Auswahl der wissenschaftlichen Literatur zum Thema „RD optimization“
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Zeitschriftenartikel zum Thema "RD optimization"
Selby, D. A. „Marketing event optimization“. IBM Journal of Research and Development 51, Nr. 3.4 (Mai 2007): 409–19. http://dx.doi.org/10.1147/rd.513.0409.
Der volle Inhalt der QuelleKanagasabai, L. „Real Power Loss Reduction by Rock Dove Optimization and Fuligo Septica Optimization Algorithms“. Journal of Engineering Sciences 7, Nr. 2 (2020): E1—E6. http://dx.doi.org/10.21272/jes.2020.7(2).e1.
Der volle Inhalt der QuelleAgrafiotis, D. K. „Multiobjective optimization of combinatorial libraries“. IBM Journal of Research and Development 45, Nr. 3.4 (Mai 2001): 545–66. http://dx.doi.org/10.1147/rd.453.0545.
Der volle Inhalt der QuelleAydin, Cem Savas, Senim Ozgurler, Mehmet Bulent Durmusoglu und Mesut Ozgurler. „Response surface approach to robust design of assembly cells through simulation“. Assembly Automation 38, Nr. 4 (03.09.2018): 450–64. http://dx.doi.org/10.1108/aa-08-2017-093.
Der volle Inhalt der QuelleAbramov, A. M., und A. S. Kovalev. „Strength calculation and optimization of axle housing form“. Izvestiya MGTU MAMI 4, Nr. 2 (20.01.2010): 12–14. http://dx.doi.org/10.17816/2074-0530-69523.
Der volle Inhalt der QuelleBournas, R. M. „Optimization of TCP segment size for file transfer“. IBM Journal of Research and Development 41, Nr. 3 (Mai 1997): 357–66. http://dx.doi.org/10.1147/rd.413.0357.
Der volle Inhalt der QuelleCascaval, C., E. Duesterwald, P. F. Sweeney und R. W. Wisniewski. „Performance and environment monitoring for continuous program optimization“. IBM Journal of Research and Development 50, Nr. 2.3 (März 2006): 239–48. http://dx.doi.org/10.1147/rd.502.0239.
Der volle Inhalt der QuelleKatircioglu, K., T. M. Brown und M. Asghar. „An SQL-based cost-effective inventory optimization solution“. IBM Journal of Research and Development 51, Nr. 3.4 (Mai 2007): 433–45. http://dx.doi.org/10.1147/rd.513.0433.
Der volle Inhalt der QuelleXiao, Wu, Yan Yan Zhou, Shou Cheng Du und Gao Hong He. „Process Parameters Optimization of the MTBE Reactive Distillation by Orthogonal Numberical Test and Least Square Method“. Advanced Materials Research 550-553 (Juli 2012): 947–52. http://dx.doi.org/10.4028/www.scientific.net/amr.550-553.947.
Der volle Inhalt der QuellePoindexter, 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, Nr. 1.2 (Januar 2007): 5–18. http://dx.doi.org/10.1147/rd.511.0005.
Der volle Inhalt der QuelleDissertationen zum Thema "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.
Der volle Inhalt der QuelleCataloged 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.
Der volle Inhalt der QuelleIn 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.
Der volle Inhalt der QuelleBücher zum Thema "RD optimization"
Sidebotham, David, Alan Forbes Merry, Malcolm E. Legget und I. Gavin Wright, Hrsg. Practical Perioperative Transoesophageal Echocardiography. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198759089.001.0001.
Der volle Inhalt der QuelleBuchteile zum Thema "RD optimization"
Kim, Byung-Gyu, und Kalyan Goswami. „RD Cost Optimization“. In 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.
Der volle Inhalt der QuelleGu, Mengjun. „Analysis of the Discriminability of High-Temperature Performance Indices of Modified Asphalt Mixtures“. In Lecture Notes in Civil Engineering, 243–51. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1748-8_20.
Der volle Inhalt der QuellePrakash Bandsode, Shirish, und Chandra Shekar Besta. „Centralized and Decentralized Control System for Reactive Distillation Diphenyl Carbonate Process“. In Distillation Processes - From Solar and Membrane Distillation to Reactive Distillation Modelling, Simulation and Optimization. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.101981.
Der volle Inhalt der QuelleMeng, Fannian, Ziqi Zhang, Liangwen Wang und Yiyang Liu. „Volute Optimization Based on NSGA-II Algorithm“. In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220230.
Der volle Inhalt der QuelleLambert, Peter, Stefaan Mys, Jozef Škorupa, Jürgen Slowack, Rik Van de Walle, Ming Yuan Yang, Christos Grecos und Vassilios Argiriou. „Fast Mode Decision in H.264/AVC“. In Handheld Computing for Mobile Commerce, 403–24. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61520-761-9.ch021.
Der volle Inhalt der Quelle(Almbhen), Manish Sharma. „WEBSITES SAFETY“. In 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "RD optimization"
Jiajun, Wu. „An Effective RD Cost Optimization Algorithm for HEVC“. In 2018 2nd IEEE Advanced Information Management,Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2018. http://dx.doi.org/10.1109/imcec.2018.8469676.
Der volle Inhalt der QuelleFernandez-Escribano, Gerardo, Hari Kalva, Pedro Cuenca und Luis Orozco-Barbosa. „RD-Optimization for MPEG-2 to H.264 Transcoding“. In 2006 IEEE International Conference on Multimedia and Expo. IEEE, 2006. http://dx.doi.org/10.1109/icme.2006.262460.
Der volle Inhalt der QuelleMilicevic, Z. M., und Z. S. Bojkovic. „RD Optimization and Skip Prediction for H. 264/AVC Standard“. In EUROCON 2005 - The International Conference on "Computer as a Tool". IEEE, 2005. http://dx.doi.org/10.1109/eurcon.2005.1630203.
Der volle Inhalt der QuelleRopert, Michael, und Francois Ropert. „RD Optimization of uniform threshold scalar quantization for Laplacian distributions“. In 2013 Picture Coding Symposium (PCS). IEEE, 2013. http://dx.doi.org/10.1109/pcs.2013.6737682.
Der volle Inhalt der Quelle„MATERIALS ІІІ-rd International scientific-technical conference COMPUTER MODELING AND OPTIMIZATION OF COMPLEX SYSTEMS“. In COMPUTER MODELING AND OPTIMIZATION OF COMPLEX SYSTEMS. Balance-Club, 2017. http://dx.doi.org/10.32434/cmocs-2017.
Der volle Inhalt der QuelleLiu, Li, und Xinhua Zhuang. „Cabac Based Bit Estimation for Fast H.264 RD Optimization Decision“. In 2009 6th IEEE Consumer Communications and Networking Conference (CCNC). IEEE, 2009. http://dx.doi.org/10.1109/ccnc.2009.4784711.
Der volle Inhalt der QuelleRusert, Thomas, Martin Spiertz und Jens-Rainer Ohm. „H.264/AVC Compatible Scalable Multiple Description Video Coding with RD Optimization“. In 2006 International Symposium on Intelligent Signal Processing and Communications. IEEE, 2006. http://dx.doi.org/10.1109/ispacs.2006.364851.
Der volle Inhalt der QuelleMAMAI, Oksana, und Igor MAMAI. „OPTIMIZATION OF THE MANAGEMENT MECHANISM FOR THE INNOVATIVE DEVELOPMENT OF THE REGION’S AGRICULTURAL SECTOR“. In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.054.
Der volle Inhalt der QuelleZhuang, Yan, Takeshi Ikenaga und Satoshi Goto. „Rate Estimation of RD Optimization for Intra Mode Decision of H.264/AVC“. In 2008 Congress on Image and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/cisp.2008.617.
Der volle Inhalt der QuellePrasad, Vikas, P. Seshu und Dnyanesh N. Pawaskar. „Controller Design and Road-Friendly Suspension Optimization: Half Vehicle Model“. In 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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