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< Page ,Total 18 >
Design, modeling and experimental verification of circular Halbach electromagnetic energy harvesting from bearing motion EI SCIE Scopus
期刊论文 | 2019 , 180 , 811-821 | Energy Conversion and Management
WoS CC Cited Count: 3 SCOPUS Cited Count: 5
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Abstract :

Recently, bearing health condition monitoring has attracted considerable attention due to its great significance to prolong the lifespan and improve the system reliability of key industrial equipments. For the purpose of improving the reliability and effectiveness of energy harvesting in the monitoring node of industrial equipments, this paper proposes circular Halbach electromagnetic energy harvesters for extracting the electrical energy from the rotational motion of bearings to supply the monitoring units. The magnetic distribution model of the circular Halbach array is derived to investigate the magnetic field enhancement using different arrangement modes and structural parameters. The effect of gap, magnetic shape and distribution radius on the magnetic field is numerically discussed to obtain the proper configuration of circular Halbach array for performance enhancement. The experimental results of the fabricated prototype demonstrate the effectiveness of the proposed model and optimization design for enhancing the energy harvesting performance. Moreover, voltage response and power output under different connection modes of multi-coil are experimentally discussed for increasing efficiency and reducing the cost and difficulty of interface circuits. Under the rotational speed from 600 rpm to 1000 rpm, the proposed harvester can generate the voltage of 2.79–4.59 V and the maximum average power of 50.8–131.1 mW. © 2018

Keyword :

Experimental verification Halbach array Health monitoring Magnetic distributions Magnetic field enhancements Modeling and optimization Performance enhancements Structural parameter

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GB/T 7714 Zhang, Ying , Cao, Junyi , Zhu, Hongyu et al. Design, modeling and experimental verification of circular Halbach electromagnetic energy harvesting from bearing motion [J]. | Energy Conversion and Management , 2019 , 180 : 811-821 .
MLA Zhang, Ying et al. "Design, modeling and experimental verification of circular Halbach electromagnetic energy harvesting from bearing motion" . | Energy Conversion and Management 180 (2019) : 811-821 .
APA Zhang, Ying , Cao, Junyi , Zhu, Hongyu , Lei, Yaguo . Design, modeling and experimental verification of circular Halbach electromagnetic energy harvesting from bearing motion . | Energy Conversion and Management , 2019 , 180 , 811-821 .
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A Wiener Process Model-based Method for Remaining Useful Life Prediction Considering Unit-to-Unit Variability EI Scopus SCIE
期刊论文 | 2019 , 66 (3) , 2092-2101 | IEEE Transactions on Industrial Electronics
WoS CC Cited Count: 6 SCOPUS Cited Count: 6
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Abstract :

Remaining useful life (RUL) prediction has attracted more and more attention in recent years because of its significance in predictive maintenance. The degradation processes of systems from the same population are generally different from each other due to their various operational conditions and health states. This behavior is defined as unit-to-unit variability (UtUV), which brings difficulty to RUL prediction. To handle this problem, this paper develops a Wiener process model (WPM)-based method for RUL prediction with the consideration of the UtUV. In this method, an age- and state-dependent WPM is specially designed to describe the various degradation processes of different units. A unit maximum likelihood estimation algorithm is proposed to estimate the UtUV parameter according to the measurements of training units, without any restriction to the distribution pattern of the parameter. The UtUV parameter is further updated via particle filtering (PF) according to the measurements of the testing unit. In the particle updating process, a fuzzy resampling algorithm is developed to handle the sample impoverishment problem of PF. With the updated parameter, the RUL is predicted through a degradation process simulation algorithm. The effectiveness of the proposed method is verified through a simulation study and a turbofan engine degradation dataset. IEEE

Keyword :

Particle Filtering Prediction algorithms Predictive models Remaining useful life predictions unit-to-unit variability Wiener process

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GB/T 7714 Li, Naipeng , Lei, Yaguo , Yan, Tao et al. A Wiener Process Model-based Method for Remaining Useful Life Prediction Considering Unit-to-Unit Variability [J]. | IEEE Transactions on Industrial Electronics , 2019 , 66 (3) : 2092-2101 .
MLA Li, Naipeng et al. "A Wiener Process Model-based Method for Remaining Useful Life Prediction Considering Unit-to-Unit Variability" . | IEEE Transactions on Industrial Electronics 66 . 3 (2019) : 2092-2101 .
APA Li, Naipeng , Lei, Yaguo , Yan, Tao , Li, Ningbo , Han, Tianyu . A Wiener Process Model-based Method for Remaining Useful Life Prediction Considering Unit-to-Unit Variability . | IEEE Transactions on Industrial Electronics , 2019 , 66 (3) , 2092-2101 .
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Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model EI SCIE Scopus
期刊论文 | 2019 , 186 , 88-100 | RELIABILITY ENGINEERING & SYSTEM SAFETY
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Abstract :

The growth of the Industrial Internet of Things (IIoT) has generated a renewed emphasis on research of prognostic degradation modeling whereby degradation signals, such as vibration signals, temperature and acoustic emissions, are used to estimate the state-of-health and predict the remaining useful life (RUL). Besides the inherent system state, external operating conditions, such as the rotational speed and load also play a significant role in the behavior of degradation signals. Time-varying operating conditions often cause two major effects on the degradation signals. First, they change the degradation rate of systems. Second, they cause signal jumps at condition change-points. These two factors make RUL prediction more difficult under time-varying operating conditions. This paper proposes a RUL prediction method by introducing these two factors into a state-space model. Changes in the degradation rate are introduced into a state transition function, and jumps in the degradation signals are introduced into a measurement function. The separate analysis of these two factors makes it possible to distinguish their own contributions to RUL prediction, thus avoiding false alarms and improving the prediction accuracy. The effectiveness of the proposed method is demonstrated using both a simulation study and an accelerated degradation test of rolling element bearings.

Keyword :

Prognostic degradation modeling Remaining useful life prediction State-space model Time-varying operating conditions

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GB/T 7714 Li, NP , Gebraeel, N , Lei, YG et al. Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model [J]. | RELIABILITY ENGINEERING & SYSTEM SAFETY , 2019 , 186 : 88-100 .
MLA Li, NP et al. "Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model" . | RELIABILITY ENGINEERING & SYSTEM SAFETY 186 (2019) : 88-100 .
APA Li, NP , Gebraeel, N , Lei, YG , Bian, LK , Si, XS . Remaining useful life prediction of machinery under time-varying operating conditions based on a two-factor state-space model . | RELIABILITY ENGINEERING & SYSTEM SAFETY , 2019 , 186 , 88-100 .
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Applications of stochastic resonance to machinery fault detection: A review and tutorial EI SCIE
期刊论文 | 2019 , 122 , 502-536 | Mechanical Systems and Signal Processing
WoS CC Cited Count: 7
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Abstract :

Fault detection is a key tool to ensure the safety and reliability of machinery. In machinery fault detection, signal processing methods are extensively applied to extract fault characteristics. Widely used signal processing methods attempt to eliminate the noise imbedded in signals for discovering fault characteristics. Different from widely used signal processing methods, stochastic resonance (SR) is able to utilize the noise imbedded in signals to extract weak fault characteristics from the signals. Therefore, it has been extensively applied to fault characteristic extraction and machinery fault detection. Up to now, massive literature on the applications of SR to machinery fault detection has been published in academic journals, conference proceedings, etc. This paper attempts to survey and summarize the current progress of SR applied in machinery fault detection, providing comprehensive references for researchers concerning with the subject and further helping them identify future trends for research. First, this paper elaborates SR from its original mechanism to fundamental theory. Then, the literature on machinery fault detection using SR is reviewed in terms of critical rotary components prone to faults, such as rolling element bearings, gears and rotors. Moreover, a tutorial on how to use SR for machinery fault detection is provided. What's more, the key issues and prospects of SR in machinery fault detection are pointed out and discussed. It is expected that this review would inspire researchers to explore the potential of SR as well as develop advanced research in this field. © 2018 Elsevier Ltd

Keyword :

Academic journal Advanced researches Fault characteristics Fundamental theory Machinery fault detection Rolling Element Bearing Rotary components Stochastic resonances

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GB/T 7714 Qiao, Zijian , Lei, Yaguo , Li, Naipeng . Applications of stochastic resonance to machinery fault detection: A review and tutorial [J]. | Mechanical Systems and Signal Processing , 2019 , 122 : 502-536 .
MLA Qiao, Zijian et al. "Applications of stochastic resonance to machinery fault detection: A review and tutorial" . | Mechanical Systems and Signal Processing 122 (2019) : 502-536 .
APA Qiao, Zijian , Lei, Yaguo , Li, Naipeng . Applications of stochastic resonance to machinery fault detection: A review and tutorial . | Mechanical Systems and Signal Processing , 2019 , 122 , 502-536 .
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An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings EI SCIE
期刊论文 | 2019 , 122 , 692-706 | Mechanical Systems and Signal Processing
WoS CC Cited Count: 7
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Abstract :

Intelligent fault diagnosis of rolling element bearings has made some achievements based on the availability of massive labeled data. However, the available data from bearings used in real-case machines (BRMs) are insufficient to train a reliable intelligent diagnosis model. Fortunately, we can easily simulate various faults of bearings in a laboratory, and the data from bearings used in laboratory machines (BLMs) contain diagnosis knowledge related to the data from BRMs. Therefore, inspired by the idea of transfer learning, we propose a feature-based transfer neural network (FTNN) to identify the health states of BRMs with the help of the diagnosis knowledge from BLMs. In the proposed method, a convolutional neural network (CNN) is employed to extract transferable features of raw vibration data from BLMs and BRMs. Then, the regularization terms of multi-layer domain adaptation and pseudo label learning are developed to impose constraints on the parameters of CNN so as to reduce the distribution discrepancy and the among-class distance of the learned transferable features. The proposed method is verified by two fault diagnosis cases of bearings, in which the health states of locomotive bearings in real cases are identified by using the data respectively collected from motor bearings and gearbox bearings in laboratories. The results show that the proposed method is able to effectively learn transferable features to bridge the discrepancy between the data from BLMs and BRMs. Consequently, it presents higher diagnosis accuracy for BRMs than existing methods. © 2018 Elsevier Ltd

Keyword :

Convolutional neural network Domain adaptation Intelligent fault diagnosis Rolling Element Bearing Transfer learning

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GB/T 7714 Yang, Bin , Lei, Yaguo , Jia, Feng et al. An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings [J]. | Mechanical Systems and Signal Processing , 2019 , 122 : 692-706 .
MLA Yang, Bin et al. "An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings" . | Mechanical Systems and Signal Processing 122 (2019) : 692-706 .
APA Yang, Bin , Lei, Yaguo , Jia, Feng , Xing, Saibo . An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings . | Mechanical Systems and Signal Processing , 2019 , 122 , 692-706 .
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Identification of magnetomechanical phenomena in a degradation process of loaded steel elements EI SCIE Scopus
期刊论文 | 2018 , 467 , 29-36 | JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS
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Abstract :

The search for new methods of determining the degree of stress of steel structures and the methods of detecting early phases of defect development are still proceeding. These allows to enhance the safety of use of machines and construction structures. The paper presents the description of a measuring apparatus, the method of measurements and the results of the research carried out at test beds with a focus of acquiring new sources of information regarding the technical condition of construction materials. In the long run, such information could prove useful for the diagnosis of objects made of various types of materials that have magnetic properties. Authors points out the basic problems encountered while searching for diagnostic information in magnetic signal, which comes from the complexity of stress effective magnetization. The proper model of magnetization was introduced which leads to original method which based on the special measurement and on dedicated processing of obtained signals. Relevant experiment was performed which concerned a two-dimensional measurement and analysis of own magnetic field of a construction steel sample for three various degrees of effort. The key point was to identify and separate reversible and irreversible magnetomechanical effects according to proposed method. The results proofs of existence of detail diagnostic information which is possible to discover. The results of the research lead to improvement of the existing measuring approach for passive magnetic methods and reduce the risk of omissions of diagnostically valuable diagnostic information. This paper ends with a summary of the obtained results while focusing in particular on the utility value of the approach for the purpose of diagnosis and its further development.

Keyword :

Diagnostics Magnetomechanical effects Magnetometer Measurement Signal analysis Steel

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GB/T 7714 Gontarz, Szymon , Szulim, Przemyslaw , Lei, Yaguo . Identification of magnetomechanical phenomena in a degradation process of loaded steel elements [J]. | JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS , 2018 , 467 : 29-36 .
MLA Gontarz, Szymon et al. "Identification of magnetomechanical phenomena in a degradation process of loaded steel elements" . | JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS 467 (2018) : 29-36 .
APA Gontarz, Szymon , Szulim, Przemyslaw , Lei, Yaguo . Identification of magnetomechanical phenomena in a degradation process of loaded steel elements . | JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS , 2018 , 467 , 29-36 .
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基于SCADA数据的风机叶片结冰检测方法
期刊论文 | 2018 , (1) , 58-62 | 发电与空调
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Abstract :

针对工作在寒冷地区的风机易出现的叶片结冰现象,提出一种基于SCADA数据的风机叶片结冰检测方法.根据叶片结冰会增大发电机的功率损耗,选择风速与网侧有功功率2个变量,利用主成分分析技术构造对叶片结冰敏感的风速与网侧有功功率在非主成分方向投影特征,通过选择最优阈值使逻辑回归分类器适用于不平衡分类,可以实现风机叶片结冰检测自动化与智能化.通过中国工业大数据创新竞赛数据验证了该方法的有效性.

Keyword :

风机叶片结冰检测 SCADA数据 非主成分方向投影特征 最优阈值选择 不平衡分类

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GB/T 7714 李宁波 , 闫涛 , 李乃鹏 et al. 基于SCADA数据的风机叶片结冰检测方法 [J]. | 发电与空调 , 2018 , (1) : 58-62 .
MLA 李宁波 et al. "基于SCADA数据的风机叶片结冰检测方法" . | 发电与空调 1 (2018) : 58-62 .
APA 李宁波 , 闫涛 , 李乃鹏 , 孔德同 , 刘庆超 , 雷亚国 . 基于SCADA数据的风机叶片结冰检测方法 . | 发电与空调 , 2018 , (1) , 58-62 .
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A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines EI SCIE Scopus
期刊论文 | 2018 , 272 , 619-628 | NEUROCOMPUTING
WoS CC Cited Count: 54 SCOPUS Cited Count: 66
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Abstract :

In traditional intelligent fault diagnosis methods of machines, plenty of actual effort is taken for the manual design of fault features, which makes these methods less automatic. Among deep learning techniques, autoencoders may be a potential tool for automatic feature extraction of mechanical signals. However, traditional autoencoders have two following shortcomings. (1) They may learn similar features in mechanical feature extraction. (2) The learned features have shift variant properties, which leads to the misclassification of mechanical health conditions. To overcome the aforementioned shortcomings, a local connection network (LCN) constructed by normalized sparse autoencoder (NSAE), namely NSAE-LCN, is proposed for intelligent fault diagnosis. We construct LCN by input layer, local layer, feature layer and output layer. When raw vibration signals are fed to the input layer, LCN first uses NSAE to locally learn various meaningful features from input signals in the local layer, then obtains shift-invariant features in the feature layer and finally recognizes mechanical health conditions in the output layer. Thus, NSAE-LCN incorporates feature extraction and fault recognition into a general-purpose learning procedure. A gearbox dataset and a bearing dataset are used to validate the performance of the proposed NSAE-LCN. The results indicate that the learned features of NSAE are meaningful and dissimilar, and LCN helps to produce shift-invariant features and recognizes mechanical health conditions effectively. Through comparing with commonly used diagnosis methods, the superiority of the proposed NSAE-LCN is verified. (C) 2017 Elsevier B.V. All rights reserved.

Keyword :

Deep learning Intelligent fault diagnosis Local connection network Normalized sparse autoencoder

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GB/T 7714 Jia, Feng , Lei, Yaguo , Guo, Liang et al. A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines [J]. | NEUROCOMPUTING , 2018 , 272 : 619-628 .
MLA Jia, Feng et al. "A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines" . | NEUROCOMPUTING 272 (2018) : 619-628 .
APA Jia, Feng , Lei, Yaguo , Guo, Liang , Lin, Jing , Xing, Saibo . A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines . | NEUROCOMPUTING , 2018 , 272 , 619-628 .
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Remaining Useful Life Prediction of Machinery Subjected to Two-Phase Degradation Process EI Scopus
会议论文 | 2018 , 8448705 | 2018 IEEE International Conference on Prognostics and Health Management, ICPHM 2018
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Abstract :

Remaining useful life (RUL) prediction of machinery is a major task in condition-based maintenance, which is able to provide crucial guidance for preventive maintenance. To guarantee the accuracy for the RUL prediction of machinery subjected to two-phase degradation process, the interactive multiple model (IMM) filtering technique has been used because of its capability in estimating the state and the phase dynamically. However, there are two limitations in the IMM based methods. 1) A crucial parameter of the IMM, i.e., the transition probabilities matrix (TPM) of the IMM, is set manually in existing IMM based methods, which often leads to inaccurate state estimation results. 2) The phase estimation is derived as one-step filtering results without considering the overall evolution of the degradation trend, which is unable to describe the phase transition, thus causing inaccurate phase estimation results. To tackle these two limitations, an improved RUL prediction method is proposed in this paper for machinery subjected to two-phase degradation process. In the proposed method, a two-phase degradation model is constructed to describe the degradation process. A nonlinear IMM technique, i.e., the interactive multiple model particle filter (IMMPF) is utilized for the state and the phase estimation, where the TPM is estimated using the numerical-integration TPM estimation (NI-TPME) algorithm instead of being pre-specified manually. The transition point (TP) distribution is adopted to reflect the overall evolution of the degradation trend, and is further used to modify the phase estimation from the IMMPF. Finally, the RUL is predicted by Monte Carlo simulation. The effectiveness of the proposed method is demonstrated by a numerical simulation study.

Keyword :

interactive multiple model particle filter remaining useful life prediction two-phase degradation process

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GB/T 7714 Yan, Tao , Lei, Yaguo , Li, Naipeng . Remaining Useful Life Prediction of Machinery Subjected to Two-Phase Degradation Process [C] //2018 IEEE International Conference on Prognostics and Health Management . 2018 : 8448705 .
MLA Yan, Tao et al. "Remaining Useful Life Prediction of Machinery Subjected to Two-Phase Degradation Process" 2018 IEEE International Conference on Prognostics and Health Management . (2018) : 8448705 .
APA Yan, Tao , Lei, Yaguo , Li, Naipeng . Remaining Useful Life Prediction of Machinery Subjected to Two-Phase Degradation Process 2018 IEEE International Conference on Prognostics and Health Management . (2018) : 8448705 .
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A Transfer Learning Method for Intelligent Fault Diagnosis from Laboratory Machines to Real-case Machines EI CPCI-S Scopus
会议论文 | 2018 , 35-40 | International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)
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Abstract :

It is difficult to train a reliable intelligent fault diagnosis model for machines used in real cases (MURC) because there are not sufficient labeled data. However, we can easily simulate various faults in a laboratory, and the data from machines used in the laboratory (MUL) contain fault knowledge related to the data from MURC. Thus, it is possible to identify the health states of MURC by using related fault knowledge contained in the data from MUL. To achieve this purpose, a transfer learning method named convolutional adaptation network (CAN) is proposed in this paper. The proposed method first uses domain-shared convolutional neural network to extract features from the collected data. Second, the distribution discrepancy between the learned features of the data from MUL and MURC is reduced by minimizing multi-kernel maximum mean discrepancy. Finally, pseudo label learning is introduced to train domain-shared classifier by using unlabeled data from MURC. The proposed method is verified by a transfer learning case, in which the health states of locomotive bearings are identified by using the fault knowledge contained in the data from motor bearings used in a laboratory. The results show that CAN is able to effectively identify the health states of MURC with the help of the data from MUL.

Keyword :

Intelligent fault diagnosis transfer learning multi-kernel maximum mean discrepancy pseudo label learning

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GB/T 7714 Yang, B , Lei, YG , Jia, F et al. A Transfer Learning Method for Intelligent Fault Diagnosis from Laboratory Machines to Real-case Machines [C] //2018 INTERNATIONAL CONFERENCE ON SENSING, DIAGNOSTICS, PROGNOSTICS, AND CONTROL (SDPC) . 2018 : 35-40 .
MLA Yang, B et al. "A Transfer Learning Method for Intelligent Fault Diagnosis from Laboratory Machines to Real-case Machines" 2018 INTERNATIONAL CONFERENCE ON SENSING, DIAGNOSTICS, PROGNOSTICS, AND CONTROL (SDPC) . (2018) : 35-40 .
APA Yang, B , Lei, YG , Jia, F , Xing, SB . A Transfer Learning Method for Intelligent Fault Diagnosis from Laboratory Machines to Real-case Machines 2018 INTERNATIONAL CONFERENCE ON SENSING, DIAGNOSTICS, PROGNOSTICS, AND CONTROL (SDPC) . (2018) : 35-40 .
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