Sungkyunkwan University · 工学
Professor Taeyoung Kim's research lab specializes in cybersecurity, blockchain technology, and intelligent systems, with a focus on securing software and data in distributed environments. The lab develops innovative solutions for software watermarking in smart contracts, noise detection in wireless communication systems using deep learning, and multimodal sentiment analysis tailored for the Korean language. Their work bridges the gap between advanced machine learning techniques and practical security challenges in emerging technologies. The lab also investigates the usability of security tools, such as Docker image vulnerability scanners, to improve software development practices.
Figures are computed from collected data and may differ slightly.
Microbial fuel cell (MFC) technology offers an alternative means for producing energy from waste products. In this review, several characteristics of MFC technology that make it revolutionary will be highlighted. First, a brief history presents how bioelectrochemical systems have advanced, ultimately describing the development of microbial fuel cells. Second, the focus is shifted to the attributes that enable MFCs to work efficiently. Next, follows the design of various MFC systems in use includ
The recent strong demands for higher data rate support to cope with the explosive mobile data crunch has initiated research on the next generation (5G) wireless mobile communication technologies that could provide with drastic capacity increase. One of the promising candidates is to use wide spectrum in the mmWave bands, where a breakthrough to overcome the unfavorable channel properties needs to be preceded. In this paper, we propose a novel hybrid beamforming scheme that jointly combines RF be
Non-invasive delivery of artificial implants, stents or devices in patients is vital for rapid and successful recovery. Unfortunately, because the delivery passage is often narrower than the size of the delivered object, a compromise between the shape that is effective at the targeted location and a thin form that allows smooth unobstructed travel to the destination is needed. We address this problem through two key technologies: 3D printing and shape memory polymers (SMPs). 3D printing can prod
In this paper, we propose a new energy and lifetime optimization techniques for emerging dark silicon manycore microprocessors considering both hard long-term reliability effects (hard errors) and transient soft errors, which have been studied less in the past. We consider a recently proposed physics-based electromigration (EM) reliability model to predict the EM-induced reliability. We employ both dynamic voltage and frequency scaling (DVFS) and dark silicon core state using ON/OFF switching ac
Polyester cloth (PC) was selected as a prospective inexpensive substitute separator material for microbial fuel cells (MFCs). PC was compared with a traditional Nafion proton exchange membrane (PEM) as an MFC separator by analyzing its physical and electrochemical properties. A single layer of PC showed higher mass transfer (<i>e.g</i>., for O₂/H⁺/ions) than the Nafion PEM; in the case of oxygen mass transfer coefficient (k<sub>o</sub>), a rate of 50.0 × 10⁻⁵ cm·s⁻¹ was observed compared with a
In this article, we propose a new dynamic reliability management (DRM) technique for emerging dark silicon manycore processors. We formulate our DRM problem as minimizing the energy consumption subject to the reliability, performance and thermal constraints. The new approach is based on a newly proposed physics-based electromigration (EM) reliability model to predict the EM reliability of full-chip power grid networks. We consider thermal design power (TDP) as the power constraint for a dark sil
This paper proposes a scheme to enhance localization in terms of accuracy and transmission overhead in wireless sensor networks. This scheme starts from a basic anchor-node-based distributed localization (ADL) using grid scan with the information of anchor nodes within two-hop distance. Even though the localization accuracy of ADL is higher than that of previous schemes (e.g., DRLS), estimation error can be propagated when the ratio of anchor nodes is low. Thus, after each normal node estimates
Forward Head Posture (FHP) is a common musculoskeletal disorder correlated with neck pain that affects a large percentage of the population. Research has shown that providing feedback for posture auto-correction can help combat FHP and reduce the associated neck pain. However, existing methods for head posture detection are immobile, invasive, and/or inaccurate. This paper presents the use and video-based validation of wireless inertial body sensors for FHP detection. In addition, the effectiven
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