Cloud movement impacts the performance of photovoltaic (PV) power plants by causing sudden fluctuations in output power, leading to voltage instability in connected electricity networks. This paper introduces a novel ...
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ISBN:
(数字)9798331518943
ISBN:
(纸本)9798331518950
Cloud movement impacts the performance of photovoltaic (PV) power plants by causing sudden fluctuations in output power, leading to voltage instability in connected electricity networks. This paper introduces a novel and cost-effective deeplearning solution for forecasting shortterm cloud movement to regulate PV power output. The methodology involves capturing raw image data through a PyCamera connected to a Raspberry Pi, followed by rigorous pre-processing and cloud detection techniques. Using colourbased segmentation, morphological operations, and contour analysis, the developed pipeline accurately identifies cloud regions in images and predicts their movement for the subsequent time interval. The proposed algorithm is designed to be efficient, lightweight, and suitable for deployment on low-cost processors. This study successfully implements and optimizes a cloud detection system for resource-constrained environments using the Raspberry Pi 4 Model B. The method significantly improves inference speed and model efficiency while addressing the high latency constraints associated with embedded devices with execution periods measured in seconds. The model performance metrics validate the approach, with an accuracy of 89.84%, indicating the practical potential of the model for real-time applications, offering a promising solution towards the integration of effective cloud forecasting into cost-sensitive energy management systems.
With the rapid development of artificial intelligence technologies, particularly deeplearning, the application of imageprocessing in emotion recognition and psychological therapy has become a growing area of researc...
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With the rapid development of artificial intelligence technologies, particularly deeplearning, the application of imageprocessing in emotion recognition and psychological therapy has become a growing area of research. As a crucial indicator of an individual's psychological state, accurate emotion recognition plays a vital role in psychological treatment and mental health management. Traditional emotion recognition methods primarily rely on subjective judgment by human experts, which has certain limitations. In contrast, deeplearning-based automated emotion recognition methods can capture emotional changes in real-time and with high accuracy through facial expressions, eye movement trajectories, and other image data, overcoming the shortcomings of traditional methods. Currently, emotion recognition technology is widely applied in fields such as psychological therapy, affective computing, and smart healthcare. However, existing research still faces challenges, including insufficient recognition accuracy, poor adaptability to individual differences, and weak integration with actual psychological therapy practices. In response to these issues, this paper proposes a deeplearning-based imageprocessing method that integrates multi-feature fusion techniques to improve the accuracy of emotion recognition. The method is applied to the detection of abnormal emotional states in psychological therapy and personalized emotion analysis. The results show that deeplearning technology can effectively recognize complex emotional changes and provide more accurate emotional intervention strategies for psychological therapy, offering significant theoretical and practical value.
In order to study the deeplearning model of urban planning image data processing and health intelligent system, based on existing remote sensing image change detection methods, the author introduces and proposes the ...
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In order to study the deeplearning model of urban planning image data processing and health intelligent system, based on existing remote sensing image change detection methods, the author introduces and proposes the use of deep belief networks in deeplearning to classify high-resolution remote sensing images and analyze urban expansion change detection. Compared with traditional methods, deeplearning has the highest overall accuracy and Kappa coefficient. deeplearning has the highest producer accuracy and relatively low misjudgment rate, making it the most suitable for studying the trend of urban built-up areas. By calculating the information entropy of the image to predict the number of hidden layer nodes, the time for deeplearning is greatly reduced. Under the same experimental conditions, the training time for each image can be shortened by 12 525 seconds has improved classification efficiency and made a significant contribution to research on urban expansion applications. Finally, the improved deep belief network was applied to classify and detect changes in the three phase remote sensing images of Beijing, and the urban expansion trend and characteristics of Beijing were analyzed. Provide technical reference and inspiration for urban planning and land use protection.
When processing the image, the image that is easy to process is not covered and missing. The image will be partially missing or pixels will be lost due to the low accuracy of the transmission equipment or the unstable...
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Experiments on public datasets suggest that this method certifies its effectiveness, reaches human-level performance, and outperforms current state-of-the-art methods with 92.8% on the extended Cohn-Kanade (CK+) and 8...
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Experiments on public datasets suggest that this method certifies its effectiveness, reaches human-level performance, and outperforms current state-of-the-art methods with 92.8% on the extended Cohn-Kanade (CK+) and 87.0% on FERPLUS.
“A locally-processed light-weight deep neural network for detecting colorectal polyps in wireless capsule endoscopes” propose a light-weight DNN model that has the potential of running locally in the WCE [2].
[...]only images indicating potential diseases are transmitted, saving energy on data transmission.
Background subtraction is a substantially important video processing task that aims at separating the foreground from a video in order to make the post-processing tasks efficient.
[...]several different techniques have been proposed for this task but most of them cannot perform well for the videos having variations in both the foreground and the background.
“Background subtraction in videos using LRMF and CWM algorithm,” a novel background subtraction technique is proposed that aims at progressively fitting a particular subspace for the background that is obtained from L1-low rank matrix regularization using the cyclic weighted median algorithm and a certain distribution of a mixture of Gaussian noise for the foreground [3].
Label-free molecular imaging based on Raman scattering is attractive for medical imaging applications. The long exposure time of Raman imaging is the most significant barrier for medical applications. Here, we will pr...
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ISBN:
(纸本)9781510647862;9781510647855
Label-free molecular imaging based on Raman scattering is attractive for medical imaging applications. The long exposure time of Raman imaging is the most significant barrier for medical applications. Here, we will present the improvement of imaging speed using deep-learning-based segmentation for coherent Raman endoscopic imaging. We used 3,600 nerve images obtained with coherent anti-Stokes Raman scattering endoscopy for the training of U-Net architecture. We investigated the shortest available exposure time relationship between the exposure time of the input images and the quality of the output images. As a result, the imaging speed accelerated to 37.5 images/min from 0.68 images/min when the segmentation quality satisfies the criterion required for medical imaging.
Crack monitoring has been a hot research topic in structural health monitoring. However, the current research on deeplearning-based crack image focuses more on cracks at a certain moment and ignores the full-time cra...
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Crack monitoring has been a hot research topic in structural health monitoring. However, the current research on deeplearning-based crack image focuses more on cracks at a certain moment and ignores the full-time crack expansion details, which are crucial for more reasonable evaluation and safety quantification of concrete structures. This paper proposes a new method based on the combination of improved You Only Look Once v7 (YOLOv7) algorithm, crack expansion benchmark method, improved deepLabv3+ algorithm, and image pro-cessing technology to monitor the whole process of crack development, including real-time crack recognition and real-time monitoring of crack dynamic expansion. The precision of the improved detection algorithm can be improved by a maximum of 5.34%, and the mean intersection over union (mIoU) of the improved segmentation algorithm can be improved by 0.15%, resulting in better segmentation results. The experimental results show that this method can efficiently and accurately achieve real-time tracking of crack dynamic expansion, especially for monitoring of tiny cracks.
Drilling is one of the most classical machining operations. real-time monitoring of drill wear can effectively testify whether the product fails to meet the specifications due to drill failure. This paper proposes a t...
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Drilling is one of the most classical machining operations. real-time monitoring of drill wear can effectively testify whether the product fails to meet the specifications due to drill failure. This paper proposes a tool wear assessment and life prediction model based on imageprocessing and deeplearning methods, which works effectively for small sample datasets and for low-quality images. The normal areas and worn areas of the drill bits are extracted using the U-Net network and traditional imageprocessing methods, respectively. Moreover, the original dataset is classified using the migration learning technique. The wear level of a drill bit can be accurately evaluated through experimental tests. Testing results show that the proposed method is more convenient and efficient than previous methods using manual measurements. These results can be applied to real-time drill wear monitoring, thus reducing part damage caused by tool wear.
Quantifying the intensity of leaf herbivory pressure is crucial for understanding the interaction between plants and herbivores in both applied and basic science. Visual estimates and digital analysis have been common...
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Quantifying the intensity of leaf herbivory pressure is crucial for understanding the interaction between plants and herbivores in both applied and basic science. Visual estimates and digital analysis have been commonly used to estimate leaf herbivore damage but are time-consuming which limits the amount of data that can be collected and prevent answering big picture questions that require large-scale sampling of herbivory pressure. Recent developments in deeplearning have provided a potential tool for automatic collection of ecological data from various sources. However, most applications have focused on identification and counting, and there is a lack of deeplearning tools for quantitative estimation of leaf herbivore damage. Here, we trained generative adversarial networks (GANs) to predict the intact status of damaged leaves and applied imageprocessing technique to estimate the area and percentage of leaf damage. We first described procedures for collecting leaf images, training GAN models, predicting intact leaves and calculating leaf area, with a Python package provided to enable hands-on application of these procedures. Then, we collected a large leaf data set to train a universal deeplearning model and developed an online app HerbiEstim to allow direct use of pretrained models to estimate herbivory damage of leaves. We tested these methods using both simulated and real leaf damage data. The procedures provided in our study greatly improved the efficiency of leaf herbivore damage estimation. Our test demonstrated that the reconstruction of damaged leaf image resembled the ground-truth image with a similarity of 98.8%. The estimation of leaf herbivore damage exhibited a high accuracy with an averaged root mean square error of 1.6% and had a general applicability to different plant taxa and leaf shapes. Overall, our work demonstrated the feasibility of applying deeplearning techniques to quantify leaf herbivory intensity. The use of GANs allows automatic e
This paper provides an in-depth literature review on imageprocessing techniques, focusing on deeplearning approaches for anomaly detection and classification in photovoltaics. It examines key components of UAV-based...
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This paper provides an in-depth literature review on imageprocessing techniques, focusing on deeplearning approaches for anomaly detection and classification in photovoltaics. It examines key components of UAV-based PV inspection, including data acquisition protocols, panel segmentation and geolocation, anomaly classification, and optimizations for model generalization. Furthermore, challenges related to domain adaptation, dataset limitations, and multimodal fusion of RGB and thermal data are also discussed. Finally, research gaps and opportunities are analyzed to create a holistic, scalable, and real-time inspection workflow for large-scale installation. This review serves as a reference for researchers and industry professionals to advance UAV-based PV inspection.
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