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Yoon, Hyunsik,Kim, Soaram,Park, Hyunggil,Nam, Giwoong,Kim, Yangsoo,Leem, Jae-Young,Kim, Min Su,Kim, Byunggu,Kim, Younggyu,Ji, Iksoo,Park, Youngbin,Kim, Ikhyun,Lee, Sang-heon,Jung, Jae Hak,Kim, Jin Soo Korean Physical Society 2014 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.64 No.1
Undoped ZnO and Ga-doped ZnO (GZO) thin films with different Ga concentrations were prepared by using the sol-gel spin-coating method. The surface morphologies and the growth orientations of the films were measured by using scanning electron microscopy and X-ray diffraction, respectively. The electrical properties were measured by using the Hall effect. The optical transmittances and reflectances of the films were measured as functions of the wavelength by UV-vis spectroscopy. The undoped ZnO thin films exhibited rough surfaces with particle-like structures. When Ga was incorporated, the particle sizes dramatically decreased without changes in the surface morphologies, and the c-axis growth orientations of the GZO thin films were significantly decreased. The optical transmittances clearly exhibited shifts in the band edge, and those in the visible range gradually increased with increasing Ga concentration. The absorption coefficients, refractive indices, extinction constants, dielectric constants, and optical conductivities of the films gradually decreased with increasing Ga concentration.
사용자 움직임 특징의 학습을 이용한 게임 몰입도 측정 시스템
김영빈(YoungBin Kim),강신진(ShinJin Kang),김영선(YoungSun Kim),이상혁(Sang-Hyeok Lee),김창헌(Chang Hun Kim) 한국HCI학회 2014 한국HCI학회 학술대회 Vol.2014 No.2
본 논문에서는 카메라를 이용하여 사용자의 정보를 획득하고, 이를 통하여 게임 몰입도를 객관적으로 평가하는 시스템을 제안한다. 제안하는 시스템은 특수한 장치를 필요로 하지 않으며, 카메라로 포착 가능한 사용자의 움직임에 기반한 특징들을 이용하여 기계학습을 수행하여 사용자의 몰입도를 객관적으로 측정할 수 있다. 실험을 통하여 PC환경기반 게임에서의 몰입도 측정을 위한 시스템 구축 및 해당 시스템의 완성도를 검증해보았다. 논문에 제시된 시스템은 PC 환경에서 제공하는 모든 장시간 몰입 콘텐츠에 대한 객관적인 몰입도를 측정하는 척도로 활용될 수 있을 것이다. This paper acquired users’ information by using a camera and then through the information it proposed measurement system which evaluates game immersion objectively. The proposed system doesn’t need any special equipment and it can measure the users’ game immersion objectively through conducting machine learning by using the characteristics based on the users’ movement which can be captured by a camera. This paper verified the system establishment for measurement of game immersion based on the PC environment and the system’s degree of completion through a test. The system which is proposed in this paper can be used as a criterion of measuring objective immersion for a long time in all the contents in PC environment.
Kim Nari,Lee Eun Sung,Won Sang Eun,Yang Mihyun,Lee Amy Junghyun,Shin Youngbin,Ko Yousun,Pyo Junhee,Park Hyo Jung,Kim Kyung Won 대한영상의학회 2022 Korean Journal of Radiology Vol.23 No.11
Immunotherapy has revolutionized and opened a new paradigm for cancer treatment. In the era of immunotherapy and molecular targeted therapy, precision medicine has gained emphasis, and an early response assessment is a key element of this approach. Treatment response assessment for immunotherapy is challenging for radiologists because of the rapid development of immunotherapeutic agents, from immune checkpoint inhibitors to chimeric antigen receptor-T cells, with which many radiologists may not be familiar, and the atypical responses to therapy, such as pseudoprogression and hyperprogression. Therefore, new response assessment methods such as immune response assessment, functional/molecular imaging biomarkers, and artificial intelligence (including radiomics and machine learning approaches) have been developed and investigated. Radiologists should be aware of recent trends in immunotherapy development and new response assessment methods.
DeepNAP: Deep neural anomaly pre-detection in a semiconductor fab
Kim, Chunggyeom,Lee, Jinhyuk,Kim, Raehyun,Park, Youngbin,Kang, Jaewoo Elsevier science 2018 Information sciences Vol.457 No.-
<P>Anomaly detection in an industrial process is crucial for preventing unexpected economic loss. Among various signals, multivariate time series signals are one of the most difficult signals to analyze for detecting anomalies. Moreover, labels for anomalous signals are often unavailable in many fields. To tackle this problem, we present DeepNAP which is an anomaly pre-detection model based on recurrent neural networks. Without any annotated data, DeepNAP successfully learns to detect anomalies using partial reconstruction. Furthermore, detecting anomalies in advance is essential for preventing catastrophic events. While previous studies focused mainly on capturing anomalies after they have occurred, DeepNAP is able to pre-detect anomalies. We evaluate DeepNAP and other baseline models on a real multivariate dataset generated from a semiconductor manufacturing fab. Compared with other baseline models, DeepNAP achieves the best performance on both the detection and pre-detection of anomalies. (C) 2018 Elsevier Inc. All rights reserved.</P>
Kim, Namdoo,Kwon, Jiwoong,Lim, Youngbin,Kang, Jooyoun,Bae, Sohyeon,Kim, Seong Keun The Royal Society of Chemistry 2018 Chemical communications Vol.54 No.69
<P>By incorporating STED (stimulated emission depletion) nanoscopy into single-molecule spectroscopy, we demonstrate that the concentration limit imposed by optical diffraction can be overcome in diffusion-based single-molecule measurement. We showed that single-molecule detection is feasible at a concentration of 5 nM, which is 100-times higher than the limit of conventional single-molecule measurements.</P>