Cluster Prototypes and Fuzzy Memberships Jointly Leveraged Cross-Domain Maximum Entropy Clustering

Qian, Pengjiang; Jiang, Yizhang; Deng, Zhaohong; Hu, Lianghai; Sun, Shuifa; Wang, Shitong; Muzic, Raymond F.

doi:10.1109/tcyb.2015.2399351

Cited by 87 publications

(29 citation statements)

References 57 publications

(50 reference statements)

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“…The learning method provides a certain reference for the target domain by referring to the source domain information. In the clustering analysis, transfer learning can effectively deal with the problem of low clustering accuracy and instability [18]. This paper introduces transfer learning into MV-FCM to improve the stability of WTs clustering results.…”

Section: B the Proposed Clustering Methods Mvt-fcmmentioning

confidence: 99%

A Wind Farm Equivalent Method Based on Multi-View Transfer Clustering and Stack Sparse Auto Encoder

Han

Miao

et al. 2020

IEEE Access

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Large-scale wind farm (WF) constitutes dozens or even hundreds of wind turbines (WTs), making it complex and even impractical to develop each individual WT in detail when building WF model. Thus, the equivalent model of WF, with a reasonable reduction of the detailed model, is essential to be developed. In this paper, we propose a multi-view transfer clustering and stack sparse auto encoder (SSAE) based WF equivalent method, which can be used in the low voltage ride through (LVRT) analysis of WF. First, to obtain distinguishable deep-level and multi-view representation of wind turbine (WT), stack sparse auto encoder (SSAE) is used to extract features from the time series of several WT physical quantities, and these features are used as the clustering indicator (CI). Then, a multi-view transfer FCM (MVT-FCM) clustering algorithm, which combines transfer learning with multi-view FCM (MV-FCM), is put forward for WTs clustering. Two transfer rules are designed in this algorithm, and the clustering center and membership degree in the source domain are transferred to guide the clustering process of target domain samples. Finally, the calculation method of equivalent parameters is presented. To verify the effectiveness of the proposed method, a modified actual system in East Inner Mongolia of China is utilized for case study, and the performance of the proposed model is compared with several state-of-the-art models. Simulation results show that the equivalent errors of the proposed model decrease at least 3% when comparing with other models. Also, the error fluctuations are within 6% under different simulation conditions, which illustrates the well-performed robustness of the proposed model. INDEX TERMS Wind farm equivalence, multi-view, transfer learning, deep learning, MVT-FCM, SSAE.

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Section: B the Proposed Clustering Methods Mvt-fcmmentioning

confidence: 99%

A Wind Farm Equivalent Method Based on Multi-View Transfer Clustering and Stack Sparse Auto Encoder

Han

Miao

et al. 2020

IEEE Access

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“…Finally, the cascade error-correction mechanism is used for correcting the predicted wrist positions. The experimental results on VideoPose2.0 [12][13][14] and VIPS-VideoPose datasets [15][16][17][18] verify the effectiveness for tracking dynamic human poses, especially on improving the wrist location accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…A human pose estimation method based on Faster R-CNN is proposed in the literature [14], for the sake of solving the problem that is difficult to get the robust appearance model of human body parts in the process of still human pose estimation. The traditional methods almost have poor robustness because artificial extracted features are vulnerable to background environments, illumination variations, and a difference in clothing.…”

Section: Related Workmentioning

confidence: 99%

A human pose estimation algorithm based on the integration of improved convolutional neural networks and multi-level graph structure constrained model

You

Zhao

2019

Pers Ubiquit Comput

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This work introduces a novel convolutional feature for the task of human pose estimation, which is a framework of fusing a convolutional neural network into a multi-level graph structure model so as to improve the pose estimation results from body-part detection and human spatial structure. In the stage of part detection, the probability vectors corresponding to human body parts in the whole image are analyzed by a convolutional neural network. And then, a novel multi-level graph structure model is designed in this method, which contains the whole body, human body parts and joints, and realizes the coarse-to-fine establishment of a more constrained human spatiTal constraint model from levels of the structures, the edges, to the pixels. The obtained probability vectors are put into the multi-level graph structure model to compute the location coordinates for each joint, successfully achieving a framework of combining the deep learning network and the multi-level graph structure model. A large number of qualitative and quantitative experimental results show that compared with other state-of-art methods, the integration of the deep learning network and the multi-level pictorial structure model can improve the accuracy of human pose estimation to a greater extent.

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“…As can be seen from the above analysis, it's a classic LASSO regression model [33]. LASSO regression is characterized by variable selection and regularization while fitting a generalized linear model.…”

Section: Our Improved Elm For Networked Control Systemsmentioning

confidence: 99%

Regularized extreme learning machine–based intelligent adaptive control for uncertain nonlinear systems in networked control systems

Chen

Sun

2019

Pers Ubiquit Comput

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Since the learning ability of adaptive control (including multi-model adaptive control) is actually very limited and the persistent excitation (PE) condition is also not satisfied in the design of uncertain nonlinear system controllers, an improved networked control method based on regularized extreme learning machine for a class of nonlinear systems. The networked controller is built to compensate for the modeling error and system uncertainties. In the designed controller, its hidden node parameters are modified using the recently proposed regularized extreme learning machine (ELM), where they are assigned random values. However, different from the original ELM algorithm, the output weight is updated based on the L 1 /L 2 norm penalty to guarantee the stability of the overall control system. The proposed model is finally applied to the internal model control in networked control system. The simulation results demonstrate good tracking performance of the proposed control scheme and have better control performance, anti-interference ability, and robustness for nonlinear system.

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Cluster Prototypes and Fuzzy Memberships Jointly Leveraged Cross-Domain Maximum Entropy Clustering

Cited by 87 publications

References 57 publications

A Wind Farm Equivalent Method Based on Multi-View Transfer Clustering and Stack Sparse Auto Encoder

A Wind Farm Equivalent Method Based on Multi-View Transfer Clustering and Stack Sparse Auto Encoder

A human pose estimation algorithm based on the integration of improved convolutional neural networks and multi-level graph structure constrained model

Regularized extreme learning machine–based intelligent adaptive control for uncertain nonlinear systems in networked control systems

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