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23 results on '"Ma, Enlin"'

1. Design and Analysis of a Coreless Disc Permanent Magnet Synchronous Motor

Author

Wang, Junjie, Huang, Xiaoyan, Ma, Enlin, Wang, Lei, Wei, Suya, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Yang, Qingxin, editor, and Li, Jian, editor

Published
2025
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12. Current Measurement Error Compensation Based on Modified Multiple Complex-Coefficient Filters in PMSM Drives

Author

Song, Pingyue, Wang, Yuankui, Ma, Enlin, Wu, Lijian, Wang, Tao, and Li, Hao

Abstract

Current measurement errors (CMEs) including offset and gain errors can lead to the first- and second-order harmonics in dq-axes currents of permanent magnet synchronous machine (PMSM) drives. The thermal drift-caused CME can change during the operation of the PMSM, which should be compensated online. This article proposed a CME online compensation strategy based on a modified multiple complex-coefficient filter (MCCF). This compensator has two improvements based on MCCF. First, the reference model of PMSM is employed to replace the original CCF to achieve fast fundamental current feedforward, which effectively suppresses the additional disturbance caused by working condition variation. In addition, the gain error coefficient is directly compensated instead of the negative sequence current to further improve the adaptability of variable operating conditions. Compared with MCCF, the proposed strategies can greatly expand the practical application under variable conditions. The effectiveness of the proposed method is verified by simulation and experiment.

Published
2025
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14. An Explainable Evaluation Model for Building Thermal Comfort in China.

Author

Liu, Haiyang and Ma, Enlin

Subjects
THERMAL comfort, MACHINE learning, ENERGY consumption, ENERGY consumption of buildings, GREENHOUSE gases, INTELLIGENT buildings
Abstract

The concentration of atmospheric greenhouse gases is being amplified by human activity. Building energy consumption, particularly for heating and cooling purposes, constitutes a significant proportion of overall energy demand. This research aims to establish a smart evaluation model to understand the thermal requirements of building occupants based on an open-access dataset. This model is beneficial for making reasonable adjustments to building thermal management, based on factors such as different regions and building user characteristics. Employing Bayesian-optimized LightGBM and SHAP (SHapley Additive exPlanations) methods, an explainable machine learning model was developed to evaluate the thermal comfort design of buildings in different areas and with different purpose. Our developed LightGBM model exhibited superior evaluation performance on the test set, outperforming other machine learning models, such as XGBoost and SVR (Support Vector Regression). The SHAP method further helps us to understand the interior evaluation mechanism of the model and the interactive effect among input features. An accurate thermal comfort design for buildings based on the evaluation model can benefit the carbon-neutral strategy. [ABSTRACT FROM AUTHOR]

Published
2023
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15. Deep learning methods for underground deformation time-series prediction

Author

Ma, Enlin, Janiszewski, Mateusz, Torkan, Masoud, Anagnostou, Georgios, Benardos, Andreas, Marinos, Vassilis P., Chang'an University, Department of Civil Engineering, Mineral Based Materials and Mechanics, Aalto-yliopisto, and Aalto University

Subjects
machine learning, deformation prediction, time-series, deep learning, underground engineering
Abstract

Prediction is a vague concept that is why we need to conceptualize it specifically for underground deformation time-series data. For this impending issue, this paper employs an advanced deep learning model Bi-LSTM-AM to address it. The results show its applicability for practical engineering. The proposed model is compared with other basic deep learning models including long short-term memory (LSTM), Bi-LSTM, gated recurrent units (GRU), and temporal convolutional networks (TCN). These models cover the most common three forms of deep learning for time-series prediction: recurrent neural networks (RNN) and convolutional neural networks (CNN). This research is supposed to benefit the underground deformation time-series prediction.

Published
2023

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