Seasonal variation in light penetration and subsurface chlorophyll-α in southern Lake Michigan observed by a glider

The Cooperative Institute for Great Lakes Research (CIGLR) in collaboration with the Great Lakes Observing System and National Oceanic and Atmospheric Administration, Great Lakes Environmental Research Laboratory (NOAA GLERL) deployed an autonomous underwater glider in southern Lake Michigan several times per year between 2012 and 2019 to collect offshore (>30 m depth) limnological measurements, including temperature, photosynthetically active radiation (beginning during 2015), and chlorophyll-α fluorescence. From these data, we calculated mixed layer depth, several measures of light penetration (diffuse attenuation coefficient, first optical depth, euphotic zone depth), and depth of the subsurface chlorophyll-α maxima. During summer, mean offshore mixed layer depth was typically 10–15 m, K_d for PAR was 0.1–0.17 m^?1, first optical depth was 6–9 m, euphotic zone depth was 35–40 m, and depth of subsurface chlorophyll-α maxima was 30–35 m. We also observed substantial spatial and temporal variation in these values across the basin and within and among seasons. Glider-based observations provide a wider horizontal and vertical perspective than other methods (e.g., ship- and satellite-based observations, buoys, and fixed moorings), and are therefore a valuable, complementary tool for Great Lakes limnology. The set of observations reported here provide seasonal and basin-scale information that may help to identify anomalies useful for future glider-assisted investigation into the role of biophysical processes in Great Lakes limnology and ecology.     

Conceptual digital twin modeling based on an integrated five-dimensional framework and TRIZ function model

Digital twin represents a fusion of the informational and physical domains, to bridge the material and virtual worlds. Existing methods of digital twin modeling are mainly based on modular representation, which limits guidance of the modeling process. Such methods do not consider the components or operational rules of the digital twin in detail, thereby preventing designers from applying these methods in their fields. With the increasing application of digital twin to various engineering fields, an effective method of modeling a multi-dimensional digital twin at the conceptual level is required. To such an end, this paper presents a method for the conceptual modeling of a digital twin based on a five-dimensional digital twin framework to represent the complex relationship between digital twin objects and their attributes. The proposed method was used to model the digital twin of an intelligent vehicle at the concept level.     

机动车车牌识别中SVM的应用

支持向量机是一类新型机器学习方法,成功应用于各个领域,算法在分类性能上优于其它学习算法。该文在字符特征提取基础上,将支持向量机理论应用于车牌识别系统,提高识别率和识别速度,具有很好的推广价值。     

特种车辆维修空间可达性评价方法研究

可达性作为维修性的定性指标,缺少定量评价的方法。传统方法只针对装备的整体进行定性评价,不能对不同的等级维修和单个维修动作进行评价。通过对维修性设计中的结构性因素进行分析,得到了空间可达性的主要影响因素。在虚拟维修仿真平台的基础上对工具旋转角度和作业空间比进行了量化评分,给出了针对维修作业过程中单个零部件拆卸的空间可达性评价方法。单个动作评价方法为等级维修的整体空间可达性评价提供了数据基础,并为新型特种车辆的维修方案论证和研制的空间可达性评价提供了手段。     

一种应用于城市环境中的车载自组网广播算法

针对城市环境中车载自组网信息发送面临的问题,本文提出一种可靠的车载自组网广播算法。首先,结合节点间的位置信息与移动方向将节点分类,基于距离机制选择中继节点,实现多跳广播;在十字路口处,为解决消息在各个路口进行广播,结合道路拓扑,将节点分为前向节点与后向节点两类;最后,针对车辆密度稀疏状况而出现通信空洞的问题,采用携带转发策略解决,节点主动探测空洞的发生,并验证消息发送方向上有节点进入通信范围,再次进行消息广播。仿真实验表明,算法在数据包延时、数据包成功发送率以及发送单一数据包平均负荷等方面具有较好的性能。     

基于种群多样性的自适应PSO算法求解VRPSPD问题

为有效求解逆向物流车辆路径(VRPSPD)模型,本文提出一种基于种群多样性的自适应PSO算法(SDAPSO)。在SDAPSO运行时,根据种群多样性,自适应地对种群中运行较差的粒子进行扰动操作,提升这些粒子向最优解收敛的能力;同时,对全局最优粒子进行概率扰动,以增加种群的多样性。标准检测函数的仿真结果表明SDAPSO算法是对基本PSO算法的有效改进。在对VRPSPD模型求解中,通过与其它粒子群算法相比,表明SDAPSO是求解该类问题的一种有效方法。     

基于交通信息和配电网全成本电价的电动汽车充电负荷调度方法

电动汽车的集中充电行为会在一定程度造成交通拥堵,为解决这一问题,综合考虑交通信息和电网信息,基于边际定价理论得到配电网节点边际电价和交通阻塞成本,构建电动汽车充电的全成本电价模型。在此基础上,提出一种考虑交通信息和配电网全成本电价的电动汽车充电负荷调度方法。通过仿真算例验证,该方法在满足电动汽车充电需求的同时可以有效缓解交通拥堵。     

基于改进深度强化学习的智能微电网群控制优化方法

针对微电网群控制的经济效益、负荷波动以及碳排放问题,提出一种基于改进深度强化学习的智能微电网群运行优化方法。首先,计及分布式电源、电动汽车及负荷特性,提出微电网的系统模型。然后,针对微电网群的运行特点,提出4个系统优化目标和5个约束条件,并且引入分时电价机制调控负荷运行。最后,利用改进深度强化学习算法对微电网群进行优化,合理调控多种能源协同出力,调整负荷状态,实现电网经济运行。仿真结果表明了所提方法的有效性,与其他方法相比,其收益较高且碳排放量较小,可实现系统的经济环保运行。     

基于模糊逻辑的电动汽车双源混合储能系统能量管理策略

针对电动汽车行驶过程中电池放电电流过大导致的电池容量衰减问题,构建了由锂离子动力电池、超级电容和多端口DC/DC变换器构成的全主动式混合储能系统,其中电流环控制器和电压环控制器分别控制输出电流和直流母线电压。结合超级电容SOC、整车需求功率和车速情况,根据建立的45条模糊控制规则,模糊逻辑控制器调节锂离子动力电池和超级电容的充放电功率,在车辆峰值功率需求较高时避免了高频电流波动对动力电池寿命的影响。同时在功率需求较低时,动力电池给超级电容充电。在HWFET工况下的实验结果表明所提出的全主动式双能量源混合储能系统和基于模糊逻辑的能量管理策略能够有效保护锂离子动力电池免受大电流波动影响,从而达到延长电池寿命的作用。     

电动车制动能量再生反馈控制研究

研究了电动汽车制动过程中的能量回馈，设计了混合励磁无刷电动机在汽车制动过程的能量再生反馈控制系统。以脉宽调制调压调速系统为例，研究各种制动态下的能量回馈过程。并将其安装在EV6600电动汽车上进行试验，结果表明：该控制系统可达到10%左右的能量回收效率。