Increased BLSS closure using mineralized human waste in plant cultivation on a neutral substrate

The purpose of this work was to study the full-scale potential use of human mineralized waste (feces and urine) as a source of mineral elements for plant cultivation in a biological life support system (BLSS). Plants that are potential candidates for a photosynthesizing link were grown on a neutral solution containing human mineralized waste. Spring wheat Triticum aestivum L., peas Pisum sativum L. Ambrosia cultivar and leaf lettuce Lactuca sativa L., Vitaminny variety, were used. The plants were grown hydroponically on expanded clay aggregates in a vegetation chamber in constant environmental conditions. During plant growth, a determined amount of human mineralized waste was added daily to the nutrient solution. The nutrient solution remained unchanged throughout the vegetation period. Estimated plant requirements for macro-elements were based on a total biological productivity of 0.04 kg day⁻¹ m⁻². As the plant requirements for potassium exceeded the potassium content of human waste, a water extract of wheat straw containing the required amount of potassium was added to the nutrient solution. The Knop’s solution was used in the control experiments.     

军用飞机维修保障信息系统体系结构建模研究

介绍了系统体系结构的建模方法,详细分析了系统体系结构的建模过程,针对军用飞机维修保障业务特点,对军用飞机维修保障信息系统的体系结构建模进行了研究,并给出了一个应用案例。本文的研究结果可为维修保障信息系统的研制提供参考。     

基于PSO-ELM的军用飞机维修保障系统效能评估研究

维修保障系统效能评估是军用飞机精细化、规范化管理的一项重要措施,科学的维修保障系统效能评估是精细化保障的基础,是提升战斗力的保证。针对军用飞机的维修保障系统效能评估问题,在总结军用飞机维修保障系统效能评估体系的基础上,提出基于粒子群优化极限学习机算法(PSO-ELM)的维修保障系统效能评估模型;并通过实例进行验证。结果表明:该算法应用于军用飞机维修保障系统的效能评估是可行的,可为军用飞机维修保障效能评估提供借鉴。     

动态保障结构下多级多层备件配置优化建模

针对作战编队执行长时间、远距离任务期间,编队后方保障站点变更的情况,结合部队维修保障的特点,基于可修复备件多级管理(METRIC)理论,通过计算编队剩余备件分布规律,建立了动态保障体系结构下基于时变可用度的三级两层备件保障模型。以备件储存空间为约束,可用度为目标,建立了分阶段边际优化模型。列举实例,采用分阶段边际算法对备件方案进行优化,对比分析了动态保障结构下和固定保障结构下装备可用度随时间的变化,并采用蒙特卡罗仿真方法对案例进行实验验证。案例结果表明:采取多个后方站点的保障方式能有效提高装备可用度;案例仿真实验结果与解析结果误差在4%以内。模型可为保障决策者制定备件方案提供辅助决策工具。     

Assessing the feasibility of involving gaseous products resulting from physicochemical oxidation of human liquid and solid wastes in the cycling of a bio-technical life support system

The study addresses the possible ways of involving gaseous products produced by “wet” incineration of human wastes mixed with H₂O₂ in an alternating electric field in the cycling of the physical model of a bio-technical life support system (BTLSS). The resulting gas mixture contains CO₂ and O₂, which are easily involved in the cycling in the closed ecosystem, and NH₃, which is unacceptable in the atmosphere of the BTLSS. NH₃ fixation has been proposed, which is followed by nitrification and involvement of the resulting products in the mass exchange of the closed system. Experiments have been performed to show that plants can be grown in the atmosphere resulting from the closing of the gas loop that includes a physicochemical installation and a growth chamber with plants representing the phototrophic compartment of the BTLSS. The results of the study suggest the conclusion that the proposed method of organic waste oxidation can be a useful tool in creating a physical model of a closed-loop integrated BTLSS.     

SVM和神经网络在电能质量扰动分类应用中的对比

提出了一种电能质量动态扰动特征向量的提取方法,分析比较了多种分类器对电能质量动态扰动的分类能力。首先采用小波包分解算法对电能质量信号某一频段内的信息进行精细分解从而提取出特征向量,然后针对该特征向量构造了相应的BP神经网络、学习向量量化(Learning vector quantization,LVQ)神经网络、自组织特征映射(Self-organizing map,SOM)神经网络及支持向量机(Support vector machine,SVM)分类器,并模拟实际电网中的复杂扰动信号提取其特征向量集,对多种分类器的分类能力进行对比。仿真结果表明,在较复杂的电能质量扰动情况中,支持向量机分类器仍能实现对信号的精确分类,对电能质量监测具有很好的应用价值。     

飞机研制的计量保障探讨

计量保障是飞机研制的重要技术基础,本文分析了飞机研制的计量保障需求,探讨了计量保障工作的思路和要点。     

风洞干扰对YF16模型实验数据的影响

本文研究了风洞干扰对ＹＦ１６模型实验数据的影响,特别着重于阻塞效应。结论是：提高大攻角实验数据准确度的关键在于减少和修正由于阻塞效应引起的系统误差。     

基于优化最小二乘支持向量机的襟翼趋势预测

将最小二乘支持向量机(LS-SVM)应用于飞机襟翼状态趋势研究。首先,通过分析飞机襟翼故障与襟翼动作耗时参数的关系,提出了利用动作耗时趋势来确定该系统未来状态的方法。然后,使用最小二乘支持向量机建立耗时回归预测模型,采用最终预报误差(FPE)准则确定回归模型嵌入维数,提出了自适应网格搜索法,优化最小二乘支持向量机的建模参数,从而实现比现有方法精度高,泛化性能好的预测模型。训练和测试样本取自飞行数据记录系统(QAR)中译码襟翼参数值。与神经网络模型的比较实践表明,该方法具有实用价值。     

DDQNC-P:A framework for civil aircraft tactical synergetic trajectory planning under adverse weather conditions

Adverse weather during aircraft operation generates more complex scenarios for tactical trajectory planning,which requires superior real-time performance and conflict-free reliability of solving methods.Multi-aircraft real-time 4D trajectory planning under adverse weather is an essen-tial problem in Air Traffic Control(ATC)and it is challenging for the existing methods to be applied effectively.A framework of Double Deep Q-value Network under the Critic guidance with heuristic Pairing(DDQNC-P)is proposed to solve this problem.An Agent for two aircraft syner-getic trajectory planning is trained by the Deep Reinforcement Learning(DRL)model of DDQNC,which completes two aircraft 4D trajectory planning tasks preliminarily under dynamic weather conditions.Then a heuristic pairing algorithm is designed to convert the multi-aircraft synergetic trajectory planning into multi-time pairwise synergetic trajectory planning,making the multi-aircraft trajectory planning problem processable for the trained Agent.This framework compresses the input dimensions of the DRL model while improving its generalization ability significantly.Sub-stantial simulations with various aircraft numbers,weather conditions,and airspace structures were conducted for performance verification and comparison.The success rate of conflict-free trajectory resolution reached 96.56％with an average calculation time of 0.41 s for 350 4D trajectory points per aircraft,finally confirming its applicability to make real-time decision-making support for con-trollers in real-world ATC systems.