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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.

In this study, we propose EnhanceCenter, a multiple-object tracking model that demonstrates enhanced tracking efficiency and stability while reducing dependencies on computationally intensive detectors. EnhanceCenter, based on the CenterTrack method, introduces three key improvements. First, a channel–spatial–spatial feature fusion module effectively utilizes object appearance information, enhancing tracking in complex scenes. Second, the backbone network weights are optimized for multiple-object tracking tasks, enabling more effective feature extraction. Lastly, an improved association method increases long-term tracking stability, maintaining consistency during occlusions or detection failures. Experiments on various MOT benchmarks demonstrated the performance of EnhanceCenter against models using high-performance detectors. On the MOT17 test set, EnhanceCenter outperformed CenterTrack with a 1.6% improvement in IDF1 and achieved a HOTA of 55.1%, surpassing leading center-point-based tracking studies, such as TransTrack and TransCenter. The MOT20 dataset showed a significant 13% improvement in IDF1 compared to CenterTrack. This research underscores the potential of lightweight detectors in achieving state-of-the-art multiple-object tracking performance, paving the way for more efficient tracking solutions in complex environments.

Climate change poses significant challenges to agriculture, leading to increased crop damage owing to extreme weather conditions. Detecting and analyzing such damage is crucial for mitigating its effects on crop yield. This study proposes a novel autoencoder (AE)-based model, termed “Memory Ganomaly,” designed to detect and analyze weather-induced crop damage under conditions of significant class imbalance. The model integrates memory modules into the Ganomaly architecture, thereby enhancing its ability to identify anomalies by focusing on normal (undamaged) states. The proposed model was evaluated using apple and peach datasets, which included both damaged and undamaged images, and was compared with existing robust Convolutional neural network (CNN) models (ResNet-50, EfficientNet-B3, and ResNeXt-50) and AE models (Ganomaly and MemAE). Although these CNN models are not the latest technologies, they are still highly effective for image classification tasks and are deemed suitable for comparative analyses. The results showed that CNN and Transformer baselines achieved very high overall accuracy (94–98%) but completely failed to identify damaged samples, with precision and recall equal to zero under severe class imbalance. Few-shot learning partially alleviated this issue (up to 75.1% recall in the 20-shot setting for the apple dataset) but still lagged behind AE-based approaches in terms of accuracy and precision. In contrast, the proposed Memory Ganomaly delivered a more balanced performance across accuracy, precision, and recall (Apple: 80.32% accuracy, 79.4% precision, 79.1% recall; Peach: 81.06% accuracy, 83.23% precision, 80.3% recall), outperforming AE baselines in precision and recall while maintaining comparable accuracy. This study concludes that the Memory Ganomaly model offers a robust solution for detecting anomalies in agricultural datasets, where data imbalance is prevalent, and suggests its potential for broader applications in agricultural monitoring and beyond. While both Ganomaly and MemAE have shown promise in anomaly detection, they suffer from limitations—Ganomaly often lacks long-term pattern recall, and MemAE may miss contextual cues. Our proposed Memory Ganomaly integrates the strengths of both, leveraging contextual reconstruction with pattern recall to enhance detection of subtle weather-related anomalies under class imbalance.

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.

This study sought to propose a big data analysis and prediction model for transmission line tower outliers to assess when something is wrong with transmission line tower big data based on deep reinforcement learning. The model enables choosing automatic cluster K values based on non-labeled sensor big data. It also allows measuring the distance of action between data inside a cluster with the Q-value representing network output in the altered transmission line tower big data clustering algorithm containing transmission line tower outliers and old Deep Q Network. Specifically, this study performed principal component analysis to categorize transmission line tower data and proposed an automatic initial central point approach through standard normal distribution. It also proposed the A-Deep Q-Learning algorithm altered from the deep Q-Learning algorithm to explore policies based on the experiences of clustered data learning. It can be used to perform transmission line tower outlier data learning based on the distance of data within a cluster. The performance evaluation results show that the proposed model recorded an approximately 2.29%~4.19% higher prediction rate and around 0.8% ~ 4.3% higher accuracy rate compared to the old transmission line tower big data analysis model.

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.

A smart grid is an intelligent power grid. In recent years, the smart grid environment and its applications are incorporated into a variety of areas. The smart grid environment, however, can expose much more information than the old environments. In the environment, smart devices can be media in the exposure of various and specific pieces of information as well as energy consumption. This poses a huge risk in that it, combined with other pieces of information, may expose much more information. The current smart grid environment raises a need to develop anonymous signature and authentication techniques to prevent privacy breaches. Trying to meet this need, the principal investigator conducted research for three years. This paper discusses both the research trends investigated by him and the limitations of the development research and future research in need. Smart grid security requires the development of encrypted anonymous authentication that is applicable to power plant security, including nuclear power plants as well as expandable test beds.

PurposeThe purpose of this study was to evaluate whether trans-tendon suture bridge repair with tenotomized pathologic biceps tendon augmentation improves mid-term clinical outcomes for high-grade partial articular-sided supraspinatus tendon avulsion (PASTA) lesions or not.MethodsA retrospective review of a consecutive series of arthroscopic trans-tendon suture bridge repair with tenotomized pathologic biceps tendon augmentation was conducted. Total 115 patients (44 men and 71 women) with minimum 2 years follow-up were enrolled in our study. Their mean age was 59.7 ± 7.6 (38–77) years and mean follow-up were 6.9 ± 2.5 (2 ~ 10) years. Clinical assessment and radiological outcomes using post-operative MRI were evaluated at last follow-up.ResultsAll these tears were high-grade PASTA lesions in which mean cuff tear size (exposed footprint) was anteroposterior length 15.7 ± 6.3 mm (5–25 mm) and mediolateral width 10.1 ± 3.6 mm 6.4 mm (5–16 mm). At last follow-up, mean pain VAS, ASES, UCLA, and SST scores were improved from pre-operative values of 5, 59, 21, and 7 to post-operative values of 1, 84.4, 29.5, and 9.4, respectively (p value < 0.001). ROM such as forward flexion, abduction, and internal rotation to the back were improved from a pre-operative mean of 148° (±24), 144° (±24), L2 (Buttock-T7) to a post-operative mean of 161° (±10), 160.0° (±12), and T12 (L3–T5), respectively (p value < 0.001). Follow-up MRI showed Sugaya classification type I in 24 patients (20.9%), type II in 78 patients (67.8%), type III in 11 patients (9.6%) and type 4 in 2 patients (1.7%) were found. As complications, shoulder stiffness was found in five patients, Popeye deformity in two patients and retear in two patients. Revision surgery of the retear was performed in 2 patients. At the last follow-up, 17% (20/115 patients) reported occasional discomfort at the extremes of range of motion during a heavy work or sports activities.ConclusionsIn high-grade PASTA lesions, arthroscopic trans-tendon suture bridge repair with tenotomized pathologic biceps tendon augmentation could be a useful treatment modality capable of preserving rotator cuff footprint, providing simultaneous biceps tenodesis, inducing better tendon healing and possibly preventing tendon buckling and residual pain of the conventional trans-tendon repair methods. These specific methods showed satisfactory outcomes and decreased residual shoulder discomfort (17%) at mid-term follow-up.Level of evidenceLevel IV, Retrospective case study.

Today, semi-structured and unstructured data are mainly collected and analyzed for data analysis applicable to various systems. Such data have a dense distribution of space and usually contain outliers and noise data. There have been ongoing research studies on clustering algorithms to classify such data (outliers and noise data). The K-means algorithm is one of the most investigated clustering algorithms. Researchers have pointed out a couple of problems such as processing clustering for the number of clusters, K, by an analyst through his or her random choices, producing biased results in data classification through the connection of nodes in dense data, and higher implementation costs and lower accuracy according to the selection models of the initial centroids. Most K-means researchers have pointed out the disadvantage of outliers belonging to external or other clusters instead of the concerned ones when K is big or small. Thus, the present study analyzed problems with the selection of initial centroids in the existing K-means algorithm and investigated a new K-means algorithm of selecting initial centroids. The present study proposed a method of cutting down clustering calculation costs by applying an initial center point approach based on space division and outliers so that no objects would be subordinate to the initial cluster center for dependence lower from the initial cluster center. Since data containing outliers could lead to inappropriate results when they are reflected in the choice of a center point of a cluster, the study proposed an algorithm to minimize the error rates of outliers based on an improved algorithm for space division and distance measurement. The performance experiment results of the proposed algorithm show that it lowered the execution costs by about 13–14% compared with those of previous studies when there was an increase in the volume of clustering data or the number of clusters. It also recorded a lower frequency of outliers, a lower effectiveness index, which assesses performance deterioration with outliers, and a reduction of outliers by about 60%.

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.