无线传感器网络中一种能量高效的分布式分簇算法

提出了一种适用于无线传感器网络的能量高效的分布式分簇算法（ＥＥＤＣ）,该算法使具有较高剩余能量及距离基站较近的节点有更大的机会成为簇头．理论分析表明该算法通信开销较小,而且有效地均衡了节点的能量消耗．为了确保ＥＥＤＣ 的正确性、完整性和可靠性,利用形式化方法———着色网对其关键属性进行建模和分析．仿真结果表明,ＥＥＤＣ 有效地延长了网络生命周期,提高了网络的能耗效率．

     

基于不均匀圆环模型的无线传感器网络分簇算法

提出一种基于不均匀圆环模型的分簇算法．该算法根据节点与基站的距离来优化簇的规模,进 一步保证了网络内节点能量负荷的均衡性;还给出了计算簇规模的方法．实验表明,本文所提算法能有效地 延长网络的生命周期．     

基于蚁群的无线传感器网络分簇路由算法

在研究经典低能量自适应分簇路由算法的基础上,提出基于蚁群的无线传感器网络分簇路由算法。该算法将蚁群算法应用到簇间路由机制中,寻找簇头到基站的最佳路径,使得离基站较远的簇头节点沿着最佳路径传输信息,有效地减少了簇头节点的能量开销。同时,在簇头选举时,该算法不仅考虑簇头节点的剩余能量,而且兼顾簇头与簇头之间的距离,使得簇头分布更加均匀。仿真结果表明,该算法和LEACH及DADC算法相比,有效地均衡了网络能量消耗,并延长了网络生命周期。     

基于能量与能耗速度的WSN分簇算法

无线传感器网络的最大局限是能量有限.为了高效利用网络能量、均衡网络负载,提出了一种基于能量与能耗速度的分簇算法.其中节点能耗速度是一个带有能耗预测信息的参量,利用它可以更有效地优化簇头选择与簇规模,该算法根据这两个参数来优化簇头的选择,能有效地延长节点的生存时间;同时,根据簇头节点与基站的距离、当前能量和能耗速度对簇规模进行约束和优化,进一步保证了簇之间的负载均衡.仿真实验表明改进后的算法有效地延长了网络的生存时间.     

无线传感器网络能量均衡的多sink分簇路由算法

针对无线传感器网络中传感器节点能量有限以及节点能耗不均衡的问题,提出了一种基于能量均衡的多sink分簇路由算法（EBMCR）。该算法在簇头选择阶段,综合考虑了节点的剩余能量级和节点到sink的距离等因素选择簇头节点;在簇间通信过程,采用多跳传输的方式,综合考虑了路径能量消耗、路径最小剩余能量和节点到sink的跳数等因素,选择节点到多个sink的最优路径。仿真结果表明,该算法能够有效地均衡网络能量,延长网络生命周期。     

基于地理信息静态分簇的无线传感器网络路由算法

通过路由算法减少节点的能耗、延长网络的寿命是无线传感器网络的研究重点之一。探讨了能量高效的分层无线传感器网络路由算法,提出一个基于地理位置信息静态分簇,根据节点剩余能量及节点在簇内的位置选择簇头的分层路由算法(GSCH),适用于静态无线传感器网络。仿真实验结果表明该路由算法有效地减少分簇成形及簇首选择的能耗,延长了整个网络的寿命。     

无线传感器网络中一种能量自适应的簇首选择机制

提出一种能量自适应的簇首选择机制．算法通过综合考虑候选节点的剩余能量级、通信能量损耗等参数来优化簇首的选择，从而有效地避免了低能量节点被选为簇首的可能性，进一步保证网络内节点能量负载的均衡性．仿真结果表明，改进的簇首选择机制能够很好地均衡网络能量损耗分布，有效延长近30%的网络生存时间．     

基于作用力模型的传感器网络簇型协议

现有的无线传感器网络簇型算法忽视了簇头和簇成员之间的通信代价不均衡和"孤立簇头"等问题.提出了一种基于作用力模型的移动簇型协议,该协议在簇头选举时兼顾节点剩余能量和节点密度;在簇头选举后,该协议使簇头节点根据基于簇成员剩余能量和距离的作用力模型自适应移动,以均衡簇头和簇成员之间的通信代价.另外,为了实现簇头节点之间的全连通,提出了中继节点选举算法.实验结果显示:该协议能够有效地均衡网络能耗,进一步提高网络稳定性.     

一种检测无线网络违规行为的改进退避算法

无线媒体访问控制（ＭＡＣ）协议通常使用分布式竞争机制来共享无线信道,但在动态和开放的网络环境中,部分违规节点会有意识地抢占信道以获取更多的信道资源．为此,通过对ＩＥＥＥ８０２．１１协议的ＤＣＦ 机制进行修改,提出一种改进的退避算法,可实现对网络中违规行为节点的有效检测,并通过惩罚机制加以纠正．仿真结果表明,该方法能够更有效地检测出无线网络中的违规行为,提高整个网络的吞吐量．     

一种基于负载均衡无线传感器网络节能分簇算法

由于无线传感器节点的能量是有限的,如何延长节点和网络的工作寿命成为一个很关键的问题.LEACH算法采用本地簇头随机轮转机制将能量负载分担给网络中的所有传感器节点,但是,簇头选举的随机性和簇内节点数目的不均衡可能导致某些节点过快耗尽能量而死亡.本文提出了一种基于负载均衡的簇头选举方案,采用粒子群优化(PSO)算法先行分簇,然后考虑能量和距离再推举出簇头.仿真结果表明,该算法比LEACH更有效地平衡了能量消耗,并显著延长了网络的存活时间.     

无线传感器网络的节能分布式分簇算法

针对无线传感器网络的异构特性,提出一种能量有效的分布式分簇算法EEDC。预先选择剩余能量较多的节点作为竞争簇头的候选簇头节点,以簇内通信代价作为候选节点竞争最终簇头的竞争参数,选择剩余能量高且通信代价低的节点作为最终的簇头节点。理论分析与仿真实验证明,EEDC能产生均匀分布的簇头集合,有效延长网络寿命。     

无线传感器网络集中控制分簇组网算法研究

为了实时监测森林气象因子,查找火灾隐患,将无线传感器网络更有效地应用到实际领域中,本文提出了一种可以有效延长网络生命周期,自组织成簇,自适应变化的网络拓扑结构的能量有效集中控制分簇算法EEDC（energy-efficient distributed clustering hierarchy）。该算法高效利用有限能源,均衡全网能量,使负载平衡,防止节点失效。     

刚度和等效质量对SEA能量放大特性的影响

对简单的SEA(series elastic actuator)模型采用解析法求解,得出了驱动器输出能量曲线,分析了其能量放大特性,解决了Paluska的SEA能量分析的无解析解问题.并针对Paluska分析中对动力学模型的过度简化,引入了传动机构的等效质量的概念,完善了SEA的能量放大作用模型.为了便于求解和分析,对SEA能量放大模型各参数进行归一化处理,并在归一化条件下通过频域转换方式进行数值求解,得出该条件下的驱动器输出能量曲线,从而得到了该模型下的SEA能量放大特性及其随系统参数(弹簧刚度、传动机构等效质量)的变化规律.     

Peeking inside the black-box: Explainable machine learning applied to household transportation energy consumption

Sustainability policies to mitigate transportation energy impacts on the urban environment are urgently needed. Energy prediction models provide critical information to decision-makers who develop sustainability policies to reduce energy use and emissions. We present a transportation energy model (TEM) that uses Explainable Artificial Intelligence (XAI) methods to predict household transportation energy consumption in this study. The TEM model uses data-driven approaches for household transportation energy prediction. Machine learning techniques in artificial intelligence (AI) predictive modeling have become popular due to their ability to capture nonlinear and complex relationships. On the other hand, developing comprehensive understanding the inference mechanisms in AI models and ensuring trust in their predictions is challenging. This is because AI models are mostly of high complexity and low interpretability; in other words, they are black-box models. This study presents a case study of how model transparency and explanation can be generated using the Local Interpretable Model-Agnostic Explanation (LIME) to support advanced machine learning techniques in the transportation energy field. The methodology has been implemented based on the Household Travel Survey (HTS) data, which is used to train the artificial neural network with a relatively high degree of accuracy. The importance and effect (local explanation) of HTS inputs (such as household travel, demographics, and neighborhood data) on transportation energy consumption for specific traffic analysis zones (TAZs) are analyzed. The results are valuable to promote intelligent and user-friendly transportation energy planning models in urban regions across the world.     

含电动汽车充电站的多端直流微电网研究

为未来大规模能源互联网的形成以及多种新能源的接入提供技术支撑,设计直流微电网的拓扑结构,提出了一种四端口环网的直流电网拓扑结构,实现交流电网、储能单元、直流负荷、风力发电和光伏发电与直流电网的互联。首先研究了交流电网与直流电网的接口方式和相关技术参数,提出了光伏发电、风力发电和储能单元等接口的技术配置。其次,研究了整个直流微电网的启停时序,设计了直流电网的接线方式、电压等级和容量,最后基于MATLAB Simulink平台搭建了直流园区系统仿真模型,然后对典型工况进行了仿真分析：（1）储能单元由放电到能量为零;（2）储能单元由充电到能量充满;（3）VSC1变换器功率反转;（4）负荷跳变;（5）储能单元由放电到充电。这些工况基本包涵了直流微电网可能出现的运行状态,对直流微电网的运行管理有较高的参考价值。     

A Clustering-tree Topology Control Based on the Energy Forecast for Heterogeneous Wireless Sensor Networks

How to design an energy-efficient algorithm to maximize the network lifetime in complicated scenarios is a critical problem for heterogeneous wireless sensor networks (HWSN). In this paper, a clustering-tree topology control algorithm based on the energy forecast (CTEF) is proposed for saving energy and ensuring network load balancing, while considering the link quality, packet loss rate, etc. In CTEF, the average energy of the network is accurately predicted per round (the lifetime of the network is denoted by rounds) in terms of the difference between the ideal and actual average residual energy using central limit theorem and normal distribution mechanism, simultaneously. On this basis, cluster heads are selected by cost function (including the energy, link quality and packet loss rate) and their distance. The non-cluster heads are determined to join the cluster through the energy, distance and link quality. Furthermore, several noncluster heads in each cluster are chosen as the relay nodes for transmitting data through multi-hop communication to decrease the load of each cluster-head and prolong the lifetime of the network. The simulation results show the efficiency of CTEF. Compared with low-energy adaptive clustering hierarchy (LEACH), energy dissipation forecast and clustering management (EDFCM) and efficient and dynamic clustering scheme (EDCS) protocols, CTEF has longer network lifetime and receives more data packets at base station.      

Periodic charging planning for a mobile WCE in wireless rechargeable sensor networks based on hybrid PSO and GA algorithm

Previous studies of periodic charging planning in Wireless Rechargeable Sensor Networks (WRSNs) assumed that the traveling energy of a mobile Wireless Charging Equipment (WCE) has sufficient energy for charging travel and the energy depletion rate at each sensor is identical. These assumptions, however, are not realistic. In fact, the traveling energy of the WCE is limited by the energy capacity of the WCE and the energy consumptions at different sensor nodes are imbalanced. In this paper, a periodic charging planning for a mobile WCE with limited traveling energy is proposed. With the optimization objective of maximizing the the docking time ratio, this periodic charging planning ensures that the energy of the nodes in the WRSN varies periodically and that nodes perpetually fail to die. To deal with the problem, a Hybrid Particle Swarm Optimization Genetic Algorithm (HPSOGA) is proposed due to the NP-Hard of the problem. Extensive simulations have been conducted, the experimental results indicate that the proposed periodic charging planning can avoid node deaths and keep the energy of sensor nodes varying periodically. Compared with the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), the algorithm HPSOGA outperforms both of these two algorithms empirically.     

Operation mode control of a hybrid power system based on fuel cell/battery/ultracapacitor for an electric tramway

This paper focuses on describing a control strategy for a real tramway, in Zaragoza (Spain), whose current propulsion system is to be replaced by a hybrid system based on fuel cell (FC) as primary energy source and batteries and ultracapacitors (UCs) as secondary energy sources. Due to its slow dynamic response, the FC needs other energy sources support during the starts and accelerations, which are used as energy storage devices in order to harness the regenerative energy generated during brakings and decelerations. The proposed energy management system is based on an operation mode control, which generates the FC reference power, and cascade controls, which define the battery and UC reference powers in order to achieve a proper control of the DC bus voltage and states of charge (SOC) of battery and UC. The simulations, performed by using the real drive cycle of the tramway, show that the proposed hybrid system and energy management system are suitable for its application in this tramway.     

Hybrid Renewable Energy Source Combined Dynamic Voltage Restorer for Power Quality Improvement

In this paper, the hybrid photovoltaic-thermoelectric generator (PV-TEG) combined dynamic voltage restorer (DVR) system is proposed for the power quality disturbances compensation in a single-phase distribution system. The stable and precise level of input voltage is essential for the smooth and trouble-free operation of the electrically sensitive loads which are connected at the utility side to avoid system malfunctions. In this context, the hybrid PV-TEG energy module combined DVR system is proposed in this paper. With the support of the hybrid energy module, the DVR will perform the power quality disturbances compensation effectively with needed voltage and /or power. In the proposed system, the PV and TEG energy sources are connected electrically in series to produce adequate voltage for the DVR operation and the fractional factor-based variable incremental conduction (FFVINC) maximum power point tracking (MPPT) control algorithm is employed to extract the possible maximum power from the PV array. The intelligent fuzzy logic controller (FLC) is chosen for implementing the MPPT control algorithm. The half-bridge voltage source inverter (VSI) circuit and in-phase voltage compensation technique are used in the DVR for better power quality disturbances compensation. The performance and usefulness of the proposed DVR system are investigated by an extensive simulation study with four different modes of operation, the study results are confirmed that the proposed system promptly identifies the power quality disturbances for compensation. Moreover, the investigation proved that the combined PV and TEG energy module can provide better energy efficiency in converting solar irradiation into electricity.     

Process energy analysis and optimization in selective laser sintering

Additive manufacturing (AM) processes are increasingly being used to manufacture complex precision parts for the automotive, aerospace and medical industries. One of the popular AM processes is the selective laser sintering (SLS) process which manufactures parts by sintering metallic, polymeric and ceramic powder under the effect of laser power. The laser energy expenditure of SLS process and its correlation to the geometry of the manufactured part and the SLS process parameters, however, have not received much attention from AM/SLS researchers. This paper presents a mathematical analysis of the laser energy required for manufacturing simple parts using the SLS process. The total energy expended is calculated as a function of the total area of sintering (TAS) using a convex hull based approach and is correlated to the part geometry, slice thickness and the build orientation. The TAS and laser energy are calculated for three sample parts and the results are provided in the paper. Finally, an optimization model is presented which computes the minimal TAS and energy required for manufacturing a part using the SLS process.