Energy-Efficient Design of Seabed Substrate Detection Model Leveraging CNN-SVM Architecture and Sonar Data

Q3 Engineering

EAI Endorsed Transactions on Energy Web Pub Date : 2024-06-04 DOI:10.4108/ew.6097

Keming Wang, Chengli Wang, Wenbing Jin, Liuming Qi

引用次数: 0

Abstract

This study introduces an innovative seabed substrate detection model that harnesses the complementary strengths of Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) to analyze sonar data with a focus on energy efficiency. The model addresses the challenges of underwater sensing and imaging, including variable lighting conditions, backscattering effects, and acoustic sensor limitations, while minimizing energy consumption. By leveraging advanced machine learning techniques, the proposed model aims to enhance seabed classification accuracy, a crucial aspect for marine operations, ecological studies, and energy-intensive underwater applications.The introduced ShuffleNet-DSE architecture demonstrates significant improvements in both accuracy and stability for seabed sediment image classification, while maintaining energy-efficient performance. This robust tool offers a valuable asset for underwater exploration, research, and monitoring efforts, especially in environments where energy resources are limited.

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利用 CNN-SVM 架构和声纳数据设计高能效海底底质探测模型

本研究介绍了一种创新的海底底质探测模型，该模型利用卷积神经网络（CNN）和支持向量机（SVM）的互补优势来分析声纳数据，重点关注能源效率。该模型解决了水下传感和成像所面临的挑战，包括多变的光照条件、后向散射效应和声学传感器的局限性，同时最大限度地降低了能耗。通过利用先进的机器学习技术，该模型旨在提高海底分类的准确性，这对于海洋作业、生态研究和能源密集型水下应用来说是至关重要的。这种强大的工具为水下勘探、研究和监测工作提供了宝贵的资产，尤其是在能源资源有限的环境中。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EAI Endorsed Transactions on Energy Web Energy-Energy Engineering and Power Technology

CiteScore

2.60

自引率

0.00%

发文量

审稿时长

10 weeks

期刊介绍： With ICT pervading everyday objects and infrastructures, the ‘Future Internet’ is envisioned to undergo a radical transformation from how we know it today (a mere communication highway) into a vast hybrid network seamlessly integrating knowledge, people and machines into techno-social ecosystems whose behaviour transcends the boundaries of today’s engineering science. As the internet of things continues to grow, billions and trillions of data bytes need to be moved, stored and shared. The energy thus consumed and the climate impact of data centers are increasing dramatically, thereby becoming significant contributors to global warming and climate change. As reported recently, the combined electricity consumption of the world’s data centers has already exceeded that of some of the world''s top ten economies. In the ensuing process of integrating traditional and renewable energy, monitoring and managing various energy sources, and processing and transferring technological information through various channels, IT will undoubtedly play an ever-increasing and central role. Several technologies are currently racing to production to meet this challenge, from ‘smart dust’ to hybrid networks capable of controlling the emergence of dependable and reliable green and energy-efficient ecosystems – which we generically term the ‘energy web’ – calling for major paradigm shifts highly disruptive of the ways the energy sector functions today. The EAI Transactions on Energy Web are positioned at the forefront of these efforts and provide a forum for the most forward-looking, state-of-the-art research bringing together the cross section of IT and Energy communities. The journal will publish original works reporting on prominent advances that challenge traditional thinking.