Галерея 3208253
Галерея 3208253
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Ignoring calls for talks, the North has ridiculed President Yoon Suk Yeol’s offer for economic benefits in exchange for denuclearisation steps
North Korea suspended cooperation with South Korea after the collapse of nuclear negotiations with the US in 2019; Kim Jong-un ramped up tensions in 2022, test-firing more than 70 missiles. Photo: dpa
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Energy Balance Modeling of CMG Rotor Subsystem
HI Construction Based on Transfer Learning
Abstract: Control moment gyro (CMG) is a critical component for attitude control of large spacecraft. To avoid its accidental failure caused by the performance degradation of high-... View more
Control moment gyro (CMG) is a critical component for attitude control of large spacecraft. To avoid its accidental failure caused by the performance degradation of high-speed rotating bearings, a metric, which is referred to as the health indicator (HI), should be constructed and monitored. Although the HI construction problem has been widely studied to evaluate the degradation process of machinery, the poor quality of telemetry data makes it impossible to apply existing solutions to the bearings in CMG. Therefore, a novel HI construction method based on energy theory and physics-inspired machine learning is proposed. Generally, the energy balance relationship of the system would change with the degradation of its components. Based on this idea, a physics-inspired convolutional neural network (CNN) model is established to formulate the relationship between monitoring signals and total energy consumed by the driving motor. Then a transfer learning strategy is developed to capture the changes in model parameters and perform HI construction. Compared with existing approaches, the proposed method does not rely on vibration signals or high-frequency data and can extract degradation information obscured by the controller and other influences. A real dataset collected from an aerospace CMG is utilized to verify the effectiveness of the proposed method. Results show that the constructed HIs have good performance in monotonicity, robustness, and time correlation. The application of constructed HIs for CMG anomaly detection is also discussed.
Date of Publication: 21 September 2022
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Control moment gyro (CMG) is the key actuator for attitude control and fast maneuvering of on-orbit spacecraft such as space stations, space telescopes, and large satellites [1]. As a critical component in various industrial machinery, bearing also plays an important role in CMG. A typical CMG system with mechanical bearings can be divided into a rotor subsystem and a gimbal subsystem [2]. The rotor subsystem is composed of a constant-speed rotor, a brushless dc motor (BLDCM), a speed controller, and a pair of rotor bearings. Since the CMG rotor is constantly operating under high-speed situations (e.g., 8000 rpm), the rotor bearing is far more likely to degrade than the other components due to gradual wear. Its performance deterioration and accidental failure may lead to instability of the entire vehicle or even termination of space missions. For example, in 2002 and 2006, the CMGs equipped on the International Space Station were shut down twice due to rotor bearings failures, which directly led to the delay of the U.S. space exploration program. Therefore, timely evaluate the health status of degrading rotor bearings in aerospace CMG has great significance to ensure the operation safety of space vehicles.
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Telemed J E Health
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Telemed J E Health. 2011 Oct; 17(8): 627–634.
Yaqin Li , Ph.D., 1 Thomas P. Karnowski , M.S., 2 Kenneth W. Tobin , Ph.D., 2 Luca Giancardo , Ph.D., 2 Scott Morris , M.D., 3 Sylvia E. Sparrow , M.D., 3 Seema Garg , M.D., Ph.D., 4 Karen Fox , Ph.D., 5 and Edward Chaum , M.D., Ph.D. 1
1 Hamilton Eye Institute, University of Tennessee Health Science Center, Memphis, Tennessee.
2 Oak Ridge National Laboratory, Oak Ridge, Tennessee.
3 The Church Health Center, Memphis, Tennessee.
4 University of North Carolina, Chapel Hill, North Carolina.
5 Delta Health Alliance, Stoneville, Mississippi.
Address correspondence to: Edward Chaum, M.D., Ph.D., Hamilton Eye Institute, University of Tennessee, 930 Madison Ave., Suite 731, Memphis, TN 38163. E-mail: ude.cshtu@muahce
Received 2011 Jan 6; Revised 2011 Mar 8; Accepted 2011 Mar 9.
Copyright 2011, Mary Ann Liebert, Inc.
Key words: diabetic retinopathy , computer-aided diagnosis , telemedicine , HIPAA compliance , healthcare outcomes , ocular telehealth , image analysis , teleophthalmology
1. Centers for Disease Control and Prevention, National Diabetes Fact Sheet. [Jul 18;2011 ]. www.cdc.gov/diabetes/pubs/pdf/ndfs_2011.pdf
2. The World Diabetes Foundation. [Jul 18;2011 ]. www.worlddiabetesfoundation.org
3. Abramoff MA. Suttorp-Schulten M. Web-based screening for diabetic retinopathy in a primary care population: The eyecheck project. Telemed E Health. 2005; 11 :668–674. [ PubMed ] [ Google Scholar ]
4. Abramoff MA. Veirgever M. Neimeijer M. Russel S. Suttorp-Schulten M. Van Ginneken B. Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes. Diabet Care. 2008; 31 :193–198. [ PMC free article ] [ PubMed ] [ Google Scholar ]
5. Cavallerano AA. Cavallerano JD. Katalinic P. Tolson AM. Aiello LP. Aiello LM. Joslin Vision Network Clinical Team, Use of Joslin Vision Network digital-video nonmydriatic retinal imaging to assess diabetic retinopathy in a clinical program. Retina. 2003; 23 :215–223. [ PubMed ] [ Google Scholar ]
6. Larsen M. Godt J. Grunkin M. Automated detection of diabetic retinopathy in a fundus photographic screening population. Invest Ophthalmol Visual Sci. 2003; 44 :767–771. [ PubMed ] [ Google Scholar ]
7. Giancardo L. Abramoff MD. Chaum E. Karnowski TP. Meriaudeau F. Tobin KW. Elliptical local vessel density: A fast and robust quality metric for fundus images. Conference Proceedings IEEE Engineering in Medicine and Biology Society (EMBS); Vancouver, BC, Canada. Aug 20–25;2008 ; pp. 3534–3537. [ PubMed ] [ Google Scholar ]
8. Giancardo L. Quality analysis of retina images for the automatic diagnosis of diabetic retinopathy, Master's Thesis, Msc Erasmus Mundis in VIBOT. Dijon, France: University of Burgundy; Jul, 2008. [ Google Scholar ]
9. Tobin KW. Chaum E. Govindasamy VP. Karnowski TP. Detection of anatomic structures in human retinal imagery. IEEE Trans Med Imaging. 2007; 26 :1729–1739. [ PubMed ] [ Google Scholar ]
10. Karnowski TP. Govindasamy VP. Tobin KW. Chaum E. Abramoff MD. Retina lesion and microaneurysm segmentation using morphological reconstruction methods with ground-truth data. Conference Proceedings IEEE Engineering in Medicine and Biology Society; Vancouver, BC, Canada. Aug 20–25;2008 ; pp. 5433–5436. [ PubMed ] [ Google Scholar ]
11. Karnowski TP. Aykac D. Chaum E. Giancardo L. Li Y. Tobin KW., Jr Abramoff MD. Practical considerations for optic nerve location in telemedicine. Conference Proceedings IEEE Engineering in Medicine and Biology Society; Minneapolis, MN. Sep 2–6;2009 ; pp. 6205–6209. [ PubMed ] [ Google Scholar ]
12. Chaum E. Karnowski TP. Govindasamy VP. Abdelrahamen M. Tobin KW. Automated diagnosis of retinopathy by content-based image retrieval. Retina. 2008; 28 :1463–1477. [ PubMed ] [ Google Scholar ]
13. Tobin KW. Abramoff MD. Chaum E. Giancardo L. Govindasamy VP. Karnowski TP. Tennant MTS. Swainson S. Using a patient image archive to diagnose retinopathy. Conference Proceedings IEEE Engineering in Medicine and Biology Society; Vancouver, BC, Canada. Aug 20–25;2008 ; pp. 5441–5444. [ PubMed ] [ Google Scholar ]
14. Tobin KW. Abdelrahman M. Chaum E. Govindasamy VP. Karnowski TP. A probabilistic framework for content-based diagnosis of retinal disease. Conference Proceedings IEEE Engineering in Medicine and Biology Society; Lyon, France. Aug 23–26;2007 ; pp. 6744–6747. [ PubMed ] [ Google Scholar ]
15. Federal Information Processing Standards Publication 140–2. Security Requirements for Cryptographic Modules. Gaithersburg, MD: Information Technology Laboratory, National Institute of Standards and Technology (NIST); 2001. [ Google Scholar ]
16. Giancardo L. Meriaudeau F. Karnowski TP. Chaum E. Tobin KW. Li Y. Proceedings of SPIE Medical Imaging. San Diego, CA: SPIE; 2010. Microaneurysms detection with the Radon Cliff Operator in retinal fundus images. [ Google Scholar ]
17. Giancardo L. Chaum E. Karnowski TP. Meriaudeau F. Tobin KW. Li Y. Proceedings of World Congress of Medical Physics and Biomedical Engineering. Munich: Springer; Sep 9–12, 2009. Bright retinal lesions detection using color fundus images containing reflective features; pp. 292–295. [ Google Scholar ]
18. Rudnisky CJ. Tennant MTS. Weis E. Ting A. Hinz BJ. Greve MDJ. Web-based grading of compressed stereoscopic digital photography versus standard slide film photography for the diagnosis of diabetic retinopathy. Ophthalmology. 2007; 114 :1748–1754. [ PubMed ] [ Google Scholar ]
19. Wei J. Valentino D. Bell D. Baker R. A Web-based telemedicine system for diabetic retinopathy screening using digital fundus photography. Telemed E Health. 2006; 12 :50–57. [ PubMed ] [ Google Scholar ]
20. Wei Z. Wu Y. Deng RH. Yu S. Yao H. Zhao Z. Ngoh LH. Han LT. Poh EWT. A secure and synthesis tele-ophthalmology system. Telemed E Health. 2008; 14 :833–845. [ PubMed ] [ Google Scholar ]
Articles from Telemedicine Journal and e-Health are provided here courtesy of Mary Ann Liebert, Inc.
1. Centers for Disease Control and Prevention, National Diabetes Fact Sheet. [Jul 18;2011 ]. www.cdc.gov/diabetes/pubs/pdf/ndfs_2011.pdf [ Ref list ]
2. The World Diabetes Foundation. [Jul 18;2011 ]. www.worlddiabetesfoundation.org [ Ref list ]
3. Abramoff MA. Suttorp-Schulten M. Web-based screening for diabetic retinopathy in a primary care population: The eyecheck project. Telemed E Health. 2005; 11 :668–674. [ PubMed ] [ Google Scholar ] [ Ref list ]
6. Larsen M. Godt J. Grunkin M. Automated detection of diabetic retinopathy in a fundus photographic screening population. Invest Ophthalmol Visual Sci. 2003; 44 :767–771. [ PubMed ] [ Google Scholar ] [ Ref list ]
7. Giancardo L. Abramoff MD. Chaum E. Karnowski TP. Meriaudeau F. Tobin KW. Elliptical local vessel density: A fast and robust quality metric for fundus images. Conference Proceedings IEEE Engineering in Medicine and Biology Society (EMBS); Vancouver, BC, Canada. Aug 20–25;2008 ; pp. 3534–3537. [ PubMed ] [ Google Scholar ] [ Ref list ]
8. Giancardo L. Quality analysis of retina images for the automatic diagnosis of diabetic retinopathy, Master's Thesis, Msc Erasmus Mundis in VIBOT. Dijon, France: University of Burgundy; Jul, 2008. [ Google Scholar ] [ Ref list ]
9. Tobin KW. Chaum E. Govindasamy VP. Karnowski TP. Detection of anatomic structures in human retinal imagery. IEEE Trans Med Imaging. 2007; 26 :1729–1739. [ PubMed ] [ Google Scholar ] [ Ref list ]
10. Karnowski TP. Govindasamy VP. Tobin KW. Chaum E. Abramoff MD. Retina lesion and microaneurysm segmentation using morphological reconstruction methods with ground-truth data. Conference Proceedings IEEE Engineering in Medicine and Biology Society; Vancouver, BC, Canada. Aug 20–25;2008 ; pp. 5433–5436. [ PubMed ] [ Google Scholar ] [ Ref list ]
11. Karnowski TP. Aykac D. Chaum E. Giancardo L. Li Y. Tobin KW., Jr Abramoff MD. Practical considerations for optic nerve location in telemedicine. Conference Proceedings IEEE Engineering in Medicine and Biology Society; Minneapolis, MN. Sep 2–6;2009 ; pp. 6205–6209. [ PubMed ] [ Google Scholar ] [ Ref list ]
14. Tobin KW. Abdelrahman M. Chaum E. Govindasamy VP. Karnowski TP. A probabilistic framework for content-based diagnosis of retinal disease. Conference Proceedings IEEE Engineering in Medicine and Biology Society; Lyon, France. Aug 23–26;2007 ; pp. 6744–6747. [ PubMed ] [ Google Scholar ] [ Ref list ]
15. Federal Information Processing Standards Publication 140–2. Security Requirements for Cryptographic Modules. Gaithersburg, MD: Information Technology Laboratory, National Institute of Standards and Technology (NIST); 2001. [ Google Scholar ] [ Ref list ]
12. Chaum E. Karnowski TP. Govindasamy VP. Abdelrahamen M. Tobin KW. Automated diagnosis of retinopathy by content-based image retrieval. Retina. 2008; 28 :1463–1477. [ PubMed ] [ Google Scholar ] [ Ref list ]
16. Giancardo L. Meriaudeau F. Karnowski TP. Chaum E. Tobin KW. Li Y. Proceedings of SPIE Medical Imaging. San Diego, CA: SPIE; 2010. Microaneurysms detection with the Radon Cliff Operator in retinal fundus images. [ Google Scholar ] [ Ref list ]
17. Giancardo L. Chaum E. Karnowski TP. Meriaudeau F. Tobin KW. Li Y. Proceedings of World Congress of Medical Physics and Biomedical Engineering. Munich: Springer; Sep 9–12, 2009. Bright retinal lesions detection using color fundus images containing reflective features; pp. 292–295. [ Google Scholar ] [ Ref list ]
4. Abramoff MA. Veirgever M. Neimeijer M. Russel S. Suttorp-Schulten M. Van Ginneken B. Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes. Diabet Care. 2008; 31 :193–198. [ PMC free article ] [ PubMed ] [ Google Scholar ] [ Ref list ]
18. Rudnisky CJ. Tennant MTS. Weis E. Ting A. Hinz BJ. Greve MDJ. Web-based grading of compressed stereoscopic digital photography versus standard slide film photography for the diagnosis of diabetic retinopathy. Ophthalmology. 2007; 114 :1748–1754. [ PubMed ] [ Google Scholar ] [ Ref list ]
5. Cavallerano AA. Cavallerano JD. Katalinic P. Tolson AM. Aiello LP. Aiello LM. Joslin Vision Network Clinical Team, Use of Joslin Vision Network digital-video nonmydriatic retinal imaging to assess diabetic retinopathy in a clinical program. Retina. 2003; 23 :215–223. [ PubMed ] [ Google Scholar ] [ Ref list ]
19. Wei J. Valentino D. Bell D. Baker R. A Web-based telemedicine system for diabetic retinopathy screening using digital fundus photography. Telemed E Health. 2006; 12 :50–57. [ PubMed ] [ Google Scholar ] [ Ref list ]
20. Wei Z. Wu Y. Deng RH. Yu S. Yao H. Zhao Z. Ngoh LH. Han LT. Poh EWT. A secure and synthesis tele-ophthalmology system. Telemed E Health. 2008; 14 :833–845. [ PubMed ] [ Google Scholar ] [ Ref list ]
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1 Hamilton Eye Institute, University of Tennessee Health Science Center, Memphis, Tennessee.
2 Oak Ridge National Laboratory, Oak Ridge, Tennessee.
2 Oak Ridge National Laboratory, Oak Ridge, Tennessee.
2 Oak Ridge National Laboratory, Oak Ridge, Tennessee.
3 The Church Health Center, Memphis, Tennessee.
3 The Church Health Center, Memphis, Tennessee.
4 University of North Carolina, Chapel Hill, North Carolina.
5 Delta Health Alliance, Stoneville, Mississippi.
1 Hamilton Eye Institute, University of Tennessee Health Science Center, Memphis, Tennessee.
In this article, we present the design and implementation of a regional ocular telehealth network for remote assessment and management of diabetic retinopathy (DR), including the design requirements, network topology, protocol design, system work flow, graphics user interfaces, and performance evaluation. The Telemedical Retinal Image Analysis and Diagnosis Network is a computer-aided, image analysis telehealth paradigm for the diagnosis of DR and other retinal diseases using fundus images acquired from primary care end users delivering care to underserved patient populations in the mid-South and southeastern United States.
Diabetic retinopathy (DR) is the leading cause of new-onset blindness in working-age adults in the industrialized world today, but timely laser treatments can preserve vision in patients with DR if the disease is diagnosed at an early stage. There are currently more than 25.8 million people with Type 1 and 2 diabetes in the United States, with over 7 million being undiagnosed. 1 The number of people over the age of 20 with prediabetes is estimated to be more than 79 million, with an incidence approaching 27% in those over the age of 65. 1 The Centers for Disease Control and Preve
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