Thematische Bibliographien / Algorithm performance

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Auswahl der wissenschaftlichen Literatur zum Thema „Algorithm performance“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 21. Juni 2025

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Zeitschriftenartikel zum Thema "Algorithm performance"

Mary, A. Viji Amutha, and Dr T. Jebarajan Dr. T. Jebarajan. "Performance Metrics of Clustering Algorithm." Indian Journal of Applied Research 4, no. 8 (2011): 165–67. http://dx.doi.org/10.15373/2249555x/august2014/47.

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Han, Xiaofeng, Xuejia Qi, Jincheng Zhang, Songlin Yang, and Shuling Chen. "The Analysis Method of Performance Comprehensive Performance Optimization Design for Multi-body Unmanned Boat." Journal of Physics: Conference Series 2219, no. 1 (2022): 012048. http://dx.doi.org/10.1088/1742-6596/2219/1/012048.

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Abstract In order to compare and analyze the comprehensive performance of multi-hull unmanned boats under specific conditions, this paper conducts in-depth research on the multi-objective decision-making theory and intelligent optimization algorithms of boat type design. First of all, this paper establishes the corresponding ship type and resistance database using the response surface fitting method through the drag resistance test of the ship model. Secondly, comprehensively considering the impact of the three major performances of multi-hull unmanned craft on the design of the boat’s speed, maneuverability and seakeeping, a comprehensive optimization mathematical model was established, and the comprehensive optimization design software was adapted. Then, using genetic algorithm, particle swarm algorithm, and chaos algorithm to conduct a preliminary calculation and analysis of the comprehensive performance of the two ship types. By comparing the calculation results, it is concluded that the particle swarm algorithm is the best algorithm among the three intelligent algorithms. Finally, the particle swarm algorithm is used to compare and analyze the comprehensive performance of the two ship types in the multi-hull unmanned craft. It is concluded that the overall performance of the three-hull unmanned craft is better at the same speed and displacement. This paper discusses the pros and cons of the three intelligent algorithms, compares and analyzes the calculation results of each optimization algorithm, and provides a practical way and method for the selection of multihull unmanned boats under the same speed and same displacement.

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Qiu, Jian Lin, Li Chen, Jian Ping Chen, Xiang Gu, and Yan Yun Chen. "Grid-Based Task Scheduling PMTS Algorithm." Applied Mechanics and Materials 121-126 (October 2011): 4491–97. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4491.

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This paper analyses the Min-min algorithm and its improved algorithms through the performances of load balance, time span, quality of service and economic principle. Based on the analysis of the merits of these algorithms, we propose an improved algorithm as PMTS (Priority-based maximum time-span algorithm) by integrating. In the instance of the application, we analyse and compare the performances of these algorithms, and experimental results show that, PMTS algorithm is better than other algorithms in the comprehensive performance of load-balance, time-span, quality of service and other aspects.

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Civicioglu, P., U. H. Atasever, C. Ozkan, E. Besdok, A. E. Karkinli, and A. Kesikoglu. "Performance Comparison Of Evolutionary Algorithms For Image Clustering." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 71–74. http://dx.doi.org/10.5194/isprsarchives-xl-7-71-2014.

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Evolutionary computation tools are able to process real valued numerical sets in order to extract suboptimal solution of designed problem. Data clustering algorithms have been intensively used for image segmentation in remote sensing applications. Despite of wide usage of evolutionary algorithms on data clustering, their clustering performances have been scarcely studied by using clustering validation indexes. In this paper, the recently proposed evolutionary algorithms (i.e., Artificial Bee Colony Algorithm (ABC), Gravitational Search Algorithm (GSA), Cuckoo Search Algorithm (CS), Adaptive Differential Evolution Algorithm (JADE), Differential Search Algorithm (DSA) and Backtracking Search Optimization Algorithm (BSA)) and some classical image clustering techniques (i.e., k-means, fcm, som networks) have been used to cluster images and their performances have been compared by using four clustering validation indexes. Experimental test results exposed that evolutionary algorithms give more reliable cluster-centers than classical clustering techniques, but their convergence time is quite long.

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Kaur, Baljit. "Performance Comparison between LMS and NLMS Algorithm." Indian Journal of Applied Research 1, no. 6 (2011): 63–65. http://dx.doi.org/10.15373/2249555x/mar2012/20.

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Saini, Deepali, and Prof Anand Rajavat. "Performance Evaluation System for Decision Tree Algorithms." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 11, no. 8 (2013): 2879–86. http://dx.doi.org/10.24297/ijct.v11i8.3006.

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In the machine learning process, classification can be described by supervise learning algorithm. Classification techniques have properties that enable the representation of structures that reflect knowledge of the domain being classified. Industries, education, business and many other domains required knowledge for the growth.Â Some of the common classification algorithms used in data mining and decision support systems is: Neural networks, Logistic regression, Decision trees etc. The decision regarding most suitable data mining algorithm cannot be made spontaneously. Selection of appropriate data mining algorithm for Business domain required comparative analysis of different algorithms based on several input parameters such as accuracy, build time and memory usage.To make analysis and comparative study, implementation of popular algorithm required on the basis of literature survey and frequency of algorithm used in present scenario. The performance of algorithms are enhanced and evaluated after applying boosting on the trees. We selected numerical and nominal types of dataset and apply on algorithms. Comparative analysis is perform on the result obtain by the system. Then we apply the new dataset in order to generate generate prediction outcome.

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YE, DESHI, and QINMING HE. "WORST-CASE PERFORMANCE EVALUATION ON MULTIPROCESSOR TASK SCHEDULING WITH RESOURCE AUGMENTATION." International Journal of Foundations of Computer Science 22, no. 04 (2011): 971–82. http://dx.doi.org/10.1142/s0129054111008519.

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We study the worst-case performance of approximation algorithms for the problem of multiprocessor task scheduling on m identical processors with resource augmentation, whose objective is to minimize the makespan. In this case, the approximation algorithms are given k (k ≥ 0) extra processors than the optimal off-line algorithm. For on-line algorithms, the Greedy algorithm and shelf algorithms are studied. For off-line algorithm, we consider the LPT (longest processing time) algorithm. Particularly, we prove that the schedule produced by the LPT algorithm is no longer than the optimal off-line algorithm if and only if k ≥ m - 2.

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Zhang, Qingyang, Tianji Peng, Guangchun Zhang, et al. "An Efficient Scheme for Coupling OpenMC and FLUENT with Adaptive Load Balancing." Science and Technology of Nuclear Installations 2021 (September 24, 2021): 1–16. http://dx.doi.org/10.1155/2021/5549602.

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This paper develops a multi-physics interface code MC-FLUENT to couple the Monte Carlo code OpenMC with the commercial computational fluid dynamics code ANSYS FLUENT. The implementations and parallel performances of block Gauss–Seidel-type and block Jacobi-type Picard iterative algorithms have been investigated. In addition, this paper introduces two adaptive load-balancing algorithms into the neutronics and thermal-hydraulics coupled simulation to reduce the time cost of computation. Considering that the different scalability of OpenMC and FLUENT limits the performance of block Gauss–Seidel algorithm, an adaptive load-balancing algorithm that can increase the number of nodes dynamically is proposed to improve its efficiency. Moreover, with the natural parallelism of block Jacobi algorithm, another adaptive load-balancing algorithm is proposed to improve its performance. A 3 x 3 PWR fuel pin model and a 1000 MWt ABR metallic benchmark core were used to compare the performances of the two algorithms and verify the effectiveness of the two adaptive load-balancing algorithms. The results show that the adaptive load-balancing algorithms proposed in this paper can greatly improve the computing efficiency of block Jacobi algorithm and improve the performance of block Gauss–Seidel algorithm when the number of nodes is large. In addition, the adaptive load-balancing algorithms are especially effective when a case demands different computational power of OpenMC and FLUENT.

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Qiu, Dong Wei, and Shan Shan Wan. "Research on Algorithms Performance about JSP Scheduling." Advanced Materials Research 457-458 (January 2012): 20–25. http://dx.doi.org/10.4028/www.scientific.net/amr.457-458.20.

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Three typical intelligent evolutionary algorithms are applied on Job Shop scheduling problem which are Quantum algorithm, Genetic Algorithm and Population Based Incremental Learning algorithm. They three algorithms have some common features in computation, encoding strategy and probability application, but with the different problems and different scale sizes of the same problem they show different performance. In this paper we take JSP as example to test their performance difference and analyze their applicability. Two benchmark Job Shop problems are used to fulfill the comparison. The results denote that Quantum algorithm is good in a great quantity of solution individual, GA is excellent in stability and PBIL had good performance in accuracy. The research also makes a reliable instruction on the application or combination of the three algorithms.

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Wei, Xian Min. "Routing Lookup Algorithm Performance Analysis and Research." Advanced Materials Research 181-182 (January 2011): 109–12. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.109.

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This paper describes several current routing lookup algorithms, to study and analysize the complexity and operating practical performance of these routing lookup algorithms. The results show that although the binary search algorithm is not greatly improved in the searching performance, but in IPv6 environment, because searching performance of multi-branch Trie tree will decrease greatly, thus the superiority of binary search algorithm will be reflected better.

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Dissertationen zum Thema "Algorithm performance"

Wang, Lingyun. "Feeder Performance Analysis with Distributed Algorithm." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/31949.

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How to evaluate the performance of an electric power distribution system unambiguously and quantitatively is not easy. How to accurately measure the efficiency of it for a whole year, using real time hour-by-hour Locational Marginal Price data, is difficult. How to utilize distributed computing technology to accomplish these tasks with a timely fashion is challenging. This thesis addresses the issues mentioned above, by investigating feeder performance analysis of electric power distribution systems with distributed algorithm. Feeder performance analysis computes a modeled circuitâ s performance over an entire year, listing key circuit performance parameters such as efficiency, loading, losses, cost impact, power factor, three phase imbalance, capacity usage and others, providing detailed operating information for the system, and an overview of the performance of every circuit in the system. A diakoptics tearing method and Graph Trace Analysis based distributed computing technology is utilized to speed up the calculation. A general distributed computing architecture is established and a distributed computing algorithm is described. To the best of the authorâ s knowledge, it is the first time that this detailed performance analysis is researched, developed and tested, using a diakoptics based tearing method and Graph Trace Analysis to split the system so that it can be analyzed with distributed computing technology. Master of Science

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Pochet, Juliette. "Evaluation de performance d’une ligne ferroviaire suburbaine partiellement équipée d’un automatisme CBTC." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLC005.

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En zone dense, la croissance actuelle du trafic sur les lignes ferroviaires suburbaines conduit les exploitants à déployer des systèmes de contrôle-commande avancés des trains, tels que les systèmes dits « CBTC » (Communication Based Train Control) jusque-là réservés aux systèmes de métro. Les systèmes CBTC mettent en œuvre un pilotage automatique des trains et permettent une amélioration significative des performances. Par ailleurs, ils peuvent inclure un module de supervision de la ligne en charge de réguler la marche des trains en cas d’aléa, améliorant ainsi la robustesse du trafic. Face au problème de régulation, la recherche opérationnelle a produit un certain nombre de méthodes permettant de répondre efficacement aux perturbations, d’une part dans le secteur métro et d’autre part dans le secteur ferroviaire lourd. En tirant profit de l’état de l’art et des avancées faites dans les deux secteurs, les travaux présentés dans ce manuscrit cherchent à contribuer à l’adaptation des fonctions de régulation des systèmes CBTC pour l’exploitation de lignes ferroviaires suburbaines. L’approche du problème débute par la construction de l’architecture fonctionnelle d’un module de supervision pour un système CBTC standard. Nous proposons ensuite une méthode de régulation basée sur une stratégie de commande prédictive et sur une optimisation multi-objectif des consignes des trains automatiques. Afin d’être en mesure d’évaluer précisément les performances d’une ligne ferroviaire suburbaine équipée d’un automatisme CBTC, il est nécessaire de s’équiper d’un outil de simulation microscopique adapté. Nous présentons dans ce manuscrit l’outil SNCF nommé SIMONE qui permet une simulation réaliste du point de vue fonctionnel et dynamique d’un système ferroviaire incluant un système CBTC. Les objectifs des travaux de thèse nous ont naturellement conduits à prendre part, avec l’équipe SNCF, à la spécification, à la conception et à l’implémentation de cet outil. Finalement, grâce à l’outil SIMONE, nous avons pu tester la méthode de régulation proposée sur des scénarios impliquant des perturbations. Afin d’évaluer la qualité des solutions, la méthode multi-objectif proposée a été comparée à une méthode de régulation individuelle basée sur une heuristique simple. La méthode de régulation multi-objectif propose de bonnes solutions au problème, dans la majorité des cas plus satisfaisantes que celles proposées par la régulation individuelle, et avec un temps de calcul jugé acceptable. Le manuscrit se termine par des perspectives de recherche intéressantes In high-density area, the demand for railway transportation is continuously increasing. Operating companies turn to new intelligent signaling and control systems, such as Communication Based Train Control (CBTC) systems previously deployed on underground systems only. CBTC systems operate trains in automatic pilot and lead to increase the line capacity without expensive modification of infrastructures. They can also include a supervision module in charge of adapting train behavior according to operating objectives and to disturbances, increasing line robustness. In the literature of real-time traffic management, various methods have been proposed to supervise and reschedule trains, on the one hand for underground systems, on the other hand for railway systems. Making the most of the state-of-the-art in both fields, the presented work intend to contribute to the design of supervision and rescheduling functions of CBTC systems operating suburban railway systems. Our approach starts by designing a supervision module for a standard CBTC system. Then, we propose a rescheduling method based on a model predictive control approach and a multi-objective optimization of automatic train commands. In order to evaluate the performances of a railway system, it is necessary to use a microscopic simulation tool including a CBTC model. In this thesis, we present the tool developed by SNCF and named SIMONE. It allows realistic simulation of a railway system and a CBTC system, in terms of functional architecture and dynamics. The presented work has been directly involved in the design and implementation of the tool. Eventually, the proposed rescheduling method was tested with the tool SIMONE on disturbed scenarios. The proposed method was compared to a simple heuristic strategy intending to recover delays. The proposed multi-objective method is able to provide good solutions to the rescheduling problem and over-performs the simple strategy in most cases, with an acceptable process time. We conclude with interesting perspectives for future work

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Hamrin, Niklas, and Nils Runebjörk. "Examining Sorting Algorithm Performance Under System Load." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209765.

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In this thesis, we conducted tests in order to determine how system load affected the speed of commonly used algorithms. The difficulty of obtaining these type of results theoretically due to a large number of factors that can affect performance motivated the use of simulation. An implementation was constructed that ran the sorting algorithms Mergesort, Quicksort and Radixsort while the system was put under different levels of stress. This stress was created by generating cache misses using the software stress-ng. The resulting completion times, as well as the cache activity was logged. The result of the simulations were mostly within expectations, faster algorithms still performed better even under heavy loads and even handled the load better than the competition I denna rapport har vi utfört tester för att utvärdera hur systembelastning påverkar hastigheten på en samling vanligt förekommande algoritmer. Svårigheterna med att teoretiskt räkna ut dessa resultat är på grund av dess många påverkande faktorer. Detta ledde till användandet av en simulation. Ett testsystem skapades för att köra sorteringsalgoritmerna Mergesort, Quicksort och Radixsort med varierad systembelastning. Denna systembelastning skapades genom att generera cachemissar med hjälp av programmet stress-ng. Dessa simulationer, såväl som cache aktiviteten loggades. Resultatet var inom förväntan, de lättare och snabbare algoritmerna klarade sig fortfarande bättre även under svår belastning, och hanterade den ökade belastningen bättre.

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Musselman, Roger D. "Robustness a better measure of algorithm performance." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Sep%5FMusselman.pdf.

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Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 2007. Thesis Advisor(s): Sanchez, Paul J. "September 2007." Description based on title screen as viewed on October 25, 2007. Includes bibliographical references (p. 55-56). Also available in print.

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Chu, Yijing, and 褚轶景. "Resursive local estimation: algorithm, performance and applications." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B49799320.

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Adaptive filters are frequently employed in many applications, such as, system identification, adaptive echo cancellation (AEC), active noise control (ANC), adaptive beamforming, speech signal processing and other related problems, in which the statistic of the underlying signals is either unknown a priori, or slowly-varying. Given the observed signals under study, we shall consider, in this dissertation, the time-varying linear model with Gaussian or contaminated Gaussian (CG) noises. In particular, we focus on recursive local estimation and its applications in linear systems. We base our development on the concept of local likelihood function (LLF) and local posterior probability for parameter estimation, which lead to efficient adaptive filtering algorithms. We also study the convergence performance of these algorithms and their applications by theoretical analyses. As for applications, another important one is to utilize adaptive filters to obtain recursive hypothesis testing and model order selection methods. It is known that the maximum likelihood estimate (MLE) may lead to large variance or ill-conditioning problems when the number of observations is limited. An effective approach to address these problems is to employ various form of regularization in order to reduce the variance at the expense of slightly increased bias. In general, this can be viewed as adopting the Bayesian estimation, where the regularization can be viewed as providing a certain prior density of the parameters to be estimated. By adopting different prior densities in the LLF, we derive the variable regularized QR decomposition-based recursive least squares (VR-QRRLS) and recursive least M-estimate (VR-QRRLM) algorithms. An improved state-regularized variable forgetting factor QRRLS (SR-VFF-QRRLS) algorithm is also proposed. By approximating the covariance matrix in the RLS, new variable regularized and variable step-size transform domain normalized least mean square (VR-TDNLMS and VSS-TDNLMS) algorithms are proposed. Convergence behaviors of these algorithms are studied to characterize their performance and provide useful guidelines for selecting appropriate parameters in practical applications. Based on the local Bayesian estimation framework for linear model parameters developed previously, the resulting estimate can be utilized for recursive nonstationarity detection. This can be cast under the problem of hypothesis testing, as the hypotheses can be viewed as two competitive models between stationary and nonstationary to be selected. In this dissertation, we develop new regularized and recursive generalized likelihood ratio test (GLRT), Rao’s and Wald tests, which can be implemented recursively in a QRRLS-type adaptive filtering algorithm with low computational complexity. Another issue to be addressed in nonstationarity detection is the selection of various models or model orders. In particular, we derive a recursive method for model order selection from the Bayesian Information Criterion (BIC) based on recursive local estimation. In general, the algorithms proposed in this dissertation have addressed some of the important problems in estimation and detection under the local and recursive Bayesian estimation framework. They are intrinsically connected together and can potentially be utilized for various applications. In this dissertation, their applications to adaptive beamforming, ANC system and speech signal processing, e.g. adaptive frequency estimation and nonstationarity detection, have been studied. For adaptive beamforming, the difficulties in determining the regularization or loading factor have been explored by automatically selecting the regularization parameter. For ANC systems, to combat uncertainties in the secondary path estimation, regularization techniques can be employed. Consequently, a new filtered-x VR-QRRLM (Fx-VR-QRRLM) algorithm is proposed and the theoretical analysis helps to address challenging problems in the design of ANC systems. On the other hand, for ANC systems with online secondary-path modeling, the coupling effect of the ANC controller and the secondary path estimator is thoroughly studied by analyzing the Fx-LMS algorithm. For speech signal processing, new approaches for recursive nonstationarity detection with automatic model order selection are proposed, which provides online time-varying autoregressive (TVAR) parameter estimation and the corresponding stationary intervals with low complexity. published_or_final_version Electrical and Electronic Engineering Doctoral Doctor of Philosophy

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Jonas, Mario Ricardo Edward. "High performance computing and algorithm development: application of dataset development to algorithm parameterization." Thesis, University of the Western Cape, 2006. http://etd.uwc.ac.za/index.php?module=etd&amp.

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A number of technologies exist that captures data from biological systems. In addition, several computational tools, which aim to organize the data resulting from these technologies, have been created. The ability of these tools to organize the information into biologically meaningful results, however, needs to be stringently tested. The research contained herein focuses on data produced by technology that records short Expressed Sequence Tags (EST's).

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Anderson, Roger J. "Characterization of Performance, Robustness, and Behavior Relationships in a Directly Connected Material Handling System." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/26967.

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In the design of material handling systems with complex and unpredictable dynamics, conventional search and optimization approaches that are based only on performance measures offer little guarantee of robustness. Using evidence from research into complex systems, the use of behavior-based optimization is proposed, which takes advantage of observed relationships between complexity and optimality with respect to both performance and robustness. Based on theoretical complexity measures, particularly algorithmic complexity, several simple complexity measures are created. The relationships between these measures and both performance and robustness are examined, using a model of a directly connected material handling system as a backdrop. The fundamental causes of the relationships and their applicability in the proposed behavior-based optimization approach are discussed. Ph. D.

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Li, Chuhe. "A sliding window BIRCH algorithm with performance evaluations." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-32397.

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An increasing number of applications covered various fields generate transactional data or other time-stamped data which all belongs to time series data. Time series data mining is a popular topic in the data mining field, it introduces some challenges to improve accuracy and efficiency of algorithms for time series data. Time series data are dynamical, large-scale and high complexity, which makes it difficult to discover patterns among time series data with common methods suitable for static data. One of hierarchical-based clustering methods called BIRCH was proposed and employed for addressing the problems of large datasets. It minimizes the costs of I/O and time. A CF tree is generated during its working process and clusters are generated after four phases of the whole BIRCH procedure. A drawback of BIRCH is that it is not very scalable. This thesis is devoted to improve accuracy and efficiency of BIRCH algorithm. A sliding window BIRCH algorithm is implemented on the basis of BIRCH algorithm. At the end of thesis, the accuracy and efficiency of sliding window BIRCH are evaluated. A performance comparison among SW BIRCH, BIRCH and K-means are also presented with Silhouette Coefficient index and Calinski-Harabaz Index. The preliminary results indicate that the SW BIRCH may achieve a better performance than BIRCH in some cases.

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Martins, Wellington Santos. "Algorithm performance on a general purpose parallel computer." Thesis, University of East Anglia, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296870.

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Balasubramanian, Priya. "Interfacing VHDL performance models to algorithm partitioning tools." Thesis, This resource online, 1996. http://scholar.lib.vt.edu/theses/available/etd-02132009-172459/.

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Bücher zum Thema "Algorithm performance"

H, Lambert Heather, and United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., eds. Preliminary flight evaluation of an engine performance optimization algorithm. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1991.

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Dunn, Ian N., and Gerard G. L. Meyer. A Parallel Algorithm Synthesis Procedure for High-Performance Computer Architectures. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-8650-4.

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L, Meyer Gerard G., ed. A parallel algorithm synthesis procedure for high-performance computer architectures. Kluwer Academic/Plenum Publishers, 2003.

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Dunn, Ian N. A Parallel Algorithm Synthesis Procedure for High-Performance Computer Architectures. Springer US, 2003.

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Smart, John R. Performance evaluation of the NEXRAD Hail Algorithm applied to Colorado thunderstorms. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, Environmental Research Laboratories, 1985.

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Orme, John S. Flight assessment of the onboard propulsion system for the performance seeking control algorithm on an F-15 aircraft. Dryden Flight Research Center, 1995.

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Kobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. National Aeronautics and Space Administration, Glenn Research Center, 2001.

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United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. On the estimation algorithm used in adaptive performance optimization of turbofan engines. National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1993.

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T, Nguyen Duc, Agarwal Tarun K, and Langley Research Center, eds. A parallel-vector algorithm for rapid structural analysis on high-performance computers. National Aeronautics and Space Administration, Langley Research Center, 1990.

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Kobayashi, Takahisa. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. National Aeronautics and Space Administration, Glenn Research Center, 2001.

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Buchteile zum Thema "Algorithm performance"

Mantaci, Roberto, and Jean-Baptiste Yunès. "Algorithm Performance." In Basics of Programming and Algorithms, Principles and Applications. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-59801-2_7.

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Goldshtein, Sasha, Dima Zurbalev, and Ido Flatow. "Algorithm Optimization." In Pro .NET Performance. Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4459-2_9.

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Bojesen, Jesper, Jyrki Katajainen, and Maz Spork. "Performance Engineering Case Study: Heap Construction." In Algorithm Engineering. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48318-7_24.

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Ewald, Roland. "Simulation Algorithm Performance Analysis." In Automatic Algorithm Selection for Complex Simulation Problems. Vieweg+Teubner Verlag, 2012. http://dx.doi.org/10.1007/978-3-8348-8151-9_3.

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Hanke, Sabine. "The Performance of Concurrent Red-Black Tree Algorithms." In Algorithm Engineering. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48318-7_23.

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Fourneau, J. M., and L. Mokdad. "A Perturbation and Reduction Based Algorithm." In Computer Performance Evaluation. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-68061-6_12.

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Bilardi, Gianfranco, Paolo D’Alberto, and Alex Nicolau. "Fractal Matrix Multiplication: A Case Study on Portability of Cache Performance." In Algorithm Engineering. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44688-5_3.

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Roe, Michael, Wei Xu, and Dongping Song. "Evaluating the Multiple Objective Genetic Algorithm." In Optimizing Supply Chain Performance. Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137501158_10.

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Roe, Michael, Wei Xu, and Dongping Song. "Evaluating the Single Objective Genetic Algorithm." In Optimizing Supply Chain Performance. Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137501158_9.

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Tewari, Ambuj, Utkarsh Srivastava, and P. Gupta. "A Parallel DFA Minimization Algorithm." In High Performance Computing — HiPC 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36265-7_4.

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Konferenzberichte zum Thema "Algorithm performance"

Jencka, Louis, and Amanda Bienz. "Compressed Cannon's Algorithm." In 2024 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, 2024. https://doi.org/10.1109/hpec62836.2024.10938440.

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Zhao, Ruiting, Jingru Ding, Tianqi Song, and Anqi Ye. "Multi-algorithm comparison and performance evaluation." In International Conference on Cloud Computing, Performance Computing, and Deep Learning, edited by Wanyang Dai and Xiangjie Kong. SPIE, 2024. http://dx.doi.org/10.1117/12.3050805.

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Huang, Xueyu, and Jinwei Yi. "Fusion strategy-driven dung beetle optimization algorithm." In International Conference on Algorithms, High Performance Computing and Artificial Intelligence, edited by Pavel Loskot and Liang Hu. SPIE, 2024. http://dx.doi.org/10.1117/12.3051352.

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Chi, Dianwei, Zehao Jia, Tiantian Huang, et al. "An improvement of a decision tree ID3 algorithm." In International Conference on Algorithms, High Performance Computing and Artificial Intelligence, edited by Pavel Loskot and Liang Hu. SPIE, 2024. http://dx.doi.org/10.1117/12.3051346.

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Yong, Gao, Wang Zhigang, Li Hengwu, Guo Hui, and Zhao Liang. "Blockchain signature acceleration engine based on SM2 algorithm." In International Conference on Algorithms, High Performance Computing and Artificial Intelligence, edited by Pavel Loskot and Liang Hu. SPIE, 2024. http://dx.doi.org/10.1117/12.3051327.

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yang, zhenhu, Ning Hu, and Lei Zhou. "An efficient multi-step parallel FFT algorithm on GPU." In International Conference on Algorithms, High Performance Computing and Artificial Intelligence, edited by Pavel Loskot and Liang Hu. SPIE, 2024. http://dx.doi.org/10.1117/12.3051802.

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Chen, Dawei, and Lu Yuan. "Federated learning with improved aggregation via optimal transport algorithm." In International Conference on Algorithms, High Performance Computing and Artificial Intelligence, edited by Pavel Loskot and Liang Hu. SPIE, 2024. http://dx.doi.org/10.1117/12.3051407.

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Mu, Zhilin, Xin Sun, and Tiande Guo. "A fast algorithm for maximizing monotone non-submodular functions." In International Conference on Algorithms, High Performance Computing and Artificial Intelligence, edited by Pavel Loskot and Liang Hu. SPIE, 2024. http://dx.doi.org/10.1117/12.3051434.

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Shen, Hongping, and Ling Wu. "Research on building optimization design based on genetic algorithm." In International Conference on Algorithms, High Performance Computing and Artificial Intelligence, edited by Pavel Loskot and Liang Hu. SPIE, 2024. http://dx.doi.org/10.1117/12.3051667.

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Liu, Tao, and Xiaohui Huang. "Apple surface defect detection algorithm based on improved YOLOv8." In International Conference on Algorithms, High Performance Computing and Artificial Intelligence, edited by Pavel Loskot and Liang Hu. SPIE, 2024. http://dx.doi.org/10.1117/12.3051665.

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Berichte der Organisationen zum Thema "Algorithm performance"

Thompson, David C., and Philippe Pierre Pebay. Performance of a streaming mesh refinement algorithm. Office of Scientific and Technical Information (OSTI), 2004. http://dx.doi.org/10.2172/919132.

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Mustacich, Robert V., William Foreman, and Paul M. Holland. Task 2 Report: Algorithm Development and Performance Analysis. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/adb174940.

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Carrano, C. Bispectral Speckle Imaging Algorithm Performance on Specific Simulated Scenarios. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1108864.

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Vouras, Peter G., and Gerard G. L. Meyer. Hybrid QR Factorization Algorithm for High Performance Computing Architectures. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada419390.

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Jimoh, Mujeeb B. Performance Testing of GPU-Based Approximate Matching Algorithm on Network Traffic. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada620807.

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Peeta, Srinivas, Amit Kumar, and Sushant Sharma. Post-Processing Techniques to Enhance Reliability of Assignment Algorithm Based Performance Measures. Purdue University, 2011. http://dx.doi.org/10.5703/1288284314643.

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Milora, S. L., S. K. Combs, M. J. Gouge, and R. W. Kincaid. QUICKGUN: An algorithm for estimating the performance of two-stage light gas guns. Office of Scientific and Technical Information (OSTI), 1990. http://dx.doi.org/10.2172/6428575.

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Sun, Xiaobai, and Nikos P. Pitsianis. FANTOM: Algorithm-Architecture Codesign for High-Performance Embedded Signal and Image Processing Systems. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada581464.

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Dahlgren, Kathryn Marie, Francesco Rizzi, Karla Vanessa Morris, and Bert Debusschere. Rexsss Performance Analysis: Domain Decomposition Algorithm Implementations for Resilient Numerical Partial Differential Equation Solvers. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1171553.

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Bambha, Neal K., and Shuvra S. Bhattacharyya. A Joint Power/Performance Optimization Algorithm for Multiprocessor Systems Using a Period Graph Construct. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada456719.

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