基于Spider卷积的三维点云分类与分割网络

针对传统的卷积神经网络（CNN）不能直接处理点云数据，需先将点云数据转换为多视图或者体素化网格，导致过程复杂且点云识别精度低的问题，提出一种新型的点云分类与分割网络Linked-Spider CNN。首先，在Spider CNN基础上通过增加Spider卷积层数以获取点云深层次特征；其次，引入残差网络的思想在每层Spider卷积增加短连接构成残差块；然后，将每层残差块的输出特征进行拼接融合形成点云特征；最后，使用三层全连接层对点云特征进行分类或者利用多层卷积层对点云特征进行分割。在ModelNet40和ShapeNet Parts数据集上将所提网络与PointNet、PointNet++和Spider CNN等网络进行对比实验，实验结果表明，所提网络可以提高点云的分类精度和分割效果，说明该网络具有更快的收敛速度和更强的鲁棒性。     

Multi-period wind integrated optimal dispatch using series PSO-DE with time-varying Gaussian membership function based fuzzy selection

Wind power has emerged as the most promising option for providing sustainable eco-friendly power supply to the modern world. Due to its unpredictable nature, integration of wind power into the conventional power grid is a very challenging task having dynamic characteristics. Due to the inherent uncertainty associated with wind availability, additional spinning reserve needs to be scheduled in order to maintain security and supply reliability. Multi-period multi-objective optimal dispatch (MPMOOD) is presented for wind integrated power system with reserve constraints. The complex relationship between wind power availability, spinning reserve allocation and their impact on economic/environmental cost are analysed using an elaborate model.A new multi-objective Series PSO-DE (SPSO-DE) hybrid algorithm is proposed where the two paradigms, differential evolution (DE) and particle swarm optimization (PSO) share domain information and maintain a synergistic cooperation to overcome their individual weaknesses. For multi-objective (MO) problems, the selection operation in SPSO-DE is replaced by a 5-class time-varying fuzzy selection mechanism (TVFSM) to avoid saturation and to increase Pareto diversity. To promote convergence towards the central part of the Pareto front and to quickly isolate the boundary solutions, Guassian membership function is employed. Elitism is applied to preserve good solutions and momentum operation is used to stop premature convergence. The proposed method expedites the search for the best solution, i.e. the solution which satisfies all the objectives of the MO problems. To test the performance and computational efficiency, the proposed method is applied on two standard test power systems.     

GCAT-U-Net嵌入全局坐标注意力机制的遥感地块分割网络

遥感影像的地块背景特征复杂,当前地块分割方法不能较好地处理模糊的边缘信息,导致分割精度不理想;文章利用注意力机制处理地块特征,提出了一种基于全局坐标注意力机制的遥感地块分割网络:GCAT-U-Net;该方法在U-Net网络基础上嵌入了全局坐标注意力机制,加强了深度神经网络对于遥感影像数据中重要特征的关注度;在公开的GID数据集上的实验结果表明,文章提出的模型将准确率从0.9041提升到了0.9227,比传统U-Net网络提高了2百分点;结合特征自身重要性和特征位置信息的全局坐标注意力机制有助于更精确的目标定位,其输出相较于嵌入单一注意力机制,地块边界更为清晰,提升效果更为显著。     

An adaptive sliding mode observer based near-optimal OER tracking control approach for PEMFC under dynamic operation condition

Tracking control of oxygen excess ratio (OER) is crucial for dynamic performance and operating efficiency of the proton exchange membrane fuel cell (PEMFC). OER tracking errors and overshoots under dynamic load limit the PEMFC output power performance, and also could lead oxygen starvation which seriously affect the life of PEMFC. To solve this problem, an adaptive sliding mode observer based near-optimal OER tracking control approach is proposed in this paper. According to real time load demand, a dynamic OER optimization strategy is designed to obtain an optimal OER. A nonlinear system model based near-optimal controller is designed to minimize the OER tracking error under variable operation condition of PEMFC. An adaptive sliding mode observer is utilized to estimate the uncertain parameters of the PEMFC air supply system and update parameters in near-optimal controller. The proposed control approach is implemented in OER tracking experiments based on air supply system of a 5 kW PEMFC test platform. The experiment results are analyzed and demonstrate the efficacy of the proposed control approach under load changes, external disturbances and parameter uncertainties of PEFMC system.     

多维不确定性影响下的水期权交易最优策略研究

为解决来水预测不确定性和水价不确定性给用水户带来的风险对冲问题,引入对冲性的水期权交易模式,建立了以用水户期望收益最大为目标的水期权交易模型,得到了最优的期权交易策略应满足的条件,并利用二分法计算得到了最优的期权交易策略,探究了来水不确定性与水价不确定性对期权交易策略的影响。结果表明,来水预测误差和水价的波动越大,用水户的期望收益越大,最优的期权交易策略能有效降低来水预测不确定性和水价不确定性带来的风险。     

某型运输机飞行状态下冷凝器风道性能

以某型运输机蒸发循环制冷系统用冷凝器为研究对象，分析了运输机与直升机、小型通航飞机蒸发循环制冷系统工作环境的区别，应用Star CCM+软件进行了仿真建模，通过对冷凝器风道的流体仿真分析，阐述了飞行状态下引起蒸发循环制冷系统压力故障的原因，在此基础上提出了冷凝器风道优化方案，并应用流体仿真分析的方法，验证了优化方案的有效性，最后在该运输机上进行了试飞验证。     

内热型超声雾化溶液再生系统最优内热量的研究

基于质量守恒、能量守恒定律，建立了内热型超声雾化溶液再生系统（IH-UARS）的再生性能预测模型并进行了充分的实验验证，通过研究不同内热量下IH-UARS的再生性能及其变化规律，寻求系统所需的最佳内热量并明确其可能的影响因素。结果表明：IH-UARS系统存在最优的内热量范围，使其再生系统性能最佳；所需最优内热量随着再生溶液流量增大呈显著的对数增长，但受空气流量的影响较弱；在该研究中的标准工况下，IH-UARS所需最优内热量约为275~350 W。此外研究发现：内热量的增长有益于促进初始浓度较高的溶液进一步浓缩再生，如当IH-UARS中内热量增至800 W时，其初始浓度为36%的溶液比24%的溶液浓度增量指标改善幅度高37%。研究所得结果可对提高溶液再生性能及经济性提供积极参考。     

Optimal design and operation of ammonia decomposition reactors

The design and steady-state operation of a packed bed reactor with tubular geometry is optimized. Direct optimal control methods are used. Two objective functions are considered: (i) minimization of the ammonia mass fraction at reactor outlet and (ii) minimization of the heat flux necessary to reach a predefined value of the ammonia mass fraction at reactor outlet. The optimization process is performed by using different controls, that is, the space distributions of (1) tube wall temperature T_w , (2) circular tube diameter D_tube , and (3) diameter d_p of the catalyst spherical particles. Results for the first objective function are as follows. The optimal distribution of T_w along the reactor consists of a constant temperature or a U-shaped space temperature distribution, respectively, depending on the allowed range of variation of T_w . The optimal space distribution of D_tube (or, in other words, the shape of the reactor tube) depends of T_w . For smaller values of T_w the tube is narrower at inlet and larger at outlet while the reverse situation happens for larger values of T_w . For lower T_w values, particles with smaller diameter d_p are placed at reactor inlet while when higher values of T_w are considered, particles with larger d_p are placed at reactor inlet. When both D_tube and d_p are used as controls, the optimization results are generally different from the results obtained from one-control optimization. Results for the second objective function are as follows. The optimal space distribution of T_w starts with high values at reactor inlet. Next, the temperature decreases abruptly towards a minimum (which is lower for longer tubes). Finally, the temperature increases smoothly towards a maximum near the reactor outlet. The required heat flux slightly decreases by increasing the tube length. The optimal D_tube ranges between its maximum allowed value (at reactor inlet) and its minimum allowed value (at reactor outlet). The best performance is obtained for catalyst particles of the smallest allowed diameter.     

随机森林算法在温度分布重建中的应用

为提高温度分布重建精度,提出了使用随机森林算法对温度测点进行优化布置的新方法。 将测点位置作为样本特征, 以不同的测点布置方式及其对应的重建误差作为样本数据集。 使用样本数据集构建随机森林模型,评估样本特征重要性,根据 特征重要性排序实现温度测点的优化布置。 设定仿真实验与燃烧实验验证优化布置算法的可行性与有效性。 分析实验数据, 所提出的优化布置算法相对于原有算法,重建精度提升了 20%以上。 研究结果表明,随机森林算法在温度分布重建中具有良好 的应用价值,并为解决工业实际问题提供了新思路。     

工业园区混合能源优化配置方法应用与实践

为了充分挖掘多能源之间的互补潜力,使工业园区混合能源系统的配置方式由传统粗放式转变为精确优化配置并降低系统配置方案的保守性,基于粒子群联合CPLEX求解算法对工业园区的系统配置和运行调度方案进行分析优化,并以某生物园区为例进行了实例分析,得到了目标函数下的最优设计方案和运行策略。仿真分析结果表明,优化配置方法的应用能够协调冷热电多种能源的混合利用,提升系统的综合经济性。同时,为工业园区能源供应项目的规划、设计提供参考性支撑,丰富设计手段。