B—P网络逐步聚类法在环境测点优选中的应用

将Ｂ－Ｐ网络原理与逐步聚类分析思想相结合,用于环境测点聚类优选。该方法用于水清河几个监测断面的优选结果是符合客观实际的。     

抽穗期低温对杂交晚粳宁67A／K1722结实率的影响

利用人工气候箱研究了抽穗期低温对杂交晚粳新组合宁67A/K1722开花量、包颈率及结实率的影响。结果表明：低温使抽穗速度明显变慢,开花量减少,包颈率呈现上升趋势,不同温度处理对结实率的影响不同,17℃与21℃处理间差异极显著,而19℃与21℃间差异不显著,不同时期处理及不同处理天数（3d,5d,7d和12d）间结实率差异不显著,表明该组合对抽穗期低温的耐受力较强,对抽穗期耐低温的鉴定指标问题进行了讨论。     

基于L-M神经网络的道路交通噪声预测研究

神经网络具有很强的预测功能.根据石家庄公路交通噪声的实测数据,利用L-M优化算法的多层神经网络预测模型进行道路交通噪声的预测,经检验,计算值与实测值接近,预测精度令人满意.     

水环境质量综合评价的新模型

为检验已订的水环境质量评价标准的合理性,解决各单项水质指标的评估结果的不相容性问题,提高水质等级模型的分辨率,提出了一种新的水质评价模型-逻辑斯谛曲线(LOG)模型,它的水质等级是连续的实数值;根据LOG模型的参数值可以分析各水质指标值对水质等级的影响程度,从而检验原订水质等级标准的合理性;并给出了基于实码遗传算法的LOG建模的实施方案.实例研究说明,这套方案是实用的和通用的,在其它环境质量综合评价中也具有广泛的应用价值.     

氨基化有机-无机杂化整体柱分离富集环境水中五价砷

将四乙氧基硅烷(TEOS)和N-(β-氨乙基)-γ-氨丙基三乙氧基硅烷(AEAPTES)作为共聚前体,采用在毛细管内原位聚合的方式制备得到氨基功能化的有机-无机杂化整体柱,将其作为针式固相微萃取(SPME)介质,建立了现场分离富集环境水中As(V)的SPME方法,优化了有机-无机杂化整体柱富集As(V)的实验条件,并研究了整体柱对As(V)的吸附/洗脱性能和富集能力,实现了环境水样中As(V)的高效、快速、高选择性检测。     

核化危害源项反演技术现状及研究展望

源项反演对缓解和遏制核化危害源、精准预测核化危害时空传输和扩散情况、辅助作战行动和保障决策等具有十分重要的意义.本文从源项反演应用平台和源项反演关键算法(包括欧拉方法、拉格朗日方法、深度学习等)两个方面,对国内外核化危害源项反演技术的现状和二者间的差距进行了总结与分析.研究指出,利用深度学习提高反演精度和速率、从单污染...     

PARAMETER ESTIMATION OF THE NONLINEAR MUSKINGUM MODEL USING HARMONY SEARCH1

ABSTRACT: A newly developed heuristic algorithm, Harmony Search, is applied to the parameter estimation problem of the nonlinear Muskingum model. Harmony Search found better values of parameters in the nonlinear Muskingum model than five other methods including another heuristic method, genetic algorithm, in terms of SSQ (the sum of the square of the deviations between the observed and routed outflows), SAD (the sum of the absolute value of the deviations between the observed and routed outflows), DPO (deviations of peak of routed and actual flows), and DPOT (deviations of peak time of routed and actual outflow). Harmony Search also has the advantage that it does not require the process of assuming the initial values of design parameters. The sensitivity analysis of Harmony Memory Considering Rate showed that relatively large values of Harmony Memory Considering Rate makes the Harmony Search converge to a better solution.     

杂交水稻节水高产栽培技术研究

通过多年的试验、示范证明,杂交稻具有旱能保产、湿能增产的特点,特别是在通气式大田水层栽插、湿润管理的条件下,具有节水、增产、改良土壤的作用。对在灌溉条件较差的丘陵山区和圩区发展水稻生产有积极的作用。     

Integrated energy management strategy of powertrain and cooling system for PHEV

ABSTRACT

Energy management strategy (EMS) is crucial in improving the fuel economy of plug-in hybrid electric vehicle (PHEV). Existing studies on EMS mostly manage powertrain and cooling system separately which cannot get the minimum total energy consumption. This paper aims to propose a novel EMS for a new type of dual-motor planetary-coupled PHEV, which considers cooling power demand and effect of temperature on fuel economy. Temperature-modified engine model, lithium-ion battery model, two motors, and cooling system models are established. Firstly, the separated EMS (S-EMS) is designed which manages powertrain and cooling system separately. Sequentially, after the analysis of thermal characteristics of the powertrain and cooling system, the thermal-based EMS (T-EMS) is then proposed to manage two systems coordinately. In T-EMS, cooling power demand and the charging/discharging energy of motors are calculated as equivalent fuel consumption and integrated into the object function. Besides, a fuzzy controller is also established to deicide the fuel-electricity equivalent factor with consideration of the effect of temperature and state of charge on powertrain efficiency. Finally, the hardware-in-loop experiment is carried out to validate the real-time effect of EMS under the New European Driving Cycle. The result shows that cooling power demand and temperature can significantly affect the fuel economy of the vehicle. T-EMS shows better performance in fuel economy than S-EMS. The equivalent fuel consumption of the cooling system of T-EMS decreases by 27% compared with that of S-EMS. The total equivalent fuel consumption over the entire trip of PHEV using T-EMS is reduced by 9.7%.     

Energy consumption estimation model for dual-motor electric vehicles based on multiple linear regression

ABSTRACT

The drive range of electric vehicle (EV) is one of the major limitations that impedes its universalism. A great deal of research has been devoted to drive range improvement of EV, an accurate and efficiency energy consumption estimation plays a crucial role in these researches. However, the majority of EV’s energy consumption estimation models are based on single motor EV, these models are not suitable for dual-motor EVs, which are composed of more complex transmission mechanisms and multiple operating modes. Thus, an energy consumption estimation model for dual-motor EV is proposed to estimate battery power. This article focuses on studying the operating modes and system efficiency in each operating mode. The limitation of working area of each mode ensures the vehicle dynamic performance, then PSO algorithm is adopted to optimize the torque (speed) distribution between two motors to improve the system efficiency in the coupled driving mode. Finally, the energy consumption estimation model is established by multiple linear regression (MLR). The result shows that the proposed model has a high precision in energy consumption estimation of dual-motor EV.