复杂中空结构材料的构筑及能源应用

随着能源问题的日益突显,开发新型多功能材料以满足能源存储与转换应用的需求变得尤为重要.在众多功能材料中,复杂中空结构材料由于其独特的结构和物理化学特性而备受关注.本文综合评述了复杂中空结构材料的普适性构筑方法(硬模板法、软模板法、自模板法、次序模板法和选择性刻蚀法)及在能源方面的应用(锂/钠/钾离子电池、锂硫电池、超级电容器、电催化、光催化及染料敏化电池等).最后,对复杂空心结构研究领域存在的问题及未来的发展方向进行了展望.     

三种类型汽车的能耗和使用成本的建模研究

本文对电动,传统,混合动力三种类型汽车的能耗和使用成本问题进行了建模、求解及分析．首先定义了电动汽车的能量利用率ηE。根据能量守恒将所有电量转换成汽油消耗量,而传统车型则直接反映到汽油的消耗上,混合动力汽车则综合电动汽车和传统汽车的能耗模型,从而统一能耗水平评价标准．这样,我们就得到了三种不同类型车的能耗模型再进行模型求解以此来分析比较三种类型车哪一种节能效果好．对电动汽车来说,使用成本主要包括驾驶维护成本、报废处理成本,而对传统汽车来说,使用成本主要包括驾驶维护成本、报废处理成本、环境成本和其他成本,混合动力汽车则结合二者使用成本．因此,根据不同车型使用成本类型建立相应的数学模型,按照建立的公式,可以得到电动汽车的使用成本LCC1、传统汽车的使用成本LCC2和混合动力汽车的使用成本LCC3,从而进行模型求解．     

一种基于WSN时变性与节点剩余能量均衡的机会路由算法

 为了解决数据报文在无线传感网络中动态路由以及网络内各节点剩余能量均衡问题,该文提出了一种机会路由算法。首先,利用热力学第2定律描述数据报文在无线传感器网络中动态路由的传输过程,其中,为了表征网络内各节点状态的时变性以及剩余能量,提出了机会熵模型;其次,以机会熵模型作为选择下一跳节点的理论依据,并结合蚁群优化(ACO)算法,设计并实现了考虑网络中各节点时变性与剩余能量均衡的机会路由算法(ACO for Time Dependent Opportunistic-routing Protocol, ATDOP),使得数据报文在转发过程时,能够有效地选择下一跳节点,从而使网络内各节点的通信资源和能量资源达到负载均衡。最后,通过实验证明,相对于已有的机会路由协议,ATDOP具有报文成功传输率高、网络有效吞吐量大以及网络工作寿命长等优点。     

基于跨层设计的无线传感器网络节能双向梯度路由算法

针对现有无线传感器网络梯度路由算法在下行路由创建过程和能量更新机制中存在冗余控制开销的问题,该文提出一种采用跨层和功率控制机制,具有节能功能的双向梯度路由算法(Cross-layer Energy-efficient Bidirectional Routing,CEBR):无需使用专门的控制分组,采用源路由方式以较小开销建立从Sink节点通往传感器节点的下行路由;通过跨层信息共享,定期采集节点剩余能量信息并按需发布;设计使用含跳数和节点剩余能量的合成路由度量标准,减少节点能量和网络带宽消耗的同时均衡节点能耗;结合RSSI(Received Signal Strength Indication)测距实现节点发射功率控制从而在数据及查询分组发送过程中节约节点能量。理论分析表明了CEBR的有效性;仿真结果显示:与现有的典型相关算法相比,CEBR能够在建立双向梯度路由的前提下,至少降低34.5%的归一化控制开销和27.12%的数据分组平均能耗,并使网络生存期延长18.98%以上。     

SB60型变频器应用于风机控制变频调速系统

在工矿企业中,风机设备应用广泛,可应用于锅炉燃烧系统、通风系统和烘干系统等.变频器的运行控制方式采用U/f控制方式.变频器外部FWD控制模式,选择X4、X5端子控制端子的通断实现变频器的升降速,变频器一旦发生故障,风机由变频运行切换为工频运行的控制,从而实现风机工频运行.     

Machine learning in vehicular networking: An overview

As vehicle complexity and road congestion increase, combined with the emergence of electric vehicles, the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential. The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks. Particularly, 5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities. Machine Learning (ML), a powerful methodology for adaptive and predictive system development, has emerged in both vehicular and conventional wireless networks. Adopting data-centric methods enables ML to address highly dynamic vehicular network issues faced by conventional solutions, such as traditional control loop design and optimization techniques. This article provides a short survey of ML applications in vehicular networks from the networking aspect. Research topics covered in this article include network control containing handover management and routing decision making, resource management, and energy efficiency in vehicular networks. The findings of this paper suggest more attention should be paid to network forming/deforming decision making. ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies, such as mobile edge computing for real-world deployment. Research datasets, simulation environment standardization, and method interpretability also require more research attention.     

风电场随机时间序列法功率预测的探究

我国能源资源总量相对较少,人均占有率低;能源消费增长较快,能源形势比较严峻;能源结构不合理,可再生能源开发利用不充分。从技术成熟性和经济可行性看,风电在可再生能源中具有最好前景。近十几年来其发电成本已得到大幅下降,风电己具有与传统常规电源发电竞争的潜力。随着我国能源结构的调整,无污染可再生的风电日益得到重视。风电场功率预测一直是风电研究热点和技术必需。     

Energy cost and error performance of range-aware, anchor-free localization algorithms

Many applications that use sensor data from a wireless sensor network (WSN) require corresponding node position information as well. Therefore, it is not surprising that a common figure of merit for localization algorithms is the accuracy of the position estimate produced. Similarly, the amount of communication required by a localization algorithm is often of paramount interest as well since it is common knowledge that communication expends the most energy in a WSN. However, localization algorithms seldom characterize their communication cost. Furthermore, when they do it is often merely qualitative and is typically described as “expensive”. For two types of range-aware, anchor-free localization algorithms we found the opposite to be true. Rather than being expensive, the communication costs were quite modest. So much so that we maintain range-aware, anchor-free localization algorithms should be chosen on the basis of the accuracy required by the intended application independent of the communication cost.In this paper, we examine the effect of node degree, node distribution, range error and network size on distance error and communication cost for both incremental and concurrent versions of range-aware, anchor-free algorithms. The concurrent algorithm is twice as accurate as the incremental, but less efficient. Furthermore, node degree influences the energy cost of the algorithms the most, but neither algorithm uses more than a surprisingly small 0.8% of a 560 mA h battery. This result indicates less energy efficient localization algorithms can be tolerated, especially if they provide better accuracy. Furthermore, if energy does need to be conserved, there is not much savings available within the localization algorithm and savings must be found in other areas such as the MAC protocol or routing algorithm.     

论细胞神经网络的理论基础

本文详细讨论了由美国伯克莱加利福尼大学电子学家L.O.Chua教授等首创的细胞神经网络理论基础,具体指出了其存在的缺陷及所构造的能量函数用来研究稳定性失效的原因,建议了理论研究的进一步方向。     

基于压电效应的智能控制广场自发电系统

本文介绍了一种基于压电效应的智能控制广场自发电系统,核心技术是通过压电材料的压电效应将机械能转化为电能,并通过能量转换模块和能量储存模块将电能储存于锂电池中,给主控制器模块即Arduino供电,主控制器模块驱动灯光和音频输出模块.整个装置所需的电能都通过压电效应产生,起到节能环保的作用.