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  4. 172 — телекомунікації та радіотехніка, Електронні комунікації та радіотехніка
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dc.contributor.advisorХимич, Григорій Петрович-
dc.contributor.advisorKhymych, Hryhorij-
dc.contributor.authorЯцюк, Ірина Євгенівна-
dc.contributor.authorYatsiuk, Iryna-
dc.date.accessioned2021-12-19T22:57:31Z-
dc.date.available2021-12-19T22:57:31Z-
dc.date.issued2021-12-
dc.date.submitted2021-12-
dc.identifier.citationЯцюк І. Є. Дослідження smart системи керування рухом транспорту по вулиці Руській міста Тернополя : кваліфікаційна робота магістра за спеціальністю „172 — телекомунікації та радіотехніка“ / І. Є. Яцюк. — Тернопіль: ТНТУ, 2021 — 87 с.uk_UA
dc.identifier.urihttp://elartu.tntu.edu.ua/handle/lib/36669-
dc.description.abstractКваліфікаційну роботу було присвячено аналізу та дослідженню «вільного» руху автотранспорту (без заторів) в умовах наявної пропускної спроможності дороги з максимальною інтенсивністю проїзду авто (вул. Руська від моста через залізничні колії до перехрестя з вул. Танцорова) з метою впровадження smart технологій на основі встановлення спеціалізованих відеокамер, регульованих на основі штучного інтелекту світлофорів та встановлення систем моніторингу якості повітря відповідно до європейських стандартівuk_UA
dc.description.abstractThe thesis was devoted to the analysis and study of "free" traffic (without congestion) in terms of available capacity of the road with maximum traffic intensity (Ruska Street from the bridge over the railway tracks to the intersection with Tantsorov Street) to implement smart technologies based on the installation of specialized video cameras regulated based on artificial intelligence of traffic lights and installation of air quality monitoring systems by European standards.uk_UA
dc.description.tableofcontentsПерелік скорочень...8 Вступ... 10 РОЗДІЛ 1 АНАЛІТИЧНА ЧАСТИНА ...12 1.1. Огляд останніх досліджень, проблеми та перспективи розвитку ...12 1.2. Можливі підходи оптимізації TST...16 1.2.1. Підходи, засновані на штучному інтелекті ...16 1.2.2. Підходи на основі метаевристики ...17 1.2.3. Багатоцільові підходи...19 1.2.4. Підходи, засновані на дворівневому програмуванні...19 1.2.5. Різні підходи ...20 1.3. Протокол національних транспортних комунікацій для інтелектуальної транспортної системи...21 1.3.1. Зв’язок від центру до польових пристроїв ...21 1.3.2. Структура стандартів NTCIP ...22 1.4. Висновки по розділу ...25 РОЗДІЛ 2 ОСНОВНА ЧАСТИНА ...26 2.1. Модель мережевого потоку...26 2.1.1. Динаміка руху на ділянках доріг у результаті рівняння безперервності...26 2.1.2. Закон Кірхгофа для динаміки руху в вузлах...27 2.2. Передбачення транспортних потоків ...29 2.2.1. Процес обслуговування та час налаштування ...30 2.2.2. Зелений час, необхідний для очищення черги...33 2.2.3. Час очікування в черзі ...34 2.3. Звичайне та самоорганізоване світлофорне керування ...35 2.3.1. Класичний підхід для управління та його обмеження...35 2.3.2. Евристика реального часу на основі самоорганізованої стратегії визначення пріоритетів ...37 2.4. Стратегія оптимізації ...39 2.5. Стратегія стабілізації...44 2.6. Висновки по розділу 2...45 РОЗДІЛ 3 НАУКОВО-ДОСЛІДНИЦЬКА ЧАСТИНА ...46 3.1. Аналіз алгоритмів роботи системи. Створення власного алгоритму...46 3.2. Підбір комплектуючих 16 камер та відеореєстратора на основі типових технічних завдань ...52 3.3. Огляд програмного забезпечення ...58 3.4. Висновки по розділу 3...60 РОЗДІЛ 4 ОХОРОНА ПРАЦІ ТА БЕЗПЕКА В НАДЗВИЧАЙНИХ СИТУАЦІЯХ...61 4.1. Класифікація безпеки життєдіяльності ...61 4.2. Фактори що впливають на функціональний стан користувачів комп'ютерів ...65 4.3. Висновки по розділу 4...69 ВИСНОВКИ ...70 ПЕРЕЛІК ВИКОРИСТАНИХ ДЖЕРЕЛ ...71 ДОДАТКИ ...79uk_UA
dc.language.isoukuk_UA
dc.publisherТНТУ імені Івана Пулюя ФПТ, м. Тернопіль, Українаuk_UA
dc.subject172uk_UA
dc.subjectтелекомунікації та радіотехнікаuk_UA
dc.subjectоптимізаціяuk_UA
dc.subjectтранспортний рухuk_UA
dc.subjectрозумне містоuk_UA
dc.subjectsmart cityuk_UA
dc.subjectoptimizationuk_UA
dc.subjecttransportation systemuk_UA
dc.titleДослідження smart системи керування рухом транспорту по вулиці Руській міста Тернополяuk_UA
dc.title.alternativeResearch of smart traffic control system on Ruska Street in Ternopiluk_UA
dc.typeMaster Thesisuk_UA
dc.rights.holder© Яцюк Ірина Євгенівна, 2021uk_UA
dc.contributor.committeeMemberХвостівський, Микола Орестович-
dc.contributor.committeeMemberKhrostirskyy, Mykola-
dc.coverage.placenameТНТУ імені Івана Пулюя ФПТ, м. Тернопіль, Українаuk_UA
dc.subject.udc621.397.74uk_UA
dc.relation.references1. Litman, T. A. (2003). Transportation cost and benefit analysis: Techniques, estimates, and implications. Victoria: Victoria Transport Policy Institute.uk_UA
dc.relation.references2. Fyhri, A., & Marit, G. (2010). Science of the Total environment noise, sleep, and poor health: Modeling the relationship between road traf fi c noise and cardiovascular problems. Science of the Total Environment, 408, 4935–4942. https://doi.org/10.1016/j.scitotenv.2010.06.057.uk_UA
dc.relation.references3. Agarwal, S., & Swami, B. L. (2011). Road traffic noise, annoyance, and community health survey - a case study for an Indian city. Noice Heal, 13, 272–277. https://doi.org/10.4103/1463-1741.82959.uk_UA
dc.relation.references4. Howell, W. C., & Fu, M. C. (2006). Simulation optimization of traffic light signal timings via perturbation analysis doctoral dissertation, University of Maryland.uk_UA
dc.relation.references5. Zhao, D., Dai, Y., & Zhang, Z. (2012). Computational intelligence in urban traffic signal control: A survey. IEEE Transactions on Systems, Man, and CyberneticsPart C: Applications and Reviews, 42, 485–494. https://doi.org/10.1109/TSMCC.2011.2161577.uk_UA
dc.relation.references6. Tan, M. K., Chuo, H. S. E., Chin, R. K. Y., et al. (2017). Genetic algorithmbased signal optimizer for the oversaturated urban signalized intersection. In 2016 IEEE Int Conf Consum electron ICCE-Asia 2016 5–8. https://doi.org/10.1109/ICCEAsia.2016.7804762uk_UA
dc.relation.references7. Sabar, N. R., Kieu, L. M., Chung, E., et al. (2017). A memetic algorithm for real-world multi-intersection traffic signal optimisation problems. Engineering Applications of Artificial Intelligence, 63, 45–53. https://doi.org/10.1016/j.engappai.2017.04.021.uk_UA
dc.relation.references8. Akcelik, R. (1981). Traffic signals: Capacity and timing analysis, (vol. 123). Melbourne: Australian Road Research Board, ARR.uk_UA
dc.relation.references9. Koukol, M., I, L. Z., Marek, L., & I, P. T. (2015). Fuzzy logic in traffic engineering : A review on signal control. Mathematical Problems in Engineering, 2015, 1–14. https://doi.org/10.1155/2015/979160.uk_UA
dc.relation.references10. Araghi, S., Khosravi, A., & Creighton, D. (2015). A review on computational intelligence methods for controlling traffic signal timing. Expert Systems with Applications, 42, 1538–1550. https://doi.org/10.1016/j.eswa.2014.09.003.uk_UA
dc.relation.references11. Yu, Q., Liu, J. G., Liu, P. H., et al. (2009). Dynamic optimization project study between the traffic organization and the traffic signal control of urban traffic. 2009 WRI World Congress Computer Science Information Engineering CSIE 2009, 3, 182–186. https://doi.org/10.1109/CSIE.2009.63.uk_UA
dc.relation.references12. Ng, K. M., Reaz, M. B. I., Ali, M. A. M., & Chang, T. G. (2013). A brief survey on advances of control and intelligent systems methods for traffic-responsive control of urban networks. Teh Vjesn, 3, 555–562.uk_UA
dc.relation.references13. Papageorgiou, M., Diakaki, C., Dinopoulou, V., et al. (2003). Review of road traffic control strategies. Proceedings of the IEEE, 91, 2043–2067.uk_UA
dc.relation.references14. Ribeiro, I. M., & Simões, M. D. L. D. O. (2016). The fully actuated traffic control problem solved by global optimization and complementarity. Engineering Optimization, 48, 199–212. https://doi.org/10.1080/0305215X.2014.995644.uk_UA
dc.relation.references15. Webster, F. V. (1958). Traffic signal setting. Road Research Laboratory Technical Paper /UK/, 39, 1–44.uk_UA
dc.relation.references16. Miller, A. J. (1963). Settings for fixed-cycle traffic signals. The Journal of the Operational Research Society, 14, 373–386. https://doi.org/10.2307/3006800.uk_UA
dc.relation.references17. Küçükoğlu, İ., Dewil, R., & Cattrysse, D. (2019). Hybrid simulated annealing and tabu search method for the electric travelling salesman problem with time windows and mixed charging rates. Expert Systems with Applications, 134, 279–303. https://doi.org/10.1016/j.eswa.2019.05.037.uk_UA
dc.relation.references18. Ratrout, N. T., & Rahman, S. M. (2009). A comparative analysis of currently used microscopic and macroscopic traffic simulation software. Arabian Journal for Science and Engineering, 34, 121–133.uk_UA
dc.relation.references18. Ratrout, N. T., & Rahman, S. M. (2009). A comparative analysis of currently used microscopic and macroscopic traffic simulation software. Arabian Journal for Science and Engineering, 34, 121–133.uk_UA
dc.relation.references20. Deng, G. (2007). Simulation-based optimization doctoral dissertation, University of Wisconsin-Madison.uk_UA
dc.relation.references21. Carson, Y., & Maria, A. (1997). Simulation optimization: Methods and applications. In S. Andradóttir, K. J. Healy, D. H. Winters, & B. L. Nelson (Eds.), Proceedings of the 1997 winter simulation conference, (pp. 118–126).uk_UA
dc.relation.references22. Araghi, S., Khosravi, A., & Creighton, D. (2015). Intelligent cuckoo search optimized traffic signal controllers for multi-intersection network. Expert Systems with Applications, 42, 4422–4431. https://doi.org/10.1016/j.eswa.2015.01.063.uk_UA
dc.relation.references23. Jin, J., Ma, X., & Kosonen, I. (2017). An intelligent control system for traffic lights with simulation-based evaluation. Control Engineering Practice, 58, 24–33. https://doi.org/10.1016/j.conengprac.2016.09.009.uk_UA
dc.relation.references24. Araghi, S., Khosravi, A., Creighton, D., & Nahavandi, S. (2017). Influence of meta-heuristic optimization on the performance of adaptive interval type2-fuzzy traffic signal controllers. Expert Systems with Applications, 71, 493–503. https://doi.org/10.1016/j.eswa.2016.10.066.uk_UA
dc.relation.references25. Miletić, M., Kapusta, B., & Ivanjko, E. (2018). Comparison of two approaches for preemptive traffic light control. In Proc Elmar - Int Symp electron, (pp. 57–62). https://doi.org/10.23919/ELMAR.2018.8534608.uk_UA
dc.relation.references26. Vogel, A., Oremovi, I., Simi, R., & Ivanjko, E. (2018). Improving traffic light control by means of fuzzy logic. In In 2018 international symposium ELMAR, (pp. 16– 19).uk_UA
dc.relation.references27. Wei, H., Zheng, G., Yao, H., & Li, Z. (2018). Intellilight: A reinforcement learning approach for intelligent traffic light control. In In proceedings of the 24th ACM SIGKDD international conference on Knowledge Discovery & Data Mining, (pp. 2496– 2505).uk_UA
dc.relation.references28. Garg, D., Chli, M., & Vogiatzis, G. (2018). Deep reinforcement learning for autonomoustraffic light control. In 2018 3rd IEEE international conference on intelligent transportation engineering, ICITE 2018, (pp. 214–218). https://doi.org/10.1109/ICITE.2018.8492537.uk_UA
dc.relation.references29. Gökçe, M. A., Öner, E., & Işık, G. (2015). Traffic signal optimization with p swarm optimization for signalized roundabouts. Simulation, 91, 456–466. https://doi.org/10.1177/0037549715581473.uk_UA
dc.relation.references30. Dabiri, S., & Abbas, M. (2016). Arterial traffic signal optimization using p swarm optimization in an integrated VISSIM-MATLAB simulation environment. In IEEE Conf Intell Transp Syst proceedings, ITSC, (pp. 766–771). https://doi.org/10.1109/ITSC.2016.7795641.uk_UA
dc.relation.references31. Panovski, D., & Zaharia, T. (2016). Simulation-based vehicular traffic lights optimization. In In 2016 12th international conference on signal-image Technology & Internet-Based Systems, (pp. 258–265). https://doi.org/10.1109/SITIS.2016.49.uk_UA
dc.relation.references32. Elgarej, M., Khalifa, M., & Youssfi, M. (2016). Traffic lights optimization with distributed ant colony optimization based on multi-agent system. International Conference Networked System, 266–279. https://doi.org/10.1007/978-3-642-60749- 3_9.uk_UA
dc.relation.references33. Jintamuttha, K., Watanapa, B., & Charoenkitkarn, N. (2016). Dynamic traffic light timing optimization model using bat algorithm. In In 2016 2nd international conference on control science and systems engineering (ICCSSE), (pp. 181–185). https://doi.org/10.1109/CCSSE.2016.7784378.uk_UA
dc.relation.references34. Ahmed, E. K. E., Khalifa, A. M. A., & Kheiri, A. (2018). Evolutionary computation for static traffic light cycle optimisation. International Conference on Computer Control Electric Electron Engineering, 2018, 1–6.uk_UA
dc.relation.references35. Chuo, H. S. E., Tan, M. K., Chong, A. C. H., et al. (2017). Evolvable traffic signal control for intersection congestion alleviation with enhanced p swarm optimisation. Proc - 2017 IEEE. In 2nd Int Conf autom control Intell Syst I2CACIS 2017 2017-Decem, (pp. 92–97). https://doi.org/10.1109/I2CACIS.2017.8239039.uk_UA
dc.relation.references36. Nguyen, P. T. M., Passow, B. N., & Yang, Y. (2016). Improving anytime behavior for traffic signal control optimization based on NSGA-II and local search. Proceedings of International Joint Conference on Neural Networks, 4611–4618. https://doi.org/10.1109/IJCNN.2016.7727804.uk_UA
dc.relation.references37. Hatri, C. E. L., & Boumhidi, J. (2016). Q-learning based intelligent multiobjective p swarm optimization of light control for traffic urban congestion management. In In 2016 4th IEEE international colloquium on information science and technology (CiSt), (pp. 794–799). https://doi.org/10.1109/CIST.2016.7804996.uk_UA
dc.relation.references38. Zheng, L., Xu, C., Jin, P. J., & Ran, B. (2019). Network-wide signal timing stochastic simulation optimization with environmental concerns. Applied Soft Computing - Journal, 77, 678–687. https://doi.org/10.1016/j.asoc.2019.01.046.uk_UA
dc.relation.references39. Hajbabaie, A., & Benekohal, R. F. (2015). A program for simultaneous network signal timing optimization and traffic assignment. IEEE Transactions on Intelligent Transportation Systems, 16, 2573–2586. https://doi.org/10.1109/TITS.2015.2413360.uk_UA
dc.relation.references40. Li, Z., Shahidehpour, M., Bahramirad, S., & Khodaei, A. (2017). Optimizing traffic signal settings in smart cities. IEEE Transactions on Smart Grid, 8, 2382–2393. https://doi.org/10.1109/TSG.2016.2526032.uk_UA
dc.relation.references41. Chen, S., & Sun, D. J. (2016). An improved adaptive signal control method for isolated signalized intersection based on dynamic programming. IEEE Intelligent Transportation Systems Magazine, 8, 4–14.uk_UA
dc.relation.references42. Ahmed, F., & Hawas, Y. E. (2015). An integrated real-time traffic signal system for transit signal priority, incident detection and congestion management. Transport Research Part C Emerging Technology, 60, 52–76. https://doi.org/10.1016/j.trc.2015.08.004.uk_UA
dc.relation.references43. Dakic, I., Raton, B., & Raton, B. (2015). Backpressure traffic control algorithms in field-like signal operations. In In 2015 IEEE 18th international conference on intelligent transportation systems, (pp. 137–142). https://doi.org/10.1109/ITSC.2015.31.uk_UA
dc.relation.references44. Pavleski, D., Koltovska-Nechoska, D., & Ivanjko, E. (2017). Evaluation of adaptive traffic control system UTOPIA using microscopic simulation. Proc Elmar - International Symposium of Electron, 17–20. https://doi.org/10.23919/ELMAR.2017.8124425.uk_UA
dc.relation.references45. Chen, X., Osorio, C., & Santos, B. F. (2017). Simulation-based travel time reliable signal control. Transportation Science, 1–22. https://doi.org/10.1287/trsc.2017.0812.uk_UA
dc.relation.references46. Baldi, S., Michailidis, I., Ntampasi, V., et al. (2019). A simulation-based traffic signal control for congested urban traffic networks. Transportation Science, 53, 6–20. https://doi.org/10.1287/trsc.2017.0754.uk_UA
dc.relation.references47. Shah, S., Mohiuddin, S., Gokce, M. A., et al. (2019). Analysis of various scenarios to mitigate congestion at a signalized roundabout using microsimulation. In 2019 innovations in intelligent systems and applications conference (ASYU), (pp. 1–6). https://doi.org/10.1109/ASYU48272.2019.8946339.uk_UA
dc.relation.references48. Zheng, L., Xue, X., Xu, C., & Ran, B. (2019). A stochastic simulation-based optimization method for equitable and efficient network-wide signal timing under uncertainties. Transport Research Part B Methodology, 122, 287–308. https://doi.org/10.1016/j.trb.2019.03.001.uk_UA
dc.relation.references49. Venayagamoorthy, G. K. (2009). A successful interdisciplinary course on computational intelligence. EEE Computer Intelligence Magnet, 4, 14–23. https://doi.org/10.1109/MCI.2008.930983.uk_UA
dc.relation.references50. Parpinelli, R. S., & Lopes, H. S. (2011). New inspirations in swarm intelligence: A survey. International Journal of Bio-Inspired Computer, 3, 1. https://doi.org/10.1504/IJBIC.2011.038700.uk_UA
dc.relation.references51. Eiben, A. E., & Smith, J. E. (2012). Introduction to evolutionary computing genetic algorithms, (2nd ed., ). Berlin: Springer Netherlands.uk_UA
dc.relation.references52. Montana, D. J., & Czerwinski, S. (1996). Evolving control laws for a network of traffic signals. In Proceedings of the 1st annual conference on genetic programming, (pp 333–338). ISBN:0-262- 61127-9.uk_UA
dc.relation.references53. Montana, D. J. (1995). Strongly typed genetic programming. Evolutionary Computation, 3, 199–230.uk_UA
dc.relation.references54. Xiang, J., & Chen, Z. (2015). Adaptive traffic signal control of bottleneck subzone based on grey qualitative reinforcement learning algorithm. In international 77 conference on pattern recognition applications and methods (ICPRAM), (pp. 295–301). https://doi.org/10.5220/0005269302950301.uk_UA
dc.relation.references55. Benhamza, K., & Seridi, H. (2015). Adaptive traffic signal control in multiple intersections network. Journal of Intelligent Fuzzy Systems, 28, 2557–2567. https://doi.org/10.3233/IFS-151535.uk_UA
dc.relation.references56. Vidhate, D. A., & Kulkarni, P. (2017). Cooperative multi-agent reinforcement learning models (CMRLM) for intelligent traffic control. In Proc - 1st Int Conf Intell Syst Inf Manag ICISIM 2017 2017-Janua, (pp. 325–331). https://doi.org/10.1109/ICISIM.2017.8122193.uk_UA
dc.relation.references57. Genders, W., & Razavi, S. (2018). Evaluating reinforcement learning state representations for adaptive traffic signal control. Procedia Computer Science, 130, 26– 33. https://doi.org/10.1016/j.procs.2018.04.008.uk_UA
dc.relation.references58. Liang, X., Du, X., Member, S., & Wang, G. (2019). A deep reinforcement learning network for traffic light cycle control. IEEE Transactions on Vehicular Technology, 68, 1243–1253. https://doi.org/10.1109/TVT.2018.2890726.uk_UA
dc.relation.references59. Bernas, M., & Płaczek, B. (2019). A neuroevolutionary approach to controlling traffic signals based on data from sensor network. Sensors, 19, 1–24. https://doi.org/10.3390/s19081776.uk_UA
dc.relation.references60. Abdelgawad, H., Abdulhai, B., El-tantawy, S., et al. (2015). Assessment of self-learning adaptive traffic signal control on congested urban areas : Independent versus coordinated perspectives. Canadian Journal of Civil Engineering, 42, 353–366. https://doi.org/10.1139/cjce-2014-0503.uk_UA
dc.relation.references61. Ozan, C., Baskan, O., Haldenbilen, S., & Ceylan, H. (2015). A modified reinforcement learning algorithm for solving coordinated signalized networks. Transport Research Part C Emerging Technology, 54, 40–55. https://doi.org/10.1016/j.trc.2015.03.010.uk_UA
dc.relation.references62. Li, Z., & Schonfeld, P. (2015). Hybrid simulated annealing and genetic algorithm for optimizing arterial signal timings under oversaturated traffic conditions. Journal of Advanced Transportation, 49, 153–170. https://doi.org/10.1002/atr.1274.uk_UA
dc.relation.references63. Gao, K., Zhang, Y., Sadollah, A., & Su, R. (2016). Optimizing urban traffic light scheduling problem using harmony search with ensemble of local search. Applied Soft Computing - Journal, 48, 359–372. https://doi.org/10.1016/j.asoc.2016.07.029.uk_UA
dc.relation.references64. Bie, Y., Cheng, S., & Liu, Z. (2017). Optimization of signal-timing parameters for the intersection with hook turns. Transport, 32, 233–241. https://doi.org/10.3846/16484142.2017.1285813.uk_UA
dc.relation.references65. Tan, M. K., Chuo, H. S. E., Chin, R. K. Y., et al. (2017). Optimization of traffic network signal timing using decentralized genetic algorithm. In 2017 IEEE 2nd international conference on automatic control and intelligent systems, I2CACIS 2017, (pp. 62–67).uk_UA
dc.relation.references66. Jovanović, A., & Teodorović, D. (2017). Pre-timed control for an undersaturated and over-saturated isolated intersection: A bee Colony optimization approach. Transportation Planning and Technology, 40, 556–576. https://doi.org/10.1080/03081060.2017.1314498.uk_UA
dc.relation.references67. Tarek, Z., Al-rahmawy, M., & Tolba, A. (2018). Fog computing for optimized traffic control strategy. Journal of Intelligent Fuzzy Systems. https://doi.org/10.3233/JIFS-18077.uk_UA
dc.relation.references68. Manandhar, B., & Joshi, B. (2018). Adaptive traffic light control with statistical multiplexing technique and p swarm optimization in smart cities. Proceedings on 2018 IEEE 3rd International Conference Comput Communication Security ICCCS, 2018, 210–217. https://doi.org/10.1109/CCCS.2018.8586845.uk_UA
dc.relation.references69. Eduardo, P., De Almeida, M., Chung, E., et al. (2017). Active control for traffic lights in regions and corridors : An approach based on evolutionary computation based on evolutionary computation. Transport Research Procedia, 25, 1769–1780. https://doi.org/10.1016/j.trpro.2017.05.140.uk_UA
dc.contributor.affiliationТернопільський національний технічний університет імені Івана Пулюя ФПТ, м. Тернопіль, Українаuk_UA
dc.coverage.countryUAuk_UA
Koleksiyonlarda Görünür:172 — телекомунікації та радіотехніка, Електронні комунікації та радіотехніка

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Kısa Öğe Kaydını Göster İstatistikler


DSpace'deki bütün öğeler, aksi belirtilmedikçe, tüm hakları saklı tutulmak şartıyla telif hakkı ile korunmaktadır.

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