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Simulating Raman spectra by combining first-principles and empirical potential approaches with application to defective MoS_(2)

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npj Computational Materials 2020年第1期6卷 1182-1188页

作者： zhennan kou Arsalan Hashemi Martti J.Puska Arkady V.Krasheninnikov Hannu-Pekka Komsa Department of Applied Physics Aalto UniversityP.O.Box 1110000076 AaltoFinland Helmholtz-Zentrum Dresden-Rossendorf Institute of Ion Beam Physics and Materials Research01328 DresdenGermany Microelectronics Research Unit University of Oulu90014 OuluFinland

Successful application of two-dimensional transition metal dichalcogenides in optoelectronic,catalytic,or sensing devices heavily relies on the materials’quality,that is,the thickness uniformity,presence of grain boundaries,and the types and concentrations of point *** spectroscopy is a powerful and nondestructive tool to probe these factors but the interpretation of the spectra,especially the separation of different contributions,is not *** to simulated spectra is beneficial,but for defective systems first-principles simulations are often computationally too expensive due to the large sizes of the systems ***,we present a combined first-principles and empirical potential method for simulating Raman spectra of defective materials and apply it to monolayer MoS_(2) with random distributions of Mo and S *** study to what extent the types of vacancies can be distinguished and provide insight into the origin of different evolutions of Raman spectra upon increasing defect *** apply to our simulated spectra the phonon confinement model used in previous experiments to assess defect concentrations,and show that the simplest form of the model is insufficient to fully capture peak shapes,but a good match is obtained when the type of phonon confinement and the full phonon dispersion relation are accounted for.

关键词： spectra principles phonon

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Early smoke and flame detection based on transformer

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Journal of Safety Science and Resilience 2023年第3期4卷 294-304页

作者： Xinzhi Wang Mengyue Li Mingke Gao Quanyi Liu zhennan Li Luyao kou School of Computer Engineering and Science Shanghai UniversityShanghai 200444China The 32nd Research Institute of China Electronics Technology Group Corporation Shanghai 201808China College of Civil Aviation Safety Engineering Civil Aviation Flight University of ChinaGuanghan 618307China Department of Engineering Physics Tsinghua UniversityBeijing 100084China

Fire-detection technology plays a critical role in ensuring public safety and facilitating the development of smart *** fire detection is imperative to mitigate potential hazards and minimize associated ***,existing vision-based fire-detection methods exhibit limited generalizability and fail to adequately consider the effect of fire object size on detection *** address this issue,in this study a decoder-free fully transformer-based(DFFT)detector is used to achieve early smoke and flame detection,improving the detection performance for fires of different *** method effectively captures multi-level and multi-scale fire features with rich semantic information while using two powerful encoders to maintain the accuracy of the single-feature map ***,data augmentation is performed to enhance the generalizability of the ***,the detection-oriented transformer(DOT)backbone network is treated as a single-layer fire-feature extractor to obtain fire-related features on four scales,which are then fed into an encoder-only single-layer dense prediction ***,the prediction module aggregates the multi-scale fire features into a single feature map using a scale-aggregated encoder(SAE).The prediction module then aligns the classification and regression features using a task-aligned encoder(TAE)to ensure the semantic interaction of the classification and regression *** results on one private dataset and one public dataset demonstrate that the adopted DFFT possesses high detection accuracy and a strong generalizability for fires of different sizes,particularly early small *** DFFT achieved mean average precision(mAP)values of 87.40%and 81.12%for the two datasets,outperforming other baseline *** exhibits a better detection performance on flame objects than on smoke objects because of the prominence of flame features.

关键词： Early fire Smoke and flame detection Fire detection Vision transformer Public safety

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