Academic literature on the topic 'Experience-Driven Optimization'
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Journal articles on the topic "Experience-Driven Optimization"
Cheng, Yihua, Xu Zhang, and Junchen Jiang. "Enabling Perception-Driven Optimization in Networking." ACM SIGMETRICS Performance Evaluation Review 51, no. 2 (September 28, 2023): 103–5. http://dx.doi.org/10.1145/3626570.3626608.
Full textP, Dr Periasamy, and Dr Dinesh N. "Data Driven Marketing Strategic Trends in 2022." International Journal of Science, Engineering and Management 9, no. 6 (June 13, 2022): 24–27. http://dx.doi.org/10.36647/ijsem/09.06.a005.
Full textShailesh, K. S., and P. V. Suresh. "Performance Driven Development Framework for Web Applications." Global Journal of Enterprise Information System 9, no. 1 (May 5, 2017): 75. http://dx.doi.org/10.18311/gjeis/2017/15870.
Full textSONG, WEI, DIAN TJONDRONEGORO, and MICHAEL DOCHERTY. "EXPLORATION AND OPTIMIZATION OF USER EXPERIENCE IN VIEWING VIDEOS ON A MOBILE PHONE." International Journal of Software Engineering and Knowledge Engineering 20, no. 08 (December 2010): 1045–75. http://dx.doi.org/10.1142/s0218194010005067.
Full textWu, Xiaojing, Zijun Zuo, and Long Ma. "Aerodynamic Data-Driven Surrogate-Assisted Teaching-Learning-Based Optimization (TLBO) Framework for Constrained Transonic Airfoil and Wing Shape Designs." Aerospace 9, no. 10 (October 17, 2022): 610. http://dx.doi.org/10.3390/aerospace9100610.
Full textPairet, Eric, Constantinos Chamzas, Yvan R. Petillot, and Lydia Kavraki. "Path Planning for Manipulation Using Experience-Driven Random Trees." IEEE Robotics and Automation Letters 6, no. 2 (April 2021): 3295–302. http://dx.doi.org/10.1109/lra.2021.3063063.
Full textEndress, Felix, Jasper Rieser, and Markus Zimmermann. "ON THE TREATMENT OF REQUIREMENTS IN DFAM: THREE INDUSTRIAL USE CASES." Proceedings of the Design Society 3 (June 19, 2023): 2815–24. http://dx.doi.org/10.1017/pds.2023.282.
Full textBorba Evangelista, Gustavo, Guilherme Conceição Rocha, and Wlamir Olivares Loesch Vianna. "Aircraft Troubleshooting Optimization Using Case-based Reasoning and Decision Analysis." Annual Conference of the PHM Society 12, no. 1 (November 3, 2020): 8. http://dx.doi.org/10.36001/phmconf.2020.v12i1.1170.
Full textNa, Chongzheng, and Huixin Liu. "A Historical Experience Surrogate Model Assisted Particle Swarm Optimization for Expensive Black-box Problems." Highlights in Science, Engineering and Technology 7 (August 3, 2022): 83–88. http://dx.doi.org/10.54097/hset.v7i.1021.
Full textEnríquez-Urbano, Juana, Marco Antonio Cruz-Chávez, Rafael Rivera-López, Martín H. Cruz-Rosales, Yainier Labrada-Nueva, and Marta Lilia Eraña-Díaz. "Metaheuristic to Optimize Computational Convergence in Convection-Diffusion and Driven-Cavity Problems." Mathematics 9, no. 7 (March 31, 2021): 748. http://dx.doi.org/10.3390/math9070748.
Full textDissertations / Theses on the topic "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.
Full textThe 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.
Full textBook chapters on the topic "Experience-Driven Optimization"
Can, Alperen, Hendrik Schulz, Ali El-Rahhal, Gregor Thiele, and Jörg Krüger. "A Practical Approach to Realize a Closed Loop Energy Demand Optimization of Milling Machine Tools in Series Production." In Lecture Notes in Mechanical Engineering, 499–507. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28839-5_56.
Full textMillan, Michael, Annika Becker, Ester Christou, Roman Flaig, Leon Gorißen, Christian Hinke, István Koren, et al. "Design Elements of a Platform-Based Ecosystem for Industry Applications." In Internet of Production, 1–22. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-98062-7_20-1.
Full textAzzam, Hammad. "Corporates in the Digital Age." In 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.
Full textJanssens, Jürgen. "Managing and Shaping Change in International Projects." In 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.
Full textJanssens, Jürgen. "Managing and Shaping Change in International Projects." In Managerial Competencies for Multinational Businesses, 150–73. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5781-4.ch008.
Full textConference papers on the topic "Experience-Driven Optimization"
Xie, Hui, Ze Zhang, and Kang Song. "A Self-optimization Algorithm of Multi-style Smart Parking Driven by Experience, Knowledge and Data." In 2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI). IEEE, 2021. http://dx.doi.org/10.1109/cvci54083.2021.9661152.
Full textAgrawal, Sunil K., Venketesh N. Dubey, John J. Gangloff, Elizabeth Brackbill, and Vivek Sangwan. "Optimization and Design of a Cable Driven Upper Arm Exoskeleton." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86516.
Full textBurns, Cliff, and Mike Wiegand. "Practical Considerations for Optimization of Propulsion Efficiency in Commercial Vessels." In SNAME 13th Propeller and Shafting Symposium. SNAME, 2012. http://dx.doi.org/10.5957/pss-2012-007.
Full textVennelakanti, Ravigopal, Malarvizhi Sankaranarayanasamy, Ramyar Saeedi, Rahul Vishwakarma, Prasun Singh, Jian Sun, Yushi Akiyama, and Hisao Adachi. "Multimodal Mobility Framework: Towards Seamless Mobility Experience." In 2021 Joint Rail Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/jrc2021-58377.
Full textCanchucaja, Ramiro. "Fast Real-Time Production Optimization for Integrated Asset Modelling Using Mixed-Integer Non-Linear Programming in Julia Language." In SPE Latin American and Caribbean Petroleum Engineering Conference. SPE, 2023. http://dx.doi.org/10.2118/213138-ms.
Full textChoi, Young H., Jin H. Hong, and Sung H. Jang. "A Study on the Feed Rate Optimization of a Ball Screw Feed Drive System for Minimum Vibrations." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85473.
Full textAntani, Kavit, Alireza Madadi, Mary E. Kurz, Laine Mears, Kilian Funk, and Maria E. Mayorga. "Robust Work Planning and Development of a Decision Support System for Work Distribution on a Mixed-Model Automotive Assembly Line." In 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.
Full textPark, Youn, Dragi Gasevski, Marlow Springer, Milos Stanic, Viraj Kulkarni, and Dhiren Marjadi. "Simulation Driven Design Workflow for Aircraft Gearbox." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15091.
Full textMaheshwari, Nitin, Sultan Lobari, and Ali Awadh Saary. "Production Optimization and Reservoir Monitoring Through Virtual Flow Metering." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211233-ms.
Full textTaruvai Sankaran, Raghuraman, Arunkumar S, Muthukumar Arunachalam, and harinadh Gudla. "Simulation Driven Optimization of Automotive Floor Console Mounting Brackets – An Overview." In 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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