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- Dai, Fei, et al.
(författare)
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Recent Progress on Hydrogen-Rich Syngas Production from Coal Gasification
- 2023
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Ingår i: Processes. - : MDPI. - 2227-9717. ; 11:6
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Tidskriftsartikel (refereegranskat)abstract
- Coal gasification is recognized as the core technology of clean coal utilization that exhibits significant advantages in hydrogen-rich syngas production and CO2 emission reduction. This review briefly discusses the recent research progress on various coal gasification techniques, including conventional coal gasification (fixed bed, fluidized bed, and entrained bed gasification) and relatively new coal gasification (supercritical water gasification, plasma gasification, chemical-looping gasification, and decoupling gasification) in terms of their gasifiers, process parameters (such as coal type, temperature, pressure, gasification agents, catalysts, etc.), advantages, and challenges. The capacity and potential of hydrogen production through different coal gasification technologies are also systematically analyzed. In this regard, the decoupling gasification technology based on pyrolysis, coal char–CO2 gasification, and CO shift reaction shows remarkable features in improving comprehensive utilization of coal, low-energy capture and conversion of CO2, as well as efficient hydrogen production. As the key unit of decoupling gasification, this work also reviews recent research advances (2019–2023) in coal char–CO2 gasification, the influence of different factors such as coal type, gasification agent composition, temperature, pressure, particle size, and catalyst on the char–CO2 gasification performance are studied, and its reaction kinetics are also outlined. This review serves as guidance for further excavating the potential of gasification technology in promoting clean fuel production and mitigating greenhouse gas emissions.
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| 2. |
- Shen, Jianbing, et al.
(författare)
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Distilled Siamese Networks for Visual Tracking
- 2022
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Ingår i: IEEE Transactions on Pattern Analysis and Machine Intelligence. - : IEEE COMPUTER SOC. - 0162-8828 .- 1939-3539. ; 44:12, s. 8896-8909
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, Siamese network based trackers have significantly advanced the state-of-the-art in real-time tracking. Despite their success, Siamese trackers tend to suffer from high memory costs, which restrict their applicability to mobile devices with tight memory budgets. To address this issue, we propose a distilled Siamese tracking framework to learn small, fast and accurate trackers (students), which capture critical knowledge from large Siamese trackers (teachers) by a teacher-students knowledge distillation model. This model is intuitively inspired by the one teacher versus multiple students learning method typically employed in schools. In particular, our model contains a single teacher-student distillation module and a student-student knowledge sharing mechanism. The former is designed using a tracking-specific distillation strategy to transfer knowledge from a teacher to students. The latter is utilized for mutual learning between students to enable in-depth knowledge understanding. Extensive empirical evaluations on several popular Siamese trackers demonstrate the generality and effectiveness of our framework. Moreover, the results on five tracking benchmarks show that the proposed distilled trackers achieve compression rates of up to 18x and frame-rates of 265 FPS, while obtaining comparable tracking accuracy compared to base models.
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