The proceedings contain 87 papers. The topics discussed include: Auto-FP: an experimental study of automated feature preprocessing for tabular data;Data-CASE: grounding data regulations for compliant data processing s...
ISBN:
(纸本)9783893180950
The proceedings contain 87 papers. The topics discussed include: Auto-FP: an experimental study of automated feature preprocessing for tabular data;Data-CASE: grounding data regulations for compliant data processingsystems;data coverage for detecting representation bias in image datasets: a crowdsourcing approach;balancing utility and fairness in submodular maximization;stateful entities: object-oriented cloud applications as distributed dataflows;learning over sets for databases;a new PET for data collection via forms with data minimization, full accuracy and informed consent;adaptive compression for databases;analysis of open government datasets from a data design and integration perspective;fine-grained geo-obfuscation to protect workers’ location privacy in time-sensitive spatial crowdsourcing;and a framework to evaluate early time-series classification algorithms.
In traditional interval-set information systems (ISISs), each attribute is single-scale. However, processing and analyzing data at different scales is often necessary for practical applications. This paper introduces ...
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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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With the increasing availability of unmanned aerial vehicles (UAV), their potential misuse has become a serious concern, posing a threat to public security. Existing tracking methods have limitations in detecting UAV ...
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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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Data dimension reduction (DDR) is all about mapping data from high dimensions to low dimensions, various techniques of DDR are being used for image dimension reduction like Random Projections, Principal Component Anal...
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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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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
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.
In recent years, Point Cloud signal processing has received increased attention. Airborne LiDAR can measure the ground and generate point clouds in a cost-effective and rapid way. In order to generate an accurate Digi...
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