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您的检索式：作者名="Jiashi Feng"
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| 1 | A Survey on Deep Learning-based Fine-grained Object Classification and Semantic Segmentation显示文摘The deep learning technology has shown impressive performance in various vision tasks such as image classification, object detection and semantic segmentation. In particular, recent advances of deep learning techniques bring encouraging performance to fine-grained image classification which aims to distinguish subordinate-level categories, such as bird species or dog breeds. This task is extremely challenging due to high intra-class and low inter-class variance. In this paper, we review four types of deep learning based fine-grained image classification approaches, including the general convolutional neural networks(CNNs), part detection based,ensemble of networks based and visual attention based fine-grained image classification approaches. Besides, the deep learning based semantic segmentation approaches are also covered in this paper. The region proposal based and fully convolutional networks based approaches for semantic segmentation are introduced respectively. | Bo Zhao Jiashi Feng Xiao Wu Shuicheng Yan | 2017 | International Journal of Automation and computing2017，14，2： | 36 |
| 2 | Bending of a Flexoelectric Semiconductor Plate显示文摘We study electromechanical responses of a flexoelectric semiconductor plate in bending under mechanical loads.A two-dimensional theory for classical bending without shear deformation is derived from the three-dimensional macroscopic theory of flexoelectric semiconductors.A simple solution is obtained for pure bending.A combination of physical and geometric parameters is introduced as a measure of the strength of the coupling between the mechanical load and the redistribution of charge carriers.A trigonometric series solution is obtained for a simply supported rectangular plate under a local normal mechanical load,which shows concentration of mobile charges and the formation of electric potential barriers near the loading area.The results are fundamental to the mechanical manipulation of charge carriers in these plates.We also analyze the buckling of a simply supported rectangular plate under in-plane compressive forces.A series of buckling loads and modes are obtained.Numerical results show that flexoelectric coupling exhibits a stiffening effect and increases the buckling load,while semiconduction weakens the flexoelectric stiffening.The distributions of mobile charges in the first few buckling modes are presented. | Yilin Qu Feng Jin Jiashi Yang | 2022 | Acta Mechanica Solida Sinica2022，35，3： | 2 |
| 3 | Linear distance co-ding for image classification显示文摘 | Wang Zilei Feng Jiashi Yan Shuicheng | 2013 | IEEE Trans on Image Proces-sing2013，22，2： | 1 |
| 4 | Deep Learning Models Based on Weakly Supervised Learning and Clustering Visualization for Disease Diagnosis显示文摘The coronavirus disease 2019(COVID-19)has severely disrupted both human life and the health care system.Timely diagnosis and treatment have become increasingly important;however,the distribution and size of lesions vary widely among individuals,making it challenging to accurately diagnose the disease.This study proposed a deep-learning disease diagnosismodel based onweakly supervised learning and clustering visualization(W_CVNet)that fused classification with segmentation.First,the data were preprocessed.An optimizable weakly supervised segmentation preprocessing method(O-WSSPM)was used to remove redundant data and solve the category imbalance problem.Second,a deep-learning fusion method was used for feature extraction and classification recognition.A dual asymmetric complementary bilinear feature extraction method(D-CBM)was used to fully extract complementary features,which solved the problem of insufficient feature extraction by a single deep learning network.Third,an unsupervised learning method based on Fuzzy C-Means(FCM)clustering was used to segment and visualize COVID-19 lesions enabling physicians to accurately assess lesion distribution and disease severity.In this study,5-fold cross-validation methods were used,and the results showed that the network had an average classification accuracy of 85.8%,outperforming six recent advanced classification models.W_CVNet can effectively help physicians with automated aid in diagnosis to determine if the disease is present and,in the case of COVID-19 patients,to further predict the area of the lesion. | Jingyao Liu Qinghe Feng Jiashi Zhao Yu Miao Wei He Weili Shi Zhengang Jiang | 2023 | Computers, Materials & Continua2023，76，9： | 0 |
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