Design and Application of an Onboard Particle Identification Platform Based on Convolutional Neural Networks Article Swipe
Chaoping Bai
,
Xin Zhang
,
Shenyi Zhang
,
Yueqiang Sun
,
Xianguo Zhang
,
Ziting Wang
,
Shuai Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14156628
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14156628
Space radiation particle detection plays a crucial role in scientific research and engineering practice, especially in particle species identification. Currently, commonly used in-orbit particle identification techniques include telescope methods, electrostatic analysis time of flight (ESA × TOF), time-of-flight energy (TOF × E), and pulse shape discrimination (PSD). However, these methods usually fail to utilize the full waveform information containing rich features, and their particle identification results may be affected by the random rise and fall of particle deposition and noise interference. In this study, a low-latency and lightweight onboard FPGA real-time particle identification platform based on full waveform information was developed utilizing the superior target classification, robustness, and generalization capabilities of convolutional neural networks (CNNs). The platform constructs diversified input datasets based on the physical features of waveforms and uses Optuna and Pytorch software architectures for model training. The hardware platform is responsible for the real-time inference of waveform data and the dynamic expansion of the dataset. The platform was utilized for deep learning training and the testing of the historical waveform data of neutron and gamma rays, and the inference time of a single waveform takes 4.9 microseconds, with an accuracy rate of over 97%. The classification expectation FOM (figure-of-merit) value of this CNN model is 133, which is better than the traditional pulse shape discrimination (PSD) algorithm’s FOM value of 0.8. The development of this platform not only improves the accuracy and efficiency of space particle discrimination but also provides an advanced tool for future space environment monitoring, which is of great value for engineering applications.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app14156628
- https://www.mdpi.com/2076-3417/14/15/6628/pdf?version=1722251674
- OA Status
- gold
- Cited By
- 1
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401122952
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4401122952Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app14156628Digital Object Identifier
- Title
-
Design and Application of an Onboard Particle Identification Platform Based on Convolutional Neural NetworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-29Full publication date if available
- Authors
-
Chaoping Bai, Xin Zhang, Shenyi Zhang, Yueqiang Sun, Xianguo Zhang, Ziting Wang, Shuai ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/app14156628Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/14/15/6628/pdf?version=1722251674Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2076-3417/14/15/6628/pdf?version=1722251674Direct OA link when available
- Concepts
-
Computer science, Waveform, Convolutional neural network, Inference, Identification (biology), Artificial neural network, Artificial intelligence, Particle identification, Computer engineering, Telecommunications, Biology, Botany, Detector, RadarTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
-
22Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4401122952 |
|---|---|
| doi | https://doi.org/10.3390/app14156628 |
| ids.doi | https://doi.org/10.3390/app14156628 |
| ids.openalex | https://openalex.org/W4401122952 |
| fwci | 1.07211444 |
| type | article |
| title | Design and Application of an Onboard Particle Identification Platform Based on Convolutional Neural Networks |
| awards[0].id | https://openalex.org/G3421559059 |
| awards[0].funder_id | https://openalex.org/F4320321001 |
| awards[0].display_name | |
| awards[0].funder_award_id | 42204180 |
| awards[0].funder_display_name | National Natural Science Foundation of China |
| biblio.issue | 15 |
| biblio.volume | 14 |
| biblio.last_page | 6628 |
| biblio.first_page | 6628 |
| topics[0].id | https://openalex.org/T11949 |
| topics[0].field.id | https://openalex.org/fields/31 |
| topics[0].field.display_name | Physics and Astronomy |
| topics[0].score | 0.9987000226974487 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/3108 |
| topics[0].subfield.display_name | Radiation |
| topics[0].display_name | Nuclear Physics and Applications |
| topics[1].id | https://openalex.org/T11176 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9979000091552734 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2740 |
| topics[1].subfield.display_name | Pulmonary and Respiratory Medicine |
| topics[1].display_name | Radiation Therapy and Dosimetry |
| topics[2].id | https://openalex.org/T11216 |
| topics[2].field.id | https://openalex.org/fields/31 |
| topics[2].field.display_name | Physics and Astronomy |
| topics[2].score | 0.9919000267982483 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/3108 |
| topics[2].subfield.display_name | Radiation |
| topics[2].display_name | Radiation Detection and Scintillator Technologies |
| funders[0].id | https://openalex.org/F4320321001 |
| funders[0].ror | https://ror.org/01h0zpd94 |
| funders[0].display_name | National Natural Science Foundation of China |
| is_xpac | False |
| apc_list.value | 2300 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2490 |
| apc_paid.value | 2300 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2490 |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.6449707746505737 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C197424946 |
| concepts[1].level | 3 |
| concepts[1].score | 0.6211998462677002 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1165717 |
| concepts[1].display_name | Waveform |
| concepts[2].id | https://openalex.org/C81363708 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5760254859924316 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[2].display_name | Convolutional neural network |
| concepts[3].id | https://openalex.org/C2776214188 |
| concepts[3].level | 2 |
| concepts[3].score | 0.4652228057384491 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q408386 |
| concepts[3].display_name | Inference |
| concepts[4].id | https://openalex.org/C116834253 |
| concepts[4].level | 2 |
| concepts[4].score | 0.45147907733917236 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2039217 |
| concepts[4].display_name | Identification (biology) |
| concepts[5].id | https://openalex.org/C50644808 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4454555809497833 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[5].display_name | Artificial neural network |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.44058772921562195 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C2779510917 |
| concepts[7].level | 3 |
| concepts[7].score | 0.43111634254455566 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q7140498 |
| concepts[7].display_name | Particle identification |
| concepts[8].id | https://openalex.org/C113775141 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3255994915962219 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q428691 |
| concepts[8].display_name | Computer engineering |
| concepts[9].id | https://openalex.org/C76155785 |
| concepts[9].level | 1 |
| concepts[9].score | 0.0 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[9].display_name | Telecommunications |
| concepts[10].id | https://openalex.org/C86803240 |
| concepts[10].level | 0 |
| concepts[10].score | 0.0 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[10].display_name | Biology |
| concepts[11].id | https://openalex.org/C59822182 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q441 |
| concepts[11].display_name | Botany |
| concepts[12].id | https://openalex.org/C94915269 |
| concepts[12].level | 2 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1834857 |
| concepts[12].display_name | Detector |
| concepts[13].id | https://openalex.org/C554190296 |
| concepts[13].level | 2 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q47528 |
| concepts[13].display_name | Radar |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.6449707746505737 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/waveform |
| keywords[1].score | 0.6211998462677002 |
| keywords[1].display_name | Waveform |
| keywords[2].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[2].score | 0.5760254859924316 |
| keywords[2].display_name | Convolutional neural network |
| keywords[3].id | https://openalex.org/keywords/inference |
| keywords[3].score | 0.4652228057384491 |
| keywords[3].display_name | Inference |
| keywords[4].id | https://openalex.org/keywords/identification |
| keywords[4].score | 0.45147907733917236 |
| keywords[4].display_name | Identification (biology) |
| keywords[5].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[5].score | 0.4454555809497833 |
| keywords[5].display_name | Artificial neural network |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.44058772921562195 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/particle-identification |
| keywords[7].score | 0.43111634254455566 |
| keywords[7].display_name | Particle identification |
| keywords[8].id | https://openalex.org/keywords/computer-engineering |
| keywords[8].score | 0.3255994915962219 |
| keywords[8].display_name | Computer engineering |
| language | en |
| locations[0].id | doi:10.3390/app14156628 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210205812 |
| locations[0].source.issn | 2076-3417 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2076-3417 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Applied Sciences |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/2076-3417/14/15/6628/pdf?version=1722251674 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Applied Sciences |
| locations[0].landing_page_url | https://doi.org/10.3390/app14156628 |
| locations[1].id | pmh:oai:doaj.org/article:6ba0348b1c1d4767b4a76de74525dcc9 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Applied Sciences, Vol 14, Iss 15, p 6628 (2024) |
| locations[1].landing_page_url | https://doaj.org/article/6ba0348b1c1d4767b4a76de74525dcc9 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5114199851 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Chaoping Bai |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210115570 |
| authorships[0].affiliations[0].raw_affiliation_string | National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China |
| authorships[0].affiliations[1].institution_ids | https://openalex.org/I4210165038 |
| authorships[0].affiliations[1].raw_affiliation_string | University of Chinese Academy of Sciences, Beijing 100049, China |
| authorships[0].institutions[0].id | https://openalex.org/I19820366 |
| authorships[0].institutions[0].ror | https://ror.org/034t30j35 |
| authorships[0].institutions[0].type | government |
| authorships[0].institutions[0].lineage | https://openalex.org/I19820366 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | Chinese Academy of Sciences |
| authorships[0].institutions[1].id | https://openalex.org/I4210115570 |
| authorships[0].institutions[1].ror | https://ror.org/02nnjtm50 |
| authorships[0].institutions[1].type | facility |
| authorships[0].institutions[1].lineage | https://openalex.org/I19820366, https://openalex.org/I4210115570 |
| authorships[0].institutions[1].country_code | CN |
| authorships[0].institutions[1].display_name | National Space Science Center |
| authorships[0].institutions[2].id | https://openalex.org/I4210165038 |
| authorships[0].institutions[2].ror | https://ror.org/05qbk4x57 |
| authorships[0].institutions[2].type | education |
| authorships[0].institutions[2].lineage | https://openalex.org/I19820366, https://openalex.org/I4210165038 |
| authorships[0].institutions[2].country_code | CN |
| authorships[0].institutions[2].display_name | University of Chinese Academy of Sciences |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Chaoping Bai |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China, University of Chinese Academy of Sciences, Beijing 100049, China |
| authorships[1].author.id | https://openalex.org/A5100674119 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-5935-5135 |
| authorships[1].author.display_name | Xin Zhang |
| authorships[1].countries | CN |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210115570 |
| authorships[1].affiliations[0].raw_affiliation_string | National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China |
| authorships[1].institutions[0].id | https://openalex.org/I19820366 |
| authorships[1].institutions[0].ror | https://ror.org/034t30j35 |
| authorships[1].institutions[0].type | government |
| authorships[1].institutions[0].lineage | https://openalex.org/I19820366 |
| authorships[1].institutions[0].country_code | CN |
| authorships[1].institutions[0].display_name | Chinese Academy of Sciences |
| authorships[1].institutions[1].id | https://openalex.org/I4210115570 |
| authorships[1].institutions[1].ror | https://ror.org/02nnjtm50 |
| authorships[1].institutions[1].type | facility |
| authorships[1].institutions[1].lineage | https://openalex.org/I19820366, https://openalex.org/I4210115570 |
| authorships[1].institutions[1].country_code | CN |
| authorships[1].institutions[1].display_name | National Space Science Center |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Xin Zhang |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China |
| authorships[2].author.id | https://openalex.org/A5083435178 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-9111-0349 |
| authorships[2].author.display_name | Shenyi Zhang |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210115570 |
| authorships[2].affiliations[0].raw_affiliation_string | National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China |
| authorships[2].institutions[0].id | https://openalex.org/I19820366 |
| authorships[2].institutions[0].ror | https://ror.org/034t30j35 |
| authorships[2].institutions[0].type | government |
| authorships[2].institutions[0].lineage | https://openalex.org/I19820366 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Chinese Academy of Sciences |
| authorships[2].institutions[1].id | https://openalex.org/I4210115570 |
| authorships[2].institutions[1].ror | https://ror.org/02nnjtm50 |
| authorships[2].institutions[1].type | facility |
| authorships[2].institutions[1].lineage | https://openalex.org/I19820366, https://openalex.org/I4210115570 |
| authorships[2].institutions[1].country_code | CN |
| authorships[2].institutions[1].display_name | National Space Science Center |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Shenyi Zhang |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China |
| authorships[3].author.id | https://openalex.org/A5110749300 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Yueqiang Sun |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210115570 |
| authorships[3].affiliations[0].raw_affiliation_string | National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China |
| authorships[3].institutions[0].id | https://openalex.org/I19820366 |
| authorships[3].institutions[0].ror | https://ror.org/034t30j35 |
| authorships[3].institutions[0].type | government |
| authorships[3].institutions[0].lineage | https://openalex.org/I19820366 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Chinese Academy of Sciences |
| authorships[3].institutions[1].id | https://openalex.org/I4210115570 |
| authorships[3].institutions[1].ror | https://ror.org/02nnjtm50 |
| authorships[3].institutions[1].type | facility |
| authorships[3].institutions[1].lineage | https://openalex.org/I19820366, https://openalex.org/I4210115570 |
| authorships[3].institutions[1].country_code | CN |
| authorships[3].institutions[1].display_name | National Space Science Center |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Yueqiang Sun |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China |
| authorships[4].author.id | https://openalex.org/A5115598697 |
| authorships[4].author.orcid | https://orcid.org/0009-0000-3701-6215 |
| authorships[4].author.display_name | Xianguo Zhang |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210115570 |
| authorships[4].affiliations[0].raw_affiliation_string | National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China |
| authorships[4].institutions[0].id | https://openalex.org/I19820366 |
| authorships[4].institutions[0].ror | https://ror.org/034t30j35 |
| authorships[4].institutions[0].type | government |
| authorships[4].institutions[0].lineage | https://openalex.org/I19820366 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Chinese Academy of Sciences |
| authorships[4].institutions[1].id | https://openalex.org/I4210115570 |
| authorships[4].institutions[1].ror | https://ror.org/02nnjtm50 |
| authorships[4].institutions[1].type | facility |
| authorships[4].institutions[1].lineage | https://openalex.org/I19820366, https://openalex.org/I4210115570 |
| authorships[4].institutions[1].country_code | CN |
| authorships[4].institutions[1].display_name | National Space Science Center |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Xianguo Zhang |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China |
| authorships[5].author.id | https://openalex.org/A5101742618 |
| authorships[5].author.orcid | https://orcid.org/0009-0006-4070-1284 |
| authorships[5].author.display_name | Ziting Wang |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210115570 |
| authorships[5].affiliations[0].raw_affiliation_string | National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China |
| authorships[5].institutions[0].id | https://openalex.org/I19820366 |
| authorships[5].institutions[0].ror | https://ror.org/034t30j35 |
| authorships[5].institutions[0].type | government |
| authorships[5].institutions[0].lineage | https://openalex.org/I19820366 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | Chinese Academy of Sciences |
| authorships[5].institutions[1].id | https://openalex.org/I4210115570 |
| authorships[5].institutions[1].ror | https://ror.org/02nnjtm50 |
| authorships[5].institutions[1].type | facility |
| authorships[5].institutions[1].lineage | https://openalex.org/I19820366, https://openalex.org/I4210115570 |
| authorships[5].institutions[1].country_code | CN |
| authorships[5].institutions[1].display_name | National Space Science Center |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Ziting Wang |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China |
| authorships[6].author.id | https://openalex.org/A5100328889 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-5821-2650 |
| authorships[6].author.display_name | Shuai Zhang |
| authorships[6].countries | CN |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210115570 |
| authorships[6].affiliations[0].raw_affiliation_string | National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China |
| authorships[6].institutions[0].id | https://openalex.org/I19820366 |
| authorships[6].institutions[0].ror | https://ror.org/034t30j35 |
| authorships[6].institutions[0].type | government |
| authorships[6].institutions[0].lineage | https://openalex.org/I19820366 |
| authorships[6].institutions[0].country_code | CN |
| authorships[6].institutions[0].display_name | Chinese Academy of Sciences |
| authorships[6].institutions[1].id | https://openalex.org/I4210115570 |
| authorships[6].institutions[1].ror | https://ror.org/02nnjtm50 |
| authorships[6].institutions[1].type | facility |
| authorships[6].institutions[1].lineage | https://openalex.org/I19820366, https://openalex.org/I4210115570 |
| authorships[6].institutions[1].country_code | CN |
| authorships[6].institutions[1].display_name | National Space Science Center |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Shuai Zhang |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/2076-3417/14/15/6628/pdf?version=1722251674 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Design and Application of an Onboard Particle Identification Platform Based on Convolutional Neural Networks |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11949 |
| primary_topic.field.id | https://openalex.org/fields/31 |
| primary_topic.field.display_name | Physics and Astronomy |
| primary_topic.score | 0.9987000226974487 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/3108 |
| primary_topic.subfield.display_name | Radiation |
| primary_topic.display_name | Nuclear Physics and Applications |
| related_works | https://openalex.org/W1974895211, https://openalex.org/W2129841057, https://openalex.org/W3040712279, https://openalex.org/W2176409448, https://openalex.org/W2364769705, https://openalex.org/W2056136368, https://openalex.org/W2374664672, https://openalex.org/W4367555392, https://openalex.org/W2538520412, https://openalex.org/W2883092465 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.3390/app14156628 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210205812 |
| best_oa_location.source.issn | 2076-3417 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2076-3417 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Applied Sciences |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/2076-3417/14/15/6628/pdf?version=1722251674 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Applied Sciences |
| best_oa_location.landing_page_url | https://doi.org/10.3390/app14156628 |
| primary_location.id | doi:10.3390/app14156628 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210205812 |
| primary_location.source.issn | 2076-3417 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2076-3417 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Applied Sciences |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2076-3417/14/15/6628/pdf?version=1722251674 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Applied Sciences |
| primary_location.landing_page_url | https://doi.org/10.3390/app14156628 |
| publication_date | 2024-07-29 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W6989661866, https://openalex.org/W2790543755, https://openalex.org/W3137620230, https://openalex.org/W2953849464, https://openalex.org/W3187874998, https://openalex.org/W2584311934, https://openalex.org/W2051468774, https://openalex.org/W2118108380, https://openalex.org/W2026494907, https://openalex.org/W2053636920, https://openalex.org/W3128341916, https://openalex.org/W3176925972, https://openalex.org/W4214543617, https://openalex.org/W3201503618, https://openalex.org/W2949676527, https://openalex.org/W4200602311, https://openalex.org/W4214853082, https://openalex.org/W4367054505, https://openalex.org/W3119962849, https://openalex.org/W4285594851, https://openalex.org/W3175799302, https://openalex.org/W2367240655 |
| referenced_works_count | 22 |
| abstract_inverted_index.a | 5, 84, 183 |
| abstract_inverted_index.In | 81 |
| abstract_inverted_index.an | 190, 242 |
| abstract_inverted_index.be | 67 |
| abstract_inverted_index.by | 69 |
| abstract_inverted_index.in | 8, 15 |
| abstract_inverted_index.is | 141, 206, 209, 251 |
| abstract_inverted_index.of | 32, 75, 110, 126, 147, 154, 168, 173, 182, 193, 202, 221, 225, 235, 252 |
| abstract_inverted_index.on | 95, 122 |
| abstract_inverted_index.to | 52 |
| abstract_inverted_index.× | 35, 40 |
| abstract_inverted_index.4.9 | 187 |
| abstract_inverted_index.CNN | 204 |
| abstract_inverted_index.E), | 41 |
| abstract_inverted_index.FOM | 199, 219 |
| abstract_inverted_index.The | 115, 138, 157, 196, 223 |
| abstract_inverted_index.and | 11, 42, 61, 73, 78, 86, 107, 128, 131, 150, 165, 175, 178, 233 |
| abstract_inverted_index.but | 239 |
| abstract_inverted_index.for | 135, 143, 161, 245, 255 |
| abstract_inverted_index.may | 66 |
| abstract_inverted_index.not | 228 |
| abstract_inverted_index.the | 54, 70, 102, 123, 144, 151, 155, 166, 169, 179, 212, 231 |
| abstract_inverted_index.was | 99, 159 |
| abstract_inverted_index.(ESA | 34 |
| abstract_inverted_index.(TOF | 39 |
| abstract_inverted_index.0.8. | 222 |
| abstract_inverted_index.133, | 207 |
| abstract_inverted_index.97%. | 195 |
| abstract_inverted_index.FPGA | 89 |
| abstract_inverted_index.also | 240 |
| abstract_inverted_index.data | 149, 172 |
| abstract_inverted_index.deep | 162 |
| abstract_inverted_index.fail | 51 |
| abstract_inverted_index.fall | 74 |
| abstract_inverted_index.full | 55, 96 |
| abstract_inverted_index.only | 229 |
| abstract_inverted_index.over | 194 |
| abstract_inverted_index.rate | 192 |
| abstract_inverted_index.rich | 59 |
| abstract_inverted_index.rise | 72 |
| abstract_inverted_index.role | 7 |
| abstract_inverted_index.than | 211 |
| abstract_inverted_index.this | 82, 203, 226 |
| abstract_inverted_index.time | 31, 181 |
| abstract_inverted_index.tool | 244 |
| abstract_inverted_index.used | 21 |
| abstract_inverted_index.uses | 129 |
| abstract_inverted_index.with | 189 |
| abstract_inverted_index.(PSD) | 217 |
| abstract_inverted_index.Space | 0 |
| abstract_inverted_index.TOF), | 36 |
| abstract_inverted_index.based | 94, 121 |
| abstract_inverted_index.gamma | 176 |
| abstract_inverted_index.great | 253 |
| abstract_inverted_index.input | 119 |
| abstract_inverted_index.model | 136, 205 |
| abstract_inverted_index.noise | 79 |
| abstract_inverted_index.plays | 4 |
| abstract_inverted_index.pulse | 43, 214 |
| abstract_inverted_index.rays, | 177 |
| abstract_inverted_index.shape | 44, 215 |
| abstract_inverted_index.space | 236, 247 |
| abstract_inverted_index.takes | 186 |
| abstract_inverted_index.their | 62 |
| abstract_inverted_index.these | 48 |
| abstract_inverted_index.value | 201, 220, 254 |
| abstract_inverted_index.which | 208, 250 |
| abstract_inverted_index.(PSD). | 46 |
| abstract_inverted_index.Optuna | 130 |
| abstract_inverted_index.better | 210 |
| abstract_inverted_index.energy | 38 |
| abstract_inverted_index.flight | 33 |
| abstract_inverted_index.future | 246 |
| abstract_inverted_index.neural | 112 |
| abstract_inverted_index.random | 71 |
| abstract_inverted_index.single | 184 |
| abstract_inverted_index.study, | 83 |
| abstract_inverted_index.target | 104 |
| abstract_inverted_index.(CNNs). | 114 |
| abstract_inverted_index.Pytorch | 132 |
| abstract_inverted_index.crucial | 6 |
| abstract_inverted_index.dynamic | 152 |
| abstract_inverted_index.include | 26 |
| abstract_inverted_index.methods | 49 |
| abstract_inverted_index.neutron | 174 |
| abstract_inverted_index.onboard | 88 |
| abstract_inverted_index.results | 65 |
| abstract_inverted_index.species | 17 |
| abstract_inverted_index.testing | 167 |
| abstract_inverted_index.usually | 50 |
| abstract_inverted_index.utilize | 53 |
| abstract_inverted_index.However, | 47 |
| abstract_inverted_index.accuracy | 191, 232 |
| abstract_inverted_index.advanced | 243 |
| abstract_inverted_index.affected | 68 |
| abstract_inverted_index.analysis | 30 |
| abstract_inverted_index.commonly | 20 |
| abstract_inverted_index.dataset. | 156 |
| abstract_inverted_index.datasets | 120 |
| abstract_inverted_index.features | 125 |
| abstract_inverted_index.hardware | 139 |
| abstract_inverted_index.improves | 230 |
| abstract_inverted_index.in-orbit | 22 |
| abstract_inverted_index.learning | 163 |
| abstract_inverted_index.methods, | 28 |
| abstract_inverted_index.networks | 113 |
| abstract_inverted_index.particle | 2, 16, 23, 63, 76, 91, 237 |
| abstract_inverted_index.physical | 124 |
| abstract_inverted_index.platform | 93, 116, 140, 158, 227 |
| abstract_inverted_index.provides | 241 |
| abstract_inverted_index.research | 10 |
| abstract_inverted_index.software | 133 |
| abstract_inverted_index.superior | 103 |
| abstract_inverted_index.training | 164 |
| abstract_inverted_index.utilized | 160 |
| abstract_inverted_index.waveform | 56, 97, 148, 171, 185 |
| abstract_inverted_index.detection | 3 |
| abstract_inverted_index.developed | 100 |
| abstract_inverted_index.expansion | 153 |
| abstract_inverted_index.features, | 60 |
| abstract_inverted_index.inference | 146, 180 |
| abstract_inverted_index.practice, | 13 |
| abstract_inverted_index.radiation | 1 |
| abstract_inverted_index.real-time | 90, 145 |
| abstract_inverted_index.telescope | 27 |
| abstract_inverted_index.training. | 137 |
| abstract_inverted_index.utilizing | 101 |
| abstract_inverted_index.waveforms | 127 |
| abstract_inverted_index.Currently, | 19 |
| abstract_inverted_index.constructs | 117 |
| abstract_inverted_index.containing | 58 |
| abstract_inverted_index.deposition | 77 |
| abstract_inverted_index.efficiency | 234 |
| abstract_inverted_index.especially | 14 |
| abstract_inverted_index.historical | 170 |
| abstract_inverted_index.scientific | 9 |
| abstract_inverted_index.techniques | 25 |
| abstract_inverted_index.development | 224 |
| abstract_inverted_index.diversified | 118 |
| abstract_inverted_index.engineering | 12, 256 |
| abstract_inverted_index.environment | 248 |
| abstract_inverted_index.expectation | 198 |
| abstract_inverted_index.information | 57, 98 |
| abstract_inverted_index.lightweight | 87 |
| abstract_inverted_index.low-latency | 85 |
| abstract_inverted_index.monitoring, | 249 |
| abstract_inverted_index.responsible | 142 |
| abstract_inverted_index.robustness, | 106 |
| abstract_inverted_index.traditional | 213 |
| abstract_inverted_index.capabilities | 109 |
| abstract_inverted_index.algorithm’s | 218 |
| abstract_inverted_index.applications. | 257 |
| abstract_inverted_index.architectures | 134 |
| abstract_inverted_index.convolutional | 111 |
| abstract_inverted_index.electrostatic | 29 |
| abstract_inverted_index.interference. | 80 |
| abstract_inverted_index.microseconds, | 188 |
| abstract_inverted_index.classification | 197 |
| abstract_inverted_index.discrimination | 45, 216, 238 |
| abstract_inverted_index.generalization | 108 |
| abstract_inverted_index.identification | 24, 64, 92 |
| abstract_inverted_index.time-of-flight | 37 |
| abstract_inverted_index.classification, | 105 |
| abstract_inverted_index.identification. | 18 |
| abstract_inverted_index.(figure-of-merit) | 200 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5100674119 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I19820366, https://openalex.org/I4210115570 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.550000011920929 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.72383698 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |