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Your search keyword '"Meng, Zuqiang"' showing total 115 results
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115 results on '"Meng, Zuqiang"'

2. A Deep Neural Network-Based Segmentation Method for Multimodal Brain Tumor Images

Author

Meng, Zuqiang, Peng, Yue, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Shi, Zhongzhi, editor, Torresen, Jim, editor, and Yang, Shengxiang, editor

Published
2024
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8. A UAV Aerial Image Target Detection Algorithm Based on YOLOv7 Improved Model.

Author

Qin, Jie, Yu, Weihua, Feng, Xiaoxi, Meng, Zuqiang, and Tan, Chaohong

Subjects
OBJECT recognition (Computer vision), FEATURE extraction, ALGORITHMS
Abstract

To address the challenges of multi-scale objects, dense distributions, occlusions, and numerous small targets in UAV image detection, we present CMS-YOLOv7, a real-time target detection method based on an enhanced YOLOv7 model. Firstly, the detection layer P2 for small targets was added to YOLOv7 to enhance the detection ability of small and medium-sized targets, and the deep detection head P5 was taken out to mitigate the influence of excessive downsampling on small target images. The anchor frame was calculated by the K-means++ method. Using the concept of Inner-IoU, the Inner-MPDIoU loss function was constructed to control the range of the auxiliary border and improve detection performance. Furthermore, the CARAFE module was introduced to replace traditional upsampling methods, offering improved integration of semantic information during the image upsampling process and enhancing feature mapping accuracy. Simultaneously, during the feature extraction stage, a non-strided convolutional SPD-Conv module was constructed using space-to-depth techniques. This module replaced certain convolutional operations to minimize the loss of fine-grained information and improve the model's ability to extract features from small targets. Experiments on the UAV aerial photo dataset VisDrone2019 demonstrated that compared with the baseline YOLOv7 object detection algorithm, CMS-YOLOv7 achieved an improvement of 3.5% mAP@0.5, 3.0% mAP@0.5:0.95, and the number of parameters decreased by 18.54 M. The ability of small target detection was significantly enhanced. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Image Semantic Description Based on Deep Learning with Multi-attention Mechanisms

Author

Yang, Jian, Meng, ZuQiang, Rannenberg, Kai, Editor-in-Chief, Sakarovitch, Jacques, Series Editor, Goedicke, Michael, Series Editor, Tatnall, Arthur, Series Editor, Neuhold, Erich J., Series Editor, Pras, Aiko, Series Editor, Tröltzsch, Fredi, Series Editor, Pries-Heje, Jan, Series Editor, Whitehouse, Diane, Series Editor, Reis, Ricardo, Series Editor, Furnell, Steven, Series Editor, Furbach, Ulrich, Series Editor, Winckler, Marco, Series Editor, Rauterberg, Matthias, Series Editor, Shi, Zhongzhi, editor, Mercier-Laurent, Eunika, editor, and Li, Jiuyong, editor

Published
2018
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13. The Optimization Algorithm of Circle Stock Problem with Standard Usage Leftover

Author

Yan, Chen, Xie, Qiqi, Chen, Qiulin, Zhang, Li, Cui, Yaodong, Meng, Zuqiang, Rannenberg, Kai, Editor-in-chief, Sakarovitch, Jacques, Series editor, Goedicke, Michael, Series editor, Tatnall, Arthur, Series editor, Neuhold, Erich J., Series editor, Pras, Aiko, Series editor, Tröltzsch, Fredi, Series editor, Pries-Heje, Jan, Series editor, Whitehouse, Diane, Series editor, Reis, Ricardo, Series editor, Furnell, Steven, Series editor, Furbach, Ulrich, Series editor, Winckler, Marco, Series editor, Rauterberg, Matthias, Series editor, Shi, Zhongzhi, editor, Goertzel, Ben, editor, and Feng, Jiali, editor

Published
2017
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14. A Fast Granular Method for Classifying Incomplete Inconsistent Data

Author

Meng, Zuqiang, Li, Hongli, Rannenberg, Kai, Editor-in-chief, Sakarovitch, Jacques, Series editor, Goedicke, Michael, Series editor, Tatnall, Arthur, Series editor, Neuhold, Erich J., Series editor, Pras, Aiko, Series editor, Tröltzsch, Fredi, Series editor, Pries-Heje, Jan, Series editor, Whitehouse, Diane, Series editor, Reis, Ricardo, Series editor, Furnell, Steven, Series editor, Furbach, Ulrich, Series editor, Winckler, Marco, Series editor, Rauterberg, Matthias, Series editor, Shi, Zhongzhi, editor, Goertzel, Ben, editor, and Feng, Jiali, editor

Published
2017
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15. An Attribute-Value Block Based Method of Acquiring Minimum Rule Sets: A Granulation Method to Construct Classifier

Author

Meng, Zuqiang, Gan, Qiuling, Rannenberg, Kai, Editor-in-chief, Sakarovitch, Jacques, Series editor, Goedicke, Michael, Series editor, Tatnall, Arthur, Series editor, Neuhold, Erich J., Series editor, Pras, Aiko, Series editor, Tröltzsch, Fredi, Series editor, Pries-Heje, Jan, Series editor, Whitehouse, Diane, Series editor, Reis, Ricardo, Series editor, Furnell, Steven, Series editor, Furbach, Ulrich, Series editor, Gulliksen, Jan, Series editor, Rauterberg, Matthias, Series editor, Shi, Zhongzhi, editor, Vadera, Sunil, editor, and Li, Gang, editor

Published
2016
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26. A Granular Evolutionary Algorithm Based on Cultural Evolution

Author

Meng, Zuqiang, Shi, Zhongzhi, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Kang, Lishan, editor, Liu, Yong, editor, and Zeng, Sanyou, editor

Published
2007
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27. A Method of Data Classification Based on Parallel Genetic Algorithm

Author

Shi, Yuexiang, Meng, Zuqiang, Cai, Zixing, Benhabib, B., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Wang, Lipo, editor, and Jin, Yaochu, editor

Published
2005
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47. The optimization of large-scale SVM using nest template ant clustering in kernel space

Author

Qin Hua, Sun Xin, Meng Zuqiang, and Ding Liduo

Subjects
Computer Science::Machine Learning, business.industry, media_common.quotation_subject, Scale (descriptive set theory), Pattern recognition, Space (commercial competition), Machine learning, computer.software_genre, Adaptability, Support vector machine, Statistics::Machine Learning, ComputingMethodologies_PATTERNRECOGNITION, Ranking SVM, Kernel (statistics), Quadratic programming, Artificial intelligence, business, Cluster analysis, computer, Mathematics, media_common
Abstract

If the training data sets are large-scale, the SVM models turn into large-scale quadratic programming problems and are hard to be effectively solved. That is the main reason why the learning efficiency of SVM is so low on large-scale data sets. According to the principle of SVM, the border support vectors play a decisive role on the SVM decision hyper-plane. The nest template ant clustering algorithm in kernel space is proposed. The algorithm is used to extract border support vectors from large-scale training datasets. When using the less border support vectors to train SVM, the scale of SVM is reduced and the training performance is improved. The algorithm has better adaptability than the kernel K-means algorithm. Experimental results on UCI datasets show that the algorithm is effective, and the classification accuracy of SVM is still maintained.

Published
2012

48. A general framework for rule acquisition based on tolerance granular computing in IIDSs

Author

Meng Zuqiang, Shi Zhongzhi, and Xu Ke

Subjects
Degree (graph theory), Computer science, Decision system, business.industry, Granular computing, Set theory, Decision rule, Artificial intelligence, Type (model theory), business, Knowledge acquisition, Variety (cybernetics)
Abstract

This paper systematically studies the problem of decision rule acquisition in inconsistent incomplete decision systems (IIDSs). First, a tolerance granular framework model based on tolerance granular computing is presented, which is suitable for variety types of decision rules in IIDSs; secondly, with the proposed model, a framework for acquiring all minimum decision rule sets for each type is given, which solves the problem of decision rule acquisition in IIDSs to a certain degree; finally, an example is given to show the efficiency of our framework.

Published
2011

49. On modeling cognitive process with granular computing

Author

Gong Tao, Shi Zhongzhi, and Meng Zuqiang

Subjects
business.industry, Computer science, Physics education, Granular computing, Educational technology, Information processing, Cognition, Fuzzy control system, Abstraction (mathematics), Transformation (function), Vector space model, Artificial intelligence, business, Representation (mathematics)
Abstract

Exploring mechanism of cognitive process is a valuable means of investigating artificial intelligence. But human beingpsilas perception of cognitive process is very limited so far, and related researches are required to be further deepened. This paper focuses on modeling cognitive process based on theory of granular computing(GrC). First, fundamental principle of GrC is used to analyze cognitive process and establish its model. Secondly, a tolerance relation on vector space(ldquorv_spacerdquo for short) is defined by using a distance fucntion dis, and a tolerance granular space model is established, in which the problem of approximate representation of unknown concept(abstraction of perceptive information) is discussed and analyzed. The analyses show that a given concept, sometimes, is difficult to be learned(i.e., difficult to be accurately represented), so appropriate granular world is required. The solution to this problem involves relationship and transformation between granular worlds. At last, such relationship and transformation are investigated, and many related properties and theorems are deduced, solving the facing problem to some extent. The theory and approach of representation of a concept in a granular world and transformation between different granular worlds are made of support cognitive processpsila granular model.

Published
2008

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