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学者姓名:陈雪峰

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< Page ,Total 37 >
Effect of oxidation on crack propagation of Si nanofilm: A ReaxFF molecular dynamics simulation study SCIE
期刊论文 | 2019 , 480 , 1100-1108 | APPLIED SURFACE SCIENCE
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Abstract :

The effect of oxidation and oxide film on mechanical properties and fracture mechanisms of Si nanostructure is an important factor for the design and application of micro- and nano-scale devices but still remain several debates. Reactive force field molecular dynamics (ReaxFF MD) simulation provides a practical way to investigate such an issue, by which we focused on the role of initial stage oxidation on crack propagation of Si nanofilm. The fracture strain and fracture stress were increased by simultaneous oxidation during uniaxial tension, displaying that the crack propagation retarded and initiated from Si/SiOx interface. The modification of stress field due to oxidation may play as a main part on related strengthening behavior, i.e. the blocking of compression in oxide film and the tension relaxation at crack tip. Moreover, we found that the crack retardation may be associate to oxide film morphology, which indicating further retardation by a high quality oxide film with smoother interface. The mechanism detailed here explained previous simulation and experiment on oxide driven strength evolution of Si surface and may shed a light on strength design of nanofilm with oxide layers.

Keyword :

Oxidation Silicon Nanofilm ReaxFF MD Crack

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GB/T 7714 Sun, Yu , Zhai, Zhi , Tian, Shaohua et al. Effect of oxidation on crack propagation of Si nanofilm: A ReaxFF molecular dynamics simulation study [J]. | APPLIED SURFACE SCIENCE , 2019 , 480 : 1100-1108 .
MLA Sun, Yu et al. "Effect of oxidation on crack propagation of Si nanofilm: A ReaxFF molecular dynamics simulation study" . | APPLIED SURFACE SCIENCE 480 (2019) : 1100-1108 .
APA Sun, Yu , Zhai, Zhi , Tian, Shaohua , Chen, Xuefeng . Effect of oxidation on crack propagation of Si nanofilm: A ReaxFF molecular dynamics simulation study . | APPLIED SURFACE SCIENCE , 2019 , 480 , 1100-1108 .
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Subspace-based MVE for performance degradation assessment of aero-engine bearings with multimodal features SCIE
期刊论文 | 2019 , 124 , 298-312 | MECHANICAL SYSTEMS AND SIGNAL PROCESSING
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Abstract :

Performance degradation assessment is an important concept in prognostics and health management (PHM) of complex engineering systems. In this study, a novel method utilizing subspace-based minimum volume ellipsoid (SMVE) for bearing performance degradation assessment is proposed. The statistical features extracted from vibration signals are seen as multimodal homologous features in time, frequency and wavelet modalities, thus it needs to consider the differences in variance for the homologous features as well as covariance among them. The subspaces are used to model the homologous features of different frequency bands since they can capture the dynamic information. Based on subspaces, the proposed SMVE model covering most or all of the normal data by a unique ellipsoid with minimum volume considers variance of each dimension adaptively. A performance degradation index is designed based on the SMVE. The proposed method is applied on the vibration signals acquired in run-to-failure tests of aero-engine bearings and common bearings. By comparison with the commonly used time-domain features and traditional minimum volume ellipsoid method, the results demonstrate the effectiveness and superiority of the proposed method. (C) 2018 Elsevier Ltd. All rights reserved.

Keyword :

Minimum volume ellipsoid Multimodal features Subspace model Performance degradation assessment

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GB/T 7714 Ma, Meng , Sun, Chuang , Zhang, Chi et al. Subspace-based MVE for performance degradation assessment of aero-engine bearings with multimodal features [J]. | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2019 , 124 : 298-312 .
MLA Ma, Meng et al. "Subspace-based MVE for performance degradation assessment of aero-engine bearings with multimodal features" . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING 124 (2019) : 298-312 .
APA Ma, Meng , Sun, Chuang , Zhang, Chi , Chen, Xuefeng . Subspace-based MVE for performance degradation assessment of aero-engine bearings with multimodal features . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2019 , 124 , 298-312 .
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Time-Varying Chatter Frequency Characteristics in Thin-Walled Workpiece Milling With B-Spline Wavelet on Interval Finite Element Method SCIE
期刊论文 | 2019 , 141 (5) | JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME
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Abstract :

As the most significant material removal method, milling plays a very important role in the manufacturing industry. However, chatter occurs frequently in milling, which will seriously affect the production efficiency. The accurate prediction of chatter frequency can contribute to chatter monitoring and the design of the controller for chatter mitigation. During thin-walled workpiece milling under chatter, a new phenomenon of time-varying chatter frequency is discovered and explained in this paper. This phenomenon can be explained as follows, with the workpiece material removal, the modal parameters change during thin-walled milling, which can cause the continuous change of chatter frequency. In order to predict the varying modal parameters, this paper provided an efficient tool, the B-spline wavelet on interval finite element method (BSWIFEM), which can possess the material removal problem more accurately and more rapidly. Based on the calculated modal parameters, the time-varying chatter frequency can be obtained with the chatter frequency calculation formulas. To verify the calculated results, a number of milling tests are implemented on thin-walled parts. The experimental results show that the calculated chatter frequency is in good agreement with the measured chatter frequency, which validates the effectiveness of the proposed method.

Keyword :

time-varying chatter frequency milling B-spline wavelet on interval finite element method thin-walled workpiece

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GB/T 7714 Wang, Chenxi , Zhang, Xingwu , Chen, Xuefeng et al. Time-Varying Chatter Frequency Characteristics in Thin-Walled Workpiece Milling With B-Spline Wavelet on Interval Finite Element Method [J]. | JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME , 2019 , 141 (5) .
MLA Wang, Chenxi et al. "Time-Varying Chatter Frequency Characteristics in Thin-Walled Workpiece Milling With B-Spline Wavelet on Interval Finite Element Method" . | JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME 141 . 5 (2019) .
APA Wang, Chenxi , Zhang, Xingwu , Chen, Xuefeng , Cao, Hongrui . Time-Varying Chatter Frequency Characteristics in Thin-Walled Workpiece Milling With B-Spline Wavelet on Interval Finite Element Method . | JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME , 2019 , 141 (5) .
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Data-driven multiscale sparse representation for bearing fault diagnosis in wind turbine SCIE
期刊论文 | 2019 , 22 (4) , 587-604 | WIND ENERGY
WoS CC Cited Count: 1
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Abstract :

With the increase of the wind turbine capacity, failures occur on the drivetrain of wind turbines frequently. Since faults of bearings in the wind turbine can lead to long downtime and even casualties, fault diagnosis of the drivetrain is very important to reduce the maintenance cost of the wind turbine and improve economic efficiency. However, the traditional diagnosis methods have difficulty in extracting the impulsive components from the vibration signal of the wind turbine because of heavy background noise and harmonic interference. In this paper, we propose a novel method based on data-driven multiscale dictionary construction. Firstly, we achieve the useful atom through training the K-means singular value decomposition (K-SVD) model with a standard signal. Secondly, we deform the chosen atom into different shapes and construct the final dictionary. Thirdly, the constructed dictionary is used to sparsely represent the vibration signal, and orthogonal matching pursuit (OMP) is performed to extract the impulsive component. The proposed method is robust to harmonic interference and heavy background noise. Moreover, the effectiveness of the proposed method is validated by numerical simulation and two experimental cases including the bearing fault of the wind turbine generator in the field test. The overall results indicate that compared with traditional methods, the proposed method is able to extract the fault characteristics from the measured signals more efficiently.

Keyword :

K-SVD bearing fault detection sparse representation wind turbine

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GB/T 7714 Guo, Yanjie , Zhao, Zhibin , Sun, Ruobin et al. Data-driven multiscale sparse representation for bearing fault diagnosis in wind turbine [J]. | WIND ENERGY , 2019 , 22 (4) : 587-604 .
MLA Guo, Yanjie et al. "Data-driven multiscale sparse representation for bearing fault diagnosis in wind turbine" . | WIND ENERGY 22 . 4 (2019) : 587-604 .
APA Guo, Yanjie , Zhao, Zhibin , Sun, Ruobin , Chen, Xuefeng . Data-driven multiscale sparse representation for bearing fault diagnosis in wind turbine . | WIND ENERGY , 2019 , 22 (4) , 587-604 .
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Multi harmonic spindle speed variation for milling chatter suppression and parameters optimization EI SCIE
期刊论文 | 2019 , 55 , 268-274 | Precision Engineering
WoS CC Cited Count: 1
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Abstract :

As one of the most common obstacles in milling process, regenerative chatter will result in lessened productivity, poorer product surface finish and decreased cutting life of the tool. In order to suppress chatter, the spindle speed variation (SSV) has been proposed and researched for a long time. However, the previous researches mainly focus on the basic waveform variation, such as sine, cosine, square and triangular waveforms and didn't consider the effect of phase on milling stability, which weren't suitable to the complex and high spindle speed milling conditions. Based on the previous studies, this paper proposes the concept of multi harmonic spindle speed variation (MHSSV) including the phase factor for chatter suppression. The dynamic equations with MHSSV are derived to describe the milling process. Due to the existence of the milling period and the speed variation period, the least common multiple of these two periods is adopted as the Floquet period in semi-discretization method (SDM) for stability analysis. Because the speed variation function can be described by some finite parameters, the genetic algorithm is used to optimize these parameters in order to suppress chatter more effectively. As a result, the optimized milling process has higher stability limits, especially in the high speed zone, which validates the effectiveness of the multi harmonic spindle speed variation. In addition, the numerical simulation of milling process is implemented and verifies the correctness of the proposed method. © 2018 Elsevier Inc.

Keyword :

Chatter suppression Milling process Multiharmonic Parameters optimization Speed variations

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GB/T 7714 Wang, Chenxi , Zhang, Xingwu , Yan, Ruqiang et al. Multi harmonic spindle speed variation for milling chatter suppression and parameters optimization [J]. | Precision Engineering , 2019 , 55 : 268-274 .
MLA Wang, Chenxi et al. "Multi harmonic spindle speed variation for milling chatter suppression and parameters optimization" . | Precision Engineering 55 (2019) : 268-274 .
APA Wang, Chenxi , Zhang, Xingwu , Yan, Ruqiang , Chen, Xuefeng , Cao, Hongrui . Multi harmonic spindle speed variation for milling chatter suppression and parameters optimization . | Precision Engineering , 2019 , 55 , 268-274 .
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Sparse estimation of propagation distances in Lamb wave inspection SCIE
期刊论文 | 2019 , 30 (5) | MEASUREMENT SCIENCE AND TECHNOLOGY
WoS CC Cited Count: 1
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Abstract :

The arrival Lamb waves of a receiver usually contain multiple wave packets corresponding to different propagation distances. It is important to accurately identify the propagation distances in Lamb wave inspection and structural health monitoring for damage location. While techniques such as multiplying group velocity and time of flight are simple and capable of estimating propagation distances, their performance is limited because accurate identification of time of flights is also a challenging task. Here a novel approach to estimate propagation distances leading to high resolution from arrival Lamb waves is presented that exploits the sparsity of distance spectrum. The unit pulse response is obtained between the transmitter and receiver and then decomposed into a sparse linear combination of propagation distance-based components. The components are pre-computed from Lamb wave propagation and scattering models, and are used to construct an overcomplete dictionary for sparse decomposition. The distance spectrum is obtained by solving a sparse reconstruction problem. Both synthetic signals with noise and experimental signals recorded on an aluminum plate are investigated. The results demonstrate the effectiveness of the proposed method.

Keyword :

structural health monitoring sparse reconstruction Lamb wave propagation distance estimation

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GB/T 7714 Xu, Caibin , Yang, Zhibo , Qiao, Baijie et al. Sparse estimation of propagation distances in Lamb wave inspection [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2019 , 30 (5) .
MLA Xu, Caibin et al. "Sparse estimation of propagation distances in Lamb wave inspection" . | MEASUREMENT SCIENCE AND TECHNOLOGY 30 . 5 (2019) .
APA Xu, Caibin , Yang, Zhibo , Qiao, Baijie , Chen, Xuefeng . Sparse estimation of propagation distances in Lamb wave inspection . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2019 , 30 (5) .
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Group sparse regularization for impact force identification in time domain SCIE
期刊论文 | 2019 , 445 , 44-63 | JOURNAL OF SOUND AND VIBRATION
WoS CC Cited Count: 4
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Abstract :

Impact force identification remains a challenging inverse problem, where a small error in structural responses may lead to a large deviation in the actual solution. Recently, sparse regularization has attracted a lot of interest in the field of force identification. However, the standard sparse regularization method for force identification does not consider the intrinsic structure of impact force, i.e., the nonzero elements occur in groups, called group sparsity. In this paper, by exploiting the group sparse structure of impact force time history, we develop a general group sparse regularization method based on minimizing mixed l(2,1)-norm for the inverse problem of impact force identification. First, the number and size of groups including a complete profile of impact force are determined for penalizing the sum of the l(2)-norm of groups associated with the pulse profile of impact force. Second, a general group sparse optimization model based on the mixed l(2,1)-norm penalty for impact force identification is constructed in time domain, leading to a non-smooth convex optimization problem. Third, given the transfer function and the impact response, an accelerated gradient descent method is developed to solve such a group sparse regularization model. Finally, experiments including identification of single and consecutive impact forces are conducted on a clamped-free thin plate to illustrate the effectiveness and applicability of the proposed approach. Experimental results demonstrate that the classical Tikhonov regularization methods can only identify the single impact force from weakly noisy responses; the group sparse regularization method can efficiently identify both single and consecutive impact forces from heavily noisy responses, and has a slightly better improvement of the peak force amplitude than the standard sparse regularization method. (C) 2019 Elsevier Ltd. All rights reserved.

Keyword :

Impact force identification Group sparse regularization Tikhonov regularization Accelerated gradient descent Mixed l(2,1)-norm

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GB/T 7714 Qiao, Baijie , Mao, Zhu , Liu, Jinxin et al. Group sparse regularization for impact force identification in time domain [J]. | JOURNAL OF SOUND AND VIBRATION , 2019 , 445 : 44-63 .
MLA Qiao, Baijie et al. "Group sparse regularization for impact force identification in time domain" . | JOURNAL OF SOUND AND VIBRATION 445 (2019) : 44-63 .
APA Qiao, Baijie , Mao, Zhu , Liu, Jinxin , Zhao, Zhibin , Chen, Xuefeng . Group sparse regularization for impact force identification in time domain . | JOURNAL OF SOUND AND VIBRATION , 2019 , 445 , 44-63 .
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Machine health monitoring based on locally linear embedding with kernel sparse representation for neighborhood optimization EI SCIE Scopus
期刊论文 | 2019 , 114 , 25-34 | MECHANICAL SYSTEMS AND SIGNAL PROCESSING
WoS CC Cited Count: 4 SCOPUS Cited Count: 5
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Abstract :

Manifold is considered to be a low dimensional surface embedded in a high dimensional vector space, and manifold learning is to find this surface based on data points sampled from this vector space. Neighborhood construction is a critical step in manifold learning to retain local relationship of data, i.e., neighbors and the connection weights. Current methods for manifold learning, including locally linear embedding, locality preserving projection, etc., assume fixed and linear neighborhood, thus lacking in adaptability for handling nonlinear system states caused by variations in machine condition or operation. To overcome this limitation, an enhanced manifold learning method is developed by utilizing kernel sparse representation to determine data neighbors and connecting weights. This enhanced manifold learning method maps data into a feature space where a kernel function is adopted to represent data by its neighbors nonlinearly. The number of data neighbors and connecting weights are determined adaptively by kernel sparse representation. It is found that the developed method enables state-related feature fusion and redundant feature elimination, thus is more effective for dimensionality reduction and feature extraction than traditional manifold learning. Analysis using vibration data measured on a gearbox with multiple faults of varying severity degrees confirmed the performance of the developed method. (C) 2018 Elsevier Ltd. All rights reserved.

Keyword :

Kernel sparse representation Gearbox fault diagnosis Manifold learning Locally linear embedding Adaptive neighborhood

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GB/T 7714 Sun, Chuang , Wang, Peng , Yan, Ruqiang et al. Machine health monitoring based on locally linear embedding with kernel sparse representation for neighborhood optimization [J]. | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2019 , 114 : 25-34 .
MLA Sun, Chuang et al. "Machine health monitoring based on locally linear embedding with kernel sparse representation for neighborhood optimization" . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING 114 (2019) : 25-34 .
APA Sun, Chuang , Wang, Peng , Yan, Ruqiang , Gao, Robert X. , Chen, Xuefeng . Machine health monitoring based on locally linear embedding with kernel sparse representation for neighborhood optimization . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2019 , 114 , 25-34 .
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A weighted multi-scale dictionary learning model and its applications on bearing fault diagnosis SCIE
期刊论文 | 2019 , 446 , 429-452 | JOURNAL OF SOUND AND VIBRATION
WoS CC Cited Count: 2
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Abstract :

Extracting impulsive information under strong background noise and harmonic interference is a challenging problem for bearing fault diagnosis. Multi-scale transforms have achieved great success in extracting impulsive feature information, however, how to choose a suitable transform is a difficult problem, especially in the case of strong noise interference. Therefore, dictionary learning methods have attracted more and more attention in recent years. A weighted multi-scale dictionary learning model (WMSDL) is proposed in this paper which integrates the multi-scale transform and fault information into a unified dictionary learning model and it successfully overcomes four disadvantages of traditional dictionary learning algorithms including lacking the multi-scale property; restricting training samples to local patches; being sensitive to strong harmonic interference; suffering from high computational complexity. Moreover, algorithmic derivation, computational complexity and parameter selection are discussed. Finally, The effectiveness of the proposed method is verified by both the numerical simulations and experiments. Comparisons with other state-of-the-art methods further demonstrate the superiority of the proposed method. (C) 2019 Elsevier Ltd. All rights reserved.

Keyword :

Fault diagnosis Sparse representation Weighted multi-scale dictionary learning Strong interference Planetary bearing

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GB/T 7714 Zhao, Zhibin , Qiao, Baijie , Wang, Shibin et al. A weighted multi-scale dictionary learning model and its applications on bearing fault diagnosis [J]. | JOURNAL OF SOUND AND VIBRATION , 2019 , 446 : 429-452 .
MLA Zhao, Zhibin et al. "A weighted multi-scale dictionary learning model and its applications on bearing fault diagnosis" . | JOURNAL OF SOUND AND VIBRATION 446 (2019) : 429-452 .
APA Zhao, Zhibin , Qiao, Baijie , Wang, Shibin , Shen, Zhixian , Chen, Xuefeng . A weighted multi-scale dictionary learning model and its applications on bearing fault diagnosis . | JOURNAL OF SOUND AND VIBRATION , 2019 , 446 , 429-452 .
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Weighted sparse representation based on failure dynamics simulation for planetary gearbox fault diagnosis SCIE
期刊论文 | 2019 , 30 (4) | MEASUREMENT SCIENCE AND TECHNOLOGY
WoS CC Cited Count: 1
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Abstract :

The most common and effective way of rotating machinery diagnostics is to extract fault impact features from the vibration signals and to carry out further processing. As for planetary gear sets, because of the simultaneous mesh of multiple gears and the effect of the carrier rotation, the fault features are submerged in the strong harmonic signals and other noise. Due to the lack of prior information on the faults, the conventional fault diagnostic methods often fail to achieve satisfactory results. To address this problem, we give the prior information of a chipped planetary gear set by dynamics simulation, and utilize the information in the time domain to improve the diagnosis performance. Firstly, a pure torsional lumped parameter model is used to simulate the system vibration response with different degrees of failure. Through statistical analysis, the margin factor, the most sensitive indicator, is selected as prior information to reflect the local gear fault among several indexes. Finally, a weighted sparse representation method based on the prior information provided above is proposed to extract the impact features. Moreover, it is found that the extracted components have a strong-weak cyclic impact feature in the chipped planetary gear sets. The features and effectiveness of the method are verified by an experiment on a planetary gearbox test rig. To validate the superiority of the proposed method, comparisons are made among several state-of-the-art feature extraction methods.

Keyword :

fault dynamics simulation weighted sparse representation impact feature extraction planetary gearbox fault diagnosis

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GB/T 7714 Sun, Ruo-Bin , Yang, Zhi-Bo , Luo, Wei et al. Weighted sparse representation based on failure dynamics simulation for planetary gearbox fault diagnosis [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2019 , 30 (4) .
MLA Sun, Ruo-Bin et al. "Weighted sparse representation based on failure dynamics simulation for planetary gearbox fault diagnosis" . | MEASUREMENT SCIENCE AND TECHNOLOGY 30 . 4 (2019) .
APA Sun, Ruo-Bin , Yang, Zhi-Bo , Luo, Wei , Qiao, Bai-Jie , Chen, Xue-Feng . Weighted sparse representation based on failure dynamics simulation for planetary gearbox fault diagnosis . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2019 , 30 (4) .
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