A substantial amount of money and time is required to optimize resources in a massive Wi-Fi network in a real-world environment. Therefore, to reduce cost, proposed algorithms are first verified through simulations before implementing them in a real-world environment. A traffic model is essential to describe user traffic for simulations. Existing traffic models are statistical models based on a discrete-time random process and combine a spatiotemporal characteristic model with the varying parameters, such as average and variance, of a statistical model. The spatiotemporal characteristic model has a mathematically strict assumption that the access points (APs) have approximately similar traffic patterns that increase during day times and decrease at night. The mathematical assumption ensures a homogeneous representation of the network traffic. It does not include heterogeneous characteristics, such as the fact that lecture buildings on campus have a high traffic during lectures, while restaurants have a high traffic only during mealtimes. Therefore, it is difficult to represent heterogeneous traffic using this mathematical model. Deep learning can be used to represent heterogeneous patterns. This study proposes a generative model for Wi-Fi traffic that considers spatiotemporal characteristics using deep learning. The proposed model learns the heterogeneous traffic patterns from the AP-level measurement data without any assumptions and generates similar traffic patterns based on the data. The result shows that the difference between the sample generated by the proposed model and the collected data is up to 72.1% less than that reported in previous studies.

Radiographic testing (RT) using X-rays is essential for ensuring weld integrity in shipbuilding. However, current RT inspection relies on manual evaluation, which is subjective, time-consuming, and costly. Therefore, automation of this inspection process is needed to improve efficiency and reliability. This automated approach involves two essential stages, extracting weld seams from raw radiographic images and detecting defects within them. Deep learning has emerged as a promising approach for this automation, but faces challenges from data heterogeneity in RT images and scarcity of weld defect data. Previous studies have addressed these stages separately or required large labeled datasets, thus lacking a complete pipeline for industrial deployment.We propose an end-to-end framework that addresses both stages with minimal data requirements. The framework first employs few-shot segmentation to extract weld seams from heterogeneous RT images using a fewlabeled examples. Subsequently, it detects weld defects through unsupervised learning trained on 183 normal weld images collected from a shipyard. Experimental results show 94.5% accuracy and 95.7% recall on shipyard data. Additionally, the framework achieves pixel-level anomaly localization without pixel-level annotations, enabling field deployment with minimal labeling burden.