一种游船多模混合推进动力系统设计

本文提出了一种游船多模混合推进动力系统,该系统以电力推进系统为基础,以柴电、锂离子电池、太阳能电池等作为动力源,具有噪音低、效率高、绿色环保等特点。文中对系统指标、系统方案、系统关键技术等进行了介绍,并分析了系统的可改进之处。

基于线性反馈的多模混合可重构PUF电路设计

物理不可克隆函数(Physical Unclonable Function,PUF)是集成电路领域的特殊识别技术。通过提取芯片制造过程中随机工艺偏差,产生无限多个特殊的密钥。通过对存储型PUF电路、线性反馈移位寄存器和仲裁器PUF电路进行研究,提出一种基于线性反馈的多模混合可重构PUF方案。首先,该方案利用存储型随机源产生随机数据,将其作为自选反馈网络的选择信号,完成反馈网络的随机选择功能。然后,线性反馈移位寄存器根据反馈信号产生随机数序列,作为仲裁器PUF的n位路径选择信号,在时钟信号控制下,实现可重构PUF的串行密钥输出。在TSMC 65 nm CMOS工艺下仿真验证,结果表明所设计的PUF电路随机性达到96. 2%,唯一性达到50. 8%。

面向城市信号灯环境的多模混合动力汽车经济性驾驶研究

以智能网联环境下的多模混合动力汽车作为研究对象,开展了面向城市道路信号交叉口的经济性驾驶研究。首先,基于车联网信息获取信号灯相位与计时数据,根据信号灯位置与道路限速等约束搭建多个信号交叉口路网模型,建立了面向控制的多模混合动力系统部件模型与整车能耗模型;其次,基于庞特里亚金极小值原理解决单车经济性驾驶问题,针对红绿灯路口进行场景分析,规划车辆在不停车通过条件下的有效速度范围,分析了连续信号交叉口的经济性速度优化问题;最后,基于模型预测控制求解多模混合动力汽车的最优能量管理问题,同时引入模式切换惩罚项以提高多模混合动力系统的运行平顺性。与全局最优的动态规划型能量管理相比,新控制策略取得了3.767 L/100 km的近优燃油经济性。

多源异构天网大数据多模混合存储体系设计

多源异构大数据存储具有广泛的应用前景,如何能够对海量数据进行高效的存储,成为数据应用的关键点。

基于系统效率最优的多模混合动力系统构型选择及匹配设计研究

随着中国政府对车辆油耗限值的要求越来越低,2020年达到5L/100km,2025年达到4L/100km,因此越来越多的中国OEM开始研发高节油率的混合动力系统。这其中,以丰田、通用为代表的具备功率分流功能的混动系统引起了越来越多的关注。但丰田的混动系统在发动机工作时只能以功率分流的形式工作,不具备发动机直接驱动功能,因此在高速行驶时整车的油耗水平偏高,而通用的双模混动系统虽然在全车速下都具备较好的油耗水平,但因其采用双排行星排、4个离合器及制动器,导致系统的成本较高。本文试图基于系统效率最优、在考虑成本的前提下,设计一种多模式混合动力系统,在保证系统成本的前提下,实现高的节油率。

双电机多模混合动力总成构型参数优化匹配

本文研究一种适用于乘用车的双电机多模混合动力总成系统。首先探究参数匹配方法,匹配动力总成系统的基本参数。其次建立CRUISE仿真平台,仿真系统的动力性与NEDC循环工况。

基于双电机多模混合动力系统的CRUISE和SIMULINK联合仿真建模

本文针对双电机多模混合动力总成系统进行研究,利用SIMULINK与CRIUSE软件建立整车的仿真平台,并进行了NEDC工况的仿真分析,为整车的动力总成的设计与分析提供了坚实基础。

目标跟踪中的混合多模方法:综述

研究了用于多目标多传感器跟踪的混合多模方法 ,对目标跟踪中现有的各种IMM方法 ,从其产生的理论基础和适用的具体环境入手 ,详细阐述了这些算法的优越性和面临的各种困难 ,比较了主要多模算法的跟踪性能。通过一些工程应用实例的研究 。

基于多模开关结构的某混合动力民用车辆能量管理策略

本文提出了一种基于多模开关结构的混合动力车辆的能量管理策略.在电机侧安装三级变速器,根据技术经验初步制定换挡策略,并基于MATLAB/Simulink建立混合动力客车仿真模型和能量管理策略模型.以百公里NEDC循环当量油耗为优化目标函数,采用直接搜索算法对多模式切换参数和传动比进行优化.结果表明,所设计的控制策略能够使发动机主要工作在低油耗范围.与单级速比相比,三级速比在低功率、高油耗区域可以提高电机的工作点,降低发动机的工作点.与基本工况相比,参数优化后NEDC循环的百公里油耗降低5%,采用三级传动的NEDC循环百公里油耗降低4.8%.

机动巨标跟踪的自适应混合多模算法

本文介绍了用于机动目标跟踪的自适应混合多模算法.这个算法不需要预先定义模型,它利用一个二级卡尔曼滤波器来估计目标的加速度,这个加速度被用于混合多模算法中具有不同确定性加速度的子滤波器中.文中给出了自适应混合多模算法的一个计算机模拟结果并和无自适应混合多模算法的结果进行了比较.

一种多模专家智能控制器研究及其应用

从智能控制概念出发，提出了一种多模混合智能控制理结构，引入了特征信息空间、行为空间、多模混合控制规则集等，使用关系运算矩阵描述了它们之间的推理关系，给出了控制器的结构及设计方法，工程应用结果验证了控制器的有效性。最后利用锥稳定性定理为闭环智能控制系统建立了稳定性分析。

非合作通信中OFDM系统盲均衡方法

针对传统的正交频分复用系统盲均衡方法中存在接收数据短、收敛速度慢的问题,提出了一种基于符号间干扰变步长迭代的多模混合算法的正交频分复用系统盲均衡新方法.该方法首先将接收数据进行了分段复用,然后将修正的恒模算法与判决引导算法进行多模混合,并在均衡过程中采用了基于符号间干扰的变步长迭代方式.仿真结果表明,在非合作通信中,该方法不但加快了收敛速度,而且保证了稳态收敛性能.

非合作通信中OFDM系统盲均衡的方法

针对传统的正交频分复用（OFDM）系统盲均衡方法中存在接收数据短、收敛速度慢的问题，提出了一种基于符号间干扰变步长迭代的多模混合算法的 OFDM 系统盲均衡新方法。该方法首先将接收数据进行了分段复用，然后将修正的恒模算法与判决引导算法进行多模混合，并在均衡过程中采用了基于符号间干扰的变步长迭代方式。仿真结果表明，在非合作通信中，该方法不但加快了收敛速度并且保证了稳态收敛性能。

Genetic Analysis on Plant Height in Rice in Different Growing Seasons

[Objective] The aim was to carry out the genetic analysis on plant height of rice（Oryza sativa L.）cultivated in different seasons.[Method] Three rice parents with great difference in plant height including CB1（83.1 cm）,CB4（105.5 cm）and CB7（115.6 cm）were chosen to construct two parental combinations：CB1×CB4 and CB7×CB4,and the corresponding filial generations P1,F1,P2,B1,B2 and F2 were obtained.The 6 populations were planted in middle and late seasons respectively to measure their height traits.The Akaike＇s information criterion（AIC）of the mixed major gene and polygene model was used to indentify the existence of major genes affecting quantitative traits in B1,B2,F2 populations.When the major genes existed,the genetic effects of the major genes and polygenes and their genetic variance were estimated through segregation analysis.[Result] One additive major gene plus additive-dominance polygenes was the most fitted genetic model for the trait in all B1,B2,F2 populations in two planting seasons.The heritability values of the major genes varied from 38.63% to 78.53% and those of polygenes varied from 1.72% to 36.04%,and the total heritability values were 45.52-92.93%.The additive effect d value of the two genetic populations under two planting seasons was-4.56,-9.16,-7.19,and-9.38,respectively,as suggested that additive effect of the major genes would decrease the express of the plant height trait.[Conclusion] The heritability of plant height trait was affected by planting seasons and the combinations clearly as a whole.

Genetic Analysis of Embryo Production Frequency in Wheat × Maize Cross

A DH population derived from C49S-87/01Y1-1069 was used to study the inheritance of wheat haploid embryo production frequency（EPF） in wheat × maize cross with the mixed major gene and polygene inheritance model of quantitative traits. The results showed that the EPF of wheat × maize cross was controlled by two dominant epistatic genes and polygene with gene effects of 1.95 for the first major gene, 6.69 for the second one and 2.80 for the polygene. The inheritability of major genes was as high as 72.09%, suggesting that the differences in EPF among wheat materials were mainly influenced by genotype. However, non-genetic factors were still important, especially for wheat materials with low EPF.

A Novel Multi-Stage Interpolation Filter Design Technique for High-Resolution Σ-Δ DAC

This paper presents an efficient way to implement an interpolation filter in a 20bit ∑-△ DAC with an oversampling ratio of 128. A multistage structure is used to reduce the complexity of filter coefficients and the fi- nite word length effect. A novel method based on mixed-radix number representation is proposed to realize a poly- phase multiplier-free half-band subfilter with a high resolution. This approach reduces the complexity of the con- trol system and saves chip area dramatically. The IC is realized in a standard 0.13μm CMOS process and the inter- polation filter occupies less than 0.63mm^2 . This realization has desirable properties of regularity with simple hard- ware devices which are suitable for VLSI and can be applied to many other high resolution data converters.

An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.

Transition Features from Simplicity-Universality to Complexity-Diversification Under UHNTF

A large unified hybrid network model with a variable speed growth （LUHNM-VSG） is proposed as third model of the unified hybrid network theoretical framework （UHNTF）. A hybrid growth ratio vg of deterministic linking number to random linking number and variable speed growth index a are introduced in it. The main effects of vg and a on topological transition features of the LUHNM-VSC are revealed. For comparison with the other models, we construct a type of the network complexity pyramid with seven levels, in which from the bottom level-1 to the top level-7 of the pyramid simplicity-universality is increasing but complexity-diversity is decreasing. The transition relations between them depend on matching of four hybrid ratios （dr, fd, gr, vg）. Thus the most of network models can be investigated in the unification way via four hybrid ratios （dr, fd, gr, vg）. The LUHNM-VSG as the level-1 of the pyramid is much better and closer to description of real-world networks as well as has potential application.

A hybrid decomposition-boosting model for short-term multi-step solar radiation forecasting with NARX neural network

Due to global energy depletion,solar energy technology has been widely used in the world.The output power of the solar energy systems is affected by solar radiation.Accurate short-term forecasting of solar radiation can ensure the safety of photovoltaic grids and improve the utilization efficiency of the solar energy systems.In the study,a new decomposition-boosting model using artificial intelligence is proposed to realize the solar radiation multi-step prediction.The proposed model includes four parts:signal decomposition(EWT),neural network(NARX),Adaboost and ARIMA.Three real solar radiation datasets from Changde,China were used to validate the efficiency of the proposed model.To verify the robustness of the multi-step prediction model,this experiment compared nine models and made 1,3,and 5 steps ahead predictions for the time series.It is verified that the proposed model has the best performance among all models.

Multi-objective coordination optimal model for new power intelligence center based on hybrid algorithm

In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network.