Falls are defined by the World Health Organization (WHO) as incidents in which an individual unintentionally falls to the ground or a lower level. Falls represent a serious public health issue, ranking as the second leading cause of death from unintentional injuries, following traffic accidents. While fall prevention is crucial, prompt intervention after a fall is equally necessary. Delayed responses can result in severe complications, reduced recovery potential, and a negative impact on quality of life. This study focuses on detecting fall situations using image-based methods. The fall images utilized in this research were created by combining three open-source datasets to enhance generalization and adaptability across diverse scenarios. Because falls must be detected promptly, the YOLO (You Only Look Once) network, known for its effectiveness in real-time detection, was applied. To better capture the complex body structures and interactions with the floor during a fall, two key techniques were integrated. First, a global attention module (GAM) based on the Convolutional Block Attention Module (CBAM) was employed to improve detection performance. Second, a Transformer-based Swin Transformer module was added to effectively learn global spatial information and enable a more detailed analysis of body movements. This study prioritized minimizing missed fall detections (false negatives, FN) as the key performance metric, since undetected falls pose greater risks than false detections. The proposed Fall Detection YOLO (FD-YOLO) network, developed by integrating the Swin Transformer and GAM into YOLOv9, achieved a high mAP@0.5 score of 0.982 and recorded only 134 missed fall incidents, demonstrating optimal performance. When implemented in environments equipped with standard camera systems, the proposed FD-YOLO network is expected to enable real-time fall detection and prompt post-fall responses. This technology has the potential to significantly improve public health and safety by preventing fall-related injuries and facilitating rapid interventions.

Given the global competitive landscape, it is imperative that businesses maintain and manage their facilities continuously to enhance efficiency and productivity for sustaining competitiveness. Hence, a new hybrid model called contrast enhancement convolutional vision transformer (CECvT) was developed in this study that enables fault diagnosis without physical contact with factory equipment to ensure accurate initial fault detection without the risk of machine damage or interference. This model leverages thermal imaging as an apt source for early anomaly detection in equipment. A new contrast enhancement module employing contrast enhancement techniques was integrated to address the edge information loss when utilizing thermal images. Moreover, the network performance was enhanced by fusing the advantages of convolutional neural network (CNN) and Transformer models. Notably, the model design allows deriving detailed feature information necessary for the initial diagnostics by harnessing multiscale information to extract and concatenate features. The proposed method’s performance was evaluated using the thermal imaging dataset provided by AI Hub. When juxtaposed with CNN, Transformer, and hybrid CNN-Transformer models, the proposed model demonstrated a superior accuracy of 96.17%. Furthermore, it achieved the most accurate diagnosis at the inception of abnormalities than the other networks. The proposed model thus has potential and is preferrable for various thermal-imaging-based fault diagnosis applications owing to its excellent performance and precision during initial diagnosis.

Computed tomography (CT) is a rapid and precise medical imaging modality, but it poses the challenge of high radiation exposure to patients. To control this issue, stringent quality control (QC) evaluations are imperative for CT. One crucial aspect of CT QC involves the evaluation of phantom images, utilizing specifically designed phantoms for accuracy management and subsequent objective evaluation. However, CT QC has qualitative evaluation methods, particularly for evaluating spatial and contrast resolutions. To solve this problem, we propose a quality control method based on deep-learning object detection for quantitatively evaluating spatial and contrast resolutions, CT Attention You Only Look Once v8 (CTA-YOLOv8). First, we utilized the YOLOv8 network as the foundational model, optimizing it for enhanced accuracy. Second, we enhanced the network’s capabilities by integrating the Convolutional Block Attention Module (CBAM) and Swin Transformers, tailored for phantom image evaluations. The CBAM module was employed internally to pinpoint the optimal position for achieving peak performance in CT QC data. Similarly, we fine-tuned the code and patch size of the Swin Transformer module to align it with YOLOv8, culminating in the identification of the optimal configuration. Our proposed CTA-YOLOv8 network showed superior agreement with qualitative evaluation methods, achieving accuracies of 92.03% and 97.56% for spatial and contrast resolution evaluations, respectively. Thus, we suggest that our method offers nearly equivalent performance to qualitative methods. The utilization of the CTA-YOLOv8 network in evaluating CT phantom images holds potential for setting a new standard in quantitative assessment methodologies.

An on-load tap changer (OLTC) is a critical component of power transformers, and the vibration signals generated during its operation provide valuable information for forecasting equipment conditions and anomalies. In this study, we propose a novel mixture-of-experts-based Kolmogorov–Arnold network (KAN) model, referred to as MoEKAN, to enhance the accuracy of time series forecasting for the vibration signals of the OLTC. The proposed MoEKAN incorporates reversible instance normalization (RevIN) to flexibly adapt to changes in data distribution and employs a transformer-based gating mechanism to dynamically integrate forecasts from various KAN expert models. In addition, multi-scale signal processing is performed to effectively capture the complex periodicity and patterns present in the vibration data. Experiments using real OLTC operational data demonstrate that the MoEKAN model achieves superior forecasting performance, recording forecasting errors with MSE, MAE, and MAPE values of 133.4579, 7.2801, and 4.4272, respectively, outperforming all comparison models. These results validate the practicality and contribution of the proposed model and confirm its potential as a highly reliable diagnostic tool for condition monitoring and predictive maintenance of OLTCs.

An on-load tap changer (OLTC) regulates transformer voltage under load, while repeated portable imaging provides records of accessible component geometry. This study proposes TapBR-SSM, which combines high-resolution HRNet features, stage-selective Mamba-inspired directional refinement, degradation-conditioned correction, and semantic–boundary–radial prediction. Training uses five loss groups for region, boundary, shape, cross-path consistency, and shallow restoration supervision. The dataset contains 330 image–mask pairs allocated to training (210), validation (60), and testing (60). Across nine training and evaluation runs, TapBR-SSM yielded mean mIoU and mDice values of 0.9806 and 0.9902, respectively, on the 60-image test set. For longitudinal analysis, the test images were organized by date into 32 cases spanning 20 August 2024 to 17 April 2025. Reference- and prediction-derived ellipse-normalized contour ratios R_c decreased by approximately 0.247% and 0.272%, respectively, between the endpoints. Across the 32 date-based cases, the prediction-minus-reference signed bias, MAE, RMSE, and maximum absolute difference were +0.000992, 0.001129, 0.001431, and 0.003988, respectively; the Pearson correlation, Spearman correlation, and Lin’s concordance correlation coefficient (CCC) values were 0.9765, 0.9531, and 0.9555, respectively.

The On-Load Tap Changer (OLTC) in power transformers is a critical component responsible for regulating the output voltage, and the early detection of OLTC faults is essential for maintaining power grid stability. In this paper, we propose a one-class deep learning anomaly detection model named “U-AttentionFlow” based on acoustic signals from the OLTC operation. The proposed model is trained exclusively on normal operating data to accurately model normal patterns and identify anomalies when new signals deviate from the learned patterns. To enhance the ability of the model to focus on significant features, we integrate the squeeze-and-excitation (SE) block and Convolutional Block Attention Module (CBAM) into the network architecture. Furthermore, static positional encoding and multihead self-attention (MHSA) are employed to effectively learn the temporal characteristics of time-series acoustic signals. We also adopted a U-Flow-style invertible multiscale coupling structure, which integrates features across multiple scales while ensuring the invertibility of the model. Experimental validation was conducted using acoustic data collected under realistic voltage and load conditions from actual ECOTAP VPD OLTC equipment, resulting in an anomaly detection accuracy of 99.15%. These results demonstrate the outstanding performance and practical applicability of the U-AttentionFlow model for OLTC anomaly detection.