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学者姓名：张延华
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Abstract ：
本发明公开了自组网场景下基于区块链和边缘计算的轨道交通网络资源分配方法，通过构建多跳传输模型、区块链模型、MEC服务器计算模型，计算任务在列车之间多跳传输的时延、经济成本和区块链系统的时延，以及MEC服务器处理任务产生的时延和经济成本，从而根据系统状态通过训练深度神经网络，指导调整卸载路由路径的选择、卸载决策和区块大小的选择，完成场景内的最优资源分配。仿真实验表明，本发明在节省系统时延和系统总经济成本方面具有一定的优势。
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| GB/T 7714 | 李萌 , 田琳琳 , 司鹏搏 et al. 自组网场景下基于区块链和边缘计算的轨道交通网络资源分配方法 : CN202310010374.0[P]. | 2023-01-04 . |
| MLA | 李萌 et al. "自组网场景下基于区块链和边缘计算的轨道交通网络资源分配方法" : CN202310010374.0. | 2023-01-04 . |
| APA | 李萌 , 田琳琳 , 司鹏搏 , 杨睿哲 , 孙艳华 , 孙恩昌 et al. 自组网场景下基于区块链和边缘计算的轨道交通网络资源分配方法 : CN202310010374.0. | 2023-01-04 . |
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Abstract ：
Heterogeneous networks (HetNets) with end-to-end (E2E) network slicing are regarded as effective approaches to meet diverse service requirements from vertical industries. Due to the dense deployment of base stations (BSs) and the complicated associations between BSs and E2E network slices (NSs) in the scenario, the handoff problem faces challenges of the huge system state space and handoff action space and the considerable communication overhead. In this paper, we take these issues into account and consider a distributed E2E NS handoff decision framework in the HetNet. A decentralized Markov decision process (DEC-MDP)-based model is formulated for the distributed E2E NS handoff problem, and the jointly observable and random characteristics of the DEC-MDP are analyzed. To obtain a theoretical performance reference, the original distributed E2E NS handoff problem is simplified, and a Nash equilibrium-based performance bound is given. More practically, the multi-agent double deep Q-network-based distributed handoff (MA-DDQN-DH) algorithm with the centralized training and decentralized executing framework is proposed. Simulation results show that the Nash equilibrium-based performance bound is reasonable, and the proposed MA-DDQN-DH algorithm performs well in the comparison.
Keyword ：
Heuristic algorithms Heuristic algorithms Bandwidth Bandwidth Distributed network slice handoff Distributed network slice handoff decentralized Markov decision process decentralized Markov decision process Nash equilibrium Nash equilibrium Quality of service Quality of service Real-time systems Real-time systems Costs Costs multi-agent deep reinforcement learning multi-agent deep reinforcement learning Training Training Network slicing Network slicing
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| GB/T 7714 | Wu, Wenjun , Yang, Feng , Gao, Yang et al. Distributed Handoff Problem in Heterogeneous Networks With End-to-End Network Slicing: Decentralized Markov Decision Process-Based Modeling and Solution [J]. | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS , 2022 , 21 (12) : 11222-11236 . |
| MLA | Wu, Wenjun et al. "Distributed Handoff Problem in Heterogeneous Networks With End-to-End Network Slicing: Decentralized Markov Decision Process-Based Modeling and Solution" . | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 21 . 12 (2022) : 11222-11236 . |
| APA | Wu, Wenjun , Yang, Feng , Gao, Yang , Wang, Xiaoxi , Si, Pengbo , Zhang, Yanhua et al. Distributed Handoff Problem in Heterogeneous Networks With End-to-End Network Slicing: Decentralized Markov Decision Process-Based Modeling and Solution . | IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS , 2022 , 21 (12) , 11222-11236 . |
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Abstract ：
本发明公开了一种基于区块链和国密算法的文件信息数据存储方法，为了更安全便捷的存储文件，防止被篡改，保护用户个人隐私和利益。将区块链和国密算法结合起来，使用去中心化的公开账本区块链做为存储工具。使用国密SM3哈希算法生成哈希值，提取文件信息，可占用较小的存储空间记录关键信息。采用POST请求的方式，安全快捷的传输数据。区块链后台系统中使用Kafka算法高效完成节点之间的共识，使数据一经上链便无法篡改。使用HTML\CSS\JavaScript搭建前端，查询到数据以及对应的区块信息在前端界面显示。最终能安全有效便捷的存储文件信息数据。
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| GB/T 7714 | 司鹏搏 , 周宇泽 , 李萌 et al. 一种基于区块链和国密算法的文件信息数据存储方法 : CN202211161053.2[P]. | 2022-09-22 . |
| MLA | 司鹏搏 et al. "一种基于区块链和国密算法的文件信息数据存储方法" : CN202211161053.2. | 2022-09-22 . |
| APA | 司鹏搏 , 周宇泽 , 李萌 , 杨睿哲 , 孙艳华 , 张延华 . 一种基于区块链和国密算法的文件信息数据存储方法 : CN202211161053.2. | 2022-09-22 . |
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Abstract ：
Driven by numerous emerging mobile devices and various Quality-of-Service (QoS) requirements, mobile-edge computing (MEC) has been recognized as a prospective paradigm to promote the computation capability of mobile devices, as well as reduce energy overhead and service latency of applications for the Internet of Things (IoT). However, there are still some open issues in the existing research works: 1) limited network and computing resource; 2) simple or nonintelligent resource management; and 3) ignored security and reliability. In order to cope with these issues, in this article, 6G and blockchain technology are considered to improve network performance and ensure the authenticity of data sharing for the MEC-enabled IoT. Meanwhile, a novel intelligent optimization method named as collective reinforcement learning (CRL) is proposed and introduced, to realize intelligent resource allocation, meet distributed training results sharing, and avoid excessive consumption of system resources. Based on the designed network model, a cloud-edge collaborative resource allocation framework is formulated. By joint optimizing the offloading decision, block interval, and transmission power, it aims to minimize the consumption overheads of system energy and latency. Then, the formulated problem is designed as a Markov decision process, and the optimal strategy can be obtained by the CRL. Some evaluation results reveal that the system performance based on the proposed scheme outperforms other existing schemes obviously.
Keyword ：
Internet of Things (IoT) Internet of Things (IoT) Internet of Things Internet of Things Blockchain Blockchain Blockchains Blockchains Resource management Resource management 6G mobile communication 6G mobile communication mobile-edge computing (MEC) mobile-edge computing (MEC) collective reinforcement learning (CRL) collective reinforcement learning (CRL) Computational modeling Computational modeling Servers Servers sixth generation (6G) sixth generation (6G) Optimization Optimization
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| GB/T 7714 | Li, Meng , Pei, Pan , Yu, F. Richard et al. Cloud-Edge Collaborative Resource Allocation for Blockchain-Enabled Internet of Things: A Collective Reinforcement Learning Approach [J]. | IEEE INTERNET OF THINGS JOURNAL , 2022 , 9 (22) : 23115-23129 . |
| MLA | Li, Meng et al. "Cloud-Edge Collaborative Resource Allocation for Blockchain-Enabled Internet of Things: A Collective Reinforcement Learning Approach" . | IEEE INTERNET OF THINGS JOURNAL 9 . 22 (2022) : 23115-23129 . |
| APA | Li, Meng , Pei, Pan , Yu, F. Richard , Si, Pengbo , Li, Yu , Sun, Enchang et al. Cloud-Edge Collaborative Resource Allocation for Blockchain-Enabled Internet of Things: A Collective Reinforcement Learning Approach . | IEEE INTERNET OF THINGS JOURNAL , 2022 , 9 (22) , 23115-23129 . |
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Abstract ：
Nowadays, the rise of the Internet of Vehicles (IoV) has led to the rapid development of smart transportation. To increase the computing capacity of mobile vehicles and decrease the content delivery latency of suppliers, mobile edge computing (MEC) is considered as an indispensable solution. However, there are some essential issues to be considered: 1) security and privacy of data transmission, and 2) reasonable resource allocation for collaborative computing and caching. In this paper, to solve above issues, blockchain technology is adopted to ensure reliable transmission and interaction of data. Meanwhile, we develop an intelligent resource framework about computing and caching for blockchain-enabled MEC systems in IoV. Through jointly considering and optimizing offloading decision of computation task carried by vehicle, caching decision, the number of offloaded consensus nodes, block interval and block size, the energy consumption and computation overheads can be decreased, and the data throughput of the blockchain can be increased significantly. Moreover, the proposed optimization problem is modeled and formulated as a Markov decision process. Facing the complexity and dynamic of resource allocation, the asynchronous advantage actor-critic approach is considered and applied to solve the optimization problem. Experiment results demonstrate that the advantages of the proposed optimization scheme are obvious compared with other existing schemes.
Keyword ：
mobile edge computing (MEC) mobile edge computing (MEC) Internet of Vehicles Internet of Vehicles blockchain blockchain resource allocation resource allocation asynchronous advantage actor-critic (A3C) approach asynchronous advantage actor-critic (A3C) approach
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| GB/T 7714 | Ye, Xinyu , Li, Meng , Yu, F. Richard et al. MEC and Blockchain-Enabled Energy-Efficient Internet of Vehicles Based on A3C Approach [J]. | 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) , 2021 . |
| MLA | Ye, Xinyu et al. "MEC and Blockchain-Enabled Energy-Efficient Internet of Vehicles Based on A3C Approach" . | 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) (2021) . |
| APA | Ye, Xinyu , Li, Meng , Yu, F. Richard , Si, Pengbo , Wang, Zhuwei , Zhang, Yanhua . MEC and Blockchain-Enabled Energy-Efficient Internet of Vehicles Based on A3C Approach . | 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM) , 2021 . |
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Abstract ：
A compact and reconfigurable low noise amplifier (LNA) is proposed by combining an input transistor, composite transistors with Darlington configuration as the amplification and output transistor, T-type structure composite resistors instead of a simplex structure resistor, a shunt inductor feedback realized by a tunable active inductor (AI), a shunt inductor peaking technique realized by another tunable AI. The division and collaboration among different resistances in the T-type structure composite resistor realize simultaneously input impedance matching, output impedance matching and good noise performance; the shunt feedback and peaking technique using two tunable AIs not only extend frequency bandwidth and improve gain flatness, but also make the gain and frequency band can be tuned simultaneously by the external bias of tunable AIs; the Darlington configuration of composite transistors provides high gain; furthermore, the adoption of the small size AIs instead of large size passive spiral inductor, and the use of composite resistors make the LNA have a small size. The LNA is fabricated and verified by GaAs/InGaP hetero-junction bipolar transistor (HBT) process. The results show that at the frequency of 7GHz, the gain S21 is maximum and up to 19dB; the S21 can be tuned from 17dB to 19dB by tuning external bias of tunable AIs, that is, the tunable amount of S21 is 2dB, and similarly at 8GHz; the tunable range of 3dB bandwidth is 1GHz. In addition, the gain S21 flatness is better than 0.4dB under frequency from 3.1GHz to 10.6GHz; the size of the LNA only has 760μm×1260μm (including PADs). Therefore, the proposed strategies in the paper provide a new solution to the design of small size and reconfigurable ultra-wideband (UWB) LNA and can be used further to adjust the variations of gain and bandwidth of radio frequency integrated circuits (RFICs) due to package, parasitic and the variation of fabrication process and temperature. Copyright © by HIGH TECHNOLOGY LETTERS PRESS.
Keyword ：
Transistors Transistors Bandwidth Bandwidth Resistors Resistors Composite structures Composite structures Electric inductors Electric inductors Ultra-wideband (UWB) Ultra-wideband (UWB) Impedance matching (electric) Impedance matching (electric) Gallium arsenide Gallium arsenide Feedback amplifiers Feedback amplifiers Low noise amplifiers Low noise amplifiers III-V semiconductors III-V semiconductors
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| GB/T 7714 | Zhang, Zheng , Zhang, Yanhua , Yang, Ruizhe et al. A compact and reconfigurable low noise amplifier employing combinational active inductors and composite resistors feedback techniques [J]. | High Technology Letters , 2021 , 27 (1) : 38-42 . |
| MLA | Zhang, Zheng et al. "A compact and reconfigurable low noise amplifier employing combinational active inductors and composite resistors feedback techniques" . | High Technology Letters 27 . 1 (2021) : 38-42 . |
| APA | Zhang, Zheng , Zhang, Yanhua , Yang, Ruizhe , Shen, Pei , Ding, Chunbao , Liu, Yaze et al. A compact and reconfigurable low noise amplifier employing combinational active inductors and composite resistors feedback techniques . | High Technology Letters , 2021 , 27 (1) , 38-42 . |
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Abstract ：
采用新型可调谐有源电感的频率可调谐高Q低噪声的带通滤波器
Keyword ：
中心频率 中心频率 有源带通滤波器 有源带通滤波器 可调谐有源电感 可调谐有源电感
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| GB/T 7714 | 张正 , 张延华 , 温晓伟 et al. 采用新型可调谐有源电感的频率可调谐高Q低噪声的带通滤波器 [J]. | 张正 , 2021 , 44 (1) : 39-45 . |
| MLA | 张正 et al. "采用新型可调谐有源电感的频率可调谐高Q低噪声的带通滤波器" . | 张正 44 . 1 (2021) : 39-45 . |
| APA | 张正 , 张延华 , 温晓伟 , 那伟聪 , 电子器件 . 采用新型可调谐有源电感的频率可调谐高Q低噪声的带通滤波器 . | 张正 , 2021 , 44 (1) , 39-45 . |
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Abstract ：
人工智能与区块链赋能物联网:发展与展望
Keyword ：
去中心化 去中心化 区块链 区块链 机器学习 机器学习 物联网(IoT) 物联网(IoT) 安全性 安全性 人工智能(AI) 人工智能(AI)
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| GB/T 7714 | 李萌 , 裴攀 , 孙恩昌 et al. 人工智能与区块链赋能物联网:发展与展望 [J]. | 李萌 , 2021 , 47 (5) : 520-529 . |
| MLA | 李萌 et al. "人工智能与区块链赋能物联网:发展与展望" . | 李萌 47 . 5 (2021) : 520-529 . |
| APA | 李萌 , 裴攀 , 孙恩昌 , 杨睿哲 , 司鹏搏 , 张延华 et al. 人工智能与区块链赋能物联网:发展与展望 . | 李萌 , 2021 , 47 (5) , 520-529 . |
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Abstract ：
区块链技术应用于物联网:发展与展望
Keyword ：
智能合约 智能合约 数字货币 数字货币 物联网(IoT) 物联网(IoT) 共识机制 共识机制 区块链 区块链
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| GB/T 7714 | 叶欣宇 , 李萌 , 赵铖泽 et al. 区块链技术应用于物联网:发展与展望 [J]. | 叶欣宇 , 2021 , 31 (1) : 48-63 . |
| MLA | 叶欣宇 et al. "区块链技术应用于物联网:发展与展望" . | 叶欣宇 31 . 1 (2021) : 48-63 . |
| APA | 叶欣宇 , 李萌 , 赵铖泽 , 司鹏搏 , 孙阳 , 张延华 et al. 区块链技术应用于物联网:发展与展望 . | 叶欣宇 , 2021 , 31 (1) , 48-63 . |
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Abstract ：
采用可调谐高Q有源电感的高优值VCO的研究
Keyword ：
优值 优值 压控振荡器 压控振荡器 可调谐有源电感 可调谐有源电感
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| GB/T 7714 | 张正 , 张延华 , 温晓伟 et al. 采用可调谐高Q有源电感的高优值VCO的研究 [J]. | 张正 , 2021 , 44 (2) : 272-277 . |
| MLA | 张正 et al. "采用可调谐高Q有源电感的高优值VCO的研究" . | 张正 44 . 2 (2021) : 272-277 . |
| APA | 张正 , 张延华 , 温晓伟 , 那伟聪 , 电子器件 . 采用可调谐高Q有源电感的高优值VCO的研究 . | 张正 , 2021 , 44 (2) , 272-277 . |
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