A Novel Deep CNN-RNN Approach for Real-time Impulsive Sound Detection to Detect Dangerous Events

Смайлов, Нуржигит; Dosbayev, Zhandos; Omarov, Nurzhan; Sadykova, Bibigul; Zhekambayeva, Maigul; Zhamangarin, Dusmat; Ayapbergenova, Assem

doi:10.14569/ijacsa.2023.0140431

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…The regression neural network is highly effective in determining the parameters of ultrasonic inspection, which has led to a significant increase in measurement accuracy, a critical aspect for ensuring the reliability and safety of industrial facilities (Fomin et al, 2023). The use of neural networks has made it possible to automate the process of analysing ultrasonic data, reducing dependence on the human factor, and speeding up the time to detect defects (Smailov et al, 2023). This contributed to the efficient use of resources and optimization of production processes.…”

Section: Discussionmentioning

confidence: 99%

Application of the regression neural network for the analysis of the results of ultrasonic testing

Andriievskyi,

Spivak,

Gogota

et al. 2024

Tehnìka ta energetika

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Conducting a study on this topic becomes relevant due to the great importance of the safety of critical infrastructure facilities and the presence of operational defects in equipment elements and pipelines, which poses serious threats, including the possibility of equipment destruction and negative environmental impact. The purpose of this work is to study the possibility of using the diffraction-time technique of ultrasonic non-destructive testing together with a deep convolutional neural network to accurately determine the numerical value of the height of an operational crack. The methods used include the analytical method, classification method, functional method, statistical method, synthesis method, and others. The study found that an automated approach to measuring crack height, based on diffraction signals and the use of neural networks, significantly improved the quality and accuracy of non-destructive testing. Ultrasonic testing is one of the most common inspection methods for detecting service cracks and is considered to be the most effective. It allows for reliable detection of defects and determination of their size without destroying the product. The results of the study emphasize the high potential and efficiency of the method in analysing the data obtained and provide confirmation of its applicability for determining the condition of objects during ultrasonic inspection. The paper emphasizes that these technologies are particularly important and effective. It is noted that their widespread use in various industries, such as medicine, aviation, and machine learning, demonstrates their power in solving complex problems. The practical significance of the work lies in the development of advanced approaches that provide new insights and methods to improve the efficiency of analysing the results, which can be applied in industry to improve the quality of control and reliability of technical facilities

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Section: Discussionmentioning

confidence: 99%

Application of the regression neural network for the analysis of the results of ultrasonic testing

Andriievskyi,

Spivak,

Gogota

et al. 2024

Tehnìka ta energetika

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“…[57], [58], [59] conducted a joint scientific study of the general principles of the application of digital technologies and artificial intelligence-based systems in the training of tourism workers and their subsequent activities. The researchers conclude that continuous technological progress poses new challenges for employees in various areas of modern business.…”

Section: Discussionmentioning

confidence: 99%

The growing use and impact of artificial intelligence technologies in the tourism industry

Indaryanto,

Harijadi,

Sinaga

2023

Sustainable Engineering and Innovation

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The research relevance is determined by the increasing role of artificial intelligence in the tourism business, which was demonstrated during the outbreak of the COVID-19 pandemic in Bali, resulting in a decline in the number of tourists visiting the island. The research aims to investigate the real-life experience of applying artificial intelligence systems in the tourism industry to address the challenges of developing the tourism industry in Bali, Indonesia. The basis of the methodological approach is a combination of theoretical methods of analysis and synthesis of the collected information covering the practical experience of the application of artificial intelligence systems in the tourism industry, as well as a survey of customers of travel agencies that have used such systems in trips to Bali. The research results show that artificial intelligence can significantly help the tourism business in Bali, as the number of tourists visiting the island dropped by 40% during the pandemic, resulting in a reduction in the number of tourism staff. The practical application of systems based on artificial intelligence opens up a wide range of business opportunities, as it allows for optimizing the activities of tourism firms and providing customers with high-quality services with highly technical equipment. The use of neural network mathematical models of tourist firms’ activity based on AI, allows them to achieve high speed of data processing, which allows tourist firms numerous competitive advantages and significantly improves the quality of their services. The practical significance of the results of this scientific research lies in the possibility of their application in the organization of the activities of enterprises in the sphere of tourism business, built on the use of digital technologies and systems based on artificial intelligence.

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Hyperparameter Optimization for Impulsive Sound Classifiers

Mendes,

Trigo,

Paulo

2024

Procedia Computer Science

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A Novel Deep CNN-RNN Approach for Real-time Impulsive Sound Detection to Detect Dangerous Events

Cited by 6 publications

References 32 publications

Application of the regression neural network for the analysis of the results of ultrasonic testing

Application of the regression neural network for the analysis of the results of ultrasonic testing

The growing use and impact of artificial intelligence technologies in the tourism industry

Hyperparameter Optimization for Impulsive Sound Classifiers

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