采用多模模型的锂离子电池荷电状态联合估计算法

针对单一的等效电路模型难以准确描述全时段的锂离子电池、估计电池荷电状态(SOC)准确度低的问题,提出采用多模模型的锂离子电池荷电状态联合估计算法。利用电化学阻抗谱分析不同SOC下锂离子电池的阻抗分布,并以此构建等效电路模型来描述整个充放电过程中的锂离子电池,得到一种基于变阶RC模型的多模模型。利用贝叶斯定阶准则综合模型的准确度和实用性来确定具体阶数,采用带有遗忘因子的递推最小二乘法对模型参数进行在线辨识,利用扩展卡尔曼滤波算法(EKF)求得锂离子电池的实时SOC。在恒流工况以及动态应力测试工况下,与传统基于一阶RC模型和二阶RC模型的EKF算法进行了多组实验对比。结果表明:采用多模模型的联合算法在不同工况下估计的SOC精度提高了30%以上,并均可在两个迭代周期内追踪到准确值。

基于多模网络模型的个性化信息推荐算法及应用

为了获得更高的信息推荐效率和更精确的推荐结果,结合k-means聚类法设计了信息推荐算法。以网络信息资源的属性及整体变化的相似度判断为基础进行信息检索,通过用户对资源的偏好程度构建多模网络模型,采用k-means聚类法建立最近邻用户集,以综合特征值为聚类起点对推荐的信息资源进行排序,从而实现信息资源的推荐。将该算法应用到就业信息推荐平台中,结果表明所提出的算法达到了较高的性能指标,对于各种创业、就业网络平台具有很强的实用性。

小圆蓝细胞瘤预测模型研究

为研究肿瘤与基因之间的关系,分析了小圆蓝细胞瘤基因表达数据,建立了分类和预测小圆蓝细胞瘤4 个亚型的多模预测模型.针对小圆蓝细胞瘤的4个亚型,该预测模型创建了4个基于神经网络的分类器,并编 码4个分类器的分类结果,获得每个数据样本的最终预测结果.研究表明,将复杂的多类分类问题分解为多个2 类分类问题是解决多类分类问题的有效方法,基于该方法建立的多模预测模型能够学习基因表达数据中蕴含的 知识,并利用获得的知识准确地分类和预测全部83个数据样本.

基于多模空腔模型的微带阵列天线馈电研究

针对微带天线的馈电方式对阻抗匹配影响较大的特点,本文在微带线侧馈模型的基础上,介绍了基于多模空腔模型的内插式馈电方法.将该方法应用于微带阵列天线模型,有效提高了天线增益,改善了方向性.HFSS仿真结果证明,改进方法使天线的方向性与增益都有所提高,阻抗匹配效果较好.

基于个性化多模网络模型的音乐推荐技术

为了获得更高的推荐效率和更精确的推荐结果,提出了一种基于个性化多模网络模型的音乐推荐技术。以音乐音高的轮廓及整体变化的相似度判断为基础进行音乐信息检索,通过用户对资源的偏好程度构建多模网络模型,采用K-means聚类法建立最近邻用户集,以综合特征值为聚类起点对推荐的音乐资源进行排序,从而实现个性化音乐的推荐。实验结果表明,所提出的算法达到了较高的性能指标,对于各种音乐播放软件具有很强的实用性。本文提出的推荐技术,采用了个性化多模网络模型,对于其他算法来说具有一定的创新,因此,该技术对于海量音乐资源的处理及推荐具有一定的实用价值。

基于光纤传感器的视频图像几何失真校正方法

视频图像在传播过程中会出现信息畸变,致使图像几何失真,为此提出基于光纤传感器的视频图像几何失真校正方法。分析视频图像像素节点在光敏区域分布情况,提取亚像素视差信息,通过面阵CCD图像传感器获取畸变视频图像;利用畸变曲线近似模型计算出视频图像畸变率,确定畸变图像坐标,并结合双线性灰度插值算法推导校正参数,实现视频图像的几何失真校正。实验结果表明,所提方法能够有效校正不同种类的视频图像几何畸变,校正后的图像与原图像相似度高超过0.97,图像边缘模糊程度降低,提高了图像的视觉观测效果。

基于IMM-UKF方法的主动段目标分级与关机识别

主动段目标的分级与关机识别对于系统把握目标模型切换并部署后续跟踪具有重要意义。建立了主动段目标的分级和关机所涉及的两级主动段模型和中段模型;引入了交互式多模型框架以应对不确定模式下的跟踪问题,引入了无迹卡尔曼滤波以解决非线性估计问题。在天基观测条件下进行了仿真实验,结果表明,利用模型概率和总体估计误差的异常变化,可有效识别主动段目标的分级和关机。

开放教育多模教学模型的探索与研究

开放大学的成立为开放教育教学模式改革带来了新的契机。本文通过对经典教育模式及开放教育现状的分析,提出了一个以获取多层次教学目标为前提,以整合优质资源为核心,有机融合各种教学元素的多模教学模型。实证研究表明,该模型在应对工学矛盾突出、学习需求多样化等开放教育难题方面,不失为一种有效的解决方案。

A multi-objective fuzzy optimization model for cropping structure and water resources and its method

Cropping structure has a close relationship with the optimal allocation of agricultural water resources. Based on the analysis of the relationship between agricultural water resources and sustainable development, this paper presents a multi objective fuzzy optimization model for cropping structure and water allocation, which overcomes the shortcoming of current models that only considered the economic objective,and ignored the social and environmental objectives. During the process, a new method named fuzzy deciding weight is developed to decide the objective weight. A case study shows that the model is reliable, the method is simple and objective, and the results are reasonable. This model is useful for agricultural management and sustainable development.

机动塔台管制系统总体设计

直升机/无人机具有飞行灵活性强、反应速度快、可进行野外起降等特点,而机动塔台管制系统对飞行员日常训练、空中交通管制、指挥调度和飞行保障等具有重要作用。介绍了机动塔台管制的任务和作用,提出其建设思路和方法,描述了其主要功能和技术体系结构,对硬件、软件物理组成进行了设计。通过计算机仿真进行分析,探讨了多模交互模型IMM在自动相关监视ADS数据与雷达数据融合应用的可行性。

BISQ model based on a Kelvin-Voigt viscoelastic frame in a partially saturated porous medium

Taking into account three important porous media mechanisms during wave propagation （the Biot-flow, squirt-flow, and solid-skeleton viscoelastic mechanisms）, we introduce water saturation into the dynamic governing equations of wave propagation by analyzing the effective medium theory and then providing a viscoelastic Biot/squirt （BISQ） model which can analyze the wave propagation problems in a partially viscous pore fluid saturated porous media. In this model, the effects of pore fluid distribution patterns on the effective bulk modulus at different frequencies are considered. Then we derive the wave dynamic equations in the time-space domain. The phase velocity and the attenuation coefficient equations of the viscoelatic BISQ model in the frequency-wavenumber domain are deduced through a set of plane harmonic solution assumptions. Finally, by means of numerical simulations, we investigate the effects of water saturation, permeability, and frequency on compressional wave velocity and attenuation. Based on tight sandstone and carbonate experimental observed data, the compressional wave velocities of partially saturated reservoir rocks are calculated. The compressional wave velocity in carbonate reservoirs is more sensitive to gas saturation than in sandstone reservoirs.

Discrimination for minimal hepatic encephalopathy based on Bayesian modeling of default mode network

In order to classify the minimal hepatic encephalopathy （MHE） patients from healthy controls, the independent component analysis （ICA） is used to generate the default mode network （DMN） from resting-state functional magnetic resonance imaging （fMRI）. Then a Bayesian voxel- wised method, graphical-model-based multivariate analysis （GAMMA）, is used to explore the associations between abnormal functional integration within DMN and clinical variable. Without any prior knowledge, five machine learning methods, namely, support vector machines （SVMs）, classification and regression trees （ CART ）, logistic regression, the Bayesian network, and C4.5, are applied to the classification. The functional integration patterns were alternative within DMN, which have the power to predict MHE with an accuracy of 98%. The GAMMA method generating functional integration patterns within DMN can become a simple, objective, and common imaging biomarker for detecting MIIE and can serve as a supplement to the existing diagnostic methods.

A Study on the Multi-Compartment Linear Circulation Pharmacokinetic Model for the Targeting Drug Delivery System

By analyzing the observed phenomena and the data collected in the study, a multi-compartment linear circulation model for targeting drug delivery system was developed and the function formulas of the drug concentration-time in blood and target organ by computing were figured out. The drug concentration-time curve for target organ can be plotted with reference to the data of drug concentration in blood according to the model. The pharmacokinetic parameters of the drug in target organ could also be obtained. The practicability of the model was further checked by the curves of drug concentration-time in blood and target organ(liver) of liver-targeting nanoparticles in animal tests. Based on the liver drug concentration-time curves calculated by the function formula of the drug in target organ, the pharmacokinetic behavior of the drug in target organ(liver) was analyzed by statistical moment, and its pharmacokinetic parameters in liver were obtained. It is suggested that the (relative targeting index( can be used for quantitative evaluation of the targeting drug delivery systems.

Multivariable Fuzzy Predictive Control Based on the Modified CPN Model

Through modifying the CPN model, a kind of multivariable fuzzy model is put forward, and the matching fuzzy multistep predictive control algorithm is deduced based on the model. The modified model works in a competitive output manner which results in its local representation property. While studying on line, only a few parameters need to be regulated. So the model has the merits of fast learning and on line self organizing modeling. The control algorithm is simple, adaptive and useful in multivariable and time delay systems. Applying the algorithm in a paper making system, simulation shows its good effect.

Relationship between Abundance and Area of Pinus tabulaeformis and Quercus liaotungensis Koidz in Lingkong Mountain Nature Reserve

The spatial distribution and species abundance of Pinus tabulaeformis and Quercus liaotungensis Koidz were analyzed with random distribution abundance model and aggregated distribution abundance model,and evaluation goodness was evaluated based on related information of sample area at 4 hm2 in Shanxi Lingkong Mountain with altitude at 1500-1 800 m.The results showed that of the 30 xylophyta plants,abundance of 20 plants was increasing in sequence and the covered spaces extended accordingly,except of 10 plant species.As pixel area extended,curve of abundance-area tended to be volatile if area in abundance sequence was smaller than that of the front one;the curve tended to be stable if the fluctuating point was removed.For the same species,the higher pixel area is,the larger the covered area of the species in corresponding pixel would be.The results of evaluation goodness indicated that aggregated distribution model is better for prediction on relationship between abundance and area,compared with random distribution abundance model.Both of the two models rely on value of m,namely,number of covered pixel given the pixel is fixed.For the species distribute dispersedly,the prediction results would be more accurate if both of the two models are made use of,or the prediction errors would be larger.Given that the total area of sample plot is fixed,the smaller the pixel area is,the more accurate the prediction would be.

An Algorithm of the Adaptive Grid and Fuzzy Interacting Multiple Model

This paper studies the algorithm of the adaptive grid and fuzzy interacting multiple model （AGFIMM） for maneuvering target tracking, while focusing on the problems of the fixed structure multiple model （FSMM） algorithm＇s cost-efficiency ratio being not high and the Markov transition probability of the interacting multiple model （IMM） algorithm being difficult to determine exactly. This algorithm realizes the adaptive model set by adaptive grid adjustment, and obtains each model matching degree in the model set by fuzzy logic inference. The simulation results show that the AGFIMM algorithm can effectively improve the accuracy and cost-efficiency ratio of the multiple model algorithm, and as a result is suitable for enineering apolications.

A vague-set-based fuzzy multi-objective decision making model for bidding purchase

A vague-set-based fuzzy multi-objective decision making model is developed for evaluating bidding plans in a bid- ding purchase process. A group of decision-makers (DMs) first independently assess bidding plans according to their experience and preferences, and these assessments may be expressed as linguistic terms, which are then converted to fuzzy numbers. The resulting decision matrices are then transformed to objective membership grade matrices. The lower bound of satisfaction and upper bound of dissatisfaction are used to determine each bidding plan’s supporting, opposing, and neutral objective sets, which together determine the vague value of a bidding plan. Finally, a score function is employed to rank all bidding plans. A new score function based on vague sets is introduced in the model and a novel method is presented for calculating the lower bound of sat- isfaction and upper bound of dissatisfaction. In a vague-set-based fuzzy multi-objective decision making model, different valua- tions for upper and lower bounds of satisfaction usually lead to distinct ranking results. Therefore, it is crucial to effectively contain DMs’ arbitrariness and subjectivity when these values are determined.

Maneuvering target tracking algorithm based on cubature Kalman filter with observation iterated update

Reasonable selection and optimization of a filter used in model estimation for a multiple model structure is the key to improve tracking accuracy of maneuvering target.Combining with the cubature Kalman filter with iterated observation update and the interacting multiple model method,a novel interacting multiple model algorithm based on the cubature Kalman filter with observation iterated update is proposed.Firstly,aiming to the structural features of cubature Kalman filter,the cubature Kalman filter with observation iterated update is constructed by the mechanism of iterated observation update.Secondly,the improved cubature Kalman filter is used as the model filter of interacting multiple model,and the stability and reliability of model identification and state estimation are effectively promoted by the optimization of model filtering step.In the simulations,compared with classic improved interacting multiple model algorithms,the theoretical analysis and experimental results show the feasibility and validity of the proposed algorithm.

Multiple Model Soft Sensor Based on Affinity Propagation, Gaussian Process and Bayesian Committee Machine

Presented is a multiple model soft sensing method based on Affinity Propagation （AP）, Gaussian process （GP） and Bayesian committee machine （BCM）. AP clustering arithmetic is used to cluster training samples according to their operating points. Then, the sub-models are estimated by Gaussian Process Regression （GPR）. Finally, in order to get a global probabilistic prediction, Bayesian committee mactnne is used to combine the outputs of the sub-estimators. The proposed method has been applied to predict the light naphtha end point in hydrocracker fractionators. Practical applications indicate that it is useful for the online prediction of quality monitoring in chemical processes.

A topographical model for precipitation pattern in the Tibetan Plateau

As the highest and most extensive plateau on earth, the Tibetan Plateau has strong thermo- dynamic effect, which not only affects regional climate around the plateau but precipitation patterns of scattered meteorological also temperature and itself. However, due to stations, its spatial precipitation pattern and, especially, the mechanism behind are poorly understood. The availability of spatially consistent satellite-derived precipitation data makes it possible to get accurate precipitation pattern in the plateau, which could help quantitatively explore the effect and mechanism of mass elevation effect on precipitation pattern. This paper made full use of TMPA 3B43 V7 monthly precipitation data to track the trajectory of precipitation and identified four routes （east, southeast, south, west directions） along which moisture-laden air masses move into the plateau. We made the assumption that precipitation pattern is the result interplay of these four moisture- laden air masses transportation routes against the distances from moisture sources and the topographic barriers along the routes. To do so, we developed a multivariate linear regression model with the spatial distribution of annual mean precipitation as the dependent variable and the topographical barriers to these four moisture sources as independent variables. The result shows that our model could explain about 7o% of spatial variation of mean annual precipitation pattern in the plateau; the regression analysis also shows that the southeast moisture source （the Bay of Bengal） contributes the most （32.56%） to the rainfall pattern of the plateau; the east and the south sources have nearly the same contribution, 23.59% and 23.48%, respectively; while the west source contributes the least, only 2o.37%. The findings of this study can greatly improve our understanding of mass elevation effect on spatial precipitation pattern.