WiMA:Towards a Multi-Criterion Association in Software Defined Wi-Fi Networks

Despite the planned installation and operations of the traditional IEEE 802.11 networks,they still experience degraded performance due to the number of inefficiencies.One of the main reasons is the received signal strength indicator(RSSI)association problem,in which the user remains connected to the access point(AP)unless the RSSI becomes too weak.In this paper,we propose a multi-criterion association(WiMA)scheme based on software defined networking(SDN)in Wi-Fi networks.An association solution based on multi-criterion such as AP load,RSSI,and channel occupancy is proposed to satisfy the quality of service(QoS).SDNhaving an overall view of the network takes the association and reassociation decisions making the handoffs smooth in throughput performance.To implementWiMA extensive simulations runs are carried out on Mininet-NS3-Wi-Fi network simulator.The performance evaluation shows that the WiMA significantly reduces the average number of retransmissions by 5%–30%and enhances the throughput by 20%–50%,hence maintaining user fairness and accommodating more wireless devices and traffic load in the network,when compared to traditional client-driven(CD)approach and state of the art Wi-Balance approach.

PARETO FRONT CAPTURE USING DETERMINISTIC OPTIMIZATION METHODS IN MULTI-CRITERION AERODYNAMIC DESIGN

Deterministic optimization methods are combined with the Pareto front concept to solve multi-criterion design problems. The algorithm and the numerical implementation are applied to aerodynamic designs. Evolutionary algorithms （EAs） and the Pareto front concept are used to solve practical design problems in industry for its robustness in capturing convex, concave, discrete or discontinuous Pareto fronts of multi-objective optimization problems. However, the process is time-consuming. Therefore, deterministic optimization methods are introduced to capture the Pareto front, and the types of the captured Pareto front are explained. Numerical experiments show that the deterministic optimization method is a good alternative to EAs for capturing any convex and some concave Pareto fronts in multi-criterion aerodynamic optimization problems due to its efficiency.

Suboptimal reliable guaranteed cost control for continuous-time systems with multi-criterion constraints

The suboptimal reliable guaranteed cost control （RGCC） with multi-criterion constraints is investigated for a class of uncertain continuous-time systems with sensor faults. A fauk model in sensors, which considers outage or partial degradation of sensors, is adopted. The influence of the disturbance on the quadratic stability of the closed-loop systems is analyzed. The reliable state-feedback controller is developed by a linear matrix inequalities （LMIs） approach, to minimize the upper bound of a quadratic cost fimction under the conditions that all the closed-loop poles be placed in a specified disk, and that the prescribed level of H∞ disturbance attenuation and the upper bound constraints of control inputs＇ magnitudes be guaranteed. Thus, with the above muki-criterion constraints, the resulting closed-loop system can provide satisfactory stability, transient property, a disturbance rejection level and minimized quadratic cost performance despite possible sensor faults.

Multi-Criterion Optimal Design of Automotive Door Based on Metamodeling Technique and Genetic Algorithm

A method for optimizing automotive doors under multiple criteria involving the side impact, stiffness, natural frequency, and structure weight is presented. Metamodeling technique is employed to construct approximations to replace the high computational simulation models. The approximating functions for stiffness and natural frequency are constructed using Taylor series approximation. Three popular approximation techniques,i.e.polynomial response surface (PRS), stepwise regression (SR), and Kriging are studied on their accuracy in the construction of side impact functions. Uniform design is employed to sample the design space of the door impact analysis. The optimization problem is solved by a multi-objective genetic algorithm. It is found that SR technique is superior to PRS and Kriging techniques in terms of accuracy in this study. The numerical results demonstrate that the method successfully generates a well-spread Pareto optimal set. From this Pareto optimal set, decision makers can select the most suitable design according to the vehicle program and its application.

On a Multi-criterion Problem of Planning Maritime Cargo Transportation

In this work, we consider a specific problem of optimal planning of maritime transportation of multiproduct cargo by ships of one （so-called ＂corporate strategy＂） or several （so-called ＂partially corporate strategy＂） companies： the core of the problem consists of the existence of the network of intermediate seaports （i.e., transitional seaports）, where for every ship arrived the cargo handling is done, and which are situated between the starting and the finishing seaports. In this work, there are mathematical models built from scratch in the form of multi-criterion optimization problem; according to the properties of the criteria and structure of the feasible solution set; are formulated different optimality conditions; are analysed different approaches for finding effective solutions （i.e., Pareto optimal solutions） and for check of the given solutions＇ effectiveness. In addition, in this work, there is considered and analysed well-known method of contraction of the Pareto boundary （goal attainment method of Gembicki）, then, it is used for reducing the built models to a one-criterion problem of linear programming.

Unsupervised construction of health indicator for rotating machinery via multi-criterion feature selection and attentive variational autoencoder

Health indicator(HI)construction is a crucial task in degradation evaluation and facilitates the prognostic and health management(PHM)of rotating machinery.Excluding interference from artificial labeling,the HI construction approaches in an unsupervised manner have attracted substantial attention.Nevertheless,current unsupervised methods generally struggle with two problems:(1)ignorance of both redundancy between features and global variability of features during the feature selection process;(2)inadequate utilization of information from different sampling moments.To tackle these problems,this work develops a novel unsupervised approach for HI construction that integrates multi-criterion feature selection and the Attentive Variational Autoencoder(Attentive VAE).Explicitly,a multi-criterion feature selection(Mc FS)algorithm together with an elaborately designed metric is proposed to determine a superior feature subset,considering the relevance,the redundancy,and the global variability of features simultaneously.Then,for the adequate utilization of the information from distinct sampling moments,a deep learning model named Attentive VAE is established.The Attentive VAE is solely fed with the selected features in the health state for model training and the HI is derived through the reconstruction error to reveal the degradation degree of machinery.Two case studies based on genuine experimental datasets are involved to quantitatively evaluate the superiority of the developed approach,demonstrating its superiority over other unsupervised methods for characterizing degradation processes.The effectiveness of both the Mc FS algorithm and the Attentive VAE is verified by ablation experiments,respectively.

Couplings in Multi-criterion Aerodynamic Optimization Problems Using Adjoint Methods and Game Strategies

The tighten couplings of game strategies with adjoint methods for multi-criterion aerodynamic design optimization are ad-dressed. Its numerical implementation is also described in details. In cooperative game,adjoint methods are coupled in parallel to compute Pareto front collaboratively. Conversely in a Nash game,adjoint methods are coupled in each player s decision making to achieve Nash equilibrium competitively. In Stackelberg game,adjoint methods used by players are nested hierarchically through incomp...

SSA-MLP模型在岩质边坡稳定性预测中的应用

岩质边坡的力学参数量化及稳定性分析对岩质边坡灾害的防治具有重要意义。Hoek-Brown(H B)准则是一种用于确定岩体力学参数的经典方法,能反映出边坡岩体变形和位移的非线性破坏特征。在此基础上,首先,提出一种麻雀搜索算法(Sparrow Search Algorithm,SSA)改进多层感知器(Multi-Layer Perceptron,MLP)的神经网络模型,并用于边坡稳定性预测、指标敏感性分析及参数反演。其次,将收集的1085组岩质边坡的几何参数和H B准则参数等作为输入变量,极限平衡理论Bishop法求解的安全系数作为输出变量,对SSA MLP模型进行训练学习和性能评估。最后,将该模型运用于25个边坡实例,验证模型的有效性。结果显示,该模型收敛速度快、精度高,为边坡稳定性分析和参数量化提供了一种新思路。

基于决策者偏好构造的概率语言型Z-number多属性决策方法

由于决策环境的日益复杂,专家评价信息往往会包含专家偏好,因此有必要对专家偏好进行识别与处理。针对这一情况,提出了基于决策者偏好构造的概率语言型Z-number并应用于多属性决策问题中。首先,针对专家偏好未知的情况,提出了一种基于修正的平均得分距离,由专家评价信息确定专家偏好。然后,依据得到的专家偏好,利用GOWPA算子与最大熵生成概率语言型Z-number。同时,获得专家评价信息的可信度,由专家可信度聚合专家信息并采用TODIM方法对方案进行排序。最后,在国家大力提倡节能减排的背景下,将所提方法应用于国家绿色矿山试点企业的选取上,验证了所提方法的有效性和可行性。

基于故障距离的微电网系统线路保护方法分析

配置快速、有效的线路保护方案是电源分散就地技术发展的关键。针对微电网线路保护问题,提出基于故障距离的微电网系统线路保护方法。通过构建包含故障信号的等效电气量方程,并推导出故障距离的解析表达式,确立1种多端线路保护的判据,再将此方法扩展到含有多个电源接入的电力网络中。算例分析表明,所提判据方法可靠性较高,可有效避免因保护装置整定值设定不合理而导致的误动或拒动问题。

低雷诺数翼型多目标优化设计研究

为消除低雷诺数下对称翼型小迎角范围内的气动力非线性现象,基于高斯过程回归模型及NSGA‑Ⅱ多目标遗传算法并耦合RANS的数值方法建立了低雷诺数翼型优化设计程序,以多个状态下小迎角范围内的平均巡航因子为目标函数开展翼型优化设计。结果表明:优化翼型具有明显的几何凸起,有效改变了分离泡的位置和类型,进而显著改善翼型气动特性,Re=4×104时小迎角范围内的升力系数非线性现象基本消失。进一步分析发现优化翼型的凸起增强了壁面的对流影响和来流抵抗逆压梯度的能力,进而改善了翼型的气动特性。

Multi-criterion system optimization model for urban multimodal traffic network

In this paper,computational models of environmental pollution and energy consumption of urban multimodal traffic network are proposed according to pertinent research and a multi-objective programming model is then developed to formulate optimization problem for such a system.Simultaneously,the main factors,such as travel time,pricing and convenience which influence travelers' choice behaviors are all considered and a combined assignment model is proposed to simulate travelers' mode and route choices.A bi-level programming model,in which the multi-objective optimization model is treated as the upper-level problem and the combined assignment model is processed as the lower-level problem,is then presented to solve multi-criterion system optimization problem for urban multimodal traffic network.The solution algorithms of the proposed models are also presented.Finally,the model and its algorithms are illustrated through a simple numerical example.

基于多对数螺旋曲线破坏机构的富水软岩隧道开挖面支护应力分析

为了避免对胡克-布朗准则进行任何线性化处理,将广义胡克-布朗(generalized Hoek-Brown, GHB)强度准则直接引入到开挖面稳定性分析中,基于极限分析理论构建了富水地层浅埋隧道开挖面多对数螺旋线破坏机构。在考虑孔隙水压力的作用下,推导出能量平衡方程,得到开挖面极限支护应力表达式,并且通过程序优化得到支护应力最优上限解。在此基础上,分析了GHB强度参数、孔隙水压力系数、岩土体重度以及隧道埋深比对开挖面稳定性的影响。最后通过归一化处理得到不同情况下的开挖面极限支护应力稳定性图表,通过该图表能够快速便捷地获得富水地层隧道开挖面维持稳定性所需的极限支护应力,为盾构隧道初步设计提供理论依据。

多判据融合的换流站监控数据异常甄别系统

换流站监控数据中存在部分异常数据,如果无法有效融合全部异常数据,会造成异常数据维度过大,导致甄别结果不精准,为此设计了基于多判据融合的换流站监控数据异常甄别系统。以MSP430单片机为核心,通过双存储器传输传感信号。设计可视甄别模块,计算两个图像圆点中心坐标距离,以此为依据,判断甄别点与设定阈值之间的关系。检测和筛选换流站监控异常数据,融合多判据数据特征,对数据进行降维处理,降低异常数据维度。构建LSTM分位数回归多判据融合模型,识别电流、电压、功率时间序列,获取置信区间范围,结合曲线斜率甄别异常数据,避免人为设置阈值给甄别结果带来的不确定性。由实验结果可知,该系统甄别出的电压、电流异常节点与实际异常节点数量一致,分别是3个和2个,说明使用该系统能够精准甄别异常数据。

基于LMD与AO-PNN的中介轴承故障诊断方法

针对航空发动机中介轴承受噪声干扰大、传递路径复杂导致采用传统方法难以进行故障诊断的问题,提出了一种基于局部均值分解(LMD)与相关系数-能量比-峭度准则、结合天鹰座优化算法(AO)优化概率神经网络(PNN)的中介轴承故障诊断方法。使用LMD对传感器采集的振动信号进行分解;利用相关系数-能量比-峭度准则判决筛选分解得到的PF分量,重构筛选后的信号;计算重构信号的多尺度排列熵(MPE),以构建特征向量;通过AO优化的PNN的平滑因子,将优化后的神经网络用于中介轴承的故障诊断。基于中介轴承故障试验数据对诊断结果进行了分析,结果表明:提出的方法可以有效诊断高背景噪声、复杂路径干扰下的航空发动机中介轴承的典型故障,与粒子群优化的概率神经网络方法(PSO-PNN)和传统的PNN方法相比,其诊断准确率分别提高了3.875%和8.125%,具有较好的全局收敛性和计算鲁棒性。

Distributed optimization using virtual and real game strategies for multi-criterion aerodynamic design

This paper introduces the virtual and real game concepts to investigate multi-criterion optimization for optimum shape design in aerodynamics. The constrained adjoint meth- odology is used as the basic optimizer. Furthermore, the above is combined with the vir- tual and real game strategies to treat single-point/multi-point airfoil optimization. In a symmetric Nash Game, each optimizer attempts to optimize one’s own target with ex- change of symmetric information with others. A Nash equilibrium is just the compromised solution among the multiple criteria. Several kinds of airfoil splitting and design cases are shown for the utility of virtual and real game strategies in aerodynamic design. Successful design results confirm the validity and efficiency of the present design method.

综合多指标评价法在河流健康评价中的应用研究综述

为了解当前综合多指标评价法的研究进展和方向,补充评价指标筛选的原则与方法,概述了国内外综合多指标评价法在河流健康评价中的研究进展。首先,通过追溯国内外文献资料,从河流健康评价对象和评价方式的角度总结了综合多指标评价的概念和评估原理,并以云南省河长制关于河流健康评价的目标-准则-指标结构为例,说明河流健康评价的3个层次结构。其次,重点强调了综合多指标评价法目前的研究进展和指标选用的合理性。最后,就综合多指标评价法存在的问题进行分析,并提出相应的解决方法,为中国河流健康管理提供参考,为各地河长制的顺利推进提供科技支撑。

面向组网雷达干扰任务的多机伴随式编队航迹预规划方法

针对多目标突防组网雷达系统场景,为有效提高干扰效果以及突防成功率,编队航迹规划尤为重要。因此,首先构建航迹规划模型,从飞行器自身约束、航迹安全性、机间协调以及任务完成效果4个方面出发,结合多机伴随式编队及其所处环境特点,提出较为完备的航迹规划准则,形成一个新的整体目标函数;其次,为有效描述每架飞机的机动特性以及伴飞干扰机与目标飞机间的联系,提高算法搜索能力,提出基于多球面矢量(multi-spherical vector-based,MS)方法;为进一步提高算法的探索和开发能力,提出多面球矢量逐航迹点学习混合粒子群优化(multi-spherical vector-based hybrid particle swarm optimization with track point by track point learning,TLHPSO)算法,并将两者相结合,形成基于多面球矢量的逐航迹点学习混合粒子群优化(MS-based hybrid particle swarm optimization with track point by track point learning,MS-TLHPSO)航迹规划方法;最后,构建相应仿真场景进行验证。对比结果表明,MS方法以及TLHPSO优化算法在寻优能力上具有明显优势;同时,所提算法在不同初始场景下最优解的平均值均优于其他算法,充分说明所提算法能够在保证稳定性的前提下规划具有更高可信度的编队航迹。

儿童阅读空间读者满意度多层次综合评价研究

科学评价是科学管理的前提。该研究融入元评价理念和方法,将属性识别模型应用于公共图书馆儿童阅读空间读者满意度评价中,得到了多层次的、定性与定量相统一的综合评价结果。通过比较分析论证了模型应用的科学性和适用性。模型应用能为读者满意度评价提供新的方法和思路,评价结果也能为相关工作提供参考。

基于多滤波降噪法的桥梁应变监测信号处理

针对桥梁应变监测信号存在多源噪声,以及现有降噪法难以准确选取有效固有模态函数(Intrinsic Mode Function,IMF)分量的问题,提出一种有效的多滤波降噪法。首先,采用自适应噪声抵消器对含噪信号进行预处理,以滤除低频噪声,再对其进行自适应噪声完备集合经验模态分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)得到IMF分量,并利用频谱分析-自相关函数双重判断准则选取IMF分量;随后,对其进行奇异值分解,利用奇异值差分谱确定各分量有效阶数;最后,将各有效IMF分量的有效阶重构得到去噪信号。通过模拟试验验证上述方法的合理性,并将其应用于桥梁应变监测信号处理。结果表明:采用上述双重判断准则选取有效IMF分量具有较好效果,且提出的多滤波降噪法在桥梁应变监测信号处理中具有显著的优越性。