Strategic Planning for Equitable RWIS Implementation:A Comprehensive Study Incorporating a Multi-variable Semivariogram Model

This paper extends the previously developed method of optimizing Road Weather Information Systems(RWIS)station placement by unveiling a sophisticated multi-variable semivariogram model that concurrently considers multiple vital road weather variables.Previous research primarily centered on single-variable analysis focusing on road surface temperature(RST).The study bridges this oversight by introducing a framework that integrates multiple critical weather variables into the RWIS location allocation framework.This novel approach ensures balanced and equitable RWIS distribution across zones and aligns the network with areas both prone to traffic accidents and areas of high uncertainty.To demonstrate the effectiveness of this refinement,the authors applied the framework to Maine’s existing RWIS network,conducted a gap analysis through varying planning scenarios and generated optimal solutions using a heuristic optimization algorithm.The analysis identified areas that would benefit most from additional RWIS stations and guided optimal resource utilization across different road types and priority locations.A sensitivity analysis was also performed to evaluate the effect of different weightings for weather and traffic factors on the selection of optimal locations.The location solutions generated have been adopted by MaineDOT for future implementations,attesting to the model’s practicality and signifying an important advancement for more effective management of road weather conditions.

Combined model based on optimized multi-variable grey model and multiple linear regression

The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.

Multi-Variable Flocking Control for Multi-Agent Systems via a Received Signal Strength Indicator

A novel flocking control approach is proposed for multi-agent systems by integrating the variables of velocities, motion directions, and positions of agents. A received signal strength indicator （RSSI） is applied as a variable to estimate the inter-distance between agents. A key parameter that contains the local information of agents is defined, and a multi-variable controller is proposed based on the parameter. For the position control of agents, the RSSI is introduced to substitute the distance as a control variable in the systems. The advantages of RSSI include that the relative distance between every two agents can be adjusted through the communication quality under different environments, and it can shun the shortage of the limit of sensors. Simulation studies demonstrate the effectiveness of the proposed control approach.

The Research and Application of Prediction Control in Multi-variable Control Process

The modern industrial control objects become more and more complicated,and higher control quality is required, so a series of new control strategies appear,applied,modified and develop quickly.This paper researches a new control strategy- prediction control-and its application in Multi-Variable Control Process.The research result is worthy for automatic control in pro- cess industry.

ENHANCING GROUND RESOLUTION OF TM6 BASED ON MULTI-VARIATE REGRESSION MODEL AND SEMI-VARIOGRAM FUNCTION

It is well known that Landsat TM images are the most widely used remote sensing data in various fields.Usually,it has 7 different electromagnetic spectrum bands,among which the sixth one has much lower ground resolution compared with the other six bands.Nevertheless,it is useful in the study of rock spectrum reflection,geothermal resources exploration,etc.To improve the ground resolution of TM6 to the level as that of the other six bands is a problem .This paper presents an algorithm based on the combination of multivariate regression model with semivariogram function which can improve the ground resolution of TM6 by "fusing" the data of other six bands.It includes the following main steps: （1） testing the correlation between TM6 and one of TM15,7.If the correlation coefficient between TM6 and another one is greater than a given threshold value,then select the band to the regression analysis as an argument.（2） calculating the size of the template window within which some parameters needed by the regression model will be calculated; （3） replacing the original pixel values of TM6 by those obtained by regression analysis; （4） using image entropy as a measurement to evaluate the quality of the fused image of TM6.The basic mechanism of the algorithm is discussed and the V C ++ program for implementing this algorithm is also presented.A simple application example is given in the last part of this paper,showing the effectiveness of the algorithm.

PRECISE INTEGRAL ALGORITHM BASED SOLUTION FORTRANSIENT INVERSE HEAT CONDUCTION PROBLEMSWITH MULTI-VARIABLES

By modeling direct transient heat conduction problems via finite element method (FEM) and precise integral algorithm, a new approach is presented to solve transient inverse heat conduction problems with multi-variables. Firstly, the spatial space and temporal domain are discretized by FEM and precise integral algorithm respectively. Then, the high accuracy semi-analytical solution of direct problem can be got. Finally, based on the solution, the computing model of inverse problem and expression of sensitivity analysis are established. Single variable and variables combined identifications including thermal parameters, boundary conditions and source-related terms etc. are given to validate the approach proposed in 1-D and 2-D cases. The effects of noise data and initial guess on the results are investigated. The numerical examples show the effectiveness of this approach.

SecMVX:Analysis on the Vulnerability of Multi-Variant Execution

As an active defenses technique,multivariant execution(MVX)can detect attacks by monitoring the consistency of heterogeneous variants with parallel execution.Compared with patch-style passive defense,MVX can defend against known and even unknown vulnerability-based attacks without relying on attack feature information.However,variants generated with software diversity technologies will introduce new vulnerabilities when they execute in parallel.First,we analyze the security of MVX theory from the perspective of formal description.Then we summarize the general forms and techniques for attacks against MVX,and analyze the new vulnerabilities arising from the combination of variant generation technologies.We propose SecMVX,a secure MVX architecture and variant generation technology.Experimental evaluations based on CVEs and SPEC 2006 benchmark show that SecMVX introduces 11.29%of the average time overhead,and avoids vulnerabilities caused by the improper combination of variant generation technologies while keeping the defensive ability of MVX.

考虑速度时变的多配送中心医药冷链配送路径优化

随着医药冷链市场需求的迅速增长,越来越多的城市、企业采用多配送中心模式,因此研究多配送中心模式下的医药冷链路径优化以减少配送成本尤为重要。在实际配送过程中,由于交通拥堵问题,配送车辆无法保持恒定的速度,进而可能导致配送成本的增加。基于此,构建考虑时变速度的多配送中心医药冷链物流路径优化模型,并结合遗传算法与粒子群算法对模型进行求解。通过数值算例的结果表明了模型的合理性和算法的有效性。

Non-Probabilistic Reliability Research on Multi-Variable Hydraulic Turbine Blade Model

Based on the interval mathematics and possibility theory, the variables existing in hydraulic turbine blade are described. Considering the multi-failure mode in turbine blade, multi-variable model is established to meet the actual situation. Thus, non-probabilistic reliability index is presented by comparing with the output range and the given range.

Multi-variable special polynomials using an operator ordering method

Using an operator ordering method for some commutative superposition operators,we introduce two new multi-variable special polynomials and their generating functions,and present some new operator identities and integral formulas involving the two special polynomials.Instead of calculating compli-cated partial differential,we use the special polynomials and their generating functions to concsely address the normalzation,photoount distributions and Wigner distributions of several quantum states that can be realized physically,the rsults of which provide real convenience for further investigating the properties and applications of these states.

电磁阀半主动悬架线性变参数μ综合鲁棒控制

针对电磁阀半主动悬架系统中的非线性和不确定性问题以及多个性能目标之间的矛盾,设计了一种线性变参数(linear parameter varying,LPV)μ综合鲁棒控制器。建立电磁阀非线性阻尼力力学模型并开展台架试验验证模型的准确性,构建了七自由度悬架系统的非线性LPV模型;对悬架参数不确定性和系统建模误差进行分析,并考虑传感器测量噪声的影响,设计了一种基于非线性和混合不确定性的LPV-μ综合鲁棒控制器;最后进行整车电磁阀悬架系统仿真和实车试验。仿真结果表明:在随机扰动下该控制器使车身垂向加速度均方根值降低46.47%,俯仰角加速度降低46.47%,侧倾角加速度降低50.68%,具有良好的抗干扰能力和可控性。试验结果表明:LPV-μ综合控制下车身垂向加速度、俯仰角加速度和侧倾角加速度均方根值分别降低36.6%、30.14%和39.47%,并且保证悬架动挠度小于0.06 m,验证了该控制策略的有效性。

最大系数多重同步挤压变换识别结构瞬时频率

针对多重同步挤压变换及其改进算法存在的未重排点问题,提出了一种基于最大系数的多重同步挤压变换(maximum coefficient based multi-synchrosqueezing transform,简称MCMSST)方法来识别时变结构非平稳响应信号的瞬时频率(instantaneous frequency,简称IF)。首先,引入傅里叶频谱来辅助多分量响应信号选取截止频率;其次,对响应信号进行短时傅里叶变换(short time fourier transform,简称STFT),针对短时傅里叶变换系数求取针对时间的偏导,从而获得估算的瞬时频率;然后,在对瞬时频率的估算值进行多次迭代的基础上,仅保留时频系数模值最大处所对应的估算瞬时频率,并将其余位置的瞬时频率值归零;最后,对时频系数模值最大处所对应的瞬时频率进行时频重排即可得到细化后的瞬时频带。由于基于MCMSST方法提取的是瞬时频带,故采用时频系数模极大值法在限定的频带范围内提取瞬时频率曲线。通过2组数值算例和1个铝合金悬臂梁质量突变试验,验证了所提方法的有效性。研究结果表明,MCMSST方法不仅彻底解决了未重排点问题,而且提高了瞬时频率的识别精度和抗噪能力。

基于暂态数据戴维南等值参数辨识的时变广义短路比计算方法

广义短路比能有效反映多馈入直流系统受端交流电网的强度,其计算通常基于直流馈入节点处受端交流电网的戴维南等值导纳矩阵,忽略了交流电网各元件动态对电网强度的影响。本文提出了一种基于暂态数据戴维南等值参数辨识的时变广义短路比计算方法,将隐含系统元件动态特性的端口节点电压与注入端口节点功率作为辨识数据,基于多端口戴维南等值模型与最小二乘法辨识受端交流电网时变导纳矩阵,进而计算出时变广义短路比,准确地反映了系统动态对受端交流电网强度的影响。最后,以单馈入直流系统算例验证了方法的精确性,以某省级电网算例验证了其实用性,分析了负荷动态特性对时变广义短路比的影响。

采用联合算法识别人-桥耦合系统时变模态参数

为更加准确地识别在役人行桥结构的时变模态参数,提出了一种基于联合方法的人行桥瞬时频率和时变阻尼比识别方法。首先,采用联合方法分别识别模态分量信号的瞬时频率和自由衰减振动信号的时频系数。然后,通过推导瞬时频率、时频系数模值和阻尼比三者之间的函数关系,估算获得人-桥耦合系统的时变阻尼比。最后,通过人-桥耦合数值算例和华威桥步行试验对联合方法进行了验证。结果表明:联合方法可以有效改善多种复杂因素下自由衰减振动信号提取结果不准确的问题,并且该方法识别的瞬时频率和时变阻尼比曲线可以清晰地表征行人空间位置变化对钢-混组合人行桥频率和阻尼比的变化趋势。

高温蒸汽养护复掺混凝土力学性能时变特性与模型研究

为研究高温蒸汽养护下粉煤灰-矿粉机制砂混凝土(简称复掺混凝土)力学性能时变特性与模型,本文结合某高速公路高温蒸汽养护复掺混凝土预制梁项目,设计了蒸汽-标准养护、蒸汽-自然养护和标准养护三种养护方式,测试了不同阶段复掺混凝土的抗压强度、劈裂抗拉强度和弹性模量,探究了抗压强度与劈裂抗拉强度、弹性模量的关系。结果表明:合适的蒸汽养护方式能促进复掺混凝土抗压强度、劈裂抗拉强度和弹性模量更快增长,其中对抗压强度形成作用最显著,后期的力学性能不倒缩;蒸汽养护恒温阶段复掺混凝土的力学性能具有最大的增长率,其次是升温阶段,降温阶段力学性能增长率最小;蒸汽-标准养护方式下复掺混凝土的抗压强度时变模型可取对数或对数与幂函数复合函数,劈裂抗拉强度、弹性模量时变模型可取幂函数。

计及信息不确定性的多端交直流混合配电网分布式二级协同控制

分布式一致性控制(distributed consensus control,DCC)因其灵活协同能力成为交直流混合配电网二次控制的新途径.然而,传统DCC并未考虑实际通信环境中存在的随机时变时延和数据丢包问题,这致使其在非理想通信环境下难以稳定跟踪一致性期望目标.为此,本文提出一种计及信息不确定性的分布式二级协同控制策略.利用变量累加补偿机制,对时变时延和数据丢包进行统一等效.进而基于附加一致性算法(surplus consensus algorithm,SCA)构建具有零稳态误差特性的估计器,并对其收敛性进行推导证明.在此基础上,设计稳态收敛性能不受通信约束干扰的SCA-DCC控制器,实现非理想通信环境下的精准平均电压偏差调节和有功功率成比例分配.随后,以时延容忍度为指标优化SCA-DCC控制器参数,满足系统在不同通信条件下的稳定需求,进一步增强控制器的通信鲁棒性.仿真结果表明,在不同功率扰动和故障工况下,与现有控制策略相比,所提控制策略能够克服时变时延和数据丢包产生的负面影响,在维持较优动态响应性能的同时使稳态电压偏差降低73.08%~85.25%,具有控制精度高和抗扰动能力强的特点.

多自由度柔性机械臂非线性连续时变反馈控制方法

针对当前多自由度柔性机械臂控制过程中,机械臂定位控制系数选择合理性较低,导致多自由度柔性机械臂控制时延较长且运动轨迹与预设轨迹存在较大差异的问题,提出多自由度柔性机械臂非线性连续时变反馈控制方法。将拉格朗日算子作为计算基础,以能量的方式构建多自由度柔性机械臂动力学模型。考虑到机械臂的负载状态,计算机械臂的重力矩,识别机械臂非线性连续时变反馈控制参数。设定非线性连续时变反馈控制规则,构建机械臂非线性控制器,实现机械臂定位控制。构建实验环节,实验结果表明:该方法可有效提升机械臂运动轨迹与预设轨迹的一致性,缩短机械臂定位控制时延,进一步提升机械臂应用效果。

基于多物理场耦合的双块式无砟轨道早期混凝土时变行为研究

针对无砟轨道道床混凝土早期开裂问题,基于多物理场耦合理论,提出一种适用于浇筑早期的双块式无砟轨道水化-热-湿-力耦合计算模型,利用既有试验验证模型合理性,获取无砟轨道道床早期各物理场的时空分布规律,进行开裂风险预测。结果表明,与试验结果对比,本文模型对早期混凝土各物理场的模拟较为合理,尤其对表面干燥下的湿度场、复杂应力场的计算具有较强的适用性;道床水化速率先迅速增大后逐渐减,至第7 d基本停滞,最大水化速率出现在浇筑后约7 h,不同深度的水化度发展一致;受水化热影响,浇筑后24 h道床温度呈升高趋势,后受环境影响程度增大,随环境温度呈日周期变化;道床相对湿度及含水量呈垂向梯度分布,支承层对道床底部的干燥作用较为明显,水化耗水是导致道床内含水量降低的主要因素;道床早期应力及开裂风险呈周期性变化,最大开裂风险达到1.0,位于轨枕与道床的结合面处,并预测了道床早期开裂的3种主要形式。

基于事件触发机制的离散时变时滞多智能体一致性研究

为降低资源的消耗和浪费,并处理时变时滞对于多智能体的影响,针对离散线性时变时滞多智能体系统,提出一种基于事件触发机制的一致性协议,以减少系统的通信次数。在事件触发机制的框架下,提出一种基于预测控制的分段时滞补偿方法来补偿时变时滞对多智能体系统一致性的影响。在此基础上,通过构造基于延迟划分的Lyapunov-Krasovskii函数分析离散线性多智能系统的稳定性。最后,通过MATLAB仿真验证所提方法的有效性。研究结果表明:事件触发机制可以有效减少系统更新次数,降低消耗,所提出的基于预测控制的分段时滞补偿方法可以弥补时变时滞对于多智能体系统的影响,且基于延迟划分的分段补偿能够降低系统的保守性。

Leader-following多自主体系统时变编队跟踪控制

考虑时变时延,研究了高阶leader-following多自主体系统的分布式时变编队跟踪控制问题.针对连续高阶多自主体系统,设计了基于观测器的时变编队跟踪控制协议,通过状态变换,将系统的时变编队跟踪控制问题,转变为误差系统的稳定性问题.然后利用图论及李雅普诺夫稳定定理,研究了系统实现时变编队跟踪控制的充分条件,并且指出时变编队参考函数与时变时延无关.最后,数据仿真结果证明了所设计的协议能使follower自主体实现时变编队,并且跟踪leader自主体的轨迹.