Search

Your search keyword '"Yang, Mingyang"' showing total 654 results
Start Over Author "Yang, Mingyang"
654 results on '"Yang, Mingyang"'

186. Land Use and Land Cover Mapping in China Using Multimodal Fine-Grained Dual Network

Author

Liu, Shang, Wang, Huadong, Hu, Yuan, Zhang, Mengting, Zhu, Yixuan, Wang, Zhibin, Li, Dongyang, Yang, Mingyang, and Wang, Fan

Abstract

With the advancement of geo-systems and the increased availability of satellite data, a plethora of land-use and land-cover (LULC) products have been developed. The existing LULC products primarily relied on time-series imagery to classify land by pixel-based classifiers, allowing for local analysis and accurate boundary detection. However, the advent of deep learning has shifted toward the use of patch-based CNN models for generating land cover maps. In this article: 1) we create a training dataset for China using a voting strategy based on three off-the-shelf available LULC products, avoiding the labor-intensive manual annotation. 2) We design a novel CNN-based model for the LULC task, called multimodal fine-grained dual network (dubbed as Dual-Net), which takes dual-date images to generate final maps and reduces the need for gap-free temporal sequences or separate cloud detection. To leverage the correlation between location, date, and category, we embed modal information (dates and geo-locations) into the model. Furthermore, by incorporating low-level constraints and using pseudolabel refinement, we continually improve the performance and achieve more refined segmentation. 3) Due to the lack of a suitable validation dataset for China, we create a new validation dataset called the China Sentinel2 Validation Dataset (CSVD) by manually annotating 733 finely labeled images of $1024 \times 1024$ pixels of China-specific Sentinel2 data. 4) Extensive experiments demonstrate that our model outperforms existing LULC products and produces more fine-grained segmentation results comparable to other patch-based products. Finally, we release annual LULC maps for China in 2020–2022 and also make our model accessible online for real-time results export.

Published
2023
Full Text
View/download PDF

187. A Partial Hierarchical Model for Online Low-Resolution Wear Particle Images Classification

Author

Xuxu Guo, Xinrong He, Yang Mingyang, Aziguli Wulamu, Suli Fan, Junnan Hu, Jia Guo, Taohong Zhang, and Rui Tan

Subjects
Article Subject, General Computer Science, Computer science, General Mathematics, Computer applications to medicine. Medical informatics, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, R858-859.7, Neurosciences. Biological psychiatry. Neuropsychiatry, 02 engineering and technology, Convolutional neural network, Hierarchical database model, Image (mathematics), 0203 mechanical engineering, Classifier (linguistics), Feature (machine learning), Layer (object-oriented design), business.industry, General Neuroscience, Pattern recognition, General Medicine, 021001 nanoscience & nanotechnology, Support vector machine, 020303 mechanical engineering & transports, ComputingMethodologies_PATTERNRECOGNITION, Artificial intelligence, Enhanced Data Rates for GSM Evolution, 0210 nano-technology, business, RC321-571
Abstract

Wear particle image analysis is an effective method to detect wear condition of mechanical devices. However, the recognition accuracy and recognition efficiency for online wear particle automatic recognition are always mutual restricted because the online wear particle images have almost no texture information and lack clarity. Especially for confusing fatigue wear particles and sliding wear particles, the online recognition is a challenging task. Based on this requirement, a super-resolution reconstruct technique and partial hierarchical convolutional neural network, SR-PHnet, is proposed to classify wear particles in one step. The structure of this network is composed by three modules, one is super-resolution layer module, the second is convolutional neural network classifier module, and the third is support vector machine (SVM) classifier module. The classification result of the second module is partial input to the third module for precision classification of fatigue and sliding particles. In addition, a new feature of radial edge factor (REF) is put forward to target fatigue and sliding wear particles. The test result shows that the new feature has the capability to distinguish fatigue and sliding particles well and time saving. The comparison experiments of the convolution neural network (CNN) method, support vector machine method (SVM) with and without REF feature, and integrated model of back-propagation (BP) and CNN are produced. The comparison results show that the online recognition speed and online recognition rate of the proposed SR-PHnet model in this paper are both improved markedly, especially for fatigue and sliding wear particles.

Published
2021

190. NudC L279P Mutation Destabilizes Filamin A by Inhibiting the Hsp90 Chaperoning Pathway and Suppresses Cell Migration

Author

Liu, Min, primary, Xu, Zhangqi, additional, Zhang, Cheng, additional, Yang, Chunxia, additional, Feng, Jiaxing, additional, Lu, Yiqing, additional, Zhang, Wen, additional, Chen, Wenwen, additional, Xu, Xiaoyang, additional, Sun, Xiaoxia, additional, Yang, Mingyang, additional, Liu, Wei, additional, Zhou, Tianhua, additional, and Yang, Yuehong, additional

Published
2021
Full Text
View/download PDF

200. A single-crystalline diamond X-ray detector based on direct sp3-to-sp2conversed graphene electrodes

Author

Yuan, Qilong, Liu, Linyue, Dai, Dan, Zhou, Yuhong, Liu, Ying, Yang, Mingyang, Qiu, Mengting, Jia, Zhenglin, Li, He, Nishimura, Kazhihito, Tian, Geng, Chee, Kuan W. A., Du, Shiyu, Lin, Cheng-Te, Jiang, Nan, and Ouyang, Xiaoping

Abstract

AbstractDiamond is an ultrawide bandgap semiconductor with excellent electronic and photonic properties, which has great potential applications in microelectronic and optoelectronic devices. As an allotrope of diamond, graphene also has many fantastic properties like diamond, which caught much attention in combing them together. In this work, a direct sp3-to-sp2conversion method was proposed to fabricate graphene layers on single crystal diamond by thermal treatment with Ni film catalyst. By optimizing the conversing conditions, a thin graphene layer with low sheet resistance was obtained on diamond. Based on this, an all-carbon sandwich structural graphene-diamond-graphene (GDG) detector was fabricated, which shows low dark current of 0.45 nA at 0.5 V μm−1applied electric field. The maximum sensitivity of this detector is obtained when the incident X-ray is 12 keV, with the value of 2.88 × 10−8C Gy−1. Moreover, the rise time and delay time of the GDG detector is about 1.2 and 22.8 ns, respectively, which are very close to that of diamond detector with Ti/Au electrode. The realization of the direct in-situsp3-to-sp2conversion on diamond shows a promising approach for fabricating diamond-based all-carbon electronic devices.

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results