Building damage due to various causes occurs frequently and has risk factors that can cause additional collapses. However, it is difficult to accurately identify objects in complex structural sites because of inaccessible situations and image noise. In conventional approaches, close-up images have been used to detect and segment damage images such as cracks. In this study, the method of using a deep learning model is proposed for the rapid determination and analysis of multiple damage types, such as cracks and concrete rubble, in disaster sites. Through the proposed method, it is possible to perform analysis by receiving image information from a robot explorer instead of a human, and it is possible to detect and segment damage information even when the damaged point is photographed at a distance. To accomplish this goal, damage information is detected and segmented using YOLOv7 and Deeplabv2. Damage information is quickly detected through YOLOv7, and semantic segmentation is performed using Deeplabv2 based on the bounding box information obtained through YOLOv7. By using images with various resolutions and senses of distance for training, damage information can be effectively detected not only at short distances but also at long distances. When comparing the results, depending on how YOLOv7 and Deeplabv2 were used, they returned better scores than the comparison model, with a Recall of 0.731, Precision of 0.843, F1 of 0.770, and mIoU of 0.638, and had the lowest standard deviation.

Recently, autonomous driving technology has been in the spotlight. However, autonomous driving is still in its infancy in the railway industry. In the case of railways, there are fewer control elements than autonomous driving of cars due to the characteristics of running on railways, but there is a disadvantage in that evasive maneuvers cannot be made in the event of a dangerous situation. In addition, when braking, it cannot be decelerated quickly for the weight of the body and the safety of the passengers. In the case of a tram, one of the railway systems, research has already been conducted on how to generate a profile that plans braking and acceleration as a base technology for autonomous driving, and to find the location coordinates of surrounding objects through object recognition. In pilot research about the tram’s automated driving, YOLOv3 was used for object detection to find object coordinates. YOLOv3 is an artificial intelligence model that finds coordinates, sizes, and classes of objects in an image. YOLOv3 is the third upgrade of YOLO, which is one of the most famous object detection technologies based on CNN. YOLO’s object detection performance is characterized by ordinary accuracy and fast speed. For this paper, we conducted a study to find out whether the object detection performance required for autonomous trams can be sufficiently implemented with the already developed object detection model. For this experiment, we used the YOLOv4 which is the fourth upgrade of YOLO.

For real-time target classification, a study was conducted to improve the AI-based target classification performance using RCS measurements that are vulnerable to noise, but can be obtained quickly. To compensate for the shortcomings of the RCS, a 1D CNN–GRU network with strengths in feature extraction and time-series processing was considered. The 1D CNN–GRU was experimentally changed and designed to fit the RCS characteristics. The performance of the proposed 1D CNN–GRU was compared and analyzed using the 1D CNN and 1D CNN–LSTM. The designed 1D CNN–GRU had the best classification performance with a high accuracy of 99.50% in complex situations, such as with different missile shapes with the same trajectory and with the same missile shapes that had the same trajectory. In addition, to confirm the general target classification performance for the RCS, a new class was verified. The 1D CNN–GRU had the highest classification performance at 99.40%. Finally, as a result of comparing three networks by adding noise to compensate for the shortcomings of the RCS, the 1D CNN–GRU, which was optimized for both the data set used in this paper and the newly constructed data set, was the most robust to noise.

Purpose: Health-related quality of life (HRQoL) is an important construct in clinical settings, and it is crucial that it should be properly measured. As the EuroQol-5-dimensions-5 levels (EQ-5D-5L) is more effective for such measurement than the 3-level model, data on economic models, clinical studies, and public health evaluations previously collected through the EQ-5D-3L need to be revaluated using the EQ-5D-5L. This study evaluated colorectal cancer (CRC) patients’ HRQoL scores using the Vietnamese EQ-5D-5L value set. Patients and Methods: The cross-sectional study included CRC patients treated at a tertiary public hospital. HRQoL was assessed using the EQ-5D-5L, and HRQoL utility scores were calculated using the Vietnamese value set. Tobit regression examined factors associated with HRQoL. Results: The analysis included 197 CRC patients. Ages ranged from 20 to 87 years (M = 57.64, SD = 13.5); 42.2% and 57.8% were diagnosed with cancer of the colon or rectum/anus, respectively. Mean EQ-5D-5L was 0.561 (range, − 0.5115 to 1). Most participants experienced anxiety/depression (88%), followed by pain/discomfort (87%), mobility (71%), usual activity (69%), and self-care (67%). Advanced CRC stage (stage II: β − 0.303, se 0.08; stage III: β − 0.305, se 0.07; stage IV: β − 0.456, se 0.07) and surgery (β − 0.113, se 0.05) were negatively associated with EQ-5D-5L scores. Advanced education (high school: β 0.273, se 0.07); college/vocational: β 0.134se 0.05; university/higher: Coef 0.213, se 0.08;) and older age (age group 35− 44: β 0.253, se 0.10; 45− 54: β 0.327, se 0.09; 55− 64: β 0.355 se 0.09; 65+ β 0.204, se 0.09) were positively associated with EQ-5D-5L scores. Conclusion: Patients in advanced CRC stages or undergoing surgery experienced lower HRQoL and higher prevalence of anxiety/depression and pain/discomfort. Older age and high educational attainment predicted high HRQoL. This study provides information on CRC patients’ health utility based on various patient characteristics, which can be used in future economic evaluations.

The purpose of this paper is to study the recognition of ships and their structures to improve the safety of drone operations engaged in shore-to-ship drone delivery service. This study has developed a system that can distinguish between ships and their structures by using a convolutional neural network (CNN). First, the dataset of the Marine Traffic Management Net is described and CNN’s object sensing based on the Detectron2 platform is discussed. There will also be a description of the experiment and performance. In addition, this study has been conducted based on actual drone delivery operations—the first air delivery service by drones in Korea.