Objective image quality assessment measures were extensively used to evaluate the performance of different imageprocessing and analysis algorithms. However, they are application-driven. In contrast, subjective assess...
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The proceedings contain 180 papers. The topics discussed include: analysis of medical data and machine-learning algorithms from the perspective of public-goods models of data-provision decision making;price competitio...
The proceedings contain 180 papers. The topics discussed include: analysis of medical data and machine-learning algorithms from the perspective of public-goods models of data-provision decision making;price competition for service provision with different bargaining abilities;survey on stakeholder cooperative behavior for designing voluntary medical data provision motivation mechanisms;analysis of excellent service systems from co-creation and emergent synthesis perspective;meta-heuristic scheduling auction applying distributed genetic algorithm;the impact of characteristic function of Shapley value mechanism in distributed machine learning environment for equipment diagnosis;automatic measurement of timber diameter using imageprocessing;intelligent scheduling based on discrete-time simulation using machine learning;and a fuzzy synthesis approach for hierarchical decision analysis to select optimum repair technique.
The paper considers the main procedures of digital processing of endoscopic images aimed at increasing their diagnostic value. It is shown that the existing approaches, solving private problems, lead to the general in...
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This research focuses on the application of artificial intelligence in the modern design field and proposes a solution to build an information visualisation design platform based on natural language processing technol...
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Inspection of aircraft skin is required as per the Corrosion Prevention and Control Program (CPCP) to ensure aircraft structural integrity. Human visual inspection is the most widely used technique in aircraft surface...
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Studying circumstellar environments is crucial for understanding exoplanets and stellar systems. Instruments like SPHERE can extract information about these environments by leveraging advanced image reconstruction met...
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ISBN:
(数字)9789464593617
ISBN:
(纸本)9798331519773
Studying circumstellar environments is crucial for understanding exoplanets and stellar systems. Instruments like SPHERE can extract information about these environments by leveraging advanced image reconstruction methods, possibly based on deep learning. This work focuses on unfolded proximal neural networks based on Condat- vii iterations and proposes a new nonlinear formulation. To evaluate and compare the performance of the proposed reconstruction strategies, two datasets dedicated to circumstellar environments analysis in the context of high-contrast imagery have been created offering different level of complexity in the evaluation of the performance.
The proceedings contain 16 papers. The topics discussed include: artificial intelligence for the future of construction;cobots and industrial robots;predictive maintenance for wind turbine bearings: an MLOps approach ...
The proceedings contain 16 papers. The topics discussed include: artificial intelligence for the future of construction;cobots and industrial robots;predictive maintenance for wind turbine bearings: an MLOps approach with the DIAFS machine learning model;development of an artificial intelligence tool and sensing in informatization systems of mobile robots;PCA-NuSVR framework for predicting local and global indicators of tunneling-induced building damage;design and deployment of data development toolkit in cloud manufacturing environments;research and development of imageprocessingalgorithms for effective recognition of various gestures in real time;machine learning models for the recognition of commands in smart home technologies;responsive dehydration: sensor-driven optimisation of production cycles in a solar dehydrator;and formation of the method of description and control of the relative position of the links of the upper limbs of the grip of an anthropomorphic robot.
Solar photovoltaic systems have emerged as a prominent source of renewable energy. However, their performance can be hindered by various factors, including faults within the PV modules or the overall system. Rapid and...
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ISBN:
(纸本)9798350309140
Solar photovoltaic systems have emerged as a prominent source of renewable energy. However, their performance can be hindered by various factors, including faults within the PV modules or the overall system. Rapid and accurate detection of these faults is crucial for maintaining optimal energy production and ensuring system safety. This project proposes a novel approach for Solar Fault Detection using Convolutional Neural Network algorithm. The proposed system leverages the power of deep learning techniques to automatically analyse images of solar panels and identify potential faults. A comprehensive dataset of annotated solar panel images is used for training the CNN model. To account for real-world scenarios, the dataset includes images captured under various lighting conditions and angles. The CNN architecture is designed to extract intricate features from the images, allowing for precise fault identification. The model is trained on a diverse set of fault types, including but not limited to, micro cracks, hot spots, shading effects, and soiling. Transfer learning techniques are also employed to improve model performance and reduce training time. To account for real-world scenarios, the dataset includes images taken under different lighting circumstances and angles. The results demonstrate high accuracy, sensitivity, and specificity in detecting faults. Additionally, the system exhibits robustness to environmental variables such as weather conditions and time of day. In this user-friendly interface is developed to facilitate easy integration of the solar panel. The interface provides real-time feedback on the status of solar panels, highlighting any detected faults and their severity levels. This project presents a state-of-the-art approach for solar fault Detection using CNN algorithms, offers a best maintenance for the solar panel damages and fault. The proposed system holds significant potential for the solar panel damages and fault. The proposed system holds
Antenna position ambiguity is a common problem that affects radar imaging systems that are mounted on mobile platforms. Existing approaches that aim to recover a sharp radar image despite this ambiguity aim to estimat...
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ISBN:
(数字)9781665496209
ISBN:
(纸本)9781665496209
Antenna position ambiguity is a common problem that affects radar imaging systems that are mounted on mobile platforms. Existing approaches that aim to recover a sharp radar image despite this ambiguity aim to estimate the shift in the antenna position by modeling the radar scene as a sparse image with a small number of targets using explicit analytical models for the statistical distribution of the targets in a radar image. The radar imaging problem is then solved by alternating between estimating the radar image, followed by estimating the shift in the antenna positions, until convergence is reached. While such approaches have shown tremendous success, they still struggle to recover the true target positions and may arrive at incorrect local optima when the measurement noise level is high. In this work, we develop a data-driven learning-based strategy for modeling the image of the radar scene instead of relying on explicit analytical models. We adopt a residual Unet architecture of a neural network to act as a denoising operator which takes a backprojected radar image as input and outputs a true target image. While deep denoisers may generally result in unstable iterative algorithms, we introduce a simple filtering step that suppresses noise belonging to the null space of the radar operator from the iterates to stabilize the iterative procedure. We evaluate the effectiveness of our solution using simulated numerical experiments and demonstrate its superiority over the analytic signal prior.
Brain tumors, arising from unregulated and accelerated cellular proliferation, provide considerable health hazards if not promptly addressed. Notwithstanding significant progress, precise segmentation and classificati...
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