backpropagation algorithm, such as long training time, and slow convergence speed.…”

Section: Improved Backpropagation (Bp) Neural Networkmentioning

confidence: 99%

Model Building for Regional Ecological Risk Prediction and Evaluation of Prediction Accuracy

Shao¹,

Li²,

Liu³

et al. 2021

Advances in Civil Engineering

View full text Add to dashboard Cite

The regional ecological risk model is built to predict the regional ecological risk level more accurately by using principal component analysis and optimizing standard BP neural network. Taking Xiangxi Tujia and Miao Autonomous Prefecture as an example, twelve primary factors affecting regional risk are selected. The sample data are processed by principal component analysis. The obtained main components are then used as input factors of the improved BP neural network, and the level of ecological risk is used as output factor. The results indicate that the error between the expected output and the actual output is 4.36% in 2016, 1.08% in 2017, and 5.18% in 2018, respectively, with all controlled within 6%. Compared with the prediction accuracy made by standard BP neural network without principal component analysis, the prediction accuracy made by improved BP neural network with principal component analysis is greatly improved. This comprehensive prediction model provides a better evaluation method for prediction of ecological risk level.

show abstract

Section: Improved Backpropagation (Bp) Neural Networkmentioning

confidence: 99%

Model Building for Regional Ecological Risk Prediction and Evaluation of Prediction Accuracy

Shao¹,

Li²,

Liu³

et al. 2021

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…Ship cabins are places for crew to be on duty and have a rest. The convention sets a high limitation for noises of this kind of cabins [1][2][3]. The related researches of sound quality show that noises in the low frequency will annoy people easily.…”

Section: Introductionmentioning

confidence: 99%

Numerical analysis and optimization of noises of ship cabins in the low frequency

Xu¹,

Huang²,

Zhu³

et al. 2017

J. vibroeng.

View full text Add to dashboard Cite

Ship cabins are places for the crew to be on duty and have a rest. The convention sets a high limitation for noises of ship cabins. This paper combined finite element method with boundary element method to study and improve the noise of ship cabins. Firstly, this paper established the finite element model of a ship cabin, computed structural response, took the structural response as boundary condition and input it into acoustic software to couple with boundary element model, realize the computation of vibro-acoustic coupling and obtain sound pressure response. Then, the numerically computational sound pressure was compared with experimental results, and a good consistency was presented. It showed that the vibro-acoustic coupling model of the ship cabin in this paper was effective. In addition, this paper also computed the noise contribution of cabin panels to observation points and obtained panels which had a serious contribution to cabin noises. Finally, this paper applied laminated plate structure to these panels and recomputed cabin noises to compare with the original structure. Results showed that peak noises of field points were obviously improved, which ensured that the noise level of ship cabins satisfied the corresponding requirements of the convention.

show abstract

An optimal design of wind turbine and ship structure based on neuro-response surface method

Lee

Shin

Kim

2015

International Journal of Naval Architecture and Ocean Engineeri

View full text Add to dashboard Cite

The geometry of engineering systems affects their performances. For this reason, the shape of engineering systems needs to be optimized in the initial design stage. However, engineering system design problems consist of multi-objective optimization and the performance analysis using commercial code or numerical analysis is generally time-consuming. To solve these problems, many engineers perform the optimization using the approximation model (response surface). The Response Surface Method (RSM) is generally used to predict the system performance in engineering research field, but RSM presents some prediction errors for highly nonlinear systems. The major objective of this research is to establish an optimal design method for multi-objective problems and confirm its applicability. The proposed process is composed of three parts: definition of geometry, generation of response surface, and optimization process. To reduce the time for performance analysis and minimize the prediction errors, the approximation model is generated using the Backpropagation Artificial Neural Network (BPANN) which is considered as Neuro-Response Surface Method (NRSM). The optimization is done for the generated response surface by non-dominated sorting genetic algorithm-II (NSGA-II). Through case studies of marine system and ship structure (substructure of floating offshore wind turbine considering hydrodynamics performances and bulk carrier bottom stiffened panels considering structure performance), we have confirmed the applicability of the proposed method for multi-objective side constraint optimization problems.

show abstract

Psycho-acoustic evaluation of the indoor noise in cabins of a naval vessel using a back-propagation neural network algorithm

Abstract: The indoor noise of a ship is usually determined using the

Cited by 3 publications

References 12 publications

Model Building for Regional Ecological Risk Prediction and Evaluation of Prediction Accuracy

Model Building for Regional Ecological Risk Prediction and Evaluation of Prediction Accuracy

Numerical analysis and optimization of noises of ship cabins in the low frequency

An optimal design of wind turbine and ship structure based on neuro-response surface method

Contact Info

Product

Resources

About