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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 ：
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 ：
Industrial Internet of Things (IIoT) has emerged with the developments of various communication technologies. In order to guarantee the security and privacy of massive IIoT data, blockchain is widely considered as a promising technology and applied into IIoT. However, there are still several issues in the existing blockchain-enabled IIoT: 1) unbearable energy consumption for computation tasks; 2) poor efficiency of consensus mechanism in blockchain; and 3) serious computation overhead of network systems. To handle the above issues and challenges, in this article, we integrate mobile-edge computing (MEC) into blockchain-enabled IIoT systems to promote the computation capability of IIoT devices and improve the efficiency of the consensus process. Meanwhile, the weighted system cost, including the energy consumption and the computation overhead, are jointly considered. Moreover, we propose an optimization framework for blockchain-enabled IIoT systems to decrease consumption, and formulate the proposed problem as a Markov decision process (MDP). The master controller, offloading decision, block size, and computing server can be dynamically selected and adjusted to optimize the devices energy allocation and reduce the weighted system cost. Accordingly, due to the high-dynamic and large-dimensional characteristics, deep reinforcement learning (DRL) is introduced to solve the formulated problem. Simulation results demonstrate that our proposed scheme can improve system performance significantly compared to other existing schemes. © 2014 IEEE.
Keyword ：
Industrial internet of things (IIoT) Industrial internet of things (IIoT) Energy utilization Energy utilization Reinforcement learning Reinforcement learning Markov processes Markov processes Deep learning Deep learning Blockchain Blockchain Energy efficiency Energy efficiency
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| GB/T 7714 | Yang, Le , Li, Meng , Si, Pengbo et al. Energy-Efficient Resource Allocation for Blockchain-Enabled Industrial Internet of Things with Deep Reinforcement Learning [J]. | IEEE Internet of Things Journal , 2021 , 8 (4) : 2318-2329 . |
| MLA | Yang, Le et al. "Energy-Efficient Resource Allocation for Blockchain-Enabled Industrial Internet of Things with Deep Reinforcement Learning" . | IEEE Internet of Things Journal 8 . 4 (2021) : 2318-2329 . |
| APA | Yang, Le , Li, Meng , Si, Pengbo , Yang, Ruizhe , Sun, Enchang , Zhang, Yanhua . Energy-Efficient Resource Allocation for Blockchain-Enabled Industrial Internet of Things with Deep Reinforcement Learning . | IEEE Internet of Things Journal , 2021 , 8 (4) , 2318-2329 . |
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Abstract ：
近年来,随着数字货币的日益兴起和商业应用,区块链技术越来越受到政府部门、工业界以及学术界的关注和认可。与此同时,移动通信网络的快速发展和广泛覆盖,促使物联网(IoT)逐渐走入日常生活,极大程度地提升了工作效率与生活品质。然而,物联网场景中仍存在能效性低、数据传输安全性差等问题,使用区块链技术可有助于促进物联网更好地发展和普及。本文对区块链技术在物联网中的应用进行了系统性综述,首先介绍了背景知识和相关研究,其次提出了区块链的基础架构模型,并且详细阐述了区块链技术应用于物联网各场景的发展现状以及相关特征,最后讨论了一些挑战及未来发展趋势。
Keyword ：
共识机制 共识机制 数字货币 数字货币 智能合约 智能合约 区块链 区块链 物联网(IoT) 物联网(IoT)
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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]. | 高技术通讯 , 2021 , 31 (01) : 48-63 . |
| MLA | 叶欣宇 et al. "区块链技术应用于物联网:发展与展望" . | 高技术通讯 31 . 01 (2021) : 48-63 . |
| APA | 叶欣宇 , 李萌 , 赵铖泽 , 司鹏搏 , 孙阳 , 张延华 . 区块链技术应用于物联网:发展与展望 . | 高技术通讯 , 2021 , 31 (01) , 48-63 . |
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Abstract ：
对采用双回转结构交叉耦合差分有源电感(DGC-DAI)的可调谐、高品质因子Q和低噪声差分有源带通滤波器(THQLNA-BPF)进行了研究。输入级,采用差分共基-共射结构,以抑制噪声和获得高频特性;输出级,采用差分共集放大器,以获得高的驱动能力和高的隔离度;有源电感滤波网络,利用DAI电感值可宽范围调谐、高Q值和低的噪声,来分别实现BPF的中心频率的宽范围调节、高Q值和良好的噪声特性;进一步地,利用变容二极管网络改善BPF中心频率的可调性和提高Q值,利用有源可调负阻网络提高BPF的Q值和进行Q值独立调节。基于WIN 0.2μm GaAs HBT工艺,利用ADS对THQLNA-BPF进行性能验证。...
Keyword ：
可调谐有源电感 可调谐有源电感 中心频率 中心频率 有源带通滤波器 有源带通滤波器
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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]. | 电子器件 , 2021 , 44 (01) : 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" . | 电子器件 44 . 01 (2021) : 39-45 . |
| APA | 张正 , 张延华 , 温晓伟 , 那伟聪 . 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 . | 电子器件 , 2021 , 44 (01) , 39-45 . |
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Abstract ：
对采用新型可调谐、高Q的有源电感(THQAI)的高优值(FOM)压控振荡器(VCO)进行了研究。在LC振荡回路模块中,利用THQAI具有高Q值、较宽调谐范围的特性,分别实现了VCO的低相位噪声和宽的频率调谐范围;在负阻电路模块中,将电流进行复用,使其只有一条直流工作支路,降低了VCO的功耗,又由于电路中的MOS晶体管始终工作在饱和区,进一步降低了VCO的相位噪声;在输出缓冲级,采用共源NMOS晶体管,放大了该VCO的输出波形。最终,这些技术手段使得VCO的调谐范围、相位噪声和功耗均得到了改善,因此获得了高的FOM值。基于TSMC 0.13μm CMOS工艺库,利用射频集成电路设计工具ADS对该...
Keyword ：
可调谐有源电感 可调谐有源电感 压控振荡器 压控振荡器 优值 优值
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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]. | 电子器件 , 2021 , 44 (02) : 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" . | 电子器件 44 . 02 (2021) : 272-277 . |
| APA | 张正 , 张延华 , 温晓伟 , 那伟聪 . 采用可调谐高Q有源电感的高优值VCO的研究 . | 电子器件 , 2021 , 44 (02) , 272-277 . |
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Abstract ：
设计了一种采用可调谐有源电感(TAI)的多频段低噪声放大器(MBLNA)。在放大级中,由电感值及Q值可多重调谐的TAI与电容值可调谐的变容二极管构成选频网络,并结合共射-共基放大电路,实现对不同频段信号进行选择放大。输入级采用带有输入串联电感与发射极电感负反馈的共射放大电路,实现了MBLNA输入阻抗的宽带匹配。输出级采用共射放大电路,在满足输出匹配的同时,再次对信号进行放大,保证了MBLNA的高增益,同时输出级与放大级构成电流复用结构,降低了整体电路功耗。基于WIN 0.2μm GaAs HBT工艺库,利用ADS对MBLNA的主要性能参数进行验证。结果表明,该MBLNA可以在1.9 GHz、2...
Keyword ：
多频段 多频段 低噪声放大器 低噪声放大器 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 , 黄鑫 et al. 采用可调谐有源电感的多频段低噪声放大器 [J]. | 微电子学 , 2021 , 51 (02) : 151-156 . |
| MLA | 张正 et al. "采用可调谐有源电感的多频段低噪声放大器" . | 微电子学 51 . 02 (2021) : 151-156 . |
| APA | 张正 , 张延华 , 黄鑫 , 那伟聪 . 采用可调谐有源电感的多频段低噪声放大器 . | 微电子学 , 2021 , 51 (02) , 151-156 . |
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