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( Kangyoon Lee ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.8
This study presents a reference model (RM) and the architecture of a cognitive health advisor (CHA) that integrates information with ambient intelligence. By controlling the information using the CHA platform, the reference model can provide various ambient intelligent solutions to a user. Herein, a novel approach to a CHA RM based on evolutional cyber-physical systems is proposed. The objective of the CHA RM is to improve personal health by managing data integration from many devices as well as conduct a new feedback cycle, which includes training and consulting to improve quality of life. The RM can provide an overview of the basis for implementing concrete software architectures. The proposed RM provides a standardized clarification for developers and service designers in the design and implementation process. The CHA RM provides a new approach to developing a digital healthcare model that includes integrated systems, subsystems, and components. New features for chatbots and feedback functions set the position of the conversational interface system to improve human health by integrating information, analytics, and decisions and feedback as an advisor on the CHA platform.
Development of model based EGR mass flow rate estimation algorithm in a diesel engine
Hyunjun Lee,Yeongseop Park,Junsoo Kim,Kangyoon Lee,Myoungho Sunwoo 한국자동차공학회 2010 한국자동차공학회 학술대회 및 전시회 Vol.2010 No.11
Recently, exhaust gas recirculation (EGR) is widely used to reduce NOx emissions in diesel engines. However, an excessive EGR rate in the cylinders leads to increment of particulate matter or misfire. In order to overcome this problem, the EGR mass flow rate should be controlled precisely. To control the EGR mass flow rate accurately, the EGR mass flow rate should be estimated correctly. This paper proposes a method to estimate the EGR mass flow rate based on a mean value engine model (MVEM) including characteristics of air mass flow into intake manifold, EGR mass flow into intake manifold, and air mass flow into cylinders. The MVEM also covers compressor, intercooler, exhaust manifold temperature, and EGR cooler model. The proposed algorithm calculates EGR mass flow rate with sensor outputs from mass-produced engines. The proposed EGR mass flow rate estimation algorithm was validated through simulation results using a 1-D engine model.
The novel high voltage IGBT with improved on-resistance and turn-off characteristics
Mankoo Lee,Samuell Shin,Kasan Ha,Kangyoon Lee,Yongseo Koo 대한전자공학회 2009 ITC-CSCC :International Technical Conference on Ci Vol.2009 No.7
In this paper, the novel 2.5KV IGBT incorporating an n-type MOSFET between adjacent cells is proposed with the aim of the improving the tradeoff relation between switching time and conduction loss. The incorporated MOSFET provides an additional base current that led to the increase of collector current of IGBT and the decrease of the on-state voltage drop. Also, the turn-off time and the static latch-up susceptibility are decreased because of the P+ region of the incorporated MOSFET, In the experimental result, with incorporating an n-type MOSFET, the turn-off time and on-state voltage drop are decreased by approximately 8% and 15% respectively, compared to a conventional IGBT. And the proposed IGBT provides higher latching current of 39% than conventional IGBT.
이강윤(Kangyoon Lee),김대경(Daekyung Kim),오병걸(Byounggul Oh),선우명호(Myoungho Sunwoo) 한국자동차공학회 2009 한국자동차공학회 부문종합 학술대회 Vol.2009 No.4
In this paper, we proposed a new boosting device for internal combustion engines using dynamic pressure. The boosting device is inspired by the concept of boost converter in electrical system. The proposed boosting device is composed of a fan blowing air into an intake manifold, a bypass, and a bypass valve. By using the fan and the bypass valve, kinetic energy of a working fluid is translated into pressure energy. Consequently, the intake manifold pressure is increased due to the pressure energy. The feasibility of the proposed boosting device was validated by a commercial 1-D engine model, GT-power.
Hwagyu Suh,Kangyoon Lee,Young-Min Lee,Je-Min Park,Byung-Dae Lee,Eunsoo Moon,Hee-Jeong Jeong,Young-In Chung,Ji-Hoon Kim,Hak-Jin Kim,Chi-Woong Mun,Tae-Hyung Kim,Young-Hoon Kim 대한노인정신의학회 2016 노인정신의학 Vol.20 No.2
Objective:The aim of this study is to determine whether there is any difference in white matter (WM) integrity between Alzheimer’s disease (AD) with metabolic syndrome (MetS) and without MetS. Methods:Altogether, 30 subjects were finally recruited from the Memory Impairment Clinics of Pusan National University Hospital in Korea. All subjects (AD with MetS : n=15, matched AD without MetS for age, gender and year of education : n=15) were underwent 3-tesla magnetic resonance imaging scans of diffusion tensor imaging. Results:The mean fractional anisotropy of the AD with MetS was lower (p<0.05) in right posterior corona radiate, right corticospinal tract and right superior longitudinal fasciculus than that of the AD without MetS. Conclusion:Our findings suggest that WM integrity damage.
가구당 기기별 에너지 사용량 예측을 위한 딥러닝 모델의 설계 및 구현
이주희 ( JuHui Lee ),이강윤 ( KangYoon Lee ) (사)한국빅데이터학회 2021 한국빅데이터학회 학회지 Vol.6 No.1
우리나라는 자원 빈국인 동시에 에너지 다소비 국가이다. 또한 전기 에너지에 대한 사용량 및 의존도가 매우 높고, 총 에너지 사용의 20% 이상은 건물에서 소비된다. 딥러닝과 머신러닝에 대한 연구가 활발해지면서 다양한 알고리즘을 에너지 효율 분야에 적용하려는 연구가 진행되고 있으며, 에너지의 효율적인 관리를 위한 건물에너지관리시스템(BEMS)의 도입이 늘어가는 추세이다. 본 논문에서는 스마트플러그를 이용하여 직접 수집한 가구당 기기별 에너지 사용량을 바탕으로 데이터베이스를 구축하였다. 또한 RNN과 LSTM 모델을 이용하여 수집한 데이터를 효과적으로 분석 및 예측하는 알고리즘을 구현하였다. 추후 이 데이터는 에너지 사용량 예측을 넘어 전력 소비 패턴 분석 등에 적용할 수 있다. 이는 에너지 효율 개선에 도움이 될 수 있으며, 미래 데이터의 예측을 통해 효과적인 전력 사용량 관리에 도움을 줄 것으로 기대된다. Korea is both a resource-poor country and a energy-consuming country. In addition, the use and dependence on electricity is very high, and more than 20% of total energy use is consumed in buildings. As research on deep learning and machine learning is active, research is underway to apply various algorithms to energy efficiency fields, and the introduction of building energy management systems (BEMS) for efficient energy management is increasing. In this paper, we constructed a database based on energy usage by device per household directly collected using smart plugs. We also implement algorithms that effectively analyze and predict the data collected using RNN and LSTM models. In the future, this data can be applied to analysis of power consumption patterns beyond prediction of energy consumption. This can help improve energy efficiency and is expected to help manage effective power usage through prediction of future data.