Watermelon Sorting Process by Frequency Identification and Artificial Neural Network

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.

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

Materials Science Forum ◽

10.4028/www.scientific.net/msf.488-489.793 ◽

2005 ◽

Vol 488-489 ◽

pp. 793-796 ◽

Cited By ~ 1

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.

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Comparison of Accuracy of Artificial Neural Network (ANN) and Kriging Methods for Estimating Chlorine Concentration

10.29007/lpmh ◽

2018 ◽

Author(s):

Faezeh Ghaleh Navi ◽

Hamed Mazandarani Zadeh ◽

Dragan Savic

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Kriging Model ◽

Coefficient Of Determination ◽

Monthly Basis ◽

Data Set ◽

The Neural Network ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Observation Wells

Groundwater is one of the major sources of fresh water. Maintenance and management of this vital resource is so important especially in arid and semi-arid regions. Reliable and accurate groundwater quality assessment is essential as a basic data for any groundwater management studies. The aim of this study is to compare the accuracy of two Artificial Neural Network (ANN) and Kriging methods in predicting chlorine in groundwater. In case of ANN, we created an appropriate emulator, which minimize the prediction error by changing the parameters of the neural network, including the number of layers. The best Kriging model is also obtained by changing the variogram function, such that the Gaussian variogram has the least error in interpolation of the amount of chlorine. To evaluate the accuracy of these two methods, the mean square error (MSE) and Coefficient of determination (R2) are used. The data set consists of the amount of chlorine, in a monthly basis, measured at 112 observation wells from 1999 to 2015 in aquifer Qazvin, Iran. MSE values for ANN and Kriging are 14.8 and 15.4, respectively, which indicate that the ANN has a better performance and is more capable of predicting chlorine values in comparison with Kriging.

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Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

Engineering and Technology Journal ◽

10.30684/etj.v38i4a.399 ◽

2020 ◽

Vol 38 (4A) ◽

pp. 510-514

Author(s):

Tay H. Shihab ◽

Amjed N. Al-Hameedawi ◽

Ammar M. Hamza

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Artificial Neural Network ◽

Random Forest ◽

Satellite Image ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Data Set ◽

Artificial Neural ◽

Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019. They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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

Studia Geotechnica et Mechanica ◽

10.1515/sgem-2016-0017 ◽

2016 ◽

Vol 38 (2) ◽

pp. 37-46 ◽

Cited By ~ 4

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.

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PENDUGAAN FLUKS PANAS DAN EVAPOTRANSPIRASI DENGAN JARINGAN SYARAF TIRUANHEAT FLUX AND EVAPOTRANSPIRATION ESTIMATION USING ARTIFICIAL NEURAL NETWORK

Agromet ◽

10.29244/j.agromet.25.1.24-28 ◽

2011 ◽

Vol 25 (1) ◽

pp. 24

Author(s):

Satyanto Krido Saptomo

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Latent Heat ◽

Measurement Data ◽

Heat Fluxes ◽

Similar Process ◽

Observation Data ◽

Data Set ◽

Dissipation Process ◽

Artificial Neural

Artificial neural network (ANN) approach was used to model energy dissipation process into sensible heat and latent heat (evapotranspiration) fluxes. The ANN model has 5 inputs which are leaf temperature Tl, air temperature Ta, net radiation Rn, wind speed uc and actual vapor pressure ea. Adjustment of ANN was conducted using back propagation technique, employing measurement data of input and output parameters of the ANN. The estimation results using the adjusted ANN shows its capability in resembling the heat dissipation process by giving outputs of sensible and latent heat fluxes closed to its respective measurement values as the measured input values are given. The ANN structure presented in this paper suits for modeling similar process over vegetated surfaces, but the adjusted parameters are unique. Therefore observation data set for each different vegetation and adjustment of ANN are required.

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Применение искусственной нейронной сети в экспериментах по ядерному спиновому эху

Письма в журнал технической физики ◽

10.21883/pjtf.2019.12.47911.17736 ◽

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.

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Adaptation of the Diagnostic Artificial Neural Network Structure When New Training Examples Appear

Proceedings of Telecommunication Universities ◽

10.31854/1813-324x-2020-6-4-120-126 ◽

2020 ◽

Vol 6 (4) ◽

pp. 120-126

Author(s):

A. Malikov

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Information Security ◽

Classification Problem ◽

Data Set ◽

Neural Network Structure ◽

Artificial Neural ◽

Training Examples ◽

Information Image

In this paper we can see that identified computer incidents are subject for diagnostics, during which the characteristics of information security violations are clarified (purpose, causes, consequences, etc.). To diagnose computer incidents, we can use methods of automation while collection and processing the events that occur as a result of the implementation of scenarios for information security violations. Artificial neural networks can be used to solve the classification problem of assigning diagnostic data set (information image of a computer incident) to one of the possible values of the violation characteristic. The purpose of this work is to adapt the structure of an artificial neural network that allows the accuracy diagnostics of computer incidents when new training examples appear.

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

Security and Communication Networks ◽

10.1155/2021/4027900 ◽

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.

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

Modeling, Control and Information Technologies ◽

10.31713/mcit.2019.20 ◽

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

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Artificial Neural Network Modelling of Photodegradation in Suspension of Manganese Doped Zinc Oxide Nanoparticles under Visible-Light Irradiation

The Scientific World JOURNAL ◽

10.1155/2014/726101 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 6

Author(s):

Yadollah Abdollahi ◽

Azmi Zakaria ◽

Nor Asrina Sairi ◽

Khamirul Amin Matori ◽

Hamid Reza Fard Masoumi ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Visible Light ◽

Visible Light Irradiation ◽

Back Propagation ◽

Light Irradiation ◽

Maximum Efficiency ◽

Data Set ◽

Artificial Neural

The artificial neural network (ANN) modeling ofm-cresol photodegradation was carried out for determination of the optimum and importance values of the effective variables to achieve the maximum efficiency. The photodegradation was carried out in the suspension of synthesized manganese doped ZnO nanoparticles under visible-light irradiation. The input considered effective variables of the photodegradation were irradiation time, pH, photocatalyst amount, and concentration ofm-cresol while the efficiency was the only response as output. The performed experiments were designed into three data sets such as training, testing, and validation that were randomly splitted by the software’s option. To obtain the optimum topologies, ANN was trained by quick propagation (QP), Incremental Back Propagation (IBP), Batch Back Propagation (BBP), and Levenberg-Marquardt (LM) algorithms for testing data set. The topologies were determined by the indicator of minimized root mean squared error (RMSE) for each algorithm. According to the indicator, the QP-4-8-1, IBP-4-15-1, BBP-4-6-1, and LM-4-10-1 were selected as the optimized topologies. Among the topologies, QP-4-8-1 has presented the minimum RMSE and absolute average deviation as well as maximum R-squared. Therefore, QP-4-8-1 was selected as final model for validation test and navigation of the process. The model was used for determination of the optimum values of the effective variables by a few three-dimensional plots. The optimum points of the variables were confirmed by further validated experiments. Moreover, the model predicted the relative importance of the variables which showed none of them was neglectable in this work.

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