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| 1 | Design and experiment of vibration plate type camellia fruit picking machine显示文摘In order to solve the problem of high missed rate of camellia fruits and high damage rate of flower buds during vibratory picking,through the study on the biological characteristics of camellia tree,the crown of camellia tree was divided into upper and lower canopy at the maximum canopy diameter.A kind of vibration plate type camellia fruit picking machine was designed.The structure and working principle of the whole machine were described.Through the analysis of the factors of camellia fruit abscission,it is concluded that frequency,amplitude and working time are the main factors affecting the magnitude of inertia force.Through preliminary research and coupling simulation,the horizontal range of each factor is determined:operation time is 5 s,10 s,15 s,frequency is 3 Hz,5 Hz,7 Hz and amplitude is 50 mm,60 mm,70 mm.Orthogonal tests were carried out on the upper and lower canopies successively,and the comprehensive scoring method was used to analyze the orthogonal test results.The results showed that the optimal combination of working parameters for picking camellia in the upper canopy was A3B3C2,that is,the operation time was 15 s,the amplitude was 70 mm and the frequency was 5 Hz.Under this condition,the missed picking rate of camellia fruits was 8.21%and the rate of flower buds damage was 9.12%.The optimal combination of working parameters for picking camellia fruits in the lower canopy was A3B1C3,that is,the operation time was 15 s,the amplitude was 50 mm and the frequency was 7 Hz.Under this condition,the rate of missing picking of camellia fruits was 6.84%and the rate of flower buds damage was 6.92%. | Delin Wu Da Ding Bowen Cui Shan Jiang Enlong Zhao Yangyang Liu Chengmao Cao | 2022 | International Journal of Agricultural and Biological Engineering2022，15，4： | 1 |
| 2 | Entropy-like distance driven fuzzy clustering with local information constraints for image segmentation显示文摘To improve the anti-noise ability of fuzzy local information C-means clustering, a robust entropy-like distance driven fuzzy clustering with local information is proposed. This paper firstly uses Jensen-Shannon divergence to induce a symmetric entropy-like divergence. Then the root of entropy-like divergence is proved to be a distance measure, and it is applied to existing fuzzy C-means(FCM) clustering to obtain a new entropy-like divergence driven fuzzy clustering, meanwhile its convergence is strictly proved by Zangwill theorem. In the end, a robust fuzzy clustering by combing local information with entropy-like distance is constructed to segment image with noise. Experimental results show that the proposed algorithm has better segmentation accuracy and robustness against noise than existing state-of-the-art fuzzy clustering-related segmentation algorithm in the presence of noise. | Wu Chengmao Cao Zhuo | 2021 | The Journal of China Universities of Posts and Telecommunications2021，28，1： | 0 |
| 3 | Memristor-based multi-channel pulse coupled neural network for image fusion显示文摘Image fusion is widely used in computer vision and image analysis.Considering that the traditional image fusion algorithm has a certain limitation in multi-channel image fusion,a memristor-based multi-channel pulse coupled neural network(M-MPCNN)for image fusion is proposed.Based on a dual-channel pulse coupled neural network(D-PCNN),a novel multi-channel pulse coupled neural network(M-PCNN)is firstly constructed in this paper.Then the exponential growth dynamic threshold model is used to improve the pulse generation of pulse coupled neural network,which can not only avoid multiple ignitions effectively,but can also improve operational efficiency and reduce complexity.At the same time,synchronous capture can also enhance image edge,which is more conducive to image fusion.Finally,the threshold and synaptic characteristics of pulse coupled neural networks(PCNNs)can be well realized by using a memristor-based pulse generator.Experimental results show that the proposed algorithm can fuse multi-source images more effectively than existing state-of-the-art fusion algorithms. | Liu Jian Wu Chengmao Tian Xiaoping | 2020 | The Journal of China Universities of Posts and Telecommunications2020，27，6： | 0 |
| 4 | Fractional order distance regularized level set method with bias correction显示文摘The existing level set segmentation methods have drawbacks such as poor convergence,poor noise resistance,and long iteration times.In this paper,a fractional order distance regularized level set segmentation method with bias correction is proposed.This method firstly introduces fractional order distance regularized term to punish the deviation between the level set function(LSF)and the signed distance function.Secondly a series of covering template is constructed to calculate fractional derivative and its conjugate of image pixel.Thirdly introducing the offset correction term and fully using the local clustering property of image intensity,the local clustering criterion of image intensity is defined and integrated with the neighborhood center to obtain the global criterion of image segmentation.Finally,the fractional distance regularization,offset correction,and external energy constraints are combined,and the energy optimization segmentation method for noisy image is established by level set.Experimental results show that the proposed method can accurately segment the image,and effectively improve the efficiency and robustness of exiting state of the art level set related algorithms. | Cai Xiumei He Ningning Wu Chengmao Liu Xiao Liu Hang | 2024 | The Journal of China Universities of Posts and Telecommunications2024，31，1： | 0 |
| 5 | Enhanced kernel-based fuzzy local information clustering integrating neighborhood membership显示文摘To enhance the segmentation performance and robustness of kernel weighted fuzzy local information C-means(KWFLICM) clustering for image segmentation in the presence of high noise, an improved KWFLICM algorithm aggregating neighborhood membership information is proposed. This algorithm firstly constructs a linear weighted membership function by combining the membership degrees of current pixel and its neighborhood pixels. Then it is normalized to meet the constraint that the sum of membership degree of pixel belonging to different classes is 1. In the end, normalized membership is used to update the clustering centers of KWFLICM algorithm. Experimental results show that the proposed adaptive KWFLICM(AKWFLICM) algorithm outperforms existing state of the art fuzzy clustering-related segmentation algorithms for image with high noise. | Song Yue Wu Chengmao Tian Xiaoping Song Qiuyu | 2021 | The Journal of China Universities of Posts and Telecommunications2021，28，6： | 0 |
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