DEVELOPMENT OF A NEURO FUZZY MODEL FOR NOISE PREDICTION IN OPENCAST MINES

2009 ◽  
Vol 17 (05) ◽  
pp. 729-745 ◽  
Author(s):  
SANTOSH KUMAR NANDA ◽  
DEBI PRASAD TRIPATHY ◽  
SARAT KUMAR PATRA
Keyword(s):  
Sound Pressure ◽  
Prediction Models ◽  
Fuzzy Model ◽  
Complex Model ◽  
Noise Prediction ◽  
Opencast Mining ◽  
Noise Levels ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Opencast Mines

The need of developing appropriate noise prediction models for finding out the accurate status of noise levels (>90 dBA) generated from various opencast mining machineries is overdue. The measured sound pressure levels (SPL) of equipments are not accurate due to instrumental error, attenuation due to geometrical aberration, atmospheric attenuation etc. Some of the popular noise prediction models e.g. VDI and ENM have been applied in mining and allied industries. Among these models, VDI2714 is simple and less complex model. In this paper, a neuro-fuzzy model is proposed to predict the machinery noise in an opencast coal mine. The proposed model is trained with VDI2714 and the model output is seen very closely to matching with VDI2714 output. The model proposed has a mean square error of 2.73%. This model takes CPU time of nearly 0.0625 sec where as it takes 0.5 sec for VDI2714 i.e. approximately twelve times faster.

scholarly journals Application of Functional Link Artificial Neural Network for Prediction of Machinery Noise in Opencast Mines

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
Santosh Kumar Nanda ◽  
Debi Prasad Tripathy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Models ◽  
Network Models ◽  
Noise Prediction ◽  
Opencast Mining ◽  
Neural Network Models ◽  
Functional Link ◽  
Opencast Mines ◽  
Artificial Neural

Functional link-based neural network models were applied to predict opencast mining machineries noise. The paper analyzes the prediction capabilities of functional link neural network based noise prediction models vis-à-vis existing statistical models. In order to find the actual noise status in opencast mines, some of the popular noise prediction models, for example, ISO-9613-2, CONCAWE, VDI, and ENM, have been applied in mining and allied industries to predict the machineries noise by considering various attenuation factors. Functional link artificial neural network (FLANN), polynomial perceptron network (PPN), and Legendre neural network (LeNN) were used to predict the machinery noise in opencast mines. The case study is based on data collected from an opencast coal mine of Orissa, India. From the present investigations, it could be concluded that the FLANN model give better noise prediction than the PPN and LeNN model.

scholarly journals Neuro-Fuzzy Transformation with Minimize Entropy Principle to Create New Features for Particulate Matter Prediction

2021 ◽  
Vol 11 (14) ◽  
pp. 6590
Author(s):  
Krittakom Srijiranon ◽  
Narissara Eiamkanitchat
Keyword(s):  
Particulate Matter ◽  
Fuzzy Model ◽  
Original Data ◽  
Northern Region ◽  
National Standard ◽  
Minimum Entropy ◽  
Fuzzy Transformation ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Entropy Principle

Air pollution is a major global issue. In Thailand, this issue continues to increase every year, similar to other countries, especially during the dry season in the northern region. In this period, particulate matter with aerodynamic diameters smaller than 10 and 2.5 micrometers, known as PM10 and PM2.5, are important pollutants, most of which exceed the national standard levels, the so-called Thailand air quality index (T-AQI). Therefore, this study created a prediction model to classify T-AQI calculated from both types of PM. The neuro-fuzzy model with a minimum entropy principle model is proposed to transform the original data into new informative features. The processes in this model are able to discover appropriate separation points of the trapezoidal membership function by applying the minimum entropy principle. The membership value of the fuzzy section is then passed to the neural section to create a new data feature, the PM level, for each hour of the day. Finally, as an analytical process to obtain new knowledge, predictive models are created using new data features for better classification results. Various experiments were utilized to find an appropriate structure with high prediction accuracy. The results of the proposed model were favorable for predicting both types of PM up to three hours in advance. The proposed model can help people who are planning short-term outdoor activities.

scholarly journals A NEURO FUZZY MODEL FOR THE INVESTIGATION OF DETERIORATION OF METALLIC PIPE CONVEYING FLUID UNDER DIFFERENT PIPE BURIAL DEPTH, SOIL TYPES AND PROPERTIES

2016 ◽  
Vol 36 (1) ◽  
pp. 72-79
Author(s):  
TT Akano ◽  
OA Fakindele ◽  
HE Mgbemere ◽  
JC Amechi
Keyword(s):  
Soil Properties ◽  
Membership Function ◽  
Fuzzy Model ◽  
Soil Types ◽  
Burial Depth ◽  
Pipe Conveying Fluid ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Conveying Fluid

Several factors may contribute directly or indirectly to the structural failure of metallic pipes. The most important of which is corrosion. Corrosivity of pipes is not a directly measurable parameter as pipe corrosion is a very random phenomenon. The main aim of the present study is to develop a neuro-fuzzy model capable of establishing corrosion rate criterion as a function of pipe burial depth, soil types, and properties for the prediction of deterioration of metallic pipe conveying fluid. The proposed model includes a fuzzy model and the artificial neural network (ANN) to determine soil corrosivity potential (CoP) based on soil properties. The combination contains the data of linguistic variables characterising various soil properties, and learning capability of the system that constructs relationships among those soil properties and CoP. Subsequently, the artificial neuro-fuzzy inference system (ANFIS) maps each element of its input membership function to an output membership function between 0 and 1 to determine the deterioration rate (CoP) of metallic fluid-conveying-pipe. Field data from buried fluid pipes were examined to illustrate the application of the proposed model. The ultimate goal is the ability to access the current and future life of oil pipe, given a set of circumstances, and also appropriate adoptable methodology in view of a preventive maintenance measure for the pipes in a given operating environment. Results reveal that with more than 40% clay content quickens corrosion of buried fluid pipes more than any other considered factor. http://dx.doi.org/10.4314/njt.v36i1.10

scholarly journals Estimate reliability of component-based software sys-tem using modified neuro fuzzy model

2017 ◽  
Vol 6 (2) ◽  
pp. 45 ◽  
Author(s):  
Ravi Kumar Sharma ◽  
Dr. Parul Gandhi
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Software Systems ◽  
Data Sets ◽  
Component Reliability ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Estimate Reliability

There are many algorithms and techniques for estimating the reliability of Component Based Software Systems (CBSSs). Accurate esti-mation depends on two factors: component reliability and glue code reliability. Still much more research is expected to estimate reliability in a better way. A number of soft computing approaches for estimating CBSS reliability has been proposed. These techniques learnt from the past and capture existing patterns in data. In this paper, we proposed new model for estimating CBSS reliability known as Modified Neuro Fuzzy Inference System (MNFIS). This model is based on four factors Reusability, Operational, Component dependency, Fault Density. We analyze the proposed model for diffent data sets and also compare its performance with that of plain Fuzzy Inference System. Our experimental results show that, the proposed model gives better reliability as compare to FIS.

New fuzzy solution for determining anticipation and evaluation behavior during car-following maneuvers

2017 ◽  
Vol 232 (7) ◽  
pp. 936-945 ◽  
Author(s):  
Ali Ghaffari ◽  
Alireza Khodayari ◽  
Ali Kamali ◽  
Farzam Tajdari ◽  
Niloofar Hosseinkhani
Keyword(s):  
Evaluation Model ◽  
Fuzzy Model ◽  
Intelligent Vehicles ◽  
Lane Change ◽  
Car Following ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Driving Assistance ◽  
Important Means ◽  
Two Stages

Nowadays, vehicles are the most important means of transportation in our daily lifes. During the last few decades, many studies have been carried out in the field of intelligent vehicles and significant results on the behavior of car-following and lane-change maneuvers have been achieved. However, the effects of lane-change on the car-following models have been relatively neglected. This effect is a temporary state in car-following behavior during which the follower vehicle considerably deviates from conventional car-following models for a limited time. This paper aims to investigate the behavior of the immediate follower during the lane-change of its leader vehicle. Based on a closer inspection of the microstructure behavior of real drivers, this temporary state is divided into two stages of anticipation and evaluation. Afterwards, a novel and adaptive neuro-fuzzy model that considers human driving factors is proposed to simulate the behavior of real drivers. Comparison between model results and real traffic data reveals that the proposed model can describe anticipation and evaluation behavior with smaller errors. The anticipation and evaluation model can modify current car-following models so as to accurately simulate the behavior of an immediate follower which leads to an enhancement of car-following applications such as driving assistance and collision avoidance systems.

scholarly journals Development of traffic noise prediction model for major arterial roads of tier-II city of India (Surat) using artificial neural network

Noise Mapping ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 172-184
Author(s):  
Ramesh B. Ranpise ◽  
B. N. Tandel ◽  
Vivek A. Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Prediction Model ◽  
Prediction Models ◽  
Traffic Noise ◽  
Road Traffic Noise ◽  
Noise Prediction ◽  
Noise Levels ◽  
Street Traffic ◽  
Artificial Neural

Abstract In the issue of expanding noise levels the world over, road traffic noise is main contributor. The investigation of street traffic noise in urban communities is a significant issue. Ample opportunity has already passed to understand the significance of noise appraisal through prediction models with the goal that assurance against street traffic noise can be actualized. Noise predictions models are utilized in an increasing range of decision-making applications. This study’s main objective is to assess ambient noise levels at major arterial roads of Surat city, compare these with prescribed standards, and develop a noise prediction model for arterial roads using an Artificial Neural Network. The feed-forward back propagation method has been used to train the model. Models have been developed using the data of three roads separately, and one final model has also been developed using the data of all three roads. Among the prediction in three arterial roads, the predicted output result from the model of Adajan-Rander showed a better correlation with a mean squared error (MSE) of 0.789 and R2 value of 0.707. But with the combined model, there is a slight deterioration in mean squared value (MSE) 1.550, with R2 not getting changed much significantly, i.e., 0.755. However, the combined model’s prediction can be adopted due to the variety of data used in its training.

A NEURO-FUZZY MODEL FOR DIMENSIONALITY REDUCTION AND ITS APPLICATION

2007 ◽  
Vol 15 (05) ◽  
pp. 571-593 ◽  
Author(s):  
VITALIY KOLODYAZHNIY ◽  
FRANK KLAWONN ◽  
KATHARINA TSCHUMITSCHEW
Keyword(s):  
Dimensionality Reduction ◽  
Treatment Plant ◽  
Fuzzy Model ◽  
Principal Component ◽  
Nonlinear Dimensionality Reduction ◽  
Training Algorithms ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Plant Data ◽  
Hidden Layer

A novel neuro-fuzzy approach to nonlinear dimensionality reduction is proposed. The approach is an auto-associative modification of the Neuro-Fuzzy Kolmogorov's Network (NFKN) with a “bottleneck” hidden layer. Two training algorithms are considered. The validity of theoretical results and the advantages of the proposed model are confirmed by an experiment in nonlinear principal component analysis and an application in the visualization of high-dimensional wastewater treatment plant data.

scholarly journals Modelling of Noise Pollution Due to Heterogeneous Highway Traffic in India

2019 ◽  
Vol 20 (1) ◽  
pp. 22-39 ◽  
Author(s):  
Aditya Kamineni ◽  
Sunil Kumar Duda ◽  
Venkaiah Chowdary ◽  
C.S.R.K. Prasad
Keyword(s):  
Traffic Noise ◽  
Noise Pollution ◽  
Heterogeneous Traffic ◽  
Noise Prediction ◽  
Highway Traffic ◽  
Noise Levels ◽  
Speed Variation ◽  
Proposed Model ◽  
Traffic Speed ◽  
The Difference

Abstract Compared to homogeneous traffic flow, traffic speed variation is drastic with the involvement of heterogeneity. With an intent of studying the negative upshot of fluctuating speeds of heterogeneous traffic on the environment, the current paper is the outcome of the research done on various highways located in the states of Andhra Pradesh and Telangana in India, with an objective of developing a comprehensive noise prediction model by taking into account the traffic and roadway factors. Quantified noise levels [Leq (dBA) and L10 (dBA)] revealed that for the traffic speed variation of 10 to 95 kmph, the traffic noise levels were significantly affected by the variations in the proportion of the vehicle. On a specific note, the proposed model can be effectively used for the highway traffic noise prediction especially for the heterogeneous traffic, as the difference between the measured and predicted noise levels are within 1 to 10 dB (A).

NEURO-FUZZY MODEL FOR ENVIRONMENTAL IMPACT ASSESSMENT

2011 ◽  
Vol 10 (3) ◽  
pp. 381-386 ◽  
Author(s):  
Alexandru Trandabat ◽  
Marius Pislaru ◽  
Silvia Avasilcai
Keyword(s):  
Environmental Impact ◽  
Impact Assessment ◽  
Environmental Impact Assessment ◽  
Fuzzy Model ◽  
Neuro Fuzzy
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