المؤلفون: Takahiro Kinoshita, Hideyuki Kanuka

المصدر: IEEE Access, Vol 12, Pp 55697-55710 (2024)

مصطلحات موضوعية: Microservice, decomposition, multi-object optimization, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

وصف الملف: electronic resource

Relation: https://ieeexplore.ieee.org/document/10501773/; https://doaj.org/toc/2169-3536

URL الوصول: https://doaj.org/article/c7f9ded87d064a72bc93ded561dfd595

المؤلفون: Jian Wang, Bin Liu, Danyu Li, Xiao Ma, Bin Zhao, Zhi Yang

المصدر: Energy Reports, Vol 9, Iss , Pp 46-57 (2023)

مصطلحات موضوعية: Power cable, Ductbank, Optimal placement, Multi-object optimization, Ampacity, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

وصف الملف: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2352484722022983; https://doaj.org/toc/2352-4847

URL الوصول: https://doaj.org/article/9c7beed2549545888e2dfb8c1bf95e38

المؤلفون: Yan Wang, Pei Hu

المصدر: IEEE Access, Vol 11, Pp 120258-120269 (2023)

مصطلحات موضوعية: Deep reinforcement learning, multi-object optimization, adaptive route planning, recommendation systems, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

وصف الملف: electronic resource

Relation: https://ieeexplore.ieee.org/document/10292867/; https://doaj.org/toc/2169-3536

URL الوصول: https://doaj.org/article/ea6c1b7d416a4e949a2494bafa602a04

المؤلفون: Maosheng Zheng, Yi Wang, Haipeng Teng

المصدر: Tehnički Glasnik, Vol 16, Iss 4, Pp 454-457 (2022)

مصطلحات موضوعية: favorable probability, "intersection" method, hybrid, multi-object optimization, response surface methodology, Technology

وصف الملف: electronic resource

Relation: https://hrcak.srce.hr/file/410693; https://doaj.org/toc/1846-6168; https://doaj.org/toc/1848-5588

URL الوصول: https://doaj.org/article/cff7d3d98a1948cb99555159ce43bcb6

المؤلفون: Xu Zhu, Chao Ni, Guilin Chen, Jiang Guo

المصدر: Sensors; Volume 23; Issue 12; Pages: 5616

مصطلحات موضوعية: tungsten heavy alloys, multi-object optimization, dung beetle algorithm, response surface method, multi-sensor

وصف الملف: application/pdf

Relation: Physical Sensors; https://dx.doi.org/10.3390/s23125616

الاتاحة: https://doi.org/10.3390/s23125616

المؤلفون: Jiayi Wen, Shaoman Liu, Yejin Lin

المصدر: Sensors; Volume 22; Issue 18; Pages: 6942

مصطلحات موضوعية: USV, trajectory design, policy gradient, multi-agent deep reinforcement learning, multi-object optimization

وصف الملف: application/pdf

Relation: Intelligent Sensors; https://dx.doi.org/10.3390/s22186942

الاتاحة: https://doi.org/10.3390/s22186942

المؤلفون: Minho Park, Jisun Kim, Changmin Pyo, Joonsik Son, Jaewoong Kim

المصدر: Materials; Volume 14; Issue 7; Pages: 1659

مصطلحات موضوعية: flux-cored arc welding, solidification crack susceptibility, 9% nickel steel (ASTM A553-1), multi object optimization, discriminant analysis

وصف الملف: application/pdf

Relation: https://dx.doi.org/10.3390/ma14071659

الاتاحة: https://doi.org/10.3390/ma14071659

المؤلفون: Zheng, Maosheng, Wang, Yi, Teng, Haipeng

المصدر: Tehnički glasnik
Volume 16
Issue 4

مصطلحات موضوعية: favorable probability, "intersection" method, hybrid, multi-object optimization, response surface methodology

وصف الملف: application/pdf

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f09cd0159a26fdf73ff96c3524c27fdc
https://doi.org/10.31803/tg-20210930051227

المؤلفون: Mukesh Kumar Gupta*1, Samar Wazir2, Md. TabrezNafis3

المصدر: INTERNATIONAL JOURNAL OF RESEARCH SCIENCE & MANAGEMENT, 5(4), 63-72, (2018-04-29)

مصطلحات موضوعية: Swarm optimization, multi-object optimization, cloud computing, genetic algorithms. General Terms Swarm intelligence, nature-inspired algorithm

Relation: https://doi.org/10.5281/zenodo.1236838; https://doi.org/10.5281/zenodo.1236839; oai:zenodo.org:1236839

الاتاحة: https://doi.org/10.5281/zenodo.1236839

المؤلفون: Zhanhong Zhou, Ming Cai

المصدر: Journal of Traffic and Transportation Engineering (English ed. Online), Vol 1, Iss 2, Pp 153-158 (2014)

مصطلحات موضوعية: intersection traffic signal control, multi-object optimization, genetic algorithm, microscopic traffic simulation, CMEM, Transportation engineering, TA1001-1280

وصف الملف: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2095756415301008; https://doaj.org/toc/2095-7564

URL الوصول: https://doaj.org/article/7055a7c0bca64dc584f2dcfb3efce778

المؤلفون: Zarri, Liam Joseph

مصطلحات موضوعية: Ecology, Conservation biology, Endangered Species Act, environmental flows, Green sturgeon, multi-object optimization, multi-species management, Winter-run chinook

وصف الملف: application/pdf

URL الوصول: https://escholarship.org/uc/item/6z00s7r7

المؤلفون: G. Berrettoni, C. Burelly, D. Capriglione, G. Betta, L. Ferrigno, G. Ficco, M. Laracca, G. Miele

المساهمون: Berrettoni, G., Burelly, C., Capriglione, D., Betta, G., Ferrigno, L., Ficco, G., Laracca, M., Miele, G.

مصطلحات موضوعية: NILM, uncertainty, energy measurement, multi object optimization

Relation: ispartofbook:Atti del V Forum Nazionale delle Misure; V Forum Nazionale delle Misure; firstpage:275; lastpage:276; numberofpages:2; http://hdl.handle.net/11573/1575453

الاتاحة: http://hdl.handle.net/11573/1575453

المؤلفون: Nitola Chaparro, Lizeth Alejandra

المساهمون: Rivera Rodríguez, Sergio Raúl, Grupo de Investigación EMC-UN

المصدر: Repositorio UN
Universidad Nacional de Colombia
instacron:Universidad Nacional de Colombia

مصطلحات موضوعية: Optimal pareto, Energías renovables, Microgrid, Renewable energies, Congestión, Pareto óptimo, Cost of operation, 118 - Fuerza y energía [110 - Metafísica], MOPSO, Costo de operación, Microred, Multi-object optimization, Congestion, Optimización multiobjetivo

وصف الملف: 1 recurso en linea (121 paginas); application/pdf

URL الوصول: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::996b0284fb75f1fcd4a309a6aa19f9d6
https://repositorio.unal.edu.co/handle/unal/79615

المؤلفون: Berrettoni, G., Burelly, C., Capriglione, D., Betta, G., Ferrigno, L., Ficco, G., Laracca, M., Miele, G.

مصطلحات موضوعية: NILM, energy measurement, multi object optimization, uncertainty

URL الوصول: https://explore.openaire.eu/search/publication?articleId=od______3686::46418e4336c14867d423b4f432500956
http://hdl.handle.net/11573/1575453

المؤلفون: Nitola Chaparro, Lizeth Alejandra

المساهمون: Rivera Rodríguez, Sergio Raúl, Grupo de Investigación EMC-UN

مصطلحات موضوعية: 110 - Metafísica::118 - Fuerza y energía, Congestión, Costo de operación, Optimización multiobjetivo, MOPSO, Pareto óptimo, Microred, Energías renovables, Congestion, Cost of operation, Multi-object optimization, Optimal pareto, Microgrid, Renewable energies

وصف الملف: 1 recurso en linea (121 paginas); application/pdf

Relation: [1] S. Huang, Q. Wu, Z. Liu, and A. H. Nielsen, “Review of congestion management methods for distribution networks with high penetration of distributed energy resources,” in IEEE PES Innovative Smart Grid Technologies Conference Europe, Jan. 2015, vol. 2015-Janua, no. January, doi:10.1109/ISGTEurope.2014.7028811.; [2] A. Abdolahi, J. Salehi, F. Samadi Gazijahani, and A. Safari, “Probabilistic multi-objective arbitrage of dispersed energy storage systems for optimal congestion management of active distribution networks including solar/wind/CHP hybrid energy system,” J. Renew. Sustain. Energy, vol. 10, no. 4, Jul. 2018, doi:10.1063/1.5035081.; [3] K. Spiliotis, S. Claeys, A. Ramos Gutierrez, and J. Driesen, “Utilizing Local Energy Storage for Congestion Management and Investment Deferral in Distribution Networks.”; [4] J. M. L. Lezama, “PROPUESTAS ALTERNATIVAS PARA MANEJO DE CONGESTIÓN EN EL MERCADO DE ENERGÍA ELÉCTRICA COLOMBIANO,” 2006.; [5] S. Gope, S. Dawn, R. Mitra, A. K. Goswami, and P. K. Tiwari, “Transmission congestion relief with integration of photovoltaic power using lion optimization algorithm,” in Advances in Intelligent Systems and Computing, 2019, vol. 816, pp. 327–338, doi:10.1007/978-981-13-1592-3_25.; [6] E. Reihani, P. Siano, and M. Genova, “A new method for peer-to-peer energy exchange in distribution grids,” Energies, vol. 13, no. 4, 2020, doi:10.3390/en13040799.; [7] K. Vijayakumar, “Multiobjective optimization methods for congestion management in deregulated power systems,” J. Electr. Comput. Eng., 2012, doi:10.1155/2012/962402.; [8] J. M. L. LEZAMA, “UNIVERSIDAD NACIONAL DE COLOMBIA SEDE MANIZALES DEPARTAMENTO DE INGENIERÍA ELÉCTRICA, ELECTRÓNICA Y COMPUTACIÓN,” Manizalez, 2006.; [9] Institute of Electrical and Electronics Engineers, IEEE Dielectrics and Electrical Insulation Society. Kolkata Chapter, IEEE Power & Energy Society. Kolkata Chapter, Institute of Electrical and Electronics Engineers. Kolkata section, and North Eastern Regional Institute of Science and Technology, 2012 1st International Conference on Power and Energy in NERIST (lCPEN) : proceedings : [held at new seminar hall of North Eastern Regional Institute of Science and Technology] : Nirjuli, Arunachal Pradesh, India : 28-29 December 2012. .; [10] J. Hazra and A. K. Sinha, “Congestion management using multiobjective particle swarm optimization,” IEEE Trans. Power Syst., vol. 22, no. 4, pp. 1726–1734, Nov. 2007, doi:10.1109/TPWRS.2007.907532.; [11] I. Kalogeropoulos and H. Sarimveis, “Predictive control algorithms for congestion management in electric power distribution grids,” Appl. Math. Model., vol. 77, pp. 635–651, Jan. 2020, doi:10.1016/j.apm.2019.07.034.; [12] W. Liu, Q. Wu, F. Wen, and J. Ostergaard, “Day-ahead congestion management in distribution systems through household demand response and distribution congestion prices,” IEEE Trans. Smart Grid, vol. 5, no. 6, pp. 2739–2747, 2014, doi:10.1109/TSG.2014.2336093.; [13] M. Kashyap and S. Kansal, “Hybrid approach for congestion management using optimal placement of distributed generator,” Int. J. Ambient Energy, vol. 39, no. 2, pp. 132–142, 2018, doi:10.1080/01430750.2016.1269676.; [14] J. Li and F. Li, “A Congestion Index considering the Characteristics of Generators & Networks.”; [15] P. Biswas and B. B. Pal, “A fuzzy goal programming method to solve congestion management problem using genetic algorithm,” Decis. Mak. Appl. Manag. Eng., vol. 2, no. 2, Oct. 2019, doi:10.31181/dmame1902040b.; [16] S. Patil and N. Asati, “CONGESTION MANAGEMENT USING GENETIC ALGORITHM,” 2019. [Online]. Available: www.irjeas.org,.; [17] H. Khani, M. R. D. Zadeh, and A. H. Hajimiragha, “Transmission Congestion Relief Using Privately Owned Large-Scale Energy Storage Systems in a Competitive Electricity Market,” IEEE Trans. Power Syst., vol. 31, no. 2, pp. 1449–1458, Mar. 2016, doi:10.1109/TPWRS.2015.2414937.; [18] K. Furusawa, H. Sugihara, K. Tsuji, and Y. Mitani, “A Study on Power Flow Congestion Relief by using Customer-side Energy Storage System,” IEEJ Trans. Power Energy, vol. 125, no. 3, pp. 293–301, 2005, doi:10.1541/ieejpes.125.293.; [19] F. D’Agostino, S. Massucco, P. Pongiglione, M. Saviozzi, and F. Silvestro, “Optimal der regulation and storage allocation in distribution networks: Volt/Var optimization and congestion relief,” in 2019 IEEE Milan PowerTech, PowerTech 2019, 2019, doi:10.1109/PTC.2019.8810422.; [20] J. Hazra, M. Padmanaban, F. Zaini, and L. C. De Silva, “Congestion relief using grid scale batteries,” in 2015 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2015, Jun. 2015, doi:10.1109/ISGT.2015.7131789.; [21] A. N. M. M. Haque, P. H. Nguyen, W. L. Kling, and F. W. Bliek, “Congestion management in smart distribution network,” in Proceedings of the Universities Power Engineering Conference, Oct. 2014, doi:10.1109/UPEC.2014.6934751.; [22] R. T. Elliott et al., “Sharing Energy Storage Between Transmission and Distribution,” IEEE Trans. Power Syst., vol. 34, no. 1, pp. 152–162, Jan. 2019, doi:10.1109/TPWRS.2018.2866420.; [23] G. Koeppel, M. Geidl, G. Andersson, G. Koeppel, M. Geidl, and G. Andersson, “‘Value of Storage Devices in Congestion Constrained Distribution Networks’ Value of Storage Devices in Congestion Constrained Distribution Networks,” 2004.; [24] B. K. Sarkar, A. De, and A. Chakrabarti, “Impact of Distributed Generation for congestion relief in power networks,” in 2012 1st International Conference on Power and Energy in NERIST, ICPEN 2012 - Proceedings, 2012, doi:10.1109/ICPEN.2012.6492324.; [25] K. Zhang, S. Troitzsch, S. Hanif, and T. Hamacher, “Coordinated Market Design for Peer-to-Peer Energy Trade and Ancillary Services in Distribution Grids Control-oriented Building Model (CoBMo) View project Platform for Interconnected Micro-grid Operation (PRIMO) View project Coordinated Market Design for Peer-to-Peer Energy Trade and Ancillary Services in Distribution Grids.” [Online]. Available: https://www.researchgate.net/publication/338501038.; [26] J. Hu, G. Yang, C. Ziras, and K. Kok, “Aggregator Operation in the Balancing Market Through Network-Constrained Transactive Energy,” IEEE Trans. Power Syst., vol. 34, no. 5, pp. 4071–4080, Sep. 2019, doi:10.1109/TPWRS.2018.2874255.; [27] J. Zhao, Y. Wang, G. Song, P. Li, C. Wang, and J. Wu, “Congestion Management Method of Low-Voltage Active Distribution Networks Based on Distribution Locational Marginal Price,” IEEE Access, vol. 7, pp. 32240–32255, 2019, doi:10.1109/ACCESS.2019.2903210.; [28] Carlos Eduardo Barón Moreno, “TESIS Programación de la operación horaria de una microred minimizando el costo de operación usando el algoritmo heurístico DEEPSO (1),” Nacional de Colombia, 2019.; [29] J. Arévalo, F. Santos, and S. Rivera, “Application of Analytical Uncertainty Costs of Solar, Wind and Electric Vehicles in Optimal Power Dispatch,” Ingeniería, vol. 22, no. 3, pp. 324–346, 2017, doi:10.14483/23448393.11673.; [30] R. S. Wibowo, F. F. Utama, D. F. U. Putra, and N. K. Aryani, “Unit commitment with non-smooth generation cost function using binary particle swarm optimization,” in Proceeding - 2016 International Seminar on Intelligent Technology and Its Application, ISITIA 2016: Recent Trends in Intelligent Computational Technologies for Sustainable Energy, Jan. 2017, pp. 571–576, doi:10.1109/ISITIA.2016.7828723.; [31] Q. Zhang, Z. Ren, R. Ma, M. Tang, and Z. He, “Research on double-layer optimized configuration of multi-energy storage in regional integrated energy system with connected distributed wind power,” Energies, vol. 12, no. 20, Oct. 2019, doi:10.3390/en12203964.; [32] C. Baron and S. Rivera, “Mono-objective minimization of operation cost for a microgrid with renewable power generation, energy storage and electric vehicles,” Rev. Int. Métodos Numéricos para Cálculo y Diseño en Ing., vol. 35, no. 3, Jul. 2019, doi:10.23967/j.rimni.2019.06.005.; [33] J. C. Arevalo, F. Santos, and S. Rivera, “Uncertainty cost functions for solar photovoltaic generation, wind energy generation, and plug-in electric vehicles: Mathematical expected value and verification by Monte Carlo simulation,” Int. J. Power Energy Convers., vol. 10, no. 2, pp. 171–207, 2019, doi:10.1504/IJPEC.2019.098621.; [34] Z. Xu, Z. Hu, Y. Song, W. Zhao, and Y. Zhang, “Coordination of PEVs charging across multiple aggregators,” Appl. Energy, vol. 136, pp. 582–589, Dec. 2014, doi:10.1016/j.apenergy.2014.08.116.; [35] A. Serpi and M. Porru, “Modelling and Design of Real-Time Energy Management Systems for Fuel Cell/Battery Electric Vehicles,” Energies, vol. 12, no. 22, p. 4260, Nov. 2019, doi:10.3390/en12224260.; [36] A. Hussain, V. H. Bui, J. W. Baek, and H. M. Kim, “Stationary energy storage system for fast EV charging stations: Simultaneous sizing of battery and converter,” Energies, vol. 12, no. 23, 2019, doi:10.3390/en12234516.; [37] R. Dufo-López and J. L. Bernal-Agustín, “Multi-objective design of PV-wind-diesel-hydrogen-battery systems,” Renew. Energy, vol. 33, no. 12, pp. 2559–2572, Dec. 2008, doi:10.1016/j.renene.2008.02.027.; [38] F. Berglund, S. Zaferanlouei, M. Korpås, and K. Uhlen, “Optimal Operation of Battery Storage for a Subscribed Capacity-Based Power Tariff Prosumer—A Norwegian Case Study,” Energies, vol. 12, no. 23, p. 4450, Nov. 2019, doi:10.3390/en12234450.; [39] C. Jankowiak, A. Zacharopoulos, C. Brandoni, P. Keatley, P. MacArtain, and N. Hewitt, “The Role of Domestic Integrated Battery Energy Storage Systems for Electricity Network Performance Enhancement,” Energies, vol. 12, no. 20, p. 3954, Oct. 2019, doi:10.3390/en12203954.; [40] B. Zhao, X. Zhang, J. Chen, C. Wang, and L. Guo, “Operation optimization of standalone microgrids considering lifetime characteristics of battery energy storage system,” IEEE Trans. Sustain. Energy, vol. 4, no. 4, pp. 934–943, 2013, doi:10.1109/TSTE.2013.2248400.; [41] T. Sikorski et al., “A case study on distributed energy resources and energy-storage systems in a virtual power plant concept: Economic aspects,” Energies, vol. 12, no. 23, 2019, doi:10.3390/en12234447.; [42] C. A. C. Coello, G. B. Lamont, and D. A. Van Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems. 2007.; [43] and E. M. T. O. R. A. Gallego Rendon, A. H. Escobar Zuluaga, Tecnicas metaheuristicas de optimizacion, Segunda. Pereira, 2008.; [44] M. Vélez Gallego and J. Montoya, “Metaheurísticos: Una alternativa para la solución de problemas combinatorios en administración de operaciones,” Metaheurísticos Una Altern. para la solución Probl. Comb. en Adm. operaciones, vol. 4, no. 8, pp. 99–115, 2007, doi:10.24050/reia.v4i8.188.; [45] K. A. Dowsland and B. A. Díaz, “Diseño de heurística y fundamentos del recocido simulado,” Intel. Artif. Rev. Iberoam. Intel. Artif., vol. 7, no. 19, p. 0, 2003.; [46] “Optimización por colonia de hormigas: aplicaciones y tendencias,” Ing. Solidar., vol. 6, no. 10, pp. 83–89, 2011.; [47] A. Jonathan, “ALGORITMO CULTURAL Y DE NUBES DE PARTICULAS MULTI-OBJETIVO PARA EVITAR,” 2017, [Online]. Available: http://oa.upm.es/47845/1/TFM_JONATHAN_AGUIRRE_SAMBONI.pdf.; [48] C. Adrián Correa Flórez, R. ANDRÉS BOLAÑOS Ingeniero Electricista Analista Programación Operación, A. MOLINA CABRERA Ingeniero Electricista, and P. Auxiliar, “Septiembre de 2008,” Sci. Tech. Año XIV, vol. 39.; [49] metode penelitian Nursalam, 2016 and A. . Fallis, “ESTUDIO COMPARATIVO DE TÉCNICAS DE OPTIMIZACIÓN MULTIOBJETIVO PARA DETERMINAR LA MÁS ADECUADA EN PROBLEMAS MULTI-CRITERIO,” J. Chem. Inf. Model., vol. 53, no. 9, pp. 1689–1699, 2013.; [50] C. A. Coello Coello and M. S. Lechuga, “MOPSO: A proposal for multiple objective particle swarm optimization,” Proc. 2002 Congr. Evol. Comput. CEC 2002, vol. 2, pp. 1051–1056, 2002, doi:10.1109/CEC.2002.1004388.; [51] J. Yang, J. Zhou, L. Liu, and Y. Li, “A novel strategy of pareto-optimal solution searching in multi-objective particle swarm optimization (MOPSO),” Comput. Math. with Appl., vol. 57, no. 11–12, pp. 1995–2000, Jun. 2009, doi:10.1016/j.camwa.2008.10.009.; [52] H. M. Khodr, F. G. Olsina, P. M. D. O. De Jesus, and J. M. Yusta, “Maximum savings approach for location and sizing of capacitors in distribution systems,” Electr. Power Syst. Res., vol. 78, no. 7, pp. 1192–1203, 2008, doi:10.1016/j.epsr.2007.10.002.; [53] S. Bhullar and S. Ghosh, “Optimal integration of multi distributed generation sources in radial distribution networks using a hybrid algorithm,” Energies, vol. 11, no. 3, 2018, doi:10.3390/en11030628.; [54] A. Chaouachi, R. M. Kamel, R. Andoulsi, and K. Nagasaka, “Multiobjective Intelligent Energy Management for a Microgrid _ Aymen Chaouachi - Academia,” IEEE Trans. Ind. Electron., vol. 60, no. 4, pp. 1688–1699, 2013, doi:10.1109/TIE.2012.2188873.; [55] U.S. Energy Information Administration (EIA), “Annual Energy Outlook 2013 with projections to 2040.” Washington, DC, p. 244, 2013.; https://repositorio.unal.edu.co/handle/unal/79615; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/

الاتاحة: https://repositorio.unal.edu.co/handle/unal/79615
https://repositorio.unal.edu.co/

المؤلفون: Zarri, Liam J., Danner, Eric M., Daniels, Miles E., Palkovacs, Eric P.

مصطلحات موضوعية: green sturgeon, Oncorhynchus tshawytscha, Acipenser medirostris, multi-object optimization, Paris Agreement, winter-run Chinook, Endangered Species Act, designer flows, multi-species management, hydropower proliferation, envir, geo

Time: 2012-2039

Relation: https://zenodo.org/record/5003176

الاتاحة: https://zenodo.org/record/5003176

المؤلفون: Mukesh Kumar Gupta*1, Samar Wazir2 & Md. TabrezNafis3

مصطلحات موضوعية: Swarm optimization, multi-object optimization, cloud computing, genetic algorithms. General Terms Swarm intelligence, nature-inspired algorithm

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::984bdf80781ea357ba9777951a56548c

المؤلفون: Kyosuke HIYAMA, Shinsuke KATO, Yunting DIAO, 刁 芸婷, 加藤 信介, 樋山 恭助

المصدر: 日本建築学会技術報告集 / AIJ Journal of Technology and Design. 2010, 16(34):1065

المؤلفون: Zarri, Liam, Danner, Eric, Daniels, Miles, Palkovacs, Eric

مصطلحات موضوعية: Endangered Species Act, designer flows, green sturgeon, multi-species management, multi-object optimization, winter-run Chinook, Paris Agreement, hydropower proliferation

جغرافية الموضوع: Sacramento River, California

Time: Sacramento River, 2012-2039

Relation: http://hdl.handle.net/10255/dryad.221487

الاتاحة: http://hdl.handle.net/10255/dryad.221487
https://doi.org/10.5061/dryad.898ks73/1

المؤلفون: Ming Cai, Zhan-hong Zhou

المصدر: Journal of Traffic and Transportation Engineering (English ed. Online), Vol 1, Iss 2, Pp 153-158 (2014)

مصطلحات موضوعية: Engineering, business.industry, lcsh:TA1001-1280, Traffic simulation, Transportation, ComputerApplications_COMPUTERSINOTHERSYSTEMS, CMEM, Signal, Multi-objective optimization, Automotive engineering, Modal, Software, Intersection, Genetic algorithm, Fuel efficiency, genetic algorithm, microscopic traffic simulation, lcsh:Transportation engineering, intersection traffic signal control, business, multi-object optimization, Simulation, Civil and Structural Engineering

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::69dc0a522dd40ce6df19a8c1c2dec803