scholarly journals Inventory Management of Railway Logistics Park Based on Artificial Neural Network

2020 ◽  
Vol 53 (5) ◽  
pp. 715-723
Author(s):  
Li Gao ◽  
Huandi Dou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inventory Management ◽  
Logistics Park ◽  
Factors Affecting ◽  
Modern Information Technology ◽  
Main Factors ◽  
Modern Information ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In recent years, China has stepped up its support to the optimization and development of railways. Meanwhile, the development of modern information technology (IT) has enhanced the economic advantages of railway logistics. To intelligently manage the inventory of railway logistics park (RLP), this paper integrates artificial neural network (ANN) into RLP inventory management. Firstly, the functional demand of RLP inventory management was analyzed comprehensively, and the main factors affecting the inventory demand were divided into different categories. Then, the authors formulated the framework of intelligent inventory management for RLP, and put forward the strategy of continuous periodic inventory monitoring. Finally, a RLP inventory prediction model was constructed based on optimized genetic algorithm (GA) and backpropagation neutral network (BPNN), and proved effective through experiments. The research results provide reference for the application of ANN in inventory management and prediction in other logistics fields.

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.

Towards Developing the Piece-Wise Linear Neural Network Algorithm for Rule Extraction

2020 ◽  
pp. 1632-1649
Author(s):  
Veronica Chan ◽  
Christine W. Chan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Equations ◽  
Rule Extraction ◽  
Ann Model ◽  
Factors Affecting ◽  
Artificial Neural ◽  
Linear Neural Network ◽  
Artificial Neural Network Ann ◽  
Hidden Neurons

This paper discusses development and application of a decomposition neural network rule extraction algorithm for nonlinear regression problems. The algorithm is called the piece-wise linear artificial neural network or PWL-ANN algorithm. The objective of the algorithm is to “open up” the black box of a neural network model so that rules in the form of linear equations are generated by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN). The preliminary results showed that the algorithm gives high fidelity and satisfactory results on sixteen of the nineteen tested datasets. By analyzing the values of R2 given by the PWL approximation on the hidden neurons and the overall output, it is evident that in addition to accurate approximation of each individual node of a given ANN model, there are more factors affecting the fidelity of the PWL-ANN algorithm Nevertheless, the algorithm shows promising potential for domains when better understanding about the problem is needed.

scholarly journals Semi-Adaptive Control Systems on Self-Balancing Robot using Artificial Neural Networks

2021 ◽  
Vol 5 (2) ◽  
pp. 176-192
Author(s):  
Eko Setiawan ◽  
Dahnial Syauqy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Control ◽  
Control System ◽  
Confusion Matrix ◽  
Sensor Data ◽  
Factors Affecting ◽  
Artificial Neural ◽  
Balancing Robot ◽  
Artificial Neural Network Ann

A self-balancing type of robot works on the principle of maintaining the balance of the load's position to remains in the center. As a consequence of this principle, the driver can go forward reverse the vehicle by leaning in a particular direction. One of the factors affecting the control model is the weight of the driver. A control system that has been designed will not be able to balance the system if the driver using the vehicle exceeds or less than the predetermined weight value. The main objective of the study is to develop a semi-adaptive control system by implementing an Artificial Neural Network (ANN) algorithm that can estimate the driver's weight and use this information to reset the gain used in the control system. The experimental results show that the Artificial Neural Network can be used to estimate the weight of the driver's body by using 50-ms-duration of tilt sensor data to categorize into three defined classes that have been set. The ANN algorithm provides a high accuracy given by the results of the confusion matrix and the precision calculations, which show 99%.

scholarly journals Photodegradation of roxarsone in the aquatic environment: influencing factors, mechanisms and artificial neural network modeling

2021 ◽  
Author(s):  
Jizhong Meng ◽  
Arong Arong ◽  
Shoujun Yuan ◽  
Wei Wang ◽  
Juliang Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Influencing Factors ◽  
Aquatic Environment ◽  
Feed Additive ◽  
Neural Network Modeling ◽  
Factors Affecting ◽  
Indirect Photolysis ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Roxarsone (ROX) is an organoarsenic feed additive, and can be discharged into aquatic environment. ROX can photodegrade into more toxic inorganic arsenics, causing arsenic pollution. However, the photodegradation behavior of ROX in aquatic environment is still unclear. To better understand ROX photodegradation behavior, this study investigated the ROX photodegradation mechanism and influencing factors, and modeled the photodegradation process. The results showed that ROX in the aquatic environment was degraded to inorganic As(III) and As(V) under light irradiation. The degradation efficiency was enhanced by 25 % with the increase of light intensity from 300 µW/cm2 to 800 µW/cm2 via indirect photolysis. The photodegradation was temperature dependence, but was only slightly affected by pH. Nitrate ion (NO3−) had an obvious influence, but sulfate, carbonate, and chlorate ions had a negligible effect on ROX degradation. Dissolved organic matter (DOM) in the solution inhibited the photodegradation. ROX photodegradation was mainly mediated by reactive oxygen species (in the form of single oxygen 1O2) generated through ROX self-sensitization under irradiation. Based on the data of factors affecting ROX photodegradation, ROX photodegradation model was built and trained by an artificial neural network (ANN), and the predicted degradation rate was in good agreement with the real values with a root mean square error of 1.008. This study improved the understanding of ROX photodegradation behavior and provided a basis for controlling the pollution from ROX photodegradation.

Towards Developing the Piece-Wise Linear Neural Network Algorithm for Rule Extraction

2017 ◽  
Vol 11 (2) ◽  
pp. 57-73 ◽  
Author(s):  
Veronica Chan ◽  
Christine Chan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Equations ◽  
Rule Extraction ◽  
Ann Model ◽  
Factors Affecting ◽  
Artificial Neural ◽  
Linear Neural Network ◽  
Artificial Neural Network Ann ◽  
Hidden Neurons

This paper discusses development and application of a decomposition neural network rule extraction algorithm for nonlinear regression problems. The algorithm is called the piece-wise linear artificial neural network or PWL-ANN algorithm. The objective of the algorithm is to “open up” the black box of a neural network model so that rules in the form of linear equations are generated by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN). The preliminary results showed that the algorithm gives high fidelity and satisfactory results on sixteen of the nineteen tested datasets. By analyzing the values of R2 given by the PWL approximation on the hidden neurons and the overall output, it is evident that in addition to accurate approximation of each individual node of a given ANN model, there are more factors affecting the fidelity of the PWL-ANN algorithm Nevertheless, the algorithm shows promising potential for domains when better understanding about the problem is needed.

scholarly journals MODEL PERUBAHAN PENUTUPAN/PENGGUNAAN LAHAN UNTUK IDENTIFIKASI LAHAN KRITIS DI KABUPATEN BOGOR, KABUPATEN CIANJUR, DAN KABUPATEN SUKABUMI

2016 ◽  
Vol 18 (1) ◽  
pp. 21 ◽  
Author(s):  
Siti Hadjar Kubangun ◽  
Oteng Haridjaja ◽  
Komarsa Gandasasmita
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Spatial Models ◽  
Change Models ◽  
The Road ◽  
Sustainable Environment ◽  
Main Factors ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<div class="WordSection1"><p class="abstrak">Pemanfaatan lahan yang melampaui kemampuan lahannya, dapat mengakibatkan degradasi lahan. Degradasi lahan jika dibiarkan akan menimbulkan lahan kritis. Dampak yang terjadi akibat lahan kritis mengakibatkan lahan mengalami penurunan kualitas sifat-sifat tanah, penurunan fungsi konservasi, fungsi produksi, hingga berpengaruh pada kehidupan sosial dan ekonomi masyarakat yang memanfaatkan lahan tersebut. Penelitian ini bertujuan untuk mengidentifikasi lahan kritis, berdasarkan pemodelan perubahan penutupan/penggunaan lahan dengan metode <em>Artificial Neural Network</em> (ANN). Hasil penelitian ini menunjukkan bahwa lahan-lahan yang tergolong kritis mencakup lahan berlereng dengan penutupan/penggunaan lahan yang telah terkonversi. Faktor utama penyebab konversi lahan adalah tingginya kebutuhan hidup terhadap pangan, sandang, dan papan, akibat meningkatnya kepadatan penduduk. Selain hal tersebut, kemiringan lereng, jarak dari jalan, dan jarak dari permukiman juga menjadi faktor penyebab perubahan lahan. Upaya pemanfaatan lahan sebaiknya didukung oleh peningkatan kualitas sumber daya manusia, yang tidak hanya berorientasi pada kebutuhan sosial dan ekonomi, namun juga berorientasi pada lingkungan yang berkelanjutan.</p><p class="katakunci"><strong>Kata kunci</strong>:  jaringan saraf tiruan, perubahan penutupan/penggunaan lahan, model spasial, lahan kritis.</p><p class="judulABS"><strong>ABSTRACT</strong></p><p class="Abstrakeng"><em>Over used of land can caused the degradation, it can be lead to the critical of the land. The impacts of this issue such as the decreasing of the soil characteristics quality, conservation function, production, affecting social and economic of the society which used the land.  This research aims to identify the critical land based on the land use cover change models with Artificial Neural Network (ANN) method. This research shows the critical lands including land with the slope which has been converted with the land use cover change models. The main factors caused land converse are the high of need of food, clothing, and shelter, cause of the increasing population density. Besides those factors, the shape of slope, distance from the road and settlements   are also the result of the land changing. The efforts in using the land should be supported by the increasing of the human resources, which are not only be oriented on the need of social and economic, but also on the sustainable environment.</em></p><p class="katakunci"><em><strong>Keywords</strong>: artificial neural network (ANN), land use cover change (LUCC), spatial models, critical land.</em></p></div><strong><br clear="all" /></strong>

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
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