Advanced Iterative Multi-Frequency Algorithm Used by Radar Remote-Sensing Systems for Oil-Spill Thickness Estimation

Daou, Georges; Maroun, Charbel Bou; Hammoud, Bilal

doi:10.1109/icecet52533.2021.9698742

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…• 2-D estimator iterative approach with multiple observations [74]: The presented algorithm uses optimized predefined 2-D constellation sets by utilizing the best pair of frequencies for each possible thickness value. Then, it processes sequentially the separate estimations done to optimize the estimation procedure.…”

Section: Estimationmentioning

confidence: 99%

“…Using dual-and tri-frequency estimators, one question will rise: which combination of frequencies is to be used? The best frequency pairs and triads, for each possible thickness between 1 and 10 mm, are derived in [74,67] respectively. [74] also proposes an advanced iterative procedure to use the 2D estimator for accurate and reliable thickness estimations.…”

Section: Estimationmentioning

confidence: 99%

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Recent Advances in Oil-Spill Monitoring Using Drone-Based Radar Remote Sensing

Hammoud¹,

Wehn²

2023

Environmental Sciences

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Oil spills are regrettably common and have socioeconomic implications on communities and disastrous consequences on the marine ecosystem and maritime life. The European Space Agency (ESA) has stated that worldwide spillage exceeds 4.5 million tons of oil annually, where 45% of the amount is due to operative discharges from ships. To alleviate the severity of oil spills and promptly react to such incidents, it is crucial to have oil-spill monitoring systems, which enable an effective contingency plan to dictate the best actions for dealing with oil spills. A quick and efficient intervention requires the (1) detection of oil slicks, (2) thickness estimation, and (3) oil classification. The European Maritime Safety Agency (EMSA) highlighted in 2016 the need to use drones as complementary systems supporting satellite maritime surveillance. While multiple sensors could be used, active radars appear to be prominent for oil spill monitoring. In this chapter, we present recent advances in drone-based radar remote sensing as an effective oil spill monitoring system. It shows from the system-level perspective the capability of radar systems on drones, using high spectral resolution and parallel scanning, to perform the above-required functionalities (1, 2, and 3) and provide valuable information to contain the damage.

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Section: Estimationmentioning

confidence: 99%

Section: Estimationmentioning

confidence: 99%

Recent Advances in Oil-Spill Monitoring Using Drone-Based Radar Remote Sensing

Hammoud¹,

Wehn²

2023

Environmental Sciences

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“…x y  1 , we use the sentence-level log-likelihood loss to train the model, which is commonly applied in many scenarios (Daou et al, 2021):…”

Section: Implementation Of Crfmentioning

confidence: 99%

A Web Semantic-Based Text Analysis Approach for Enhancing Named Entity Recognition Using PU-Learning and Negative Sampling

Zhang,

et al. 2023

International Journal on Semantic Web and Information Systems

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The NER task is largely developed based on well-annotated data. However, in many scenarios, the entities may not be fully annotated, leading to serious performance degradation. To address this issue, the authors propose a robust NER approach that combines a novel PU-learning algorithm and negative sampling. Unlike many existing studies, the proposed method adopts a two-step procedure for handling unlabeled entities, thereby enhancing its capability to mitigate the impact of such entities. Moreover, this algorithm demonstrates high versatility and can be integrated into any token-level NER model with ease. The effectiveness of the proposed method is verified on several classic NER models and datasets, demonstrating its strong ability to handle unlabeled entities. Finally, the authors achieve competitive performances on synthetic and real-world datasets.

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“…Currently, researchers both domestically and internationally have shifted their focus from traditional research based on dictionaries and machine learning to sentiment classification methods grounded in deep learning (Barbosa et al, 2022;Daou et al, 2021). For instance, Abdi et al (2019) acknowledged the strengths of CNN (convolutional neural network) and RNN, proposing an LSTM (long short-term memory) model that elevates the accuracy of sentiment classification in reviews by over 5% through multi-feature fusion.…”

Section: Introductionmentioning

confidence: 99%

Short Text Semantic Sentiment Analysis Based on Dual Channel Aspect Attention in Intelligent Systems

2024

International Journal on Semantic Web and Information Systems

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Traditional deep learning models for text sentiment analysis fail to fully harness the contextual semantic information of aspect nodes or use prior sentiment resources. This paper proposes a dual channel sentiment analysis model named M2BERT-BLSTM AA that is based on an enhanced Bidirectional Encoder Representations from Transformers(BERT)and Bidirectional Long short-term memory(BLSTM) model and incorporates a Dual Attention Mechanism. Firstly, an emotional resource database is constructed using existing emotional resources. Secondly, vectors are concatenated following mean and max pooling along the dimension of sentence length. These semantic features mitigate evaluation imbalance.Then the text and sentiment information are encoded separately, using distinct Attention Mechanism(Att-M) to extract contextual relationships and emotional features. The model's Aspect-Based multi-class sentiment prediction accuracies on the three Chinese datasets of Meituan ordering, restaurants, and laptops are 75.2%, 87.5%, and 75%, respectively, showing improved performance on sentiment classification.

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Advanced Iterative Multi-Frequency Algorithm Used by Radar Remote-Sensing Systems for Oil-Spill Thickness Estimation

Cited by 4 publications

References 10 publications

Recent Advances in Oil-Spill Monitoring Using Drone-Based Radar Remote Sensing

Recent Advances in Oil-Spill Monitoring Using Drone-Based Radar Remote Sensing

A Web Semantic-Based Text Analysis Approach for Enhancing Named Entity Recognition Using PU-Learning and Negative Sampling

Short Text Semantic Sentiment Analysis Based on Dual Channel Aspect Attention in Intelligent Systems

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