scholarly journals Emotional artificial neural network (EANN)-based prediction model of maximum A-weighted noise pressure level

Noise Mapping ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 1-9
Author(s):  
Sergey V. Kuznetsov ◽  
Waluyo Adi Siswanto ◽  
Fairuza Musovna Sabirova ◽  
Inna Genadievna Pustokhina ◽  
Lyubov Anatolievna Melnikova ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Efficiency ◽  
Field Measurements ◽  
Pressure Level ◽  
Noise Prediction ◽  
The Neural Network ◽  
Train Speed ◽  
Artificial Neural ◽  
Living Organisms

Abstract Noise is considered one of the most critical environmental issues because it endangers the health of living organisms. For this reason, up-to-date knowledge seeks to find the causes of noise in various industries and thus prevent it as much as possible. Considering the development of railway lines in underdeveloped countries, identifying and modeling the causes of vibrations and noise of rail transportation is of particular importance. The evaluation of railway performance cannot be imagined without measuring and managing noise. This study tried to model the maximum A-weighted noise pressure level with the information obtained from field measurements by Emotional artificial neural network (EANN) models and compare the results with linear and logarithmic regression models. The results showed the high efficiency of EANN models in noise prediction so that the prediction accuracy of 95.6% was reported. The results also showed that in noise prediction based on the neural network-based model, the independent variables of train speed and distance from the center of the route are essential in predicting.

scholarly journals Application of artificial neural networks to predict the deflections of reinforced concrete beams

2016 ◽  
Vol 38 (2) ◽  
pp. 37-46 ◽  
Author(s):  
Mateusz Kaczmarek ◽  
Agnieszka Szymańska
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Reinforced Concrete ◽  
Concrete Beams ◽  
Reinforced Concrete Beams ◽  
The Neural Network ◽  
Eurocode 2 ◽  
Artificial Neural ◽  
Concrete Elements

Abstract Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies. Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.

Watermelon Sorting Process by Frequency Identification and Artificial Neural Network

2021 ◽  
Author(s):  
Komsan Wongkalasin ◽  
Teerapon Upachaban ◽  
Wacharawish Daosawang ◽  
Nattadon Pannucharoenwong ◽  
Phadungsak Ratanadecho
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Selection Process ◽  
Network Processor ◽  
Data Set ◽  
The Neural Network ◽  
Frequency Identification ◽  
Sorting Process ◽  
Artificial Neural ◽  
Reasonable Prediction

This research aims to enhance the watermelon’s quality selection process, which was traditionally conducted by knocking the watermelon fruit and sort out by the sound’s character. The proposed method in this research is generating the sound spectrum through the watermelon and then analyzes the response signal’s frequency and the amplitude by Fast Fourier Transform (FFT). Then the obtained data were used to train and verify the neural network processor. The result shows that, the frequencies of 129 and 172 Hz were suit to be used in the comparison. Thirty watermelons, which were randomly selected from the orchard, were used to create a data set, and then were cut to manually check and match to the fruits’ quality. The 129 Hz frequency gave the response ranging from 13.57 and above in 3 groups of watermelons quality, including, not fully ripened, fully ripened, and close to rotten watermelons. When the 172 Hz gave the response between 11.11–12.72 in not fully ripened watermelons and those of 13.00 or more in the group of close to rotten and hollow watermelons. The response was then used as a training condition for the artificial neural network processor of the sorting machine prototype. The verification results provided a reasonable prediction of the ripeness level of watermelon and can be used as a pilot prototype to improve the efficiency of the tools to obtain a modern-watermelon quality selection tool, which could enhance the competitiveness of the local farmers on the product quality control.

A Model on the Correlation between Composition and Mechanical Properties of Mg-Al-Zn Alloys by Using Artificial Neural Network

2005 ◽  
Vol 488-489 ◽  
pp. 793-796 ◽  
Author(s):  
Hai Ding Liu ◽  
Ai Tao Tang ◽  
Fu Sheng Pan ◽  
Ru Lin Zuo ◽  
Ling Yun Wang
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Artificial Neural Network ◽  
Magnesium Alloys ◽  
Tensile Yield Strength ◽  
Data Set ◽  
The Neural Network ◽  
Artificial Neural ◽  
Prediction Of Mechanical Properties ◽  
Artificial Neural Network Ann

A model was developed for the analysis and prediction of correlation between composition and mechanical properties of Mg-Al-Zn (AZ) magnesium alloys by applying artificial neural network (ANN). The input parameters of the neural network (NN) are alloy composition. The outputs of the NN model are important mechanical properties, including ultimate tensile strength, tensile yield strength and elongation. The model is based on multilayer feedforward neural network. The NN was trained with comprehensive data set collected from domestic and foreign literature. A very good performance of the neural network was achieved. The model can be used for the simulation and prediction of mechanical properties of AZ system magnesium alloys as functions of composition.

scholarly journals Применение искусственной нейронной сети в экспериментах по ядерному спиновому эху

2019 ◽  
Vol 45 (12) ◽  
pp. 16
Author(s):  
С.Н. Полулях ◽  
А.И. Горбованов
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Nuclear Spin ◽  
Spin Echo ◽  
Gaussian Form ◽  
The Neural Network ◽  
Network Application ◽  
Artificial Neural

The possibility of artificial neural network application to detect nuclear spin echo signals under conditions when the echo amplitude is comparable to the amplitude of the noise is demonstrated. Data obtained by superimposing the model echo signals of a Gaussian form on experimentally recorded noise signals is proposed to use for training the neural network.

scholarly journals Intrusion Detection for Industrial Control Systems Based on Open Set Artificial Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Chao Wang ◽  
Bailing Wang ◽  
Yunxiao Sun ◽  
Yuliang Wei ◽  
Kai Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Control Systems ◽  
Industrial Control ◽  
Industrial Control Systems ◽  
Open Set ◽  
The Neural Network ◽  
Center Loss ◽  
Artificial Neural

The security of industrial control systems (ICSs) has received a lot of attention in recent years. ICSs were once closed networks. But with the development of IT technologies, ICSs have become connected to the Internet, increasing the potential of cyberattacks. Because ICSs are so tightly linked to human lives, any harm to them could have disastrous implications. As a technique of providing protection, many intrusion detection system (IDS) studies have been conducted. However, because of the complicated network environment and rising means of attack, it is difficult to cover all attack classes, most of the existing classification techniques are hard to deploy in a real environment since they cannot deal with the open set problem. We propose a novel artificial neural network based-methodology to solve this problem. Our suggested method can classify known classes while also detecting unknown classes. We conduct research from two points of view. On the one hand, we use the openmax layer instead of the traditional softmax layer. Openmax overcomes the limitations of softmax, allowing neural networks to detect unknown attack classes. During training, on the other hand, a new loss function termed center loss is implemented to improve detection ability. The neural network model learns better feature representations with the combined supervision of center loss and softmax loss. We evaluate the neural network on NF-BoT-IoT-v2 and Gas Pipeline datasets. The experiments show our proposed method is comparable with the state-of-the-art algorithm in terms of detecting unknown classes. But our method has a better overall classification performance.

scholarly journals The implementation of optimized neural network to improve the accuracy of flow measurement by ultrasonic flowmeter

2019 ◽  
pp. 193-194
Author(s):  
Oleksandr Ihorovich Parfeniuk ◽  
Oleksandr Mykolaiovych Naumchuk ◽  
Olena Olehivna Poliukhovych ◽  
Pawel Mazurek
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flow Measurement ◽  
Ultrasonic Flowmeter ◽  
Structural Scheme ◽  
Nonlinear Characteristics ◽  
The Neural Network ◽  
Artificial Neural

It is proposed the technology of intellectual measurement of expenses with the use of an artificial neural network for overcoming the constraints caused by nonlinear characteristics of ultrasonic flowmeters. It is presented structural scheme of the proposed technology and structure of the model of the neural network

Artificial Neural Networks

2011 ◽  
pp. 222-243
Author(s):  
Joarder Kamruzzaman ◽  
Ruhul Sarker
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Network Models ◽  
Neural Network Models ◽  
Network Applications ◽  
The Neural Network ◽  
Artificial Neural ◽  
Neural Network Applications

The primary aim of this chapter is to present an overview of the artificial neural network basics and operation, architectures, and the major algorithms used for training the neural network models. As can be seen in subsequent chapters, neural networks have made many useful contributions to solve theoretical and practical problems in finance and manufacturing areas. The secondary aim here is therefore to provide a brief review of artificial neural network applications in finance and manufacturing areas.

scholarly journals Analyses of the method development of decisions in an expert system with the use of information from an artificial neural network

2018 ◽  
Vol 178 ◽  
pp. 07002 ◽  
Author(s):  
Stanisław Duer ◽  
Konrad Zajkowski ◽  
Serghei Scaticailov ◽  
Paweł Wrzesień
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Expert System ◽  
Intelligent Systems ◽  
Method Development ◽  
Diagnostic System ◽  
Intelligence System ◽  
Intelligent Maintenance ◽  
The Neural Network ◽  
Artificial Neural

The present article covers the use of an artificial intelligence system in the organization of the prevention of technical objects. For this purpose, the composition of this system including an intelligent diagnostic system and an intelligent maintenance system was characterized and described. An artificial neural network and an expert system, which work among others on the basis of the information developed by the neural network, perform a special function in these systems. It was mentioned in the article that the mathematical model of the regeneration process of the functional properties (prevention) of an object forms the basis of the organization of the prevention activities of technical devices and objects with the use of intelligent systems. This model demonstrated the possibilities and directions for the use of maintenance intelligent systems.

NEURAL NETWORK AIDED EVALUATION OF LANDSLIDE SUSCEPTIBILITY IN SOUTHERN ITALY

2012 ◽  
Vol 23 (01) ◽  
pp. 1250002 ◽  
Author(s):  
SALVATORE RAMPONE ◽  
ALESSIO VALENTE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Landslide Hazard ◽  
Hazard Mapping ◽  
The Neural Network ◽  
Network Output ◽  
Artificial Neural ◽  
Validation Set

Landslide hazard mapping is often performed through the identification and analysis of hillslope instability factors. In heuristic approaches, these factors are rated by the attribution of scores based on the assumed role played by each of them in controlling the development of a sliding process. The objective of this research is to forecast landslide susceptibility through the application of Artificial Neural Networks. In particular, given the availability of past events data, we mainly focused on the Calabria region (Italy). Vectors of eight hillslope factors (features) were considered for each considered event in this area (lithology, permeability, slope angle, vegetation cover in terms of type and density, land use, yearly rainfall and yearly temperature range). We collected 106 vectors and each one was labeled with its landslide susceptibility, which is assumed to be the output variable. Subsequently a set of these labeled vectors (examples) was used to train an artificial neural network belonging to the category of Multi-Layer Perceptron (MLP) to evaluate landslide susceptibility. Then the neural network predictions were verified on the vectors not used in the training (validation set), i.e. in previously unseen locations. The comparison between the expected output and the artificial neural network output showed satisfactory results, reporting a prediction discrepancy of less than 4.3%. This is an encouraging preliminary approach towards a systematic introduction of artificial neural network in landslide hazard assessment and mapping in the considered area.

scholarly journals Prediction of Slag Characteristics Based on Artificial Neural Network for Molten Gasification of Hazardous Wastes

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5115
Author(s):  
Xiongchao Lin ◽  
Wenshuai Xi ◽  
Jinze Dai ◽  
Caihong Wang ◽  
Yonggang Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Layer Structure ◽  
Hazardous Materials ◽  
Hazardous Wastes ◽  
Slag Viscosity ◽  
The Neural Network ◽  
Independent Test ◽  
Artificial Neural ◽  
Hidden Layer

Molten gasification is considered as a promising technology for the processing and safe disposal of hazardous wastes. During this process, the organic components are completely converted while the hazardous materials are safely embedded in slag via the fusion-solidification-vitrification transformation. Ideally, the slag should be glassy with low viscosity to ensure the effective immobilization and steady discharge of hazardous materials. However, it is very difficult to predict the characteristics of slag using existing empirical equations or conventional mathematical methods, due to the complex non-linear relationship among the phase transformation, vitrification transition and chemical composition of slag. Equipped with a strong nonlinear mapping ability, an artificial neural network may be able to predict the properties of slags if a large amount of data is available for training. In this work, over 10,000 experimental data points were used to train and develop a slag classification model (glassy vs. non-glassy) based on a neural network. The optimal structure of the neural network was figured out and validated. The results suggest that the classification accuracy for the independent test samples reached 93.3%. Using 1 and 0 as model inputs to represent mildly reducing and inert atmospheres, a double hidden layer structure in the neural network enabled the accurate classification of slags under various atmospheres. Furthermore, the neural network for the prediction of glassy slag viscosity was optimized; it featured a double hidden layer structure. Under a mildly reducing atmosphere, the absolute error from the independent test data was generally within 4 Pa·s. By adding a gas atmosphere into the input of the neural network using a simple normalization method, a multi-atmosphere slag viscosity prediction model was developed. Said model is much more accurate than its counterpart that does not consider the effect of the atmosphere. In summary, the artificial neural network proved to be an effective approach to predicting the slag properties under different atmospheres. The data-driven models developed in this work are expected to facilitate the commercial deployment of molten gasification technology.

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