Stochastic network equilibrium model with reliable travel time confidence level

In order to ensure on-time arrival when travelersmake their trips, the stochastic network assignment modelunder uncertainty of travel time is investigated. First, basedon travelers＇ route choice behavior, the reliable travel timeconfidence level （RTTCL）, which is the probability that a triparrives within the shortest average travel time plus theacceptable travel time difference, is defined. Then, areliability-based user equilibrium （RUE） model, whichhypothesizes that for each OD pair no traveler can improvehis/her RTTCL by unilaterally changing routes, is built.Since the traditional traffic assignment algorithms are notfeasible to solve the RUE model, a quasi method of successiveaverage （QMSA） is developed. Using Nguyen-Dupuis andSioux Falls networks, the model and the algorithm are tested.The results show that the QMSA algorithm can rapidlyconverge to a high accuracy for solving the proposed RUEmodel, and the RUE model can provide a good response totravelers＇ behavior in the stochastic network.

Malicious Node Detection Using Confidence Level Evaluation in a Grid-Based Wireless Sensor Network

In this paper, we present a malicious node detection scheme using confidence-level evaluation in a grid-based wireless sensor network. The sensor field is divided into square grids, where sensor nodes in each grid form a cluster with a cluster head. Each cluster head maintains the confidence levels of its member nodes based on their readings and reflects them in decision-making. Two thresholds are used to distinguish between false alarms due to malicious nodes and events. In addition, the center of an event region is estimated, if necessary, to enhance the event and malicious node detection accuracy. Experimental results show that the scheme can achieve high malicious node detection accuracy without sacrificing normal sensor nodes.

Confidence Level Based on Ridge Estimator in Process Measurement and Its Application

Ordinary least squares(OLS) algorithm is widely applied in process measurement, because the sensor model used to estimate unknown parameters can be approximated through multivariate linear model. However, with few or noisy data or multi-collinearity, unbiased OLS leads to large variance. Biased estimators, especially ridge estimator, have been introduced to improve OLS by trading bias for variance. Ridge estimator is feasible as an estimator with smaller variance. At the same confidence level, with additive noise as the normal random variable, the less variance one estimator has, the shorter the two-sided symmetric confidence interval is. However, this finding is limited to the unbiased estimator and few studies analyze and compare the confidence levels between ridge estimator and OLS. This paper derives the matrix of ridge parameters under necessary and sufficient conditions based on which ridge estimator is superior to OLS in terms of mean squares error matrix, rather than mean squares error.Then the confidence levels between ridge estimator and OLS are compared under the condition of OLS fixed symmetric confidence interval, rather than the criteria for evaluating the validity of different unbiased estimators. We conclude that the confidence level of ridge estimator can not be directly compared with that of OLS based on the criteria available for unbiased estimators, which is verified by a simulation and a laboratory scale experiment on a single parameter measurement.

Evaluating the confidence level of Traditional Chinese Medicine in nursing undergraduates at Chinese medical university

Objective:To observe the confidence level prevailing with regard to the practice of Traditional Chinese Medicine(TCM)among undergraduate nursing students who have undertaken TCM courses at Shanxi Medical University.Methods:This cross-sectional study was conducted at Shanxi University of Chinese Medicine.A questionnaire survey was administered through the Questionnaire Star website(https://www.questionstar.com/).The confidence level was examined from 6 aspects,namely cognition of TCM culture and theory,advantages and characteristics of TCM diagnosis and treatment,feelings about TCM achievements,personal behaviors related to TCM,attitudes toward Western medicine,and the future of TCM.The mean score of the observed items was calculated,and was found to be positively related to the confidence level concerning TCM(the score was between 1 and 5).Results:A total of 120 participants voluntarily enrolled themselves in the study.The results showed that the mean score of observed items were generally about 4.A relatively strong confidence was shown in understating TCM advantages and characteristics in diagnosis and treatment,and also in feelings toward TCM achievements.Conclusions:The majority of nursing undergraduates had confidence in TCM.However,some aspects such as the understanding of TCM culture and the correlation between TCM and Western medicine may need to be improved through education.

Confidence Level of System Spares OptimizationUsing Fuzzy Theory

To obtain a higher readiness for a complex system, the common method is to store some spares. Confidence level of system spares is an important parameter to control the probability of the spares required. By introducing fuzzy theory, this paper allocates the confidence level of system spares to its subsystems, thus to achieve a rational management of system spares,

Aggregation operators and CRITIC-VIKOR method for confidence complex q-rung orthopair normal fuzzy information and their applications

Supply chain management is an essential part of an organisation's sustainable programme.Understanding the concentration of natural environment,public,and economic influence and feasibility of your suppliers and purchasers is becoming progressively familiar as all industries are moving towards a massive sustainable potential.To handle such sort of developments in supply chain management the involvement of fuzzy settings and their generalisations is playing an important role.Keeping in mind this role,the aim of this study is to analyse the role and involvement of complex q-rung orthopair normal fuzzy(CQRONF)information in supply chain management.The major impact of this theory is to analyse the notion of confidence CQRONF weighted averaging,confidence CQRONF ordered weighted averaging,confidence CQRONF hybrid averaging,confidence CQRONF weighted geometric,confidence CQRONF ordered weighted geometric,confidence CQRONF hybrid geometric operators and try to diagnose various properties and results.Furthermore,with the help of the CRITIC and VIKOR models,we diagnosed the novel theory of the CQRONF-CRITIC-VIKOR model to check the sensitivity analysis of the initiated method.Moreover,in the availability of diagnosed operators,we constructed a multi-attribute decision-making tool for finding a beneficial sustainable supplier to handle complex dilemmas.Finally,the initiated operator's efficiency is proved by comparative analysis.

非对称自信水平下突发事件响应等级研判方法——基于自信度调整视角

新形势下我国社会安全事件系统性风险凸显造成应急响应等级研判难度跃升,针对专家非对称自信水平下突发事件响应等级研判难题,建立基于信任的专家自信度调整策略,提出专家自信水平影响的应急响应等级研判方法。该方法利用专家提供的信任度和熟悉度构成二元函数描述专家间信任关系,构建基于信任机制的专家自信度调整策略,通过分析非对称自信水平中蕴含的专家研判不确定性,基于证据理论提出应急响应等级研判问题建模方法。在此基础上,综合证据的来源重要度和自身可信度,提出考虑决策者风险偏好的证据可靠度计算方法,赋予证据不同重要性以实现专家研判信息的准确融合,为突发事件应急响应等级综合研判提供决策支持。最后,利用算例验证了方法的实用性与先进性。结果表明:相比投票、权威等常用决策方法,本文所提方法适用性更广、可信性更高、实用性更强。

建构水平对文化自信的影响

文化自信是完成“我—我们—文化大我”自我过程中重要的心理构念,其建构会受到表征文化事物抽象水平的影响。研究分别采用问卷法和实验法,探究文化建构水平对文化自信的影响。研究发现,建构水平对文化自信具有提升作用,该提升作用仅体现在文化自信的文化认知和行为层面。建构水平越高的个体对文化信息的评价越抽象,更注重文化信息所包含的意义和价值,其文化自信水平也更高。建构水平对文化自信的影响需要以文化内容作为桥梁才能凸显,即建构水平对文化自信的提升作用具有文化领域性。

文化自信视域下大学生思想引领的载体建构——以华南农业大学“青年讲中国”论坛为例

中国特色社会主义文化自信是中国国家文化的重要体现,是大学生理想信念和价值观、世界观形成的重要基石,优秀校园文化讲座是大学生思想引领、培育文化自信的重要载体。该文以校园文化讲座为研究对象,探索不同历史时期校园文化讲座的历史使命和活动特点,以及中外校园文化讲座的比较,分析当前校园文化讲座存在的若干问题,并以华南农业大学“青年讲中国·创新创业论坛”为例,研究文化自信建设视域下,优秀校园文化讲座如何从顶层设计、内涵建设及品牌打造等方面入手,做好大学生的思想引领和价值观塑造。该论坛历经10年打造,受众10万人次,取得一系列的成绩和经验,被多家主流媒体报道,深受大学生喜欢。

基于数据优化和CQR-CNN-BiGRU模型的光伏功率超短期区间预测

现有光伏功率区间预测算法通常侧重于提高覆盖率和缩小预测区间,未能充分考虑实际调度中的经济成本和风险成本,从而限制了这些算法在调度决策方面的实际应用。引入了“最优置信度”概念,提出了一种创新的光伏功率区间预测方法,将数据优化与CQR算法以及CNN-BiGRU神经网络模型相结合。首先对经预处理后的光伏功率序列进行优化,采用分解-重构的思想,将光伏功率序列分解为趋势分量、周期分量和随机分量;然后结合多个气象因素,分别输入经改进麻雀搜索算法优化的CQRCNN-BiGRU神经网络中,建立各自的区间预测模型,叠加三个分量的区间预测结果,实现光伏功率的区间预测。仿真结果表明,所提方法的预测区间能够更接近预设的最优置信度,同时能够快速、有效地获得更高质量的预测区间。

基于Level Set方法的Visible Human Being虚拟人图像处理

提出了将基于区域的VectorConfidenceConnected的低级分割方法和基于边界的LevelSet高级分割方法相组合的分割方法,使得边界的平滑性得到了保持;既有效地结合了医学专家的医学背景知识,又提高了分割处理的速度,很好的处理了医学图像中常见的拓扑结构,实验结果表明该方法切实可行。

基于区间概率不确定集的微电网两阶段自适应鲁棒优化调度

光伏出力的随机性和负荷用电的波动性对微电网的优化调度影响显著,为此提出了预测-调节-决策一体化的策略框架。基于高斯过程回归(Gaussian process regression,GPR)将光伏出力和负荷用电典型日历史数据自适应生成的置信区间与鲁棒优化中不确定集的构建相结合,建立了基于区间概率不确定集的自适应鲁棒优化调度模型。首先,通过GPR生成自适应鲁棒优化调度模型中不确定集的固定项,然后调节决策环节所考虑的风险水平以确定不确定集中的波动项,进而确定衡量不同调度保守度下的不确定集边界;接着采用预测区间质量评测指标来考核各个不确定集所对应的区间优劣。最后,通过改进的IEEE-37节点微电网系统验证了所提模型在有效抵御光伏出力和负荷用电波动的同时保持较低的运行成本。

计及不确定性的随机暂态稳定约束最优潮流

为应对电力系统中不确定性对系统安全稳定造成的显著影响,提出一种计及不确定性的随机暂态稳定约束最优潮流方法。首先,采用威布尔和正态分布分别描述风电和负荷两种不确定性变量。其次,设置相应的置信水平,基于机会约束理论建立相应的概率约束,以期望值形式表达目标函数,从而建立计及不确定性的随机暂态稳定约束最优潮流模型。然后,通过半不变量法和Gram-Charlier级数求取电力系统输出变量的累积分布函数,并利用改进量子粒子群算法进行求解。最后,算例分析验证了所提方法的优越性和有效性。

洁净室洁净度分级标准探究

鉴于科技的进步和实验经验,ISO/TC 209出台ISO14644-1:2015《按粒子浓度划出空气洁净度等级》Classification of air cleanliness by particle concentration比ISO14644-1:1999《空气洁净度等级》Classification of air cleanliness技术概念更清晰,使用更方便;实事求是,更赋灵活性:分级表中,所有浓度值都是累积,包括所有大于等于关注粒径(Considered particle size)的粒子的最大允许浓度值(Maximun allowable concentration siae),浓度限值。区域粒子浓度太高,浓度限值不适用;或者由于低浓度时采样和统计方法的局限性区域分级不适用。按统计学技术概念,决定检测洁净度最少采样点数N_(L);N_(L)值与洁净度无直接关联。作为标准应用的补充,超净环境检测需关注超高过滤器滤材最易穿透粒径MPPS,Most penetrating particle size。

基于模态信息度评估策略的掌纹掌静脉特征识别方法

多模态生物特征识别技术凭借其出色的识别效果和稳健的可靠性引起了业界的广泛关注。然而,传统多模态生物特征识别方法通常直接在特征层或决策层进行融合,很少考虑模态样本质量不同导致的融合效果差异。此外,由于缺乏大规模公开多模态生物特征数据库,多模态生物特征识别方法的研究受到一定程度上的限制。因此,设计了一款手部多模态数据采集设备,并自建了手部多模态数据库,用于多模态生物特征识别方法的验证与评估;提出了一种基于模态信息度评估策略的掌纹掌静脉特征识别方法,利用样本标签对应的类别置信度来评估每个模态特征的信息度,从而使模型在融合过程中根据不同模态对身份识别的贡献率进行自适应的权重分配。实验表明该方法在2个公开的数据库以及自建数据库上均取得了最高识别率。

基于网台分选的目标跳频序列置信区间的预测

通信频率的不断变化给非合作通信网络的侦察和预测带来了困难。由于跳频序列通常由具有一定规律性的特定模型生成,因此跳频序列是可预测的。通过提取跳频信号参数,基于时、频二维信息的网台分选算法,研究了基于网台分选的目标跳频序列置信区间预测。利用截获的大量跳频信号数据,构建长短时记忆神经网络模型,用于跳频序列预测。仿真结果表明,即使在一定噪声的影响下,此分选方法依旧可以准确地分选目标跳频信号。此外,基于LSTM的预测方法可以有效地预测跳频序列置信水平和置信区间。

管理者自信程度与企业研发投资——基于A股上市公司的经验证据

选取2011—2020沪深A股1911家上市公司的面板数据作为样本,通过主成分分析法构造一个综合指标来定义管理者自信程度,考察管理者自信程度对企业R&D投入的影响。研究结果发现管理者自信显著提高了企业的R&D投资水平。使用工具变量法缓解内生性以及安慰剂检验后,结果依然稳健。进一步研究发现,管理者政治背景、技术背景和海外背景对这种关系有着显著的正向调节作用,说明管理者的上述背景会增强他们的创新意识,从而加大企业的研发创新投入。异质性分析表明,较国有企业而言,非国有企业中管理层的自信程度对企业R&D投资水平的促进作用更加明显;由于资源的获取性和管理者决策权力差异,管理者政治背景、技术背景和海外背景的调节作用在非国有企业中也更加明显。

基于置信水平的钢轨焊接接头设备状态评定方法

[目的]为解决我国无缝线路钢轨设备状态评定体系不完善的问题,需要建立基于置信水平的钢轨焊接接头设备状态评定方法。[方法]阐述了目前我国规定的高速铁路钢轨平直度允许偏差、焊接接头平直度允许偏差。以我国某条已开通运营13年的高速铁路为案例,对该线钢轨焊接接头平直度数据进行分析,认为钢轨接头平直度检测数据总体服从正态分布。根据正态分布及置信区间原理,对均值和标准差评定法、置信水平评定法进行对比,提出了基于置信水平的钢轨焊接接头设备状态评定方法,得到高速铁路钢轨设备状态的评定标准。[结果及结论]该方法能够客观、直观地反映出钢轨焊接接头的设备状态,可以准确地掌握钢轨设备状态。该方法可为分析钢轨设备变化规律,以及制定大中修的标准、计划及方案等提供很好的理论依据。

基于未确知测度理论的矿井巷道安全风险评价分析

矿井巷道的安全性是保证矿山掘进与煤炭开采的重要保障之一,对其进行安全风险评价也是工程开展过程中不可或缺的关键环节。过往的评价方法往往更依赖于主观的定性分析,缺乏定量以及对各影响因素之间的主次关系考虑。基于此,从人、机、环、管4个方面出发,选取其中17个关键影响因素,运用未确知测度理论,科学建立了矿井巷道的安全评价体系;利用熵权法计算各影响因素的权重,再依据置信度识别准则对结果进行科学判定;最终对结果进行重要性排序,构建基于未确知测度理论的矿井巷道安全风险评价方法。结合工程实例分析结果表明,该评价方法客观全面,能够准确判别影响巷道安全状态的关键因素,与工程实际情况基本相符,证明该方法具有较高的准确性以及实际应用价值,可用于类似条件矿井巷道的综合安全评价。

多模态深层次高置信度融合跟踪算法

为解决单目标跟踪中因目标外观及环境变化导致的跟踪失败问题,提出一种多模态深层次高置信度融合跟踪算法.首先构建目标颜色模型和基于双线性插值HOG特征形状模型的高维度多模态模型,之后对候选目标利用粒子滤波进行搜索.针对模型融合的难点,通过准确量化形状和颜色模型多种置信度并设计高置信度融合准则,以实现该多模态模型中不同置信度的深层次自适应加权平衡融合.最后针对模型更新参数固定的问题,设计非线性分级平衡更新策略.经过在OTB-2015数据集上的测试,发现该算法的平均CLE和OS在所有参照算法表现中均表现最佳,其值分别为30.57和0.609.此外,其FPS为15.67,满足了跟踪算法在一般情况下的实时性要求.在某些常见的特定场景中,其精确率、成功率指标在多数情况下的表现也超过了同类顶尖算法.