Zobrazeno 1 - 10
of 456
pro vyhledávání: '"Kim, Joongheon"'
Polarization reconfigurable (PR) antennas enhance spectrum and energy efficiency between next-generation node B(gNB) and user equipment (UE). This is achieved by tuning the polarization vectors for each antenna element based on channel state informat
Externí odkaz:
http://arxiv.org/abs/2409.20065
Autor:
Kim, Gyu Seon, Cho, Yeryeong, Chung, Jaehyun, Park, Soohyun, Jung, Soyi, Han, Zhu, Kim, Joongheon
Achieving global space-air-ground integrated network (SAGIN) access only with CubeSats presents significant challenges such as the access sustainability limitations in specific regions (e.g., polar regions) and the energy efficiency limitations in Cu
Externí odkaz:
http://arxiv.org/abs/2406.16994
Autor:
Shim, Joo Yong, Kim, Joongheon
Unlike closed systems, where the total energy and information are conserved within the system, open systems interact with the external environment which often leads to complex behaviors not seen in closed systems. The random fluctuations that arise d
Externí odkaz:
http://arxiv.org/abs/2405.14283
Autor:
Park, Soohyun, Kim, Joongheon
This paper introduces a novel run-time testing, analysis, and code optimization (TACO) method for quantum neural network (QNN) software in advanced Internet-of-Things (IoT) systems, which visually presents the learning performance that is called a ba
Externí odkaz:
http://arxiv.org/abs/2401.10914
Spurred by consistent advances and innovation in deep learning, object detection applications have become prevalent, particularly in autonomous driving that leverages various visual data. As convolutional neural networks (CNNs) are being optimized, t
Externí odkaz:
http://arxiv.org/abs/2401.01370
For Industry 4.0 Revolution, cooperative autonomous mobility systems are widely used based on multi-agent reinforcement learning (MARL). However, the MARL-based algorithms suffer from huge parameter utilization and convergence difficulties with many
Externí odkaz:
http://arxiv.org/abs/2308.01519
This paper presents the deep learning-based recent achievements to resolve the problem of autonomous mobility control and efficient resource management of autonomous vehicles and UAVs, i.e., (i) multi-agent reinforcement learning (MARL), and (ii) neu
Externí odkaz:
http://arxiv.org/abs/2307.09711
The prodigious growth of digital health data has precipitated a mounting interest in harnessing machine learning methodologies, such as natural language processing (NLP), to scrutinize medical records, clinical notes, and other text-based health info
Externí odkaz:
http://arxiv.org/abs/2306.16367
The development of urban-air-mobility (UAM) is rapidly progressing with spurs, and the demand for efficient transportation management systems is a rising need due to the multifaceted environmental uncertainties. Thus, this paper proposes a novel air
Externí odkaz:
http://arxiv.org/abs/2306.04137
Clustering data is an unsupervised learning approach that aims to divide a set of data points into multiple groups. It is a crucial yet demanding subject in machine learning and data mining. Its successful applications span various fields. However, c
Externí odkaz:
http://arxiv.org/abs/2305.15417