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        Nano-Sized Particles of Prussian Blue Analogue KxCoy[Fe(CN)6] and KxNiy[Fe(CN)6] : Synthesis and Their Properties

        니구엔반민,Phung Kim Phu,양인상 한국물리학회 2008 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.53 No.6

        Recently, striking effects of light irradiation on the magnetic state were reported for Prussian blue analogues AxCoy[Fe(CN)6] (A = Na, K, Rb, Cs). The physical and optical properties are dependent on the size of the particles of the measured samples. However, there have been few report on the formation of nano-sized particles of the material and the effect of size of the particles on the properties of the compounds. In this report, we investigate the effect of size of the particles on the properties of the Prussian blue analogue KxCoy[Fe(CN)6] and KxNiy[Fe(CN)6]. We present a novel synthesis method of the KxCoy[Fe(CN)6] and KxNiy[Fe(CN)6] nano-particles. The results of the Xray diffraction (XRD), scanning electron microscopy (SEM), UV-vis absorption and magnetization measurements of the compounds are presented.

      • SCIESCOPUSKCI등재

        A Magnetization Study of Prussian-blue Analogue Na<SUB>x</SUB>Mn<SUB>y</SUB>[Fe(CN)<SUB>6</SUB>]

        Nguyen Van Minh,Phung Kim Phu,Nguyen Minh Thuan,In-Sang Yang 한국자기학회 2008 Journal of Magnetics Vol.13 No.4

        In this report, we present the results of a study on the effects of the particle size on the properties of the Prussian blue (PB) analog NaxMny[Fe(CN)?]. A novel synthesis method of the NaxMny[Fe(CN)?] nano-particles using an organic solvent, formamide, is employed. The size of the PB particles is found to be 100-150 ㎚ for the samples prepared in the formamide solvent, which is much smaller than that of the samples prepared using water only. The broadening of the X-ray diffraction peaks of the nano-sized PB samples is attributed to the lattice disorder and a dramatic reduction in the particle size. The compositions of the samples are confirmed by an energy-dispersive X-ray analysis (EDAX), and the result proves that the samples are actually NaxMny[Fe(CN)?] Prussian blue. The UV-vis spectra show a broad intervalence charge-transfer (CT) band in the visible region between 400 and 700 ㎚, and the absorption decreases abruptly in the green region for the nano-sized PB sample. A divergence between the field cooled (FC) and zero field cooled (ZFC) magnetization curves is observed for the nano-sized PB sample at 11 K, indicating that nanoparticles in the sample are single domain superparamagnets with a blocking temperature of 11 K. Our results reveal that the nano-sized PB samples show significantly different optical and magnetic properties than those of the bulk PB samples.

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        CNN Based Face Tracking and Re-identification for Privacy Protection in Video Contents

        박태미,닌펑푸,김형원,Park, TaeMi,Phu, Ninh Phung,Kim, HyungWon The Korea Institute of Information and Commucation 2021 한국정보통신학회논문지 Vol.25 No.1

        최근 유튜브와 같이 영상 콘텐츠를 보거나 제작하는 것에 관한 관심이 급증하고 있습니다. 그러나 개인 정보 보호 기술이 없이 동영상을 제작하게 되면, 출연을 원하지 않는 사람들이 공개적으로 노출되어 개인 정보 보호권을 침해할 수 있습니다. 본 논문은 이러한 문제를 해결하기 위해 얼굴을 식별하여 특정한 얼굴만 화면에 나오고 그 외에 다른 얼굴들은 Gaussian blur filter를 이용하여 흐리게 하여서 초상권을 보호하는 기술을 제안합니다. 이 논문의 핵심은 실시간 비디오에서 인물의 초상권을 보호하기 위한 주요 기술인 얼굴 식별 기술의 정확도를 높이기 위한 노력입니다. 본 논문은 얼굴 식별의 정확도를 높이기 위하여 추적 알고리즘을 사용하였으며 실시간 비디오에 적용하기 위하여 알고리즘을 변경하였습니다. 이 논문에서는 추적 알고리즘이 있는 경우와 없는 경우를 비교하여 결과를 보여줍니다. Recently there is sharply increasing interest in watching and creating video contents such as YouTube. However, creating such video contents without privacy protection technique can expose other people in the background in public, which is consequently violating their privacy rights. This paper seeks to remedy these problems and proposes a technique that identifies faces and protecting portrait rights by blurring the face. The key contribution of this paper lies on our deep-learning technique with low detection error and high computation that allow to protect portrait rights in real-time videos. To reduce errors, an efficient tracking algorithm was used in this system with face detection and face recognition algorithm. This paper compares the performance of the proposed system with and without the tracking algorithm. We believe this system can be used wherever the video is used.

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