Gotowa bibliografia na temat „Experience-Driven Optimization”
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Artykuły w czasopismach na temat "Experience-Driven Optimization"
Cheng, Yihua, Xu Zhang i Junchen Jiang. "Enabling Perception-Driven Optimization in Networking". ACM SIGMETRICS Performance Evaluation Review 51, nr 2 (28.09.2023): 103–5. http://dx.doi.org/10.1145/3626570.3626608.
Pełny tekst źródłaP, Dr Periasamy, i Dr Dinesh N. "Data Driven Marketing Strategic Trends in 2022". International Journal of Science, Engineering and Management 9, nr 6 (13.06.2022): 24–27. http://dx.doi.org/10.36647/ijsem/09.06.a005.
Pełny tekst źródłaShailesh, K. S., i P. V. Suresh. "Performance Driven Development Framework for Web Applications". Global Journal of Enterprise Information System 9, nr 1 (5.05.2017): 75. http://dx.doi.org/10.18311/gjeis/2017/15870.
Pełny tekst źródłaSONG, WEI, DIAN TJONDRONEGORO i MICHAEL DOCHERTY. "EXPLORATION AND OPTIMIZATION OF USER EXPERIENCE IN VIEWING VIDEOS ON A MOBILE PHONE". International Journal of Software Engineering and Knowledge Engineering 20, nr 08 (grudzień 2010): 1045–75. http://dx.doi.org/10.1142/s0218194010005067.
Pełny tekst źródłaWu, Xiaojing, Zijun Zuo i Long Ma. "Aerodynamic Data-Driven Surrogate-Assisted Teaching-Learning-Based Optimization (TLBO) Framework for Constrained Transonic Airfoil and Wing Shape Designs". Aerospace 9, nr 10 (17.10.2022): 610. http://dx.doi.org/10.3390/aerospace9100610.
Pełny tekst źródłaPairet, Eric, Constantinos Chamzas, Yvan R. Petillot i Lydia Kavraki. "Path Planning for Manipulation Using Experience-Driven Random Trees". IEEE Robotics and Automation Letters 6, nr 2 (kwiecień 2021): 3295–302. http://dx.doi.org/10.1109/lra.2021.3063063.
Pełny tekst źródłaEndress, Felix, Jasper Rieser i Markus Zimmermann. "ON THE TREATMENT OF REQUIREMENTS IN DFAM: THREE INDUSTRIAL USE CASES". Proceedings of the Design Society 3 (19.06.2023): 2815–24. http://dx.doi.org/10.1017/pds.2023.282.
Pełny tekst źródłaBorba Evangelista, Gustavo, Guilherme Conceição Rocha i Wlamir Olivares Loesch Vianna. "Aircraft Troubleshooting Optimization Using Case-based Reasoning and Decision Analysis". Annual Conference of the PHM Society 12, nr 1 (3.11.2020): 8. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1170.
Pełny tekst źródłaNa, Chongzheng, i Huixin Liu. "A Historical Experience Surrogate Model Assisted Particle Swarm Optimization for Expensive Black-box Problems". Highlights in Science, Engineering and Technology 7 (3.08.2022): 83–88. http://dx.doi.org/10.54097/hset.v7i.1021.
Pełny tekst źródłaEnríquez-Urbano, Juana, Marco Antonio Cruz-Chávez, Rafael Rivera-López, Martín H. Cruz-Rosales, Yainier Labrada-Nueva i Marta Lilia Eraña-Díaz. "Metaheuristic to Optimize Computational Convergence in Convection-Diffusion and Driven-Cavity Problems". Mathematics 9, nr 7 (31.03.2021): 748. http://dx.doi.org/10.3390/math9070748.
Pełny tekst źródłaRozprawy doktorskie na temat "Experience-Driven Optimization"
Mossina, Luca. "Applications d'apprentissage automatique à la résolution de problèmes récurrents en optimisation combinatoire". Electronic Thesis or Diss., Toulouse, ISAE, 2020. http://www.theses.fr/2020ESAE0043.
Pełny tekst źródłaThe interest is on those decision problems for which an optimal or quasi-optimal solution is sought, and for which it is necessary to solve successive instances (recurrent problems) that are variations of a common original problem.The structure of such problems is analysed to identify the characteristics that can be exploited and transferred from one resolution to another, to incrementally improve the quality of the optimization process. The research is characterized by the interaction between a process of statistical learning (from optimization data) and a process of optimization. The information extracted from past resolutions is generalized to the current problem and integrated into the optimization algorithm to make its execution more resource-efficient.In particular, this thesis presents three contributions.The first, introduces a method that generates a simpler sub-problem to an instance of a recurrent problem, using multi-label classification. A subset of decision variables is selected and set to a reference value. The solution to the remaining sub-problem, while not guaranteed to be optimal for the original problem, can be obtained faster.The second employs Supervised Learning, classification and regression, to predict an additional constraint to a reference recurrent problem modelled via Mathematical Programming. When a new instance is solved, the model predicts how much of the solution to the reference problem is still applicable, allowing for a more rapid resolution.In the third, the dynamic control of the parameters of Evolutionary Algorithms is framed as a Reinforcement Learning problem. The control policies obtained guarantee that the optimization algorithm reaches an optimal solution within the shortest, average time
Teng, Sin Yong. "Intelligent Energy-Savings and Process Improvement Strategies in Energy-Intensive Industries". Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-433427.
Pełny tekst źródłaCzęści książek na temat "Experience-Driven Optimization"
Can, Alperen, Hendrik Schulz, Ali El-Rahhal, Gregor Thiele i Jörg Krüger. "A Practical Approach to Realize a Closed Loop Energy Demand Optimization of Milling Machine Tools in Series Production". W Lecture Notes in Mechanical Engineering, 499–507. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28839-5_56.
Pełny tekst źródłaMillan, Michael, Annika Becker, Ester Christou, Roman Flaig, Leon Gorißen, Christian Hinke, István Koren i in. "Design Elements of a Platform-Based Ecosystem for Industry Applications". W Internet of Production, 1–22. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-98062-7_20-1.
Pełny tekst źródłaAzzam, Hammad. "Corporates in the Digital Age". W Technology Optimization and Change Management for Successful Digital Supply Chains, 39–52. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7700-3.ch003.
Pełny tekst źródłaJanssens, Jürgen. "Managing and Shaping Change in International Projects". W Research Anthology on Digital Transformation, Organizational Change, and the Impact of Remote Work, 1199–222. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-7297-9.ch060.
Pełny tekst źródłaJanssens, Jürgen. "Managing and Shaping Change in International Projects". W Managerial Competencies for Multinational Businesses, 150–73. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5781-4.ch008.
Pełny tekst źródłaStreszczenia konferencji na temat "Experience-Driven Optimization"
Xie, Hui, Ze Zhang i Kang Song. "A Self-optimization Algorithm of Multi-style Smart Parking Driven by Experience, Knowledge and Data". W 2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI). IEEE, 2021. http://dx.doi.org/10.1109/cvci54083.2021.9661152.
Pełny tekst źródłaAgrawal, Sunil K., Venketesh N. Dubey, John J. Gangloff, Elizabeth Brackbill i Vivek Sangwan. "Optimization and Design of a Cable Driven Upper Arm Exoskeleton". W ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86516.
Pełny tekst źródłaBurns, Cliff, i Mike Wiegand. "Practical Considerations for Optimization of Propulsion Efficiency in Commercial Vessels". W SNAME 13th Propeller and Shafting Symposium. SNAME, 2012. http://dx.doi.org/10.5957/pss-2012-007.
Pełny tekst źródłaVennelakanti, Ravigopal, Malarvizhi Sankaranarayanasamy, Ramyar Saeedi, Rahul Vishwakarma, Prasun Singh, Jian Sun, Yushi Akiyama i Hisao Adachi. "Multimodal Mobility Framework: Towards Seamless Mobility Experience". W 2021 Joint Rail Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/jrc2021-58377.
Pełny tekst źródłaCanchucaja, Ramiro. "Fast Real-Time Production Optimization for Integrated Asset Modelling Using Mixed-Integer Non-Linear Programming in Julia Language". W SPE Latin American and Caribbean Petroleum Engineering Conference. SPE, 2023. http://dx.doi.org/10.2118/213138-ms.
Pełny tekst źródłaChoi, Young H., Jin H. Hong i Sung H. Jang. "A Study on the Feed Rate Optimization of a Ball Screw Feed Drive System for Minimum Vibrations". W ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85473.
Pełny tekst źródłaAntani, Kavit, Alireza Madadi, Mary E. Kurz, Laine Mears, Kilian Funk i Maria E. Mayorga. "Robust Work Planning and Development of a Decision Support System for Work Distribution on a Mixed-Model Automotive Assembly Line". W ASME 2012 International Manufacturing Science and Engineering Conference collocated with the 40th North American Manufacturing Research Conference and in participation with the International Conference on Tribology Materials and Processing. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/msec2012-7350.
Pełny tekst źródłaPark, Youn, Dragi Gasevski, Marlow Springer, Milos Stanic, Viraj Kulkarni i Dhiren Marjadi. "Simulation Driven Design Workflow for Aircraft Gearbox". W ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15091.
Pełny tekst źródłaMaheshwari, Nitin, Sultan Lobari i Ali Awadh Saary. "Production Optimization and Reservoir Monitoring Through Virtual Flow Metering". W ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211233-ms.
Pełny tekst źródłaTaruvai Sankaran, Raghuraman, Arunkumar S, Muthukumar Arunachalam i harinadh Gudla. "Simulation Driven Optimization of Automotive Floor Console Mounting Brackets – An Overview". W WCX World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2018. http://dx.doi.org/10.4271/2018-01-1020.
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