Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1007/s00521-015-2153-z |
Estimation of landmine characteristics in sandy desert using neural networks | |
Ali, Hussein F. M.1; El-Bab, Ahmed M. R. Fath1,2; Zyada, Zakarya1,3,4; Megahed, Said M.1 | |
通讯作者 | Ali, Hussein F. M. |
来源期刊 | NEURAL COMPUTING & APPLICATIONS |
ISSN | 0941-0643 |
EISSN | 1433-3058 |
出版年 | 2017 |
卷号 | 28期号:7页码:1801-1815 |
英文摘要 | Many places in the world are heavily contaminated with landmines, which cause that many resources are not utilized. This makes landmine detection and removal challenges for research. To guarantee reliable landmine sensing system, deep analysis and many test cases are required. The proposed concept is based on application of 1 kPa external constant pressure (lower than the landmine activation pressure) to the sand surface. The resultant contact pressure distribution is dependent on the imbedded object characteristics (type and depth). Then neural networks (NN) are trained to find the inverse solution of the sand-landmine problem. In other words, when the contact pressure is known, NN can estimate the imbedded object type and depth. In this work, using finite element modeling, the existence of landmines in sand is modeled and analyzed. The resultant contact pressure distribution for five objects (1-anti-tank, 2-anti-personnel, 3-can with diameter and height of 200 mm, 4-spherical rock with 200 mm diameter, and 5-sand without any object) in sand at different depths is used in training NN. Three NN are developed to estimate the landmine characteristics. The first one is perceptron type which classifies the introduced objects in sand. The other two feed-forward NN (FFNN) are developed to estimate the depth of two landmine types. The NN detection rates of anti-tank and anti-personnel landmines are 100 and 67 % in training, and 95 and 70 % in validation, respectively. As test cases, the detection rates of the NN in case of landmine inclination angles (0A degrees aEuro"30A degrees) are studied. The results show same detection rates as those at no inclination. A random noise 10 % of the average signal does not affect NN detection rates, which are the same as 95 and 70 % as in validation for anti-tank and anti-personnel, respectively, while with 20 % noise detection rates are decreases to 90 and 50 % for anti-tank and anti-personnel, respectively. |
英文关键词 | Landmine detection Contact sensing Finite element Artificial neural networks Inverse solution |
类型 | Article |
语种 | 英语 |
国家 | Egypt ; Malaysia |
收录类别 | SCI-E |
WOS记录号 | WOS:000404928900019 |
WOS关键词 | SURFACES ; SYSTEM ; MODEL |
WOS类目 | Computer Science, Artificial Intelligence |
WOS研究方向 | Computer Science |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/201243 |
作者单位 | 1.EJUST, Innovat Sch, Mechatron & Robot Engn Dept, Alexandria, Egypt; 2.Assiut Univ, Dept Mech Engn, Fac Engn, Assiut, Egypt; 3.UTM, Fac Mech Engn FKM, Utm Skudai 81310, Johor, Malaysia; 4.Tanta Univ, Mech Power Engn Dept On Leave, Fac Engn, Tanta 31511, Egypt |
推荐引用方式 GB/T 7714 | Ali, Hussein F. M.,El-Bab, Ahmed M. R. Fath,Zyada, Zakarya,et al. Estimation of landmine characteristics in sandy desert using neural networks[J],2017,28(7):1801-1815. |
APA | Ali, Hussein F. M.,El-Bab, Ahmed M. R. Fath,Zyada, Zakarya,&Megahed, Said M..(2017).Estimation of landmine characteristics in sandy desert using neural networks.NEURAL COMPUTING & APPLICATIONS,28(7),1801-1815. |
MLA | Ali, Hussein F. M.,et al."Estimation of landmine characteristics in sandy desert using neural networks".NEURAL COMPUTING & APPLICATIONS 28.7(2017):1801-1815. |
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