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.     

矿用柴油机车中新型无源液位传感器设计

针对目前矿用柴油机车液位传感器无法动态调整水箱剩余水位的报警高度、可靠性较差等问题,提出一种新型无源液位传感器的设计方案。该液位传感器采用干簧管和电阻阵列等无源器件,实现了对水箱水位高度的连续监测,并可设置报警深度,可靠性和耐高温性能较好。     

A comparative study between dynamic adapted PSO and VNS for the vehicle routing problem with dynamic requests

Combinatorial optimization problems are usually modeled in a static fashion. In this kind of problems, all data are known in advance, i.e. before the optimization process has started. However, in practice, many problems are dynamic, and change while the optimization is in progress. For example, in the dynamic vehicle routing problem (DVRP), new orders arrive when the working day plan is in progress. In this case, routes must be reconfigured dynamically while executing the current simulation. The DVRP is an extension of a conventional routing problem, its main interest being the connection to many real word applications (repair services, courier mail services, dial-a-ride services, etc.). In this article, a DVRP is examined, and solving methods based on particle swarm optimization and variable neighborhood search paradigms are proposed. The performance of both approaches is evaluated using a new set of benchmarks that we introduce here as well as existing benchmarks in the literature. Finally, we measure the behavior of both methods in terms of dynamic adaptation.     

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。     

某型装甲车驾驶员终端仿真训练系统设计与实现

针对部队、院校装备教学训练需求,以某型装甲车驾驶员终端为研究对象,研究并分析了某型装甲车辆驾驶员终端结构、工作原理以及对外的接口规范、标准,采用NI公司的虚拟仪表控件、MapInfo公司的MapX地图控件等控件,设计并实现了基于半实物的装甲车驾驶员终端训练系统。系统设计既能满足装备仿真训练,也可以接入实际装备,完成与实际装备的互联和通信,满足部队、院校教学训练的需要,达到了预期的目标。     

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。     

基于机会约束的电动汽车充光储一体化充电站容量优化方法

针对电动汽车充电站建设运营成本高,土地利用率较低,功能单一的缺点,提出了电动汽车充光储一体化充电站的容量优化方法。考虑光伏出力及电动汽车充电负荷的不确定性,研究在不确定性环境下的容量配置问题,以保证优化方案的可靠性。基于机会约束规划,以建设运营成本最小为目标建立电动汽车充光储一体化充电站容量配置模型,通过序列运算理论将模型中含有随机变量的约束进行确定性转化,采用CPLEX作为模型的求解工具。算例验证了所提模型及方法的有效性。