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A Rolling Bearing Fault Diagnosis Method Based on Improved CEEMDAN and RCMFE

JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2024年第1期33卷 2450014-2450014页

作者： Luo, Zhiyong Zhu, Guangming Dong, Xin Tan, Hongkai Li, Jialin Chongqing Univ Posts & Telecommun Sch Adv Mfg Engn Chongqing Peoples R China

Considering the problem of residual noise and spurious modes in the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), a rolling element bearing malfunction diagnostic method based on improved CEEMDAN (ICEEMDAN) is proposed. First, different from the CEEMDAN, which directly adds Gaussian white noise with a mean of zero, the proposed method adds the kth component obtained from white noise decomposed by empirical mode decomposition (EMD) to the vibration signal, and then the ICEEMDAN is employed to decompose the signal into several intrinsic mode functions (IMFs). Second, aiming at the uncertainty problem of entropy estimation in multi-scale fuzzy entropy (MFE), a refined composite multi-scale fuzzy entropy (RCMFE) is proposed to obtain the characteristic from the selected IMFs. Finally, smoothing factor of PNN is determined by fruit fly optimization algorithm (FOA), and the extracted features are input into the FOA-PNN model to achieve condition identification. Experimental results illustrate that the identification accuracy is more than 99%, which indicates its high effectiveness and superiority.

关键词： Rolling bearing fault diagnosis empirical mode decomposition multi-scale fuzzy entropy fruit fly optimization algorithm probabilistic neural network

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Prediction and determination of mildew grade in grain storage based on FOA-SVM algorithm

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Food Quality and Safety 2023年第1期7卷 145-151页

作者： Jianghao Yuan Fang Tang Zhihui Qi Huiyi Zhao Academy of National Food and Strategic Reserves Administration BeijingChina Hebei Agricultural University BaodingChina National Engineering Research Center of Grain Storage and Logistics BiingChina

Grain mildew is a significant hazard that causes food loss and poses a serious threat to human health when ***,effective prediction and determination of mildew grade is essential for the prevention and control of mildew and global food *** the present study,a model for predicting and determining the mildew grade of rice was constructed using logistic regression,back propagation neural network and GS-SVM(a grid search-based support vector machine algorithm)based on laboratory culture data and actual data from a granary,*** results show that the GS-SVM model has a better prediction effect,but the model cannot automatically adjust the parameters and is more subjective,and the accuracy may decrease when the data set ***,this paper establishes a new model for a support vector machine based on a fruit fly optimization algorithm(FOA-SVM),which can achieve automatic parameter search and automatically adjust its parameters to find the best result when the data set changes,with a strong ability of self-adjustment of *** addition,the FOA-SVM converges quickly and the model is *** results of this study provide a technical method for early identification of mildew grade during grain storage,which is beneficial for the prevention and control of rice mildew during grain storage.

关键词： Identification of grain mildew support vector machine fruit fly optimization algorithm grain security

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Multi-threshold image segmentation using an enhanced fruit fly optimization for COVID-19 X-ray images

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第PartA期86卷 105147页

作者： Hao, Shuhui Huang, Changcheng Heidari, Ali Asghar Xu, Zhangze Chen, Huiling Alabdulkreem, Eatedal Elmannai, Hela Wang, Xianchuan Wenzhou Univ Key Lab Intelligent Informat Safety & Emergency Zh Wenzhou 325035 Peoples R China Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Comp Sci POB 84428 Riyadh 11671 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Wenzhou Med Univ Informat Technol Ctr Wenzhou 325035 Peoples R China

Since the outbreak of COVID-19, it has seriously endangered the health of human beings. Computer automatic segmentation of COVID-19 X-ray images is an important means to assist doctors in rapid and accurate diagnosis. Therefore, this paper proposes a modified FOA (EEFOA) with two optimization strategies added to the original FOA, including elite natural evolution (ENE) and elite random mutation (ERM). To be specific, ENE and ERM can effectively speed up the convergence and deal with the problem of local optima, respectively. The outstanding performance of EEFOA was confirmed by experimental results comparing EEFOA with the original FOA, other FOA variants, and advanced algorithms at CEC2014. After that, EEFOA is implemented for multi-threshold image segmentation (MIS) of COVID-19 X-ray images, where a 2D histogram consisting of the original greyscale image and the non-local means image is used to represent the image information, and Re & PRIME;nyi's entropy is used as the objective function to find the maximum value. The evaluation results of the MIS segmentation experiments show that, whether high or low threshold, EEFOA can achieve higher quality segmentation results and greater robustness than other advanced segmentation methods.

关键词： Meta -heuristic algorithm fruit fly optimization algorithm Multi -threshold image segmentation COVID-19 X-ray images

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The fruit fly optimization algorithms for Patient-Centered Care Based on Interval Trapezoidal Type-2 Fuzzy Numbers

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2019年第4期21卷 1270-1287页

作者： Hu, Junhua Chen, Panpan Yang, Yan Liu, Yongmei Chen, Xiaohong Cent South Univ Sch Business Changsha 410083 Hunan Peoples R China

Patient-centered care is an important part of the integrative medicine that places the patient at the center of the delivery of treatment, improves the efficiency of care and responds the patient's needs and preferences. However, the weighting approach for patient-centered group decision making has merely studied the consistency of decision makers (DMs) and group decision. In this study, we aim to develop the fruit fly optimization algorithm (FOA) under the interval trapezoidal type-2 fuzzy numbers (ITrT2FNs). By lowering the deviation distance between each DM's decision matrix and the group matrix, the optimal weight of DMs can be obtained. Then a novel patient-centered group decision-making model based on the ITrT2FNs and the multi-attributive border approximation area comparison(MABAC) method is proposed. Moreover, the entropy weight method is developed to determine the criteria weights. Finally, the new model is applied to address the realistic breast cancer treatment selection problem, and a comparative analysis is implemented to verify the flexibility and rationality of the extended FOA.

关键词： Patient-centered group decision making Weights of decision makers fruit fly optimization algorithm Extended MABAC Interval trapezoidal type-2 fuzzy numbers

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The Text Mining and Classification Analyses of Tumor Based on Twitter

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IETE JOURNAL OF RESEARCH 2023年第4期69卷 1945-1951页

作者： Wu, Shianghau Macau Univ Sci & Technol Sch Business Taipa Macau Peoples R China

The study is combined with the text mining and classification method to analyze the tumor-related tweets on Twitter in order to comprehend the public opinion focus on the social media. The contribution of the study included the following two points. First, the study used the text mining method to explore the content of tumor-related tweets and found the keywords according to their frequencies. Second, the study applied the classification analysis with four different algorithms to explore the relationship and the importance of the keywords. The study also found the random forests model with the best performance in four models and concluded that the Twitter users focused more on surgery issues, psychological issues and the technical issues on the topic of tumor.

关键词： Ada Boost algorithm classification fruit fly optimization algorithm random forests text mining tumor

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Improved Watershed Segmentation and DualNet Deep Learning Classifiers for Breast Cancer Classification

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SN Computer Science 2024年第5期5卷 458页

作者： Kadadevarmath, Jyoti Reddy, A. Padmanabha Vijayanagara Sri Krishnadevaraya University Karnataka Ballari 583101 India

Breast cancer is a deadly, noncommunicable disease that affects women worldwide. Early detection is crucial in providing effective treatment and improving survival rates. Towards that, a novel DualNet-deep-learning-classifier model for efficient and accurate breast cancer detection is proposed. The proposed model includes five major phases: “pre-processing, segmentation, feature extraction, feature selection, and breast cancer detection”. The pre-processing phase involves noise removal via Wiener filtering and image contrast enhancement via a contrast stretching approach. Then, from the pre-processed mammogram images, the ROI region is identified using the new gradient-based watershed segmentation approach. Subsequently, from the identified ROI regions the texture [grey level run-length matrix (GLRLM), multi-threshold rotation invariant LBP (MT-RILBP) (proposed)], color (color correlogram), and shape features (Zernike moment) are extracted;and among the extracted features, the optimal features are chosen using a hybrid optimization model-flyBird optimization algorithm (FBO), which incorporates both the “fruit fly optimization algorithm and bird mating optimizer”. The breast cancer classification phase uses a DualNet-deep-learning-classifiers approach that includes “long short-term memory networks (LSTM), convolutional spiking neural networks (CSNN), and a new optimized autoencoder (OptAuto)”. The LSTM and CSNN are trained using the identified optimal features. The outcome from LSTM and CSNN is fed as input to OptAuto, wherein the outcome regarding the presence/absence of breast cancer is identified. Moreover, the weight function of the autoencoder is tuned using the new FBO. The proposed model is evaluated in terms of “sensitivity, accuracy, specificity, precision, TPR, FPR, TNR, F1-score, and recall”. Overall, the proposed model holds promise for accurate and efficient breast cancer detection, with potential for future clinical applications. © The Author(s), under

关键词： Bird mating optimizer Breast cancer diagnosis CSNN DualNet-deep-learning-classifiers fruit fly optimization algorithm LSTM OptAuto

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Multi sensor moving image fusion analysis algorithm on the basis of neural network technology

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2023年第3期23卷 1209-1224页

作者： Zhan, Keqiang Fujian Polytech Informat Technol Internet Things & Artificial Intelligence Coll Fuzhou 350003 Fujian Peoples R China

Image fusion can extract the useful information of each channel about the same target to the maximum extent, and get high quality image. However, in this process, the image quality may be affected by noise and reduced. To reduce the image noise's influence on the image fusion effect as well as improve the image fusion quality, a multi sensor moving image fusion analysis algorithm on the basis of neural network technology is proposed. This study designed a window adaptive strategy, use the probability density function, and built an impulse noise model, and use this model to divide each pixel in the image into noise points or signal points to obtain image impulse noise detection results, and use bilateral filtering algorithm to achieve image denoising processing;The fruit fly optimization algorithm is adopted to detect the edge of the multi sensor moving image, extract the image's main edge points, and remove the detail edge points and noise points;nonlinear convolutional layer is used to replace most fusion layers to improve the dense network model, and the cross-entropy loss is used as the loss function in training the network, then use guided filters to generate guide maps, and generate final fusion images. According to experimental results, the noise detection method in this paper can also maintain 79.21% non-noise extraction rate under the noise density of 0.7. The highest correlation coefficient between the proposed algorithm and the standard image is 37.41. Its peak signal-to-noise ratio is as low as 0.09 and as high as 0.52. It has a minimum root mean square error of 8.52. The above values are better than other measured methods, and its edge miss rate can be as low as 1%, the image resolution is higher. It can be seen that its image denoising effect is better. Image denoising effect, and low edge missed detection rate, which effectively improves the effect of image fusion.

关键词： Neural network technology multi sensor image fusion impulse noise model fruit fly optimization algorithm improved dense network

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Research on intelligent slice planning method for free-form surfaces of shaped workpieces

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第12期37卷 11214-11232页

作者： Du, Yuxiao Chen, Yihang Cai, Shuting Chen, Kongyang Li, Xianghuan Guangdong Univ Technol Sch Automation Guangzhou Peoples R China Guangzhou Univ Inst Artificial Intelligence & Blockchain Guangzhou Peoples R China Pazhou Lab Guangzhou Peoples R China Jiangnan Univ Jiangsu Key Lab Media Design & Software Technol Wuxi Peoples R China Guangdong Univ Technol Guangzhou Peoples R China

A surface slicing planning method based on the K-means clustering algorithm and improved fruit fly optimization algorithm (FOA) is proposed to address the efficiency problem in the surface processing of special shaped workpieces. The NURBS surface reconstruction is performed on the complex surface of the selected workpiece, and the K-means clustering algorithm with the curvature-distance factor is used to subdivide the surface, and the FOA with the hopping strategy is used to plan the optimal connection path for each subdivision with different starting points. This improves the local search capability of the FOA by improving the mixing of different classes that occurs when the surface is subdivided. Finally, the proposed method is demonstrated to be effective for surface slicing by simulating the slicing plan on the constructed heterogeneous workpiece surface, and the shortest connectivity paths are obtained for different starting points.

关键词： curved surface slice fruit fly optimization algorithm K-means clustering traveling salesman problem

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Many-objective optimization based path planning of multiple UAVs in oilfield inspection

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APPLIED INTELLIGENCE 2022年第11期52卷 12668-12683页

作者： Li, Kun Yan, Xinxin Han, Ying Ge, Fawei Jiang, Yu Liaoning Tech Univ Fac Elect & Control Engn Huludao 125105 Liaoning Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China CCTEG Shenyang Res Inst Fushun 113122 Liaoning Peoples R China

Unmanned aerial vehicles (UAVs) inspection can be considered as a path planning problem for the UAVs, which needs UAVs to traverse all task points one by one by avoiding some obstacles. In most current researches, only single objective function is used in the path planning for the UAVs, and some other objectives are seldom considered together, like: the number of UAVs, the required inspection time and so on. Therefore, in order to overcome the defect of using single objective function to plan the path of UAVs, a many-objective optimization based multiple UAVs path planning method is studied in this paper. Firstly, four objective functions are chosen which include the flight distance, the flight stability, the number of UAVs and the time offset of reaching the task point;considering the arrival time of UAVs at each task point is an interval value in the practical application, the time offset of reaching the task point is set as an interval objective function, which is handled by the matter-element extension method in this paper. Then, an improved NSGA-III algorithm is proposed to solve the established many-objective optimization problem, which uses fruit fly optimization algorithm (FOA) to replace the genetic algorithm (GA) in the NSGA-III algorithm. Finally, several evaluation indictors (Spread, generational distance (GD), inverse generational distance (IGD), and running time) are used to choose the optimal flight paths of multiple UAVs. Through simulation comparisons with other algorithms, it is concluded that the improved NSGA-III algorithm is more effective in the path planning of UAVs.

关键词： Oilfield inspection Many-objective optimization NSGA-III algorithm Path planning fruit fly optimization algorithm

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Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: method and analysis

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 1期38卷 743-771页

作者： Yu, Helong Li, Wenshu Chen, Chengcheng Liang, Jie Gui, Wenyong Wang, Mingjing Chen, Huiling Jilin Agr Univ Coll Informat Technol Changchun 130118 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Univ Technol Sydney Sch Comp Sci Fac Engn & IT Ultimo Australia Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam

The fruit fly optimization algorithm (FOA) is a recent algorithm inspired by the foraging behavior of fruit fly populations. However, the original FOA easily falls into the local optimum in the process of solving practical problems, and has a high probability of escaping from the optimal solution. In order to improve the global search capability and the quality of solutions, a dynamic step length mechanism, abandonment mechanism and Gaussian bare-bones mechanism are introduced into FOA, termed as BareFOA. Firstly, the random and ambiguous behavior of fruit flies during the olfactory phase is described using the abandonment mechanism. The search range of fruit fly populations is automatically adjusted using an update strategy with dynamic step length. As a result, the convergence speed and convergence accuracy of FOA have been greatly improved. Secondly, the Gaussian bare-bones mechanism that overcomes local optimal constraints is introduced, which greatly improves the global search capability of the FOA. Finally, 30 benchmark functions for CEC2017 and seven engineering optimization problems are experimented with and compared to the best-known solutions reported in the literature. The computational results show that the BareFOA not only significantly achieved the superior results on the benchmark problems than other competitive counterparts, but also can offer better results on the engineering optimization design problems.

关键词： Swarm intelligence fruit fly optimization algorithm Gaussian bare-bones Dynamic step length Engineering design problems

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