A select link analysis method based on logit–weibit hybrid model

Select link analysis provides information of where traffic comes from and goes to at selected links.This disaggregate information has wide applications in practice.The state-of-the-art planning software packages often adopt the user equilibrium(UE) model for select link analysis.However,empirical studies have repeatedly revealed that the stochastic user equilibrium model more accurately predicts observed mean and variance of choices than the UE model.This paper proposes an alternative select link analysis method by making use of the recently developed logit–weibit hybrid model,to alleviate the drawbacks of both logit and weibit models while keeping a closed-form route choice probability expression.To enhance the applicability in large-scale networks,Bell’s stochastic loading method originally developed for logit model is adapted to the hybrid model.The features of the proposed method are twofold:(1) unique O–D-specific link flow pattern and more plausible behavioral realism attributed to the hybrid route choice model and(2) applicability in large-scale networks due to the link-based stochastic loading method.An illustrative network example and a case study in a large-scale network are conducted to demonstrate the efficiency and effectiveness of the proposed select link analysis method as well as applications of O–D-specific link flow information.A visualizationmethod is also proposed to enhance the understanding of O–D-specific link flow originally in the form of a matrix.

System modeling and dynamic analysis of ISG hybrid power shafting

A system model is established to analyze the dynamic performance of an integrated starter and generator （ISG） hybrid power shafting. The model couples the electromechanical coupling shaft dynamics, the bearing hydrodynamic lubrication and the engine block stiffness. The model is com- pared with the model based on ADAMS or the model neglecting the bearing hydrodynamics. The bearing eccentricity and the oil film pressure have been calculated under different hybrid conditions or at the different motor power levels. It＇ s found that the bearing hydrodynamics decreases the cal- culation results of the bearing peak load. Changes of the hybrid conditions or the motor power have no significant effect on the main bearing, but have impact on the motor bearing. A hybrid power sys- tem composed of a 1.6 L engine and a 45 kW ISG motor can operate safely.

Hybrid Malware Variant Detection Model with Extreme Gradient Boosting and Artificial Neural Network Classifiers

In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining organizations.This paper presents an innovative Application Programmable Interface(API)-based hybrid model designed to enhance the detection performance of malware variants.This model integrates eXtreme Gradient Boosting(XGBoost)and an Artificial Neural Network(ANN)classifier,offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware authors.The model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features,providing a holistic and comprehensive view of malware behavior.From these features,we construct two XGBoost predictors,each of which contributes a valuable perspective on the malicious activities under scrutiny.The outputs of these predictors,interpreted as malicious scores,are then fed into an ANN-based classifier,which processes this data to derive a final decision.The strength of the proposed model lies in its capacity to leverage behavioral and signature-based features,and most importantly,in its ability to extract and analyze the hidden relations between these two types of features.The efficacy of our proposed APIbased hybrid model is evident in its performance metrics.It outperformed other models in our tests,achieving an impressive accuracy of 95%and an F-measure of 93%.This significantly improved the detection performance of malware variants,underscoring the value and potential of our approach in the challenging field of cybersecurity.

Time-series analysis with a hybrid Box-Jenkins ARIMA

Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, Box-Jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades. More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model’s unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.

MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection

In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making decisions based on the extracted knowledge is becoming increasingly important in all business domains. Nevertheless, high-dimensional data remains a major challenge for classification algorithms due to its high computational cost and storage requirements. The 2016 Demographic and Health Survey of Ethiopia (EDHS 2016) used as the data source for this study which is publicly available contains several features that may not be relevant to the prediction task. In this paper, we developed a hybrid multidimensional metrics framework for predictive modeling for both model performance evaluation and feature selection to overcome the feature selection challenges and select the best model among the available models in DM and ML. The proposed hybrid metrics were used to measure the efficiency of the predictive models. Experimental results show that the decision tree algorithm is the most efficient model. The higher score of HMM (m, r) = 0.47 illustrates the overall significant model that encompasses almost all the user’s requirements, unlike the classical metrics that use a criterion to select the most appropriate model. On the other hand, the ANNs were found to be the most computationally intensive for our prediction task. Moreover, the type of data and the class size of the dataset (unbalanced data) have a significant impact on the efficiency of the model, especially on the computational cost, and the interpretability of the parameters of the model would be hampered. And the efficiency of the predictive model could be improved with other feature selection algorithms (especially hybrid metrics) considering the experts of the knowledge domain, as the understanding of the business domain has a significant impact.

PERFORMANCE EVALUATION METHOD FOR BUSINESS PROCESS OF MACHINERY MANUFACTURER BASED ON DEA/AHP HYBRID MODEL

A set of indices for performance evaluation for business processes with multiple inputs and multiple outputs is proposed, which are found in machinery manufacturers. Based on the traditional methods of data envelopment analysis （DEA） and analytical hierarchical process （AHP）, a hybrid model called DEA/AHP model is proposed to deal with the evaluation of business process performance. With the proposed method, the DEA is firstly used to develop a pairwise comparison matrix, and then the AHP is applied to evaluate the performance of business process using the pairwise comparison matrix. The significant advantage of this hybrid model is the use of objective data instead of subjective human judgment for performance evaluation. In the case study, a project of business process reengineering （BPR） with a hydraulic machinery manufacturer is used to demonstrate the effectiveness of the DEA/AHP model.

Nonlinear Coupled Dynamics Analysis of A Truss Spar Platform

Accurate prediction of the offshore structure motion response and associate mooring line tension is important in both technical applications and scientific research. In our study, a truss spar platform, operated in Gulf of Mexico, is numerically simulated and analyzed by an in-house numerical code ＇COUPLE＇. Both the platform motion responses and associated mooring line tension are calculated and investigated through a time domain nonlinear coupled dynamic analysis. Satisfactory agreement between the simulation and corresponding field measurements is in general reached, indicating that the numerical code can be used to conduct the time-domain analysis of a truss spar interacting with its mooting and riser system. Based on the comparison between linear and nonlinear results, the relative importance of nonlinearity in predicting the platform motion response and mooring line tensions is assessed and presented. Through the coupled and quasi-static analysis, the importance of the dynamic coupling effect between the platform hull and the mooting/riser system in predicting the mooting line tension and platform motions is quantified. These results may provide essential information pertaining to facilitate the numerical simulation and design of the large scale offshore structures.

Design of Intelligent Network Performance Analysis & Forecast Support System

A system designed for supporting the network performance analysis and forecast effort is presented, based on the combination of offline network analysis and online real-time performance forecast. The off-line analysis will perform analysis of specific network node performance, correlation analysis of relative network nodes performance and evolutionary mathematical modeling of long-term network performance measurements. The online real-time network performance forecast will be based on one so-called hybrid prediction modeling approach for short-term network, performance prediction and trend analysis. Based on the module design, the system proposed has good intelligence, scalability and self-adaptability, which will offer highly effective network performance analysis and forecast tools for network managers, and is one ideal support platform for network performance analysis and forecast effort.

基于磁热耦合法的非对称混合磁极永磁电机热分析

针对传统内置永磁电机损耗高、发热量大的问题,提出非对称混合磁极永磁电机拓扑结构.介绍非对称混合磁极永磁电机的拓扑结构,对比分析两者的电磁特性与损耗分布特性.针对非对称混合磁极永磁电机分布式绕组的结构特点,对绕组进行等效处理,确定等效导热系数,建立集中参数热网络模型.建立单向磁热耦合模型,计算电机各部件的温度分布,验证了热网络模型的正确性.考虑到温度对永磁材料的影响,建立双向磁热耦合模型,对比分析不同电流密度对电机温升的影响规律.试制一台样机并搭建温升试验平台进行温升试验,验证了新型拓扑结构的有效性与合理性以及磁热双向耦合法计算结果的准确性.

船舶混合动力系统能量管理预测控制方法研究

针对混合动力系统控制稳定性和节能减排问题,明确模型预测控制在船舶能量管理中的作用、研究现状及趋势。借助CiteSpace对船舶能量管理进行可视化分析,理清了船舶能量管理的发展脉络,并通过不同能量管理策略的对比分析揭示模型预测控制在船舶能量管理中的重要性;从预测建模、优化目标及约束、求解和改进策略等3个方面开展了船舶能量管理预测控制方法的分析;最后从考虑可再生能源的能量管理策略、建模与验证的标准化、协同优化和多维度评估等方面对船舶能量管理预测控制的未来研究进行展望。结果表明:模型预测控制在智能船舶、多能源船舶等复杂的动力系统的实时控制中具有重要潜力。与其他算法结合开展多目标算法融合是提升控制精度和计算实时性的重要途径,开展多维度测试评估有利于推动策略的实船应用。

柔性直流输电系统三端口混合参数建模及其稳定性分析

模块化多电平换流器(MMC)与系统网络之间的交互是诱发振荡的主要原因,然而现有MMC单侧阻抗/导纳建模方法往往没有计及对端换流站的动态过程,因此该文重点通过建立可以考虑交直流耦合的换流器三端口混合参数模型实现整个双端柔直系统的稳定性分析。首先,该文基于谐波状态空间法推导考虑桥臂间谐波动态交互过程的MMC换流器交直流端口功率守恒方程,并据此建立换流器的三端口混合参数模型;其次,基于所建立的三端口混合参数模型和广义奈奎斯特判据,对MMC互联系统的稳定性进行分析,因该方法可避免计算开环传递函数右半平面零极点的数量,与传统单侧阻抗稳定性分析方法相比,可实现系统稳定性的准确判断;再次,推导了特征值相位关于三端口混合参数元素的灵敏度计算公式,揭示了系统振荡的诱因,进一步结合控制参数灵敏度分析,实现了一种用于改善互联系统直流侧稳定性的直流电流前馈附加阻尼控制策略;最后,结合时域仿真算例验证了稳定性分析和改善措施的有效性。

基于深度学习的特高压三端混合直流输电线路波形特征故障区域判别方法

针对将现有直流线路故障区域识别方法应用于特高压三端混合直流输电线路时,存在难以区分T区两侧故障、耐过渡电阻能力弱和阈值整定困难的问题,提出一种利用深度学习及波形特征进行特高压三端混合直流输电线路故障区域识别的方法。首先,对三端混合直流线路不同故障区域进行故障特征分析;然后,对线模电压和线模电流进行多尺度小波分解,提取线模电流中低频分量和线模电压高频分量,结合正负极电压波形特征,组成深度学习模型的输入量,并将故障区域作为输出量,构建深度学习故障区域识别模型;最后,用训练过的深度学习模型对获取的故障特征量进行处理,以实现故障区域识别的目的。通过大量仿真实验,验证了所提故障区域识别方法具有准确率高和基本不受过渡电阻影响的特性。

汽车尾气温差发电系统瞬态回收性能分析

为了预测温差发电(thermoelectric generator, TEG)系统的动态特性,基于COMSOL Multiphy-sics建立了用于求解温差发电系统温度场分布的瞬态计算流体力学(computational fluid dynamics, CFD)模型和用于研究温差发电模块瞬态响应特性的分析模型,提出了混合瞬态CFD-分析模型,并经过瞬态试验验证.结果表明:由于热惯性的影响,TEG系统的转化效率会出现一个瞬时的较高值;相较于尾气温度和质量流量的瞬态波动,热电半导体的热端温度和冷端温度会存在时滞;在美国环保局的高速公路燃油经济性测试(highway fuel economy test, HWFET)模式循环工况下,瞬态模型求解得到整个温差发电系统的平均输出功率、平均转化效率分别为35.63 W和3.40%,瞬态模型的输出电压平均误差为6.41%;该模型能够以较高的精度及较短的计算时间预测温差发电系统在瞬态热源激励下的瞬态响应特性.

考虑OD需求聚类的区域多层公路交通网络混合路径诱导模型

随着交通出行需求的快速增长,我国高速公路网络面临的运输压力日渐增加,经常出现严重的交通拥堵现象。为了缓解高速公路交通拥堵,并实施更有针对性的交通管控措施,提出了考虑OD需求聚类的区域多层公路交通网络混合路径诱导模型。首先,利用湖南省高速公路以及国道、省道的地理信息数据构建区域多层公路交通网络。然后,根据OD对间距离和OD交通量的差异,利用K-均值聚类算法对OD对进行聚类分析,将OD对划分为3个不同的类别。最后,应用遗传算法筛选出各类OD对中对拥堵贡献较大的出行群体,并建立有针对性的混合路径诱导模型,对拥堵贡献较大和拥堵贡献较小的出行群体分别应用不同的路径诱导方案。当OD需求扩样系数设置为6时,对OD对聚类可以将总出行成本进一步降低35186.03 min。在不进行OD对聚类时,使用规划路径的出行总数为79140,而实施OD对聚类后,使用规划路径的出行总数为70374。使用诱导路径的出行的平均出行时间由121.47 min下降为85.61 min,极少数出行(3.75%)的时间增加,且增加最大值低于3 min。对多个不同扩样系数进行敏感性分析进一步说明了考虑OD需求聚类的混合路径诱导模型具有良好的拥堵缓解效果。考虑OD需求聚类的区域多层公路交通网络混合路径诱导模型可以用于识别对拥堵贡献较大的关键出行群体,进而制定有针对性的路径诱导策略,在缓解高速公路交通拥堵的同时能够减少对大多数出行者的影响,降低路径诱导策略的实施难度。另外,研究结果还表明:对出行距离较长的出行群体实施路径诱导能够更加有效地缓解区域多层公路交通网络中的交通拥堵。

肉羊不同杂交组合生长曲线模型筛选及分析

为研究肉羊不同杂交组合生长发育规律及评价适宜推广的杂交模式,以引进品种澳洲白、杜泊和萨福克羊为父本,湖羊为母本进行杂交试验。测定不同杂交组合1~6月龄体重,利用Brody、Logistic、Gompertz和Von Bertalanffy 4种模型分别拟合各杂交组合体重发育过程,分析体重参数和模型估计值,评价杂交优势。结果表明:4种模型的拟合度(R^(2))均在0.95以上,其中Von Bertalanffy模型估计值最接近各个杂交组合的实测值,拟合效果最佳,拐点体重介于7.64~9.98 kg之间,拐点日龄在25~35 d之间。澳湖、杜湖和萨湖3个杂交组合最大日增重均在200 g左右(P>0.05),表明三者体重发育相近且适宜推广。Von Bertalanffy模型符合杂交羔羊体重实际生长现状,可为杂交羔羊早期体重发育评估提供依据。

中压直流配电场景下混合型MMC过调制工况对运行稳定性的影响分析

混合型模块化多电平换流器(hybrid MMC)可通过过调制运行实现中压直流(MVDC)配电网的直流故障穿越.现有研究未充分分析过调制工况下混合型MMC的运行稳定性,无法满足中压直流配电网的运行需求.为此,本文通过与正常工况进行对比,分析了过调制工况对混合型MMC运行稳定性的影响.首先,在电气与控制部分统一的dq坐标系下,构建考虑直流调制比的混合型MMC小信号模型,并将其与详细电磁暂态(EMT)仿真结果对比,验证所建立模型的准确性;其次,对比正常及过调制工况下的根轨迹,进而从控制参数可行域的角度分析了过调制工况对系统运行稳定性的影响;然后,基于主导模态特征根的虚部计算系统失稳时的振荡频率,并将其与相同控制参数下电磁暂态仿真结果进行对比,验证上述稳定性分析结果的正确性;最后,基于参与因子法计算各状态变量对于主导模态的参与程度,从而揭示对系统稳定性影响较大的关键状态变量.理论分析及仿真结果表明,混合型MMC的内部模态会随着交流及直流电流控制器参数的增大而逐渐趋于不稳定;相较于正常工况,过调制工况下混合型MMC控制参数可行域扩大,小信号稳定性提高.

Hybrid Ⅲ 50th颈部模型低速后碰撞响应的仿真分析

为了明确Hybrid Ⅲ 50th假人颈部的低速后碰撞动力学响应特性,提出了提高其生物逼真度的方法和途径。针对头颈部低速后碰撞条件下的动力学响应,比较分析了Hybrid Ⅲ和HBM-neck模型。建立了Hybrid Ⅲ头颈部模型,并通过标定试验进行了验证。结合基于解剖学结构的HBM-neck模型,在国外志愿者进行的低速后碰撞试验条件下,对比了HybridⅢ和HBM-neck颈部模型。结果表明,HybridⅢ假人的颈部模型刚度高于HBM-neck颈部模型,合理调整HybridⅢ颈部模型的材料特性和关节位置可以获得类似人体颈部的响应特性。

A dynamic exploratory hybrid modelling framework for simulating complex and uncertain system

Complex disaster systems involve various components and mechanisms that could interact in complex ways and change over time,leading to significant deep uncertainty.Due to deep uncertainty,decision-makers have severe inadequacy of knowledge and often encounter unpredictable surprises that may emerge in the future,thus making it difficult to specify appropriate models and parameters to describe the system of interest.In this paper,we propose a dynamic exploratory hybrid modeling framework that fits data,models,and computational ex-periments together to simulate complex systems with deep uncertainty.In the framework,one needs to develop multiple plausible models from a hybrid modeling perspective and perform enormous computational experi-ments to explore the diversity of future scenarios.Real-time data is then incorporated into diverse forecasts to dynamically adjust the simulation system.This ultimately enables an ongoing modeling and analysis process in which deep uncertainty would be gradually mitigated.Our approach has been applied to a human-involved car-following system simulation under complex traffic conditions.The results show that the proposed approach can improve the prediction accuracy while enhancing the sensitivity of the simulation system to uncertain changes in the system of interest.

基于试验子结构恢复力修正的不完整边界条件混合试验方法

相比于传统拟静力和拟动力试验,混合试验通常存在较为复杂的试验子结构边界条件。受到实验室加载条件的限制,子结构的边界条件往往难以完全实现,这将对混合试验的保真度产生很大影响,即混合试验中的不完整边界条件问题。为解决不完整边界条件问题,提出一种基于试验子结构恢复力修正的不完整边界条件混合试验方法。该方法为试验子结构建立两套数值模型,并通过两套数值模型修正试验子结构的不完整恢复力。两套数值模型的区别在于边界条件的设定:第一套数值模型具有完整的边界条件;第二套数值模型具有与试验子结构相同的边界条件,为不完整边界条件。该研究分析了边界自由度耦合程度、数值模型误差对该方法精度的影响,验证了该方法对存在模型误差的数值模型具有较好的鲁棒性;最后,采用所提方法对一座两层两跨抗弯框架结构进行了抗震性能数值分析。结果表明:与常规混合试验方法和弱耦合混合试验方法相比,该方法不仅具有解决不完整边界条件问题的能力,而且也表现出较高的仿真精度。

基于全局敏感性的自适应模型更新混合模拟方法

基于全局敏感性(global sensitivity, GS)的模型更新混合模拟方法可以有效提升混合模拟精度并拥有较高的计算效率,然而该方法易陷入局部最优值。为解决此问题,在GS方法的基础上引入了动量和自适应步长的概念,提出了基于全局敏感性的自适应(global sensitivity adaptive, GS-A)模型更新混合模拟方法。以钢框架模型为例,对8个本构参数和模型几何参数进行参数识别,利用均匀设计试验方法,建立11个模型更新案例,分别进行了不更新、GS方法和GS-A方法的数值模拟分析。33组数值模拟案例参数识别过程、位移时程的对比分析表明:GS-A方法相对于GS方法,解决了参数组合陷入局部最优的问题,具有较高参数识别的精度和稳定性,提升了模型更新效果。