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Journal articles on the topic 'Penalty Decomposition'

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Author: Grafiati

Published: 10 March 2023

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1

Dong, Zhengshan, Geng Lin, and Niandong Chen. "An Inexact Penalty Decomposition Method for Sparse Optimization." Computational Intelligence and Neuroscience 2021 (July 14, 2021): 1–8. http://dx.doi.org/10.1155/2021/9943519.

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The penalty decomposition method is an effective and versatile method for sparse optimization and has been successfully applied to solve compressed sensing, sparse logistic regression, sparse inverse covariance selection, low rank minimization, image restoration, and so on. With increase in the penalty parameters, a sequence of penalty subproblems required being solved by the penalty decomposition method may be time consuming. In this paper, an acceleration of the penalty decomposition method is proposed for the sparse optimization problem. For each penalty parameter, this method just finds some inexact solutions to those subproblems. Computational experiments on a number of test instances demonstrate the effectiveness and efficiency of the proposed method in accurately generating sparse and redundant representations of one-dimensional random signals.

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2

Wang, Yuanxin. "An Adaptive Variational Mode Decomposition Technique with Differential Evolution Algorithm and Its Application Analysis." Shock and Vibration 2021 (November 11, 2021): 1–5. http://dx.doi.org/10.1155/2021/2030128.

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Variational mode decomposition is an adaptive nonrecursive signal decomposition and time-frequency distribution estimation method. The improper selection of the decomposition number will cause under decomposition or over decomposition, and the improper selection of the penalty factor will affect the bandwidth of modal components, so it is very necessary to look for the optimal parameter combination of the decomposition number and the penalty factor of variational mode decomposition. Hence, differential evolution algorithm is used to look for the optimization combination of the decomposition number and the penalty factor of variational mode decomposition because differential evolution algorithm has a good ability at global searching. The method is called adaptive variational mode decomposition technique with differential evolution algorithm. Application analysis and discussion of the adaptive variational mode decomposition technique with differential evolution algorithm are employed by combining with the experiment. The conclusions of the experiment are that the decomposition performance of the adaptive variational mode decomposition technique with differential evolution algorithm is better than that of variational mode decomposition.

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3

Lu, Zhaosong, and Yong Zhang. "Sparse Approximation via Penalty Decomposition Methods." SIAM Journal on Optimization 23, no. 4 (January 2013): 2448–78. http://dx.doi.org/10.1137/100808071.

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4

Lu, Zhaosong, Yong Zhang, and Xiaorui Li. "Penalty decomposition methods for rank minimization." Optimization Methods and Software 30, no. 3 (August 8, 2014): 531–58. http://dx.doi.org/10.1080/10556788.2014.936438.

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5

Xu, Huaqing, Tieding Lu, Jean-Philippe Montillet, and Xiaoxing He. "An Improved Adaptive IVMD-WPT-Based Noise Reduction Algorithm on GPS Height Time Series." Sensors 21, no. 24 (December 11, 2021): 8295. http://dx.doi.org/10.3390/s21248295.

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To improve the reliability of Global Positioning System (GPS) signal extraction, the traditional variational mode decomposition (VMD) method cannot determine the number of intrinsic modal functions or the value of the penalty factor in the process of noise reduction, which leads to inadequate or over-decomposition in time series analysis and will cause problems. Therefore, in this paper, a new approach using improved variational mode decomposition and wavelet packet transform (IVMD-WPT) was proposed, which takes the energy entropy mutual information as the objective function and uses the grasshopper optimisation algorithm to optimise the objective function to adaptively determine the number of modal decompositions and the value of the penalty factor to verify the validity of the IVMD-WPT algorithm. We performed a test experiment with two groups of simulation time series and three indicators: root mean square error (RMSE), correlation coefficient (CC) and signal-to-noise ratio (SNR). These indicators were used to evaluate the noise reduction effect. The simulation results showed that IVMD-WPT was better than the traditional empirical mode decomposition and improved variational mode decomposition (IVMD) methods and that the RMSE decreased by 0.084 and 0.0715 mm; CC and SNR increased by 0.0005 and 0.0004 dB, and 862.28 and 6.17 dB, respectively. The simulation experiments verify the effectiveness of the proposed algorithm. Finally, we performed an analysis with 100 real GPS height time series from the Crustal Movement Observation Network of China (CMONOC). The results showed that the RMSE decreased by 11.4648 and 6.7322 mm, and CC and SNR increased by 0.1458 and 0.0588 dB, and 32.6773 and 26.3918 dB, respectively. In summary, the IVMD-WPT algorithm can adaptively determine the number of decomposition modal functions of VMD and the optimal combination of penalty factors; it helps to further extract effective information for noise and can perfectly retain useful information in the original time series.

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Lee, Ju Hwan and 윤자영. "Wage Penalty and Decomposition of Care Employment." Korean Journal of Social Welfare Studies 46, no. 4 (December 2015): 33–57. http://dx.doi.org/10.16999/kasws.2015.46.4.33.

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Wang, Caihua, Juan Liu, Wenwen Min, and Aiping Qu. "A Novel Sparse Penalty for Singular Value Decomposition." Chinese Journal of Electronics 26, no. 2 (March 1, 2017): 306–12. http://dx.doi.org/10.1049/cje.2017.01.025.

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8

Lazarov, Raytcho D., Stanimire Z. Tomov, and Panayot S. Vassilevski. "Interior Penalty Discontinuous Approximations of Elliptic Problems." Computational Methods in Applied Mathematics 1, no. 4 (2001): 367–82. http://dx.doi.org/10.2478/cmam-2001-0024.

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AbstractThis paper studies an interior penalty discontinuous approximation of elliptic problems on nonmatching grids. Error analysis, interface domain decomposition type preconditioners, as well as numerical results illustrating both discretization errors and condition number estimates of the problem and reduced forms of it are presented.

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9

Ouellet, Yanick, and Claude-Guy Quimper. "The SoftCumulative Constraint with Quadratic Penalty." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 3813–20. http://dx.doi.org/10.1609/aaai.v36i4.20296.

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The Cumulative constraint greatly contributes to the success of constraint programming at solving scheduling problems. The SoftCumulative, a version of the Cumulative where overloading the resource incurs a penalty is, however, less studied. We introduce a checker and a filtering algorithm for the SoftCumulative, which are inspired by the powerful energetic reasoning rule for the Cumulative. Both algorithms can be used with classic linear penalty function, but also with a quadratic penalty function, where the penalty of overloading the resource increases quadratically with the amount of the overload. We show that these algorithms are more general than existing algorithms and vastly outperform a decomposition of the SoftCumulative in practice.

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10

Zhao, Ming-Min, Qingjiang Shi, Yunlong Cai, Min-Jian Zhao, and Quan Yu. "Decoding Binary Linear Codes Using Penalty Dual Decomposition Method." IEEE Communications Letters 23, no. 6 (June 2019): 958–62. http://dx.doi.org/10.1109/lcomm.2019.2911277.

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11

Wang, Hongjun, Xiang Zhang, Zhengbo Wang, and Shucong Liu. "Impact Load Sparse Recognition Method Based on Mc Penalty Function." Applied Sciences 12, no. 16 (August 15, 2022): 8147. http://dx.doi.org/10.3390/app12168147.

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The rotor system is an important part of large-scale rotating machinery. Bearings, as a key component of the rotor system, play a vital role in the healthy operation of the rotor system. The bearings operate under harsh conditions such as high temperature, high pressure, and high speed. They are complex and extremely prone to failure, especially when the bearing is affected by impact load, which seriously affects the remaining service life of the bearing. Uneven bearing friction, caused by the impact, is one of the main factors that cause premature failure of the bearing. The early identification of shock loads and reasonable measures are extremely important for the safe operation of equipment. This paper proposes an impact load identification method based on the sparse decomposition of the Mini-max concave penalty function (Mini-max concave penalty function, MC). The method uses the MC penalty function to reconstruct the regularized sparse recognition model, and then uses the improved original dual interior point method to solve the problem. This model realizes the identification of vibration and shock loads. Relevant experimental verification was carried out, and the results show that the sparse decomposition result based on the MC penalty function is better than the L1-regularized sparse decomposition result, and the noise is well suppressed in the non-loaded area of the impact load. This method can be applied to the early fault diagnosis of the vibration signal of the gas turbine rotor.

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12

Dong, Zhengshan, and Wenxing Zhu. "An improvement of the penalty decomposition method for sparse approximation." Signal Processing 113 (August 2015): 52–60. http://dx.doi.org/10.1016/j.sigpro.2015.01.012.

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Liu, Jian, Yuhu Cheng, Xuesong Wang, and Xiaoluo Cui. "Supervised Penalty Matrix Decomposition for Tumor Differentially Expressed Genes Selection." Chinese Journal of Electronics 27, no. 4 (July 1, 2018): 845–51. http://dx.doi.org/10.1049/cje.2017.09.023.

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14

Tokareva, O. N. "Two-level decomposition in sequential quadratic programming with penalty functions." Cybernetics 26, no. 6 (1991): 891–902. http://dx.doi.org/10.1007/bf01069495.

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15

Chen, Xieqi, Jianhui Lin, Hang Jin, Andy Tan, and Lei Yan. "Acoustics Source Identification of Diesel Engines Based on Variational Mode Decomposition, Fast Independent Component Analysis, and Hilbert Transformation." Shock and Vibration 2021 (January 20, 2021): 1–18. http://dx.doi.org/10.1155/2021/8832932.

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Diesel engines are widely used in railway systems, particularly in freight trains. Despite their high efficiency in energy conversion, they usually generate high levels of acoustics pollution during operation. In order to mitigate this problem, a series of active/passive acoustics control methods are used to reduce noise. Most of these methods are only effective if the prior knowledge of sources is given. In other words, it is essential to recognize the acoustics source. Variational mode decomposition (VMD) is a signal processing method that enhances the signal corrupted by background noise. However, the decomposed results of VMD depend on their mode parameter and penalty parameter. Therefore, an evaluation method based on system modal parameters (natural frequency and damping ratio) is proposed to select the mode parameter, and the penalty parameter can be selected from the power spectra of signals. In order to increase the accuracy of decomposition for diesel engines and find out the sources of acoustics, a method combining VMD, fast independent component analysis, and Hilbert transformation (VMD-FastICA-HT) is proposed for the separation and identification of different sources for diesel engines. The optimization results indicate that when the penalty parameter value is 1.5 to 16 times the maximum signal amplitude, better decomposition results can be achieved. Therefore, the separated independent acoustics are more accurate in source identification. Furthermore, both simulation data and in situ operational data of diesel engines for vehicles are used to validate the effectiveness of the proposed method.

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16

Rolli, Fernando, João Fradinho, Alessandro Giorgetti, Paolo Citti, and Gabriele Arcidiacono. "Axiomatic decomposition of a zero-sum game: the penalty shoot-out case." MATEC Web of Conferences 223 (2018): 01005. http://dx.doi.org/10.1051/matecconf/201822301005.

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The game of soccer has offered matter of wide scientific analysis about the effective application of the game theory in real-life. The field observations have often detected divergent behaviors from theoretical predictions. The basic problem comes from the fact that it is difficult to build scientific models reflecting reality as closely as possible. Axiomatic Design offers us a powerful tool of rational decomposition of a real and complex issue into elementary components. Independence Axiom guarantees that game decomposition will define a set of elementary actions logically consistent and free of redundancies. At the same time, Information Axiom can allow to select among alternative strategies, those that they predict the actions with a higher probability rate of success. In this paper, it is suggested the use of the Axiomatic Design methodology in the Collectively Exhaustive and Mutually Exclusive (CEME) mode, as a tool of analysis of the penalty shoot-out in extra time. This methodology allows to define the game strategies for goalkeepers and penalty takers. It will be analyzed both, the case when the opponents' behavior is well known and the situation when the statistics about the opponents are unknown. Axiomatic Design allows the process of decomposition to be simplified, enabling the selection of optimal game strategies. These strategies correspond to Nash’s equilibrium solutions when you already know about your opponents' game behavior. On the contrary, when penalty takers whose behavior is unknown, then it is always possible to define a strategy corresponding to the Bayesian equilibrium game solutions.

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17

He, Zhitao, Haiyang Zhang, Jun Wang, Xin Jin, Song Gao, and Jing Pang. "BP-AdaBoost Algorithm Based on Variational Mode Decomposition Optimized by Envelope Entropy for Diagnosing the Working Conditions of a Slideway Seedling-Picking Mechanism." Applied Engineering in Agriculture 37, no. 4 (2021): 665–75. http://dx.doi.org/10.13031/aea.14124.

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Highlights A method of monitoring the working conditions of a slideway seedling-picking mechanism based on variational mode decomposition (VMD), envelope entropy, and energy entropy is proposed. Based on the criterion of envelope entropy minimization, the combination of the decomposition layer number and penalty factor in VMD is optimized to yield a satisfactory decomposition effect of the analyzed vibration signal. The BP-AdaBoost algorithm is used to improve the working condition classification performance for the slideway seedling-picking mechanism. The working-condition identification effect with the proposed method are compared with those through EMD-based, LMD-based, and EEMD-based methods. Abstract . The slideway seedling-picking mechanism is a type of rotating machinery. This study proposes a novel method of identifying the working conditions of slideway seedling-picking mechanisms for early fault diagnosis by utilizing a back-propagation adaptive boosting (BP-AdaBoost) algorithm based on variational mode decomposition (VMD) optimized by the envelope entropy. The experimental results demonstrate that the proposed method can effectively verify the four working conditions (normal state, slideway failure, cam failure, and spring failure). The overall recognition accuracy reaches 90.0% under the optimal combination of the decomposition layer number K and penalty factor a in VMD determined through the envelope entropy minimization criterion. Classification comparisons with empirical mode decomposition (EMD), local mean decomposition (LMD) and ensemble empirical mode decomposition (EEMD) integrated into the BP-AdaBoost algorithm indicate that the overall recognition accuracy of the proposed method is 18.1%, 16.9%, and 15.6% higher than the accuracies of the three conventional methods, respectively. Compared with the K-means, support vector machine (SVM) algorithms, BP-AdaBoost algorithm demonstrates a more dependable capability for identifying the working conditions. This study provides a useful reference for monitoring the working conditions of slideway seedling-picking mechanisms. Keywords: BP-AdaBoost algorithm, Energy entropy, Envelope entropy, Slideway seedling-picking mechanism, Variational mode decomposition, Working conditions.

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18

Li, Ziyue, Nurettin Dorukhan Sergin, Hao Yan, Chen Zhang, and Fugee Tsung. "Tensor Completion for Weakly-Dependent Data on Graph for Metro Passenger Flow Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4804–10. http://dx.doi.org/10.1609/aaai.v34i04.5915.

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Low-rank tensor decomposition and completion have attracted significant interest from academia given the ubiquity of tensor data. However, low-rank structure is a global property, which will not be fulfilled when the data presents complex and weak dependencies given specific graph structures. One particular application that motivates this study is the spatiotemporal data analysis. As shown in the preliminary study, weakly dependencies can worsen the low-rank tensor completion performance. In this paper, we propose a novel low-rank CANDECOMP / PARAFAC (CP) tensor decomposition and completion framework by introducing the L1-norm penalty and Graph Laplacian penalty to model the weakly dependency on graph. We further propose an efficient optimization algorithm based on the Block Coordinate Descent for efficient estimation. A case study based on the metro passenger flow data in Hong Kong is conducted to demonstrate an improved performance over the regular tensor completion methods.

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19

Qiao, Junfei, Hongbiao Zhou, Cuili Yang, and Shengxiang Yang. "A decomposition-based multiobjective evolutionary algorithm with angle-based adaptive penalty." Applied Soft Computing 74 (January 2019): 190–205. http://dx.doi.org/10.1016/j.asoc.2018.10.028.

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20

Shi, Qingjiang, Mingyi Hong, Xiao Fu, and Tsung-Hui Chang. "Penalty Dual Decomposition Method for Nonsmooth Nonconvex Optimization—Part II: Applications." IEEE Transactions on Signal Processing 68 (2020): 4242–57. http://dx.doi.org/10.1109/tsp.2020.3001397.

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21

Jin, Zheng-Fen, Zhongping Wan, Xiaoke Zhao, and Yunhai Xiao. "A penalty decomposition method for rank minimization problem with affine constraints." Applied Mathematical Modelling 39, no. 16 (August 2015): 4859–70. http://dx.doi.org/10.1016/j.apm.2015.03.054.

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22

Rahman, Mustafizur, and Md Al-Hasan. "Male–Female Wage Gap and Informal Employment in Bangladesh: A Quantile Regression Approach." South Asia Economic Journal 20, no. 1 (March 2019): 106–23. http://dx.doi.org/10.1177/1391561418824477.

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This article undertakes an examination of Bangladesh’s latest available Quarterly Labour Force Survey 2015–2016 data to draw in-depth insights on gender wage gap and wage discrimination in Bangladesh labour market. The mean wage decomposition shows that on average a woman in Bangladesh earns 12.2 per cent lower wage than a man, and about half of the wage gap can be explained by labour market discrimination against women. Quantile counterfactual decomposition shows that women are subject to higher wage penalty at the lower deciles of the wage distribution with the wage gap varying between 8.3 per cent and 19.4 per cent at different deciles. We have found that at lower deciles, a significant part of the gender wage gap is on account of the relatively larger presence of informal employment. Conditional quantile estimates further reveal that formally employed female workers earn higher wage than their male counterparts at the first decile but suffer from wage penalty at the top deciles. JEL: C21, J31, J46, J70

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23

Rasti, Behnood, Pedram Ghamisi, and Magnus Ulfarsson. "Hyperspectral Feature Extraction Using Sparse and Smooth Low-Rank Analysis." Remote Sensing 11, no. 2 (January 10, 2019): 121. http://dx.doi.org/10.3390/rs11020121.

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In this paper, we develop a hyperspectral feature extraction method called sparse and smooth low-rank analysis (SSLRA). First, we propose a new low-rank model for hyperspectral images (HSIs) where we decompose the HSI into smooth and sparse components. Then, these components are simultaneously estimated using a nonconvex constrained penalized cost function (CPCF). The proposed CPCF exploits total variation penalty, ℓ 1 penalty, and an orthogonality constraint. The total variation penalty is used to promote piecewise smoothness, and, therefore, it extracts spatial (local neighborhood) information. The ℓ 1 penalty encourages sparse and spatial structures. Additionally, we show that this new type of decomposition improves the classification of the HSIs. In the experiments, SSLRA was applied on the Houston (urban) and the Trento (rural) datasets. The extracted features were used as an input into a classifier (either support vector machines (SVM) or random forest (RF)) to produce the final classification map. The results confirm improvement in classification accuracy compared to the state-of-the-art feature extraction approaches.

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24

Bi, Shujun, Xiaolan Liu, and Shaohua Pan. "Exact Penalty Decomposition Method for Zero-Norm Minimization Based on MPEC Formulation." SIAM Journal on Scientific Computing 36, no. 4 (January 2014): A1451—A1477. http://dx.doi.org/10.1137/110855867.

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25

Yang, Shengxiang, Shouyong Jiang, and Yong Jiang. "Improving the multiobjective evolutionary algorithm based on decomposition with new penalty schemes." Soft Computing 21, no. 16 (February 18, 2016): 4677–91. http://dx.doi.org/10.1007/s00500-016-2076-3.

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26

Pinar, Mustafa C., and Stavros A. Zenios. "Parallel Decomposition of Multicommodity Network Flows Using a Linear-Quadratic Penalty Algorithm." ORSA Journal on Computing 4, no. 3 (August 1992): 235–49. http://dx.doi.org/10.1287/ijoc.4.3.235.

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27

Zhao, Ming-Min, Qingjiang Shi, Yunlong Cai, Min-Jian Zhao, and Yongjie Li. "Distributed Penalty Dual Decomposition Algorithm for Optimal Power Flow in Radial Networks." IEEE Transactions on Power Systems 35, no. 3 (May 2020): 2176–89. http://dx.doi.org/10.1109/tpwrs.2019.2952433.

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28

Liu, Qiegen, Biao Xiong, and Minghui Zhang. "Adaptive Sparse Norm and Nonlocal Total Variation Methods for Image Smoothing." Mathematical Problems in Engineering 2014 (2014): 1–18. http://dx.doi.org/10.1155/2014/426125.

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In computer vision and graphics, it is challenging to decompose various texture/structure patterns from input images. It is well recognized that how edges are defined and how this prior information guides smoothing are two keys in determining the quality of image smoothing. While many different approaches have been reported in the literature, sparse norm and nonlocal schemes are two promising tools. In this study, by integrating a texture measure as the spatially varying data-fidelity/smooth-penalty weight into the sparse norm and nonlocal total variation models, two new methods are presented for feature/structure-preserving filtering. The first one is a generalized relative total variation (i.e., GRTV) method, which improves the contrast-preserving and edge stiffness-enhancing capabilities of the RTV by extending the range of the penalty function’s norm from 1 to [0, 1]. The other one is a nonlocal version of generalized RTV (i.e., NLGRTV) for which the key idea is to use a modified texture-measure as spatially varying penalty weight and to replace the local candidate pixels with the nonlocal set in the smooth-penalty term. It is shown that NLGRTV substantially improves the performance of decomposition for regions with faint pixel-boundary.

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Yu, Chengbo, and Youyu Mi. "Optimised VMD based on Improved Grey Wolf for Human Pulse Wave Characterisation." Journal of Physics: Conference Series 2356, no. 1 (October 1, 2022): 012023. http://dx.doi.org/10.1088/1742-6596/2356/1/012023.

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To address the difficulty in determining the penalty factor α mode and number K for the Variational Mode Decomposition (VMD) human pulse wave signal, which makes it difficult to extract and analyse its features accurately, an Improved Grey Wolf Optimization (IGWO) method is proposed to optimise the VMD. In this paper, the optimal modal number K and penalty factor α in the VMD are determined by IGWO using Minimum Average Mutual Information (MAMI) as the fitness function. The proposed method is used to completely decompose the pulse wave into K Intrinsic Mode Functions (IMF), and the marginal spectral analysis of the decomposed modes is performed to calculate the spectral energy ratio. And the effectiveness of the proposed method is demonstrated experimentally.

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Liang, Tao, and Hao Lu. "A Novel Method Based on Multi-Island Genetic Algorithm Improved Variational Mode Decomposition and Multi-Features for Fault Diagnosis of Rolling Bearing." Entropy 22, no. 9 (September 7, 2020): 995. http://dx.doi.org/10.3390/e22090995.

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Aiming at the problem that it is difficult to extract fault features from the nonlinear and non-stationary vibration signals of wind turbine rolling bearings, which leads to the low diagnosis and recognition rate, a feature extraction method based on multi-island genetic algorithm (MIGA) improved variational mode decomposition (VMD) and multi-features is proposed. The decomposition effect of the VMD method is limited by the number of decompositions and the selection of penalty factors. This paper uses MIGA to optimize the parameters. The improved VMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMF), and a group of components containing the most information is selected through the Holder coefficient. For these components, multi-features based on Renyi entropy feature, singular value feature, and Hjorth parameter feature are extracted as the final feature vector, which is input to the classifier to realize the fault diagnosis of rolling bearing. The experimental results prove that the proposed method can more effectively extract the fault characteristics of rolling bearings. The fault diagnosis model based on this method can accurately identify bearing signals of 16 different fault types, severity, and damage points.

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Kong Deming, 孔德明, 张春祥 Zhang Chunxiang, 崔耀耀 Cui Yaoyao, 李雨蒙 Li Yumeng, and 王书涛 Wang Shutao. "Detection of Oil Species in Mixed Oil Based on Alternating Penalty Trilinear Decomposition." Acta Optica Sinica 38, no. 11 (2018): 1130005. http://dx.doi.org/10.3788/aos201838.1130005.

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Zhao, Jingtao, Suping Peng, and Wenfeng Du. "Seismic small-scale discontinuity sparsity-constraint inversion method using a penalty decomposition algorithm." Journal of Geophysics and Engineering 13, no. 1 (January 29, 2016): 109–15. http://dx.doi.org/10.1088/1742-2132/13/1/109.

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Murata, Naoki, Shoichi Koyama, Norihiro Takamune, and Hiroshi Saruwatari. "Sparse Representation Using Multidimensional Mixed-Norm Penalty With Application to Sound Field Decomposition." IEEE Transactions on Signal Processing 66, no. 12 (June 15, 2018): 3327–38. http://dx.doi.org/10.1109/tsp.2018.2830318.

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Cai, Gaigai, Ivan W. Selesnick, Shibin Wang, Weiwei Dai, and Zhongkui Zhu. "Sparsity-enhanced signal decomposition via generalized minimax-concave penalty for gearbox fault diagnosis." Journal of Sound and Vibration 432 (October 2018): 213–34. http://dx.doi.org/10.1016/j.jsv.2018.06.037.

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Xia, A.-Lin, Hai-Long Wu, Shu-Fang Li, Shao-Hua Zhu, Le-Qian Hu, and Ru-Qin Yu. "Alternating penalty quadrilinear decomposition algorithm for an analysis of four-way data arrays." Journal of Chemometrics 21, no. 3-4 (2007): 133–44. http://dx.doi.org/10.1002/cem.1051.

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Shi, Yan, Zhou Chen, and Chang-Hong Liang. "An interior penalty Galerkin domain decomposition method based on high-order basis function." Microwave and Optical Technology Letters 57, no. 8 (May 28, 2015): 1961–65. http://dx.doi.org/10.1002/mop.29235.

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Wu, Shuonan, Shihua Gong, and Jinchao Xu. "Interior penalty mixed finite element methods of any order in any dimension for linear elasticity with strongly symmetric stress tensor." Mathematical Models and Methods in Applied Sciences 27, no. 14 (November 15, 2017): 2711–43. http://dx.doi.org/10.1142/s0218202517500567.

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We propose two classes of mixed finite elements for linear elasticity of any order, with interior penalty for nonconforming symmetric stress approximation. One key point of our method is to introduce some appropriate nonconforming face-bubble spaces based on the local decomposition of discrete symmetric tensors, with which the stability can be easily established. We prove the optimal [Formula: see text]-error estimate for displacement and optimal [Formula: see text] error estimate for stress by adding an interior penalty term. The elements are easy to be implemented thanks to the explicit formulations of its basis functions. Moreover, the method can be applied to arbitrary simplicial grids for any spatial dimension in a unified fashion. Numerical tests for both 2D and 3D are provided to validate our theoretical results.

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Yang, Jingzong, Chengjiang Zhou, and Xuefeng Li. "Research on Fault Feature Extraction Method Based on Parameter Optimized Variational Mode Decomposition and Robust Independent Component Analysis." Coatings 12, no. 3 (March 21, 2022): 419. http://dx.doi.org/10.3390/coatings12030419.

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The variational mode decomposition mode (VMD) has a reliable mathematical derivation and can decompose signals adaptively. At present, it has been widely used in mechanical fault diagnosis, financial analysis and prediction, geological signal analysis, and other fields. However, VMD has the problems of insufficient decomposition and modal aliasing due to the unclear selection method of modal component k and penalty factor α. Therefore, it is difficult to ensure the accuracy of fault feature extraction and fault diagnosis. To effectively extract fault feature information from bearing vibration signals, a fault feature extraction method based on VMD optimized with information entropy, and robust independent component analysis (RobustICA) was proposed. Firstly, the modal component k and penalty factor α in VMD were optimized by the principle of minimum information entropy to improve the effect of signal decomposition. Secondly, the optimal parameters weresubstituted into VMD, and several intrinsic mode functions (IMFs) wereobtained by signal decomposition. Secondly, the kurtosis and cross-correlation coefficient criteria were comprehensively used to evaluate the advantages and disadvantages of each IMF.And then, the optimal IMFs were selected to construct the observation signal channel to realize the signal-to-noise separation based on RobustICA. Finally, the envelope demodulation analysis of the denoised signal was carried out to extract the fault characteristic frequency. Through the analysis of bearing simulation signal and actual data, it shows that this method can extract the weak characteristics of rolling bearing fault signal and realize the accurate identification of fault. Meanwhile, in the bearing simulation signal experiment, the results of kurtosis value, cross-correlation coefficient, root mean square error, and mean absolute error are 6.162, 0.681, 0.740, and 0.583, respectively. Compared with other traditional methods, better index evaluation value is obtained.

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Shao, Yang, Zhen Peng, Kheng Hwee Lim, and Jin-Fa Lee. "Non-conformal domain decomposition methods for time-harmonic Maxwell equations." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 468, no. 2145 (April 4, 2012): 2433–60. http://dx.doi.org/10.1098/rspa.2012.0028.

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We review non-conformal domain decomposition methods (DDMs) and their applications in solving electrically large and multi-scale electromagnetic (EM) radiation and scattering problems. In particular, a finite-element DDM, together with a finite-element tearing and interconnecting (FETI)-like algorithm, incorporating Robin transmission conditions and an edge corner penalty term , are discussed in detail. We address in full the formulations, and subsequently, their applications to problems with significant amounts of repetitions. The non-conformal DDM approach has also been extended into surface integral equation methods. We elucidate a non-conformal integral equation domain decomposition method and a generalized combined field integral equation method for modelling EM wave scattering from non-penetrable and penetrable targets, respectively. Moreover, a plane wave scattering from a composite mockup fighter jet has been simulated using the newly developed multi-solver domain decomposition method.

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Zhou, Guo, Wang, Du, Wang, Han, Wang, et al. "Research on Fault Extraction Method of Variational Mode Decomposition Based on Immunized Fruit Fly Optimization Algorithm." Entropy 21, no. 4 (April 15, 2019): 400. http://dx.doi.org/10.3390/e21040400.

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In recent years, a new method of fault diagnosis, named variational mode decomposition (VMD), has been widely used in industrial production, but the decomposition accuracy of VMD is determined by two parameters, which are respectively the decomposition layer number k and the penalty factor α, if the parameters are not properly selected, there will be over-decomposition or under-decomposition. In order to find an approach to determine the parameters adaptively, a method to optimize VMD by using the immune fruit fly optimization algorithm (IFOA) is proposed in this paper. In this method, permutation entropy is used as the fitness function, firstly, the immune fruit fly optimization algorithm is used to search the combined parameters of k and α in VMD, searching for the best combination parameters of k and α by iteration, and then uses the combined parameters to perform VMD, finally, the center frequency is determined through frequency spectrum analysis. The method mentioned is applied to the fault extraction of a simulated signal and a measured signal of a wind turbine gearbox, and the fault frequency is successfully extracted. Using ensemble empirical mode decomposition (EEMD) and singular spectrum decomposition (SSD) to compare with the proposed method, which validated feasibility of the proposed method.

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Göttlich, Simone, Falk M. Hante, Andreas Potschka, and Lars Schewe. "Penalty alternating direction methods for mixed-integer optimal control with combinatorial constraints." Mathematical Programming 188, no. 2 (May 10, 2021): 599–619. http://dx.doi.org/10.1007/s10107-021-01656-9.

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AbstractWe consider mixed-integer optimal control problems with combinatorial constraints that couple over time such as minimum dwell times. We analyze a lifting and decomposition approach into a mixed-integer optimal control problem without combinatorial constraints and a mixed-integer problem for the combinatorial constraints in the control space. Both problems can be solved very efficiently with existing methods such as outer convexification with sum-up-rounding strategies and mixed-integer linear programming techniques. The coupling is handled using a penalty-approach. We provide an exactness result for the penalty which yields a solution approach that convergences to partial minima. We compare the quality of these dedicated points with those of other heuristics amongst an academic example and also for the optimization of electric transmission lines with switching of the network topology for flow reallocation in order to satisfy demands.

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Rahman, Mustafizur, Debapriya Bhattacharya, and Md Al-Hasan. "Dimensions of Informality in Bangladesh Labour Market and the Consequent Wage Penalty." South Asia Economic Journal 20, no. 2 (July 26, 2019): 224–47. http://dx.doi.org/10.1177/1391561419850303.

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The article examines the various dimensions and underlying causes of informal employment in the Bangladesh labour market and the associated wage penalty. Based on labour force survey data for successive points, we trace the dynamics of employment in Bangladesh along the informal–formal divide over time. Given that wage differential remains a key feature concerning the two market segments, we have carried out mean and quantile decomposition exercises to estimate the wage penalty originating from informality. We find significant wage gaps between formal- and informal-paid employees, formal paid and informal day labour, and formal paid and informal self-employed. The wage gaps range between 65.0 per cent and 225.0 per cent. The gap arises from a combination of observed differences in human capital and job characteristics, and the wage premium accruing from formal employment. JEL: C21, J31

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Xie, Peng, Li Xu, Jun-Hui Yin, Hao Wang, Zhong-Hai Yang, and Bin Li. "An Interior Penalty Domain Decomposition Method for Thermal Analysis of 3-D Integrated Systems." IEEE Transactions on Components, Packaging and Manufacturing Technology 11, no. 3 (March 2021): 395–406. http://dx.doi.org/10.1109/tcpmt.2021.3054243.

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Shi, Qingjiang, and Mingyi Hong. "Penalty Dual Decomposition Method for Nonsmooth Nonconvex Optimization—Part I: Algorithms and Convergence Analysis." IEEE Transactions on Signal Processing 68 (2020): 4108–22. http://dx.doi.org/10.1109/tsp.2020.3001906.

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Laptin, Yu P. "Exact Penalty Functions and Convex Extensions of Functions in Schemes of Decomposition in Variables*." Cybernetics and Systems Analysis 52, no. 1 (January 2016): 85–95. http://dx.doi.org/10.1007/s10559-016-9803-8.

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Singh, Sahjendra N., James H. Myatt, Gregory A. Addington, Siva Banda, and James K. Hall. "Optimal Feedback Control of Vortex Shedding Using Proper Orthogonal Decomposition Models." Journal of Fluids Engineering 123, no. 3 (May 1, 2001): 612–18. http://dx.doi.org/10.1115/1.1385513.

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This paper treats the question of control of two-dimensional incompressible, unsteady wake flow behind a circular cylinder at Reynolds number Re=100. Two finite-dimensional lower order models based on proper orthogonal decomposition (POD) are considered for the control system design. Control action is achieved via cylinder rotation. Linear optimal control theory is used for obtaining stabilizing feedback control systems. An expression for the region of stability of the system is derived. Simulation results for 18-mode POD models obtained using the control function and penalty methods are presented. These results show that in the closed-loop system mode amplitudes asymptotically converge to the chosen equilibrium state for each flow model for large perturbations in the initial states.

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Madalozzo, Regina. "An analysis of income differentials by marital status." Estudos Econômicos (São Paulo) 38, no. 2 (2008): 267–92. http://dx.doi.org/10.1590/s0101-41612008000200003.

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Unmarried cohabitation has become a more frequently observed phenomenon over the last three decades, and not only in the United States. The objective of this work is to examine income differentials between married women and those who remain single or cohabitate. The empirical literature shows that, while the marriage premium is verified in different studies for men, the result for women is not conclusive. The main innovation of my study is the existence of controls for selection. In this study, we have two sources of selectivity: into the labor force and into a marital status category. The switching regressions and the Oaxaca decomposition results demonstrate the existence of a significant penalty for marriage. Correcting for both types of selection, the difference in wages varies between 49% and 53%, when married women are compared with cohabiting ones, and favors non-married women. This result points to the existence of a marriage penalty.

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Jin, Zhihao, Guangdong Chen, and Zhengxin Yang. "Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM." Entropy 24, no. 7 (July 3, 2022): 927. http://dx.doi.org/10.3390/e24070927.

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In order to further improve the accuracy of fault identification of rolling bearings, a fault diagnosis method based on the modified particle swarm optimization (MPSO) algorithm optimized least square support vector machine (LSSVM), combining parameter optimization variational mode decomposition (VMD) and multi-scale permutation entropy (MPE), was proposed. Firstly, to solve the problem of insufficient decomposition and mode mixing caused by the improper selection of mode component K and penalty factor α in VMD algorithm, the whale optimization algorithm (WOA) was used to optimize the penalty factor and mode component number in the VMD algorithm, and the optimal parameter combination (K, α) was obtained. Secondly, the optimal parameter combination (K, α) was used for the VMD of the rolling bearing vibration signal to obtain several intrinsic mode functions (IMFs). According to the Pearson correlation coefficient (PCC) criterion, the optimal IMF component was selected, and its optimal multi-scale permutation entropy was calculated to form the feature set. Finally, K-fold cross-validation was used to train the MPSO-LSSVM model, and the test set was input into the trained model for identification. The experimental results show that compared with PSO-SVM, LSSVM, and PSO-LSSVM, the MPSO-LSSVM fault diagnosis model has higher recognition accuracy. At the same time, compared with VMD-SE, VMD-MPE, and PSO-VMD-MPE, WOA-VMD-MPE can extract more accurate features.

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Xuan, Menghui, Sixia Zhao, Mengnan Liu, Liyou Xu, and Xiaoliang Chen. "Inspection method of combine assembly quality based on optimized VMD." Journal of Physics: Conference Series 2125, no. 1 (November 1, 2021): 012021. http://dx.doi.org/10.1088/1742-6596/2125/1/012021.

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Abstract Aiming at the problems of low assembly accuracy and difficult to detect assembly quality of combine, a method of combine assembly quality detection based on sparrow search algorithm (SSA) optimized variational mode decomposition (VMD) and particle swarm optimization (PSO) optimized least squares support vector machine (LSSVM) was proposed, Firstly, the sparrow search algorithm is used to obtain the optimal VMD decomposition modal parameter K and penalty factor α, then the combined vibration signal of combine harvester is decomposed into intrinsic modal components of different center frequencies by using the best parameter combination [K, α]. Finally, the feature vector is used as the input of LSSVM classifier to classify different fault features. The analysis results show that the classification accuracy of SSA-VMD joint feature extraction method is 99.5%, which is 17.5% and 9.5% higher than ensemble empirical mode decomposition (EEMD) and fixed parameter VMD, which verifies the superiority of this method in the detection of combine assembly quality.

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Li, Zhiyuan, Shun Li, Jiandong Mao, Juan Li, Qiang Wang, and Yi Zhang. "A Novel Lidar Signal-Denoising Algorithm Based on Sparrow Search Algorithm for Optimal Variational Modal Decomposition." Remote Sensing 14, no. 19 (October 5, 2022): 4960. http://dx.doi.org/10.3390/rs14194960.

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Atmospheric lidar is susceptible to the influence of light attenuation, sky background light, and detector dark currents during the detection process. This results in a large amount of noise in the lidar return signal. To reduce noise and extract a useful signal, a novel denoising method combined with variational modal decomposition (VMD), the sparrow search algorithm (SSA) and singular value decomposition (SVD) is proposed. The SSA is used to optimize the number of decomposition layers K and the quadratic penalty factor α values of the VMD algorithm. Some intrinsic mode function (IMF) components obtained from the VMD-SSA decomposition are grouped and reconstructed according to the interrelationship number selection criterion. Then, the reconstructed signal is further denoised by combining the strong noise-reduction ability of SVD to obtain a clean lidar return signal. To verify the effectiveness of the VMD-SSA-SVD method, the method is compared and analysed with wavelet packet decomposition, empirical modal decomposition (EMD), ensemble empirical modal decomposition (EEMD), and adaptive noise-complete ensemble empirical modal decomposition (CEEMD), and its noise-reduction effect is considerably improved over that of the other four methods. The method can eliminate the complex noise in the lidar return signal while retaining all the details of the signal. The signal is not distorted, the waveform is smoother, and far-field noise interference can be suppressed. The denoised signal is closer to the real signal with higher accuracy, which shows the feasibility and the practicality of the proposed method.

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