Literatura académica sobre el tema "RD optimization"
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Artículos de revistas sobre el tema "RD optimization"
Selby, D. A. "Marketing event optimization". IBM Journal of Research and Development 51, n.º 3.4 (mayo de 2007): 409–19. http://dx.doi.org/10.1147/rd.513.0409.
Texto completoKanagasabai, L. "Real Power Loss Reduction by Rock Dove Optimization and Fuligo Septica Optimization Algorithms". Journal of Engineering Sciences 7, n.º 2 (2020): E1—E6. http://dx.doi.org/10.21272/jes.2020.7(2).e1.
Texto completoAgrafiotis, D. K. "Multiobjective optimization of combinatorial libraries". IBM Journal of Research and Development 45, n.º 3.4 (mayo de 2001): 545–66. http://dx.doi.org/10.1147/rd.453.0545.
Texto completoAydin, Cem Savas, Senim Ozgurler, Mehmet Bulent Durmusoglu y Mesut Ozgurler. "Response surface approach to robust design of assembly cells through simulation". Assembly Automation 38, n.º 4 (3 de septiembre de 2018): 450–64. http://dx.doi.org/10.1108/aa-08-2017-093.
Texto completoAbramov, A. M. y A. S. Kovalev. "Strength calculation and optimization of axle housing form". Izvestiya MGTU MAMI 4, n.º 2 (20 de enero de 2010): 12–14. http://dx.doi.org/10.17816/2074-0530-69523.
Texto completoBournas, R. M. "Optimization of TCP segment size for file transfer". IBM Journal of Research and Development 41, n.º 3 (mayo de 1997): 357–66. http://dx.doi.org/10.1147/rd.413.0357.
Texto completoCascaval, C., E. Duesterwald, P. F. Sweeney y R. W. Wisniewski. "Performance and environment monitoring for continuous program optimization". IBM Journal of Research and Development 50, n.º 2.3 (marzo de 2006): 239–48. http://dx.doi.org/10.1147/rd.502.0239.
Texto completoKatircioglu, K., T. M. Brown y M. Asghar. "An SQL-based cost-effective inventory optimization solution". IBM Journal of Research and Development 51, n.º 3.4 (mayo de 2007): 433–45. http://dx.doi.org/10.1147/rd.513.0433.
Texto completoXiao, Wu, Yan Yan Zhou, Shou Cheng Du y Gao Hong He. "Process Parameters Optimization of the MTBE Reactive Distillation by Orthogonal Numberical Test and Least Square Method". Advanced Materials Research 550-553 (julio de 2012): 947–52. http://dx.doi.org/10.4028/www.scientific.net/amr.550-553.947.
Texto completoPoindexter, D. J., S. R. Stiffler, P. T. Wu, P. D. Agnello, T. Ivers, S. Narasimha, T. B. Faure et al. "Optimization of silicon technology for the IBM System z9". IBM Journal of Research and Development 51, n.º 1.2 (enero de 2007): 5–18. http://dx.doi.org/10.1147/rd.511.0005.
Texto completoTesis sobre el tema "RD optimization"
Whitcomb, Jacob A. "The value of power grid flexibility : applied optimization methods for bulk electricity storage and technology RD&D". Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/105303.
Texto completoCataloged from PDF version of thesis.
Includes bibliographical references (pages 104-110).
As power systems adapt to include aging infrastructure, new socio-political priorities, and renewable electricity resources, grid operators look to a more flexible grid. Electricity storage flexibility is one strategy gaining interest. Clean energy advocates see benefits in terms of greater renewables integration and lower emissions; grid operators see storage as an improved security system in the face of supply and demand variability and uncertainty. However, as power systems are designed for reliable and efficient operations using available technologies, newer, better-performing technologies such as energy storage devices may not always win the market. Several market barriers to storage remain, including high storage capital costs and a lack of trusted tools for modeling and estimating the lifetime value of new capacity investments [1]. Most storage modeling strategies omit constraints that describe the technical operating boundaries of different power generating technologies, which can lead an overestimation of total operating costs for the power system [2]. I describe a mixed integer linear optimization framework for estimating the optimal control and value of energy storage in a virtual power generation system with economic, regulatory, and technical performance characteristics. The model consists of power plant commitment, dispatch, and selective capacity expansion constraints that simulate optimal investments and operations of the power generation system. A new formulation for modeling energy storage is also developed in order to improve the accuracy of round-trip efficiencies and allow for the inclusion of minimum storage output constraints. Using this model, I solve for break-even target prices for storage capital costs under a range of scenarios (storage futures scenarios). A second challenge slowing the adoption of storage is a lack of spending on performance improvements and cost-reductions. A two-factor learning curve and optimization approach is developed to solve for the optimal portfolio of research, development, demonstration, and diffusion investments (RDD&D) over multiple investment periods. Using the target capital costs from unit commitment model output as the investment model input value, innovating firms and policy planners may better identify cost targets and investment strategies for reaching target levels of storage deployment. Electricity storage becomes more valuable as net load variability increases. The impact of net load variability is tested by changing the level of renewable generation resources in the system. The current capital cost of storage-here, compressed air energy storage (CAES)-generally exceeds the target cost needed to make CAES economical when it is used to provide load following, load shifting, and operating reserve services in high-voltage power generation systems. Scenario analysis shows that when renewables generation reaches 35%, CAES becomes economical in limited quantities due to the added value from providing renewables integration and greater operating reserves. Using this framework, I identify different levels of cost reductions needed to drive improved adoption and make several RDD&D recommendations.
by Jacob Whitcomb.
S.M. in Engineering and Management
Zouidi, Naïma. "Complexity reduction of VVC encoder using machine learning techniques : intra-prediction". Electronic Thesis or Diss., Rennes, INSA, 2023. http://www.theses.fr/2023ISAR0016.
Texto completoIn July 2020, the new video coding standard Versatile Video Coding (VVC), was released by the Joint Video Expert Team (JVET). This standard enables a higher level of versatility with a better compression performance compared to its predecessor, High Efficiency Video Coding (HEVC). Indeed, it introduces several new coding tools such as finer-granularity Intra prediction Modes (IPMs), and nested Multi-type Tree (QTMT) and finer-granularity Intra Prediction Modes (IPM). Because finding the best encoding decisions is usually preceded by optimizing the Rate Distortion (RD) cost, introducing new coding tools or enhancing existing ones would require additional computations. In fact, the VVC is 31 times more complex than the HEVC. Therefore, the aim of this thesis is to reduce the computational complexity of the VVC. First, it studies the upper bound of complexity reduction in the intra mode decision of the VVC. Then, proposes two fast decision algorithms for the intra mode decision based on machine learning algorithms such as Multi-Task Learning (MTL) and Light-Gradient Boosting Machine (Light-GBM) were proposed
Keil, Wolfgang. "Optimization principles and constraints shaping visual cortical architecture". Doctoral thesis, 2012. http://hdl.handle.net/11858/00-1735-0000-000D-F0B0-0.
Texto completoLibros sobre el tema "RD optimization"
Sidebotham, David, Alan Forbes Merry, Malcolm E. Legget y I. Gavin Wright, eds. Practical Perioperative Transoesophageal Echocardiography. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198759089.001.0001.
Texto completoCapítulos de libros sobre el tema "RD optimization"
Kim, Byung-Gyu y Kalyan Goswami. "RD Cost Optimization". En SpringerBriefs in Electrical and Computer Engineering, 53–61. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39241-7_5.
Texto completoGu, Mengjun. "Analysis of the Discriminability of High-Temperature Performance Indices of Modified Asphalt Mixtures". En Lecture Notes in Civil Engineering, 243–51. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1748-8_20.
Texto completoPrakash Bandsode, Shirish y Chandra Shekar Besta. "Centralized and Decentralized Control System for Reactive Distillation Diphenyl Carbonate Process". En Distillation Processes - From Solar and Membrane Distillation to Reactive Distillation Modelling, Simulation and Optimization. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.101981.
Texto completoMeng, Fannian, Ziqi Zhang, Liangwen Wang y Yiyang Liu. "Volute Optimization Based on NSGA-II Algorithm". En Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220230.
Texto completoLambert, Peter, Stefaan Mys, Jozef Škorupa, Jürgen Slowack, Rik Van de Walle, Ming Yuan Yang, Christos Grecos y Vassilios Argiriou. "Fast Mode Decision in H.264/AVC". En Handheld Computing for Mobile Commerce, 403–24. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-761-9.ch021.
Texto completo(Almbhen), Manish Sharma. "WEBSITES SAFETY". En Futuristic Trends in Information Technology Volume 2 Book 20, 121–27. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2023. http://dx.doi.org/10.58532/v2bs20p1ch9.
Texto completoActas de conferencias sobre el tema "RD optimization"
Jiajun, Wu. "An Effective RD Cost Optimization Algorithm for HEVC". En 2018 2nd IEEE Advanced Information Management,Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2018. http://dx.doi.org/10.1109/imcec.2018.8469676.
Texto completoFernandez-Escribano, Gerardo, Hari Kalva, Pedro Cuenca y Luis Orozco-Barbosa. "RD-Optimization for MPEG-2 to H.264 Transcoding". En 2006 IEEE International Conference on Multimedia and Expo. IEEE, 2006. http://dx.doi.org/10.1109/icme.2006.262460.
Texto completoMilicevic, Z. M. y Z. S. Bojkovic. "RD Optimization and Skip Prediction for H. 264/AVC Standard". En EUROCON 2005 - The International Conference on "Computer as a Tool". IEEE, 2005. http://dx.doi.org/10.1109/eurcon.2005.1630203.
Texto completoRopert, Michael y Francois Ropert. "RD Optimization of uniform threshold scalar quantization for Laplacian distributions". En 2013 Picture Coding Symposium (PCS). IEEE, 2013. http://dx.doi.org/10.1109/pcs.2013.6737682.
Texto completo"MATERIALS ІІІ-rd International scientific-technical conference COMPUTER MODELING AND OPTIMIZATION OF COMPLEX SYSTEMS". En COMPUTER MODELING AND OPTIMIZATION OF COMPLEX SYSTEMS. Balance-Club, 2017. http://dx.doi.org/10.32434/cmocs-2017.
Texto completoLiu, Li y Xinhua Zhuang. "Cabac Based Bit Estimation for Fast H.264 RD Optimization Decision". En 2009 6th IEEE Consumer Communications and Networking Conference (CCNC). IEEE, 2009. http://dx.doi.org/10.1109/ccnc.2009.4784711.
Texto completoRusert, Thomas, Martin Spiertz y Jens-Rainer Ohm. "H.264/AVC Compatible Scalable Multiple Description Video Coding with RD Optimization". En 2006 International Symposium on Intelligent Signal Processing and Communications. IEEE, 2006. http://dx.doi.org/10.1109/ispacs.2006.364851.
Texto completoMAMAI, Oksana y Igor MAMAI. "OPTIMIZATION OF THE MANAGEMENT MECHANISM FOR THE INNOVATIVE DEVELOPMENT OF THE REGION’S AGRICULTURAL SECTOR". En RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.054.
Texto completoZhuang, Yan, Takeshi Ikenaga y Satoshi Goto. "Rate Estimation of RD Optimization for Intra Mode Decision of H.264/AVC". En 2008 Congress on Image and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/cisp.2008.617.
Texto completoPrasad, Vikas, P. Seshu y Dnyanesh N. Pawaskar. "Controller Design and Road-Friendly Suspension Optimization: Half Vehicle Model". En ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22051.
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