Deep Learning and Artificial Neural Networks for Spacecraft Dynamics, Navigation and Control

Silvestrini, Stefano; Lavagna, Michèle

doi:10.3390/drones6100270

Cited by 32 publications

(16 citation statements)

References 74 publications

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“…It also has a spreading base that receives external inputs and determines effective ones. The ANN input layer receives feature vectors extracted by CNN models and hybrid features between CNN models [ 40 ]. The input layer consists of units with the same number of features extracted from the previous stage.…”

Section: Methodsmentioning

confidence: 99%

AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features

et al. 2023

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Melanoma is one of the deadliest types of skin cancer that leads to death if not diagnosed early. Many skin lesions are similar in the early stages, which causes an inaccurate diagnosis. Accurate diagnosis of the types of skin lesions helps dermatologists save patients’ lives. In this paper, we propose hybrid systems based on the advantages of fused CNN models. CNN models receive dermoscopy images of the ISIC 2019 dataset after segmenting the area of lesions and isolating them from healthy skin through the Geometric Active Contour (GAC) algorithm. Artificial neural network (ANN) and Random Forest (Rf) receive fused CNN features and classify them with high accuracy. The first methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid models CNN-ANN and CNN-RF. CNN models (AlexNet, GoogLeNet and VGG16) receive lesions area only and produce high depth feature maps. Thus, the deep feature maps were reduced by the PCA and then classified by ANN and RF networks. The second methodology involved analyzing the area of skin lesions and diagnosing their type early using the hybrid CNN-ANN and CNN-RF models based on the features of the fused CNN models. It is worth noting that the features of the CNN models were serially integrated after reducing their high dimensions by Principal Component Analysis (PCA). Hybrid models based on fused CNN features achieved promising results for diagnosing dermatoscopic images of the ISIC 2019 data set and distinguishing skin cancer from other skin lesions. The AlexNet-GoogLeNet-VGG16-ANN hybrid model achieved an AUC of 94.41%, sensitivity of 88.90%, accuracy of 96.10%, precision of 88.69%, and specificity of 99.44%.

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Section: Methodsmentioning

confidence: 99%

AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features

et al. 2023

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“…Neural Networks (FNNs), Recurrent Neural Networks (RNNs), or multilayer perceptrons (MLPs). FNNs are basic NNs with input, hidden and output layers that data flows in one direction without looping back to the input [77]. They are appropriate for problems with defined input-output relationships.…”

Section: Data-driven Modelingmentioning

confidence: 99%

AI-coherent data-driven forecasting model for a combined cycle power plant

Danish

Nazari

Senjyu

2023

Energy Conversion and Management

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“…2022 Visual Navigation Agricultural robot navigation [12]. 2022 Spacecraft Navigation Deep learning for spacecraft dynamics control, guidance and navigation [40]. 2022 Visual Navigation Unmanned underwater vehicles [41].…”

Section: Year Topic Highlightmentioning

confidence: 99%

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

Golroudbari¹,

Sabour²

2023

Preprint

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This review paper presents a comprehensive overview of end-to-end deep learning frameworks used in the context of autonomous navigation, including obstacle detection, scene perception, path planning, and control. The paper aims to bridge the gap between autonomous navigation and deep learning by analyzing recent research studies and evaluating the implementation and testing of deep learning methods. It emphasizes the importance of navigation for mobile robots, autonomous vehicles, and unmanned aerial vehicles, while also acknowledging the challenges due to environmental complexity, uncertainty, obstacles, dynamic environments, and the need to plan paths for multiple agents. The review highlights the rapid growth of deep learning in engineering data science and its development of innovative navigation methods. It discusses recent interdisciplinary work related to this field and provides a brief perspective on the limitations, challenges, and potential areas of growth for deep learning methods in autonomous navigation. Finally, the paper summarizes the findings and practices at different stages, correlating existing and future methods, their applicability, scalability, and limitations. The review provides a valuable resource for researchers and practitioners working in the field of autonomous navigation and deep learning.

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Deep Learning and Artificial Neural Networks for Spacecraft Dynamics, Navigation and Control

Cited by 32 publications

References 74 publications

AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features

AI Techniques of Dermoscopy Image Analysis for the Early Detection of Skin Lesions Based on Combined CNN Features

AI-coherent data-driven forecasting model for a combined cycle power plant

Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation -- A Comprehensive Review

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