Hyoungjoo Lee

Learn More
Keystroke dynamics-based authentication (KDA) is to verify a user’s identity using not only the password but also keystroke dynamics. With a small number of patterns available, data quality is of great importance in KDA applications. Recently, the authors have proposed to employ artificial rhythms and tempo cues to improve data quality: consistency and(More)
In this paper, a generalized MMSE beamforming is proposed for downlink multiple input multiple output (MIMO) systems. Unlike the conventional MMSE beamforming, cellular environments are considered where each user is randomly distributed in a cell and has the different temporal correlation of fading channel. We apply the proposed generalized MMSE beamforming(More)
Novelty detection is concerned with identifying abnormal system behaviours and abrupt changes from one regime to another. This paper proposes an on-line (causal) novelty detection method capable of detecting both outliers and regime change points in sequential time-series data. Our approach is based on a Kalman filter in order to model time-series data and(More)
Nowadays, the semiconductor manufacturing becomes very complex, consisting of hundreds of individual processes. If a faulty wafer is produced in an early stage but detected at the last moment, unnecessary resource consumption is unavoidable. Measuring every wafer’s quality after each process can save resources, but it is unrealistic and impractical because(More)
In this paper, we propose a practical downlink multiuser multiple-input–multiple-output (MU-MIMO) system. The proposed MU-MIMO system focuses on improving two limiting factors for practical implementations of MU-MIMO: 1) feedback rate and 2) computational complexity. First, users efficiently feed their channel-state information back with low feedback rate(More)
We show that the novelty detection approach is a viable solution to the class imbalance and examine which approach is suitable for different degrees of imbalance. In experiments using SVM-based classifiers, when the imbalance is extreme, novelty detectors are more accurate than balanced and unbalanced binary classifiers. However, with a relatively moderate(More)