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data-driven optimization of reliability using buffered failure probability

STRUCTURAL SAFETY 2022年 98卷

作者： Byun, Ji-Eun Royset, Johannes O. Tech Univ Munich Engn Risk Anal Grp Munich Germany Naval Postgrad Sch Operat Res Dept Monterey CA USA

Design and operation of complex engineering systems rely on reliability optimization. Such optimization requires us to account for uncertainties expressed in terms of complicated, high-dimensional probability distributions, for which only samples or data might be available. However, using data or samples often degrades the computational efficiency, particularly as the conventional failure probability is estimated using the indicator function whose gradient is not defined at zero. To address this issue, by leveraging the buffered failure probability, the paper develops the buffered optimization and reliability method (BORM) for efficient, data-driven optimization of reliability. The proposed formulations, algorithms, and strategies greatly improve the computational efficiency of the optimization and thereby address the needs of high-dimensional and nonlinear problems. In addition, an analytical formula is developed to estimate the reliability sensitivity, a subject fraught with difficulty when using the conventional failure probability. The buffered failure probability is thoroughly investigated in the context of many different distributions, leading to a novel measure of tail-heaviness called the buffered tail index. The efficiency and accuracy of the proposed optimization methodology are demonstrated by three numerical examples. Although they might show slight deviations from a target failure probability because of sampling errors and other inaccuracies, the results underline unique advantages and potentials of the buffered failure probability for datadriven reliability analysis.

关键词： Reliability optimization data-driven optimization Buffered failure probability Superquantile Tail index Reliability sensitivity

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data-driven optimization of the periodic beam with multiple acoustic black holes

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JOURNAL OF SOUND AND VIBRATION 2021年 493卷 115816-115816页

作者： He, Meng-Xin Ding, Qian Tianjin Univ Dept Mech Tianjin 300072 Peoples R China

This paper presents data-driven optimization study of a periodic beam with multiple acoustic black holes (ABH) for better dynamic performance. The vibration and sound radiation of the structure are analyzed by the spectral element method (SEM). A data-driven multi-objective optimization method is proposed to investigate the design problems for minimizing the structural weight and maximizing the sound transmission loss. Several cases are presented to demonstrate the accuracy of the SEM compared with the finite element method, and the multi-objective optimal designs of the ABH beam. Numerical results indicate that the data-driven method can significantly improve the computational efficiency. Pareto solutions provide a variety of design options and reveal the mechanisms about band gaps of the periodic beam. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： data-driven optimization Spectral element method Noise reduction Multi-objective optimization Acoustic black holes

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data-driven optimization of a gas turbine combustor: A Bayesian approach addressing NOX emissions, lean extinction limits, and thermoacoustic stability

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data-CENTRIC ENGINEERING 2024年 5卷 e32-e32页

作者： Reumschuessel, Johann Moritz von Saldern, Jakob G. R. Cosic, Bernhard Paschereit, Christian Oliver TU Berlin Chair Fluid Dynam Muller Breslau Str 8 D-10623 Berlin Germany TU Berlin Lab Flow Instabil & Dynam Muller Breslau Str 8 D-10623 Berlin Germany MAN Energy Solut SE Steinbrinkstr 1 D-46145 Oberhausen Germany

The design of gas turbine combustors for optimal operation at different power ratings is a multifaceted engineering task, as it requires the consideration of several objectives that must be evaluated under different test conditions. We address this challenge by presenting a data-driven approach that uses multiple probabilistic surrogate models derived from Gaussian process regression to automatically select optimal combustor designs from a large parameter space, requiring only a few experimental data points. We present two strategies for surrogate model training that differ in terms of required experimental and computational efforts. Depending on the measurement time and cost for a target, one of the strategies may be preferred. We apply the methodology to train three surrogate models under operating conditions where the corresponding design objectives are critical: reduction of NOx emissions, prevention of lean flame extinction, and mitigation of thermoacoustic oscillations. Once trained, the models can be flexibly used for different forms of a posteriori design optimization, as we demonstrate in this study.

关键词： Bayesian statistics data-driven optimization emission reduction gas turbine combustion surrogate modeling thermoacoustics

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LIGHTWEIGHT VOICE ANONYMIZATION BASED ON data-driven optimization OF CASCADED VOICE MODIFICATION MODULES

LIGHTWEIGHT VOICE ANONYMIZATION BASED ON DATA-DRIVEN OPTIMIZ...

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IEEE Spoken Language Technology Workshop (SLT)

作者： Kai, Hiroto Takamichi, Shinnosuke Shiota, Sayaka Kiya, Hitoshi Tokyo Metropolitan Univ Tokyo Japan Univ Tokyo Tokyo Japan

ISBN: (纸本)9781728170664

In this paper, we propose a voice anonymization framework based on data-driven optimization of cascaded voice modification modules. With increasing opportunities to use speech dialogue with machines nowadays, research regarding privacy protection of speaker information encapsulated in speech data is attracting attention. Anonymization, which is one of the methods for privacy protection, is based on signal processing manners, and the other one based on machine learning ones. Both approaches have a trade off between intelligibility of speech and degree of anonymization. The proposed voice anonymization framework utilizes advantages of machine learning and signal processing-based approaches to find the optimized trade off between the two. We use signal processing methods with training data for optimizing hyperparameters in a data-driven manner. The speech is modified using cascaded lightweight signal processing methods and then evaluated using black-box ASR and ASV, respectively. Our proposed method succeeded in deteriorating the speaker recognition rate by approximately 22% while simultaneously improved the speech recognition rate by over 3% compared to a signal processing-based conventional method.

关键词： Voice anonymization Voice Privacy data-driven optimization cascaded voice modification

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Psychoacoustically-motivated Adaptive β-order Generalized Spectral Subtraction Based on data-driven optimization

Psychoacoustically-motivated Adaptive β-order Generalized S...

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9th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2008)

作者： Li, Junfeng Jiang, Hui Akagi, Masato Japan Adv Inst Sci & Technol Sch Informat Sci Tokyo Japan York Univ Dept Comp Sci & Engn N York ON Canada

ISBN: (纸本)9781615673780

To mitigate the performance limitations caused by the constant spectral order beta in the traditional spectral subtraction methods, we previously presented an adaptive beta-order generalized spectral subtraction (GSS) in which the spectral order beta is updated in a heuristic way [10]. In this paper, we propose a psychoacoustically-motivated adaptive beta-order GSS, by considering that different frequency bands contribute different amounts to speech intelligibility (i.e., the band-importance function). Specifically, in this proposed adaptive beta-order GSS, the tendency of spectral order beta to change with the input local signal-to-noise ratio (SNR) is quantitatively approximated by a sigmoid function, which is derived through a data-driven optimization procedure by minimizing the intelligibility-weighted distance between the desired speech spectrum and its estimate. The inherent parameters of the sigmoid function are further optimized with the data-driven optimization procedure. Experimental results indicate that the proposed psychoacoustically-motivated adaptive beta-order GSS yields great improvements over the traditional spectral subtraction methods with the intelligibility-weighted measures.

关键词： Psychoacoustically-motivated adaptive beta-order GSS data-driven optimization Importance function Speech intelligibility Sigmoid function

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On Regularization Schemes for data-driven optimization 31

On Regularization Schemes for Data-Driven Optimization

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31st Chinese Control And Decision Conference (CCDC)

作者： Ni, Wei Jiang, Zhong-Ping Nanchang Univ Sch Sci Nanchang 330031 Jiangxi Peoples R China NYU Tandon Sch Engn New York NY 11201 USA

ISBN: (纸本)9781728101057

This paper presents several regularization schemes for data-driven optimization problems. data-driven optimization is a challenging task because the model or the distribution describing the inherent stochastic disturbance is unknown but instead only a set of data sampled from it can be available. Most existing literature tackles the datadriven optimization problem by using the approach of distributionally robust optimization which is however an infinite-dimensional optimization problem and thus is required to be transformed into a tractable finite-dimensional optimization problem. As a different line, the regularization schemes presented in this paper contribute to solving the data-driven minimization problem by approximating the unknown expectation with some data-dependent surrogates in a high confidence and by minimizing the surrogates in a tractable way. The tradeoff between the confidence level and the optimization error is analyzed.

关键词： data-driven optimization distributionally robust optimization phi-divergence chi-distribution empirical likelihood Cressie-Read Likelihood

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Topics in Deep Learning and data-driven optimization

Topics in Deep Learning and Data-Driven Optimization

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作者： Bahamou, Achraf Columbia University

学位级别：Ph.D., Doctor of Philosophy

data-driven optimization has become an increasingly popular approach for solving complex problems in various domains, such as finance, online retail, and engineering. However, in many real-world applications, the amount of available data can vary significantly, ranging from limited to large data sets. Both of these regimes present unique modeling and optimization challenges. In this thesis, we explore two distinct problems in two different data availability and model complexity regimes. In the first part(Chapters 2 and 3), we focus on the development of novel optimization algorithms for training deep neural network(DNN) models on large data sets, in particular, we develop practical optimization methods that incorporate curvature information in an economical way to accelerate the optimization process. The performance of the proposed methods is compared to that of several state-of-the-art methods used to train DNNs, to validate their effectiveness both in terms of time efficiency and generalization power. In the second part of the dissertation(Chapters 4), we focus on data-driven pricing in the limited data regime. Morespecifically, we study the fundamental problem of a seller pricing a product based on historical information consisting of the observed demand at a single historical price point. We develop a novel framework that allows characterizing optimal performance for deterministic or more general randomized mechanisms and leads to fundamental novel insights on the value of limited demand data for pricing.

关键词： data-driven optimization Deep learning optimization Pricing details Online retail

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Offline data-driven optimization: Benchmarks, Algorithms and Applications

Offline Data-Driven Optimization: Benchmarks, Algorithms and...

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作者： Geng, Xinyang University of California Berkeley

学位级别：Ph.D., Doctor of Philosophy

Black-box model-based optimization problems, where the goal is to find a design input that maximizes an unknown objective function, are ubiquitous in a wide range of domains, such as the design of proteins, DNA sequences, aircraft, and robots. Solving model-based optimization problems typically requires actively querying the unknown objective function on design proposals, which means physically building the candidate molecule, aircraft, or robot, testing it to obtain the result. This process can be expensive and time consuming, and one might instead prefer to optimize for the best design using only the data one already has. This setting, called offline model-based optimization (MBO), poses substantial and different algorithmic challenges than more commonly studied online techniques. In this thesis, I will cover how to build benchmarks and algorithms to tackle these challenges. In particular, I will first define the offline MBO problem formally, and identify the common challenging properties associated with real-world offline MBO problems. I will then present Design-Bench, a benchmark for evaluating offline MBO methods with a suite of diverse and realistic tasks derived from real-world optimization problems. With the benchmark set up, I will describe conservative objective models (COMs), a surprisingly simple but effective method for tackling offline MBO problems. Finally, I will cover applications of offline MBO in computational chemistry and synthetic biology to demonstrate how variants of COMs can be applied to solve real-world scientific problems.

关键词： Back-box optimization data-driven optimization Offline model-based optimization Design inputs Online techniques

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Lightweight and irreversible speech pseudonymization based on data-driven optimization of cascaded voice modification modules

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COMPUTER SPEECH AND LANGUAGE 2022年第0期72卷 101315-101315页

作者： Kai, Hiroto Takamichi, Shinnosuke Shiota, Sayaka Kiya, Hitoshi Tokyo Metropolitan Univ Grad Sch Syst Design 6-6 Asahigaoka Hino Tokyo Japan Univ Tokyo Grad Sch Informat Sci & Technol Bunkyo Ku 7-3-1 Hongo Tokyo Japan

In this paper, we propose a speech pseudonymization framework that utilizes cascaded and superposition-based voice modification modules. With increasing opportunities to use spoken dialogue systems nowadays, research regarding protecting the privacy of speaker information encapsulated in speech data is attracting attention. Pseudonymization, which is one method for voice privacy protection, aims to keep the intelligibility of speech while simultaneously suppressing speaker-specific information. One motivation of our framework is to achieve a reliable pseudonymization performance with light computation. To do this, we utilize the advantages of both machine learning-based and signal processing-based approaches. The advantages are (1) using signal processing-based methods parameterized with few hyperparameters and (2) using machine learning-based optimization to optimize all hyperparameters on the basis of black-box systems consisting of automatic speaker verification and automatic speech recognition. Our method of cascading signal processing modules, which are jointly optimized in a data-driven manner, can pseudonymize speech in a lightweight manner. Additionally, we discuss irreversible pseudonymization approaches and propose a superposition approach, yet another pseudonymization method that is more irreversible than the cascade method in terms of estimating the adequate parameters to recover the original signal. From the experimental results conducted under the VoicePrivacy 2020 protocols, we can demonstrate that (1) our cascade method succeeds in deteriorating the speaker recognition rate by over 24% while simultaneously improving the speech recognition rate by approximately 8% compared with a signal processing-based baseline system of VoicePrivacy 2020 and that (2) our superposition method works comparable to our cascade method in terms of pseudonymization performance.

关键词： Speech pseudonymization Voice privacy protection data-driven optimization Cascaded voice modification Irreversibility

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Recovery of energy losses using an online data-driven optimization technique

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ENERGY CONVERSION AND MANAGEMENT 2020年 225卷 113339-113339页

作者： Ashuri, Turaj Li, Yaoyu Hosseini, Seyed Ehsan Kennesaw State Univ Southern Polytech Coll Engn & Engn Technol Marietta GA 30060 USA Univ Texas Dallas Erik Jonsson Sch Engn & Comp Sci Richardson TX 75080 USA Arkansas Tech Univ Coll Engn & Appl Sci Russellville AR 72801 USA

This paper investigates the energy loss due to rotor aging for a wind turbine and its recovery. Energy loss for a wind turbine is caused by formation of dust and bugs, and corrosion and erosion of the surface of the rotor by sand and rain. We present a real-lime data-driven optimization algorithm that uses dither and demodulation signals to recover the energy loss by extracting online the sensitivity of the function of interest to optimize. We use a viscid-inviscid model to represent aerodynamic performance loss of the rotor due to aging. We employ lime-domain aeroservoelastic simulation of the CART3 wind turbine of the National Renewable Energy Laboratory to provide a comprehensive assessment of aging and its recovery. We also investigate the impact of aging on structural loads and levelized cost of energy. Using annual energy production and WindPACT cost models of the National Renewable Energy Laboratory, the levelized cost of energy enables an overall assessment of aging and its recovery. To evaluate the impact of the energy loss recovery on structural loading, ultimate and fatigue loads for power production design load case are used. The results of this study show 6.9% reduction in the annual energy production due to aging for a class C wind condition based on IEC61400 standard. This energy loss increases the levelized cost of energy by 7.5%. Our online optimization algorithm can recover 1.7% of the energy losses, and it results in 2.0% reduction in the levelized cost of energy. Results of the ultimate loads indicate that an aged rotor reduces structural loading on most components except the main shaft where the bending moment shows an increase of 5.3%. Rotor aging also reduces the fatigue loads in most components except the tower bottom fore-aft moment with an increase of 5.5%. The statistical inference of the results shows that the proposed optimization algorithm is effective in recovering aging related energy losses of wind turbines with a confidence level of

关键词： data-driven optimization Energy loss recovery Annual energy production Levelized cost of energy Ultimate and fatigue structural loads Extremum seeking controls Hypothesis testing

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