Results 11 to 20 of about 729,060 (268)
Markov State Models: To Optimize or Not to Optimize [PDF]
Journal of Chemical Theory and Computation, 2023 Markov state models (MSM) are a popular statistical method for analyzing the conformational dynamics of proteins, including protein folding. With all statistical and machine learning (ML) models choices must be made about the modeling pipeline that cannot be directly learned from the data.
Robert Arbon +2 more
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Modelling and Optimization in Microgrids [PDF]
Energies, 2016 The expansion of renewable energies is progressing strongly. The influence on the power supply networks by the volatility of the infeed must be met with new concepts. In this paper we investigate the possibilities of integrating microgrids as a cooperating unit in the power supply network to support further expansion of RES power plants.
Tobias Porsinger +3 more
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Cache Optimization Models and Algorithms [PDF]
Foundations and Trends® in Communications and Information Theory, 2020 Caching refers to the act of replicating information a a faster (or closer) medium with the purpose of improving performance. This deceptively simple idea has given rise to some of the hardest optimization problems in the fields of computer systems, networking, and the Internet, many of which remain unsolved several years after their conception.
Georgios S. Paschos +2 more
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Encrypted traffic classification method based on convolutional neural network
网络与信息安全学报, 2022 Aiming at the problems of low accuracy, weak generality, and easy privacy violation of traditional encrypted network traffic classification methods, an encrypted traffic classification method based on convolutional neural network was proposed, which ...
Rongna XIE +3 more
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Modeling and Optimization of Risk
Surveys in Operations Research and Management Science, 2011 Abstract This paper surveys the most recent advances in the context of decision making under uncertainty, with an emphasis on the modeling of risk-averse preferences using the apparatus of axiomatically defined risk functionals, such as coherent measures of risk and deviation measures, and their connection to utility theory, stochastic dominance, and
Department of Mechanical and Industrial Engineering, University of Iowa, Iowa City, IA 52242, United States ( host institution ) +3 more
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Systematic Modeling for Optimization [PDF]
, 2014 Optimization usually requires models, which are computationally speaking less expensive than models commonly used for simulations. At the same time, process optimization and model predictive control etc. require dependable accuracies in addition to the fastness.
Esche, Erik +2 more
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Autoregressive optimal transport models
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2023 Abstract Series of univariate distributions indexed by equally spaced time points are ubiquitous in applications and their analysis constitutes one of the challenges of the emerging field of distributional data analysis. To quantify such distributional time series, we propose a class of intrinsic autoregressive models that operate in the
Changbo Zhu, Hans-Georg Müller
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Study on Safety Analysis Method to Task Process of Civil Aircraft Weather Radar System
Xibei Gongye Daxue Xuebao, 2020 To solve the task-process-safety problem of airborne weather radar system, a set of case-inspired safety analysis method is proposed based on the STAMP(Systems-Theoretic Accident Model and Process).
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Applied Sciences, 2021 A study on object detection utilizing deep learning is in continuous progress to promptly and accurately determine the surrounding situation in the driving environment.
Seong-Eun Ryu, Kyung-Yong Chung
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Optimal Policy in OG Models [PDF]
Journal of Economic Theory, 2000 A mathematical model of general stationary overlapping generations economies with long-lived consumers, many commodities and productions is considered. An economy is regulated by government via fiscal policy (transfers and taxes). Its aim is to maximize a utilitarian social welfare function, which is a possibly discounted sum of such generations ...
Christian Ghiglino, Mich Tvede
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