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A new six-DoF parallel mechanism for captive model test

Lu Yun, Wu Jinbo, Li Weijia, Wu Yaozhong

Polish Maritime Research

2020

nr 3

4--15

In order to obtain the hydrodynamic coefficients that can save cost and meet the accuracy requirements, a new hydrodynamic test platform based on a 6DoF (six degrees of freedom) parallel mechanism is proposed in this paper. The test platform can drive the ship to move in six degrees of freedom. By using this experimental platform, the corresponding hydrodynamic coefficients can be measured. Firstly, the structure of the new device is introduced. The working principle of the model is deduced based on the mathematical model. Then the hydrodynamic coefficients of a test ship model of a KELC tank ship with a scale of 1:150 are measured and 8 typical hydrodynamic coefficients are obtained. Finally, the measured data are compared with the value of a real ship. The deviation is less than 10% which meets the technical requirements of the practical project. The efficiency of measuring the hydrodynamic coefficients of physical models of ships and offshore structures is improved by the device. The method of measuring the hydrodynamic coefficients by using the proposed platform provides a certain reference for predicting the hydrodynamic performance of ships and offshore structures.

HWDCNN: Multi-class recognition in breast histopathology with Haar wavelet decomposed image based convolution neural network

Kausar Tasleem, Wang MingJiang, Idrees Muhammad, Lu Yun

Biocybernetics and Biomedical Engineering

2019

Vol. 39, no. 4

967--982

Among the predominant cancers, breast cancer is one of the main causes of cancer deaths impacting women worldwide. However, breast cancer classification is challenging due to numerous morphological and textural variations that appeared in intra-class images. Also, the direct processing of high resolution histological images is uneconomical in terms of GPU memory. In the present study, we have proposed a new approach for breast histopathological image classification that uses a deep convolution neural network (CNN) with wavelet decomposed images. The original microscopic image patches of 2048 1536 3 pixels are decomposed into 512 384 3 using 2-level Haar wavelet and subsequently used in proposed CNN model. The image decomposition step considerably reduces convolution time in deep CNNs and computational resources, without any performance downgrade. The CNN model extracts the deep features from Haar wavelet decomposed images and incorporates multi-scale discriminant features for precise prognostication of class labels. This paper also solves the demand for massive histopathology dataset by means of transfer learning and data augmentation techniques. The efficacy of proposed approach is corroborated on two publicly available breast histology datasets-(a) one provided as a part of international conference on image analysis and recognition (ICIAR 2018) grand challenge and (b) on BreakHis data. On the ICIAR 2018 validation data, our model showed an accuracy of 98.2% for both 4-class and 2-class recognition. Further, on hidden test data of the ICIAR 2018, we achieved an accuracy of 91%, outperforming existing state-of-the-art results significantly. Besides, on BreakHis dataset, the model achieved competing performance with 96.85% multi-class accuracy.