{"title":"基于模糊逻辑的运输机尾流轨迹自动识别","authors":"Aziz Al-Mahadin, F. Bouslama","doi":"10.1109/ISCMI.2017.8279613","DOIUrl":null,"url":null,"abstract":"Aircraft trailing vortices result sometimes in significant disturbance to following aircraft. Separation standards between leading and following aircraft are sometimes over estimated, hence reducing airport capacity. An important contribution to the formation and revision of vortex separations lies in the recognition of wake vortex traverse by pilot reports together with a manual analysis of the flight data routinely recorded by flight data recorders (FDRs). This process has many disadvantages and, therefore, it is desirable to have an automatic identification technique, which can save time, and is both accurate and simple to implement. In this paper, fuzzy logic (FL) is used to model and identify vortex encounters. FL tolerates data imprecision and cope well with complexities in modeling the vortex encounters. Fuzzy linguistic variables are used to model FDR data. Fuzzy rules are derived from a collection of 54 pilot reports of vortex encounters and 210 flight records from FDRs. FL identification models are analyzed and the fuzzy rule base is optimized. An average success rate of identification of 83.7% is obtained. This automatic identification system should enable the aviation authorities to review regularly the appropriateness of wake vortex separation criteria to enhance safety and increase airport capacities.","PeriodicalId":119111,"journal":{"name":"2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Automatic identification of wake vortex traverse by transport aircraft using fuzzy logic\",\"authors\":\"Aziz Al-Mahadin, F. Bouslama\",\"doi\":\"10.1109/ISCMI.2017.8279613\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aircraft trailing vortices result sometimes in significant disturbance to following aircraft. Separation standards between leading and following aircraft are sometimes over estimated, hence reducing airport capacity. An important contribution to the formation and revision of vortex separations lies in the recognition of wake vortex traverse by pilot reports together with a manual analysis of the flight data routinely recorded by flight data recorders (FDRs). This process has many disadvantages and, therefore, it is desirable to have an automatic identification technique, which can save time, and is both accurate and simple to implement. In this paper, fuzzy logic (FL) is used to model and identify vortex encounters. FL tolerates data imprecision and cope well with complexities in modeling the vortex encounters. Fuzzy linguistic variables are used to model FDR data. Fuzzy rules are derived from a collection of 54 pilot reports of vortex encounters and 210 flight records from FDRs. FL identification models are analyzed and the fuzzy rule base is optimized. An average success rate of identification of 83.7% is obtained. This automatic identification system should enable the aviation authorities to review regularly the appropriateness of wake vortex separation criteria to enhance safety and increase airport capacities.\",\"PeriodicalId\":119111,\"journal\":{\"name\":\"2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI)\",\"volume\":\"114 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCMI.2017.8279613\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 4th International Conference on Soft Computing & Machine Intelligence (ISCMI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCMI.2017.8279613","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic identification of wake vortex traverse by transport aircraft using fuzzy logic
Aircraft trailing vortices result sometimes in significant disturbance to following aircraft. Separation standards between leading and following aircraft are sometimes over estimated, hence reducing airport capacity. An important contribution to the formation and revision of vortex separations lies in the recognition of wake vortex traverse by pilot reports together with a manual analysis of the flight data routinely recorded by flight data recorders (FDRs). This process has many disadvantages and, therefore, it is desirable to have an automatic identification technique, which can save time, and is both accurate and simple to implement. In this paper, fuzzy logic (FL) is used to model and identify vortex encounters. FL tolerates data imprecision and cope well with complexities in modeling the vortex encounters. Fuzzy linguistic variables are used to model FDR data. Fuzzy rules are derived from a collection of 54 pilot reports of vortex encounters and 210 flight records from FDRs. FL identification models are analyzed and the fuzzy rule base is optimized. An average success rate of identification of 83.7% is obtained. This automatic identification system should enable the aviation authorities to review regularly the appropriateness of wake vortex separation criteria to enhance safety and increase airport capacities.