Multi-source Fuzzy Information Fusion Method Based on Bayesian Optimal Classifier

为了做常规贝叶斯的最佳的分类器，拥有处理模糊信息并且认识到推理过程的自动化的能力，一个新贝叶斯的最佳的分类器被建议，模糊信息嵌入。它不能仅仅有效地处理模糊信息，而且保留贝叶斯的最佳的分类器的学习性质。另外根据模糊集合理论的进化，含糊的集合也是嵌入的进它产生含糊的贝叶斯的最佳的分类器。它能同时从积极、反向的方向模仿模糊信息的双重的特征。进一步，贝叶斯的最佳的分类器也是的集合对从积极、反向、不确定的方面就模糊信息的三方面的特征而言求婚了。最后，一个知识库的人工的神经网络(KBANN ) 被介绍认识到贝叶斯的最佳的分类器的自动推理。它不仅减少贝叶斯的最佳的分类器的计算费用而且改进它学习质量的分类。

Primary exploration of nonlinear information fusion control theory

By introducing Information fusion techniques into a control field, a new theory of information fusion control （IFC） is proposed. Based on the theory of information fusion estimation, optimal control of nonlinear discrete control system is investi- gated. All information on control strategy, including ideal control strategy, expected object trajectory and dynamics of system, are regarded as measuring information of control strategy. Therefore, the problem of optimal control is transferred into the one of information fusion estimation. Firstly, the nonlinear information fusion estimation theorems are described. Secondly, an algorithm of nonlinear IFC theory is detailedly deduced. Finally, the simulation results of manipulator shift control are given, which show the feasibility and effectiveness of the presented algorithm.

State Accurate Representation and Performance Prediction Algorithm Optimization for Industrial Equipment Based on Digital Twin

The combination of the Industrial Internet of Things(IIoT)and digital twin(DT)technology makes it possible for the DT model to realize the dynamic perception of equipment status and performance.However,conventional digital modeling is weak in the fusion and adjustment ability between virtual and real information.The performance prediction based on experience greatly reduces the inclusiveness and accuracy of the model.In this paper,a DT-IIoT optimization model is proposed to improve the real-time representation and prediction ability of the key equipment state.Firstly,a global real-time feedback and the dynamic adjustment mechanism is established by combining DT-IIoT with algorithm optimization.Secondly,a strong screening dual-model optimization(SSDO)prediction method based on Stacking integration and fusion is proposed in the dynamic regulation mechanism.Lightweight screening and multi-round optimization are used to improve the prediction accuracy of the evolution model.Finally,tak-ing the boiler performance of a power plant in Shanxi as an example,the accurate representation and evolution prediction of boiler steam quantity is realized.The results show that the real-time state representation and life cycle performance prediction of large key equipment is optimized through these methods.The self-lifting ability of the Stacking integration and fusion-based SSDO prediction method is 15.85%on average,and the optimal self-lifting ability is 18.16%.The optimization model reduces the MSE loss from the initial 0.318 to the optimal 0.1074,and increases R2 from the initial 0.731 to the optimal 0.9092.The adaptability and reliability of the model are comprehensively improved,and better prediction and analysis results are achieved.This ensures the stable operation of core equipment,and is of great significance to comprehensively understanding the equipment status and performance.

Exploring the Potentialities of Automatic Extraction of University Webometric Information

Purpose:The main objective of this work is to show the potentialities of recently developed approaches for automatic knowledge extraction directly from the universities’websites.The information automatically extracted can be potentially updated with a frequency higher than once per year,and be safe from manipulations or misinterpretations.Moreover,this approach allows us flexibility in collecting indicators about the efficiency of universities’websites and their effectiveness in disseminating key contents.These new indicators can complement traditional indicators of scientific research(e.g.number of articles and number of citations)and teaching(e.g.number of students and graduates)by introducing further dimensions to allow new insights for“profiling”the analyzed universities.Design/methodology/approach:Webometrics relies on web mining methods and techniques to perform quantitative analyses of the web.This study implements an advanced application of the webometric approach,exploiting all the three categories of web mining:web content mining;web structure mining;web usage mining.The information to compute our indicators has been extracted from the universities’websites by using web scraping and text mining techniques.The scraped information has been stored in a NoSQL DB according to a semistructured form to allow for retrieving information efficiently by text mining techniques.This provides increased flexibility in the design of new indicators,opening the door to new types of analyses.Some data have also been collected by means of batch interrogations of search engines(Bing,www.bing.com)or from a leading provider of Web analytics(SimilarWeb,http://www.similarweb.com).The information extracted from the Web has been combined with the University structural information taken from the European Tertiary Education Register(https://eter.joanneum.at/#/home),a database collecting information on Higher Education Institutions(HEIs)at European level.All the above was used to perform a clusterization of 79 Italian universities based on structural and digital indicators.Findings:The main findings of this study concern the evaluation of the potential in digitalization of universities,in particular by presenting techniques for the automatic extraction of information from the web to build indicators of quality and impact of universities’websites.These indicators can complement traditional indicators and can be used to identify groups of universities with common features using clustering techniques working with the above indicators.Research limitations:The results reported in this study refers to Italian universities only,but the approach could be extended to other university systems abroad.Practical implications:The approach proposed in this study and its illustration on Italian universities show the usefulness of recently introduced automatic data extraction and web scraping approaches and its practical relevance for characterizing and profiling the activities of universities on the basis of their websites.The approach could be applied to other university systems.Originality/value:This work applies for the first time to university websites some recently introduced techniques for automatic knowledge extraction based on web scraping,optical character recognition and nontrivial text mining operations(Bruni&Bianchi,2020).

基于多源信息融合和WOA-CNN-LSTM的外脚手架隐患分类预警研究

面对施工现场外脚手架隐患信息的多样性,传统的基于传感器监测的单一信号预警研究存在容错力不佳、含有信息有限等问题。针对施工现场外脚手架“图像+监测”数据,提出一种基于数据层和特征层信息融合的脚手架隐患分类预警方法。首先,利用Revit三维建模软件建立外脚手架实体模型,对不同初始隐患下的外脚手架进行有限元分析,划分隐患预警等级;其次,利用无迹卡尔曼滤波算法(Unscented Kalman Filter,UKF)及卷积长短时记忆网络(Convolutional Neural Network-Long Short Term Memory Network,CNN-LSTM)实现脚手架同类信息数据层融合及异类信息特征层融合;最后,通过实时收集西安市某在建项目落地式双排扣件式钢管脚手架隐患信息,对其进行分类预警,并使用鲸鱼优化算法(Whale Optimization Algorithm,WOA)对CNN-LSTM网络进行参数优化,发现隐藏节点个数为30、学习率为0.0072、正则化系数为1×10^(-4)时分类效果最佳,优化后预警精度达到了91.4526%。通过可视化WOA-CNN-LSTM、CNN-LSTM、CNN-SVM(Support Vector Machine,支持向量机)及CNN-GRU(Gate Recurrent Unit,门控循环单元)分类预警结果,证实了优化后的CNN-LSTM网络在脚手架分类预警方面的优越性。

“人-车-桩-路-网”深度耦合下的配电网协同规划与运行优化

电动汽车(EV)作为连接交通电气化和电网清洁化的纽带和桥梁,可实现电力-交通-信息之间的深度耦合,形成电力-交通一体化的规划及运行架构。配电-交通融合系统(DTIS)中,EV充放电行为多时空动态交织、电能流-交通流-信息流深度融合、多利益主体动态博弈等大量不确定性因素使配电网的规划及运行优化的边界都将发生重大转变,但同时也给挖掘利用EV的移动储能特性和提升配电网灵活性带来了契机。为此,在分析“人-车-桩-路-网”深度耦合下配电网的形态演化特征基础上,剖析了新形态下配电网协同规划与运行优化所面临的新挑战,进而针对“人-车-桩-路-网”耦合下的EV灵活性建模、灵活域高效构建及预测、协同规划、运行优化4个方面关键技术展开讨论,并对相关技术问题的研究方向进行了展望。

基于多传感器信息融合的机床测量数据自动补偿系统

机床测量数据自动补偿能够减少加工误差,为提高机床加工精度,设计基于多传感器信息融合的机床测量数据自动补偿系统。首先设计温度传感器、单片机主控与补偿脉冲发生装置,获取机床运动数据,然后采用多传感器融合算法融合采集信息,对信息中误差处理,采用粗糙集理论寻找最佳补偿值,实现机床测量数据自动补偿。实验结果表明,该系统能够有效减少加工误差,可以满足实际应用要求。

面向安全高效运行的重载列车智能操控关键技术

面向我国重载铁路自动化和智能化发展以及重载铁路运输需求持续增长对既有列车运行控制带来的严峻挑战,文章围绕重载列车智能操控关键技术展开了研究现状介绍,并分析了目前亟待解决的难题与未来发展趋势。首先,针对列车纵向动力学建模,已有研究多考虑单一环境、固定工况下机理建模及分析,难以精确表征列车运行过程中的内、外部参数时空渐变特性,因此,在已有机理模型基础上考虑未建模动态,并进行降阶等效是未来一个重要研究方向。其次,长编组列车车辆间非线性强耦合特性、空气制动特性对列车运行过程中纵向冲动的影响依旧为重载列车操纵优化带来了巨大的挑战,借鉴高速列车多车协同控制技术,研究考虑主动抑制纵向冲动的重载列车多机车分布式协同控制具有重要意义。最后,结合数据驱动与人工智能算法、多源信息融合等新兴技术分析了列车控制领域一些新的机遇,而耦合仿真平台与评估体系的搭建也将成为重载列车安全运行分析、评估与优化的一种有效途径。

SELF-TUNING WEIGHTED MEASUREMENT FUSION WHITE NOISE DECONVOLUTION ESTIMATOR

For the multi-sensor linear discrete time-invariant stochastic systems with correlated measurement noises and unknown noise statistics,an on-line noise statistics estimator is obtained using the correlation method.Substituting it into the optimal weighted fusion steady-state white noise deconvolution estimator based on the Kalman filtering,a self-tuning weighted measurement fusion white noise deconvolution estimator is presented.By the Dynamic Error System Analysis(DESA) method,it proved that the self-tuning fusion white noise deconvolution estimator converges to the steady-state optimal fusion white noise deconvolution estimator in a realization.Therefore,it has the asymptotically global optimality.A simulation example for the tracking system with 3 sensors and the Bernoulli-Gaussian input white noise shows its effectiveness.

远程光纤传感网中滤波降噪方法的优化

为实现远程光纤传感网络在噪声干扰条件下的高精度检测传输,提出一种优化差分(optimized difference,OD)的降噪方法。基于移动平均法构建新差分算法,并结合低通算法、中值算法预判边缘信息及预测信号数值;采用双重判别弥补边缘信息误判的缺陷,优化准确定位边缘信息的能力;融合加权平均法与卡尔曼滤波器(Kalman filter,KF),优化信号预测的精度。实验验证结果表明:采用该方法进行数据预处理将使系统能够正常检测数据,测量精度可达0.76%,提高检测系统的抗噪性能。与先进小波阈值降噪方法的对比实验结果表明:斜率优化方法在≥10 dB噪声干扰下,测量精度相对提高0.62倍以上。

模糊集合下多传感器信息融合算法仿真

多传感器数据融合通常涉及大量数据,受到噪声、不完整性、不准确性等因素的影响,导致融合结果的不确定性增加。为此,提出一种基于模糊集合的多传感器信息融合算法。利用局部保持投影(Locality Preserving Projection, LPP)处理多传感器信息,采用主成分分析(Principal Component Analysis, PCA)分离特征值较大的反射信号,删除特征值较小的随机噪声。利用隶属函数得到不同传感器提供的可信度,将支持度以及可信度转换为基本概率分配函数,引入证据理论(Dempster-Shafer, D-S),实现多传感器信息融合优化。仿真分析表明,所提方法可以得到高精度和高效率的多传感器信息融合结果,峰值信噪比达60dB以上,信噪比一直处于12dB以上,融合最长耗时仅为2.01ms,使其融合性能得到有效优化。

基于多尺度融合的轻量级脑肿瘤分割算法

脑肿瘤是一种常见的神经系统疾病,准确的肿瘤分割对于诊断和治疗至关重要。然而,传统的自动分割方法受限于计算复杂度和精度,限制了其实际的临床应用。此外,脑肿瘤在不同尺度下具有多样性,因此需要一种方法来融合多尺度信息以提高分割精度。首先设计一种轻量级脑肿瘤分割模型,通过减小参数量和计算复杂度,使其更适合点对点临床分析;其次,引入了多尺度信息融合策略和注意力机制,以考虑不同尺度下的脑肿瘤特征,提高分割准确度和鲁棒性;最后,实验优化后的模型完整肿瘤、核心区域、增强区域的Dice分数分别为0.851、0.834、0.778,参数量和计算复杂度仅为0.73 M和0.20 G,优于最先进的分割方法。

多传感器智能车辆的编队运动自适应控制

面向具备多传感器的车辆编队问题,文中主要研究基于预定运动轨迹的自适应运动算法。首先,利用Kalman滤波处理多个传感器的数据信息得到最优数据融合估计,以获取车辆编队的最优位置信息。然后,通过车辆之间交换位置估计信息得到下一步车辆编队运动规划。分别考虑了无领导车的固定双向通信集群移动和有领导车的固定单向通信集群移动2种车辆编队运动,提出的方法不同于仅控制编队车辆向目的地移动的情况,而是通过车辆与相邻车辆节点交换位置信息,使得车辆编队可沿着预定的轨迹向终点位置移动。为了满足车辆之间维持理想距离的实际需求,分别引入使队列沿预定路径运动的牵引力和拓扑力2种虚拟力模型,其中牵引力主要引导车辆沿预定路径到达目的地,而拓扑力主要维持各跟随车辆之间的连接拓扑。仿真结果表明:所提方法相较于已有方法能有效缩短车辆安全距离、提升道路利用率,具有良好的适用性。

Self-tuning measurement fusion white noise deconvolution estimator with correlated noises

For the multisensor linear discrete time-invariant stochastic systems with correlated noises and unknown noise statistics,an on-line noise statistics estimator is presented by using the correlation method.Substituting it into the steady-state Riccati equation,the self-tuning Riccati equation is obtained.Using the Kalman filtering method,based on the self-tuning Riccati equation,a self-tuning weighted measurement fusion white noise deconvolution estimator is presented.By the dynamic error system analysis（DESA） method,it is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steadystate white noise deconvolution estimator in a realization,so that it has the asymptotic global optimality.A simulation example for Bernoulli-Gaussian input white noise shows its effectiveness.

基于多源信息融合的新型电力系统短路故障自动定位研究

传统的短路故障定位方法通常对获取的短路故障特征量处理较单一,导致短路故障自动定位精度低,为此,研究基于多源信息融合的新型电力系统短路故障自动定位。首先,利用多源信息融合获取短路故障特征量,得到更详细的短路故障数据;其次,设置线路监测点,实时监测新型电力系统的运行状态,当监测到短路故障时,需确定短路故障区间;最后,进行短路故障点测距定位,实现新型电力系统短路故障自动定位。结果表明,与红外热像仪法和数字测量仪法相比,基于多源信息融合的新型电力系统短路故障自动定位方法在定位短路故障时,定位精度更高,具有实际的应用价值。

GF-6与Sentinel-2A数据多层次优选的杨树食叶虫害信息提取研究

以天津市蓟州区于桥水库北部为研究区,选用高分六号(GF-6)全色数据和哨兵二号(Sentinel-2A)多光谱数据,通过对遥感数据的虫害敏感波段、融合波段和分类识别特征的3个层次优选处理,进而提高杨树食叶虫害遥感识别信息的准确度.遥感影像经过预处理后,首先根据光谱统计特征差值最大化方法,优选出虫害区识别敏感的特征波段;然后利用波段相关系数大小优选融合波段方案,进行高通滤波(high-pass filtering,HPF)融合和Gram-Schmidt(GS)融合处理;最后通过Relief算法对植被指数等19种特征进行优选,确定食叶虫害遥感识别的特征.结果表明:(1)经过敏感波段优选后的HPF+GS融合,Sentinel-2A数据的红边波段、近红外波段、短波红外波段受虫害胁迫区杨树林的反射率值有明显变化,在分类时特征贡献度提升明显;(2)经过HPF+GS组合融合处理后,融合后的影像细节信息突出,植被监测光谱红边敏感波段的空间分辨率明显提高;(3)经过多层次的特征优选处理后的分类精度明显提高.未经过优选处理遥感影像分类精度为83.15%,Kappa系数为0.6785;经过优选后影像的分类精度为90.64%,Kappa系数0.8116,表明进行多次特征优选处理能够较为有效地提取食叶虫害影像信息.

Two-stage prediction and update particle filtering algorithm based on particle weight optimization in multi-sensor observation

The reasonable measuring of particle weight and effective sampling of particle state are consid- ered as two important aspects to obtain better estimation precision in particle filter. Aiming at the comprehensive treatment of above problems, a novel two-stage prediction and update particle filte- ring algorithm based on particle weight optimization in multi-sensor observation is proposed. Firstly, combined with the construction of muhi-senor observation likelihood function and the weight fusion principle, a new particle weight optimization strategy in multi-sensor observation is presented, and the reliability and stability of particle weight are improved by decreasing weight variance. In addi- tion, according to the prediction and update mechanism of particle filter and unscented Kalman fil- ter, a new realization of particle filter with two-stage prediction and update is given. The filter gain containing the latest observation information is used to directly optimize state estimation in the frame- work, which avoids a large calculation amount and the lack of universality in proposal distribution optimization way. The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

基于视频识别技术的电气火灾自动报警系统

为了提高电气火灾自动报警准确性和实时性,设计基于视频识别技术的电气火灾自动报警系统。首先通过火灾探测器采集电气火灾视频数据,采用D-S证据理论算法对数据进行融合处理,然后采集盒式滤波器对电气火灾视频进行去噪处理,从电气火灾视频序列提取闪烁和色彩特征,神经网络根据电气火灾特征建立电气火灾检测模型,根据检测结果进行电气火灾自动报警,测试结果表明:所提系统减少了电气火灾自动报警响应时间,提高了电气火灾识别准确性,可以满足电气火灾自动报警实际应用要求。

基于改进智能优化算法的自主导航物流小车路径决策方法

针对目前多自主导航小车(Automatic guided vehicles,AGV)物流系统实现过程复杂、路径规划以及调度性能差等问题,提出了一种基于信息物理系统(Cyber physical system,CPS)的多AGV路径规划与调度控制方法,设计了包含物理感知层、网络传输层和控制层的AGV物流系统架构,建立了通过时间参数反映货物不平衡状态的虚拟路径网络模型。提出了一种考虑时间约束的改进A算法的多AGV路径规划方法,还提出一种基于列表的调度控制算法以避免多AGV碰撞与冲突问题。在实验环节,以仿真模拟系统对所提方法进行测试。实验结果表明,所提方法的分拣效率约为D规划的1.3倍,约为A规划的1.79倍。此外,平均停车时间、死锁时间、平均全局路径占用率、平均规划时间等指标验证了所提算法具有应用更轻、更高效的优势,该方法具有广阔的应用前景。

复合型火灾报警系统设计

本文设计了一种复合型火灾自动报警系统。本系统基于单片机STC89C52,结合DS18B20温度传感器、MQ-2气体传感器、红外光敏传感器和LCD1602液晶显示屏、蜂鸣器等器件,利用多传感器信息融合技术,将火灾现场的火焰、烟雾和温度等物理信息转化成电信号,传输到单片机控制模块进行判断处理,随后显示在液晶屏并完成声光报警。