Metallography is the study of the structure of metals and alloys. Metallographic analysis can be regarded as a detection tool to assist in identifying a metal or alloy, to evaluate whether an alloy is processed correctly, to inspect multiple phases within a material, to locate and characterize imperfections such as voids or impurities, or to find the damaged areas of metallographic images. However, the defect detection of metallography is evaluated by human experts, and its automatic identification is still a challenge in almost every real solution. Deep learning has been applied to different problems in computer vision since the proposal of AlexNet in 2012. In this study, we propose a novel convolutional neural network architecture for metallographic analysis based on a modified residual neural network (ResNet). Multi-scale ResNet (M-ResNet), the modified method, improves efficiency by utilizing multi-scale operations for the accurate detection of objects of various sizes, especially small objects. The experimental results show that the proposed method yields an accuracy of 85.7% (mAP) in recognition performance, which is higher than existing methods. As a consequence, we propose a novel system for automatic defect detection as an application for metallographic analysis.

Underwater images often come with blurriness, lack of contrast, and low saturation due to the physics of light propagation, absorption, and scattering in seawater. To improve the visual quality of underwater images, many have proposed image processing methods that vary based on different approaches. We use a generative adversarial network (GAN)-based solution and generate high-quality underwater images equivalent to given raw underwater images by training our network to specify the differences between high-quality and raw underwater images. In our proposed method, which is called dilated GAN (DGAN), we add an additional loss function using structural similarity. Moreover, this method can not only determine the realness of the entire image but also functions with classification ability on each constituent pixel in the discriminator. Finally, using two different datasets, we compare the proposed model with other enhancement methods. We conduct several comparisons and demonstrate via full-reference and nonreference metrics that the proposed approach is able to simultaneously improve clarity and correct color and restores the visual quality of the images acquired in typical underwater scenarios.

With the increasing application of steel materials, the metallographic analysis of steel has gained importance. At present, grain size analysis remains the task of experts who must manually evaluate photos of the structure. Given the software currently available for this task, it is impossible to effectively determine the grain size because of the limitations of traditional algorithms. Artificial intelligence is now being applied in many fields. This paper uses the concept of deep learning to propose a fast image classifier (FIC) to classify grain size. We establish a classification model based on the grain size of steel in metallography. This model boasts high performance, fast operation, and low computational costs. In addition, we use a real metallographic dataset to compare FIC with other deep learning network architectures. The experimental results show that the proposed method yields a classification accuracy of 99.7%, which is higher than existing methods, and boasts computational demands, which are far lower than with other network architectures. We propose a novel system for automatic grain size determination as an application for metallographic analysis.

With the rapid development of deepfake technology, it is finding applications in virtual movie production and entertainment. However, its potential for malicious use, such as generating false information, fake news, or synthetic pornography, poses significant threats to national and social security. Various research disciplines are actively engaged in developing deepfake video detection technologies to mitigate the risks associated with malicious deepfake content. Therefore, the importance of deepfake video detection technology cannot be overemphasized. This study addresses the challenge posed by images in nonexistent datasets by analyzing deepfake video detection methods. Using temporal and spatial detection techniques and employing 68 facial landmarks for alignment and feature extraction, this research integrates the attention-guided data augmentation (AGDA) strategy to enhance generalization capabilities. The detection performance is evaluated on four datasets: UADFV, FaceForensics++, Celeb-DF, and DFDC, with superior results compared to alternative approaches. To evaluate the study’s ability to accurately discriminate authenticity, detection experiments are conducted on both genuine and deepfake videos synthesized using the DeepFaceLab and FakeApp frameworks. The experimental results show better performance in detecting deepfake videos than other methods compared.

The maritime sector faces increasing pressure to reduce emissions, especially in ports, pushing governments and shipowners towards greener energy sources. Conventional diesel generator (DG) powered vessels experience increased fuel consumption and emissions during low-power demand due to fluctuating loads with changing sea conditions. Integrating battery energy storage can absorb excess power, optimize DG operation, reduce costs, and manage variable loads. Traditional shipboard power systems (SPS) rely on centralized control schemes, which pose the risk of single points of failure, scalability issues, and increased latency due to centralized decision-making. Decentralized control improves resilience and scalability by eliminating single points of failure and enabling local decision-making, which improves response times and system robustness. Although recent research has explored decentralized control strategies for AC or DC-based SPS, there is limited work on hybrid AC-DC SPS architectures. This paper proposes a decentralized control strategy for integrating multiple power sources within a hybrid AC-DC network to optimize their operation. This approach allows vessels to operate in various modes, including full diesel, hybrid, and zero emission, and seamlessly transition between these modes as needed. The effectiveness of the proposed control scheme is validated through simulation and high-fidelity software-in-the-loop (SIL) results in OPAL-RT 5700, demonstrating adaptive power sharing among different resources.

Neural style transfer (NST) is a technique based on the deep learning of a convolutional neural network (CNN) to create entertaining pictures by cleverly stylizing ordinary pictures with the predetermined visual art style. However, three issues must be carefully investigated during the generation of neural-stylized artwork: the color scheme, the strength of style of the strokes, and the adjustment of image contrast. To solve these problems and select image colorization based on personal preference, in this paper, we propose modified universal-style transfer (UST) method combined with the image fusion and color enhancement methods to design a good post-processing framework to tackle the three above-mentioned issues simultaneously. This work provides more visual effects for stylized images, and also can integrate into the UST method effectively. In addition, the proposed method is suitable for stylized images generated by any NST method, but it also works similarly to the Multi-Style Transfer (MST) method, which mixes two different stylized images. Finally, our proposed method successfully combined the modified UST method and post-processing method to meet personal preference.