In today’s world of massive data and interconnected networks,it’s crucial to burgeon a secure and efficient digital watermarking method to protect the copyrights of digital content.Existing research primarily focuse...In today’s world of massive data and interconnected networks,it’s crucial to burgeon a secure and efficient digital watermarking method to protect the copyrights of digital content.Existing research primarily focuses on deep learning-based approaches to improve the quality of watermarked images,but they have some flaws.To overcome this,the deep learning digital image watermarking model with highly secure algorithms is proposed to secure the digital image.Recently,quantum logistic maps,which combine the concept of quantum computing with traditional techniques,have been considered a niche and promising area of research that has attracted researchers’attention to further research in digital watermarking.This research uses the chaotic behaviour of the quantum logistic map with Rivest–Shamir–Adleman(RSA)and Secure Hash(SHA-3)algorithms for a robust watermark embedding process,where a watermark is embedded into the host image.This way,the quantum chaos method not only helps limit the chance of tampering with the image content through reverse engineering but also assists in maintaining a high level of imperceptibility and strong robustness with efficient extraction or detection of watermark images.Lifting Wavelet Transformation(LWT)is a potential and computationally efficient version of traditional Discrete Wavelet Transform(DWT)where the host image is divided into four sub-bands to offer a multi-resolution view of an image with greater flexibility in watermarking methodologies.Furthermore,considering the robustness against attacks,a pre-trained Residual Neural Network(ResNet-50),a convolutional neural network with 50 layers deep,is used to better learn the complex features and efficiently extract the watermark from the image.By integrating RSA and SHA-3 algorithms,the proposed model demonstrates improved imperceptibility,robustness,and accuracy in watermark extraction compared to traditional methods.It achieves a Peak Signal-to-Noise Ratio(PSNR)of 49.83%,a Structural Similarity Index Measure(SSIM)of 0.98,and a Number of Pixels Change Rate(NPCR)of 99.79%,respectively.These results reflect the model’s effectiveness in delivering superior quality and security.Consequently,our proposed approach offers accurate results,exceptional invisibility,and enhanced robustness compared to the existing digital image watermarking techniques.展开更多
为提高电力设备状态监测的智能化水平,设计一种基于无线移动通信技术的电力故障诊断系统。该系统采用“采集-传输-诊断”三层架构,集成STM32H743处理器、5G/窄带物联网(Narrow Band Internet of Things,NB-IoT)双模通信和ResNet-50改进...为提高电力设备状态监测的智能化水平,设计一种基于无线移动通信技术的电力故障诊断系统。该系统采用“采集-传输-诊断”三层架构,集成STM32H743处理器、5G/窄带物联网(Narrow Band Internet of Things,NB-IoT)双模通信和ResNet-50改进模型,通过自适应采样机制、MQTT 5.0和深度学习算法实现电力设备状态的实时监测与智能诊断。实验结果表明,该系统在有效采样数据占比、平均通信时延、数据丢包率、平均诊断准确率以及整体能耗5项关键性能指标上均显著优于传统数据采集与监控(Supervisory Control And Data Acquisition,SCADA)系统,为智能电网建设提供可靠的技术支撑。展开更多
文摘In today’s world of massive data and interconnected networks,it’s crucial to burgeon a secure and efficient digital watermarking method to protect the copyrights of digital content.Existing research primarily focuses on deep learning-based approaches to improve the quality of watermarked images,but they have some flaws.To overcome this,the deep learning digital image watermarking model with highly secure algorithms is proposed to secure the digital image.Recently,quantum logistic maps,which combine the concept of quantum computing with traditional techniques,have been considered a niche and promising area of research that has attracted researchers’attention to further research in digital watermarking.This research uses the chaotic behaviour of the quantum logistic map with Rivest–Shamir–Adleman(RSA)and Secure Hash(SHA-3)algorithms for a robust watermark embedding process,where a watermark is embedded into the host image.This way,the quantum chaos method not only helps limit the chance of tampering with the image content through reverse engineering but also assists in maintaining a high level of imperceptibility and strong robustness with efficient extraction or detection of watermark images.Lifting Wavelet Transformation(LWT)is a potential and computationally efficient version of traditional Discrete Wavelet Transform(DWT)where the host image is divided into four sub-bands to offer a multi-resolution view of an image with greater flexibility in watermarking methodologies.Furthermore,considering the robustness against attacks,a pre-trained Residual Neural Network(ResNet-50),a convolutional neural network with 50 layers deep,is used to better learn the complex features and efficiently extract the watermark from the image.By integrating RSA and SHA-3 algorithms,the proposed model demonstrates improved imperceptibility,robustness,and accuracy in watermark extraction compared to traditional methods.It achieves a Peak Signal-to-Noise Ratio(PSNR)of 49.83%,a Structural Similarity Index Measure(SSIM)of 0.98,and a Number of Pixels Change Rate(NPCR)of 99.79%,respectively.These results reflect the model’s effectiveness in delivering superior quality and security.Consequently,our proposed approach offers accurate results,exceptional invisibility,and enhanced robustness compared to the existing digital image watermarking techniques.
文摘为提高电力设备状态监测的智能化水平,设计一种基于无线移动通信技术的电力故障诊断系统。该系统采用“采集-传输-诊断”三层架构,集成STM32H743处理器、5G/窄带物联网(Narrow Band Internet of Things,NB-IoT)双模通信和ResNet-50改进模型,通过自适应采样机制、MQTT 5.0和深度学习算法实现电力设备状态的实时监测与智能诊断。实验结果表明,该系统在有效采样数据占比、平均通信时延、数据丢包率、平均诊断准确率以及整体能耗5项关键性能指标上均显著优于传统数据采集与监控(Supervisory Control And Data Acquisition,SCADA)系统,为智能电网建设提供可靠的技术支撑。
