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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We present a novel method to control the rebounding behavior of small mm-sized solid balls by employing magnetoactive elastomers (MAEs) with microstructured surfaces. An MAE is a composite material consisting of mu m-...
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ISBN:
(纸本)9780791888322
We present a novel method to control the rebounding behavior of small mm-sized solid balls by employing magnetoactive elastomers (MAEs) with microstructured surfaces. An MAE is a composite material consisting of mu m-sized ferromagnetic particles dispersed in a soft elastomer (e.g., polydimethylsiloxane) matrix. In the act of rebounding, the ball hits an MAE surface and bounces back. The MAE samples contained 75 wt.% of iron. This composite material is known to respond to an applied magnetic field with increased stiffness (due to the magnetorheological effect) and plasticity. To adjust the rebound properties, the top layer of the MAE material was additionally modified by micromachining lamellar structures with different dimensions on the 100 mu m scale via laser ablation. Due to the resulting high aspect ratio, these surface structures were sensitive to the magnetic field direction. The lamellas could stand up straight or lay down flat. The rebound behavior was evaluated by using a custom build apparatus that facilitates dropping of the balls in a precise and repeatable manner. A ball was dropped from different heights. The ball trajectory was captured with a high-speed camera to investigate the rebound properties. The recorded video was processed using a custom software written in Python. The experimental procedure and data processingalgorithms are presented in detail. The results for the samples with different geometrical dimensions are provided as examples. It is made evident that the magnetic field influences the rebound properties of small non-magnetic balls impinging microstructured MAE surfaces. The change in surface topography is an effective way to control the ball rebound. The fabrication flexibility in geometrical dimensions of surface microstructures opens a convenient way to tune the desired response to magnetic fields. The presented idea may find applications in impact mitigation or small-scale sorting machinery, e.g. for recycling.
Medical image analysis is an invaluable tool in medicine. Different imaging modalities provide an effective means for mapping images that can feed machine and deep learning models which can significantly contribute to...
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A comprehensive benchmark is yet to be established in the image Manipulation Detection & Localization (IMDL) field. The absence of such a benchmark leads to insufficient and misleading model evaluations, severely ...
This paper presents an innovative way of image compression using Field-Programmable Gate Array (FPGA) implementation of the Integer Wavelet Transform (IWT) and Discrete Wavelet Transform (DWT) algorithms. For situatio...
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ISBN:
(数字)9798350384369
ISBN:
(纸本)9798350384376
This paper presents an innovative way of image compression using Field-Programmable Gate Array (FPGA) implementation of the Integer Wavelet Transform (IWT) and Discrete Wavelet Transform (DWT) algorithms. For situations where resources are limited, the flexibility and adaptability offered by the FPGA architecture are ideal. Our technique strikes a compromise between compression effectiveness and image quality by utilizing DWT for multi-resolution analysis and IWT for spatial redundancy reduction. Real-time processing and resource optimization are ensured by the FPGA implementation. FPGA-optimized algorithms that tackle resource constraints are among the contributions. Evaluations demonstrate enhanced signal-to-noise ratios, compression ratios, and execution times. This study highlights how fast FPGA can compress images, especially for embedded systems and space missions. The study not only improves image compression but also highlights how FPGA can be used to increase the effectiveness of signal processingalgorithms.
Bangladesh having a growing agricultural economy quick detection of plant leaf diseases is a primary requirement. Disease discovery with the existing diagnostic procedures demands a longer time. Therefore, growers fre...
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ISBN:
(纸本)9789811628771;9789811628764
Bangladesh having a growing agricultural economy quick detection of plant leaf diseases is a primary requirement. Disease discovery with the existing diagnostic procedures demands a longer time. Therefore, growers frequently miss the best time for stopping and treating diseases. Further, early identification and classification of pumpkin leaf diseases extremely needed. This paper proposes to discover the pumpkin leaf diseases by utilizing a modern imageprocessing procedure convolutional neural network (CNN). CNN applied for image classification and recognition because of its high accuracy. Besides, a comparison of traditional machine learning algorithms like support vector machines (SVM), K-nearest neighbor (KNN), decision tree, and Naive Bayes with the performance of CNN is demonstrated in our work. Tensorflow library was adopted to implement the CNN algorithm and Scikit-learn used in terms of utilizing the above-mentioned traditional machine learning algorithms. Finally, we detected the pumpkin leaf diseases by the algorithm that exhibits an assuring accuracy to our suggested approach.
The use of machine learning (ML) in the medical field is hindered by the scarcity of high-quality data. This work tackles the deficiency of echocardiogram pictures (echoCG) by using advanced generative models for synt...
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