scholarly journals Preliminary study on fault detection using artificial neural network for water-cooled reactors

2020 ◽  
Vol 14 (4) ◽  
pp. 7469-7480
Author(s):  
J. A. Karim ◽  
T. Lanyau ◽  
M. Maskin ◽  
M. A. S. Anuar ◽  
A. Che Soh ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Cooling System ◽  
Detection System ◽  
Real Data ◽  
The Real ◽  
Proposed Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In the PUSPATI TRIGA reactor (RTP), many variables and instruments need to be monitored to make sure it is functioning and running accordingly. The late detection of faults may result in accidents and affect workers’ safety and health. Therefore, an intelligent fault detection system is needed to detect faults in the process plant and alert for any safe point breach. This work was carried out to discover the use of an artificial neural network (ANN) to model and develop a fault detection programme in the RTP cooling system. Using actual data from the reactor to train the multilayer network model with backpropagation algorithm. Referring to the real data from the reactor, the simulation results demonstrate a good correlation between the proposed model using ANN and the real plants with a residual mean of below 1%. The preliminary results for fault detection show that ANN was able to predict the value of failure in residual factor by comparing the normal state and fault state of the plant. The proposed model using ANN method proofed that it could quickly diagnose the single fault and perform for any given failure. The research outcome could contribute to the improvement in frontier technologies and advanced manufacturing in Malaysia.

scholarly journals Determination of the Factors Affecting King Abdul Aziz University Published Articles in ISI by Multilayer Perceptron Artificial Neural Network

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 766
Author(s):  
Rashad A. R. Bantan ◽  
Ramadan A. Zeineldin ◽  
Farrukh Jamal ◽  
Christophe Chesneau
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Research Study ◽  
Real Data ◽  
Factors Affecting ◽  
King Abdulaziz University ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Deanship of scientific research established by the King Abdulaziz University provides some research programs for its staff and researchers and encourages them to submit proposals in this regard. Distinct research study (DRS) is one of these programs. It is available all the year and the King Abdulaziz University (KAU) staff can submit more than one proposal at the same time up to three proposals. The rules of the DSR program are simple and easy so it contributes in increasing the international rank of KAU. The authors are offered financial and moral reward after publishing articles from these proposals in Thomson-ISI journals. In this paper, multiplayer perceptron (MLP) artificial neural network (ANN) is employed to determine the factors that have more effect on the number of ISI published articles. The proposed study used real data of the finished projects from 2011 to April 2019.

scholarly journals A multilayer perceptron artificial neural network approach for improving the accuracy of intrusion detection systems

2020 ◽  
Vol 9 (4) ◽  
pp. 609
Author(s):  
Abdulrahman Jassam Mohammed ◽  
Muhanad Hameed Arif ◽  
Ali Adil Ali
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Multilayer Perceptron ◽  
Detection System ◽  
Denial Of Service ◽  
Detection Accuracy ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<p>Massive information has been transmitted through complicated network connections around the world. Thus, providing a protected information system has fully consideration of many private and governmental institutes to prevent the attackers. The attackers block the users to access a particular network service by sending a large amount of fake traffics. Therefore, this article demonstrates two-classification models for accurate intrusion detection system (IDS). The first model develops the artificial neural network (ANN) of multilayer perceptron (MLP) with one hidden layer (MLP1) based on distributed denial of service (DDoS). The MLP1 has 38 input nodes, 11 hidden nodes, and 5 output nodes. The training of the MLP1 model is implemented with NSL-KDD dataset that has 38 features and five types of requests. The MLP1 achieves detection accuracy of 95.6%. The second model MLP2 has two hidden layers. The improved MLP2 model with the same setup achieves an accuracy of 2.2% higher than the MLP1 model. The study shows that the MLP2 model provides high classification accuracy of different request types.</p>

scholarly journals Predicting the Duration of Concrete Operations Via Artificial Neural Network and by Focusing on Supply Chain Parameters

2014 ◽  
Vol 61 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Mojtaba Maghrebi ◽  
Claude Sammut ◽  
Travis S. Waller
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supply Chain ◽  
Theoretical Method ◽  
Construction Sites ◽  
Proposed Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Concrete Operations ◽  
Ready Mixed Concrete

Abstract Being able to precisely predict the duration of concrete operations can help construction managers to organize sites and machineries more efficiently, especially when there is limited space for equipment on site. Currently there is no theoretical method for estimating the duration of the concrete pouring process. Normally, the maximum capacity of pumping facilities on construction sites is not used, and concrete pumps are idle for a considerable time as a result of the arrival of concrete trucks being delayed. In the light of this issue, this paper considers the supply chain parameters of Ready Mixed Concrete (RMC) as a means of solving this problem. Artificial Neural Network (ANN) is hired for modelling/predicting the productivity of a concrete operation. The proposed model is tested with a real database of an RMC in the Sydney metropolitan area that has 17 depots and around 200 trucks. Results show that there is an improvement in the achieved results when these are compared to the results of relevant studies that only considered the construction parameters for predicting the productivity of concrete operations

scholarly journals A Conceptual Artificial Neural Network Model in Warehouse Receiving Management

2021 ◽  
Vol 11 (2) ◽  
pp. 130-136
Author(s):  
Judy X Yang ◽  
◽  
Lily D Li ◽  
Mohammad G. Rasul
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mechanical Design ◽  
Joint Probability ◽  
Initial Result ◽  
Ann Model ◽  
Accuracy Rate ◽  
Proposed Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The purpose of this research is to explore a suitable Artificial Neural Network (ANN) method applying to warehouse receiving management. A conceptual ANN model is proposed to perform identification and counting of components. The proposed model consists of a standard image library, an ANN system to present objects for identification from the real-time images and to count the number of objects in the image. The authors adopted four basic mechanical design shapes as the attributes of images for shape analysis and pre-defined features; the joint probability from Bayes theorem and image pixel values for object counting is applied in this research. Compared to other ANNs, the proposed conceptual model is straightforward to perform classification and counting. The model is tested by employing a mini image dataset which is industrial enterprise relevant. The initial result shows that the proposed model has achieved an accuracy rate of 80% in classification and a 97% accuracy rate in counting. The development of the model is associated with a few challenges, including exploring algorithms to enhance the accuracy rate for component identification and testing the model in a larger dataset.

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