Over the past decade, advancements in cellular phone technology have drastically transformed mobile phones from mere communication devices into powerful mini-computers. The integration of high-quality cameras with sma...
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Bearing faults pose a significant threat to the reliability and efficiency of rotating machinery systems, particularly in wind turbines. Traditional fault diagnosis algorithms often face limitations, such as processin...
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ISBN:
(纸本)9798350360875;9798350360868
Bearing faults pose a significant threat to the reliability and efficiency of rotating machinery systems, particularly in wind turbines. Traditional fault diagnosis algorithms often face limitations, such as processing single signal inputs from each sensor without considering interferences between components. To overcome these shortcomings and leverage the potential of processing multi-dimensional signal inputs, we propose SiamFD-net, a deep learning-based method. Focusing on wind turbine bearing fault diagnosis, our model employs a Siamese network architecture, which distinguishes between similar and dissimilar input pairs. This approach not only addresses the limitations of traditional algorithms but also contributes to mitigating the class imbalance problem commonly encountered in fault diagnosis. Experimental results using the Doosan Wind Turbine bearing dataset demonstrate the comprehensive view and diagnosis capability of our model. Furthermore, we validate the effectiveness of SiamFDnet using the Case Western Reserve University (CWRU) bearing fault diagnosis benchmark dataset. Our code is openly accessible at: https://***/SiamFDnet.
One of the first signs of structural deterioration is cracks in the concrete surface, which is important for maintenance because prolonged exposure will seriously harm the environment. The highly regarded method for i...
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Natural gas extraction systems often encounter manufacturing defects or develop defects over time, leading to gas leaks. These leaks pose challenges, causing revenue losses and environmental pollution. Detecting gas l...
ISBN:
(纸本)9781959025030
Natural gas extraction systems often encounter manufacturing defects or develop defects over time, leading to gas leaks. These leaks pose challenges, causing revenue losses and environmental pollution. Detecting gas leaks in the vast array of extraction, transfer, and storage equipment within these systems can be arduous, allowing leaks to persist unnoticed. Additionally, natural gas leaks are not visible to naked eyes, further complicating their detection. We developed a novel deep learning imageprocessing model that utilizes videos captured by a specialized Optical Gas Imaging (OGI) camera to detect natural gas leaks. The temporal deep learning algorithm is designed to identify patterns associated with gas leaks and improve its performance through supervised learning. Our model incorporates algorithms to detect background environments, motion, equipment, and classify gas leaks. Our model employs leak identification algorithms to determine the presence of gas leaks. These algorithms calculate the probability of detected motion indicating a gas leak based on long-term and short-term background subtraction, detected motion, motion duration, equipment location, and telemetry data. To minimize false positives, we have developed image segmentation and object detection models to identify known objects, such as equipment, people, and cars, within the video footage. To train our model we collect more than 10,000 short videos from real fields and include simulated data with known rate controlled gas release in different situations. Data consist of wide range of weather situations including different temperature, wind speed, humidity in sunny, rainy, and snowy fields. We validated our model by conducting experiments involving actual footage from the field. The model achieved a 98% true positive rate, and a 100% true negative rate, correctly refraining from sending an alarm for all non-releases. Additionally, we developed a postprocessing algorithm capable of estimating the
The increasing spread of data and text documents such as articles, web pages, books, posts on social networks, etc. on the Internet, creates a fundamental challenge in various fields of text processing under the title...
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ISBN:
(纸本)9798350314557
The increasing spread of data and text documents such as articles, web pages, books, posts on social networks, etc. on the Internet, creates a fundamental challenge in various fields of text processing under the title of "automatic text summarization". Manual processing and summarization of large volumes of textual data is a very difficult, expensive, time-consuming, and impossible process for human users. Text summarization systems are divided into extractive and abstract categories. In the extractive summarization method, the final summary of a text document is extracted from the important sentences of the same document without any kind of change. In this method, it is possible to repeat a series of sentences repeatedly and interfere with pronouns. But in the abstract summarization method, the final summary of a textual document is extracted from the meaning of the sentences and words of the same document or other documents. Many of the performed works have used extraction methods or abstracts to summarize the collection of web documents, each of which has advantages and disadvantages in the results obtained in terms of similarity or size. In this research, by developing a crawler, extracting the popular text posts from the Instagram social network, suitable pre-processing, and combining the set of extractive and abstract algorithms, the researcher showed how to use each of the abstract algorithms. and used extraction as a supplement to increase the accuracy and accuracy of another algorithm. Observations made on 820 popular text posts on the Instagram social network show the accuracy (80%) of the proposed system.
The fundamental components of automated retinal blood vessel segmentation for eye disease screening systems are segmentation algorithms, retinal blood vessel datasets, classification algorithms, performance measure pa...
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Over the years there has been huge improvements in the performance of imageprocessingalgorithms due to increase in computation power of Devices as well as use of Neural Networks. This paper focuses on comparison of ...
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With the rapid development of artificial intelligence technology, visual inspection and imageprocessingalgorithms have been continuously improved in accuracy and efficiency, and intelligent inspection systems based ...
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The quality of image and videos plays a vital role in case of real-Time systems. images are captured without sufficient illumination, lead to low dynamic range and high propensity for generating high noise levels. The...
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To address issues of insufficient sensitivity and weak anti-noise characteristics in existing image sharpness evaluation algorithms, we propose a method combining local variance and gradient analysis. Traditional meth...
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