Comparison Between Measured Traffic Noise in Klang Valley, Malaysia and Existing Prediction Models

Herni,; Ramdzani,; Jailani, Mohd Nor Mohd; Hamidi, Hamidi; Noorhazlinda,

doi:10.26480/gwk.02.2017.10.14

Cited by 29 publications

(9 citation statements)

References 19 publications

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“…In order to prove the validity of the big data attribute selection method in the submarine optical fiber network fault diagnosis database based on support vector machine, we use MATLAB 2008a as the platform and Intel P4 2G processor to perform the simulation experiment [19][20][21][22][23].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Research on Big Data Attribute Selection Method in Submarine Optical Fiber Network Fault Diagnosis Database

Chen

2017

Polish Maritime Research

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At present, in the fault diagnosis database of submarine optical fiber network, the attribute selection of large data is completed by detecting the attributes of the data, the accuracy of large data attribute selection cannot be guaranteed. In this paper, a large data attribute selection method based on support vector machines (SVM) for fault diagnosis database of submarine optical fiber network is proposed. Mining large data in the database of optical fiber network fault diagnosis, and calculate its attribute weight, attribute classification is completed according to attribute weight

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Section: Experimental Results and Analysismentioning

confidence: 99%

Research on Big Data Attribute Selection Method in Submarine Optical Fiber Network Fault Diagnosis Database

Chen

2017

Polish Maritime Research

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“…The underlying models use many global structural characteristics (roads, buildings, specific sites) and recurring activities (traffic flow and speed, the composition of the vehicle fleet) along with time period and associated meteorological conditions. The modeling provides estimated equivalent noise levels (LAeq) data, covering the whole urban area, with variable resolutions and accuracies (Gille, Marquis-Favre, & Weber, 2017;Halim, Abdullah, Mohd. Nor, Aziz, & Rahman, 2017;Picaut et al, 2017).…”

Section: The Challenge Of Monitoring Actual Urban Activitymentioning

confidence: 99%

Mobile crowd-sensing as a resource for contextualized urban public policies: a study using three use cases on noise and soundscape monitoring

et al. 2019

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Environmental noise is a major pollutant in contemporary cities and calls for the active monitoring of noise levels to spot the locations where it most affects the people's health and well-being. However, due to the complex relationship between environmental noise and its perception by the citizens, it is not sufficient to quantitatively measure environmental noise. We need to collect and aggregate contextualized-both quantitative and qualitative-data about the urban environmental noise so as to be able to study the objective and subjective relationships between sound and living beings. This complex knowledge is a prerequisite for making efficient territorial public policies for soundscapes that are inclined towards living beings welfare. In this paper, we investigate how Mobile Phone Sensing (MPS)-aka crowdsensing-enables the gathering of such knowledge, provided the implementation of sensing protocols that are customized according to the context of use and the intended exploitation of the data. Through three case studies that we carried out in France and Finland, we show that MPS is not solely a tool that contributes to sensitizing citizens and decision-makers about noise pollution; it also contributes to increasing our knowledge about the impact of the environmental noise on people's health and well-being in relation to its physical and subjective perception.

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“…From this error signal, we can correct the weight of each neuron. This adjustment of weight in each layer occurs repeatedly, during the forward propagation of signal and backward propagation of error [22]. The continuous adjustment of weight is also a learning and training process for the network.…”

Section: Tide Height Forecastmentioning

confidence: 99%

Calculation and Measurement of Tide Height for the Navigation of Ship at High Tide Using Artificial Neural Network

Bing-Dong²,

Bao

2018

Polish Maritime Research

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Accurate tide height is crucial for the safe navigation of large deep-draft ships when they enter and leave the port. We have proposed an accurate forecasting method for the tide heights from the observation data and neural networks, which can easily calculate the tidal window period of large deep-draft ships' navigation through long channels at high tide. Moreover, an artificial neural network is established for the tide height from the observation of tide heights before their current time node. For an ideal forecast, the neural network was optimized for one year with the tide height data of Huanghua Port. In case of large ships, their tidal characteristics of channels for are complex. A new method is proposed for the observation of multiple stations and artificial neural networks of each observation station. When ships are navigating through the port, the tide height is predicted from the observed data and forecast tide heights of multiple observation stations. Thus, a valid tidal window period is secured when the ships enter the port. Comparative analysis of the ship's tidal window period with that of the measured one can lead us to conclude that the forecasted data has a strong correlation with the measurement. So, our proposed algorithm can accurately predict the tide height and calculate the node timing when the ship enters and depart the port. Finally, these results can be applied for the safe navigation of large deep-draft ships when the port is at high tide.

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Comparison Between Measured Traffic Noise in Klang Valley, Malaysia and Existing Prediction Models

Cited by 29 publications

References 19 publications

Research on Big Data Attribute Selection Method in Submarine Optical Fiber Network Fault Diagnosis Database

Research on Big Data Attribute Selection Method in Submarine Optical Fiber Network Fault Diagnosis Database

Mobile crowd-sensing as a resource for contextualized urban public policies: a study using three use cases on noise and soundscape monitoring

Calculation and Measurement of Tide Height for the Navigation of Ship at High Tide Using Artificial Neural Network

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