利用人工智能技术从声导航和测距波中预测岩石和矿物

Akshat Khare, Kanchana Mani
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引用次数: 0

摘要

由于声波穿透海洋的深度比雷达和光波要深,声纳(声音导航和测距)被用来探测和绘制海洋地图。在采矿业工作时,工程师可能会发现SONAR是一个非常宝贵的工具,可以通过绘制频率信号来帮助他们可视化岩石和矿物的位置。假设一个物体在声脉冲的范围内,如果目标在声脉冲的范围内,声脉冲将被目标反射,并向声呐发射机的方向发出回波。发射器使用电源接收信号,并计算出信号的强度。它建立了脉冲产生和接收其相应信号之间的时间暂停。它分析脉冲发射和其匹配接收之间的持续时间,从而估计物体的距离和位置。工程师使用声波来确定物品。在人工智能的帮助下,将绕过评估、组织和识别项目的过程,以实现基于计划带宽立即识别项目的目标。在该方法中,采用PCA和t-SNE进行特征提取。利用逻辑回归和随机森林树等分类方法,准确率分别达到72%和91%。同样的,我们也使用了CNN和LSTM模型,最终它们的准确率分别达到了80.77%和99%左右。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Rock and Mineral from Sound Navigation and Ranging Waves using Artificial Intelligence Techniques
Since sound waves penetrate the sea more deeply than radar and light waves, SONAR (Sound Navigation and Ranging) is used to explore and map the ocean. When working in the mining industry, engineers may find SONAR to be an invaluable tool for helping them visualize the location of rocks and minerals by charting frequency signals. Assuming an object is within the sound pulse's range, the sound pulse will reflect off the target and send an echo in the direction of the sonar transmitter if the target is within the range of the sound pulse. The transmitter uses the power source to receive signals and figure out how strong the signals are. It establishes the pause in time between the generation of the pulse and the receiving of its corresponding signal. It analyzes the duration between the emission of the pulse and its matching reception, which estimates the distance and location of the matter. Engineers determine the item using an audio wave. With the assistance of AI, the process of evaluating, organizing, and identifying the item is going to be circumvented in order to accomplish the objectives of immediately identifying the item based on the scheduled bandwidths. In this proposed method, PCA and t-SNE are employed to extract features. Utilizing classification approaches such as Logistic Regression and Random Forest Tree, an accuracy of 72% and 91%, respectively, was attained. Similarly, CNN and LSTM models are also employed and finally they have yielded an accuracy of about 80.77% and 99% respectively.
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