In the wake of natural disasters, terrestrial communication infrastructure is often rendered inoperative, severely hampering emergency response efforts. This paper proposes a resilient communication framework leveraging High-Altitude Platforms (HAPs) to provide rapid, wide-area connectivity in disaster-stricken regions. We integrate a reinforcement learning (RL)-based algorithm that dynamically allocates bandwidth and adjusts coverage in real time, enabling efficient and adaptive resource management based on environmental feedback and user demand. The proposed system enhances communication reliability, reduces latency, and optimizes spectral efficiency under uncertain and evolving disaster scenarios. Simulation results demonstrate the effectiveness of the RL-driven HAP network in maintaining robust communication links and improving overall disaster response coordination.
Human Pose Estimation (HPE) aims to predict the positional coordinates of body keypoints in images. While significant progress has been made in HPE, certain challenges persist. For example, the potential for robust occlusion can result in considerable confidence in predicting false-positive keypoints. This can cause problems in applications where high detection accuracy is required, such as in a traffic environment. Most existing methods focus on predicting the positional coordinates of each human joint, but they fail to consider the keypoint occlusion problem and detect the visibility of keypoints in the image. In this paper, we propose a visibility-guided human pose estimation model, named ViPose. We extract the visibility information of keypoints to detect whether a specific keypoint of the subject in the image is obscured by other objects, visible or not captured. ViPose is composed of two modules: HPE keypoint coordinate predictor module, and keypoint visibility detector module. To verify the effectiveness of ViPose, we conduct experiments on both the general MSCOCO dataset and the diver monitoring DriPE dataset. The experimental results show that ViPose can achieve good performance with fewer parameters.
This paper presents a method for transmitting multiple synchronization signal blocks (SSBs) in 5G New Radio (NR) systems. When multiple SSBs with same physical cell identity are transmitted, the peak-to-average power ratio can increase. To reduce the PAPR, non-cell defining SSB is transmitted with a symbol delay relative to the cell-defining SSB. The simulation results show the reduction of PAPR under multiple SSBs transmission.
The objective of this study is to define a systematic taxonomy for assessing the fairness of Korean-based large language models (LLMs). To achieve this, various international documents, datasets, and academic papers are collected and analyzed for their presented fairness and bias classification methods. As a result, we propose a classification framework that includes superclasses for fairness assessment and subclasses reflecting the specific characteristics of Korean society. The proposed taxonomy provides a more systematic and detailed basis for assessing the fairness of Korean-based LLMs. This approach is expected to effectively address ethical issues in Korean language models and contribute to the development of AI technology suitable for Korean society.
