Review Of The Artificial Neural Network Application In Prediciting Blast Vibration

The blasting method is one of the best hard rock excavation methods in mining activities. This method has negative impacts, one of which is the vibrations generated by the residual energy of the explosion. This impact will affect the environment around the blasting area, both slope stability, tunnels, infrastructure, and human settlements if it is close to the blasting site. Therefore, it needs initial planning and prediction to anticipate the blasting vibration that occurs. In general, the blast vibration can be predicted using the scale distance method which uses two parameters, namely the maximum amount of explosive material per time delay and the distance of measurement from the location of the explosion. This method has been widely researched to produce several empirical equations from each explosion location studied. However, as technology develops, several studies have tried to use artificial intelligence technology, one of which is the artificial neural network algorithm as a new approach for predicting detonation vibrations. In this method, the development of the parameters used in predicting the weighting of the most influential parameters from the formation of detonation vibrations can be carried out. This study will review several studies related to the use of artificial neural networks in predicting blasting vibrations in the studies that have been carried out and also compare with prediction methods using several empirical equations.

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An Automatic Logistics System using Artificial Intelligence

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1183.0782s419 ◽

2019 ◽

Vol 8 (2S4) ◽

pp. 924-926

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Neural Networks ◽

Artificial Neural Network ◽

Optimal Route ◽

Logistics System ◽

The Neural Network ◽

Artificial Intelligence Technology ◽

Artificial Neural ◽

Set Up

In this paper, we propose a method to utilize machine learning to automate the system of classifying and transporting large quantities of logistics. First, establish an environment similar to the task of transferring logistics to the desired destination, and set up basic rules for classification and transfer. Next, each of the logistics that need sorting and transportation is defined as one entity, and artificial intelligence is introduced so that each individual can go to an optimal route without collision between the objects to the destination. Artificial intelligence technology uses artificial neural networks and uses genetic algorithms to learn neural networks. The artificial neural network is generated by each chromosome, and it is evolved based on the most suitable artificial neural network, and a score is given to each operation to evaluate the fitness of the neural network. In conclusion, the validity of this algorithm is evaluated through the simulation of the implemented system.

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Micrometeoroid and Orbital Debris Impact Detection and Location Based on FBG Sensor Network Using Combined Artificial Neural Network and Mahalanobis Distance Method

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2021.3091501 ◽

2021 ◽

pp. 1-1

Author(s):

Jing Jin ◽

Yunhong Zhu ◽

Yibo Zhang ◽

Dongwei Zhang ◽

Zuchen Zhang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Sensor Network ◽

Mahalanobis Distance ◽

Orbital Debris ◽

Distance Method ◽

Impact Detection ◽

Fbg Sensor ◽

Artificial Neural ◽

Debris Impact

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Embedding Parameters Based Distinguish between Normal and Epileptic EEG Using Artificial Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.20-23.588 ◽

2010 ◽

Vol 20-23 ◽

pp. 588-593

Author(s):

Ye Yuan

Keyword(s):

Neural Network ◽

Time Series ◽

Artificial Neural Network ◽

Delay Time ◽

Probabilistic Neural Network ◽

Embedding Dimension ◽

Artificial Neural ◽

Two Parameters ◽

Electroencephalogram Eeg ◽

Better Than

The embedding parameters of electroencephalogram (EEG) time series, i.e., the embedding dimension and delay time, are used together as the input features of artificial neural network for distinguishing between normal and epileptic EEG time series. Cao’s method and mutual information method are applied for computing the embedding dimension and delay time of normal and epileptic EEG time series, respectively. The probabilistic neural network (PNN) is used in this paper for distinguishing between normal and epileptic EEG time series. The results of the simulation show that the overall accuracy as high as 100% can be achieved by using the method proposed in this paper, and that the accuracy obtained based on the both parameters is better than that obtained based on each of the two parameters respectively.

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Comparison of empirical equations and artificial neural network results in terms of kinematic viscosity prediction of fuels based on hazelnut oil methyl ester

Environmental Progress & Sustainable Energy ◽

10.1002/ep.12410 ◽

2016 ◽

Vol 35 (6) ◽

pp. 1827-1841 ◽

Cited By ~ 4

Author(s):

Tanzer Eryilmaz ◽

Mevlut Arslan ◽

Murat Kadir Yesilyurt ◽

Alper Taner

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Methyl Ester ◽

Kinematic Viscosity ◽

Hazelnut Oil ◽

Empirical Equations ◽

Viscosity Prediction ◽

Artificial Neural

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Data Processing and Artificial Neural Network in the Background of Big Data

Journal of Electronic Research and Application ◽

10.26689/jera.v4i2.1160 ◽

2020 ◽

Vol 4 (2) ◽

Author(s):

Yang You

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Artificial Neural Network ◽

Big Data ◽

Back Propagation ◽

Human Beings ◽

Back Propagation Algorithm ◽

Emerging Industries ◽

Artificial Intelligence Technology ◽

Artificial Neural

The existing significance of big data technology lies not only in collecting massive information, but also in professional processing and analysis. It transforms information into data and extracts valuable knowledge from data. The advent of the era of big data has brought us a new development model, but also produced many emerging industries, such as cloud computing, artificial intelligence and so on. Based on this, this paper studies the artificial neural network and back propagation algorithm in this context, so that computer technology can better serve human beings, which is of great significance to promote the further development of artificial intelligence technology.

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