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Cascade conditional generative adversarial nets for spatial-spectral hyperspectral sample generation

Science China(Information Sciences) 2020年第4期63卷 81-96页

作者： Xiaobo LIU Yulin QIAO Yonghua XIONG Zhihua CAI Peng LIU School of Automation China University of Geosciences (Wuhan) Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Computer Science China University of Geosciences (Wuhan) The Beibu Gulf Big Data Resources Utilization Laboratory Beibu Gulf University

Sample generation is an effective way to solve the problem of the insufficiency of training data for hyperspectral image classification. The generative adversarial network（GAN） is one of the popular deep learning methods, which utilizes adversarial training to generate the region of samples based on the required class label. In this paper, we propose cascade conditional generative adversarial nets for hyperspectral image complete spatial-spectral sample generation, named C;GAN. The C;GAN includes two stages. The stageone model consists of the spatial information generation with a window size that entails feeding random noise and the required class label. The second stage is the spatial-spectral information generation that generates spectral information of all bands in the spatial region by feeding the label regions. The visualization and verification of generated samples based on the Pavia University and Salinas datasets show superior performance, which demonstrates that our method is useful for hyperspectral image classification.

关键词： hyperspectral image sample generation generative adversarial nets(GAN) deep learning spatial-spectral information

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Location and Lightpath Selection in Multi-Domain Optical data Center Networks

Location and Lightpath Selection in Multi-Domain Optical Dat...

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Mobile Internet, Cloud computing and Information Security (MICCIS), International Conference on

作者： Changle Xu Xu Zhang Chuan Feng Xiaoxue Gong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

In the era of booming fifth-generation fixed networks (F5G), the service demands for high bandwidth continue to grow. Meanwhile, in multi-domain network scenarios, service requests are vulnerable to physical layer attacks initiated by malicious clients. In this paper, a risk-aware heuristic algorithm is proposed to address the problem of lightpath provisioning in multi-domain elastic optical data center networks (EODCNs). Simulation results show that the blocking probability of the proposed algorithm is lower than the comparison algorithm, 8.2% and 8.8% lower in NSFNET topology and US-Backbone topology, respectively.

关键词： Integrated optics Multiplexing data centers Network topology Optical design Simulation Optical fiber networks

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Multi User Offloading Strategy for Edge computing in Charging Load Forecasting Scenario

Multi User Offloading Strategy for Edge Computing in Chargin...

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Chinese automation Congress (CAC)

作者： Hongyu Long Zhaocheng Huang Chao Liu Daolin Xu Tao Li Fan Ye Key Laboratory of Big Data Intelligent Computing School of Automation/School of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China School of Automation/School of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China Economic and Technical Research Institute of State Grid Chongqing Electric Power Company Chongqing China

In order to mitigate the impact of large-scale new energy vehicle charging access to the grid, edge computing technology is widely used in smart charging perception scenarios. By deploying various edge terminal devices or application software at the perception sites, it can collect and predict the power load in various regions, so as to effectively guide and control the charging amount of new energy vehicles. However, with the increase of computing demand brought by the increase of edge device nodes, the edge server may face the problem of insufficient computing resources, which will affect the low latency characteristics. To alleviate this problem, this paper abstracts each edge terminal device as an independent user with task computing needs, establishes a multi-user edge computing system, and proposes a multi-level resource allocation strategy for edge computing, Decompose and allocate communication channel resources and server resources in multiple rounds. This method can reduce task latency by optimizing the task offloading ratio and resource allocation method of users while meeting their basic needs. The simulation results show that this method can effectively reduce the latency of task computation in multi user scenarios.

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Segment Routing based In-Band Network Telemetry in IPv6 over Optical Networks

Segment Routing based In-Band Network Telemetry in IPv6 over...

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Mobile Internet, Cloud computing and Information Security (MICCIS), International Conference on

作者： Wei Liu Xu Zhang Chuan Feng Xiaoxue Gong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

With the rise of automated network operations, network telemetry has become increasingly important. Optical networks play a key role in modern communication systems because of their high bandwidth, low latency, and low loss. To better manage and monitor Internet Protocol version 6 (IPv6) over optical networks, programmable data planes (PDP) are used to achieve flexible data collection. Meanwhile, to solve the scalability problem of the control plane in the software-defined network (SDN), segment routing over IPv6 (SRv6) is introduced. By programming the source node on the path, SRv6 wisely instructs packets to collect the appropriate telemetry data at the segment nodes to balance the trade-off between bandwidth usage and monitoring coverage. To efficiently achieve the observability of IPv6 over optical networks, we model the system and propose an SRv6-based in-band network telemetry (INT) algorithm. Simulation results verify the performance of our algorithm.

关键词： Protocols Simulation Bandwidth Optical fiber networks Routing Universal Serial Bus Topology

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Electric Vehicle Load Forecasting in Mountainous Cities Considering Traffic Behavior

Electric Vehicle Load Forecasting in Mountainous Cities Cons...

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Chinese automation Congress (CAC)

作者： Hongyu Long Xiao Yin Chao Liu Yuansen Xu Tian Tian Fan Ye Key Laboratory of Big Data Intelligent Computing School of Automation/School of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China School of Automation/School of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China Economic and Technical Research Institute of State Grid Chongqing Electric Power Company Chongqing China

In response to the unique topographical challenges posed by mountainous environments, this paper investigates a probabilistic model of electric vehicle charging load, taking into account the unique traffic behavior and regional characteristics of mountainous cities. Regarding the traffic behavior, first, a traffic road network model for mountainous cities is proposed. Next, the travel chain theory is combined with the road chain to consider the functional partition of the city, which can better describe the travel patterns and charging behavior of users. Based on the analysis of traffic behavior, by exploring the influences of ambient temperature, road grade, and the undulating nature of mountainous roads, an improved Floyd shortest path algorithm is used for path decision-making, leading to the development of an EV power consumption model specifically tailored for mountainous cities. Based on this model, the prediction of EV load in mountainous cities is subsequently conducted. Overall, this study proposes a more realistic EV load prediction model by integrating traffic behavior, regional characteristics and user travel patterns, and the simulation results demonstrate the adaptability as well as the effectiveness of the method for application in mountainous cities.

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A Survivable Routing Algorithm with Hybrid Protection in Elastic Optical Networks

A Survivable Routing Algorithm with Hybrid Protection in Ela...

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Mobile Internet, Cloud computing and Information Security (MICCIS), International Conference on

作者： Hankun Zeng Xu Zhang Chuan Feng Xiaoxue Gong School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350389906

ISBN: (纸本)9798350389913

Spectral efficiency is a crucial factor in the survivability of multi-class traffic in elastic optical networks (EONs). In this paper, we propose a spectrum-efficient hybrid protection scheme, namely dedicated path protection (DPP) and shared backup path protection (SBPP), to address the survivability issue in EONs. Meanwhile, we design a survivable routing, modulation level, and spectrum assignment (RMLSA) algorithm to achieve hybrid protection in EONs. The simulation results demonstrate that the proposed algorithm has a lower blocking rate and Spectrum occupation ratio.

关键词： Time-frequency analysis Spectral efficiency Simulation Modulation Information security Optical fiber networks Routing

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Synthesis of Decoherence-Free Modes in Linear Quantum Passive Systems via Robust Pole Placement

Synthesis of Decoherence-Free Modes in Linear Quantum Passiv...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Zibo Miao Yu Pan Qing Gao Guangdong Key Laboratory of Intelligent Morphing Mechanisms School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Shenzhen China State Key Laboratory of Industrial Control Technology The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique.

关键词： Perturbation methods Design methodology Quantum optics Robustness Feedback control Closed loop systems Tuning Cybernetics

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CLSDRL:A routing optimization method for traffic feature extraction

CLSDRL:A routing optimization method for traffic feature ext...

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2021 International Conference on Networking and Network Applications, NaNA 2021

作者： Xia, Hong Li, Jianguo Chen, Yanping Lv, Ning Wang, ZhongMin Dong, Qingyi Xi'an University of Posts and Telecommunications School of Computer Science and Technology Shaanxi Xi'an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an710121 China Xi'an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi'an710121 China

ISBN: (纸本)9781665441582

In order to solve the impact of the temporal and spatial characteristics of traffic on network routing optimization, this paper proposes convolution long-short memory neural network deep reinforcement learning (CLSDRL) model for routing optimization. The CLSDRL model consists of deep deterministic policy gradients (DDPG) deep couple with convolution neural network (CNN) and long-short memory neural network (LSTM). After extracting the spatial and temporal characteristics of network traffic with CNN and LSTM, routing decisions are made with DDPG algorithm. Experiments are conducted under different load intensities, and the network performance is evaluated by the average network delay and packet loss rate, experimental results show that this method can improve significantly network performance. © 2021 IEEE.

关键词： Reinforcement learning

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Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction

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Computers in Biology and Medicine 2024年 176卷 108565-108565页

作者： Ma, Hongcheng Wu, Yajing Tang, Yongqiang Chen, Rui Xu, Tao Zhang, Wensheng School of Information and Communication Engineering Hainan University Haikou China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Taiyuan China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

Epilepsy is a prevalent chronic disorder of the central nervous system. The timely and accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make patients adopt reasonable preventive measures before seizures occur and thus reduce harm to patients. In recent years, deep learning-based methods have made significant progress in solving the problem of epileptic seizure prediction. However, most current methods mainly focus on modeling short- or long-term dependence in EEG, while neglecting to consider both. In this study, we propose a Parallel Dual-Branch Fusion Network (PDBFusNet) which aims to combine the complementary advantages of Convolutional Neural Network (CNN) and Transformer. Specifically, the features of the EEG signal are first extracted using Mel Frequency Cepstral Coefficients (MFCC). Then, the extracted features are delivered into the parallel dual-branches to simultaneously capture the short- and long-term dependencies of EEG signal. Further, regarding the Transformer branch, a novel feature fusion module is developed to enhance the ability of utilizing time, frequency, and channel information. To evaluate our proposal, we perform sufficient experiments on the public epileptic EEG dataset CHB-MIT, where the accuracy, sensitivity, specificity and precision are 95.76%, 95.81%, 95.71% and 95.71%, respectively. PDBFusNet shows superior performance compared to state-of-the-art competitors, which confirms the effectiveness of our proposal. © 2024 Elsevier Ltd

关键词： Electroencephalography

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Enhancing EEG-Based Cross-Subject Emotion Recognition via Adaptive Source Joint Domain Adaptation

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IEEE Transactions on Affective computing 2024年

作者： Liu, Ke Luo, Xin Zhu, Wenrui Yu, Zhuliang Yu, Hong Xiao, Bin Wu, Wei Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing China South China University of Technology College of Automation Science and Engineering Guangzhou510641 China Shanghai Jiao Tong University School of Medicine Department of Neurology Songjjiang Hospital and Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China

EEG emotion recognition is crucial in both human-machine interaction and healthcare. However, recognizing emotions across different subjects remains challenging due to individual variability. While existing multi-source domain adaptation methods have been utilized for cross-subject EEG emotion decoding, they often struggle with irrelevant or weakly relevant source domains, leading to negative transfer. Additionally, variations within subdomains are often neglected in these studies. We propose a joint domain adaptation method, Adaptive Source Joint Domain Adaptation (ASJDA) to address these issues. ASJDA utilizes an unsupervised adaptive source selection strategy to select a subset of source domains by evaluating the Jensen-Shannon divergence between the source and target domains, choosing those most relevant to the target. Subsequently, it implements joint domain adaptation with these chosen sources at both the domain and category subdomain levels. Our proposed method outperforms existing state-of-the-art methods, achieving cross-subject accuracies of 96.81% in SEED, 89.69% in SEED-IV, and 69.31% in DEAP. This work significantly advances the state of the art in EEG emotion recognition by effectively addressing the challenges of cross-subject variability. The source code for ASJDA is available at https://***/Pam098/ASJDA. © 2010-2012 IEEE.

关键词： adaptive source selection cross-subject domain adaptation EEG emotion recognition

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