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Electric vehicle routing problem: A systematic review and a new comprehensive model with nonlinear energy recharging and consumption
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“When a capsule network (CapsNet) is combined with an improved routing approach (Xiao et al., 2020; Xiao et al., 2021) for classification, it offers greater feature abstraction and representation capabilities without requiring a lot of data or parameters.”
From Deepfake detection using deep learning methods: A systematic and comprehensive review (2024) · cites this paper as Xiao et al., 2021
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Metadata
- Authors
- Xiao, Y., Zhang, Y., Kaku, I., Kang, R., & Pan, X.
- Affiliations
- Beihang University (CN)
- Tokyo City University (JP)
- Corresponding author
- Kang, Rui
- Pan, Xing
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 151
- Pages
- 111567
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W3195019475
- Cited by
- 236
- References
- 78
- Citations by year
- 2026: 19 · 2025: 42 · 2024: 43 · 2023: 74 · 2022: 54 · 2021: 4
- FWCI
- 4.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Renewable and Sustainable Energy Reviews (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Natural Science Foundation of China (71871003, 719710009)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Vehicle Routing Optimization Methods
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Automotive Engineering › Electric and Hybrid Vehicle Technologies
- Keywords
- electric vehicle routing problem, electric vehicles, energy loss, piecewise linear approximation, emission cost, transport logistics, fleet planning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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