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1,122 results on '"Feng,Weiwei"'

8. PanelNet: Understanding 360 Indoor Environment via Panel Representation

Author

Yu, Haozheng, He, Lu, Jian, Bing, Feng, Weiwei, and Liu, Shan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Indoor 360 panoramas have two essential properties. (1) The panoramas are continuous and seamless in the horizontal direction. (2) Gravity plays an important role in indoor environment design. By leveraging these properties, we present PanelNet, a framework that understands indoor environments using a novel panel representation of 360 images. We represent an equirectangular projection (ERP) as consecutive vertical panels with corresponding 3D panel geometry. To reduce the negative impact of panoramic distortion, we incorporate a panel geometry embedding network that encodes both the local and global geometric features of a panel. To capture the geometric context in room design, we introduce Local2Global Transformer, which aggregates local information within a panel and panel-wise global context. It greatly improves the model performance with low training overhead. Our method outperforms existing methods on indoor 360 depth estimation and shows competitive results against state-of-the-art approaches on the task of indoor layout estimation and semantic segmentation., Comment: To appear in CVPR 2023

Published
2023

41. FAULT DIAGNOSIS OF ROLLING BEARING BASED ON LEARNING SAMPLE SELECTION VIA CORRELATION ENERGY FLUCTUATION EVALUATION AND DEEP BELIEF NEURAL NETWORK (MT)

Author

QIN Bo, LUO QuanYi, FENG WeiWei, ZHANG Peng, ZHAO ZhenHua, LI ZiXian, and WANG Zhuo

Subjects
Learning sample screening, Correlation energy fluctuation evaluation, Rolling bearing, Deep belief neural network, Fault recognition rate, Mechanical engineering and machinery, TJ1-1570, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

The data-driven intelligent diagnosis of rolling bearing status suffers from low recognition rate due to the poor quality of learning samples in the process of identification model construction. To address this problem, a method is proposed to improve the recognition rate of the rolling bearing intelligent diagnosis model by selecting learning samples using the deep belief neural network. First, aiming at the time-varying modulation characteristics of the rolling bearing vibration signal, a finite number of intrinsic mode function components were decomposed using variational mode decomposition, which represents different components of the original signal. Secondly, according to the fault energy fluctuations and correlation of each component, the proportion of fault information in each intrinsic mode function components is quantitatively evaluated. On the basis of this, the vibration signal is screened and reconstructed to obtain learning samples. Finally, the obtained samples are used as the input of the deep belief network, and the fault identification model of the rolling bearing is constructed accordingly. Experimental results show that the proposed method is capable of screening out the intrinsic mode function components of the rolling bearing vibration signal, which contains the main components of the fault and realizes the construction of learning sample sets. Moreover, it improves the fault recognition rate of the rolling bearing state identification model based on vibration data.

Published
2023
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49. Research progress on the application of machine learning in assisted ultrasound diagnosis of adnexal masses

Author

HE Xin, CHEN Hui, FENG Weiwei

Subjects
adnexal mass, ovarian malignant tumor, ultrasound scoring system, diagnostic model, machine learning, Medicine
Abstract

Ovarian cancer is the second most common cause of gynecologic cancer death in women around the world. Around 75% of patients present with stage Ⅲ/Ⅳ disease at diagnosis, with five-year survival rates below 45%. Ovarian cancer is the main adnexal malignant mass. Thus, accurate non-invasive risk stratification of adnexal masses is essential for optimal management and outcomes. In recent years, the field of artificial intelligence is developing rapidly. As a branch of artificial intelligence, machine learning could learn efficiently from complex and large amounts of data, which has infinite potential to differentiate benign and malignant adnexal masses. Logistic regression(LR), artificial neural network (ANN), support vector machine (SVM), deep learning convolution neural networks (DCNN) have been widely applied in this field, and achieved good diagnostic performance. This paper will review the history and progress of machine learning in ultrasound diagnosis of benign and malignant adnexal masses.

Published
2022
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