Title: GitHub - cap-ntu/iTCM-Datasets: These datasets are collected for the GBIC project to conduct research about indoor human thermal comfort. The GBIC research project proposes to develop online thermal comfort models via a deep-learning approach and apply them to behavioral studies to drive “greener, smarter and healthier buildings” in the tropics (e.g., Singapore). Leveraging privacy-preserving data analytics over information acquired from smartphone crowdsourcing and in-situ wearables measurements, the project plans to develop and validate an integrative, economical and scalable thermal comfort management system.
Open Graph Title: GitHub - cap-ntu/iTCM-Datasets: These datasets are collected for the GBIC project to conduct research about indoor human thermal comfort. The GBIC research project proposes to develop online thermal comfort models via a deep-learning approach and apply them to behavioral studies to drive “greener, smarter and healthier buildings” in the tropics (e.g., Singapore). Leveraging privacy-preserving data analytics over information acquired from smartphone crowdsourcing and in-situ wearables measurements, the project plans to develop and validate an integrative, economical and scalable thermal comfort management system.
X Title: GitHub - cap-ntu/iTCM-Datasets: These datasets are collected for the GBIC project to conduct research about indoor human thermal comfort. The GBIC research project proposes to develop online thermal comfort models via a deep-learning approach and apply them to behavioral studies to drive “greener, smarter and healthier buildings” in the tropics (e.g., Singapore). Leveraging privacy-preserving data analytics over information acquired from smartphone crowdsourcing and in-situ wearables measurements, the project plans to develop and validate an integrative, economical and scalable thermal comfort management system.
Description: These datasets are collected for the GBIC project to conduct research about indoor human thermal comfort. The GBIC research project proposes to develop online thermal comfort models via a deep-learning approach and apply them to behavioral studies to drive “greener, smarter and healthier buildings” in the tropics (e.g., Singapore). Leveraging privacy-preserving data analytics over information acquired from smartphone crowdsourcing and in-situ wearables measurements, the project plans to develop and validate an integrative, economical and scalable thermal comfort management system. - cap-ntu/iTCM-Datasets
Open Graph Description: These datasets are collected for the GBIC project to conduct research about indoor human thermal comfort. The GBIC research project proposes to develop online thermal comfort models via a deep-lear...
X Description: These datasets are collected for the GBIC project to conduct research about indoor human thermal comfort. The GBIC research project proposes to develop online thermal comfort models via a deep-lear...
Opengraph URL: https://github.com/cap-ntu/iTCM-Datasets
X: @github
Domain: patch-diff.githubusercontent.com
| route-pattern | /:user_id/:repository |
| route-controller | files |
| route-action | disambiguate |
| fetch-nonce | v2:84b8af40-b25c-aa0f-9b72-c2d76a20c1a0 |
| current-catalog-service-hash | f3abb0cc802f3d7b95fc8762b94bdcb13bf39634c40c357301c4aa1d67a256fb |
| request-id | CC66:19115:1B5E78C:23F0632:6992BEBA |
| html-safe-nonce | 0bd3ab7aa7d233855ce78d74ba7ac921ff3dbfe753d7cc59194a9c734a8b7de6 |
| visitor-payload | eyJyZWZlcnJlciI6IiIsInJlcXVlc3RfaWQiOiJDQzY2OjE5MTE1OjFCNUU3OEM6MjNGMDYzMjo2OTkyQkVCQSIsInZpc2l0b3JfaWQiOiIxNDA3ODQyNzU3NTg2ODk0NTIzIiwicmVnaW9uX2VkZ2UiOiJpYWQiLCJyZWdpb25fcmVuZGVyIjoiaWFkIn0= |
| visitor-hmac | 381f234f389d3c9c1a462c2550ff364bbd6d4f77bdf41db1143c9f47bd6b6e61 |
| hovercard-subject-tag | repository:244315838 |
| github-keyboard-shortcuts | repository,copilot |
| google-site-verification | Apib7-x98H0j5cPqHWwSMm6dNU4GmODRoqxLiDzdx9I |
| octolytics-url | https://collector.github.com/github/collect |
| analytics-location | / |
| fb:app_id | 1401488693436528 |
| apple-itunes-app | app-id=1477376905, app-argument=https://github.com/cap-ntu/iTCM-Datasets |
| twitter:image | https://opengraph.githubassets.com/7306ab2a0a775a6cc94390d94b94e967f7a47aa81da10a5fa3fbb2623d28a6c5/cap-ntu/iTCM-Datasets |
| twitter:card | summary_large_image |
| og:image | https://opengraph.githubassets.com/7306ab2a0a775a6cc94390d94b94e967f7a47aa81da10a5fa3fbb2623d28a6c5/cap-ntu/iTCM-Datasets |
| og:image:alt | These datasets are collected for the GBIC project to conduct research about indoor human thermal comfort. The GBIC research project proposes to develop online thermal comfort models via a deep-lear... |
| og:image:width | 1200 |
| og:image:height | 600 |
| og:site_name | GitHub |
| og:type | object |
| hostname | github.com |
| expected-hostname | github.com |
| None | 42c603b9d642c4a9065a51770f75e5e27132fef0e858607f5c9cb7e422831a7b |
| turbo-cache-control | no-preview |
| go-import | github.com/cap-ntu/iTCM-Datasets git https://github.com/cap-ntu/iTCM-Datasets.git |
| octolytics-dimension-user_id | 15702366 |
| octolytics-dimension-user_login | cap-ntu |
| octolytics-dimension-repository_id | 244315838 |
| octolytics-dimension-repository_nwo | cap-ntu/iTCM-Datasets |
| octolytics-dimension-repository_public | true |
| octolytics-dimension-repository_is_fork | false |
| octolytics-dimension-repository_network_root_id | 244315838 |
| octolytics-dimension-repository_network_root_nwo | cap-ntu/iTCM-Datasets |
| turbo-body-classes | logged-out env-production page-responsive |
| disable-turbo | false |
| browser-stats-url | https://api.github.com/_private/browser/stats |
| browser-errors-url | https://api.github.com/_private/browser/errors |
| release | 84dcb133269e3cfe6e0296cc85fbacb92cae92bb |
| ui-target | full |
| theme-color | #1e2327 |
| color-scheme | light dark |
Links:
Viewport: width=device-width