scholarly journals 2D Detection Model of Defect on the Surface of Ceramic Tile by an Artificial Neural Network

2021 ◽  
Vol 1764 (1) ◽  
pp. 012176
Author(s):  
B Mariyadi ◽  
N Fitriyani ◽  
T R Sahroni
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ceramic Tile ◽  
Detection Model ◽  
Artificial Neural

scholarly journals Crack Detection in Folded Plates With Back-Propagated Artificial Neural Network

2021 ◽  
Author(s):  
Can Gonenli ◽  
Oguzhan Das ◽  
Duygu Bagci Das
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Damage Detection ◽  
Crack Detection ◽  
Structural Integrity ◽  
Engineering Structures ◽  
Detection Techniques ◽  
Detection Model ◽  
Artificial Neural ◽  
Destructive Methods

Abstract Engineering structures may face various damages such as crack, delamination, and fatigue in several circumstances. Localizing such damages becomes essential to understand the health of the structures since they may not be able to operate anymore. Among the damage detection techniques, non-destructive methods are considerably more preferred than destructive methods since damage can be located without affecting the structural integrity. However, these methods have several drawbacks in terms of detecting abilities, time consumption, cost, and hardware or software requirements. Employing artificial intelligence techniques could overcome such issues and could provide a powerful damage detection model if the technique is utilized correctly. In this study, the crack localization in flat and folded plate structures has been conducted by employing a Back-propagated Artificial Neural Network (BPANN). For this purpose, cracks with 18 different dimensions have been modeled in flat and four different folded structures by utilizing the Finite Element Method. The dataset required to perform the crack localization procedure includes the first ten natural frequencies of all structures as input variables. As output variables, the dataset contains a total of 500 crack locations for five structures. It is concluded that the BPANN can localize all cracks with an average accuracy of 95.12%.

scholarly journals Analysis of the detection of an attack based on SQL injection using an impulse artificial neural network

2021 ◽  
pp. 57-67
Author(s):  
Anastasiya Arkhipova ◽  
◽  
Pavel Polyakov ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Neural System ◽  
Mathematical Basis ◽  
Sql Injection ◽  
The Real ◽  
Detection Model ◽  
Injection Attacks ◽  
Artificial Neural ◽  
Sql Injection Attacks

This article presents the results of testing to create a specialized system that helps prevent cyberattacks, thus popularizing the construction of intelligent applications. Based on the results obtained, it can be argued that the tests carried out are satisfactory. The mathematical basis for building a neural network model is the HESADM model (Hybrid Artificial Intelligence Framework). The presented system allows you to form a set of rules using fuzzy logical neurons. This paper presents an approach to the formation of a fuzzy neural network used for detecting SQL injection attacks. The methodology used in this paper is an impulse artificial neural network (SANN), which uses an evolving neural network system (eCOS) and a multi-layer approach of an impulse artificial neural network to classify the exact type of intrusion or network anomaly with minimal computational potential. The impulse artificial neural system forms itself continuously, adapting to the input data, being in a functioning or not state, being under the supervision of an administrator. This system finds application to several other complex problems of the real world, proving its efficiency, including in the field of information security. The considered model is a hybrid evolving pulse anomaly detection model (HESADM), which works on impulses that occur in the system, while neurons are used to monitor the algorithm using a single training pass. In the system, traffic-oriented data is used by importing classes that use variable encoding. The data used is obtained by converting the real characteristics of network traffic into certain time stamps.

scholarly journals Automated Valve Fault Detection Based on Acoustic Emission Parameters and Artificial Neural Network

2019 ◽  
Vol 255 ◽  
pp. 02013
Author(s):  
M. Ali Al-Obaidi Salah ◽  
K.H. Hui ◽  
L.M. Hee ◽  
M. Salman Leong ◽  
Ali Abdul-Hussain Mahdi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Fault Detection ◽  
Reciprocating Compressor ◽  
Operational Conditions ◽  
Detection Model ◽  
Machinery Condition Monitoring ◽  
Artificial Neural ◽  
Popular Classes

Reciprocating compressor is one of the most popular classes of machines use with wide applications in the industry. However, valve failures in this machine often results unplanned shutdown. Therefore, the effective valve fault detection technique is very necessary to ensure safe operation and to reduce the unplanned shutdown. This paper propose an artificial intelligence (AI) model to detect valve condition in reciprocating compressor based on acoustic emission (AE) parameters measurement and artificial neural network (ANN). A set of experiments were conducted on an industrial reciprocating air compressor with several operational conditions including good valve and faulty valve to acquire AE signal. A fault detection model was then developed from the combination of healthy-faulty data using ANN tool box available in MATLAB. The results of the model validation demonstrated accuracy of valves condition classification exceeding 97%. Eventually, the authors intend to do more efforts for programming this model in smart portable device which can be one of the innovative engineering technologies in the field of machinery condition monitoring in the near future.

scholarly journals Self-Improving Generative Artificial Neural Network for Pseudorehearsal Incremental Class Learning

Algorithms ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 206 ◽  
Author(s):  
Diego Mellado ◽  
Carolina Saavedra ◽  
Steren Chabert ◽  
Romina Torres ◽  
Rodrigo Salas
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Novelty Detection ◽  
Activation Function ◽  
Learning Sequence ◽  
Data Set ◽  
Detection Model ◽  
Neural Network System ◽  
Small Set ◽  
Artificial Neural

Deep learning models are part of the family of artificial neural networks and, as such, they suffer catastrophic interference when learning sequentially. In addition, the greater number of these models have a rigid architecture which prevents the incremental learning of new classes. To overcome these drawbacks, we propose the Self-Improving Generative Artificial Neural Network (SIGANN), an end-to-end deep neural network system which can ease the catastrophic forgetting problem when learning new classes. In this method, we introduce a novel detection model that automatically detects samples of new classes, and an adversarial autoencoder is used to produce samples of previous classes. This system consists of three main modules: a classifier module implemented using a Deep Convolutional Neural Network, a generator module based on an adversarial autoencoder, and a novelty-detection module implemented using an OpenMax activation function. Using the EMNIST data set, the model was trained incrementally, starting with a small set of classes. The results of the simulation show that SIGANN can retain previous knowledge while incorporating gradual forgetfulness of each learning sequence at a rate of about 7% per training step. Moreover, SIGANN can detect new classes that are hidden in the data with a median accuracy of 43 % and, therefore, proceed with incremental class learning.

scholarly journals Self-Improving Generative Artificial Neural Network for Pseudo-Rehearsal Incremental Class Learning

2019 ◽  
Author(s):  
Diego Mellado ◽  
Carolina Saavedra ◽  
Steren Chabert ◽  
Romina Torres ◽  
Rodrigo Salas
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Novelty Detection ◽  
Activation Function ◽  
Learning Sequence ◽  
Data Set ◽  
Detection Model ◽  
Neural Network System ◽  
Small Set ◽  
Artificial Neural

Deep learning models are part of the family of artificial neural networks and, as such, it suffers of catastrophic interference when they learn sequentially. In addition, most of these models have a rigid architecture which prevents the incremental learning of new classes. To overcome these drawbacks, in this article we propose the Self-Improving Generative Artificial Neural Network (SIGANN), a type of end-to-end Deep Neural Network system which is able to ease the catastrophic forgetting problem when leaning new classes. In this method, we introduce a novelty detection model to automatically detect samples of new classes, moreover an adversarial auto-encoder is used to produce samples of previous classes. This system consists of three main modules: a classifier module implemented using a Deep Convolutional Neural Network, a generator module based on an adversarial autoencoder; and a novelty detection module, implemented using an OpenMax activation function. Using the EMNIST data set, the model was trained incrementally, starting with a small set of classes. The results of the simulation show that SIGANN is able to retain previous knowledge with a gradual forgetfulness for each learning sequence. Moreover, SIGANN can detect new classes that are hidden in the data and, therefore, proceed with incremental class learning.

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