Research on Individual Recognition and Matching of Whale and Dolphin Based on EfficientNet Model

Wu, Siwen; Wang, Jiale; Ping, Yihan; Zhang, Xuan

doi:10.1109/icbaie56435.2022.9985881

Cited by 17 publications

(11 citation statements)

References 11 publications

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“…E cientNet-B5 achieves higher accuracy than the smaller E cientNet-B4 but requires less computing power than the larger E cientNet-B6. E cientNet-B5 performs particularly well when working with high-resolution images [24]. E cientNet balances CNN characteristics such as width, depth, and resolution with a technique called composite scaling.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Achieving High Accuracy in Lemon Quality Classification: A Comparative Study of Deep Learning and Transformer Models

Dümen,

Yılmaz,

Adem

et al. 2023

Preprint

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Agricultural product quality assessment is important for the efficiency and marketability of production. Quality assessment improves industry standards, increasing sales and reducing crop loss. Maintaining quality is of paramount importance for all processes, from production to sales. Artificial intelligence has recently been frequently used for product quality assessment in the agricultural field. Both in the literature and in practice, deep learning and machine learning methods are used to process images of agricultural products and evaluate their quality. They are classified according to specified standards. In this study, firstly, data augmentation operations were performed on the lemon dataset consisting of two classes, bad quality and good quality, by using rescaling, random zoom, random flip, and random rotation methods. Afterward, eight different deep-learning methods and two different transformer methods were used for classification. As a result of the study calculated the most successful result as 99.84% accuracy, 99.95% recall, and 99.66% precision with the ViT method. This value is the highest accuracy value in the literature. When the experimental results are evaluated, it shows that lemon classification processes are successfully performed using the ViT method.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Achieving High Accuracy in Lemon Quality Classification: A Comparative Study of Deep Learning and Transformer Models

Dümen,

Yılmaz,

Adem

et al. 2023

Preprint

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“…Fault Diagnosis and Reporting: The HIPPS system should also include fault diagnosis capabilities to detect issues with components such as sensors and actuators. Upon detecting faults, the system should report them promptly and take appropriate actions to ensure system reliability and stability 10 .…”

Section: Hipps System Control Objectivesmentioning

confidence: 99%

Reliability analysis of localized replacement of hipps high voltage protection system

Luo,

Feng,

Zhang

et al. 2023

Third International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2023)

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The transfer system of Hongliu Compressor Station of the West East Gas Pipeline two is the only peak shaving hub of the West–East Gas Pipeline network in Gansu Province, which is responsible for the liaison and joint commissioning of the West East Gas Pipeline one, two, and three trunk lines. The original peak shaving high-voltage protection system was designed and produced by Yokogawa Corporation in Japan and was put into operation in 2009. As of 2022, the HIPPS system control unit is about to reach its designed service life, and the performance and reliability of various control components in the control unit have significantly decreased, unable to meet the designed SIL3 level requirements. Moreover, the control system has been discontinued, and on-site operation and maintenance are subject to dual constraints of technology and spare parts, it seriously affects the stability of the joint debugging of the first, second, and third lines, and the upgrading and transformation of the control system is imperative. We plan to use the SIL3 level safety instrument control system TCS-900 series PLC with central control technology to replace the HIPPS system domestically and improve the operational reliability of the interconnection and distribution overpressure protection system.

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“…However, there are still some challenges such as the evaluation of encryption strength [31,32], robustness to different types of attacks [33] and adaptability of the algorithms to large-scale images [34][35][36]. Future research can address these issues and propose more efficient and secure CNN encryption algorithms [24,30,37,38].…”

Section: Related Workmentioning

confidence: 99%

Image Encryption Algorithm Combining Chaotic Image Encryption and Convolutional Neural Network

Feng

et al. 2023

Electronics

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With the rapid development of information technology, the security of images has emerged as a significant area of research. This study presents an algorithm that integrates chaotic image encryption and a convolutional neural network (CNN) to enhance security and efficiency. The algorithm applies the properties of randomness and nonlinear mapping of chaotic sequences with the advanced feature extraction capabilities of a CNN model to achieve robust image encryption. First, we outline the fundamentals of chaotic image encryption and CNN. Chaotic image encryption employs chaotic sequence generation and nonlinear mapping to scramble pixel values for encryption purposes, while a CNN, as a deep-learning model with a local perceptual field and weight sharing, effectively extracts high-level image features. Subsequently, we provide a detailed description of the specific steps involved in combining chaotic image encryption and the CNN. These steps include chaotic sequence generation, pixel value mapping, feature extraction, and key management. The algorithm achieves high-strength encryption by performing dissimilarity operations between the chaotic sequence and image pixel values, as well as extracting high-level image features using the CNN. Finally, we conduct experimental evaluations of the algorithm and compare it with traditional chaotic image encryption methods. The experimental results demonstrate that the image encryption algorithm exhibits significant improvements in encryption quality and security while offering advantages in computational performance and encryption/decryption speed.

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Research on Individual Recognition and Matching of Whale and Dolphin Based on EfficientNet Model

Cited by 17 publications

References 11 publications

Achieving High Accuracy in Lemon Quality Classification: A Comparative Study of Deep Learning and Transformer Models

Achieving High Accuracy in Lemon Quality Classification: A Comparative Study of Deep Learning and Transformer Models

Reliability analysis of localized replacement of hipps high voltage protection system

Image Encryption Algorithm Combining Chaotic Image Encryption and Convolutional Neural Network

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