Changes
On December 16, 2024 at 8:20:12 PM UTC, admin:
-
Changed value of field
doi_status
toTrue
in A Multi-Task Learning Model for Super Resolution of Wireless Channel Characteristics -
Changed value of field
doi_date_published
to2024-12-16
in A Multi-Task Learning Model for Super Resolution of Wireless Channel Characteristics -
Added resource Original Metadata to A Multi-Task Learning Model for Super Resolution of Wireless Channel Characteristics
f | 1 | { | f | 1 | { |
2 | "access_rights": "", | 2 | "access_rights": "", | ||
3 | "author": "Xiping Wang", | 3 | "author": "Xiping Wang", | ||
4 | "author_email": "", | 4 | "author_email": "", | ||
5 | "citation": [], | 5 | "citation": [], | ||
6 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | 6 | "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", | ||
7 | "defined_in": "https://doi.org/10.48550/arXiv.2209.04207", | 7 | "defined_in": "https://doi.org/10.48550/arXiv.2209.04207", | ||
8 | "doi": "10.57702/lr8o0z6s", | 8 | "doi": "10.57702/lr8o0z6s", | ||
n | 9 | "doi_date_published": null, | n | 9 | "doi_date_published": "2024-12-16", |
10 | "doi_publisher": "TIB", | 10 | "doi_publisher": "TIB", | ||
n | 11 | "doi_status": false, | n | 11 | "doi_status": true, |
12 | "domain": "https://service.tib.eu/ldmservice", | 12 | "domain": "https://service.tib.eu/ldmservice", | ||
13 | "extra_authors": [ | 13 | "extra_authors": [ | ||
14 | { | 14 | { | ||
15 | "extra_author": "Zhao Zhang", | 15 | "extra_author": "Zhao Zhang", | ||
16 | "orcid": "" | 16 | "orcid": "" | ||
17 | }, | 17 | }, | ||
18 | { | 18 | { | ||
19 | "extra_author": "Danping He", | 19 | "extra_author": "Danping He", | ||
20 | "orcid": "" | 20 | "orcid": "" | ||
21 | }, | 21 | }, | ||
22 | { | 22 | { | ||
23 | "extra_author": "Ke Guan", | 23 | "extra_author": "Ke Guan", | ||
24 | "orcid": "" | 24 | "orcid": "" | ||
25 | }, | 25 | }, | ||
26 | { | 26 | { | ||
27 | "extra_author": "Dongliang Liu", | 27 | "extra_author": "Dongliang Liu", | ||
28 | "orcid": "" | 28 | "orcid": "" | ||
29 | }, | 29 | }, | ||
30 | { | 30 | { | ||
31 | "extra_author": "Jianwu Dou", | 31 | "extra_author": "Jianwu Dou", | ||
32 | "orcid": "" | 32 | "orcid": "" | ||
33 | }, | 33 | }, | ||
34 | { | 34 | { | ||
35 | "extra_author": "Bo Sun", | 35 | "extra_author": "Bo Sun", | ||
36 | "orcid": "" | 36 | "orcid": "" | ||
37 | } | 37 | } | ||
38 | ], | 38 | ], | ||
39 | "groups": [ | 39 | "groups": [ | ||
40 | { | 40 | { | ||
41 | "description": "", | 41 | "description": "", | ||
42 | "display_name": "Super Resolution", | 42 | "display_name": "Super Resolution", | ||
43 | "id": "88fad46a-2463-4346-85f0-4fe8edf4614e", | 43 | "id": "88fad46a-2463-4346-85f0-4fe8edf4614e", | ||
44 | "image_display_url": "", | 44 | "image_display_url": "", | ||
45 | "name": "super-resolution", | 45 | "name": "super-resolution", | ||
46 | "title": "Super Resolution" | 46 | "title": "Super Resolution" | ||
47 | }, | 47 | }, | ||
48 | { | 48 | { | ||
49 | "description": "", | 49 | "description": "", | ||
50 | "display_name": "Wireless Channel Modeling", | 50 | "display_name": "Wireless Channel Modeling", | ||
51 | "id": "b3c3da64-a183-4d44-ae24-0f2fb31725cd", | 51 | "id": "b3c3da64-a183-4d44-ae24-0f2fb31725cd", | ||
52 | "image_display_url": "", | 52 | "image_display_url": "", | ||
53 | "name": "wireless-channel-modeling", | 53 | "name": "wireless-channel-modeling", | ||
54 | "title": "Wireless Channel Modeling" | 54 | "title": "Wireless Channel Modeling" | ||
55 | } | 55 | } | ||
56 | ], | 56 | ], | ||
57 | "id": "125527aa-d042-45cb-b193-61f5ec51ea0c", | 57 | "id": "125527aa-d042-45cb-b193-61f5ec51ea0c", | ||
58 | "isopen": false, | 58 | "isopen": false, | ||
59 | "landing_page": "", | 59 | "landing_page": "", | ||
60 | "license_title": null, | 60 | "license_title": null, | ||
61 | "link_orkg": "", | 61 | "link_orkg": "", | ||
62 | "metadata_created": "2024-12-16T20:20:11.425342", | 62 | "metadata_created": "2024-12-16T20:20:11.425342", | ||
n | 63 | "metadata_modified": "2024-12-16T20:20:11.425347", | n | 63 | "metadata_modified": "2024-12-16T20:20:11.814373", |
64 | "name": | 64 | "name": | ||
65 | rning-model-for-super-resolution-of-wireless-channel-characteristics", | 65 | rning-model-for-super-resolution-of-wireless-channel-characteristics", | ||
66 | "notes": "Channel modeling has always been the core part in | 66 | "notes": "Channel modeling has always been the core part in | ||
67 | communication system design and development, especially in 5G and 6G | 67 | communication system design and development, especially in 5G and 6G | ||
68 | era. Traditional approaches like stochastic channel modeling and | 68 | era. Traditional approaches like stochastic channel modeling and | ||
69 | ray-tracing (RT) based channel modeling depend heavily on measurement | 69 | ray-tracing (RT) based channel modeling depend heavily on measurement | ||
70 | data or simulation, which are usually expensive and time consuming. In | 70 | data or simulation, which are usually expensive and time consuming. In | ||
71 | this paper, we propose a novel super resolution (SR) model for | 71 | this paper, we propose a novel super resolution (SR) model for | ||
72 | generating channel characteristics data.", | 72 | generating channel characteristics data.", | ||
n | 73 | "num_resources": 0, | n | 73 | "num_resources": 1, |
74 | "num_tags": 5, | 74 | "num_tags": 5, | ||
75 | "organization": { | 75 | "organization": { | ||
76 | "approval_status": "approved", | 76 | "approval_status": "approved", | ||
77 | "created": "2024-11-25T12:11:38.292601", | 77 | "created": "2024-11-25T12:11:38.292601", | ||
78 | "description": "", | 78 | "description": "", | ||
79 | "id": "079d46db-32df-4b48-91f3-0a8bc8f69559", | 79 | "id": "079d46db-32df-4b48-91f3-0a8bc8f69559", | ||
80 | "image_url": "", | 80 | "image_url": "", | ||
81 | "is_organization": true, | 81 | "is_organization": true, | ||
82 | "name": "no-organization", | 82 | "name": "no-organization", | ||
83 | "state": "active", | 83 | "state": "active", | ||
84 | "title": "No Organization", | 84 | "title": "No Organization", | ||
85 | "type": "organization" | 85 | "type": "organization" | ||
86 | }, | 86 | }, | ||
87 | "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559", | 87 | "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559", | ||
88 | "private": false, | 88 | "private": false, | ||
89 | "relationships_as_object": [], | 89 | "relationships_as_object": [], | ||
90 | "relationships_as_subject": [], | 90 | "relationships_as_subject": [], | ||
t | 91 | "resources": [], | t | 91 | "resources": [ |
92 | { | ||||
93 | "cache_last_updated": null, | ||||
94 | "cache_url": null, | ||||
95 | "created": "2024-12-16T18:25:45", | ||||
96 | "data": [ | ||||
97 | "dcterms:title", | ||||
98 | "dcterms:accessRights", | ||||
99 | "dcterms:creator", | ||||
100 | "dcterms:description", | ||||
101 | "dcterms:issued", | ||||
102 | "dcterms:language", | ||||
103 | "dcterms:identifier", | ||||
104 | "dcat:theme", | ||||
105 | "dcterms:type", | ||||
106 | "dcat:keyword", | ||||
107 | "dcat:landingPage", | ||||
108 | "dcterms:hasVersion", | ||||
109 | "dcterms:format", | ||||
110 | "mls:task", | ||||
111 | "datacite:isDescribedBy" | ||||
112 | ], | ||||
113 | "description": "The json representation of the dataset with its | ||||
114 | distributions based on DCAT.", | ||||
115 | "format": "JSON", | ||||
116 | "hash": "", | ||||
117 | "id": "fb847b21-36ef-4e5c-bc49-70b5bd895e0c", | ||||
118 | "last_modified": "2024-12-16T20:20:11.806679", | ||||
119 | "metadata_modified": "2024-12-16T20:20:11.817081", | ||||
120 | "mimetype": "application/json", | ||||
121 | "mimetype_inner": null, | ||||
122 | "name": "Original Metadata", | ||||
123 | "package_id": "125527aa-d042-45cb-b193-61f5ec51ea0c", | ||||
124 | "position": 0, | ||||
125 | "resource_type": null, | ||||
126 | "size": 1195, | ||||
127 | "state": "active", | ||||
128 | "url": | ||||
129 | resource/fb847b21-36ef-4e5c-bc49-70b5bd895e0c/download/metadata.json", | ||||
130 | "url_type": "upload" | ||||
131 | } | ||||
132 | ], | ||||
92 | "services_used_list": "", | 133 | "services_used_list": "", | ||
93 | "state": "active", | 134 | "state": "active", | ||
94 | "tags": [ | 135 | "tags": [ | ||
95 | { | 136 | { | ||
96 | "display_name": "convolutional neural network", | 137 | "display_name": "convolutional neural network", | ||
97 | "id": "b3a4edde-7f64-4c62-8318-8d4c7a03605f", | 138 | "id": "b3a4edde-7f64-4c62-8318-8d4c7a03605f", | ||
98 | "name": "convolutional neural network", | 139 | "name": "convolutional neural network", | ||
99 | "state": "active", | 140 | "state": "active", | ||
100 | "vocabulary_id": null | 141 | "vocabulary_id": null | ||
101 | }, | 142 | }, | ||
102 | { | 143 | { | ||
103 | "display_name": "multi-task learning", | 144 | "display_name": "multi-task learning", | ||
104 | "id": "bcad4df5-310a-41e2-b58a-634318066212", | 145 | "id": "bcad4df5-310a-41e2-b58a-634318066212", | ||
105 | "name": "multi-task learning", | 146 | "name": "multi-task learning", | ||
106 | "state": "active", | 147 | "state": "active", | ||
107 | "vocabulary_id": null | 148 | "vocabulary_id": null | ||
108 | }, | 149 | }, | ||
109 | { | 150 | { | ||
110 | "display_name": "ray-tracing", | 151 | "display_name": "ray-tracing", | ||
111 | "id": "3b1abbc5-700e-4917-b53e-aea2fb69cc87", | 152 | "id": "3b1abbc5-700e-4917-b53e-aea2fb69cc87", | ||
112 | "name": "ray-tracing", | 153 | "name": "ray-tracing", | ||
113 | "state": "active", | 154 | "state": "active", | ||
114 | "vocabulary_id": null | 155 | "vocabulary_id": null | ||
115 | }, | 156 | }, | ||
116 | { | 157 | { | ||
117 | "display_name": "super resolution", | 158 | "display_name": "super resolution", | ||
118 | "id": "f62fae5d-e220-4228-bd45-6312fe21657a", | 159 | "id": "f62fae5d-e220-4228-bd45-6312fe21657a", | ||
119 | "name": "super resolution", | 160 | "name": "super resolution", | ||
120 | "state": "active", | 161 | "state": "active", | ||
121 | "vocabulary_id": null | 162 | "vocabulary_id": null | ||
122 | }, | 163 | }, | ||
123 | { | 164 | { | ||
124 | "display_name": "wireless channel modeling", | 165 | "display_name": "wireless channel modeling", | ||
125 | "id": "c8935eb0-1dbc-4609-ba49-999dc6a81237", | 166 | "id": "c8935eb0-1dbc-4609-ba49-999dc6a81237", | ||
126 | "name": "wireless channel modeling", | 167 | "name": "wireless channel modeling", | ||
127 | "state": "active", | 168 | "state": "active", | ||
128 | "vocabulary_id": null | 169 | "vocabulary_id": null | ||
129 | } | 170 | } | ||
130 | ], | 171 | ], | ||
131 | "title": "A Multi-Task Learning Model for Super Resolution of | 172 | "title": "A Multi-Task Learning Model for Super Resolution of | ||
132 | Wireless Channel Characteristics", | 173 | Wireless Channel Characteristics", | ||
133 | "type": "dataset", | 174 | "type": "dataset", | ||
134 | "version": "" | 175 | "version": "" | ||
135 | } | 176 | } |