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- Bhat, Goutam, et al.
(författare)
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NTIRE 2022 Burst Super-Resolution Challenge
- 2022
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Ingår i: 2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW 2022). - : IEEE. - 9781665487399 - 9781665487405 ; , s. 1040-1060
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Konferensbidrag (refereegranskat)abstract
- Burst super-resolution has received increased attention in recent years due to its applications in mobile photography. By merging information from multiple shifted images of a scene, burst super-resolution aims to recover details which otherwise cannot be obtained using a simple input image. This paper reviews the NTIRE 2022 challenge on burst super-resolution. In the challenge, the participants were tasked with generating a clean RGB image with 4x higher resolution, given a RAW noisy burst as input. That is, the methods need to perform joint denoising, demosaicking, and super-resolution. The challenge consisted of 2 tracks. Track 1 employed synthetic data, where pixel-accurate high-resolution ground truths are available. Track 2 on the other hand used real-world bursts captured from a handheld camera, along with approximately aligned reference images captured using a DSLR. 14 teams participated in the final testing phase. The top performing methods establish a new state-of-the-art on the burst super-resolution task.
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| 2. |
- Hu, Jutao, et al.
(författare)
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The origin of anomalous hydrogen occupation in high entropy alloys
- 2022
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Ingår i: Journal of Materials Chemistry A. - : Royal Society of Chemistry (RSC). - 2050-7488 .- 2050-7496. ; 10:13, s. 7228-7237
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Tidskriftsartikel (refereegranskat)abstract
- Metal hydrogen storage materials have been the focus of intensive research in the field of hydrogen-based economy. An outstanding question is that the number of hydrogen atoms accommodated in metal hydrides is generally much below the number of interstices, which limits their hydrogen storage capacities. Unlike traditional FCC metal hydrides where hydrogen can only occupy tetrahedral interstices, this study demonstrates that hydrogen can also occupy octahedral interstices in FCC high entropy alloy (HEA) hydrides, which leads to the violation of the Switendick criterion. For Ti25V25Nb25Ta25 and Ti25V25Nb25Zr25 HEAs, nearly 20% and 17.5% of octahedral interstices can be occupied by hydrogen, respectively. The anomalous hydrogen occupation mainly originates from the intrinsic electron delocalization between hydrogen atoms in HEA hydrides, which presents a sharp contrast to traditional metal hydrides. Such electron delocalization decreases repulsive interactions between hydrogens and promotes the electron localization at octahedral interstices. Additionally, this study reveals that hydrogen occupation at octahedral interstices enhances the structural disordering and decreases the thermal stability of HEA hydrides, which will be beneficial to reduce the dehydrogenation temperature. The presented results may provide a new strategy for the design of high-density storage materials.
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| 3. |
- Yangyang, Zhao, et al.
(författare)
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Irregular Demand Pattern Analysis Under Unplanned Disruptions in Urban Rail Systems
- 2024
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Konferensbidrag (refereegranskat)abstract
- Stations’ irregular demand evolutions exhibit different patterns under unplanned disruptions given the disruption types, operation management, and passenger travel choices. Existing studies mainly focus on irregular demand detection, but lack a deep analysis of its patterns that are essential in guiding targeted countermeasure development, such as train timetable adjustment and emergency evacuation planning. Daily demand fluctuations and imbalanced disruption types make clustering and analyzing irregular demand patterns a challenging task. To address the problem, this paper presents a robust demand decomposition and clustering model for irregular demand pattern analysis under unplanned disruptions. It consists of, an irregular demand detection model, i.e., a robust principal component analysis (RPCA)-based model, and an irregular demand pattern clustering model, i.e., a robust discretization-based clustering model. The RPCA-based model is used to identify irregular demand by decomposing observed demand into regular and irregular demand. The robust discretization-based clustering model, which considers the non-sparse issue and the imbalanced issue of irregular demand sequences (caused by the daily demand fluctuations and imbalanced disruption types, respectively), is developed with a customized discrete input module, distance metric module, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (H-DBSCAN) module, to identify irregular demand patterns. We validate the proposed model using synthetic and real-world data from the Hong Kong Mass Transit Railway system. Compared with other baseline models, the results demonstrate that the proposed model can effectively identify irregular demand patterns rather than wrongly mixing them or classifying them as outliers. The factors that lead to each identified entry/exit irregular demand pattern are analyzed at a station level and illustrated through binary tree-based architectures, which enable operators to predict irregular demand patterns under unplanned disruptions.
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