Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open‐Pit Mine Slope

Jiang, Song; Lian, Minjie; Lu, Caiwu; Gu, Qinghua; Ruan, Shunling; Xie, Xiaochun

doi:10.1155/2018/1048756

Cited by 69 publications

(43 citation statements)

References 51 publications

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“…Some typical images are shown in Fig.5 (e). In our work, we 3 The Fifteen Scene Categories database is available at: http://www-cvr.ai. uiuc.edu/ponce grp/data/ first exploit k-means based feature extraction method 4 [61] to extract the features of CIFAR-10 database, and then utilize the principal component analysis (PCA) [62] algorithm to reduce the feature of each sample to 1000 dimensions to improve the computational efficiency.…”

Section: B Experiments and Analysis On Real-world Databasesmentioning

confidence: 99%

Adaptive Locality Preserving Regression

Wen

Zhong

Zhang

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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This paper proposes a novel discriminative regression method, called adaptive locality preserving regression (ALPR) for classification. In particular, ALPR aims to learn a more flexible and discriminative projection that not only preserves the intrinsic structure of data, but also possesses the properties of feature selection and interpretability. To this end, we introduce a target learning technique to adaptively learn a more discriminative and flexible target matrix rather than the pre-defined strict zero-one label matrix for regression. Then a locality preserving constraint regularized by the adaptive learned weights is further introduced to guide the projection learning, which is beneficial to learn a more discriminative projection and avoid overfitting. Moreover, we replace the conventional 'Frobenius norm' with the special l2,1 norm to constrain the projection, which enables the method to adaptively select the most important features from the original high-dimensional data for feature extraction. In this way, the negative influence of the redundant features and noises residing in the original data can be greatly eliminated. Besides, the proposed method has good interpretability for features owning to the row-sparsity property of the l2,1 norm. Extensive experiments conducted on the synthetic database with manifold structure and many realworld databases prove the effectiveness of the proposed method.

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Section: B Experiments and Analysis On Real-world Databasesmentioning

confidence: 99%

Adaptive Locality Preserving Regression

Wen

Zhong

Zhang

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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“…However, when the VMD method decomposes the signal, the decomposition effect is seriously affected by the number of decomposition components [56][57][58][59][60]. Some other methods have been proposed to realize signal analysis and fault diagnosis in recent years [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80].…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

Liu

Yuan

et al. 2019

Applied Sciences

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The variational mode decomposition (VMD) method for signal decomposition is severely affected by the number of components of the VMD method. In order to determine the decomposition modal number, K, in the VMD method, a new center frequency method of the multi-threshold is proposed in this paper. Then, an improved VMD (MTCFVMD) algorithm based on the center frequency method of the multi-threshold is obtained to decompose the vibration signal into a series of intrinsic modal functions (IMFs). The Hilbert transformation is used to calculate the envelope signal of each IMF component, and the maximum frequency value of the power spectral density is obtained in order to effectively and accurately extract the fault characteristic frequency and realize the fault diagnosis. The rolling element vibration data of the motor bearing is used to test the effectiveness of proposed methods. The experiment results show that the center frequency method of the multi-threshold can effectively determine the number, K, of decomposed modes. The proposed fault diagnosis method based on MTCFVMD and Hilbert transformation can effectively and accurately extract the fault characteristic frequency, rotation frequency, and frequency doubling, and can obtain higher diagnostic accuracy.

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“…Some have focused on the evaluation of CO 2 emissions in manufacturing [7], transportation [8,9], households [10,11], and buildings [12,13], and have found that these sectors are the main sources of CO 2 emission in urban areas. On the other hand, some scholars have suggested that the driving forces of urban CO 2 emission include population [14], the economy [15][16][17], technology [18][19][20], energy structure [21], urbanization [22], and spatial patterns [23,24], and have achieved many valuable conclusions. For example, the growing urban population and changing age structure exert positive effects on city-level emissions, while the downsizing of families and expansion of the migrant urban population fuel emissions [25]; growth in economic activity is the most fundamental contributor to the rising city-level emissions [26,27]; advancing technologies, in particular, breakthroughs in energy are the major means of lowering emissions [28] and rising emissions could be curbed by the adjustment of industrial structure and energy consumption patterns [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Spatial Association and Effect Evaluation of CO2 Emission in the Chengdu-Chongqing Urban Agglomeration: Quantitative Evidence from Social Network Analysis

et al. 2018

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Urban agglomeration, an established urban spatial pattern, contributes to the spatial association and dependence of city-level CO 2 emission distribution while boosting regional economic growth. Exploring this spatial association and dependence is conducive to the implementation of effective and coordinated policies for regional level CO 2 reduction. This study calculated CO 2 emissions from 2005-2016 in the Chengdu-Chongqing urban agglomeration with the IPAT model, and empirically explored the spatial structure pattern and association effect of CO 2 across the area leveraged by the social network analysis. The findings revealed the following: (1) The spatial structure of CO 2 emission in the area is a complex network pattern, and in the sample period, the CO 2 emission association relations increased steadily and the network stabilization remains strengthened; (2) the centrality of the cities in this area can be categorized into three classes: Chengdu and Chongqing are defined as the first class, the second class covers Deyang, Mianyang, Yibin, and Nanchong, and the third class includes Zigong, Suining, Meishan, and Guangan-the number of cities in this class is on the rise; (3) the network is divided into four subgroups: the area around Chengdu, south Sichuan, northeast Sichuan, and west Chongqing where the spillover effect of CO 2 is greatest; and (4) the higher density of the global network of CO 2 emission considerably reduces regional emission intensity and narrows the differences among regions. Individual networks with higher centrality are also found to have lower emission intensity.

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Ensemble Prediction Algorithm of Anomaly Monitoring Based on Big Data Analysis Platform of Open‐Pit Mine Slope

Cited by 69 publications

References 51 publications

Adaptive Locality Preserving Regression

Adaptive Locality Preserving Regression

Fault Diagnosis for a Bearing Rolling Element Using Improved VMD and HT

Spatial Association and Effect Evaluation of CO2 Emission in the Chengdu-Chongqing Urban Agglomeration: Quantitative Evidence from Social Network Analysis

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