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Preparation and Photocatalytic Activity of ABi2O6/BiOCl (A = Mg, Sr, Ca) Heterojunction
Zhenyu Huang,Yan Wang,정동운 대한화학회 2018 Bulletin of the Korean Chemical Society Vol.39 No.9
MgBi2O6, CaBi2O6, and SrBi2O6 were successfully synthesized by a substitution reaction of NaBiO3 with MgCl2, CaCl2, and Sr(NO3)2, respectively. Then, ABi2O6/BiOCl (A = Mg, Ca, Sr) heterojunction compounds with different ABi2O6:BiOCl ratios were prepared by treating ABi2O6 with an appropriate amount of HCl. The color of the ABi2O6/BiOCl heterojunction was changed from dark to bright as the amount of BiOCl was increased. The absorbances of the ABi2O6/BiOCl heterojunctions in the visible range were decreased as the ratio of BiOCl became higher. The photocatalytic activities of the heterojunction samples were higher than that of sole ABi2O6 or BiOCl. The highest photocatalytic efficiency was achieved when the ABi2O6:BiOCl ratio was 15:85. The organic pollutant was decomposed completely within 1 h under visible light when the 15:85 catalyst was used. The reactive species in the photocatalytic reaction with the ABi2O6/BiOCl photocatalysts were ·O2- and h+ H+.
Yan Liu,Fuli Wang,Yulu Xiong,Zhenyu Liu,Ruicheng Ma,Fei Chu 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.3
To improve the accuracy of feature representation and abnormal condition identification, a new abnormal condition identification method, named integrating multiple binary neural networks based on condition-relevantinformation (CRI-MBNN), is presented for the electro-fused magnesia smelting process in this study. Firstly, thefeatures related to each specific abnormal condition, which is named condition-relevant information (CRI), are analyzed and extracted from the multi-source heterogeneous information with the help of limited and consensus domainknowledge. Then, the CRI is fused at the feature-level to provide a comprehensive representation of each abnormalcondition. Furthermore, for each abnormal condition, a binary neural network (BNN) is established based on thefused feature. They are further integrated according to the frequency of each condition in the actual productionprocess to form the final abnormal condition identification network, i.e., CRI-MBNN. Finally, the effectiveness andfeasibility of the proposed CRI-MBNN are verified by the electro-fused magnesia smelting process.
CT Synthesis from MRI Using Generative Adversarial Network with Frequency-Aware Discriminator
Li Yan,Xu Sisi,Qi Zhenyu 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.1
The pursuit of generating computed tomography (CT) from magnetic resonance imaging (MRI) remains a key area of research with the goal of advancing modern radiation therapy. There has been an increased emphasis on leveraging deep learning methodologies, particularly the generative adversarial network (GAN), to convert MRI into CT. The efectiveness of GAN training hinges on the capacity of its discriminator model to identify and rectify faws in the synthetic CT, providing valuable feedback to the generator model. Acknowledging the multi-scale complexity of human anatomy, this study introduces an innovative discriminator model, designed to assess the synthetic performance across varying scales and frequencies of tissues and organs. We evaluated the signifcance of this frequency-aware discriminator by contrasting it with two commonly used discriminator models: the convolutional neural network discriminator and PatchGAN. We conducted our testing within three existing GAN frameworks on a dataset of 78 nasopharyngeal carcinoma patients. The experimental outcomes revealed that our model managed to decrease the mean absolute error between synthetic and actual CT by an average of 0.18–1.55 Hounsfeld Units within these frameworks. Additionally, it enhanced the visual quality of synthetic CT, ofering superior local structures and patterns. These fndings suggest that our newly developed discriminator can ofer comprehensive guidance to the generator, thereby enhancing CT synthetic performance.
Fan, Qun,Choi, Changhyeok,Yan, Chao,Liu, Yongchao,Qiu, Jieshan,Hong, Song,Jung, Yousung,Sun, Zhenyu The Royal Society of Chemistry 2019 Chemical communications Vol.55 No.29
<P>We report ambient electrochemical N2 fixation at low overpotentials by using two-dimensional (2D) β-boron. The metal-free catalyst afforded both an excellent NH3 yield rate and faradaic efficiency, approximately two times higher than those of bulk boron. We found that several binding sites, especially those involving icosahedral boron in the 2D material, can indeed catalyze N2 reduction efficiently with strong N2 adsorption, thus benefiting initial activation.</P>
Zou Bowen,Wang Wenlin,Liu Jian,Yan Zhenyu,Liu Gaojun,Wang Jun,Wei Guanxiang 한국원자력학회 2020 Nuclear Engineering and Technology Vol.52 No.8
A physical protection system (PPS) is used for the protection of critical facilities. This paper proposes a structure analytic hierarchy approach (SAHA) for the hierarchical evaluation of the PPS effectiveness in critical infrastructure. SAHA is based on the traditional analysis methods “estimate of adversary sequence interruption, EASI”. A community algorithm is used in the building of the SAHA model. SAHA is applied to cluster the associated protection elements for the topological design of complicated PPS with graphical vertexes equivalent to protection elements.