scholarly journals Artificial neural network based meta-heuristic for performance improvement in physical internet supply chain network

2021 ◽  
Vol 24 (2) ◽  
pp. 1161
Author(s):  
Chouar Abdelsamad ◽  
Tetouani Samir ◽  
Soulhi Aziz ◽  
Elalami Jamila
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supply Chain ◽  
Logistics System ◽  
Physical Internet ◽  
Feed Forward Neural Networks ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Cost ◽  
The University

<p>Nowadays, reducing total costs while enhancing customer satisfaction is a major task for many supply chain systems. To deal with this issue, the physical internet (PI) paradigm can be represented as a potential replacement for the current logistics system. This paper devoted the cost reduction and lead time improvement in a PI-SCN using a hybrid framework based on an artificial neural network (ANN) and an improved slime mould algorithm (ISMA). To address the performance of the proposed framework, a real-case study in Morocco is considered. The new trainer ISMA’s performance has been investigated in three approximation datasets from the University of California at Irvine (UCI) machine-learning repository regarding nine recent metaheuristics. The experimental results highlight the effectiveness of ISMA according to other meta heuristics for training feed-forward neural networks (FNNs) to converge speed and to avoid local minima.</p>

scholarly journals Artificial neural network vector controlled common high-side switch asymmetric converter fed switched reluctance motor drive

2021 ◽  
Vol 24 (2) ◽  
pp. 697
Author(s):  
Ashok Kumar Kolluru ◽  
Malligunta Kiran Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Machine ◽  
Switched Reluctance Motor ◽  
Switched Reluctance ◽  
Reluctance Machine ◽  
Reluctance Motor ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Cost

<p>The best alternative machine for synchronous and induction machine is switched reluctance machine for various applications. An artificial neural network (ANN) based vector controller is implemented for novel converter to drive switched reluctance motor (SRM) in this paper. To reduce the cost and simplified the controller an effective configuration of converter is proposed with only 4 pulse-withmodulation (PWM) based switches. The 6 pole stator and 4 pole rotor machine is considered in this paper to present results based on MATLAB. The ripples in torque are reduced by proposing vector controller by using novel configuration of converter. Generally SRM machines are having high ripples in torque, hence less number of switches will be feasible solution to drive the machine in order to reduce ripples. The proposed controller can also help to operate system with less ripples in torque since the controller having both torque and flux hysteresis controllers. The extensive results are presented on Simulink platform to validate the proposed method under both steady state as well as transient conditions.</p>

scholarly journals Using Artificial Neural Network to Predict Rate of Penetration from Dynamic Elastic Properties in Nasiriya Oil Field

2020 ◽  
Vol 21 (2) ◽  
pp. 7-14
Author(s):  
Yasser A. Khudhaier ◽  
Fadhil S. Kadhim ◽  
Yousif K. Yousif
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Elastic Properties ◽  
Average Rate ◽  
Oil Field ◽  
Rate Of Penetration ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Cost ◽  
Dynamic Elastic Properties

   The time spent in drilling ahead is usually a significant portion of total well cost. Drilling is an expensive operation including the cost of equipment and material used during the penetration of rock plus crew efforts in order to finish the well without serious problems. Knowing the rate of penetration should help in speculation of the cost and lead to optimize drilling outgoings. Ten wells in the Nasiriya oil field have been selected based on the availability of the data. Dynamic elastic properties of Mishrif formation in the selected wells were determined by using Interactive Petrophysics (IP V3.5) software based on the las files and log record provided. The average rate of penetration and average dynamic elastic properties for the studied wells was determined and listed with depth. Laboratory measurements were conducted on core samples selected from two wells from the studied wells. Ultrasonic device was used to measure the transit time of compressional and shear waves and to compare these results with log records. The reason behind that is to check the accuracy of the Greenberg-Castagna equation that was used to estimate the shear wave in order to calculate dynamic elastic properties. The model was built using Artificial Neural Network (ANN) to predict the rate of penetration in Mishrif formation in the Nasiriya oil field for the selected wells. The results obtained from the model were compared with the provided rate of penetration from the field and the Mean Square Error (MSE) of the model was 3.58 *10-5.

scholarly journals Predicting the Land Value using Regression Techniques and Artificial Neural Network

2019 ◽  
Vol 9 (1S3) ◽  
pp. 431-436
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tamil Nadu ◽  
Research Work ◽  
Land Value ◽  
Regression Techniques ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Cost ◽  
Cost Of Construction

Land value can be an important factor which influences the cost of construction on working in the project. The land has socio-economic and environmental values and the confronted problems on land involves the increasing costs for developing the land such as built up, agricultural, residential, commercial and industrial areas. Hence this paper concentrates on prediction of land value by considering some important factors that affects it. The study area has been selected under Tirupur district, being a developing one in Tamil Nadu. The eleven areas in four different taluks under Tirupur district were chosen for research work. The average values of monthly variation are taken for the chosen factor for the years from 2001 to 2017. Using regression analysis and artificial neural network, the prediction has been done for the future land value. The performance of both the model executed good and fit for forecasting results. Though both the model showed better results, Artificial Neural Network (ANN) showed accuracy than regression method.

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 Very Short-Term Power Forecasting of High Concentrator Photovoltaic Power Facility by Implementing Artificial Neural Network

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3493
Author(s):  
Yaser I. Alamin ◽  
Mensah K. Anaty ◽  
José Domingo Álvarez Hervás ◽  
Khalid Bouziane ◽  
Manuel Pérez García ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Data ◽  
Short Term ◽  
Prediction Horizon ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Short Term Prediction ◽  
The University

Concentrator photovoltaic (CPV) is used to obtain cheaper and more stable renewable energy. Methods which predict the energy production of a power system under specific circumstances are highly important to reach the goal of using this system as a part of a bigger one or of making it integrated with the grid. In this paper, the development of a model to predict the energy of a High CPV (HCPV) system using an Artificial Neural Network (ANN) is described. This system is located at the University of Rabat. The performed experiments show a quick prediction with encouraging results for a very short-term prediction horizon, considering the small amount of data available. These conclusions are based on the processes of obtaining the ANN models and detailed discussion of the results, which have been validated using real data.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

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

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.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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