Yonsei University · 情報科学
Professor Beakcheol Jang's research lab specializes in wireless networking, machine learning, and intelligent data processing, with a focus on developing energy-efficient communication protocols, advanced indoor positioning systems, and deep learning-based text and signal classification techniques. The lab explores practical applications of reinforcement learning, such as Q-learning optimization, and leverages neural networks—including CNNs and LSTMs—for natural language processing and big data analytics. A key research direction involves enhancing the accuracy and efficiency of wireless systems, particularly in challenging environments like indoor spaces and sensor networks.
Figures are computed from collected data and may differ slightly.
Q-learning is arguably one of the most applied representative reinforcement learning approaches and one of the off-policy strategies. Since the emergence of Q-learning, many studies have described its uses in reinforcement learning and artificial intelligence problems. However, there is an information gap as to how these powerful algorithms can be leveraged and incorporated into general artificial intelligence workflow. Early Q-learning algorithms were unsatisfactory in several aspects and cover
There is a need to extract meaningful information from big data, classify it into different categories, and predict end-user behavior or emotions. Large amounts of data are generated from various sources such as social media and websites. Text classification is a representative research topic in the field of natural-language processing that categorizes unstructured text data into meaningful categorical classes. The long short-term memory (LSTM) model and the convolutional neural network for sent
Big web data from sources including online news and Twitter are good resources for investigating deep learning. However, collected news articles and tweets almost certainly contain data unnecessary for learning, and this disturbs accurate learning. This paper explores the performance of word2vec Convolutional Neural Networks (CNNs) to classify news articles and tweets into related and unrelated ones. Using two word embedding algorithms of word2vec, Continuous Bag-of-Word (CBOW) and Skip-gram, we
Fingerprint-based wireless indoor positioning approaches are widely used for location-based services because wireless signals, such as Wi-Fi and Bluetooth, are currently pervasive in indoor spaces. The working principle of fingerprinting technology is to collect the fingerprints from an indoor environment, such as a room or a building, in advance, create a fingerprint map, and use this map to estimate the user's current location. The fingerprinting technology is associated with a high level of a
Due to its usefulness and wide deployment, IEEE 802.11 has been the subject of numerous studies, but still lacks a complete analytical model. Hidden terminals are common in IEEE 802.11 and cause the degradation of throughput. Despite the importance of the hidden terminal problem, there have been a relatively small number of studies that consider the effect of hidden terminals on IEEE 802.11 throughput, and many are not accurate for a wide range of conditions. In this paper, we present an accurat
Energy efficiency of the MAC protocol is a key design factor for wireless sensor networks (WSNs). Due to the importance of the problem, a number of energy efficient MAC protocols have been developed for WSNs. Preamble-sampling based MAC protocols (e.g., B-MAC and X-MAC) have overheads due to their preambles, and are inefficient at large wakeup intervals. SCP-MAC, a very energy efficient scheduling MAC protocol, minimizes the preamble by combining preamble sampling and scheduling techniques; howe
Recently, as the paradigm of medical services has shifted from treatment to prevention, there is a growing interest in smart healthcare that can provide users with healthcare services anywhere, at any time, using information and communications technologies. With the development of the smart healthcare industry, there is a growing need for collecting large-scale personal health data to exploit the knowledge obtained through analyzing them for improving the smart healthcare services. Although such
Crude oil plays an important role in the global economy, as it contributes one-third of the energy consumption worldwide. However, despite its importance in policymaking and economic development, forecasting its price is still challenging due to its complexity and irregular price trends. Although a significant amount of research has been conducted to improve forecasting using external factors as well as machine-learning and deep-learning models, only a few studies have used hybrid models to impr
The stress placed on global power supply systems by the growing demand for electricity has been steadily increasing in recent years. Thus, accurate forecasting of energy demand and consumption is essential to maintain the lifestyle and economic standards of nations sustainably. However, multiple factors, including climate change, affect the energy demands of local, national, and global power grids. Therefore, effective analysis of multivariable data is required for the accurate estimation of ene
Open papers in the app to read, cite, and organize with AI.
