基于时间序列关联分析的稳定运行规则提取方法

提出一种考虑运行方式变化时序的稳定规则提取方法。利用主成分分析进行敏感特征提取。对传统方法在数据标准化中存在的问题,通过数据均值化处理,保留原数据中各变量变异程度的差异信息,实现在保证原始数据分类识别     

基于等价型模糊神经网络的战场辐射源识别算法

针对多变量、环境干扰复杂的现代战场电子对抗辐射源识别非线性系统,应用早期的识别方法已经无法满足辐射源识别系统的精度要求,采用等价型模糊神经网络,产生模糊规则和隶属函数,再基于这些规则产生模糊逻辑推理来构造模式识别算法.仿真结果表明:该算法与传统模式识别方法相比具有更高的识别能力和较强的抗干扰能力,大大提高了战场辐射源识别的正确率和可靠性.     

基于脉冲重复间隔的活动雷达库关联算法

电子支援侦察系统具有对战场环境中活动雷达进行跟踪、监视的功能,上述功能是通过将雷达脉冲序列与活动雷达库中的记录进行关联实现的。文中提出了一种基于生物信息学中动态规划思想的关联算法,该算法通过提取雷达脉冲序列脉冲重复间隔变化规律,并将此作为识别特征对雷达批数据进行关联,仿真说明了该算法即使在有脉冲丢失和干扰脉冲的情况下仍具有较好的处理效果。     

不确定信息下基于拍卖算法的多无人机同时到达攻击多目标

针对不确定信息下的多无人机同时到达攻击多目标的控制策略问题,将拍卖算法和区间一致性算法相结合,提出了不确定信息下的多无人机同时到达的控制方法。首先通过拍卖算法,给出了多无人机对多目标的任务分配方法;然后定义了区间一致性算法,提出了不确定信息下的多无人机同时到达的控制方法,实现了多无人机到达多目标的时间上趋于一致;最后进行仿真,验证了该方法的可行性和有效性。     

不确定退化测量数据下的剩余寿命估计

剩余寿命估计是工程系统预测与健康管理的关键.目前,基于观测的系统退化数据进行剩余寿命估计得到了很大的关注.由于系统随机退化过程和测量误差的影响,测量数据中不可避免包含退化随机性和测量不确定性.然而,现有基于观测数据的剩余寿命估计研究中,没有将退化随机性和测量不确定性对估计的剩余寿命分布的影响同时考虑.鉴于此,提出了一种基于Wiener过程且同时考虑随机退化和不确定测量的退化建模方法,利用Kalman滤波技术,实现了潜在退化状态的实时估计.在退化状态估计的基础上,得到了同时考虑退化状态不确定性和测量不确定性的解析剩余寿命分布.此外,提出了一种基于极大似然方法的退化模型参数估计方法.最后,通过陀螺仪的退化测量数据验证了本文提出的方法优于不考虑测量不确定性的方法,可以提高剩余寿命估计的准确性.     

Stochastic collocation-based nonlinear analysis of concrete bridges with uncertain parameters

This paper focuses on the stochastic response of concrete bridges considering uncertainty in bearing and abutment stiffness. A multi-span simply supported bridge with concrete girders is selected. A 3D-dimensional model is prepared, and nonlinear response history analyses are performed. For the numerical dynamic simulation, the non-sampling stochastic method based on generalized polynomial chaos (gPC) expansion is utilised. The uncertain parameters include the vertical and shear stiffness of bearings and the lateral stiffness of abutments are presented by the truncated gPC expansions. Furthermore, the system response such as base shear, acceleration, velocity and displacement in different columns is presented by gPC expansion with unknown deterministic coefficients. The stochastic Galerkin projection is employed to calculate a set of deterministic equations. A non-intrusive solution, as a set of collocation points, determines the unknown gPC coefficients of the system response and the results are compared with Monte Carlo simulations. The key advantage of spectral discretization is the combination of the mentioned method with the spatial discretization, e.g. finite element model. This study also emphasises the accuracy in results and time efficiency of the proposed non-sampling method for uncertainty quantification of stochastic systems comparing to sampling procedure (e.g. Monte Carlo simulation).     

Bangla language modeling algorithm for automatic recognition of hand-sign-spelled Bangla sign language

Because of using traditional hand-sign segmentation and classification algorithm,many diversities of Bangla language including joint-letters,dependent vowels etc.and representing 51 Bangla written characters by using only 36 hand-signs,continuous hand-sign-spelled Bangla sign language(BdSL)recognition is challenging.This paper presents a Bangla language modeling algorithm for automatic recognition of hand-sign-spelled Bangla sign language which consists of two phases.First phase is designed for hand-sign classification and the second phase is designed for Bangla language modeling algorithm(BLMA)for automatic recognition of hand-sign-spelled Bangla sign language.In first phase,we have proposed two step classifiers for hand-sign classification using normalized outer boundary vector(NOBV)and window-grid vector(WGV)by calculating maximum inter correlation coefficient(ICC)between test feature vector and pre-trained feature vectors.At first,the system classifies hand-signs using NOBV.If classification score does not satisfy specific threshold then another classifier based on WGV is used.The system is trained using 5,200 images and tested using another(5,200×6)images of 52 hand-signs from 10 signers in 6 different challenging environments achieving mean accuracy of 95.83%for classification with the computational cost of 39.972 milliseconds per frame.In the Second Phase,we have proposed Bangla language modeling algorithm(BLMA)which discovers all"hidden characters"based on"recognized characters"from 52 hand-signs of BdSL to make any Bangla words,composite numerals and sentences in BdSL with no training,only based on the result of first phase.To the best of our knowledge,the proposed system is the first system in BdSL designed on automatic recognition of hand-sign-spelled BdSL for large lexicon.The system is tested for BLMA using hand-sign-spelled 500 words,100 composite numerals and 80 sentences in BdSL achieving mean accuracy of 93.50%,95.50%and 90.50%respectively.     

Relevancy and role of whole-farm models in supporting smallholder farmers in planning their agricultural season

The way a model is designed to assist farmers in their decision-making may influence how it is understood and perceived by farmers and shape interactions between farmers and model users (researcher, advisor). This study compared the strengths and weaknesses of three types of whole farm models used by researchers to assist 18 crop-livestock farmers in Burkina Faso in planning the next agricultural season. Due to its simplicity, the static simulation tool of annual farm stocks and flows led to superior changes in the farmers' knowledge and practices. The rule-based dynamic simulation tool helped the researchers grasp farmers' decision-making processes but was difficult for farmers to understand due to the discrepancy between its multi-annual time step and the farmers' short-term planning horizon. The optimisation tool stimulated more strategic discussions regarding paths to improve farm income despite a design that was distant from the farmers' reality.     

Rule induction for hierarchical attributes using a rough set for the selection of a green fleet

Rough set theory (RST) has been the subject of much study and numerous applications in many areas. However, most previous studies on rough sets have focused on finding rules where the decision attribute has a flat, rather than hierarchical structure. In practical applications, attributes are often organized hierarchically to represent general/specific meanings. This paper (1) determines the optimal decision attribute in a hierarchical level-search procedure, level by level, (2) merges the two stages, generating reducts and inducting decision rules, into a one-shot solution that reduces the need for memory space and the computational complexity and (3) uses a revised strength index to identify meaningful reducts and to improve their accuracy. The selection of a green fleet is used to validate the superiority of the proposed approach and its potential benefits to a decision-making process for transportation industry.     

用语图分析揭示语言系统中的隐性规律 ——赢家通吃和赢多输少算法

该文用“图”这一数学工具,通过定量分析来揭示语言系统中的隐性规律,设计了“赢家通吃”和“赢多输少”两种生成算法,将理想算法“步步竞争、择优而行”的博弈论思路贯彻到非理想状态。两种新算法都较前人有更好的概括能力。赢多输少算法更兼顾了充分概括和适度概括均衡。生成语图后,该设计着重准确率的最小简图和着重覆盖率的最大简图归纳算法,挖掘控制的主流规则、分析语言系统的语言学规律。在最小简图基础上提出控制度公式以评价语言系统。