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261 results on '"Shang, Zhigang"'

3. Research on Small Sample Ship Target Detection Based on SAR Image

Author

Li, Kaiyu, Wang, Lu, Zhao, Chunhui, Shang, Zhigang, Liu, Haodong, Qi, Yuhang, 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, Dong, Jian, editor, Zhang, Long, editor, and Cheng, Deqiang, editor

Published
2024
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4. Cross-Medium Communication: Utilizing Relay to Achieve Air-Sea Cross-Medium Communication Technology and Applications

Author

Shang, Zhigang, Zhang, Hongyu, 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, Dong, Jian, editor, Zhang, Long, editor, and Cheng, Deqiang, editor

Published
2024
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5. A Direction-Aware Inshore Ship Detection Method for SAR Images

Author

Liu, Haodong, Wang, Lu, Zhao, Chunhui, Shang, Zhigang, Li, Kaiyu, Sun, Bailiang, 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, Dong, Jian, editor, Zhang, Long, editor, and Cheng, Deqiang, editor

Published
2024
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6. Environment Driven and Task Driven Modeling of Air-Sea Cross-Domain Channels

Author

Shang, Zhigang, Yu, Han, Wen, Tao, Li, Mo, Sun, Ruofan, 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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22. From Signal to Image Then to Feature: Decoding Pigeon Behavior Outcomes During Goal-Directed Decision-Making Task Using Time-Frequency Textural Features

Author

Li, Mengmeng, Shang, Zhigang, Yang, Lifang, Wang, Haofeng, Zhao, Kun, Wan, Hong, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Gedeon, Tom, editor, Wong, Kok Wai, editor, and Lee, Minho, editor

Published
2019
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29. Phase–Amplitude Coupling between Theta Rhythm and High-Frequency Oscillations in the Hippocampus of Pigeons during Navigation.

Author

Yang, Long, Chen, Xi, Yang, Lifang, Li, Mengmeng, and Shang, Zhigang

Subjects
THETA rhythm, HIPPOCAMPUS (Brain), OSCILLATIONS, PIGEONS, SPATIAL ability, NAVIGATION
Abstract

Simple Summary: The hippocampus (Hp) is involved in various aspects of spatial cognition during navigation, reflected in multiple neural oscillations across different frequencies. Given the structure homology between the avian and mammalian Hp, does it exhibit similar cross-frequency interaction mechanisms? Unlike rodents, birds can navigate by walking and flying. So, does the avian Hp share the same information interaction under different locomotor modes? In this study, we employed rock pigeons with exceptional spatial navigation abilities and conducted two goal-directed navigational experiments: a ground maze task with a circuitous paradigm and an outdoor homing flight task. These experiments were designed to explore the interactions between different frequency bands in the avian Hp during walking and flying navigation. The study revealed that phase–amplitude coupling (PAC) existed in the avian Hp during the navigational active phases. Meanwhile, the sub-frequency bands modulated by theta oscillations are different in different modes of locomotion. These findings support evidence for cross-frequency interactions in the avian Hp during spatial navigation and contribute to explaining the role of coupling, at least partially, in the ongoing cognitive demands during the navigation process. Navigation is a complex task in which the hippocampus (Hp), which plays an important role, may be involved in interactions between different frequency bands. However, little is known whether this cross-frequency interaction exists in the Hp of birds during navigation. Therefore, we examined the electrophysiological characteristics of hippocampal cross-frequency interactions of domestic pigeons (Columba livia domestica) during navigation. Two goal-directed navigation tasks with different locomotor modes were designed, and the local field potentials (LFPs) were recorded for analysis. We found that the amplitudes of high-frequency oscillations in Hp were dynamically modulated by the phase of co-occurring theta-band oscillations both during ground-based maze and outdoor flight navigation. The high-frequency amplitude sub-frequency bands modulated by the hippocampal theta phase were different at different tasks, and this process was independent of the navigation path and goal. These results suggest that phase–amplitude coupling (PAC) in the avian Hp may be more associated with the ongoing cognitive demands of navigational processes. Our findings contribute to the understanding of potential mechanisms of hippocampal PAC on multi-frequency informational interactions in avian navigation and provide valuable insights into cross-species evolution. [ABSTRACT FROM AUTHOR]

Published
2024
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30. The Hippocampus in Pigeons Contributes to the Model-Based Valuation and the Relationship between Temporal Context States.

Author

Yang, Lifang, Jin, Fuli, Yang, Long, Li, Jiajia, Li, Zhihui, Li, Mengmeng, and Shang, Zhigang

Subjects
PIGEONS, HIPPOCAMPUS (Brain), REINFORCEMENT learning, VALUATION, VALUES (Ethics)
Abstract

Simple Summary: Model-based decision-making guides organism behavior by representing the relationships between states. Previous studies have shown that the mammalian hippocampus (Hp) plays a key role in model-based learning. However, the hippocampal neural mechanisms of birds for model-based learning are largely unknown. We trained pigeons to perform a two-step task. Using a combination of neural analysis and computational modeling, we show that the pigeons use model-based inferences to learn multi-step tasks, and multiple LFP frequency bands collaboratively contribute to model-based learning. Specifically, the high-frequency (12–100 Hz) oscillations represent model-based valuations, while the low-frequency (1–12 Hz) neural similarity is influenced by the relationship between temporal context states. These findings expand the understanding of the hippocampus' role in avian model-based learning. Model-based decision-making guides organism behavior by the representation of the relationships between different states. Previous studies have shown that the mammalian hippocampus (Hp) plays a key role in learning the structure of relationships among experiences. However, the hippocampal neural mechanisms of birds for model-based learning have rarely been reported. Here, we trained six pigeons to perform a two-step task and explore whether their Hp contributes to model-based learning. Behavioral performance and hippocampal multi-channel local field potentials (LFPs) were recorded during the task. We estimated the subjective values using a reinforcement learning model dynamically fitted to the pigeon's choice of behavior. The results show that the model-based learner can capture the behavioral choices of pigeons well throughout the learning process. Neural analysis indicated that high-frequency (12–100 Hz) power in Hp represented the temporal context states. Moreover, dynamic correlation and decoding results provided further support for the high-frequency dependence of model-based valuations. In addition, we observed a significant increase in hippocampal neural similarity at the low-frequency band (1–12 Hz) for common temporal context states after learning. Overall, our findings suggest that pigeons use model-based inferences to learn multi-step tasks, and multiple LFP frequency bands collaboratively contribute to model-based learning. Specifically, the high-frequency (12–100 Hz) oscillations represent model-based valuations, while the low-frequency (1–12 Hz) neural similarity is influenced by the relationship between temporal context states. These results contribute to our understanding of the neural mechanisms underlying model-based learning and broaden the scope of hippocampal contributions to avian behavior. [ABSTRACT FROM AUTHOR]

Published
2024
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32. A Feature Subset Evaluation Method Based on Multi-objective Optimization

Author

Li, Mengmeng, Shang, Zhigang, Yue, Caitong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Shi, Yuhui, editor, Tan, Kay Chen, editor, Zhang, Mengjie, editor, Tang, Ke, editor, Li, Xiaodong, editor, Zhang, Qingfu, editor, Tan, Ying, editor, Middendorf, Martin, editor, and Jin, Yaochu, editor

Published
2017
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35. HVS-Inspired Dimensionality Reduction Model Based on Factor Analysis

Author

Shang, Zhigang, Li, Mengmeng, Dong, Yonghui, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Gong, Maoguo, editor, Pan, Linqiang, editor, Song, Tao, editor, and Zhang, Gexiang, editor

Published
2016
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36. Digital Twin for Transportation Big Data: A Reinforcement Learning-Based Network Traffic Prediction Approach

Author

Nie, Laisen, Wang, Xiaojie, Zhao, Qinglin, Shang, Zhigang, Feng, Li, and Li, Guojun

Abstract

Vehicular Ad-Hoc Networks (VANETs), as the crucial support of Intelligent Transportation Systems (ITS), have received great attention in recent years. With the rapid development of VANETs, various services have generated a great deal of data that can be used for transportation planning and safe driving. Especially, with the advent of Coronavirus Disease 2019 (COVID-19), the transportation system has been impacted, thus novel modes of transportation planning and intelligent applications are necessary. Digital twins can provide powerful support for artificial intelligence applications in Transportation Big Data (TBD). The features of VANETs are varying, which arises the main challenge of digital twins applying in TBD. Network traffic prediction, as part of digital twins, is useful for network management and security in VANETs, such as network planning and anomaly detection. This paper proposes a network traffic prediction algorithm aiming at time-varying traffic flows with a large number of fluctuations. This algorithm combines Deep Q-Learning (DQN) and Generative Adversarial Networks (GAN) for network traffic feature extraction. DQN is leveraged to carry out network traffic prediction, in which GAN is involved to represent Q-network. Meanwhile, the generative network can increase the number of samples to improve the prediction error. We evaluate the performance of our method by implementing it on three real network traffic data sets. Finally, we compare the two state-of-the-art competing methods with our method.

Published
2024
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39. Path Planning Using Neighborhood Based Crowding Differential Evolution

Author

Qu, Boyang, Xu, Yanping, Wang, Dongyun, Song, Hui, Shang, Zhigang, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tan, Ying, editor, Shi, Yuhui, editor, and Coello, Carlos A. Coello, editor

Published
2014
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