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7th International Conference on Information Technology and Quantitative Management (ITQM) - Information Technology and Quantitative Management Based on Artificial Intelligence

作者： Roman, Raul-Cristian Precup, Radu-Emil Bojan-Dragos, Claudia-Adina Szedlak-Stinean, Alexandra-Iulia Politehn Univ Timisoara Dept Automat & Appl Informat Bd V Parvan 2 Timisoara 300223 Romania

A novel mix of two data-driven algorithms is proposed in this paper. The mix of the algorithms aims to exploit the main advantage of data-driven Virtual Reference Feedback Tuning (VRFT) algorithm, that is represented by the automatic computation of the optimal parameters using a metaheuristic Grey Wolf Optimizer (GWO) for the Compact Form Dynamic Linearization (CFDL) version of the authors' Model-Free Adaptive Control Takagi-Sugeno Fuzzy Algorithm (CFDL-PDTSFA), so the parameters of the CFDL-PDTSFA are optimally tuned in a model-free manner via VRFT. Three specific optimization problems are defined and solved by Model-Free Adaptive Control, VRFT and GWO algorithms. The new resulted algorithm is validated using experimental results to the arm angular position of the nonlinear tower crane system laboratory equipment. (C) 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-ne-nd/4.0/) Peer-review under responsibility of the scientific committee of the 7th International Conference on Information Technology and Quantitative Management (ITQM 2019)

关键词： data-driven algorithms model-free adaptive fuzzy control tower crane systems virtual reference feedback tuning

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On Matching, and Even Rectifying, Dynamical Systems through Koopman Operator Eigenfunctions

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SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS 2018年第2期17卷 1925-1960页

作者： Bollt, Erik M. Li, Qianxiao Dietrich, Felix Kevrekidis, Ioannis Clarkson Univ Dept Math Dept Elect & Comp Engn Dept Phys Potsdam NY 13699 USA Agcy Sci Technol & Res Inst High Performance Comp Singapore 138632 Singapore Johns Hopkins Univ Dept Chem & Biomol Engn Dept Appl Math & Stat Baltimore MD 21218 USA JHMI Baltimore MD 21218 USA

Matching dynamical systems, through different forms of conjugacies and equivalences, has long been a fundamental concept, and a powerful tool, in the study and classification of nonlinear dynamic behavior (e.g., through normal forms). In this paper we will argue that the use of the Koopman operator and its spectrum is particularly well suited for this endeavor, both in theory, but also especially in view of recent data-driven algorithm developments. We believe, and document through illustrative examples, that this can nontrivially extend the use and applicability of the Koopman spectral theoretical and computational machinery beyond modeling and prediction, towards what can be considered as a systematic discovery of "Cole-Hopf-type" transformations for dynamics.

关键词： Koopman operator rectification conjugacy flow box DMD EDMD dynamical systems data-driven algorithms

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Analysis of Two Dimensionality Reduction Techniques for Fast Simulation of the Spectral Radiances in the Hartley-Huggins Band

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ATMOSPHERE 2019年第3期10卷 142-142页

作者： del Aguila, Ana Efremenko, Dmitry S. Molina Garcia, Victor Xu, Jian German Aerosp Ctr DLR Remote Sensing Technol Inst D-82234 Oberpfaffenhofen Germany

The new generation of atmospheric composition sensors such as TROPOMI is capable of providing spectra of high spatial and spectral resolution. To process this vast amount of spectral information, fast radiative transfer models (RTMs) are required. In this regard, we analyzed the efficiency of two acceleration techniques based on the principal component analysis (PCA) for simulating the Hartley-Huggins band spectra. In the first one, the PCA is used to map the data set of optical properties of the atmosphere to a lower-dimensional subspace, in which the correction function for an approximate but fast RTM is derived. The second technique is based on the dimensionality reduction of the data set of spectral radiances. Once the empirical orthogonal functions are found, the whole spectrum can be reconstructed by performing radiative transfer computations only for a specific subset of spectral points. We considered a clear-sky atmosphere where the optical properties are defined by Rayleigh scattering and trace gas absorption. Clouds can be integrated into the model as Lambertian reflectors. High computational performance is achieved by combining both techniques without losing accuracy. We found that for the Hartley-Huggins band, the combined use of these techniques yields an accuracy better than 0.05% while the speedup factor is about 20. This innovative combination of both PCA-based techniques can be applied in future works as an efficient approach for simulating the spectral radiances in other spectral regions.

关键词： PCA data-driven algorithms Hartley-Huggins band dimensionality reduction techniques two-stream multi-stream

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Combined Model-Free Adaptive Control with Fuzzy Component by Virtual Reference Feedback Tuning for Tower Crane Systems

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Procedia Computer Science 2019年 162卷 267-274页

作者： Raul-Cristian Roman Radu-Emil Precup Claudia-Adina Bojan-Dragos Alexandra-Iulia Szedlak-Stinean Politehnica University of Timişoara Dept. Automation and Applied Informatics Bd. V. Parvan 2 300223 Timişoara Romania

A novel mix of two data-driven algorithms is proposed in this paper. The mix of the algorithms aims to exploit the main advantage of data-driven Virtual Reference Feedback Tuning (VRFT) algorithm, that is represented by the automatic computation of the optimal parameters using a metaheuristic Grey Wolf Optimizer (GWO) for the Compact Form Dynamic Linearization (CFDL) version of the authors’ Model-Free Adaptive Control Takagi-Sugeno Fuzzy Algorithm (CFDL-PDTSFA), so the parameters of the CFDL-PDTSFA are optimally tuned in a model-free manner via VRFT. Three specific optimization problems are defined and solved by Model-Free Adaptive Control, VRFT and GWO algorithms. The new resulted algorithm is validated using experimental results to the arm angular position of the nonlinear tower crane system laboratory equipment.

关键词： data-driven algorithms model-free adaptive fuzzy control tower crane systems virtual reference feedback tuning

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Optimal Reissue Policies for Reducing Tail Latency 17

Optimal Reissue Policies for Reducing Tail Latency

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29th ACM Symposium on Parallelism in algorithms and Architectures (SPAA)

作者： Kaler, Tim He, Yuxiong Elnikety, Sameh MIT CSAIL Cambridge MA 02139 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781450345934

Interactive services send redundant requests to multiple different replicas to meet stringent tail latency requirements. These additional (reissue) requests mitigate the impact of non-deterministic delays within the system and thus increase the probability of receiving an on-time response. There are two existing approaches of using reissue requests to reduce tail latency. (1) Reissue requests immediately to one or more replicas, which multiplies the load and runs the risk of overloading the system. (2) Reissue requests if not completed after a fixed delay. The delay helps to bound the number of extra reissue requests, but it also reduces the chance for those requests to respond before a tail latency target. We introduce a new family of reissue policies, Single-Time / Random (SINGLER), that reissue requests after a delay d with probability q. SINGLER employs randomness to bound the reissue rate, while allowing requests to be reissued early enough so they have sufficient time to respond, exploiting the benefits of both immediate and delayed reissue of prior work. We formally prove, within a simplified analytical model, that SINGLER is optimal even when compared to more complex policies that reissue multiple times. To use SINGLER for interactive services, we provide efficient algorithms for calculating optimal reissue delay and probability from response time logs through data-driven approach. We apply iterative adaptation for systems with load-dependent queuing delays. The key advantage of this data-driven approach is its wide applicability and effectiveness to systems with various design choices and workload properties. We evaluated SINGLER policies thoroughly. We use simulation to illustrate its internals and demonstrate its robustness to a wide range of workloads. We conduct system experiments on the Redis key-value store and Lucene search server. The results show that for utilizations ranging from 40-60%, SINGLER reduces the 99th-percentile latency of Redis by 30-7

关键词： performance optimization interactive services tail-latency distributed computing reissue policy data-driven algorithms redundancy

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A Comparative Study on data-driven Prognostic Approaches Using Fleet Knowledge

A Comparative Study on Data-Driven Prognostic Approaches Usi...

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IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

作者： Cristaldi, Loredana Leone, Giacomo Ottoboni, Roberto Subbiah, Subanatarajan Turrin, Simone Politecn Milan Dept Elect Engn Milan Italy ABB AG Corp Res Ctr Germany Ind Software & Applicat Ladenburg Germany

ISBN: (纸本)9781467392204

Recently, Prognostics and Heath Management techniques have been deeply investigated with the aim to reduce life-cycle cost of products and systems. The increasing availability of condition monitoring data in substantial quantities for multitudes of homogeneous products and the need for generic algorithms that are applicable to complex systems motivates the development of new data-driven prognostic approaches. In this paper, two data-driven algorithms, one based on a statistical approach and another based on Neural Network, are discussed and tested for an application case. The analysis of the results has shown that both the considered approaches are characterized by reliable prediction performances on Remaining Useful Life calculation, thus resulting as potential tools for the application of a Condition-Based Maintenance strategy.

关键词： prognostics and health management data-driven algorithms fleet management condition-based maintenance

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Advanced Analytics for Train Delay Prediction Systems by Including Exogenous Weather data 3

Advanced Analytics for Train Delay Prediction Systems by Inc...

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3rd IEEE/ACM International Conference on data Science and Advanced Analytics (DSAA)

作者： Oneto, Luca Fumeo, Emanuele Clerico, Giorgio Canepa, Renzo Papa, Federico Dambra, Carlo Mazzino, Nadia Anguita, Davide Univ Genoa DIBRIS Via Opera Pia 13 I-16145 Genoa Italy Rete Ferroviaria Italiana SpA Via Don Vincenzo Minetti 6-5 I-16126 Genoa Italy Ansaldo STS SpA Via Paolo Mantovani 3-5 I-16151 Genoa Italy

ISBN: (纸本)9781509052066

State-of-the-art train delay prediction systems neither exploit historical data about train movements, nor exogenous data about phenomena that can affect railway operations. They rely, instead, on static rules built by experts of the railway infrastructure based on classical univariate statistics. The purpose of this paper is to build a data-driven train delay prediction system that exploits the most recent analytics tools. The train delay prediction problem has been mapped into a multivariate regression problem and the performance of kernel methods, ensemble methods and feed-forward neural networks have been compared. Firstly, it is shown that it is possible to build a reliable and robust data-driven model based only on the historical data about the train movements. Additionally, the model can be further improved by including data coming from exogenous sources, in particular the weather information provided by national weather services. Results on real world data coming from the Italian railway network show that the proposal of this paper is able to remarkably improve the current state-of-the-art train delay prediction systems. Moreover, the performed simulations show that the inclusion of weather data into the model has a significant positive impact on its performance.

关键词： data-driven algorithms Delay Prediction Exogenous Weather data Intelligent Transportation Systems Railway

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A markov chain approximation to choice modeling 13

A markov chain approximation to choice modeling

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Proceedings of the fourteenth ACM conference on Electronic commerce

作者： Jose Blanchet Guillermo Gallego Vineet Goyal Columbia University New York USA

ISBN: (纸本)9781450319621

Assortment planning is an important problem that arises in many industries such as retailing and airlines. One of the key challenges in an assortment planning problem is to identify the "right model" for the substitution behavior of customers from the data. Error in model selection can lead to highly sub-optimal decisions. In this paper, we present a new choice model that is a simultaneous approximation for all random utility based discrete choice models including the multinomial logit, the nested logit and mixtures of multinomial logit models. Our model is based on a new primitive for substitution behavior where substitution from one product to another is modeled as a state transition of a Markov chain. In particular, we consider a Markov chain where there is a state for each product, and model the substitution behavior as follows: a customer arrives in the state corresponding to his most preferred product. If that product is not available, he/she transitions to other product states according to the transition probabilities of the Markov chain. Therefore, the preferences of the customers are approximated by Markovian transitions in this choice *** show that the choice probabilities computed by our model are a good approximation to the true choice probabilities of any random utility discrete based choice model under mild conditions. Moreover, they are exact if the underlying model is a Multinomial logit model. We also give a data-driven procedure to estimate the parameters of the Markov chain model that does not require any knowledge of the latent choice model. Furthermore, we show that the assortment optimization problem under our choice model can be solved efficiently in polynomial time. In an assortment optimization problem, the goal is to find an assortment (or offer set) that maximizes the total expected revenue. The result is quite surprising as we can not even express the choice probabilities for different offer sets using a simple functional form in the

关键词： choice modeling assortment optimization revenue management data-driven algorithms

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Adaptive data-driven Inventory Control with Censored Demand Based on Kaplan-Meier Estimator

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OPERATIONS RESEARCH 2011年第4期59卷 929-941页

作者： Huh, Woonghee Tim Levi, Retsef Rusmevichientong, Paat Orlin, James B. Univ British Columbia Sauder Sch Business Vancouver BC V6T 1Z2 Canada MIT Alfred P Sloan Sch Management Cambridge MA 02139 USA Cornell Univ Sch Operat Res & Informat Engn Ithaca NY 14853 USA

Using the well-known product-limit form of the Kaplan-Meier estimator from statistics, we propose a new class of nonparametric adaptive data-driven policies for stochastic inventory control problems. We focus on the distribution-free newsvendor model with censored demands. The assumption is that the demand distribution is not known and there are only sales data available. We study the theoretical performance of the new policies and show that for discrete demand distributions they converge almost surely to the set of optimal solutions. Computational experiments suggest that the new policies converge for general demand distributions, not necessarily discrete, and demonstrate that they are significantly more robust than previously known policies. As a by-product of the theoretical analysis, we obtain new results on the asymptotic consistency of the Kaplan-Meier estimator for discrete random variables that extend existing work in statistics. To the best of our knowledge, this is the first application of the Kaplan-Meier estimator within an adaptive optimization algorithm, in particular, the first application to stochastic inventory control models. We believe that this work will lead to additional applications in other domains.

关键词： inventory control data-driven algorithms statistics

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Termination detection in data-driven parallel computations/applications

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2003年第3期63卷 312-326页

作者： Khokhar, AA Hambrusch, SE Kocalar, E Univ Illinois Dept Elect & Comp Sci Chicago IL 60607 USA Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA WebTV Networks Mountain View CA 94043 USA

High-performance computing applications with data-driven communication and computation characteristics require synchronization routines in the form of eureka, barrier, or termination synchronization. In this paper, we consider termination synchronization for two different execution models, the AP and the APS model. In the AP model, processors are either active or passive and a passive processor can be made active by another active processor. In the APS model, processors can also be in a server state. A passive processor entering the server state does not become active again. In addition, a server processor cannot change the status of other processors. We describe and analyze solutions for both models and present experimental work highlighting the differences between the models. We show that in almost all situations the use of an AP algorithm to detect termination in an APS environment will result in loss of performance. Our experimental work on the Cray T3E provides insight into where and why this performance loss occurs. (C) 2003 Elsevier Science (USA). All rights reserved.

关键词： data-driven algorithms termination detection tree and ring networks synchronization

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