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2,037 results on '"Ji Hongbing"'

5. Geochemical Implications of Rare Earth Elements in Terra Rossa in Tropical Karst Area: A Case Study in Northern Vietnam

Author

Zhang Liankai, Ji Hongbing, Wang Shijie, Luo Gang, Liu Xiuming, Wei Xiao, Nguyen QuocDinh, and Nguyen DaiTrung

Subjects
carbonate rocks, terra rossa, rare earth elements, geochemistry, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

Research on weathered crusts on carbonate rock is essential for paleoenvironmental studies in karst areas. Terra rossa, widely distributed in tropical karst areas, has not been studied in terms of its material sources and geochemistry. Two typical terra rossa profiles on dolomite (SC profile located at Sang Cai, Hoa Binh province) and limestone (TG profile located at Tong Gia, Lao Cai province) in Northern Vietnam were selected to examine the geochemical characteristics and the evolutionary processes of rare earth elements (REEs). Chondrite and bedrock normalized patterns indicated that these two profiles are in situ weathering crusts, meaning they are the residual material remaining after chemical weathering of the lower carbonate rocks. The average value of total REE in the SC profile is 381.19 ppm, which is 30 times higher than the bedrock. In the TG profile, the value is 386.26 ppm, 13 times higher than the bedrock. Compared with the profiles in nearby subtropical areas in Southeast China, the REE enrichment coefficients of terra rossa in Northern Vietnam are much higher. The REE depletion was also different between the SC and TG profiles. The light and heavy REE fractionations in the SC profile are higher than in the TG profile. Paleoclimate inversion analysis shows that the SC profile experienced a stable oxidation condition, whereas the TG profile was subjected to several reducing environments since a weathering crust formed.

Published
2020
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10. Cluster-CAM: Cluster-Weighted Visual Interpretation of CNNs' Decision in Image Classification

Author

Feng, Zhenpeng, Ji, Hongbing, Dakovic, Milos, Cui, Xiyang, Zhu, Mingzhe, and Stankovic, Ljubisa

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Despite the tremendous success of convolutional neural networks (CNNs) in computer vision, the mechanism of CNNs still lacks clear interpretation. Currently, class activation mapping (CAM), a famous visualization technique to interpret CNN's decision, has drawn increasing attention. Gradient-based CAMs are efficient while the performance is heavily affected by gradient vanishing and exploding. In contrast, gradient-free CAMs can avoid computing gradients to produce more understandable results. However, existing gradient-free CAMs are quite time-consuming because hundreds of forward interference per image are required. In this paper, we proposed Cluster-CAM, an effective and efficient gradient-free CNN interpretation algorithm. Cluster-CAM can significantly reduce the times of forward propagation by splitting the feature maps into clusters in an unsupervised manner. Furthermore, we propose an artful strategy to forge a cognition-base map and cognition-scissors from clustered feature maps. The final salience heatmap will be computed by merging the above cognition maps. Qualitative results conspicuously show that Cluster-CAM can produce heatmaps where the highlighted regions match the human's cognition more precisely than existing CAMs. The quantitative evaluation further demonstrates the superiority of Cluster-CAM in both effectiveness and efficiency., Comment: 10 pages

Published
2023

11. A DNN-Based GLMB Algorithm for Multi-sensor Multi-target Tracking with Dense Group Clutter

Author

Zhang, Yongquan, Shang, Aomen, Li, Siwei, Su, Zhenzhen, Ji, Hongbing, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Qu, Yi, editor, Gu, Mancang, editor, Niu, Yifeng, editor, and Fu, Wenxing, editor

Published
2024
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12. Efficient Multiple Extended Target Tracking Based on Minimum Spanning Tree

Author

Su, Zhenzhen, Qiao, Renzhong, Zhang, Yongquan, Ji, Hongbing, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Qu, Yi, editor, Gu, Mancang, editor, Niu, Yifeng, editor, and Fu, Wenxing, editor

Published
2024
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13. Maneuvering Multi-target Tracking Based on DLSTM and Dynamic Association Gate

Author

Zhang, Yongquan, Zhang, Bingjie, Shi, Zhenyun, Li, Siwei, Su, Zhenzhen, Ji, Hongbing, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Qu, Yi, editor, Gu, Mancang, editor, Niu, Yifeng, editor, and Fu, Wenxing, editor

Published
2024
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14. A Fusion Method with Information Gains for Distributed Multi-sensor Network

Author

Wang, Peng, Ji, Hongbing, Zhang, Yongquan, Xu, Yiming, Zhang, Wenbo, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Qu, Yi, editor, Gu, Mancang, editor, Niu, Yifeng, editor, and Fu, Wenxing, editor

Published
2024
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15. A BiGRU Based Adaptive Gain Estimation for Radar Multi-target Tracking

Author

Liu, Long, Xu, Qing, Zhang, Mengxuan, Ji, Hongbing, Zhao, Qiubo, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Liu, Qingshan, editor, Wang, Hanzi, editor, Ma, Zhanyu, editor, Zheng, Weishi, editor, Zha, Hongbin, editor, Chen, Xilin, editor, Wang, Liang, editor, and Ji, Rongrong, editor

Published
2024
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17. VS-CAM: Vertex Semantic Class Activation Mapping to Interpret Vision Graph Neural Network

Author

Feng, Zhenpeng, Cui, Xiyang, Ji, Hongbing, Zhu, Mingzhe, and Stankovic, Ljubisa

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Graph convolutional neural network (GCN) has drawn increasing attention and attained good performance in various computer vision tasks, however, there lacks a clear interpretation of GCN's inner mechanism. For standard convolutional neural networks (CNNs), class activation mapping (CAM) methods are commonly used to visualize the connection between CNN's decision and image region by generating a heatmap. Nonetheless, such heatmap usually exhibits semantic-chaos when these CAMs are applied to GCN directly. In this paper, we proposed a novel visualization method particularly applicable to GCN, Vertex Semantic Class Activation Mapping (VS-CAM). VS-CAM includes two independent pipelines to produce a set of semantic-probe maps and a semantic-base map, respectively. Semantic-probe maps are used to detect the semantic information from semantic-base map to aggregate a semantic-aware heatmap. Qualitative results show that VS-CAM can obtain heatmaps where the highlighted regions match the objects much more precisely than CNN-based CAM. The quantitative evaluation further demonstrates the superiority of VS-CAM., Comment: 10 pages, 10 figures

Published
2022

19. Analytical Interpretation of Latent Codes in InfoGAN with SAR Images

Author

Feng, Zhenpeng, Dakovic, Milos, Ji, Hongbing, Zhu, Mingzhe, and Stankovic, Ljubisa

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing, Electrical Engineering and Systems Science - Signal Processing
Abstract

Generative Adversarial Networks (GANs) can synthesize abundant photo-realistic synthetic aperture radar (SAR) images. Some recent GANs (e.g., InfoGAN), are even able to edit specific properties of the synthesized images by introducing latent codes. It is crucial for SAR image synthesis since the targets in real SAR images are with different properties due to the imaging mechanism. Despite the success of InfoGAN in manipulating properties, there still lacks a clear explanation of how these latent codes affect synthesized properties, thus editing specific properties usually relies on empirical trials, unreliable and time-consuming. In this paper, we show that latent codes are disentangled to affect the properties of SAR images in a non-linear manner. By introducing some property estimators for latent codes, we are able to provide a completely analytical nonlinear model to decompose the entangled causality between latent codes and different properties. The qualitative and quantitative experimental results further reveal that the properties can be calculated by latent codes, inversely, the satisfying latent codes can be estimated given desired properties. In this case, properties can be manipulated by latent codes as we expect., Comment: 13 pages, 14 figures

Published
2022

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