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IEEE 2030.5 1

IEEE 802-16e 1

IEEE80216 1

IP语音 1

Mesh 1

QoS 1

WEP 1

WLAN 1

修正路由 1

公平 1

可交易需求响应 1

多代理系统 1

多跳双向认证 1

安全分析 1

容量 1

密钥更新 1

无线局域网 1

智能电网 1

省电类型；状态转变损失；IEEE 802.16e/m；服务质量 1

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使用IEEE 2030.5 标准在电网边缘进行可交易需求响应操作 Article

Javad Fattahi, Mikhak Samadi, Melike Erol-Kantarci, Henry Schriemer

《工程（英文）》 2020年第6卷第7期页码 801-811 doi: 10.1016/j.eng.2020.06.005

摘要：完整的基于实验室的实施（据我们所知）首次实现了全面的TDR案例，该案例完全符合电气与电子工程师协会（IEEE）2030.5标准，解决了网络安全智能能源规范（SEP）应用协议的互操作性。基于在较长时间内获取的一组智能电表数据，我们参与了多个TDR场景，并通过符合IEEE 2030.5标准的全功能实现证明了我们的方案可以在现实条件下将网络峰值功耗降低22%。

关键词：可交易需求响应 IEEE 2030.5 智能电网多代理系统邻域协调

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基于最小速率保证的IEEE 802-16e公平调度算法

史俊财,胡爱群,关艳峰

《中国工程科学》 2008年第10卷第2期页码 54-59

摘要：

针对IEEE 802-16e协议的特点，提出了一种基于最小速率保证的IEEE 802-16e 公平调度（MTRFS）算法。

关键词： IEEE 802-16e 调度算法 QoS 公平

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基于多跳双向认证的802.16Mesh网络SA管理机制

王兴建,胡爱群,黄玉划

《中国工程科学》 2006年第8卷第9期页码 69-73

摘要：

IEEE802.16-2004无线城域网（wireless-MAN）标准支持的多跳（Mesh）网络是一种树状网络和adhoc网络结合的新型网络。

关键词： IEEE80216 Mesh 节点多跳双向认证修正路由

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A novel method to investigate voltage stability of IEEE-14 bus wind integrated system using PSAT

Satish KUMAR,Ashwani KUMAR,N. K. SHARMA

《能源前沿（英文）》 2020年第14卷第2期页码 410-418 doi: 10.1007/s11708-016-0440-8

摘要： The maximum demand of power utilization is increasing exponentially from base load to peak load in day to day life. This power demand may be either industrial usage or household applications. To meet this high maximum power demand by the consumer, one of the options is the integration of renewable energy resources with conventional power generation methods. In the present scenario, wind energy system is one of the methods to generate power in connection with the conventional power systems. When the load on the conventional grid system increases, various bus voltages of the system tend to decrease, causing serious voltage drop or voltage instability within the system. In view of this, identification of weak buses within the system has become necessary. This paper presents the line indices method to identify these weak buses, so that some corrective action may be taken to compensate for this drop in voltage. An attempt has been made to compensate these drops in voltages by integration of renewable energy systems. The wind energy system at one of the bus in the test system is integrated and the performance of the system is verified by calculating the power flow (PF) using the power system analysis tool box (PSAT) and line indices of the integrated test system. The PF and load flow results are used to calculate line indices for the IEEE-14 bus test system which is simulated on PSAT.

关键词： voltage stability line indices power system analysis tool box (PSAT) wind system line loading power flow (PF)

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针对Wimax中多用户的考虑状态转移损失的省电算法 Article

Bo LI,Sung-kwon PARK

《信息与电子工程前沿（英文）》 2016年第17卷第10期页码 1085-1094 doi: 10.1631/FITEE.1500314

摘要：概要：在IEEE 802.16e/m标准中，为了节省移动台（mobile subscriber station, MSS）的电量，定义了三种省电类型（power saving classes, PSC）

关键词：省电类型；状态转变损失；IEEE 802.16e/m；服务质量

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WLAN 802.11/11b数据加密机制的安全分析

宋宇波,胡爱群,蔡天佑

《中国工程科学》 2004年第6卷第10期页码 32-38

摘要：

在802.11标准中的加密采用WEP协议，用于提供链路层数据传输的安全保护。目前，在原有EP的基础上提出了一些改进方案，能提高WEP的安全性能，但理论上缺少严密的安全分析。笔者通过数学模型对这些解决方案以及原有WEP协议进行量化分析，推导出机制内各模块与整个安全机制间安全性能的对应函数关系，并比较了这些方案间安全性能的差异，证明这些安全机制可以提高原有WEP的安全性能，在理论上为用户提供如何构造满足所需安全性能的WLAN数据加密增强机制。

关键词： WLAN 安全分析 WEP 密钥更新

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无线局域网上IP语音传输的容量分析

陈立全,胡爱群,周雪莉

《中国工程科学》 2005年第7卷第7期页码 81-85

摘要：通过对无线局域网媒体接入层机制的分析，考虑了碰撞概率因素，提出了采用马尔可夫链模型来推导在IEEE 80211b/a/g标准下单个接入点同时支持IP语音用户最大容量的方法，计算出针对不同IP语音编码标准如

关键词：无线局域网 IP语音容量马尔可夫链

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Impact evaluation of large scale integration of electric vehicles on power grid

Rabah BOUDINA, Jie WANG, Mohamed BENBOUZID, Farid KHOUCHA, Mohamed BOUDOUR

《能源前沿（英文）》 2020年第14卷第2期页码 337-346 doi: 10.1007/s11708-018-0550-6

摘要： As the world witnesses a continual increase in the global energy demand, the task of meeting this demand is becoming more difficult due to the limitation in fuel resources as well as the greenhouse gases emitted which accelerate the climate change. As a result, introducing a policy that promotes renewable energy (RE) generation and integration is inevitable for sustainable development. In this endeavor, electrification of the transport sector rises as key point in reducing the accelerating environment degradation, by the deployment of new type of vehicles referred to as PHEV (plug-in hybrid electric vehicle). Besides being able to use two kinds of drives (the conventional internal combustion engine and the electric one) to increase the total efficiency, they come with a grid connection and interaction capability known as the vehicle-to-grid (V2G) that can play a supporting role for the whole power system by providing many ancillary services such as energy storage mean and power quality enhancer. Unfortunately, all these advantages do not come alone. The uncontrolled large scale EV integration may present a real challenge and source of possible failure and instability for the grid. In this work the large scale integration impact of EVs will be investigated in details. The results of power flow analysis and the dynamic response of the grid parameters variation are presented, taking the IEEE 14 bus system as a test grid system.

关键词： PHEV vehicle-to-grid (V2G) technical impact IEEE 14 bus power flow analysis

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人工智能在空间碎片对航天器损伤评估中的应用 Editorial

包为民1,殷春2,黄雪刚3,易伟4,Sara DADRAS5

《信息与电子工程前沿（英文）》 2022年第23卷第4期页码 511-514 doi: 10.1631/FITEE.2220000

摘要： Since the first artificial satellite was launched in 1957, increasing human space activities have led to a deteriorating space debris environment. A huge amount of tiny space debris (from millimeter to micron level) appears in the Earth’s orbit, and its hypervelocity impact will cause serious damage to the structure and functional units of the spacecraft, including cabin’s outer surface, thermal barrier materials, thermal control coatings, solar panels, pipes, and cables. To ensure the safe operation of spacecraft and the completion of space missions, it is necessary to detect and evaluate the impact damage caused by space debris to provide risk warning and timely repair. Due to the complex outer surface materials of spacecraft and the unpredictability of impact damage events, the collected damage detection data present various complex characteristic information. Traditional damage identification and evaluation methods based on manual extraction of feature parameters have difficulty in accurately describing the above complex feature information. In recent years, the application of artificial intelligence (AI) technology in space debris impact perception, damage detection, risk assessment, etc. has begun to receive extensive attention from scholars and engineers, and some breakthroughs have been made in solving such very difficult engineering and technical problems. However, there are still many difficult problems to be solved in the application of AI technology to deal with the issue of space debris. With this background, several important tendencies have emerged in the use of AI methods for spacecraft damage detection and evaluation. 1. Various AI learning algorithms (such as neural networks and deep learning) are used and combined to effectively detect and classify damage features. AI learns in a variety of ways, and each learning algorithm is good at solving different problems. Combining multiple AI learning algorithms in different scenarios can improve detection efficiency and classify damage features. 2. Modifications and enhancements to the learning algorithm are explored to perform damage pattern recognition and evaluation more accurately and effectively. To improve the performance of the learning algorithm, modifications and enhancements are essential. Modifications and enhancements to the algorithm itself, including the setting of the loss function, optimization of iterative steps, and judgment of termination conditions, will have a significant impact on the performance of the learning algorithm. In addition, the complex learning algorithm network itself has a large number of parameters that need to be optimized. In fact, the optimization method of network parameters has become one of the core factors that determine the performance of the learning algorithm. 3. AI learning algorithms and models should preferably be extended to suit spacecraft damage detection and evaluation systems. In combination with specific spacecraft damage detection and assessment systems, existing learning algorithms and models can be extended by, e.g., preprocessing the actual input test data to obtain better algorithm iterative calculation results, classifying different damage detection scenarios, applying different optimization modules to obtain better performance comparison test results, and giving reasonable classification criteria for damage assessment results. 4. AI technology is used to analyze the data characteristics of various spacecraft impact damage samples to guide the space debris protection design of spacecraft. The advantage of AI technology is that it can analyze typical characteristics from a large number of data samples. By analyzing the impact damage samples of various types of spacecraft and according to the detection data characteristics under different impact conditions, researchers can obtain the damage type and damage degree of the spacecraft’s space debris protection structure. Therefore, engineers can improve the safety of spacecraft in orbit by optimizing the protective structure of the spacecraft. 5. AI technology is used to model and analyze space debris to realize the monitoring, early warning, mitigation, and removal of space debris to reduce the impact of space debris on spacecraft. Using AI technology to model and analyze space debris has a stronger expressive ability, which can express complex and qualitative empirical knowledge that is difficult to describe with mathematical formulas. AI modeling can be modified and expanded according to the new understanding of space debris model knowledge, and the system can be more flexible to adapt to new needs. The clearer the modeling and analysis results of space debris are, the more accurate the monitoring, early warning, mitigation, and removal of debris impacts are, thereby greatly reducing the impact of space debris on spacecraft. In short, spacecraft damage feature extraction and damage assessment are critical to the development of the aerospace industry, and these challenges call for new methods and techniques to stimulate the continuous efforts of aerospace equipment research, pattern recognition, and AI. In this context, the journal has organized a special feature on the application of AI in the space environment and spacecraft. This special feature focuses on spacecraft damage detection and assessment methods based on AI learning from detection data, including the hierarchical correlation analysis of spacecraft damage characteristics and detection data, and the construction of spacecraft damage assessment models based on AI analysis methods. After a rigorous review process, five research articles were selected for this feature.

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Developing a power monitoring and protection system for the junction boxes of an experimental seafloor observatory network

Jun WANG,De-jun LI,Can-jun YANG,Zhi-feng ZHANG,Bo JIN,Yan-hu CHEN

《信息与电子工程前沿（英文）》 2015年第16卷第12期页码 1034-1045 doi: 10.1631/FITEE.1500099

摘要： A power monitoring and protection system based on an embedded processor was designed for the junction boxes (JBs) of an experimental seafloor observatory network in China. The system exhibits high reliability, fast response, and high real-time performance. A two-step power management method which uses metal-oxide-semiconductor field-effect transistors (MOSFETs) and a mechanical contactor in series was adopted to generate a reliable power switch, to limit surge currents and to facilitate automatic protection. Grounding fault diagnosis and environmental monitoring were conducted by designing a grounding fault detection circuit and by using selected sensors, respectively. The data collected from the JBs must be time-stamped for analysis and for correlation with other events and data. A highly precise system time, which is necessary for synchronizing the times within and across nodes, was generated through the IEEE 1588 (precision clock synchronization protocol for networked measurement and control systems) time synchronization method. In this method, time packets were exchanged between the grandmaster clock at the shore station and the slave clock module of the system. All the sections were verified individually in the laboratory prior to a sea trial. Finally, a subsystem for power monitoring and protection was integrated into the complete node system, installed in a frame, and deployed in the South China Sea. Results of the laboratory and sea trial experiments demonstrated that the developed system was effective, stable, reliable, and suitable for continuous deep-sea operation.

关键词： Power monitoring and protection Embedded processor Seafloor observatory network IEEE 1588 Junction boxes

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智简无线网络理论与技术 Editorial

张平1,彭木根1,崔曙光2,张朝阳3,毛国强4,全智5,Tony Q. S. QUEK6,荣波7

《信息与电子工程前沿（英文）》 2022年第23卷第1期页码 1-4 doi: 10.1631/FITEE.2210000

摘要： With the acceleration of a new round of global scientific, technological, and industrial revolution, the next generation of information and communication technology, i.e., 6G, will inject new momentum into industry transformation and upgrad-ing, as well as into economic innovation and development. This will subsequently promote a global industrial integration. Wireless communication will be ubiquitous in all areas of future society, supporting novel applications with various performance requirements, such as immersive- or interactive-experience applications requiring a large bandwidth, autonomous driving and vehicle-to-everything applications requiring ultra-high reliability and ultra-low latency, and applications for industrial Internet requiring massive machine-type connectivity. Facing the challenges of the post-Moore and post-pandemic era, wireless communication needs breakthroughs in network architecture to improve the intelligence, security, robustness, bandwidth, and heterogeneity. With this background, several important tendencies have emerged in the development of 6G wireless communications: 1. Future wireless networks will evolve from “human-to-human” communications into intelligent “human-to-machine” communications. In addition to enabling communications among humans, future wireless networks will be able to support close connections among humans and machines. The behavior and intent of humans will be sensed and communicated to machines that will accordingly adjust their operations. Typical scenarios include smart building, intelligent transportation, mixed reality (MR), and others. 2. Network nodes will evolve from carrying out only traditional communications to carrying out communication, sensing, computation, management, and caching in an integrated manner. To meet the diverse service requirements of mobile MR, intelligent transportation, industrial Internet of Things, and other areas, future networks will possess multiple functionalities. For example, by sensing human head position, pre-caching necessary content, and rendering high-quality images, network nodes can provide fully immersive MR experiences. In addition, with artificial intelligence (AI), network nodes can manage multi-dimensional resources in an on-demand fashion, where intent-driven network management and control can be realized. 3. Network architecture will focus on collaborations between the cloud and the network edge, which will become more heterogenous. To shorten latency and alleviate the backhaul/fronthaul burden, the network edge must collaborate with the cloud. The first method of collaboration is that the cloud finishes AI model training and then deploys AI models into the network edge, which supports the so-called edge intelligence. In the second method, users demanding high throughput are served via a cloud radio access mode, while users requiring ultra-low latency can benefit from edge computation and caching. As for architecture heterogeneity, future networks are envisioned to incorporate unmanned aerial vehicle (UAV) networks, satellite communica-tion networks, and dense cellular networks, bringing three-dimensional and hierarchical network coverage. In short, the evolution of existing 5G technolo-gies and the development of 6G need to address more stringent and diverse application scenarios, a more strict energy constraint, and the orchestration of multi-dimensional resources. These challenges call for an intellicise wireless network operation paradigm, where “intellicise” is a new adjective that we coin, standing for intelligence-endogenous and primitive-concise. Built upon the integration of AI and next-generation networking technologies, an intellicise wireless network continually explores and exploits new intelligent primitives, e.g., semantic base (Seb) in semantic communications, proactively takes sys-tematic entropy reduction as the global optimization objective, adaptively reshapes the core models of information systems, and ultimately endows itself with endogenous intelligence and primitive conciseness. In this context, the journal has organized a special feature on the theory and techniques for intellicise wireless networks. This special feature covers information theory, architecture design, and intellicise wireless networks for achieving air-space-ground-sea integration, resource management, hardware testbeds and platforms, as well as related applications. In addition, this feature is intended to provide a review of advancements and future research directions in the research field of intellicise wireless networks. After a rigorous review process, six papers have been selected for this feature, including one review article and five research articles.

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在光纤激光器中利用Ti3C2 MXene材料产生矢量孤子和类噪声脉冲 Research

王帅1,李雷1,宋宇峰2,唐定远3,沈德元1,赵鹭明1,4

《信息与电子工程前沿（英文）》 2021年第22卷第3期页码 287-436 doi: 10.1631/FITEE.2000033

摘要：本文利用Ti3C2 MXene材料作为可饱和吸收体，搭建了铥钬共掺光纤激光器，观察到矢量孤子和类噪声脉冲的形成。其中，可饱和吸收体是通过将Ti3C2溶液滴在侧面抛光的D形光纤上自然挥发后制备而成。观察到的矢量孤子光谱上同时存在两组Kelly边带。通过调节光纤激光器中的偏振控制器，可以将矢量孤子转变为光谱带宽约3.3 nm的类噪声脉冲。据我们所知，这是首次利用Ti3C2 MXene材料作为可饱和吸收体从光纤激光器中获得矢量孤子。

关键词：矢量孤子；类噪声脉冲；MXene；光纤激光器

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一种K自适应的广域网SDN控制器部署方法 Article

Peng XIAO,Zhi-yang LI,Song GUO,Heng QI,Wen-yu QU,Hai-sheng YU

《信息与电子工程前沿（英文）》 2016年第17卷第7期页码 620-633 doi: 10.1631/FITEE.1500350

摘要：软件定义网络（software-defined networking）作为一种新技术框架，正成为未来网络技术的核心。软件定义网络的核心思想就是控制平面和数据平面分离，方便管理和控制编程。虽然软件定义网络已在数据中心这样的局域网中得到了应用和部署，但在更大规模的广域网上部署依然面临着很多问题，如SDN域划分、控制器部署等问题。本文提出了一种基于谱聚类的SDN控制器部署方法，通过此方法能将较大的网络划分成小的SDN域并选择其控制器位置。通过分析模型的矩阵扰动和本征间隙，能够自动得到SDN域个数，以达到较好的划分效果和控制器部署方案。最后，为了验证算法的有效性，本文提出了一个新的实验框架，并基于广域网拓扑结构进行了实验。实验结果表明，该方法能很好的解决SDN域划分和控制器部署问题。

关键词：软件定义网络；控制器部署；K自适应方法

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标题作者时间类型操作

使用IEEE 2030.5 标准在电网边缘进行可交易需求响应操作

Javad Fattahi, Mikhak Samadi, Melike Erol-Kantarci, Henry Schriemer

期刊论文

基于最小速率保证的IEEE 802-16e公平调度算法

史俊财,胡爱群,关艳峰

期刊论文

基于多跳双向认证的802.16Mesh网络SA管理机制

王兴建,胡爱群,黄玉划

期刊论文