Seabed classification using physics-based modeling and machine learning

Frederick, Christina; Villar, Soledad; Michalopoulou, Zoi‐Heleni

doi:10.1121/10.0001728

Cited by 43 publications

(11 citation statements)

References 62 publications

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“…The stage of resource and energy prediction and evaluation based on the theory of seeking difference is mainly marked by the theory and method of "geological anomaly mineralization and metallogenic prediction" initiated in 1990 and "comprehensive information metallogenic prediction" [2]. In the stage of "digital ore prospecting" and resource and energy prediction and evaluation, the application of data science in mineral exploration is emphasized, and the practical problems in mineral exploration are solved by data analysis theory and method, with "triple" metallogenic prediction and resource evaluation theory and method as the main symbol [3]. The introduction of nonlinear and complexity theory and model into mineral resource evaluation is a rising research field in the world, and the representative one is the prediction theory and model of multiple formation proposed by Cheng Qiuming.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Geological Mineral Energy and Classification Based on Machine Learning

2021

Wireless Communications and Mobile Computing

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In order to mine geological mineral energy and study on geological mineral energy classification, a method based on a wireless sensor was proposed. Of logistic regression, artificial neural networks, random forests, and main wireless sensor algorithms of support vector machine (SVM) with the model in the application of the energy mineral resource prediction practice effects are reviewed and discuss the practical application in the process of sample selection, the wrong points existing in the cost, the uncertainty evaluation, and performance evaluation of the model using wireless sensor algorithm, random forest of the probability distribution of mineralization in the study area is calculated, and five prospecting potential areas are delineated. The results show that the ratio of ore-bearing unit and non-ore-bearing unit is 1 : 1, and the best random forest training model is obtained. 70% of the training sample set was randomly selected as the training set, and the remaining 30% was used as the test set to construct the random forest model. The training accuracy of the model is 96.7%, and the testing accuracy is 96.5%. Both model training accuracy and model testing accuracy are very high, which proves the accuracy of RF model construction and achieves satisfactory results. In this study, a wireless sensor is successfully applied to 3D mineral energy prediction, which makes a positive exploration for mineral resource prediction and evaluation in the future. Finally, the prediction of mineral resource energy based on a wireless sensor is an important trend of future development.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Geological Mineral Energy and Classification Based on Machine Learning

2021

Wireless Communications and Mobile Computing

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“…1D-VGG19 16 is a classical convolutional neural network that has been shown to have concise structure and large number of parameters. In the 1D-VGG19 network, each convolutional layer uses a 3 × 1 convolutional kernel, which consists of two or four convolutional layers stacked to form a convolutional sequence.…”

Section: D-vgg19mentioning

confidence: 99%

Evaluation of rice freshness based on a modified VGG network and terahertz imaging

Wang

Zhang

et al. 2023

Earth and Space: From Infrared to Terahertz (ESIT 2022)

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The freshness of rice reflects the time that has elapsed since it was harvested and the extent of deterioration in the quality of the rice that has occurred during storage. Therefore, it is crucial to detect the freshness of rice samples; here, we undertake that task using terahertz images and a modified VGG network. Terahertz imaging is non-destructive, permits molecular fingerprinting, and is low in energy consumption. Terahertz imaging technology uses terahertz rays to irradiate the sample and obtains a terahertz image of the sample by processing and analyzing the transmission and reflection spectra of the sample. Terahertz imaging technology has been widely used in applications related to material identification, medical diagnoses, quality detection of agricultural products, and safety inspections. In this paper, terahertz images of rice stored for various lengths of time were analyzed using a terahertz imaging system. Due to a large amount of data and inconspicuous features of the terahertz image, the traditional 1D-VGG network is relatively insufficient in computing power. Thus, it is not well suited to the extraction of features from within the images. To resolve this issue, the Inception-ResNet-A asymmetric convolution module in the Inception-ResNet-V2 network has great computing power,which is introduced into the VGG19 network structure. This proposed network is found to increase identification accuracy up to 99.8%. This work indicates that terahertz images combined with the modified 1D-VGG network represent an efficient and practical method for identifying rice freshness; this work thus has great potential for use as a tool for ensuring food quality and safety.

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“…Machine learning for underwater applications. Finally, there has been significant interest in applying machine learning for various underwater problems, such as recognition of marine animals using their sound or images [22,43], classifying seabeds [7], robotic navigation [13], and image enhancement [40]. However, past systems for underwater ML required instruments with dedicated energy sources (typically underwater drones).…”

Section: Related Workmentioning

confidence: 99%

Towards battery-free machine learning and inference in underwater environments

Zhao

Afzal

Akbar

et al. 2022

Proceedings of the 23rd Annual International Workshop on Mobile Computing Systems and Applications

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This paper is motivated by a simple question: Can we design and build battery-free devices capable of machine learning and inference in underwater environments? An affirmative answer to this question would have significant implications for a new generation of underwater sensing and monitoring applications for environmental monitoring, scientific exploration, and climate/weather prediction.To answer this question, we explore the feasibility of bridging advances from the past decade in two fields: battery-free networking and low-power machine learning. Our exploration demonstrates that it is indeed possible to enable battery-free inference in underwater environments. We designed a device that can harvest energy from underwater sound, power up an ultra-low-power microcontroller and on-board sensor, perform local inference on sensed measurements using a lightweight Deep Neural Network, and communicate the inference result via backscatter to a receiver. We tested our prototype in an emulated marine bioacoustics application, demonstrating the potential to recognize underwater animal sounds without batteries. Through this exploration, we highlight the challenges and opportunities for making underwater batteryfree inference and machine learning ubiquitous. CCS Concepts• Computing methodologies → Supervised learning; • Hardware → Sensor devices and platforms; Renewable energy; Wireless integrated network sensors; • Applied computing → Environmental sciences.

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Seabed classification using physics-based modeling and machine learning

Cited by 43 publications

References 62 publications

[Retracted] Geological Mineral Energy and Classification Based on Machine Learning

[Retracted] Geological Mineral Energy and Classification Based on Machine Learning

Evaluation of rice freshness based on a modified VGG network and terahertz imaging

Towards battery-free machine learning and inference in underwater environments

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