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Chao Tan

Bio: Chao Tan is an academic researcher from China University of Mining and Technology. The author has contributed to research in topics: Computer science & Fuzzy logic. The author has an hindex of 9, co-authored 33 publications receiving 244 citations.

Papers
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Journal ArticleDOI
TL;DR: An improved strategy based on confidence level was presented for evidence theory to reduce the conflicts between evidences and enhance the fusion effect and BPAs function was constructed reasonably through extracting weights from preliminary prediction values of four neural networks.

42 citations

Journal ArticleDOI
12 Jan 2016-Sensors
TL;DR: An improved fly optimization algorithm (IFOA) to optimize the parameters of LSSVM was presented and the L SSVM coupled with IFOA (IFoa-LSSVM) was used to identify the shearer cutting pattern and comparison results indicate that the proposed approach was feasible, efficient and outperformed the others.
Abstract: Shearers play an important role in fully mechanized coal mining face and accurately identifying their cutting pattern is very helpful for improving the automation level of shearers and ensuring the safety of coal mining. The least squares support vector machine (LSSVM) has been proven to offer strong potential in prediction and classification issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. In this paper, an improved fly optimization algorithm (IFOA) to optimize the parameters of LSSVM was presented and the LSSVM coupled with IFOA (IFOA-LSSVM) was used to identify the shearer cutting pattern. The vibration acceleration signals of five cutting patterns were collected and the special state features were extracted based on the ensemble empirical mode decomposition (EEMD) and the kernel function. Some examples on the IFOA-LSSVM model were further presented and the results were compared with LSSVM, PSO-LSSVM, GA-LSSVM and FOA-LSSVM models in detail. The comparison results indicate that the proposed approach was feasible, efficient and outperformed the others. Finally, an industrial application example at the coal mining face was demonstrated to specify the effect of the proposed system.

32 citations

Journal ArticleDOI
TL;DR: The Preaching Optimization Algorithm is explained in detail and compared with other existing methods to evaluate its comprehensive performance and indicates the proposed algorithm has strong competitiveness both accuracy and robustness in solving optimization problems.
Abstract: Swarm intelligence algorithms have been widely used in both research and engineering fields, but they face the problems of low accuracy and premature convergence, which limit their further applications. Inspired by the preachers’ social behaviors, a novel meta-heuristic swarm intelligence algorithm, Preaching Optimization Algorithm, is proposed in this paper. Its convergence accuracy is effectively improved by improving the initial range of offspring individuals. Meanwhile, by introducing the combined weight including individual fitness and position relationship between individuals, the diversity of individuals is improved, thus reducing the possibility of algorithm premature convergence. In this paper, the parameter sensitivity of the Preaching Optimization Algorithm is analyzed firstly. Secondly, the proposed algorithm is evaluated by comparing it with the other meta-heuristic algorithms on CEC’17 benchmark functions. The results indicate the proposed algorithm has strong competitiveness both accuracy and robustness in solving optimization problems. Finally, the Preaching Optimization Algorithm is used to solve the typical problems in engineering and image threshold segmentation, which further verifies the excellent optimization performance of the proposed algorithm. In this paper, the Preaching Optimization Algorithm is explained in detail and compared with other existing methods to evaluate its comprehensive performance.

24 citations

Journal ArticleDOI
TL;DR: Three regularization methods are introduced in this paper to solve the overfitting problem of CNN and speed up the convergence: dropout, weight regularization, and batch normalization.
Abstract: Accurate identification of the distribution of coal seam is a prerequisite for realizing intelligent mining of shearer. This paper presents a novel method for identifying coal and rock based on a deep convolutional neural network (CNN). Three regularization methods are introduced in this paper to solve the overfitting problem of CNN and speed up the convergence: dropout, weight regularization, and batch normalization. Then the coal-rock image information is enriched by means of data augmentation, which significantly improves the performance. The shearer cutting coal-rock experiment system is designed to collect more real coal-rock images, and some experiments are provided. The experiment results indicate that the network we designed has better performance in identifying the coal-rock images.

24 citations

Journal ArticleDOI
TL;DR: A novel shearer cutting state recognition method based on composite multi-scale permutation (CMPE), Laplacian score (LS) and fly optimization algorithm-based support vector machine classification (FOA-SVM) is presented to overcome the shortcomings of MPE.

23 citations


Cited by
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Journal ArticleDOI

[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Journal ArticleDOI
TL;DR: In this paper, the authors offer a new book that enPDFd the perception of the visual world to read, which they call "Let's Read". But they do not discuss how to read it.
Abstract: Let's read! We will often find out this sentence everywhere. When still being a kid, mom used to order us to always read, so did the teacher. Some books are fully read in a week and we need the obligation to support reading. What about now? Do you still love reading? Is reading only for you who have obligation? Absolutely not! We here offer you a new book enPDFd the perception of the visual world to read.

2,250 citations

Proceedings ArticleDOI
15 Mar 2006
TL;DR: In this article, damage pre-cursors based residual life computation approach for various package elements to prognosticate electronic systems prior to appearance of any macro-indicators of damage has been presented.
Abstract: In this paper, damage pre-cursors based residual life computation approach for various package elements to prognosticate electronic systems prior to appearance of any macro-indicators of damage has been presented. In order to implement the system-health monitoring system, precursor variables or leading indicators-of-failure have been identified for various package elements and failure mechanisms. Model-algorithms have been developed to correlate precursors with impending failure for computation of residual life. Package elements investigated include, first-level interconnects, dielectrics, chip interconnects, underfills and semiconductors. Examples of damage proxies include, phase growth rate of solder interconnects, intermetallics, normal stress at chip interface, and interfacial shear stress

331 citations

Journal ArticleDOI
TL;DR: Results for every optimization task demonstrate that LSEOFOA can provide a high-performance and self-assured tradeoff between exploration and exploitation, and overall research findings show that the proposed model is superior in terms of classification accuracy, Matthews correlation coefficient, sensitivity, and specificity.

212 citations

Journal ArticleDOI
TL;DR: In this article, a hybrid information fusion approach that integrates cloud model (CM), Dempster-Shafer (D-S) evidence theory and Monte Carlo (MC) simulation technique was developed to perceive safety risk of tunnel-induced building damage under uncertainty.

113 citations