Indicators used in the data collection phase.
<div><p>Ramp controllers are required to manage their workloads effectively while handling complex operational tasks, a crucial part of improving aviation safety. The ability to detect their instantaneous workload is vital for ensuring operational effectiveness and preventing hazardous i...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2024
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| الموضوعات: | |
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إضافة وسم
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| _version_ | 1852025318708084736 |
|---|---|
| author | Quan Shao (20148209) |
| author2 | Kaiyue Jiang (9530750) Ruoheng Li (20148212) |
| author2_role | author author |
| author_facet | Quan Shao (20148209) Kaiyue Jiang (9530750) Ruoheng Li (20148212) |
| author_role | author |
| dc.creator.none.fl_str_mv | Quan Shao (20148209) Kaiyue Jiang (9530750) Ruoheng Li (20148212) |
| dc.date.none.fl_str_mv | 2024-11-08T18:35:45Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0313565.t002 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Indicators_used_in_the_data_collection_phase_/27639329 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biochemistry Neuroscience Cancer shanghai hongqiao airport preventing hazardous incidents novel methodology aimed improving aviation safety ensuring operational effectiveness best classification accuracy 98 %, achieved calculating feature weights calculate feature weights following data construction centered data construction workload assessment within supervised learning techniques ramp control tasks different workload levels optimal feature combination using real data feature combination data alignment unsupervised learning ramp controller instantaneous workload cumulative workload type data train classifiers threshold calculations provide insights paper introduces knn classifier fatigue characteristics eye tracker eye movement crucial part 8 controllers |
| dc.title.none.fl_str_mv | Indicators used in the data collection phase. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>Ramp controllers are required to manage their workloads effectively while handling complex operational tasks, a crucial part of improving aviation safety. The ability to detect their instantaneous workload is vital for ensuring operational effectiveness and preventing hazardous incidents. This paper introduces a novel methodology aimed at enhancing the evaluation of the ramp controller’s cumulative workload by incorporating and optimizing the feature combination from eye movement, respiratory, and fatigue characteristics. Specifically, a 90-minute simulated experiment related to ramp control tasks, using real data from Shanghai Hongqiao Airport, is conducted to collect multi-type data from 8 controllers. Following data construction and the extraction of multi-type, the workloads of all samples are categorized through unsupervised learning. Subsequently, supervised learning techniques are used to calculate feature weights and train classifiers after data alignment. The optimal feature combination is established by calculating feature weights, and the best classification accuracy is over 98%, achieved by the KNN classifier. Furthermore, numerical evaluation and threshold calculations for different workload levels are interpreted. It is promising to provide insights into future works towards human-centered data construction, processing, and interpretation to promote the progress of workload assessment within the aviation industry.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_5dacf4f25a95a67d77c4d7fb90433f49 |
| identifier_str_mv | 10.1371/journal.pone.0313565.t002 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/27639329 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Indicators used in the data collection phase.Quan Shao (20148209)Kaiyue Jiang (9530750)Ruoheng Li (20148212)BiochemistryNeuroscienceCancershanghai hongqiao airportpreventing hazardous incidentsnovel methodology aimedimproving aviation safetyensuring operational effectivenessbest classification accuracy98 %, achievedcalculating feature weightscalculate feature weightsfollowing data constructioncentered data constructionworkload assessment withinsupervised learning techniquesramp control tasksdifferent workload levelsoptimal feature combinationusing real datafeature combinationdata alignmentunsupervised learningramp controllerinstantaneous workloadcumulative workloadtype datatrain classifiersthreshold calculationsprovide insightspaper introducesknn classifierfatigue characteristicseye trackereye movementcrucial part8 controllers<div><p>Ramp controllers are required to manage their workloads effectively while handling complex operational tasks, a crucial part of improving aviation safety. The ability to detect their instantaneous workload is vital for ensuring operational effectiveness and preventing hazardous incidents. This paper introduces a novel methodology aimed at enhancing the evaluation of the ramp controller’s cumulative workload by incorporating and optimizing the feature combination from eye movement, respiratory, and fatigue characteristics. Specifically, a 90-minute simulated experiment related to ramp control tasks, using real data from Shanghai Hongqiao Airport, is conducted to collect multi-type data from 8 controllers. Following data construction and the extraction of multi-type, the workloads of all samples are categorized through unsupervised learning. Subsequently, supervised learning techniques are used to calculate feature weights and train classifiers after data alignment. The optimal feature combination is established by calculating feature weights, and the best classification accuracy is over 98%, achieved by the KNN classifier. Furthermore, numerical evaluation and threshold calculations for different workload levels are interpreted. It is promising to provide insights into future works towards human-centered data construction, processing, and interpretation to promote the progress of workload assessment within the aviation industry.</p></div>2024-11-08T18:35:45ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0313565.t002https://figshare.com/articles/dataset/Indicators_used_in_the_data_collection_phase_/27639329CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/276393292024-11-08T18:35:45Z |
| spellingShingle | Indicators used in the data collection phase. Quan Shao (20148209) Biochemistry Neuroscience Cancer shanghai hongqiao airport preventing hazardous incidents novel methodology aimed improving aviation safety ensuring operational effectiveness best classification accuracy 98 %, achieved calculating feature weights calculate feature weights following data construction centered data construction workload assessment within supervised learning techniques ramp control tasks different workload levels optimal feature combination using real data feature combination data alignment unsupervised learning ramp controller instantaneous workload cumulative workload type data train classifiers threshold calculations provide insights paper introduces knn classifier fatigue characteristics eye tracker eye movement crucial part 8 controllers |
| status_str | publishedVersion |
| title | Indicators used in the data collection phase. |
| title_full | Indicators used in the data collection phase. |
| title_fullStr | Indicators used in the data collection phase. |
| title_full_unstemmed | Indicators used in the data collection phase. |
| title_short | Indicators used in the data collection phase. |
| title_sort | Indicators used in the data collection phase. |
| topic | Biochemistry Neuroscience Cancer shanghai hongqiao airport preventing hazardous incidents novel methodology aimed improving aviation safety ensuring operational effectiveness best classification accuracy 98 %, achieved calculating feature weights calculate feature weights following data construction centered data construction workload assessment within supervised learning techniques ramp control tasks different workload levels optimal feature combination using real data feature combination data alignment unsupervised learning ramp controller instantaneous workload cumulative workload type data train classifiers threshold calculations provide insights paper introduces knn classifier fatigue characteristics eye tracker eye movement crucial part 8 controllers |