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Fault severity assessment of rolling bearings method based on improved VMD and LSTM

JOURNAL OF VIBROENGINEERING 2020年第6期22卷 1338-1356页

作者： Ji, Xiaofei Liang, Zhihua Cao, Jiangtao Wei, Peng Liaoning Shihua Univ Sch Informat & Control Engn Fushun Liaoning Peoples R China Shenyang Aerosp Univ Sch Automat Shenyang Liaoning Peoples R China

In order to solve the problem of selection of appropriate wavelet basis function and clearly show the physical meaning of Empirical Mode Decomposition (EMD), an improved Variational Mode Decomposition (VMD) method with Long Short-Term Memory (LSTM) neural network is proposed. With the Cuckoo Search (cs) algorithm, the central frequency updating rules of VMD are optimized. And the low efficiency and local optimum problem is avoided. Meanwhile the decomposition layer number is found by the instantaneous frequency theory. For improving the prediction accuracy in traditional regression prediction methods, a LSTM neural network is designed for regression prediction of time sequence characteristics. The proposed method is implemented on actual bearings data which is derived from the bearing laboratory of Case West Reserve University in the United States and the University of Cincinnati Bearing Data Center. The experimental results showed that the improved VMD method was more robust and more accurate than the other traditional methods. And it has some practical value for real application and guiding significance for theory.

关键词： improved VMD cs algorithm LSTM neural network fault severity assessment of rolling bearings

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Improved Cuckoo Search algorithm Based on Firefly Mechanism 4

Improved Cuckoo Search Algorithm Based on Firefly Mechanism

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4th IEEE International Conference on Computational Intelligence and Applications (ICCIA)

作者： Chen, Jiajia He, Miaomiao Deng, Huiwen Sci Southwest Univ Sch Comp & Informat Chongqing 400715 Peoples R China Southwest Univ Inistitute Log & Intelligence Chongqing 400715 Peoples R China

ISBN: (纸本)9781728121284

Cuckoo Search (cs) algorithm has the characteristics of few parameters, easy realization and strong exploration ability. However, cs algorithm relies heavily on the random step in the process of exploration and lacks communication with excellent individuals in the process of optimization, which leads to slow convergence speed of cs algorithm. Firefly algorithm (FA) achieves the goal of optimizing by constantly approaching individuals who are better than themselves. Inspired by the firefly algorithm, Our search algorithm introduces the firefly mechanism into the cs algorithm to increase the information exchange between particles to improve the convergence speed of the algorithm. cs-FA has no random term of FA, because cuckoo search algorithm has powerful global exploring ability. The simulation results show that our algorithm cs-FA has better optimization ability and faster convergence speed than cs algorithm, FA and also PSO algorithm.

关键词： convergence speed cs algorithm FA algorithm cs-FA algorithm

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Software Cost Estimation Using Cuckoo Search 1st

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1st International Conference on Computational Intelligence (ICCI)

作者： Kumari, Sweta Pushkar, Shashank BIT Dept CSE Ranchi Bihar India

ISBN: (纸本)9789811025259;9789811025242

One of the important aspects of any software organization is to use models that can accurately estimate the software development effort. However, development of accurate estimation model is still a challenging issue for software engineering research community. This paper proposes a new model for software cost estimation that uses Cuckoo Search (cs) algorithm for finding the optimal parameter of the cost estimation model. The proposed model has been tested on NASA software project datasets. Experimental results show that the proposed model has improved the performance of the estimated effort with respect to MMRE (Mean Magnitude of Relative Error) and PRED (Prediction).

关键词： Software cost estimation cs algorithm COCOMO model MMRE and PRED

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Communication Anti-jamming Strategy Engine Based on Cuckoo Search algorithm 3

Communication Anti-jamming Strategy Engine Based on Cuckoo S...

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3rd Asia-Pacific Conference on Communications Technology and Computer Science (ACCTcs)

作者： Han, Mingyang Sun, Xuebin Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9798350310801

In this article, a communication anti-jamming strategy engine based on cuckoo search algorithm is proposed to solve the problem of communication anti-jamming decision after receiving interference in wireless ad hoc networks. The algorithm can be initialized by using the solution of the previous decision, so as to obtain the anti-jamming strategy faster, determine the transmission power and channel selection faster, and reduce the amount of calculation. The simulation results show that the algorithm can improve the convergence speed of the cuckoo algorithm in the interference environment without increasing the complexity, and has better real-time performance, which is more in line with the application scenarios of communication anti-interference.

关键词： communication anti-interference cs algorithm nest initialization decision Engine

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Multilevel Thresholding for Coastal Video Image Segmentation Based on Cuckoo Search algorithm 19

Multilevel Thresholding for Coastal Video Image Segmentation...

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5th International Conference on Computing and Artificial Intelligence (ICCAI)

作者： Widyantara, I. Made Oka Pramaita, Nyoman Asana, I. Made Dwi Putra Adnyana, Ida Bagus Putu Pawana, I. Gusti Ngurah Agung Udayana Univ Elect Engn Dept Kuta Selatan Indonesia STMIK STIKOM Indonesia Informat Dept Denpasar Indonesia Udayana Univ Dept Civil Engn Kuta Selatan Indonesia

ISBN: (纸本)9781450361064

In the coastal video image segmentation, images are partitioned into land and sea classes, and each of these classes could have different segmentation qualities. In order to cope with variations in image quality and opaque areas, this paper has proposed a multilevel threshold technique based on the Cuckoo Search (cs) algorithm as an optimization algorithm for selecting optimum threshold values. The optimum threshold values are determined by maximizing Otsu's or Kapur's objective function using cs algorithm. The cs algorithm uses McCulloch's method for Levy flight generation and combined with Otsu's and Kapur's objective functions to analyze cs algorithm performance. Based on the evaluations of PSNR, MSE, FSIM and CPU time parameters, the McCulloch's method based on cs algorithm with Otsu's objective function is the most promising and computationally efficient for segmenting coastal video images.

关键词： cs algorithm McCulloch's method Coastal video image Multilevel thresholding segmentation Kapur method Otsu method

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A Fabric Defects Detection Method Using SAR Imaging

A Fabric Defects Detection Method Using SAR Imaging

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6th International Conference on Wireless Communications, Networking and Mobile Computing (WICOM)

作者： Zhang, Long He, Xiao Hui Si, Dong Sen Zhang, Hai Bin Xian Polytech Univ Coll Elect & Informat Xian Peoples R China Xi An Jiao Tong Univ Inst Comp Architect & Network Xian Peoples R China Xidian Univ Sch Comp Sci & Technol Xian Peoples R China

ISBN: (纸本)9781424437092

The existing fabric defection method mainly depend on the brightness of visual light or gray intensity of image, therefore, the effect of classification and identifition is deteriorated when the high quality fabric image unable to obtained. Aim at solving difficulties or fatal disadvantage in existing fabric defects detection system, a novel system of fabric defects detection system by using the synthetic Aperture radar (SAR) is presented. Different from the mechanism used in imaging approach, some vital disadvantage can be avoided in SAR fabric defect detection system. Theory of SAR method is introduced, key problem in SAR technique is brought forward and solved, the numerical example study and analysis and the result of real data processing verified the effectiveness of proposed method.

关键词： Fabirc Defects formatting SAR imaging cs algorithm

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A REFINED CHIRP SCALING algorithm FOR HIGH-RESOLUTION SPACEBORNE SAR BASED ON THE FOURTH-ORDER MODEL

A REFINED CHIRP SCALING ALGORITHM FOR HIGH-RESOLUTION SPACEB...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Wang, Pengbo Han, Yu Chen, Jie Cui, Zhongma Yang, Wei Li, Shuang Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Beijing Inst Remote Sensing Equipment Beijing 100039 Peoples R China

ISBN: (纸本)9781479911141

With the resolution of SAR developing continually, it is found that in the process of image processing, using equivalent squint range model would bring large phase error. So using traditional cs algorithm for processing will encounter difficulties. In order to solve this problem because of unsuitable slant range model, this paper introduces the fourth-order slant range model to replace the original model, and conducts a study on cs algorithm based on the fourth-order slant range model. This paper introduces the theoretical formulation and simulation results of the new algorithm. This algorithm can perform well for both low-resolution space-borne SAR and high-resolution space-borne SAR.

关键词： synthetic aperture radar equivalent squint range model fourth-order slant range model cs algorithm high-resolution space-borne SAR

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Research for imaging of high resolution airborne SAR data based on parameter estimation

Research for imaging of high resolution airborne SAR data ba...

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5th International Symposium on Multispectral Image Processing and Pattern Recognition

作者： Chao, Shuyuan Chen, Baixiao Zhou, Peng Xidian Univ Natl Key Lab Radar Signal Proc Xian 710071 Peoples R China

ISBN: (纸本)9780819469502

This paper proposes an algorithm combining classic cs algorithm with Dechirp technique for the process of imaging with big range cell migrations and partial aperture data. A great deal of interpolation will be needed if the azimuth pulse compression is accomplished by using a matched filter with only partial aperture data. So the cs algorithm is used to correct the range cell migration and complete the range pulse compression, while the Dechirp technique to accomplish the azimuth pulse compression. In addition, motion compensation is added because of the instability of the airborne SAR that leads to a debasement of the image. A curve of displacements in the LOS direction and phases of azimuth errors can be fitted by estimating the data, Thus it is easy to compensate them.

关键词： high resolution SAR cs algorithm Dechirp technique motion compensation

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Integrating Cuckoo search-Grey wolf optimization and Correlative Naive Bayes classifier with Map Reduce model for big data classification

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DATA & KNOWLEDGE ENGINEERING 2020年 127卷 101788-101788页

作者： Banchhor, Chitrakant Srinivasu, N. Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram AP India

Big data is progressively being used in various areas, such as industry, financial dealing, medicine, and so on, as it can handle the challenges in processing large amounts of data. One of the data mining techniques used widely and effectively to classify big data is the MapReduce model. In this paper, an approach for the classification of big data is developed using Cuckoo-Grey wolf based Correlative Naive Bayes classifier and MapReduce Model (CGCNB-MRM). Accordingly, a novel classifier, named Cuckoo-Grey wolf based Correlative Naive Bayes classifier (CG-CNB), is designed by modifying CNB classifier with a newly developed optimization algorithm, Cuckoo-Grey Wolf based Optimization (CGWO). CGWO algorithm is designed by the effective integration of Cuckoo Search (cs) algorithm into Grey Wolf Optimizer (GWO), to optimize the CNB model by the optimal selection of the model parameters. Finally, the proposed CGCNB-MRM approach performs the classification for each data samples based on the probability index table and the posterior probability of the data. Three metrics, such as accuracy, sensitivity, and specificity, are utilized for the performance evaluation of the proposed CGCNB-MRM approach, where it could achieve 80.7% accuracy with 84.5% sensitivity and 76.9% specificity and thus, prove its effectiveness in big data classification.

关键词： Big data classification CNB classifier GWO cs algorithm MapReduce model

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An automatic data process line identification method for dam safety monitoring data outlier detection

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STRUCTURAL CONTROL & HEALTH MONITORING 2022年第7期29卷 e2948-e2948页

作者： Zheng, Sen Shao, Chenfei Gu, Chongshi Xu, Yanxin Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing Peoples R China Hohai Univ Natl Engn Res Ctr Water Resources Efficient Utili Nanjing Jiangsu Peoples R China

In order to discover anomalies of dam structure behaviors and evaluate the operation status timely, it is quite demanding to analyze the dam safety monitoring data that has been collected from the instruments. However, outliers in original monitoring data may affect the accuracy of dam performance assessment, which need to be detected before analyzing monitoring data. Model-based methods have been applied in outlier detection as a kind of common method for a long time, but they generally rely heavily on model accuracy and easily lead to misjudgment of outliers once the data structure is complex. Considering the monitoring data of dam effect variables (e.g., deformation, cracking, or seepage) tend to show strong continuity, complex periodic and trending changes with the environment, valid monitoring data can reflect the variation trend by forming a data process line. Therefore, data that deviate from the process line can be detected as outliers. In this paper, an automatic process line identification method for dam safety monitoring data outlier detection is proposed. First, after drawing a scatter plot of the dam monitoring data, a binary image of the scatter plot is inputted into the computer program. Afterwards, the binary image would be processed by Gaussian blur and image binarization techniques, and then the continuous points could be identified. After constant adjustment of the vertical ordinate range and introducing Cuckoo Search (cs) algorithm, the optimal process line identification and outlier detection were finally completed. The case studies demonstrate the proposed method can enhance the efficiency of outlier detection.

关键词： cs algorithm dam safety monitoring data process line image processing outlier detection

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