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Journal articles on the topic 'Active subspace'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Holodnak, John T., Ilse C. F. Ipsen, and Ralph C. Smith. "A Probabilistic Subspace Bound with Application to Active Subspaces." SIAM Journal on Matrix Analysis and Applications 39, no. 3 (January 2018): 1208–20. http://dx.doi.org/10.1137/17m1141503.

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2

Xie, Ziqi, and Lihong Wang. "Active Block Diagonal Subspace Clustering." IEEE Access 9 (2021): 83976–92. http://dx.doi.org/10.1109/access.2021.3087575.

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3

Yu, Yu Min. "The Characteristics of Affine Bivariate Pseudoframes of Subspace Associated with a Bivariate Filter Functions." Key Engineering Materials 439-440 (June 2010): 926–31. http://dx.doi.org/10.4028/www.scientific.net/kem.439-440.926.

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Frame theory has been the focus of active research for twenty years, both in theory and applications. In this work, the notion of the bivariate generalized multiresolution structure (BGMS) of subspace is proposed. The characteristics of bivariate affine pseudoframes for subspaces is investigated. The construction of a GMS of Paley-Wiener subspace of is studied. The pyramid decomposition scheme is obtained based on such a GMS and a sufficient condition for its existence is provided. A constructive method for affine frames of based on a BGMS is established.

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4

Li, Changsheng, Kaihang Mao, Lingyan Liang, Dongchun Ren, Wei Zhang, Ye Yuan, and Guoren Wang. "Unsupervised Active Learning via Subspace Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 8332–39. http://dx.doi.org/10.1609/aaai.v35i9.17013.

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Unsupervised active learning has been an active research topic in machine learning community, with the purpose of choosing representative samples to be labelled in an unsupervised manner. Previous works usually take the minimization of data reconstruction loss as the criterion to select representative samples which can better approximate original inputs. However, data are often drawn from low-dimensional subspaces embedded in an arbitrary high-dimensional space in many scenarios, thus it might severely bring in noise if attempting to precisely reconstruct all entries of one observation, leading to a suboptimal solution. In view of this, this paper proposes a novel unsupervised Active Learning model via Subspace Learning, called ALSL. In contrast to previous approaches, ALSL aims to discovery the low-rank structures of data, and then perform sample selection based on learnt low-rank representations. To this end, we devise two different strategies and propose two corresponding formulations to perform unsupervised active learning with and under low-rank sample representations respectively. Since the proposed formulations involve several non-smooth regularization terms, we develop a simple but effective optimization procedure to solve them. Extensive experiments are performed on five publicly available datasets, and experimental results demonstrate the proposed first formulation achieves comparable performance with the state-of-the-arts, while the second formulation significantly outperforms them, achieving a 13\% improvement over the second best baseline at most.

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Xu, Yong Fan. "Study of Matrix Multipliers for Normalized Frame Multi-Wavelets and Applications in Engineering Material Technology." Advanced Materials Research 753-755 (August 2013): 2321–24. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2321.

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Frame theory has been the focus of active research for twenty years, both in theory and applications. Matrix Fourier multipliers send every orthonoamal wavelet to an orthonoamal wavelet. In this work, the notion of the bivariate generalized multiresolution structure (BGMS) of subspace is proposed. The characteristics of bivariate affine pseudoframes for subspaces is investigated. The construction of a GMS of Paley-Wiener subspace of is studied. The pyramid decomposition scheme is obtained based on such a GMS and a sufficient condition for its existence is provided. A constructive method for affine frames of based on a BGMS is established.

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Erdal, Daniel, and Olaf A. Cirpka. "Global sensitivity analysis and adaptive stochastic sampling of a subsurface-flow model using active subspaces." Hydrology and Earth System Sciences 23, no. 9 (September 18, 2019): 3787–805. http://dx.doi.org/10.5194/hess-23-3787-2019.

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Abstract. Integrated hydrological modeling of domains with complex subsurface features requires many highly uncertain parameters. Performing a global uncertainty analysis using an ensemble of model runs can help bring clarity as to which of these parameters really influence system behavior and for which high parameter uncertainty does not result in similarly high uncertainty of model predictions. However, already creating a sufficiently large ensemble of model simulation for the global sensitivity analysis can be challenging, as many combinations of model parameters can lead to unrealistic model behavior. In this work we use the method of active subspaces to perform a global sensitivity analysis. While building up the ensemble, we use the already-existing ensemble members to construct low-order meta-models based on the first two active-subspace dimensions. The meta-models are used to pre-determine whether a random parameter combination in the stochastic sampling is likely to result in unrealistic behavior so that such a parameter combination is excluded without running the computationally expensive full model. An important reason for choosing the active-subspace method is that both the activity score of the global sensitivity analysis and the meta-models can easily be understood and visualized. We test the approach on a subsurface-flow model including uncertain hydraulic parameters, uncertain boundary conditions and uncertain geological structure. We show that sufficiently detailed active subspaces exist for most observations of interest. The pre-selection by the meta-model significantly reduces the number of full-model runs that must be rejected due to unrealistic behavior. An essential but difficult part in active-subspace sampling using complex models is approximating the gradient of the simulated observation with respect to all parameters. We show that this can effectively and meaningfully be done with second-order polynomials.

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7

Liu, Yanbei, Kaihua Liu, Changqing Zhang, Xiao Wang, Shaona Wang, and Zhitao Xiao. "Entropy-based active sparse subspace clustering." Multimedia Tools and Applications 77, no. 17 (April 20, 2018): 22281–97. http://dx.doi.org/10.1007/s11042-018-5945-1.

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8

Seshadri, P., S. Yuchi, G. T. Parks, and S. Shahpar. "Supporting multi-point fan design with dimension reduction." Aeronautical Journal 124, no. 1279 (July 27, 2020): 1371–98. http://dx.doi.org/10.1017/aer.2020.50.

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AbstractMotivated by the idea of turbomachinery active subspace performance maps, this paper studies dimension reduction in turbomachinery 3D CFD simulations. First, we show that these subspaces exist across different blades—under the same parametrisation—largely independent of their Mach number or Reynolds number. This is demonstrated via a numerical study on three different blades. Then, in an attempt to reduce the computational cost of identifying a suitable dimension reducing subspace, we examine statistical sufficient dimension reduction methods, including sliced inverse regression, sliced average variance estimation, principal Hessian directions and contour regression. Unsatisfied by these results, we evaluate a new idea based on polynomial variable projection—a non-linear least-squares problem. Our results using polynomial variable projection clearly demonstrate that one can accurately identify dimension reducing subspaces for turbomachinery functionals at a fraction of the cost associated with prior methods. We apply these subspaces to the problem of comparing design configurations across different flight points on a working line of a fan blade. We demonstrate how designs that offer a healthy compromise between performance at cruise and sea-level conditions can be easily found by visually inspecting their subspaces.

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9

Liu, Guangcan, and Shuicheng Yan. "Active Subspace: Toward Scalable Low-Rank Learning." Neural Computation 24, no. 12 (December 2012): 3371–94. http://dx.doi.org/10.1162/neco_a_00369.

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We address the scalability issues in low-rank matrix learning problems. Usually these problems resort to solving nuclear norm regularized optimization problems (NNROPs), which often suffer from high computational complexities if based on existing solvers, especially in large-scale settings. Based on the fact that the optimal solution matrix to an NNROP is often low rank, we revisit the classic mechanism of low-rank matrix factorization, based on which we present an active subspace algorithm for efficiently solving NNROPs by transforming large-scale NNROPs into small-scale problems. The transformation is achieved by factorizing the large solution matrix into the product of a small orthonormal matrix (active subspace) and another small matrix. Although such a transformation generally leads to nonconvex problems, we show that a suboptimal solution can be found by the augmented Lagrange alternating direction method. For the robust PCA (RPCA) (Candès, Li, Ma, & Wright, 2009 ) problem, a typical example of NNROPs, theoretical results verify the suboptimality of the solution produced by our algorithm. For the general NNROPs, we empirically show that our algorithm significantly reduces the computational complexity without loss of optimality.

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10

N., Navaneeth, and Souvik Chakraborty. "Surrogate assisted active subspace and active subspace assisted surrogate—A new paradigm for high dimensional structural reliability analysis." Computer Methods in Applied Mechanics and Engineering 389 (February 2022): 114374. http://dx.doi.org/10.1016/j.cma.2021.114374.

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Zhang, Jihui, Thushara Abhayapala, Wen Zhang, and Prasanga Samarasinghe. "Active Noise Control over Space: A Subspace Method for Performance Analysis." Applied Sciences 9, no. 6 (March 25, 2019): 1250. http://dx.doi.org/10.3390/app9061250.

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In this paper, we investigate the maximum active noise control performance over a three-dimensional (3-D) spatial space, for a given set of secondary sources in a particular environment. We first formulate the spatial active noise control (ANC) problem in a 3-D room. Then we discuss a wave-domain least squares method by matching the secondary noise field to the primary noise field in the wave domain. Furthermore, we extract the subspace from wave-domain coefficients of the secondary paths and propose a subspace method by matching the secondary noise field to the projection of primary noise field in the subspace. Simulation results demonstrate the effectiveness of the proposed algorithms by comparison between the wave-domain least squares method and the subspace method, more specifically the energy of the loudspeaker driving signals, noise reduction inside the region, and residual noise field outside the region. We also investigate the ANC performance under different loudspeaker configurations and noise source positions.

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Zhang, Rong Rong, Yue Hua Li, and Jian Qiao Wang. "Laplacian Transductive Optimal Design for Face Classification." Applied Mechanics and Materials 513-517 (February 2014): 1101–4. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.1101.

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The problem of face classification is essentially a nonlinear subspace classification problem. The features of different face samples lie on different nonlinear subspaces. If the most representative samples of each subspace are selected as the training set, we can enhance the reliability of the classification, as well as reduce the computation. In this paper, a manifold based active learning algorithm, called Laplacian Transductive Optimal Design (LTOD), is presented to select the most representative samples. LTOD reconstructs each sample by the graph Laplacian matrix to make sure that the reconstructed samples and the original samples share the same manifold structure. Then, the samples which can be used to best reconstruct the whole sample set are selected as the training set. The experimental results on Yale face database have demonstrated the effectiveness of our method.

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13

Khatamsaz, Danial, Abhilash Molkeri, Richard Couperthwaite, Jaylen James, Raymundo Arróyave, Ankit Srivastava, and Douglas Allaire. "Adaptive active subspace-based efficient multifidelity materials design." Materials & Design 209 (November 2021): 110001. http://dx.doi.org/10.1016/j.matdes.2021.110001.

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14

Pichara, Karim, and Alvaro Soto. "Active learning and subspace clustering for anomaly detection." Intelligent Data Analysis 15, no. 2 (March 11, 2011): 151–71. http://dx.doi.org/10.3233/ida-2010-0461.

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15

Chen, Yanxi, Gen Li, and Yuantao Gu. "Active Orthogonal Matching Pursuit for Sparse Subspace Clustering." IEEE Signal Processing Letters 25, no. 2 (February 2018): 164–68. http://dx.doi.org/10.1109/lsp.2017.2741509.

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16

Nie, Zhenhua, Yongkang Xie, Jun Li, Hong Hao, and Hongwei Ma. "Damage detection in bridges under moving loads based on subspace projection residuals." Advances in Structural Engineering 25, no. 5 (January 6, 2022): 979–1001. http://dx.doi.org/10.1177/13694332211056107.

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This paper proposes a data-driven method using subspace projection residual of the responses to identify the damage locations in bridges subjected to moving loads. In this method, a moving window with a certain length determined by the sampling frequency and the fundamental frequency of the measured responses is used to cut out the acceleration responses of the bridge subjected to a moving vehicle. The characteristic subspaces of the windowed signals are subsequently extracted to calculate the local damage index using the subspace projection residual. When the window moves to the damage location, the orthogonality between the active subspace of the damaged state and the null subspace of the healthy state is invalid, which leads to a relatively large projection residual that can be used to localize the damage. To improve the reliability of the proposed approach, a one-side upper confidence limit is introduced. A simply supported beam bridge subjected to a moving mass is simulated to verify the effectiveness of the proposed method. Numerical results indicate that the proposed approach can accurately localize the single and multiple damages, even when the responses are smeared with a significant noise. Experimental tests conducted on a steel beam bridge model also demonstrate the performance and accuracy of the proposed approach. The results demonstrate that the proposed method can localize the damage even with a small number of sensors, indicating the method has a good and promising performance for practical engineering applications.

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17

Boulemnadjel, Amel, Fella Hachouf, Amel Hebboul, and Khalifa Djemal. "Active learning for improving a soft subspace clustering algorithm." Engineering Applications of Artificial Intelligence 46 (November 2015): 196–208. http://dx.doi.org/10.1016/j.engappai.2015.08.005.

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18

Zhou, Tong, and Yongbo Peng. "Active learning and active subspace enhancement for PDEM-based high-dimensional reliability analysis." Structural Safety 88 (January 2021): 102026. http://dx.doi.org/10.1016/j.strusafe.2020.102026.

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19

Alswaitti, Mohammed, Kamran Siddique, Shulei Jiang, Waleed Alomoush, and Ayat Alrosan. "Dimensionality Reduction, Modelling, and Optimization of Multivariate Problems Based on Machine Learning." Symmetry 14, no. 7 (June 21, 2022): 1282. http://dx.doi.org/10.3390/sym14071282.

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Simulation-based optimization design is becoming increasingly important in engineering. However, carrying out multi-point, multi-variable, and multi-objective optimization work is faced with the “Curse of Dimensionality”, which is highly time-consuming and often limited by computational burdens as in aerodynamic optimization problems. In this paper, an active subspace dimensionality reduction method and the adaptive surrogate model were proposed to reduce such computational costs while keeping a high precision. In this method, the active subspace dimensionality reduction technique, three-layer radial basis neural network approach, and polynomial fitting process were presented. For the model evaluation, a NASA standard test function problem and RAE2822 airfoil drag reduction optimization were investigated in the experimental design problem. The efficacy of the method was proved by both the experimental examples in which the adaptive surrogate model in a dominant one-dimensional active subspace is given and the optimization efficiency was improved by two orders. Furthermore, the results show that the constructed surrogate model reduced dimensionality and alleviated the complexity of conventional multivariate surrogate modeling with high precision.

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20

Zhang, Long, Nana Wang, Jieli Wei, and Zhuyin Ren. "Exploring active subspace for neural network prediction of oscillating combustion." Combustion Theory and Modelling 25, no. 3 (April 16, 2021): 570–87. http://dx.doi.org/10.1080/13647830.2021.1915500.

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21

Zhang, Lihe, Jiayu Sun, Tiantian Wang, Yifan Min, and Huchuan Lu. "Visual Saliency Detection via Kernelized Subspace Ranking With Active Learning." IEEE Transactions on Image Processing 29 (2020): 2258–70. http://dx.doi.org/10.1109/tip.2019.2945679.

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22

Lewis, Allison, Ralph Smith, and Brian Williams. "Gradient free active subspace construction using Morris screening elementary effects." Computers & Mathematics with Applications 72, no. 6 (September 2016): 1603–15. http://dx.doi.org/10.1016/j.camwa.2016.07.022.

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23

Cui, Chunfeng, Kaiqi Zhang, Talgat Daulbaev, Julia Gusak, Ivan Oseledets, and Zheng Zhang. "Active Subspace of Neural Networks: Structural Analysis and Universal Attacks." SIAM Journal on Mathematics of Data Science 2, no. 4 (January 2020): 1096–122. http://dx.doi.org/10.1137/19m1296070.

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24

Junyan Wang and Kap Luk Chan. "Incorporating Patch Subspace Model in Mumford–Shah Type Active Contours." IEEE Transactions on Image Processing 22, no. 11 (November 2013): 4473–85. http://dx.doi.org/10.1109/tip.2013.2274385.

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25

Li, Jichao, Jinsheng Cai, and Kun Qu. "Surrogate-based aerodynamic shape optimization with the active subspace method." Structural and Multidisciplinary Optimization 59, no. 2 (August 24, 2018): 403–19. http://dx.doi.org/10.1007/s00158-018-2073-5.

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Nguyen, Hai, Jonathan Wittmer, and Tan Bui-Thanh. "DIAS: A Data-Informed Active Subspace Regularization Framework for Inverse Problems." Computation 10, no. 3 (March 11, 2022): 38. http://dx.doi.org/10.3390/computation10030038.

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This paper presents a regularization framework that aims to improve the fidelity of Tikhonov inverse solutions. At the heart of the framework is the data-informed regularization idea that only data-uninformed parameters need to be regularized, while the data-informed parameters, on which data and forward model are integrated, should remain untouched. We propose to employ the active subspace method to determine the data-informativeness of a parameter. The resulting framework is thus called a data-informed (DI) active subspace (DIAS) regularization. Four proposed DIAS variants are rigorously analyzed, shown to be robust with the regularization parameter and capable of avoiding polluting solution features informed by the data. They are thus well suited for problems with small or reasonably small noise corruptions in the data. Furthermore, the DIAS approaches can effectively reuse any Tikhonov regularization codes/libraries. Though they are readily applicable for nonlinear inverse problems, we focus on linear problems in this paper in order to gain insights into the framework. Various numerical results for linear inverse problems are presented to verify theoretical findings and to demonstrate advantages of the DIAS framework over the Tikhonov, truncated SVD, and the TSVD-based DI approaches.

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Wang, Nana, Tianwei Yang, and Zhuyin Ren. "Active Subspace Variation and Modeling Uncertainty in a Supersonic Flame Simulation." AIAA Journal 59, no. 5 (May 2021): 1798–807. http://dx.doi.org/10.2514/1.j059977.

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Constantine, Paul G., Eric Dow, and Qiqi Wang. "Active Subspace Methods in Theory and Practice: Applications to Kriging Surfaces." SIAM Journal on Scientific Computing 36, no. 4 (January 2014): A1500—A1524. http://dx.doi.org/10.1137/130916138.

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Blanding, W. R., P. K. Willett, Y. Bar-Shalom, and R. Lynch. "Directed subspace search ML-PDA with application to active sonar tracking." IEEE Transactions on Aerospace and Electronic Systems 44, no. 1 (January 2008): 201–16. http://dx.doi.org/10.1109/taes.2008.4516999.

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Constantine, Paul G., Brian Zaharatos, and Mark Campanelli. "Discovering an active subspace in a single-diode solar cell model." Statistical Analysis and Data Mining: The ASA Data Science Journal 8, no. 5-6 (July 24, 2015): 264–73. http://dx.doi.org/10.1002/sam.11281.

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31

Gao, Chuanqiang, Weiwei Zhang, Jiaqing Kou, Yilang Liu, and Zhengyin Ye. "Active control of transonic buffet flow." Journal of Fluid Mechanics 824 (July 5, 2017): 312–51. http://dx.doi.org/10.1017/jfm.2017.344.

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Transonic buffet is a phenomenon of aerodynamic instability with shock wave motions which occurs at certain combinations of Mach number and mean angle of attack, and which limits the aircraft flight envelope. The objective of this study is to develop a modelling method for unstable flow with oscillating shock waves and moving boundaries, and to perform model-based feedback control of the two-dimensional buffet flow by means of trailing-edge flap oscillations. System identification based on the ARX algorithm is first used to derive a linear model of the input–output dynamics between the flap rotation (the control input) and the lift and pitching moment coefficients (system outputs). The model features a pair of unstable complex-conjugate poles at the characteristic buffet frequency. An appropriate reduced-order model (ROM) with a lower dimension is further obtained by a balanced truncation method that keeps the pair of unstable poles in the unstable subspace but truncates the dynamics in the stable subspace. Based on this balanced ROM, two kinds of feedback control are designed by pole assignment and linear quadratic methods respectively. These independent designs, however, result in similar suboptimal static output feedback control laws. When introduced in numerical simulations, they are both able to completely suppress the buffet instability. Furthermore, the resulting controllers are even able to stabilize buffet flows with nonlinear disturbances and in off-design flow conditions, thus implying their robustness. The analysis of the feedback control laws indicates that parameters (frequency and phase) corresponding to the ‘anti-resonance’ of the linear input–output model are vital for optimal control. The best performance is obtained when the control operates close to the ‘anti-resonance’, which is supported by the optimal frequency and the phase of the open-loop control as well as by the optimal phase of the closed-loop control.

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Kovtun, Viacheslav, Ivan Izonin, and Michal Gregus. "Modeling a session of subject-system interaction in a wireless communication infrastructure with a mixed resource." PLOS ONE 17, no. 7 (July 18, 2022): e0271536. http://dx.doi.org/10.1371/journal.pone.0271536.

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The article examines the subject-system interaction session, where the system is understood as a base station, and the subject is understood as a mobile communication device. The peculiarity of the study is taking into account the phenomenon relevant to modern communication infrastructures, which is that the base station supports the division of information traffic into a subspace of guaranteed personalized traffic and a subspace of general-purpose traffic. The study considers a highly critical empirical emergency when the general-purpose traffic subspace may cease to be available at any time. The presented mathematical apparatus describes the impact of such an emergency on the active communication sessions supported by the system in receiving new incoming requests of increasing intensity. To characterize this emergency situation, expressions adapted for practical application are presented to calculate such qualitative parameters as the probability of stability, the probability of failure, and unavailability.

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Li, Shanshan. "A Cross-Media Advertising Design and Communication Model Based on Feature Subspace Learning." Computational Intelligence and Neuroscience 2022 (May 17, 2022): 1–10. http://dx.doi.org/10.1155/2022/5874722.

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This paper uses feature subspace learning and cross-media retrieval analysis to construct an advertising design and communication model. To address the problems of the traditional feature subspace learning model and make the samples effectively maintain their local structure and discriminative properties after projection into the feature space, this paper proposes a discriminative feature subspace learning model based on Low-Rank Representation (LRR), which explores the local structure of samples through Low-Rank Representation and uses the representation coefficients as similarity constraints of samples in the projection space so that the projection subspace can better maintain the local nearest-neighbor relationship of samples. Based on the common subspace learning, this paper uses the extreme learning machine method to improve the cross-modal retrieval accuracy, mining deeper data features and maximizing the correlation between different modalities, so that the learned shared subspace is more discriminative; meanwhile, it proposes realizing cross-modal retrieval by the deep convolutional generative adversarial network, using unlabeled samples to further explore the correlation of different modal data and improve the cross-modal performance. The clustering quality of images and audios is corrected in the feature subspace obtained by dimensionality reduction through an optimization algorithm based on similarity transfer. Three active learning strategies are designed to calculate the conditional probability of unannotated samples around user-annotated samples in the correlation feedback process, thus improving the efficiency of cross-media retrieval in the case of limited feedback samples. The experimental results show that the method accurately measures the cross-media relevance and effectively achieves mutual retrieval between image and audio data. Through the study of cross-media advertising design and communication models based on feature subspace learning, it is of positive significance to advance commercial advertising design by guiding designers and artists to better utilize digital media technology for artistic design activities at the level of theoretical research and applied practice.

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Su, Xingyu, Weiqi Ji, and Zhuyin Ren. "Uncertainty analysis in mechanism reduction via active subspace and transition state analyses." Combustion and Flame 227 (May 2021): 135–46. http://dx.doi.org/10.1016/j.combustflame.2020.12.053.

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Wu, Yisheng, Cheng Guan, Zhen Huang, and Dong Han. "Fuel octane number prediction based on topological indices and active subspace method." Fuel 293 (June 2021): 120494. http://dx.doi.org/10.1016/j.fuel.2021.120494.

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Lin, Bangde Liu &. Guang. "High-Dimensional Nonlinear Multi-Fidelity Model with Gradient-Free Active Subspace Method." Communications in Computational Physics 28, no. 5 (June 2020): 1937–69. http://dx.doi.org/10.4208/cicp.oa-2020-0195.

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37

Constantine, Paul G., Eric Dow, and Qiqi Wang. "Erratum: Active Subspace Methods in Theory and Practice: Applications to Kriging Surfaces." SIAM Journal on Scientific Computing 36, no. 6 (January 2014): A3030—A3031. http://dx.doi.org/10.1137/140983598.

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Hu, Xingzhi, Xiaoqian Chen, Yong Zhao, Zhouhui Tuo, and Wen Yao. "Active subspace approach to reliability and safety assessments of small satellite separation." Acta Astronautica 131 (February 2017): 159–65. http://dx.doi.org/10.1016/j.actaastro.2016.10.042.

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39

Vohra, Manav, Alen Alexanderian, Hayley Guy, and Sankaran Mahadevan. "Active subspace-based dimension reduction for chemical kinetics applications with epistemic uncertainty." Combustion and Flame 204 (June 2019): 152–61. http://dx.doi.org/10.1016/j.combustflame.2019.03.006.

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40

Nestorović-Trajkov, Tamara, and Ulrich Gabbert. "Active control of a piezoelectric funnel-shaped structure based on subspace identification." Structural Control and Health Monitoring 13, no. 6 (2006): 1068–79. http://dx.doi.org/10.1002/stc.94.

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41

Demo, Nicola, Marco Tezzele, Andrea Mola, and Gianluigi Rozza. "Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing." Journal of Marine Science and Engineering 9, no. 2 (February 11, 2021): 185. http://dx.doi.org/10.3390/jmse9020185.

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In the field of parametric partial differential equations, shape optimization represents a challenging problem due to the required computational resources. In this contribution, a data-driven framework involving multiple reduction techniques is proposed to reduce such computational burden. Proper orthogonal decomposition (POD) and active subspace genetic algorithm (ASGA) are applied for a dimensional reduction of the original (high fidelity) model and for an efficient genetic optimization based on active subspace property. The parameterization of the shape is applied directly to the computational mesh, propagating the generic deformation map applied to the surface (of the object to optimize) to the mesh nodes using a radial basis function (RBF) interpolation. Thus, topology and quality of the original mesh are preserved, enabling application of POD-based reduced order modeling techniques, and avoiding the necessity of additional meshing steps. Model order reduction is performed coupling POD and Gaussian process regression (GPR) in a data-driven fashion. The framework is validated on a benchmark ship.

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42

Li, Chun-xiao, Huan-cai Lu, Ming-fei Guo, Hang-fang Zhao, and Jiang-ming Jin. "Decomposition of the Time Reversal Operator for Target Detection." Mathematical Problems in Engineering 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/597474.

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A thorough theory of detection problem using active time reversal has been investigated in several recent papers. Although active time reversal method is theoretically superior to the others, its practical implementation for target detection is far more difficult. This paper investigates the detection problem using passive decomposition of the time reversal operator (DORT) method. Provided that the signal components can be modeled as a linear combination of basis vectors with an unknown signal subspace, the generalized likelihood ratio test (GLRT) is derived based on Neyman-Person lemma with the unknown signal subspace replaced by its maximum likelihood estimation. The test statistics is one of the dominant eigenvalues of the time reversal operator for a point-like scatterer. Finally, the performance of the DORT detector is investigated with acoustic data collected from a waveguide tank. The experimental results show that the DORT detector can provide, respectively, 1.4 dB, 1.1 dB, and 0.8 dB performance gains over the energy detector given false alarms rate of 0.0001, 0.001, and 0.01.

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Lin, D. D., and T. J. Lim. "Subspace-Based Active User Identification for a Collision-Free Slotted Ad Hoc Network." IEEE Transactions on Communications 52, no. 4 (April 2004): 612–21. http://dx.doi.org/10.1109/tcomm.2004.826415.

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Jiang, Xiong, Xingzhi Hu, Gang Liu, Xiao Liang, and Ruili Wang. "A generalized active subspace for dimension reduction in mixed aleatory-epistemic uncertainty quantification." Computer Methods in Applied Mechanics and Engineering 370 (October 2020): 113240. http://dx.doi.org/10.1016/j.cma.2020.113240.

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Leon, Lider S., Paul R. Miles, Ralph C. Smith, and William S. Oates. "Active subspace analysis and uncertainty quantification for a polydomain ferroelectric phase-field model." Journal of Intelligent Material Systems and Structures 30, no. 14 (July 3, 2019): 2027–51. http://dx.doi.org/10.1177/1045389x19853636.

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We perform parameter subset selection and uncertainty analysis for phase-field models that are applied to the ferroelectric material lead titanate. A motivating objective is to determine which parameters are influential in the sense that their uncertainties directly affect the uncertainty in the model response, and fix noninfluential parameters at nominal values for subsequent uncertainty propagation. We employ Bayesian inference to quantify the uncertainties of gradient exchange parameters governing 180° and 90° tetragonal phase domain wall energies. The uncertainties of influential parameters determined by parameter subset selection are then propagated through the models to obtain credible intervals when estimating energy densities quantifying polarization and strain across domain walls. The results illustrate various properties of Landau and electromechanical coupling parameters and their influence on domain wall interactions. We employ energy statistics, which quantify distances between statistical observations, to compare credible intervals constructed using a complete set of parameters against an influential subset of parameters. These intervals are obtained from the uncertainty propagation of the model input parameters on the domain wall energy densities. The investigation provides critical insight into the development of parameter subset selection, uncertainty quantification, and propagation methodologies for material modeling domain wall structure evolution, informed by density functional theory simulations.

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Gilbert, James M., Jennifer L. Jefferson, Paul G. Constantine, and Reed M. Maxwell. "Global spatial sensitivity of runoff to subsurface permeability using the active subspace method." Advances in Water Resources 92 (June 2016): 30–42. http://dx.doi.org/10.1016/j.advwatres.2016.03.020.

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Wang, Xiao Juan, and Min Xu. "Aero-Elastic Active Control Modeling via Improved POD Method." Applied Mechanics and Materials 117-119 (October 2011): 339–46. http://dx.doi.org/10.4028/www.scientific.net/amm.117-119.339.

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The main goal of present paper is to construct an efficient reduced order model (ROM) for aerodynamic system modeling. Proper Orthogonal Decomposition (POD) is presented to address the problem. First, the snapshots are collected to form the POD kernel, and then Singular Values Decomposition (SVD) is used to obtain POD modes, finally POD-ROM can be constructed by projecting full order aerodynamic system to POD modes subspace. However, the robustness of ROM constructed via conventional POD method is not guaranteed. To improve the robustness of conventional POD method, balanced truncation modification was introduced. Aero-elastic active control case was chosen for this new method validation. The results demonstrate POD method with balanced modification is efficient and accurate enough for aeroelastic system analysis.

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Hobbs, Nicole, Iman Hajizadeh, Mudassir Rashid, Kamuran Turksoy, Marc Breton, and Ali Cinar. "Improving Glucose Prediction Accuracy in Physically Active Adolescents With Type 1 Diabetes." Journal of Diabetes Science and Technology 13, no. 4 (January 18, 2019): 718–27. http://dx.doi.org/10.1177/1932296818820550.

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Background: Physical activity presents a significant challenge for glycemic control in individuals with type 1 diabetes. As accurate glycemic predictions are key to successful automated decision-making systems (eg, artificial pancreas, AP), the inclusion of additional physiological variables in the estimation of the metabolic state may improve the glucose prediction accuracy during exercise. Methods: Predictor-based subspace identification is applied to a dynamic glucose prediction model including heart rate measurements along with variables representing the carbohydrate consumption and insulin boluses. To demonstrate the improvement in prediction ability due to the additional heart rate variable, the performance of the proposed modeling technique is evaluated with (SID-HR) and without heart rate (SID-2) as an additional input using experimental data involving adolescents at ski camp. Furthermore, the performance of the proposed approach is compared to that of the metabolic state observer (MSO) model currently used in the University of Virginia AP algorithm. Results: The addition of heart rate in the subspace-based model (SID-HR) yields a statistically significant improvement in the root-mean-square error compared to the SID-2 model ( P < .001) and the standard MSO ( P < .001). Furthermore, the SID-HR model performed favorably in comparison to the SID-2 and MSO models after accounting for its increased complexity. Conclusions: Directly considering the effects of physical activity levels on glycemic dynamics through the inclusion of heart rate as an additional input variable in the glucose dynamics model improves the glucose prediction accuracy. The proposed methodology could improve exercise-informed model-based predictive control algorithms in artificial pancreas systems.

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LUO, YAN-AN, FENG PAN, PING-ZHI NING, and J. P. DRAAYER. "INTRUDER LEVELS AND VIBRATIONAL MODES IN THE SD-PAIR SHELL MODEL." International Journal of Modern Physics E 14, no. 08 (November 2005): 1205–12. http://dx.doi.org/10.1142/s0218301305003806.

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The influence of the intruder level on vibrational modes in atomic nuclei is examined within the framework of the nucleon-pair shell model truncated to a SD-subspace. The vibrational character of the spectra is found to depend upon the size of the active model space and not on the parity of the populated levels.

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Zhang, Kai, and Xuejia Lai. "Another Perspective on Automatic Construction of Integral Distinguishers for ARX Ciphers." Symmetry 14, no. 3 (February 24, 2022): 461. http://dx.doi.org/10.3390/sym14030461.

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This paper introduces a method to construct integral distinguishers for ARX ciphers. The basic idea of this method is to utilize the symmetry between the zero-correlation linear distinguishers and integral distinguishers. Combined with an automatic searching method on zero-correlation linear distinguishers of ARX ciphers, a subspace for the distinguishers is constructed. This subspace can finally be turned into an integral distinguisher based on the symmetry between these two distinguishers. Three ARX block ciphers, HIGHT, LEA and SPECK, are used to validate the effectiveness of this method. For LEA, four nine-round integral distinguishers are constructed, which is one more round than the previous best result derived with division property. For SPECK32, two more six-round integral distinguishers are constructed, whose number of active bits is reduced by one bit.

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