This Volume 3376 of the conference proceedings contains 22 papers. Topics discussed include sensorfusion, sensorfusionarchitectures, feature and decision level fusion, data and image level fusion and sensorfusion ...
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This Volume 3376 of the conference proceedings contains 22 papers. Topics discussed include sensorfusion, sensorfusionarchitectures, feature and decision level fusion, data and image level fusion and sensorfusionalgorithms.
In dynamic industrial environments, strategic sensor placement is key to accurately monitoring equipment and detecting critical events. Despite progress in Industry 4.0 and the Internet of Things, research on optimal ...
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In dynamic industrial environments, strategic sensor placement is key to accurately monitoring equipment and detecting critical events. Despite progress in Industry 4.0 and the Internet of Things, research on optimal sensor placement remains limited. This study addresses this gap by analyzing how sensor placement impacts event detection, using chemical detection as a case study with an open dataset. Detecting gases is challenging due to their dispersion. Effective algorithms and well-planned sensor locations are required for reliable results. Using deep convolutional neural networks (DCNNs) and decision tree (DT) methods, we implemented and tested detection models on a public dataset of chemical substances collected at five locations. In addition, we also implemented a multi-objective optimization approach based on the non-dominated sorting genetic algorithm ii (NSGA-ii) to identify optimal sensor configurations that balance high detection accuracy with cost efficiency in sensor deployment. Using the refined sensor placement, the DCNN model achieved 100% accuracy using only 30% of the available sensors.
Condition monitoring of rotating shafts is a critical task in the maintenance of mechanical systems. Rotating shafts are essential components in many machines, and their failure can result in serious consequences, inc...
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Condition monitoring of rotating shafts is a critical task in the maintenance of mechanical systems. Rotating shafts are essential components in many machines, and their failure can result in serious consequences, including system downtime, production loss, equipment damage, and safety issues. Advanced sensor technologies and deep learning algorithms have facilitated data collection and processing, providing vital insights into system health. However, despite the vast availability of sensors, the data used to train these algorithms often consists of single-nature signals, and systems are typically damaged to simulate different faulty scenarios. Additionally, the perceived opacity of deep learning algorithms, often referred to as black-box models, has raised concerns about their credibility in critical domains. Hence, this paper addresses these challenges by (i) proposing various explainable artificial intelligence (XAI) methods in a rotating shaft case study, (ii) using a novel data-fusion approach to combine multiple signals, and (iii) leveraging data from an innovative experimental set-up mimicking real-world industrial machines. The employed experimental set-up is equipped with a diverse array of sensors capable of capturing signals of varying nature, and it streamlines the automated introduction of four distinct fault types in an innovative manner. Applied to convolutional neural networks, the employed XAI methods enhance transparency in deep learning models, providing practical insights for complex systems. This approach not only addresses the limitations associated with single-nature signals and simulated faults but also contributes to the credibility and interpretability of deep learning models in critical applications.
Deep learning (DL)-based systems have emerged as powerful methods for the diagnosis and treatment of plant stress, offering high accuracy and efficiency in analyzing imagery data. This review paper aims to present a t...
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Deep learning (DL)-based systems have emerged as powerful methods for the diagnosis and treatment of plant stress, offering high accuracy and efficiency in analyzing imagery data. This review paper aims to present a thorough overview of the state-of-the-art DL technologies for plant stress detection. For this purpose, a systematic literature review was conducted to identify relevant articles for highlighting the technologies and approaches currently employed in the development of a DL-based plant stress detection system, specifically the advancement of image-based data collection systems, image preprocessing techniques, and deep learning algorithms and their applications in plant stress classification, disease detection, and segmentation tasks. Additionally, this review emphasizes the challenges and future directions in collecting and preprocessing image data, model development, and deployment in real-world agricultural settings. Some of the key findings from this review paper are: Training data: (i) Most plant stress detection models have been trained on Red Green Blue (RGB) images;(ii) Data augmentation can increase both the quantity and variation of training data;(iii) Handling multimodal inputs (e. g., image, temperature, humidity) allows the model to leverage information from diverse sources, which can improve prediction accuracy;Model Design and Efficiency: (i) Self-supervised learning (SSL) and Few-shot learning (FSL)-based methods may be better than transfer learning (TL)-based models for classifying plant stress when the number of labeled training images are scarce;(ii) Custom designed DL architectures for a specific stress and plant type can have better performance than the state-of-the-art DL architectures in terms of efficiency, overfitting, and accuracy;(iii) The multi-task learning DL structure reuses most of the network architecture while performing multiple tasks (e.g., estimate stress type and severity) simultaneously, which makes the learning much
This paper aims to develop a comprehensive approach for determining the attitude of a nanosatellite. Accurate determination of these parameters is essential for the proper operation of the nanosatellite and its succes...
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ISBN:
(纸本)9783031734199;9783031734205
This paper aims to develop a comprehensive approach for determining the attitude of a nanosatellite. Accurate determination of these parameters is essential for the proper operation of the nanosatellite and its successful mission completion. To achieve this goal, a detailed simulation of the magnetometer and sun sensor measurements in the body and orbital frame of the nanosatellite is conducted. These measurements are critical for determining the attitude of the nanosatellite and ensuring that it is pointing in the right direction. The attitude determination process is carried out using the Three-Axis Attitude Determination (TRIAD) algorithm, which is a popular and effective approach for estimating the attitude of a nanosatellite. The optimization of TRIAD with three different approaches is analyzed to ensure high accuracy and efficiency in determining the attitude of the nanosatellite. A covariance analysis is performed to evaluate the accuracy and reliability of the attitude determination process. The analysis provides valuable insights into the error sources and uncertainties associated with the measurements and estimation process. This information is used to improve the performance of the system and enhance the accuracy of the results. Overall, the results of this paper are expected to contribute significantly to the development of efficient and accurate methods for determining the attitude of a nanosatellite.
The proceedings contains 31 papers from the conference on sensor fusion: architectures, algorithms, and applications VI. Topics discussed include: adaptive sequential Bayesian classification using Page's test;onto...
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The proceedings contains 31 papers from the conference on sensor fusion: architectures, algorithms, and applications VI. Topics discussed include: adaptive sequential Bayesian classification using Page's test;ontology-based multiagent approach to data fusion;unified approach to the fusion of imperfect data;fusion approach to stereo vision uncertainity;robust fusion with reliabilities weights;distributed air-to-ground targeting;and fuser design for thick film pH sensor electrodes using emperical data.
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