• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.119-120
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• 2011

Kawazoe는 1997년 p-type TOS를 만들기 위해서는 3가지가 충족되어야 한다고 언급한바 있다. 첫 번째, 가시광영역에서 투명하기 위해서 cation의 d10s0이 가득 차야 한다. 가득 차지 않은 d10 shell은 광 흡수가 가능하여 투과도를 떨어뜨린다. N-type을 예로 들어 ZnO, TiO, In2O3가 각각 Zn2+, Ti4+, In3+가 되어 d shell을 가득 차게 만드는 것을 볼 수 있다. 두 번째, cation d10s0 shell은 산소의 2p shell과 overlap 되어야 한다. 이 valence band는 홀 전도를 더욱 좋게 한다. 예를 들어 Cu1+(3d), Ag1+(4d)가 해당한다. 세 번째로, 양이온과 산소간의 공유결합을 강하게 하기 위해서 결정학적 구조는 매우 중요하다. Delafossite 구조는 산소가 pseudo-tetrahedral 구조로서 공유결합에 유리하다. 이러한 환경은 O2- (2p6)을 형성하고 홀의 이동도를 증가시킨다. 예를 들어 Cu2O의 경우 앞의 2가지를 만족시키지만 광학적 특성에서 좋지 않다. 그 이유가 3번째 언급한 결정학적인 요인에 있다. 결정 계의 환경은 Cu2O를 따라가면서 3차원적인 연결을 2차원적으로 변형된 delafossite 구조에서는 quantum well이 형성되어 band gap이 커진다. 본 연구에서는 전기적 이방성을 가지고 있는 delafossite CuCrO2 상에서 우선배향을 일으키는 인자 중 기판을 변화시켜 실험을 진행하였다. 결과적으로 기판변화를 통해 우선배향조절이 가능하였으며 CuCrO2 박막을 시켰으며, 결정방향에 따른 전기적 물성의 이방성에 관한 연구는 계속 진행 중에 있다. c-plane sapphire 기판위에는 [00l]로 성장하는 반면, c-plane STO 기판 위에는 [015] 방향으로 성장하는 것을 확인하였다. 이러한 원인은 기판과 증착되는 박막간의 mismatch를 최소화 하여 strain을 줄이고, 계면에서의 Broken boning 수를 줄여 계면에너지를 낮추는 방법이기 때문일 것으로 예상된다. C-plane sapphire 기판위에 증착될 경우 증착온도가 증가함에 따라 c-축으로의 성장이 온전해지며 이에 따라 캐리어농도의 감소와 모빌리티의 증가가 급격하게 변하는 것을 확인할 수 있다. 반면 c-plane STO 기판에서는 증착온도에 따른 박막의 배향변화가 없으며 전기적 물성 변화 또한 비교적 작은 것을 간접적으로 확인하였다.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.217-226
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• 2021

The use of personal mobility(PM) is increasing because of preading shared mobility, but, it causes risk of accidents and fear to pedestrians because of lack of sound. The purpose of this study is to find out the effects of the alert sound in PM and driving direction on risk perception of pedestrians, awareness on PM driver, and allowance for driving on each road. The 8 videos were produced from a pedestrian perspective, depending on the alert sound type(silent, vess, bell, voice) and direction of driving(front, back). After watching each video, participants completed surveys. There was a non-effect on the driving direction. Regardless of the alert sound type, all of the sounds reduced the risk perception. Also, the awareness of PM drivers and allowance for driving on each road were positive. This study found that PM needed an alert sound for pedestrian safety. We expect the results of the study to help improve awareness of PM and make policies and regulations.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.2
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• pp.62-73
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• 2022

As the competition among the autonomous vehicle (AV, here after) developers are getting fierce, Korean government has been supporting developers by deregulating safety standards and providing financial subsidies. Recently, some OEMs announced their plans to market Lv3 and Lv4 automated driving systems. However, these market changes raised concern among public road management sectors for monitoring road conditions and alleviating hazardous conditions for AVs and human drivers. In this regards, the authors proposed a methodology for monitoring road infrastructure to identify hazardous factors for AVs and categorizing the hazards based on their level of impact. To evaluate the degrees of the harm on AVs, the authors suggested a methodology for managing road hazard factors based on vehicle performance features including vehicle body, sensors, and algorithms. Furthermore, they proposed a method providing AVs and road management authorities with potential risk information on road by delivering them on the monitoring map with node and link structure.

• Korean Journal of Remote Sensing
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• v.38 no.5_2
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• pp.725-735
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• 2022

Currently, disasters occurring in Korea are characterized by unpredictability and complexity. Due to these features, property damage and human casualties are increasing. Since the initial response process of these disasters is directly related to the scale and the spread of damage, optimal decision-making is essential, and information of the site must be obtained through timely applicable sensors. However, it is difficult to make appropriate decisions because indiscriminate information is collected rather than necessary information in the currently operated Disaster and Safety Situation Office. In order to improve the current situation, this study proposed a framework that quickly collects various disaster image information, extracts information required to support decision-making, and utilizes it. To this end, a web-based display system and a smartphone application were proposed. Data were collected close to real time, and various analysis results were shared. Moreover, the capability of supporting decision-making was reviewed based on images of actual disaster sites acquired through CCTV, smartphones, and UAVs. In addition to the reviewed capability, it is expected that effective disaster management can be contributed if institutional mitigation of the acquisition and sharing of disaster-related data can be achieved together.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.1-25
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• 2022

After the first Covid-19 confirmed case occurred in Korea in January 2020, interest in personal transportation such as public bicycles not public transportation such as buses and subways, increased. The demand for 'Ddareungi', a public bicycle operated by the Seoul Metropolitan Government, has also increased. In this study, a demand prediction model of a GRU(Gated Recurrent Unit) was presented based on the rental history of public bicycles by time zone(2019~2021) in Seoul. The usefulness of the GRU method presented in this study was verified based on the rental history of Around Exit 1 of Yeouido, Yeongdengpo-gu, Seoul. In particular, it was compared and analyzed with multiple linear regression models and recurrent neural network models under the same conditions. In addition, when developing the model, in addition to weather factors, the Seoul living population was used as a variable and verified. MAE and RMSE were used as performance indicators for the model, and through this, the usefulness of the GRU model proposed in this study was presented. As a result of this study, the proposed GRU model showed higher prediction accuracy than the traditional multi-linear regression model and the LSTM model and Conv-LSTM model, which have recently been in the spotlight. Also the GRU model was faster than the LSTM model and the Conv-LSTM model. Through this study, it will be possible to help solve the problem of relocation in the future by predicting the demand for public bicycles in Seoul more quickly and accurately.