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Dr. Lei Fu
College of Mechanical Engineering, Zhejiang University of Technology

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0 Deep Learning
0 Feature Extraction
0 Feature Selection
0 Machine Learning
0 model analysis

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Journal article
Published: 11 August 2021 in IEEE Transactions on Industrial Informatics
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Currently, with the growing applicability of nonlinear electrical devices, power quality disturbances (PQDs) often occur in power systems. Previous works usually extracted statistical features from electrical signals manually and constructed classifiers with traditional machine learning methods for PQD monitoring. Furthermore, noisy tags or unlabeled data (different from noisy signals) are usually ignored in the traditional training stage, and these methods fail to meet the high accuracy and automation demands of real-world scenarios. To overcome the shortcomings of existing methods, this paper proposes a practical method called PowerCog for accurately recognizing PQDs in noisy environments. First, an input voltage waveform signal is divided into several intrinsic mode functions by an empirical wavelet transform (EWT), and these functions are then aligned into columns to form a matrix. Second, tri-training is utilized for label refactoring to improve the generalization ability of the model in a noisy environment. Then, an optimized convolutional neural network (CNN) structure combined with principal component analysis (PCA) is deployed to extract and select the universal features automatically. Finally, a support vector machine (SVM) classifier is constructed to recognize PQD patterns. Several comparative experiments are performed to verify the effectiveness and accuracy of PowerCog in complex environments.

ACS Style

Lei Fu; Ke Yan; Tiantian Zhu. PowerCog: A Practical Method for Recognizing Power Quality Disturbances Accurately in a Noisy Environment. IEEE Transactions on Industrial Informatics 2021, PP, 1 -1.

AMA Style

Lei Fu, Ke Yan, Tiantian Zhu. PowerCog: A Practical Method for Recognizing Power Quality Disturbances Accurately in a Noisy Environment. IEEE Transactions on Industrial Informatics. 2021; PP (99):1-1.

Chicago/Turabian Style

Lei Fu; Ke Yan; Tiantian Zhu. 2021. "PowerCog: A Practical Method for Recognizing Power Quality Disturbances Accurately in a Noisy Environment." IEEE Transactions on Industrial Informatics PP, no. 99: 1-1.

Journal article
Published: 08 December 2020 in Applied Sciences
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The modeling of the minimum fluidization velocity (U0mf) and the incipient fluidization pressure drop (ΔPmf) is a valuable research topic in the fluidization field. In this paper, first, a series of experiments are carried out by changing the particle size and material mass to explore their effects on U0mf and ΔPmf. Then, an Ergun equation modifying method and the dimensional analysis method are used to obtain the modeling correlations of U0mf and ΔPmf by fitting the experimental data, and the advantages and disadvantages of the two methods are discussed. The experimental results show that U0mf increases significantly with increasing particle size but has little relationship with the material mass; ΔPmf increases significantly with increasing material mass but has little relationship with the particle size. Experiments with small particles show a significant increase at large superficial gas velocity; we propose a conjecture that the particles’ collision with the fluidization chamber’s top surface causes this phenomenon. The fitting accuracy of the modified Ergun equation is lower than that of the dimensionless model. When using the Ergun equation modifying method, it is deduced that the gas drag force is approximately 0.8995 times the material total weight at the incipient fluidized state.

ACS Style

Sheng Fang; Yanding Wei; Lei Fu; Geng Tian; Haibin Qu. Modeling of the Minimum Fluidization Velocity and the Incipient Fluidization Pressure Drop in a Conical Fluidized Bed with Negative Pressure. Applied Sciences 2020, 10, 8764 .

AMA Style

Sheng Fang, Yanding Wei, Lei Fu, Geng Tian, Haibin Qu. Modeling of the Minimum Fluidization Velocity and the Incipient Fluidization Pressure Drop in a Conical Fluidized Bed with Negative Pressure. Applied Sciences. 2020; 10 (24):8764.

Chicago/Turabian Style

Sheng Fang; Yanding Wei; Lei Fu; Geng Tian; Haibin Qu. 2020. "Modeling of the Minimum Fluidization Velocity and the Incipient Fluidization Pressure Drop in a Conical Fluidized Bed with Negative Pressure." Applied Sciences 10, no. 24: 8764.

Journal article
Published: 09 September 2020 in Applied Sciences
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Web shell is a malicious script file that can harm web servers. Web shell is often used by intruders to perform a series of malicious operations on website servers, such as privilege escalation and sensitive information leakage. Existing web shell detection methods have some shortcomings, such as viewing a single network traffic behavior, using simple signature comparisons, and adopting easily bypassed regex matches. In view of the above deficiencies, a web shell detection method based on multiview feature fusion is proposed based on the PHP language web shell. Firstly, lexical features, syntactic features, and abstract features that can effectively represent the internal meaning of web shells from multiple levels are integrated and extracted. Secondly, the Fisher score is utilized to rank and filter the most representative features, according to the importance of each feature. Finally, an optimized support vector machine (SVM) is used to establish a model that can effectively distinguish between web shell and normal script. In large-scale experiments, the final classification accuracy of the model on 1056 web shells and 1056 benign web scripts reached 92.18%. The results also surpassed well-known web shell detection tools such as VirusTotal, ClamAV, LOKI, and CloudWalker, as well as the state-of-the-art web shell detectionmethods.

ACS Style

Tiantian Zhu; Zhengqiu Weng; Lei Fu; Linqi Ruan. A Web Shell Detection Method Based on Multiview Feature Fusion. Applied Sciences 2020, 10, 6274 .

AMA Style

Tiantian Zhu, Zhengqiu Weng, Lei Fu, Linqi Ruan. A Web Shell Detection Method Based on Multiview Feature Fusion. Applied Sciences. 2020; 10 (18):6274.

Chicago/Turabian Style

Tiantian Zhu; Zhengqiu Weng; Lei Fu; Linqi Ruan. 2020. "A Web Shell Detection Method Based on Multiview Feature Fusion." Applied Sciences 10, no. 18: 6274.

Journal article
Published: 14 August 2020 in IEEE Transactions on Information Forensics and Security
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Gait authentication, especially sensor-based patterns, has been studied by researchers for decades. Nowadays, gait authentication has become an important facet of biometric systems due to the so-called unique characteristics of each user. With the development of various technologies (i.e., hardware, data processing, features extraction, and learning algorithms), the performance of sensor-based authentication methods is gradually improving. But we have found that the vulnerability of most existing methods can be compromised easily. In this paper, we propose a novel attack model, called one cycle attack, to bypass existing gait authentication methods. Firstly, the gait sequence is divided into multiple gait cycles. By adopting the K-mean algorithm, we get the average distance of each feature sample (extracted from the gait cycle) to its closest cluster center, and its result confirms that independent individuals may have similar gait cycles. Secondly, using six state-of-the-art models it was found that the adversarial gait cycle found with the clustering method can bypass the victim’s model rapidly. Furthermore, to improve the accuracy of sensor-based gait authentication methods to fight against attacks, we present a WPD-LSTM (Wavelet Packet Decomposition and Long Short-Term Memory) multi-cycle defense model which considers the contextual contents of the neighboring gait cycles in the gait sequence. Experimental results on two datasets (the largest public sensor-based gait database OU-ISIR and new dataset from our laboratory) show that our attack model can bypass most of the victims’ models within a limited number of attempts. Specifically, we can compromise 20%-80% of users within 5 attempts by utilizing imitation. On the contrary, the success rate of attackers has been greatly mitigated by deploying our multi-cycle defense model.

ACS Style

Tiantian Zhu; Lei Fu; Qiang Liu; Zi Lin; Yan Chen; Tieming Chen. One Cycle Attack: Fool Sensor-Based Personal Gait Authentication With Clustering. IEEE Transactions on Information Forensics and Security 2020, 16, 553 -568.

AMA Style

Tiantian Zhu, Lei Fu, Qiang Liu, Zi Lin, Yan Chen, Tieming Chen. One Cycle Attack: Fool Sensor-Based Personal Gait Authentication With Clustering. IEEE Transactions on Information Forensics and Security. 2020; 16 (99):553-568.

Chicago/Turabian Style

Tiantian Zhu; Lei Fu; Qiang Liu; Zi Lin; Yan Chen; Tieming Chen. 2020. "One Cycle Attack: Fool Sensor-Based Personal Gait Authentication With Clustering." IEEE Transactions on Information Forensics and Security 16, no. 99: 553-568.

Journal article
Published: 11 July 2020 in Sensors
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With the popularity of smartphones and the development of hardware, mobile devices are widely used by people. To ensure availability and security, how to protect private data in mobile devices without disturbing users has become a key issue. Mobile user authentication methods based on motion sensors have been proposed by many works, but the existing methods have a series of problems such as poor de-noising ability, insufficient availability, and low coverage of feature extraction. Based on the shortcomings of existing methods, this paper proposes a hybrid deep learning system for complex real-world mobile authentication. The system includes: (1) a variational mode decomposition (VMD) based de-noising method to enhance the singular value of sensors, such as discontinuities and mutations, and increase the extraction range of the feature; (2) semi-supervised collaborative training (Tri-Training) methods to effectively deal with mislabeling problems in complex real-world situations; and (3) a combined convolutional neural network (CNN) and support vector machine (SVM) model for effective hybrid feature extraction and training. The training results under large-scale, real-world data show that the proposed system can achieve 95.01% authentication accuracy, and the effect is better than the existing frontier methods.

ACS Style

Tiantian Zhu; Zhengqiu Weng; Guolang Chen; Lei Fu. A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion Sensors. Sensors 2020, 20, 3876 .

AMA Style

Tiantian Zhu, Zhengqiu Weng, Guolang Chen, Lei Fu. A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion Sensors. Sensors. 2020; 20 (14):3876.

Chicago/Turabian Style

Tiantian Zhu; Zhengqiu Weng; Guolang Chen; Lei Fu. 2020. "A Hybrid Deep Learning System for Real-World Mobile User Authentication Using Motion Sensors." Sensors 20, no. 14: 3876.

Journal article
Published: 15 November 2019 in Applied Sciences
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Power quality disturbances (PQDs) have a large negative impact on electric power systems with the increasing use of sensitive electrical loads. This paper presents a novel hybrid algorithm for PQD detection and classification. The proposed method is constructed while using the following main steps: computer simulation of PQD signals, signal decomposition, feature extraction, heuristic selection of feature selection, and classification. First, different types of PQD signals are generated by computer simulation. Second, variational mode decomposition (VMD) is used to decompose the signals into several instinct mode functions (IMFs). Third, the statistical features are calculated in the time series for each IMF. Next, a two-stage feature selection method is imported to eliminate the redundant features by utilizing permutation entropy and the Fisher score algorithm. Finally, the selected feature vectors are fed into a multiclass support vector machine (SVM) model to classify the PQDs. Several experimental investigations are performed to verify the performance and effectiveness of the proposed method in a noisy environment. Moreover, the results demonstrate that the start and end points of the PQD can be efficiently detected.

ACS Style

Lei Fu; Tiantian Zhu; Guobing Pan; Sihan Chen; Qi Zhong; Yanding Wei. Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection. Applied Sciences 2019, 9, 4901 .

AMA Style

Lei Fu, Tiantian Zhu, Guobing Pan, Sihan Chen, Qi Zhong, Yanding Wei. Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection. Applied Sciences. 2019; 9 (22):4901.

Chicago/Turabian Style

Lei Fu; Tiantian Zhu; Guobing Pan; Sihan Chen; Qi Zhong; Yanding Wei. 2019. "Power Quality Disturbance Recognition Using VMD-Based Feature Extraction and Heuristic Feature Selection." Applied Sciences 9, no. 22: 4901.

Journal article
Published: 09 October 2019 in Energies
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Photovoltaic (PV) power generation is greatly affected by meteorological environmental factors, with obvious fluctuations and intermittencies. The large-scale PV power generation grid connection has an impact on the source-load stability of the large power grid. To scientifically and rationally formulate the power dispatching plan, it is necessary to realize the PV output prediction. The output prediction of single power plants is no longer applicable to large-scale power dispatching. Therefore, the demand for the PV output prediction of multiple power plants in an entire region is becoming increasingly important. In view of the drawbacks of the traditional regional PV output prediction methods, which divide a region into sub-regions based on geographical locations and determine representative power plants according to the correlation coefficient, this paper proposes a multilevel spatial upscaling regional PV output prediction algorithm. Firstly, the sub-region division is realized by an empirical orthogonal function (EOF) decomposition and hierarchical clustering. Secondly, a representative power plant selection model is established based on the minimum redundancy maximum relevance (mRMR) criterion. Finally, the PV output prediction for the entire region is achieved through the output prediction of representative power plants of the sub-regions by utilizing the Elman neural network. The results from a case study show that, compared with traditional methods, the proposed prediction method reduces the normalized mean absolute error (nMAE) by 4.68% and the normalized root mean square error (nRMSE) by 5.65%, thereby effectively improving the prediction accuracy.

ACS Style

Lei Fu; Yiling Yang; Xiaolong Yao; Xufen Jiao; Tiantian Zhu; Fu; Yang; Yao; Jiao; Zhu. A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion. Energies 2019, 12, 3817 .

AMA Style

Lei Fu, Yiling Yang, Xiaolong Yao, Xufen Jiao, Tiantian Zhu, Fu, Yang, Yao, Jiao, Zhu. A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion. Energies. 2019; 12 (20):3817.

Chicago/Turabian Style

Lei Fu; Yiling Yang; Xiaolong Yao; Xufen Jiao; Tiantian Zhu; Fu; Yang; Yao; Jiao; Zhu. 2019. "A Regional Photovoltaic Output Prediction Method Based on Hierarchical Clustering and the mRMR Criterion." Energies 12, no. 20: 3817.

Journal article
Published: 10 August 2019 in Energies
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Condition monitoring is used to assess the reliability and equipment efficiency of wind turbines. Feature extraction is an essential preprocessing step to achieve a high level of performance in condition monitoring. However, the fluctuating conditions of wind turbines usually cause sudden variations in the monitored features, which may lead to an inaccurate prediction and maintenance schedule. In this scenario, this article proposed a novel methodology to detect the multiple levels of faults of rolling bearings in variable operating conditions. First, signal decomposition was carried out by variational mode decomposition (VMD). Second, the statistical features were calculated and extracted in the time domain. Meanwhile, a permutation entropy analysis was conducted to estimate the complexity of the vibrational signal in the time series. Next, feature selection techniques were applied to achieve improved identification accuracy and reduce the computational burden. Finally, the ranked feature vectors were fed into machine learning algorithms for the classification of the bearing defect status. In particular, the proposed method was performed over a wide range of working regions to simulate the operational conditions of wind turbines. Comprehensive experimental investigations were employed to evaluate the performance and effectiveness of the proposed method.

ACS Style

Lei Fu; Tiantian Zhu; Kai Zhu; Yiling Yang. Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy. Energies 2019, 12, 3085 .

AMA Style

Lei Fu, Tiantian Zhu, Kai Zhu, Yiling Yang. Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy. Energies. 2019; 12 (16):3085.

Chicago/Turabian Style

Lei Fu; Tiantian Zhu; Kai Zhu; Yiling Yang. 2019. "Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy." Energies 12, no. 16: 3085.