Pn – III – pte raport ştiinţific şi tehnic în extenso

4.DISEMINAREA REZULTATELOR

Pe parcursul desfăşurării etapei întâi a prezentului proiect, a fost organizată o prezentare a obiectivelor acestuia către principalul deţinător de baraje din zonă - SC Hidroelectica SA Sucursala Cluj.

Durata scurtă a etapei prezente a limitat diseminarea rezultatelor obţinute, care se va realiza în special pe durata etapei a doua. În acest scop a fost elaborată pagina web a proiectului http://www.automation.ro/damfu/ unde sunt postate datele publice ale proiectului, precum şi documentaţia extinsă cu rezultatele etapei.

5.CONCLUZII

Prezentul proiect ’DAMFU’, finanţat în cadrul PN-III–PTE, se realizează în parteneriat de IPA SA - (coordonator) şi Universitatea Tehnică Cluj-Napoca (partener). Prin proiect se propune dezvoltarea unui produs-sistem complex, destinat urmăririi comportării barajelor prin fuziunea informaţiilor. Precursori, ai acestui proiect, sunt rezultatele proiectului „FUZIBAR-705/2006 PNCD MENER”, rezultate cu nivel de maturitate tehnologica TRL4. Scopul etapei întâi a proiectului a fost analiza pentru stabilirea ‘tipului de produs’, respectiv, definirea caracteristicilor tehnice şi funcţionale ale produsului. Din analiza făcută a rezultat că soluţiile ştiinţifice şi tehnice propuse de proiectul DAMFU, având drept precursori rezultatele elaborate şi experimentate în cadrul proiectului FUZIBAR, corespund scopului propus, răspunzând necesităţilor echipării barajelor din România şi sunt la nivelul mondial al cunoştiinţelor în domeniu. Rezultatelele activităţilor desfăşurate în cadrul etapei 1 a proiectului, sunt următoarele documente, disponibile pe pagina web a proiectului:

1.Studiu privind stadiul actual al sistemelor complexe de supraveghere a barajelor, prezentat pe scurt în prezentul Raport. În studiu se face analiza comparativă a soluţiilor de urmărire a comportării barajelor în ţară şi pe plan mondial, prin achiziţie automată a datelor de la AMC-urile din baraje şi se inventariază soluţiile moderne de utilizare a sensorilor optici distribuiţi, a tehnicilor computer vision şi a metodelor de fuziune a informaţiilor multisensoriale.

A fost analizat gradul de aliniere a soluţiilor sistemului initial (TRL4 FUZIBAR) la tendinţele mondiale din domeniu şi s-au stabilit caracteristicilor tehnice ale sistemului DAMFU.

2.Referenţial iniţial pentru definirea cerinţelor tehnice ale produsului-sistem DAMFU. Acest document conţine principalele condiţii tehnice, generale şi funcţionale pe care trebuie să le realizeze sistemul complex, obiectiv al proiectului DAMFU.

Apreciem faptul că obiectivele etapei întâi au fost atinse şi constitue premize ale desfăşurării în continuare ale proiectului.

Cluj-Napoca, 28.11.2016

Director de proiect,

CS I. Ioan STOIAN

Bibliografie
[Stu1] Studiu Tehnic Proiect FUZIBAR MENER 705/2007- 

[Rap2] Raport final Proiect FUZIBAR MENER 705/2007- 

[GCH3] GHID PRIVIND ECHIPAREA CONSTRUCŢIILOR HIDROTEHNICE  cu AMC - U. T. C.  BUCUREŞTI   Catedra Constuctii Hidrotehnice  CONTRACT Nr. 437 / 22.12.2009

[Npc4] NORMATIV PT. URMARIREA COMPORTARII CONSTRUCŢIILOR HIDROTEHNICE NP 087/03

[Dab5] Dispositif d’Ascultation des Barages – Partie 3 Edision avril 2005 Comite Suisse des Barages
[Far07] C. R. Farrar and K. Worden, “An introduction to structural health monitoring.,”Philos. Trans. A. Math. Phys. Eng. Sci., vol. 365, no. 1851, pp. 303–315, 2007.

[Gli13] B. Glisic, D. Hubbell, D. H. Sigurdardottir, and Y. Yao, “Damage detection andcharacterization using long-gauge and distributed fiber optic sensors,” Opt. Eng., vol.52, p. 87101, 2013.

[Bao12] X. Bao and L. Chen, “Recent progress in distributed fiber optic sensors,” Sensors(Basel), vol. 12, no. 7, pp. 8601–8639, 2012.

[Hig11] J. M. Lopez-Higuera, L. Rodriguez Cobo, A. Quintela Incera, and A. Cobo, “Fiber Optic Sensors in Structural Health Monitoring,” J. Light. Technol., vol. 29, no. 4, pp. 587–608, 2011.

[Gup06] B. D. Gupta, Fiber optic sensors: principles and applications. New Delhi: New India Publishing, 2006.

[Boy08] R. Boyd, Nonlinear Optics, 3rd ed. Rochester, New York: Academic Press, 2008.

[Rod15] G. Rodriguez, J. R. . Casas, and S. Villalba, “SHM by DOFS in civil engineering: a review,” Struct. Monit. Maint., vol. 2, no. 4, pp. 357–382, Dec. 2015.

[Ina05] D. Inaudi and B. Glisic, “Application of distributed Fiber Optic Sensory for SHM,” in Proceeding of the ISHMII-2, 2005, pp. 163–169.

[Auf03] M. Aufleger, M. Conrad, T. Strobl, A. I. H. Malkawi, and Y. Duan, “Distributed Fibre Optic Temperature Measurements in RCC-Dams in Jordan and China,” in RCC Dams- Roller Compacted Concrete Dams: Proceedings of the IV International Symposium on Roller Compacted Concrete Dams, 2003, p. 401.

[Rad15] K. Radzicki and S. Bonelli, “Physical and parametric monitoring of leakages in earthdams using analysis of fibre optic distributed temperature measurements withIRFTA model,” in 24th ater. Struct., vol. 24, no. 3, p. 35005, 2015.

[The99] L. Thévenaz, M. Facchini, A. Fellay, P. Robert, D. Inaudi, and B. Dardel, “Monitoring of large structure using distributed Brillouin fibre sensing,” Int. Conf. Opt. Fiber Sensors, pp. 5–8, 1999.

[Shi09] Shi, B., Sui, H., Liu, J. & Zhang, D.: ”The BOTDR-based distributed monitoring system for slope engineering.”, Engineering Geology for Tomorrow’s Cities. Geological Society, London, Engineering Geology Special Publications, 22, 2009

[Roz05] Rozycki, A., Ruiz, J. M. and Caudra, A. : “Detection and evaluation of horizontal fractures in earth dams using the self potential method”, Eng. Geo., 2005, vol. 82, no. 3, pp. 145–153.

[Vog01] Vogel, B., Cassens, C., Graupner, A. and Trostel, A.: “Leakage detection systems by using distributed fiber optical temperature measurements”, Proc. SPIE Smart Structures and Materials 2001: Sensory Phenomena and Measurement Instrumentation for Smart Structures and Materials, D. I. E. Udd, Ed., 2001, vol. 4328, pp. 23–34, ser. 0277-786X/01.

[Joh04] Johansson, S. and Sjödahl, P.: “Downstream seepage detection using temperature measurements and visual inspection—Monitoring experiences from Rřsvatn field test dam and large embankment dams in Sweden”, Proc. Int. Seminar on Stability and Breaching of Embankment Dams, Oslo, Norway, Oct. 2004, p. 21.

[Fuh93] Fuhr, P.L. and Huston, D. : ”Multiplexed fiber optic pressure and vibration sensors for hydroelectric dam monitoring”, Smart Materials and Structures, 2, 1993, 260-263.

[Kha08] Khan, A. A., Vrabie, V.,. Mars, I. J., Girard, A. and D’Urso, G. : “A source separation technique for processing of thermometric data from fiber-optic DTS measurements for water leakage identification in dikes”, IEEE Sensors J, vol. 8, no. 7, 2008, pp. 1118-1129.

[Gro01] St. Großwig., Graupner, A., Hurtig, E., Kühn, K. and Trostel A.:” Distributed fibre optical temperature sensing technique- A variable tool for monitoring tanks” , Proceedings of the 8th International Symposium on Temperature and Thermal Measurements in Industry and Science, 2001

[Kro97] Kronenberg, P., Casanova, N., Inaudi, D. and Vurpillot, S. : “Dam monitoring with fiber optics deformation sensors”, SPIE Conference on Smart Structures and Materials, San Diego, USA, 1997
[Ber07] Berberan, A., Portela, E., Boavida, J., “Assisted visual inspection of dams for structural safety control”, The International Journal on Hydropower and Dams, Volume 14, issue 2, 2007.

[Rid10] P. Ridao, M. Carreras, D. Ribas, R. Garcia, "Visual inspection of hydroelectric dams using an autonomous underwater vehicle", J. Field Robot., vol. 27, no. 6, pp. 759-778, 2010

[Eag16] “Hydrodam Visual Inspection”, https://eaglesight.org/eagle-sight-industrial/2016/9/20/hydrodam-visual-inspection

[Ber11] Berberan, A., Ferreira, I., Portela, E., Oliveira, S., Oliveira, A. and Baptista, B., 2011. “Overview on terrestrial laser scanning as a tool for dam surveillance”. în 6th International Dam Engineering Conference. LNEC, Lisboa.

[Boa09] J. Boavida, A. Oliveira, A. Berberan, “Dam monitoring using combined terrestrial imaging systems”, http://www.riegl.com/uploads/tx_pxpriegldownloads/CESDecJan09Boavida.pdf

[DSFS] ***, “Dam Safety Fact Sheet”, http://dnr.wi.gov/topic/Dams/documents/FSInspections.pdf

[Gor12] Mihaela Gordan, Ovidiu Dancea, Mihaela Cîşlariu, Ioan Stoian, Aurel Vlaicu, „Sustainable Natural Resources Management –Chapter 1: Fuzzy Image Processing, Analysis and Visualization Methods for Hydro-Dams and Hydro-Sites Surveillance and Monitoring”, Edited by Abiud Kaswamila, Published by InTech, January, 2012, pp. 3-26, ISBN: 978-953-307-670-6

[Sha08] Sham FC, Chen N, Long L. Surface crack detection by flash thermography on concrete surface. Insight-Non-Destructive Testing and Condition Monitoring. 2008 May 1;50(5):240-3.

[Nis12] Nishikawa T, Yoshida J, Sugiyama T, Fujino Y. Concrete crack detection by multiple sequential image filtering. Computer‐Aided Civil and Infrastructure Engineering. 2012 Jan 1;27(1):29-47.

[Jah11] Jahanshahi MR, Masri SF. Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures. Automation în Construction. 2012 Mar 31;22:567-76.

[Kab09] Kabir S, Rivard P, He DC, Thivierge P. Damage assessment for concrete structure using image processing techniques on acoustic borehole imagery. Construction and Building Materials. 2009 Oct 31;23(10):3166-74.

[Hut06] Hutchinson, T.C. and Chen, Z., 2006. Improved image analysis for evaluating concrete damage. Journal of Computing în Civil Engineering, 20(3), pp.210-216.

[Mat11] Mata J. Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models. Engineering Structures. 2011 Mar 31;33(3):903-10.

[Yan15] Yang, Y. and Nagarajaiah, S. (2015). "Dynamic Imaging: Real-Time Detection of Local Structural Damage with Blind Separation of Low-Rank Background and Sparse Innovation." J. Struct. Eng., 10.1061/(ASCE)ST.1943-541X.0001334, 04015144.
[Als10] Alsaade, F. (2010). Neuro-Fuzzy Logic Decision in a Multimodal Biometrics Fusion System. Scientific Journal of King Faisal University (Basic and Applied Sciences), 11(2), 14.

[Arg14] Arguedas, V. F., Zhang, Q., & Izquierdo, E. (2014). Multimodal fusion in surveillance applications. In Fusion in Computer Vision (pp. 161-184). Springer International Publishing.

[Con07] Conti, V., Milici, G., Ribino, P., Sorbello, F., & Vitabile, S. (2007, September). Fuzzy fusion in multimodal biometric systems. In International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (pp. 108-115). Springer Berlin Heidelberg.

[Dru99] Druzdzel, M. J., & Flynn, R. R. (1999). Decision support systems. Encyclopedia of library and information science. A. Kent. Marcel Dekker, Inc. Last Login, 10(03), 2010.

[Fou15] Fourati, H. (2015). Multisensor Data Fusion: From Algorithms and Architectural Design to Applications (Book).

[Fox07] Fox, N. A., Gross, R., Cohn, J. F., & Reilly, R. B. (2007). Robust biometric person identification using automatic classifier fusion of speech, mouth, and face experts. IEEE Transactions on multimedia, 9(4), 701-714.

[Hel99] Hellwich, O., & Wiedemann, C. (1999, December). Multisensor data fusion for automated scene interpretation. In Remote Sensing (pp. 284-295). International Society for Optics and Photonics.

[ISO9241] ISO 9241-210:2010, Ergonomics of human-system interaction -- Part 210: Human-centred design for interactive systems.

[Kan91] Kandel, A. (1991). Fuzzy expert systems. CRC press.

[Kha13] Khanna, P., & Sasikumar, M. (2013). Rule Based System for Recognizing Emotions Using Multimodal Approach. IJACSA) International Journal of Advanced Computer Science and Applications, 4(7).

[Kry07] Kryszczuk, K., Richiardi, J., Prodanov, P., & Drygajlo, A. (2007). Reliability-based decision fusion in multimodal biometric verification systems. EURASIP Journal on Advances in Signal Processing, 2007(1), 1-9.

[Lah15] Lahat, D., Adali, T., & Jutten, C. (2015). Multimodal data fusion: an overview of methods, challenges, and prospects. Proceedings of the IEEE, 103(9), 1449-1477.

[Mra12]Mráček, Š., Váňa, J., Drahanský, M., Dvořák, R., & Yanushkevich, S. (2012). 3D and Thermo-Face Fusion. INTECH Open Access Publisher.

[Nis15] Nishida, N., & Nakayama, H. (2015, November). Multimodal Gesture Recognition Using Multi-stream Recurrent Neural Network. In Pacific-Rim Symposium on Image and Video Technology (pp. 682-694). Springer International Publishing.

[Pav97] Pavlovic, V.I., Berry, G.A., Huang, T.S. (1997). "Integration of audio/visual information for use in human-computer intelligent interaction". Proceedings of the 1997 International Conference on Image Processing (ICIP '97), Volume 1, pp. 121-124.

[Rao15] Raol, J. R. (2015). Data Fusion Mathematics: Theory and Practice. CRC Press.

[Rus05] Russ, G., Sallans, B., Hareter, H. (2005). "Semantic Based Information Fusion in a Multimodal Interface". International Conference on Human-Computer Interaction (HCI'05), Las Vegas, Nevada, USA, 20–23 June, pp 94-100.

[Sha05] Shahbazian, E., Rogova, G., & Valin, P. (2005). Data fusion for situation monitoring, incident detection, alert and response management (Vol. 198). IOS Press.

[Sim00] Simard, P., Link, N. K., & Kruk, R. V. (2000, June). Evaluation of algorithms for fusing infrared and synthetic imagery. In AeroSense 2000 (pp. 127-138). International Society for Optics and Photonics.

[Sir16] Sireesha, V., and K. Sandhyarani. "Iris Recognition Using Combined Feature Vector." International Journal of Advanced Research in Computer Science 7.4 (2016).

[Spr82] Sprague Jr, R. H., & Carlson, E. D. (1982). Building effective decision support systems. Prentice Hall Professional Technical Reference.

[Sun06] Sun, Y.; Chen, F.; Shi, Y.D.; Chung, V. (2006). "A novel method for multi-sensory data fusion in multimodal human computer interaction". In Proceedings of the 20th conference of the computer-human interaction special interest group (CHISIG) of Australia on Computer-human interaction: design: activities, artefacts and environments, Sydney, Australia, pp. 401-404

[Tao14] C. Taouche, M. C. Batouche, M. Berkane and A. Taleb-Ahmed, "Multimodal biometric systems," 2014 International Conference on Multimedia Computing and Systems (ICMCS), Marrakech, 2014, pp. 301-308.

[Tur11] Turban, E., Sharda, R., & Delen, D. (2011). Decision support and business intelligence systems. Pearson Education India.

[Wu14] Wu, D., & Shao, L. (2014, November). Multimodal dynamic networks for gesture recognition. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 945-948). ACM.

[Zou05] Zou, X., & Bhanu, B. (2005, June). Tracking Humans using Multi-modal Fusion. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)-Workshops (pp. 4-4). IEEE.

Etapa 1 – Raport tehnico-ştiinţific în extenso