연구동향 분석

기계학습을 이용한 항공기 착륙 시 구조건전성 모니터링 및 경착륙 식별 = Structural Health Monitoring and Hard Landing Detection on Aircraft Landing using Machine Learning

정선호 2020년

논문상세정보
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' 기계학습을 이용한 항공기 착륙 시 구조건전성 모니터링 및 경착륙 식별 = Structural Health Monitoring and Hard Landing Detection on Aircraft Landing using Machine Learning' 의 주제별 논문영향력
논문영향력 선정 방법
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주제
  • 구조 건전성 모니터링
  • 구조해석
  • 기계학습
동일주제 총논문수 논문피인용 총횟수 주제별 논문영향력의 평균
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' 기계학습을 이용한 항공기 착륙 시 구조건전성 모니터링 및 경착륙 식별 = Structural Health Monitoring and Hard Landing Detection on Aircraft Landing using Machine Learning' 의 참고문헌

  • 통계학 탐구, ㈜박영사, Seoul
    김성주 Korea [2016]
  • “항공기용 타이어의 인증기술동향,”
    박근영 이경철 진영권 최주원 Current Industrial and Technological Trends in Aerospace(KARI), Vol. 4, No. 2, pp. 68-75 [2006]
  • “A Study on Anomaly Detection Based on Machine Learning,”
    김용대 박아연 오미애 진재현 Korea Institute for Health and Social Affairs 연구보고서 -16, 2018 [2018]
  • telephone interview and e-mail communication with Lacagnina ,
    [2004]
  • [95] Peter J. Rousseeuw and Annick M. Leroy, Robust Regression and Outlier Detection, John Wiley & Sons Inc., New York, N.Y., U.S.A., 1987.
  • [8] “Hard Landing,” retrieved 26 June 2020 from https://www.wingsherald.co m/embraer-emb-121a1-made-a-hard-emergency-landing-in-farmland-field-in-brazil.
  • [83] Diederik P. Kingma and Max Welling, “Auto-Encoding Variational Bayes,” arXiv:1312.6114, pp. 1-14, 2013.
  • [7] “Hard Landing,” retrieved 26 June 2020 from https://www.cbc.ca/news/can ada/nova-scotia/air-canada-flight-624-crash-hard-landing-too-soft-a-term-1.3015457.
  • [78] “Deep Learning,” retrieved 26 June 2020 from https://untitledtblog.tistory. com/154.
  • [77] “The Neural Network Zoo,” retrieved 26 June 2020 from https://www.asi movinstitute.org/neural-network-zoo.
  • [73] Laurene V. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms, and Applications, 1st ed., Prentice Hall, Inc., N.J., U.S.A., 1994.
  • [70] “Landing Gear Types,” retrieved 26 June 2020 from https://www.flight-mechanic.com/landing-gear-types-fixed-and-retractable-land ing-gear-part-two.
  • [6] “Hard Landing,” retrieved 26 June 2020 from https://www.aviation-acciden ts.net/mandala-airlines-boeing-b737-200-pk-ril-flight-mdl260.
  • [5] “Hard Landing,” retrieved 26 June 2020 from https://news.aviation-safety.n et/2018/01/23/atsb-releases-preliminary-report-hard-landing-accident-atr-72-60 0-canberra-airport-australia-19-november-2017.
  • [56] H. B. Pacejka, Tyre and Vehicle Dynamics, Elsevier, 2006.
  • [47] Rajesh Rajamani, Vehicle Dynamics and Control, Springer, Boston, M.A., U.S.A., 2005.
  • [45] Mike Blundell and Damian Harty, The Multibody Systems Approach to Vehicle Dynamics, Elsevier Butterworth-Heinemann, Burlington, M.A., U.S.A., Ch. 5, 2004.
  • [42] D. I. Hwang, K. M. Jeong, H. S. Jeong, C. M. Lee, K. W. Kim and G. H. Bae, “Estimation of the Wear Life of Aircraft Tire,” The Korean Society of Automotive Engineers Spring Conference, pp. 825-829, April 2009.
  • [40] Mokhtar S. Bazaraa, Hanif D. Sherali and C. M. Shetty, Nonlinear Programming: Theory and Algorithms, 3rd ed., John Wiley & Sons, Inc., Hoboken, N.J., U.S.A., 2006.
  • [37] Gordon J. Van Wylen, Richard E. Sonntag andClaus Borgnakke, Fundamentals ofClassical Thermodynamics, 4th ed, John Wiley Sons, Inc., New York, N.Y, U.S.A., 1993.
    [1993]
  • [27] E. Cross, P. Sartor, K. Worden and P. Southern, “Prediction of Landing Gear Loads using Machine Learning Techniques,” 6th European Workshop on Structural Health Monitoring(EWSHM 2012), Dresden, Germany, pp. 1056-1063, July 2012.
  • [20] Javier Gómez-Escalonilla, Jaime García, Ma Mar Andrés and José I. Armijo, “Strain Predictions using Artificial Neural Networks for a Full-Scale Fatigue Monitoring System,” AIAC-13 Thirteenth Australian International Aerospace Congress Sixth DSTO International Conference on Health & Usage Monitoring. Melbourne, A.U, March 2009.
  • [14] National Aeronautics and Space Administration(NASA) Aviation Safety Reporting System(ASRS) report no. 491679. Nov. 2000.
    ASRS ) report no . 491679
  • [13] National Aeronautics and Space Administration(NASA) Aviation Safety Reporting System(ASRS) report no. 464683. Feb. 2000.
    ASRS ) report no . 464683
  • [12] Peduzzi, Lauren; spokesperson for the U.S. National Transportation Safety Board (NTSB). Telephone interview by Lacagnina, Mark. Alexandria, Virginia, U.S. June 23, 2004. Flight Safety Foundation, Alexandria, Virginia, U.S.
  • [11] Guilldou, Alain;Chief of the Division of Information andCommunication, Bureau d’Enquêtes et d’Analyses pour la Sécurité de L’AviationCivile. E-mailCommunication with Lacagnina, Mark. Alexandria, Virginia, U.S. July 29, 2004. Flight Safety Foundation, Alexandria, Virginia, U.S.
  • [10] Stonier, John; Transportation Safety Board ofCanada. Telephone interview by Lacagnina, Mark. Alexandria, Virginia, U.S. July 16, 2004. Flight Safety Foundation, Alexandria, Virginia, U.S.
  • Using Rotations to Build Aerospace Coordinate Systems
    [2008]
  • Tyre Modelling for Use in Vehicle Dynamics Studies
    [1987]
  • Training feedforward networks with the Marquardt algorithm
    Vol . 5 , No . 6 , pp . 989-993 [1994]
  • Tire Shear Force Generation during Combined Steering and Braking Maneuvers
    Vol . 86 , Section 4 , pp . 2953-2969 [1977]
  • Tire Modeling and Friction Estimation
    [2007]
  • Tire Model for Simulations of Vehicle Motion on High and Low Friction Road Surfaces
    pp . 1025-1034 , [2000]
  • Theoretical and Experimental Studies on the Dynamic Cornering Properties of Tires
    [1981]
  • Theoretical Study of the Effect of Tractive and Braking Forces on Cornering Characteristics of Tires
    [1969]
  • The perceptron : a probabilistic model for information storage and organization in the brain.
    Vol . 65 , No . 6 , pp . 386-408 [1958]
  • Study on Cornering Properties of Tire and Vehicle
    Vol . 18 , No . 3 , pp . 136-169 [1990]
  • Structural Health Management an Evolution in Design , 'Structural Health Monitoring 2009 : From System Integration to Autonomous Systems
    pp . 3-13 ,
  • Statistical Summary of Commercial Jet Airplane Accidents : Worldwide Operations 1959-2018 50th ed.
    [2019]
  • Statistical Summary of Commercial Jet Airplane Accidents : Worldwide Operations 1959-2002.
    [2003]
  • Stabilized Approach and Flare are Keys to Avoiding Hard Landings
    Vol . 23 . No . 8 , p.1-25 [2004]
  • Simulation Model of an Aircraft Landing GearConsidering Elastic Properties of the Shock Absorber
    Vol . 221 , No . 1 , pp . 77-86 [2007]
  • Shimmy Analysis of a Simple Aircraft Nose Landing Gear Model using Different Mathematical Methods
    Vol . 1 , Issue 8 , pp . 545-555 [1997]
  • Shear Force Development by Pneumatic Tyres in Steady StateConditions : A Review of Modelling Aspects
    Vol . 20 , Issue 3-4 , pp . 121-176 [1991]
  • Recent Advances in Anomaly Detection Methods Applied to Aviation
    Vol . 6 , No . 11 : 117 [2019]
  • Prediction of Landing Gear Loads using Machine Learning Techniques
    Vol . 15 , No . 5. pp . 568-582 [2016]
  • Operational Risk Assessment for Unmanned Aircraft Vehicles by using Structural Health and Event Management
    [2007]
  • Novelty detection : a review—part 2 : : neural network based approaches .
    Vol . 83 , No . 12 , pp . 2499-2521 [2003]
  • Novelty detection : a review—part 1 : statistical approaches .
    Vol . 83 , No . 12 , pp . 2481-2497 [2003]
  • Neurocomputing : Picking the Human Brain
    Vol . 25 , No . 3 , pp . 36-41 [1988]
  • Neural Networks Theory and Applications, Jinhan M&B, Seoul
    김대수 Korea [2006]
  • Neural Network Design
    [2014]
  • Modélisation du pneumatique pour l ’ étude du compartement routier des véhicules automobiles , ” Ph.D. Dissertation , Ecole Nationale Superieure des Arts
    [1986]
  • Modeling Aircraft Wing Load from Flight Data using Neural Networks
    [2003]
  • Mechanical Properties of Pneumatic Tires with Special Reference to Modern Aircraft Tires
    [1958]
  • Main Causes of Hard Landings : Soviet study covers 10 years of accidents and incidents
    Vol . 48 , No . 2 , pp . 1-4 [1991]
  • MSC Nastran Product Information & Documentation
    [2018]
  • Long short-term memory
    Vol . 9 , No . 8 , pp . 1735-1780 [1997]
  • Lateral Forces on Rolling Pneumatic Tire
    96 , Vol . 29 [1954]
  • Indirect Aircraft Structural Monitoring using Artificial Neural Networks
    Vol . 112 , No . 1131 , pp . 251-265 [2008]
  • Hard-landing Simulation by a Hierarchical Aircraft Landing Model and an Extended Inertia Relief Technique
    Vol . 16 , No . 3 , pp . 394-406 [2015]
  • Gradient-Based Learning Applied to Document Recognition
    Vol . 86 , No . 11 , pp . 2278-2324 [1998]
  • Global Aviation Tires The Goodyear Tire & Rubber Co. 700-862-931-522
    [2002]
  • Generative adversarial nets ''
    [2014]
  • Fluid Mechanics , 7th ed. , McGraw-Hill
    [2011]
  • Flight Loads Estimation using Local Model Networks
    [2014]
  • Finding structure in time
    Vol . 14 , No . 2 , pp.179-211 [1990]
  • Estimation of the Wear Life of Aircraft Tire
    pp . 825-829
  • Estimation of Maximum Strains and Loads in Aircraft Landing using Artificial Neural Network
    Vol . 21 , pp . 117-132 [2020]
  • Error Analysis forCo-simulation of MATLAB/Simulink and ADAMS
    pp . 319-322
  • Error Analysis for Cosimulation of MATLAB/Simulink and ADAMS
    pp . 319-322 [2015]
  • Discontinuous Time-Integration Method for Dynamic Contact/Impact Problems
    Vol . 37 , No . 7 , pp . 874-880 [1999]
  • Development of a Parametric-based Indirect Aircraft Structural Usage Monitoring System using Artificial Neural Networks
    Vol . 111 , No . 1118 , pp . 209-230 [2007]
  • Deep residual learning for image recognition
    pp . 770-778
  • Contribution to the Modelling of Aircraft Tyre-road Interaction
    [2010]
  • Conditional Maintenance Inspection.
    Issue 14
  • Comparison of Stochastic Global Optimization Methods to Estimate Neural Network Weights
    Vol . 26 , pp . 145-158 , [2007]
  • Comparison between Pneumatic and Non-pneumatic Tire for Energy Loss
    pp . 1081-1088 [2011]
  • Comparative Study on the Several Types of Double-Acting Oleo-Pneumatic Shock Absorbers of Aircraft Part I . Mathematical Modeling
    Vol . 45 , No . 11 , pp . 939-950 [2017]
  • Comparative Study on the Several Types of Double-Acting Oleo-Pneumatic Shock Absorbers of Aircraft : Part II . Mathematical Modeling
    Vol . 45 , No . 11 , pp . 951-966 [2017]
  • Backpropagation applied to handwritten zip code recognition
    Vol . 1 , No . 4 , pp . 541-551 [1989]
  • Auto-association by multilayer perceptrons and singular value decomposition ,
    Vol . 59 , pp.291-294 [1988]
  • Artificial Neural Networks for Structural Health Monitoring of Helicopter Harsh Landings
    Vol . 390 , pp . 192-197 [2013]
  • Approximate Surface Method for the Application of CFD Pressure to FEM Model
    [2017]
  • Application of Aircraft Neural Networks in Aircraft Maintenance , Repair and Overhaul Solutions
    [2008]
  • Anomaly detection : A survey
    Vol . 41 , No . 3 , pp . 1-72 [2009]
  • Analysis of Landing Gear Behavior
    [1953]
  • An Interactive Tire Model for Driver/Vehicle Simulation
    [1988]
  • An Analytical Tire Model for vehicle Simulation in Normal Driving Conditions
    [2000]
  • Aircraft Landing Gear Spin-up , Spring-back Load Analysis using Explicit Finite Element Method
    pp . 886-891 [2010]
  • Aircraft Landing Gear Design : Principles and Practices , American Institute of Aeronautics and Astronautics
    [1988]
  • Adaptive Real-time Anomaly Detection using Inductively Generated Sequential Patterns
  • Adams/Tire help - Adams 2015.1
    [2015]
  • Accident/Incident Reporting Manual ( ADREP Manual )Doc 9156-AN/900
    Appendix 4 [1987]
  • A Unified Semi-empirical Tire Model with Higher Accuracy and Less Parameters.
    pp . 1513-1520 [1990]
  • A Predictive Aircraft Landing Speed Model using Neural Network
    [2012]
  • A Practical Approach for the Indirect Prediction of Structural Fatigue from Measured Flight Parameters
    Vol . 211 , No . 1 , pp . 29-38 [1997]
  • A New Mathematical-Physical 2D Tire Model for Handling Optimization on a Vehicle
    pp . 1540-1547 [1999]
  • A New Interpolative Model of the Mechanical Characteristics of the Tire as an Input to Handling Models
  • A Neural Network Prototype for Predicting F-14B Strains at the B.L.10 Longeron
    [1992]
  • A Methodology to Predict the Empennage In-Flight Loads of a General Aviation Aircraft using Backpropagation Neural Networks
    [2001]
  • A Comparative Evaluation of Five Tire Traction Models
    [1974]