Although access control based on human face recognition has become popular in consumer applications, it still has several implementation issues before it can realize a stand-alone access control system. Owing to a lack of computational resources, lightweight and computationally efficient face recognition algorithms are required. The conventional access control systems require significant active cooperation from the users despite its non-aggressive nature. The lighting/illumination change is one of the most difficult and challenging problems for human-face-recognition-based access control applications. This paper presents the design and implementation of a user-friendly, stand-alone access control system based on human face recognition at a distance. The local binary pattern (LBP)-AdaBoost framework was employed for face and eyes detection, which is fast and invariant to illumination changes. It can detect faces and eyes of varied sizes at a distance. For fast face recognition with a high accuracy, the Gabor-LBP histogram framework was modified by substituting the Gabor wavelet with Gaussian derivative filters, which reduced the facial feature size by 40% of the Gabor-LBP-based facial features, and was robust to significant illumination changes and complicated backgrounds. The experiments on benchmark datasets produced face recognition accuracies of 97.27% on an E-face dataset and 99.06% on an XM2VTS dataset, respectively. The system achieved a 91.5% true acceptance rate with a 0.28% false acceptance rate and averaged a 5.26 frames/sec processing speed on a newly collected face image and video dataset in an indoor office environment.

PV (photovoltaic) capacity is steadily increasing every year, and the rate of increase is also increasing. A desert area with a large equipment installation area and abundant solar radiation is a good candidate. PV power plants installed in the desert have advantages in themselves, but when combined with desert aquacultures, additional benefits can be obtained while compensating for the shortcomings of the aquaculture industry. The importance of the aquaculture industry is increasing, with aquaculture products approaching half of the total supply of marine products due to sea environmental pollution and reduced resources. Moreover, in deserts, where marine products are difficult to obtain, aquaculture is a good way to save marine products. However, one of the many problems that complicate the introduction of aquaculture in the desert is that it is difficult to supply and demand electricity because the site is not near a viable electric grid. However, combination with PV can solve this problem. This paper investigates the solar power and aquaculture industry in the desert and explains the limitations and challenges of the solar power and aquaculture industry in the desert. Based on this, we hope to increase interest in the solar power and aquaculture industry in the desert and help with future research.

Recently, with the discovery of various security threats, diversification of hacking attacks, and changes in the network environment such as the Internet of Things, security threats on the network are increasing. Attack graph is being actively studied to cope with the recent increase in cyber threats. However, the conventional attack graph generation method is costly and time-consuming. In this paper, we propose a cheap and simple method for generating the attack graph. The proposed approach consists of learning and generating stages. First, it learns how to generate an attack path from the attack graph, which is created based on the vulnerability database, using machine learning and deep learning. Second, it generates the attack graph using network topology and system information with a machine learning model that is trained with the attack graph generated from the vulnerability database. We construct the dataset for attack graph generation with topological and system information. The attack graph generation problem is recast as a multi-output learning and binary classification problem. It shows attack path detection accuracy of 89.52% in the multi-output learning approach and 80.68% in the binary classification approach using the in-house dataset, respectively.

Object detection automatically identifies and locates specific objects within images or videos, and plays a critical role in various fields. Among the many approaches, the YOLO model has significantly improved the accuracy and speed of object detection, thereby garnering attention for real-time processing. However, it exhibits limitations in detecting small or densely packed objects. This study introduces YOLO-RACE (You Only Look Once with ReAssembly and Convolutional block attEntion), an improved model based on YOLOv8 by incorporating the CARAFE upsampling technique, ResBlock, and the Convolutional Block Attention Module. YOLOv8 was selected from among various YOLO models because of its ability to deliver high accuracy in complex object detection tasks while maintaining its lightweight architecture and overall efficiency. The proposed model was evaluated using the VisDrone 2019, Pascal VOC, and SKU-110 K datasets. The experimental results demonstrated a precision of 0.419, recall of 0.321, mAP50 of 0.316, mAP50@95 of 0.18, and F1-score of 0.364 on the VisDrone 2019 dataset. On the Pascal VOC dataset, the model achieved a precision of 0.764, recall of 0.699, mAP50 of 0.773, mAP50@95 of 0.559, and F1-score of 0.73. Furthermore, on the SKU-110 K dataset, the model attained a precision of 0.904, recall of 0.841, mAP50 of 0.902, mAP50@95 of 0.575, and F1-score of 0.871. Based on these comparative results, the proposed model achieved superior performance compared with YOLOv8 and its variants, as well as with other state-of-the-art models such as YOLOv9, YOLOv10, YOLOv11, GELAN, and EfficientDet. This code is available at https://github.com/AnBLab-BAE/YOLO-RACE.git.

In the field of computer vision, convolutional neural network (CNN)-based models have demonstrated high accuracy and good generalization performance. However, in semantic segmentation, CNN-based models have a problem—the spatial and global context information is lost owing to a decrease in resolution during feature extraction. High-resolution networks (HRNets) can resolve this problem by keeping high-resolution processing layers parallel. However, information loss still occurs. Therefore, in this study, we propose an HRNet combined with an attention module to address the issue of information loss. The attention module is strategically placed immediately after each convolution to alleviate information loss by emphasizing the information retained at each stage. To achieve this, we employed a squeeze-and-excitation (SE) block as the attention module, which can seamlessly integrate into any model and enhance the performance without imposing significant parameter increases. It emphasizes the spatial and global context information by compressing and recalibrating features through global average pooling (GAP). A performance comparison between the existing HRNet model and the proposed model using various datasets show that the mean class-wise intersection over union (mIoU) and mean pixel accuracy (MeanACC) improved with the proposed model, however, there was a small increase in the number of parameters. With cityscapes dataset, MeanACC decreased by 0.1% with the proposed model compared to the baseline model, but mIoU increased by 0.5%. With the LIP dataset, the MeanACC and mIoU increased by 0.3% and 0.4%, respectively. The mIoU also decreased by 0.1% with the PASCAL Context dataset, whereas the MeanACC increased by 0.7%. Overall, the proposed model showed improved performance compared to the existing model.

Deep learning technologies have significantly advanced the field of single-image super-resolution (SISR), yet existing methods often prioritize peak signal-to-noise ratio (PSNR) over visual quality and realism. In this study, we propose NeXtSRGAN, which integrates a ConvNeXt-based discriminator to overcome these limitations and achieve more realistic and high-quality super-resolution (SR) images. NeXtSRGAN enhances image realism through its novel discriminator structure and generator residual block design, leveraging a relativistic discriminator and residual scaling. Experimental results on benchmark datasets demonstrate that NeXtSRGAN surpasses existing methods, including enhanced SRGAN (ESRGAN), with an average PSNR improvement of over 1.58 dB and an SSIM enhancement of over 0.05. Notably, NeXtSRGAN exhibits exceptional performance in facial image SR, as confirmed by the KID-F metric. By focusing on perceptual quality rather than solely PSNR, NeXtSRGAN sets a new standard for image restoration and holds promise for applications in various domains, such as video surveillance, medical imaging, and satellite photos. This code is available at https://github.com/PomKlementieff/NeXtSRGAN.