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Evaluating the Efficacy of Latent Variables in Mitigating Data Poisoning Attacks in the Context of Bayesian Networks:An Empirical Study
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作者 Shahad Alzahrani Hatim Alsuwat Emad Alsuwat 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第5期1635-1654,共20页
Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent ... Bayesian networks are a powerful class of graphical decision models used to represent causal relationships among variables.However,the reliability and integrity of learned Bayesian network models are highly dependent on the quality of incoming data streams.One of the primary challenges with Bayesian networks is their vulnerability to adversarial data poisoning attacks,wherein malicious data is injected into the training dataset to negatively influence the Bayesian network models and impair their performance.In this research paper,we propose an efficient framework for detecting data poisoning attacks against Bayesian network structure learning algorithms.Our framework utilizes latent variables to quantify the amount of belief between every two nodes in each causal model over time.We use our innovative methodology to tackle an important issue with data poisoning assaults in the context of Bayesian networks.With regard to four different forms of data poisoning attacks,we specifically aim to strengthen the security and dependability of Bayesian network structure learning techniques,such as the PC algorithm.By doing this,we explore the complexity of this area and offer workablemethods for identifying and reducing these sneaky dangers.Additionally,our research investigates one particular use case,the“Visit to Asia Network.”The practical consequences of using uncertainty as a way to spot cases of data poisoning are explored in this inquiry,which is of utmost relevance.Our results demonstrate the promising efficacy of latent variables in detecting and mitigating the threat of data poisoning attacks.Additionally,our proposed latent-based framework proves to be sensitive in detecting malicious data poisoning attacks in the context of stream data. 展开更多
关键词 Bayesian networks data poisoning attacks latent variables structure learning algorithms adversarial attacks
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Discovering Latent Variables for the Tasks With Confounders in Multi-Agent Reinforcement Learning
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作者 Kun Jiang Wenzhang Liu +2 位作者 Yuanda Wang Lu Dong Changyin Sun 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第7期1591-1604,共14页
Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning(MARL). It is significantly more difficult for those tasks with latent variables that ... Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning(MARL). It is significantly more difficult for those tasks with latent variables that agents cannot directly observe. However, most of the existing latent variable discovery methods lack a clear representation of latent variables and an effective evaluation of the influence of latent variables on the agent. In this paper, we propose a new MARL algorithm based on the soft actor-critic method for complex continuous control tasks with confounders. It is called the multi-agent soft actor-critic with latent variable(MASAC-LV) algorithm, which uses variational inference theory to infer the compact latent variables representation space from a large amount of offline experience.Besides, we derive the counterfactual policy whose input has no latent variables and quantify the difference between the actual policy and the counterfactual policy via a distance function. This quantified difference is considered an intrinsic motivation that gives additional rewards based on how much the latent variable affects each agent. The proposed algorithm is evaluated on two collaboration tasks with confounders, and the experimental results demonstrate the effectiveness of MASAC-LV compared to other baseline algorithms. 展开更多
关键词 latent variable model maximum entropy multi-agent reinforcement learning(MARL) multi-agent system
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Temporally Preserving Latent Variable Models:Offline and Online Training for Reconstruction and Interpretation of Fault Data for Gearbox Condition Monitoring
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作者 Ryan Balshaw P.Stephan Heyns +1 位作者 Daniel N.Wilke Stephan Schmidt 《Journal of Dynamics, Monitoring and Diagnostics》 2024年第2期156-177,共22页
Latent variable models can effectively determine the condition of essential rotating machinery without needing labeled data.These models analyze vibration data via an unsupervised learning strategy.Temporal preservati... Latent variable models can effectively determine the condition of essential rotating machinery without needing labeled data.These models analyze vibration data via an unsupervised learning strategy.Temporal preservation is necessary to obtain an informative latent manifold for the fault diagnosis task.In a temporalpreserving context,two approaches exist to develop a condition-monitoring methodology:offline and online.For latent variable models,the available training modes are not different.While many traditional methods use offline training,online training can dynamically adjust the latent manifold,possibly leading to better fault signature extraction from the vibration data.This study explores online training using temporal-preserving latent variable models.Within online training,there are two main methods:one focuses on reconstructing data and the other on interpreting the data components.Both are considered to evaluate how they diagnose faults over time.Using two experimental datasets,the study confirms that models from both training modes can detect changes in machinery health and identify faults even under varying conditions.Importantly,the complementarity of offline and online models is emphasized,reassuring their versatility in fault diagnostics.Understanding the implications of the training approach and the available model formulations is crucial for further research in latent variable modelbased fault diagnostics. 展开更多
关键词 Condition monitoring unsupervised learning latent variable models temporal preservation training approaches
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基于KDLV算法的配电网环网柜故障检测模型
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作者 邹勋 李鹏 苗爱敏 《工业安全与环保》 2024年第10期6-12,共7页
环网柜作为配电系统中的关键连接点,其状态监测数据通常具有非线性、动态性和多变量相关性等特点。为了克服传统环网柜故障检测模型对状态特征信息提取不够充分导致监测效果不佳的问题,提出一种基于核动态潜变量(KDLV)算法的故障检测模... 环网柜作为配电系统中的关键连接点,其状态监测数据通常具有非线性、动态性和多变量相关性等特点。为了克服传统环网柜故障检测模型对状态特征信息提取不够充分导致监测效果不佳的问题,提出一种基于核动态潜变量(KDLV)算法的故障检测模型。首先利用KDLV算法将提取的多变量信息划分为动态潜变量部分和静态潜变量部分。然后对动态部分构建VAR模型,并构造特征空间的T^(2)监控统计量;对静态部分构建NPE模型,并分别构造数据特征空间和残差空间的T^(2)和SPE监控统计量。最后综合状态数据的动静态统计限,实现对实时数据的在线监控。通过现场试验数据验证,模型的故障检测率为100%,误报率最高为1.20%,相较于传统的环网柜故障检测模型,提出的模型更具优势。 展开更多
关键词 环网柜 动态潜变量 故障检测 核函数 数据驱动 数据挖掘
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A semantic and emotion-based dual latent variable generation model for a dialogue system 被引量:1
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作者 Ming Yan Xingrui Lou +2 位作者 Chien Aun Chan Yan Wang Wei Jiang 《CAAI Transactions on Intelligence Technology》 SCIE EI 2023年第2期319-330,共12页
With the development of intelligent agents pursuing humanisation,artificial intelligence must consider emotion,the most basic spiritual need in human interaction.Traditional emotional dialogue systems usually use an e... With the development of intelligent agents pursuing humanisation,artificial intelligence must consider emotion,the most basic spiritual need in human interaction.Traditional emotional dialogue systems usually use an external emotional dictionary to select appropriate emotional words to add to the response or concatenate emotional tags and semantic features in the decoding step to generate appropriate responses.However,selecting emotional words from a fixed emotional dictionary may result in loss of the diversity and consistency of the response.We propose a semantic and emotion-based dual latent variable generation model(Dual-LVG)for dialogue systems,which is able to generate appropriate emotional responses without an emotional dictionary.Different from previous work,the conditional variational autoencoder(CVAE)adopts the standard transformer structure.Then,Dual-LVG regularises the CVAE latent space by introducing a dual latent space of semantics and emotion.The content diversity and emotional accuracy of the generated responses are improved by learning emotion and semantic features respectively.Moreover,the average attention mechanism is adopted to better extract semantic features at the sequence level,and the semi-supervised attention mechanism is used in the decoding step to strengthen the fusion of emotional features of the model.Experimental results show that Dual-LVG can successfully achieve the effect of generating different content by controlling emotional factors. 展开更多
关键词 conditional variational autoencoder dual latent space emotional responses latent variable generation
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Bayesian Inference of Spatially Correlated Binary Data Using Skew-Normal Latent Variables with Application in Tooth Caries Analysis
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作者 Solaiman Afroughi 《Open Journal of Statistics》 2015年第2期127-139,共13页
The analysis of spatially correlated binary data observed on lattices is an interesting topic that catches the attention of many scholars of different scientific fields like epidemiology, medicine, agriculture, biolog... The analysis of spatially correlated binary data observed on lattices is an interesting topic that catches the attention of many scholars of different scientific fields like epidemiology, medicine, agriculture, biology, geology and geography. To overcome the encountered difficulties upon fitting the autologistic regression model to analyze such data via Bayesian and/or Markov chain Monte Carlo (MCMC) techniques, the Gaussian latent variable model has been enrolled in the methodology. Assuming a normal distribution for the latent random variable may not be realistic and wrong, normal assumptions might cause bias in parameter estimates and affect the accuracy of results and inferences. Thus, it entails more flexible prior distributions for the latent variable in the spatial models. A review of the recent literature in spatial statistics shows that there is an increasing tendency in presenting models that are involving skew distributions, especially skew-normal ones. In this study, a skew-normal latent variable modeling was developed in Bayesian analysis of the spatially correlated binary data that were acquired on uncorrelated lattices. The proposed methodology was applied in inspecting spatial dependency and related factors of tooth caries occurrences in a sample of students of Yasuj University of Medical Sciences, Yasuj, Iran. The results indicated that the skew-normal latent variable model had validity and it made a decent criterion that fitted caries data. 展开更多
关键词 Spatial Data latent variable Autologistic Model SKEW-NORMAL Distribution BAYESIAN INFERENCE TOOTH CARIES
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基于LVS的网站服务器群的负载均衡 被引量:4
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作者 杨和东 《现代图书情报技术》 CSSCI 北大核心 2001年第4期17-19,共3页
对自由软件 L VS进行了推介性描述说明 ,同时针对其现实应用中可能遇到的问题 ,提出了解决思路。
关键词 负载均衡服务器 lvs ASP LINUX操作系统 稳定性 安全性 状态信息处理
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Latent Variable Regression for Supervised Modeling and Monitoring 被引量:5
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作者 Qinqin Zhu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2020年第3期800-811,共12页
A latent variable regression algorithm with a regularization term(r LVR) is proposed in this paper to extract latent relations between process data X and quality data Y. In rLVR,the prediction error between X and Y is... A latent variable regression algorithm with a regularization term(r LVR) is proposed in this paper to extract latent relations between process data X and quality data Y. In rLVR,the prediction error between X and Y is minimized, which is proved to be equivalent to maximizing the projection of quality variables in the latent space. The geometric properties and model relations of rLVR are analyzed, and the geometric and theoretical relations among r LVR, partial least squares, and canonical correlation analysis are also presented. The rLVR-based monitoring framework is developed to monitor process-relevant and quality-relevant variations simultaneously. The prediction and monitoring effectiveness of rLVR algorithm is demonstrated through both numerical simulations and the Tennessee Eastman(TE) process. 展开更多
关键词 Data ANALYTICS inferential MONITORING latent variablE regression REGULARIZATION
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New approaches to cognitive work analysis through latent variable modeling in mining operations 被引量:1
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作者 S.Li Y.A.Sari M.Kumral 《International Journal of Mining Science and Technology》 SCIE EI CSCD 2019年第4期549-556,共8页
This paper discusses the utilization of latent variable modeling related to occupational health and safety in the mining industry.Latent variable modeling,which is a statistical model that relates observable and laten... This paper discusses the utilization of latent variable modeling related to occupational health and safety in the mining industry.Latent variable modeling,which is a statistical model that relates observable and latent variables,could be used to facilitate researchers’understandings of the underlying constructs or hypothetical factors and their magnitude of effect that constitute a complex system.This enhanced understanding,in turn,can help emphasize the important factors to improve mine safety.The most commonly used techniques include the exploratory factor analysis(EFA),the confirmatory factor analysis(CFA)and the structural equation model with latent variables(SEM).A critical comparison of the three techniques regarding mine safety is provided.Possible applications of latent variable modeling in mining engineering are explored.In this scope,relevant research papers were reviewed.They suggest that the application of such methods could prove useful in mine accident and safety research.Application of latent variables analysis in cognitive work analysis was proposed to improve the understanding of human-work relationships in mining operations. 展开更多
关键词 latent variables EXPLORATORY FACTOR ANALYSIS Confirmatory FACTOR ANALYSIS Structural equation modeling OCCUPATIONAL health and SAFETY Mine SAFETY
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Latent Variable Modeling Approach for Assessing Social Impacts of Mine Closure 被引量:1
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作者 Mallikarjun Rao Pillalamarry Khanindra Pathak 《Open Journal of Applied Sciences》 2014年第14期573-587,共15页
Mining stimulates environmental and economic impacts on the neighboring community right from the inception to the closure of its operations. The society in the neighborhood of mining gradually adopts a characteristic ... Mining stimulates environmental and economic impacts on the neighboring community right from the inception to the closure of its operations. The society in the neighborhood of mining gradually adopts a characteristic life-style that is highly influenced by the mining. In order to sustain the societal development beyond the mine closure, it is necessary to plan post mining activities in the area. Thus, it is essential to predict the impacts of mine closure well before the closure. Many societal and family attributes are affected by mine closure. Impact on these attributes is reflected on the overall quality of life of the neighboring community. There are no adequate indicators and/or methodology available to measure social impacts of mine closure on a neighboring community. This paper made an attempt to develop such methodology to predict the degree of adverse effects of mine closure on the quality of life of neighboring communities using the Structural Equation Modeling (SEM) and the Latent Variables Interaction Model (LVM). 展开更多
关键词 MINE CLOSURE SOCIAL IMPACTS Structural Equation MODELING latent variable MODELING
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隐变量模型及其在贝叶斯运营模态分析的应用
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作者 朱伟 李宾宾 +1 位作者 谢炎龙 陈笑宇 《振动工程学报》 EI CSCD 北大核心 2024年第9期1476-1484,共9页
贝叶斯FFT算法是运营模态分析的最新一代算法,以其准确性高、计算速度快、可有效进行不确定性度量等优点受到广泛关注。然而,现有贝叶斯FFT算法针对不同情况(稀疏模态、密集模态、多步测试等)需采用不同优化算法,且编程实现极为复杂。为... 贝叶斯FFT算法是运营模态分析的最新一代算法,以其准确性高、计算速度快、可有效进行不确定性度量等优点受到广泛关注。然而,现有贝叶斯FFT算法针对不同情况(稀疏模态、密集模态、多步测试等)需采用不同优化算法,且编程实现极为复杂。为此,本文旨在提出针对不同情况的贝叶斯FFT算法的统一框架,并实现模态参数的高效求解;视结构模态响应为隐变量,建立贝叶斯模态识别单步测试和多步测试的隐变量模型框架;针对提出的隐变量模型运用期望最大化算法实现各种情况下模态参数的统一贝叶斯推断,利用隐变量解耦模态参数优化过程,采用Louis等式间接求取似然函数的Hessian矩阵。通过两个实际工程测试案例,并与现有方法对比,验证所提方法的准确性和高效性。分析结果表明,本文所提算法与现有方法结果相同,但其推导简单、易编程,尤其对于密集模态识别问题具有明显的计算优势。本文为贝叶斯模态识别建立起统一的隐变量模型框架,在很大程度上简化原本繁琐且冗长的推导过程,提高计算效率,同时也为应用变分贝叶斯、吉布斯采样等算法求解贝叶斯模态识别问题提供了可能。 展开更多
关键词 运营模态分析 参数识别 隐变量模型 期望最大化 不确定性
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留守初中生抑郁症状对非自杀性自伤行为影响的纵向研究 被引量:1
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作者 尹斐 姜文龙 +2 位作者 周郁秋 杨金伟 杨楠 《中国健康教育》 北大核心 2024年第3期250-255,共6页
目的 评估留守初中生抑郁症状与非自杀性自伤行为变化趋势,探讨二者发展关系及时序效应。方法 通过多阶段分层整群随机抽样,使用儿童抑郁量表(CDS)和青少年非自杀性自伤评定问卷(ANSAQ),对372名留守初中生进行为期1年的3次纵向调查。结... 目的 评估留守初中生抑郁症状与非自杀性自伤行为变化趋势,探讨二者发展关系及时序效应。方法 通过多阶段分层整群随机抽样,使用儿童抑郁量表(CDS)和青少年非自杀性自伤评定问卷(ANSAQ),对372名留守初中生进行为期1年的3次纵向调查。结果 留守初中生抑郁症状和非自杀性自伤行为呈线性增长。抑郁初始水平正向预测非自杀性自伤初始水平与增长速率(β=0.473,β=0.577;P<0.01),抑郁增长速率正向预测非自杀性自伤增长速率(β=0.806,P<0.001);交叉滞后回归分析显示:Tn抑郁可正向预测Tn+1非自杀性自伤行为(β=0.365,β=0.322;P<0.001),Tn非自杀性自伤行为不能预测Tn+1抑郁(β=0.117,β=0.094;P>0.05)。结论 留守初中生抑郁症状和非自杀性自伤行为随时间不断增长,抑郁症状是非自杀性自伤的前因变量,抑郁症状可预测非自杀性自伤发生。 展开更多
关键词 留守初中生 抑郁 非自杀性自伤 潜变量增长模型 交叉滞后模型
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典型相关分析与结构方程模型方法的比较研究 被引量:1
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作者 刘怡然 安奉钧 《统计与决策》 CSSCI 北大核心 2024年第10期40-45,共6页
典型相关分析(CCA)和结构方程模型(SEM)的应用日益广泛,但由于二者存在较大的相似性而常使研究者在使用过程中面临选择困难等问题,因而准确理解和区分二者之间的差异至关重要。文章基于SPSS和AMOS操作环境对典型相关分析和结构方程模型... 典型相关分析(CCA)和结构方程模型(SEM)的应用日益广泛,但由于二者存在较大的相似性而常使研究者在使用过程中面临选择困难等问题,因而准确理解和区分二者之间的差异至关重要。文章基于SPSS和AMOS操作环境对典型相关分析和结构方程模型在函数式、基本图形、二阶因素、中介效应及使用条件等方面进行系统性比较,研究结果表明:(1)CCA可以进行线性组合计算,直接计算一个潜变量与另一组显变量的关系,能有效处理二阶因素计算问题。(2)SEM可以同时考虑多个潜变量之间的关系、计算和呈现误差方差和残差、准确地计算和展示中介效应、运用辅助性标准来判断模型适配度并通过调整变量之间的逻辑联系来修正模型,其输出结果全面而精确。(3)CCA适用于含有两个潜变量的简单模型,而SEM适用于含有多个潜变量的复杂模型。在特定条件下,可以结合使用CCA和SEM,或用CCA代替SEM。 展开更多
关键词 典型相关分析 结构方程模型 潜变量 负荷量 适配度
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基于ICLV模型的通勤方式选择行为 被引量:12
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作者 付学梅 隽志才 《系统管理学报》 CSSCI 北大核心 2016年第6期1046-1050,共5页
基于ICLV(Integrated Choice and Latent Variable)模型,结合2013年绍兴市居民出行调查数据,研究通勤者的方式选择行为,包括小汽车、公交车、摩托车和电动车等4种当地居民在日常生活中较常用的交通方式。模型不仅分析了可观测的个人及... 基于ICLV(Integrated Choice and Latent Variable)模型,结合2013年绍兴市居民出行调查数据,研究通勤者的方式选择行为,包括小汽车、公交车、摩托车和电动车等4种当地居民在日常生活中较常用的交通方式。模型不仅分析了可观测的个人及家庭的社会经济属性对通勤方式的影响,而且通过潜变量模型构建通勤者个人对各类出行方式的态度等不可见因素,并将其纳入选择模型。结果表明,潜在的心理因素同样对方式选择行为有重要影响,并能够揭示选择行为的内在原因。该研究可为交通需求管理策略制定者以及交通规划者提供指导意见,从而促进交通系统的可持续发展。 展开更多
关键词 通勤方式 潜变量 多项Probit 选择和潜变量的集成模型
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基于ICLV的托运人货运服务选择行为建模 被引量:7
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作者 陶学宗 张戎 《交通运输系统工程与信息》 EI CSCD 北大核心 2015年第2期122-128,共7页
忽略托运人的认知活动对其货运选择行为的影响,将会导致模型设定错误和参数估计的不一致性.为解决这个问题,考虑托运人的认知活动对其选择行为的影响,引入潜变量的概念.建立了基于ICLV的货运服务选择行为模型,给出相应的求解步骤,并以... 忽略托运人的认知活动对其货运选择行为的影响,将会导致模型设定错误和参数估计的不一致性.为解决这个问题,考虑托运人的认知活动对其选择行为的影响,引入潜变量的概念.建立了基于ICLV的货运服务选择行为模型,给出相应的求解步骤,并以南昌市出口集装箱运输为例进行了实证分析.结果表明,反映托运人认知活动的潜变量对其货运服务选择行为有显著影响,基于ICLV的货运服务选择行为模型比传统的Logit模型具有更好的拟合度和解释能力,因而可以更加准确地分析托运人的货运选择行为,并为改进运输服务、制定运输政策提供理论支持. 展开更多
关键词 综合交通运输 货运建模 集成潜变量的选择模型 选择行为 托运人
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考虑预约响应式农村客运的内蒙古农牧民出行方式选择行为研究
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作者 肖振朝 霍月英 +1 位作者 王亚宁 杨晓芳 《内蒙古大学学报(自然科学版)》 CAS 2024年第1期82-91,共10页
为揭示考虑预约响应式农村客运的内蒙古农牧民出行方式选择行为及其影响因素,基于计划行为理论选取反映农牧民对预约响应式农村客运的态度潜变量,利用验证性因子分析预测态度潜变量得分;以个体属性、历史出行特征、出行方式属性、潜变... 为揭示考虑预约响应式农村客运的内蒙古农牧民出行方式选择行为及其影响因素,基于计划行为理论选取反映农牧民对预约响应式农村客运的态度潜变量,利用验证性因子分析预测态度潜变量得分;以个体属性、历史出行特征、出行方式属性、潜变量得分为解释变量,以农牧民的方式选择结果为反应变量,采用混合Logit模型构建内蒙古农牧民出行方式选择模型。模型分析发现:年龄在60岁以上、月收入3000元以上、日常出行次数在一天两次以上以及日常出行费用在20元以上的农牧民对预约响应式农村客运的使用意向强烈。行为意向、行为态度、主观规范以及感知易用性对方式选择行为影响显著,总体来说内蒙古农牧民对预约响应式农村客运持积极态度。研究成果为内蒙古地区发展预约响应式农村客运提供决策依据。 展开更多
关键词 预约响应式农村客运 方式选择行为 混合Logit模型 使用意向 态度潜变量
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动态内全潜结构投影的空间扩展故障检测方法
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作者 孔祥玉 陈雅琳 +2 位作者 罗家宇 安秋生 杨治艳 《控制理论与应用》 EI CAS CSCD 北大核心 2024年第1期72-82,共11页
动态内偏最小二乘(DiPLS)方法是基于数据驱动的潜结构投影的动态扩展算法,用于动态特征提取和关键性能指标预测.在大型装备系统中,传感器采集的当前时刻样本受历史样本的影响且可能包含较大噪声.在动态特征提取中,因DiPLS算法未按降序... 动态内偏最小二乘(DiPLS)方法是基于数据驱动的潜结构投影的动态扩展算法,用于动态特征提取和关键性能指标预测.在大型装备系统中,传感器采集的当前时刻样本受历史样本的影响且可能包含较大噪声.在动态特征提取中,因DiPLS算法未按降序提取主成分,导致残差空间仍存在较大变异,动态和静态信息难以有效分离,影响故障检测性能.为此,本文提出了一种基于动态内全潜结构投影的故障检测方法(DiTPLS).首先,使用动态内偏最小二乘方法和向量自回归模型建立动态模型并检测故障,用于捕捉质量相关动态信息;基于结构化动态主成分分析算法建立一种改进的动态潜在变量模型,用于残差分解,提取质量无关的动态信息和静态信息,并构造合适的统计量进行故障检测.数值仿真和田纳西–伊斯曼过程实验验证了DiTPLS算法的有效性. 展开更多
关键词 DiPLS算法 结构化动态PCA算法 动态潜变量 数据驱动 故障检测
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化零为整的宏观社会数据生成:基于潜变量模型和动态贝叶斯方法
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作者 张高祥 陈哲 陈云松 《社会》 CSSCI 北大核心 2024年第3期173-219,共47页
对因果机制和对宏观检验的探寻催生了定量社会学研究对区群层面数据的需求,然而这类高质量的追踪数据资源相对稀缺。传统研究通常通过综合多个来源的个体社会调查数据来构建面板数据集以改善宏观数据匮乏现状,但其亦受制于社会调查在时... 对因果机制和对宏观检验的探寻催生了定量社会学研究对区群层面数据的需求,然而这类高质量的追踪数据资源相对稀缺。传统研究通常通过综合多个来源的个体社会调查数据来构建面板数据集以改善宏观数据匮乏现状,但其亦受制于社会调查在时间和空间分布上的稀疏性以及不同调查间的差异性。本文引介了一种可用于生成区群层面跨时空面板数据的动态贝叶斯潜变量建模框架,并通过应用实例展示了该方法的具体应用过程,比较了动态贝叶斯方法相较于几种常用的缺失值插补方法的优势。本文的示例结果表明,动态贝叶斯潜变量模型在跨时空、多维度的信息整合和参数不确定性探索方面具有重要的优势,可以实现对调查数据缺失年份或地区的估计和插补,大大缓解了社会学研究中面板数据不足的问题。 展开更多
关键词 数据生成 维度整合 潜变量 贝叶斯项目反应模型 动态线性模型
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基于SGPLVM-LSSVM算法的U形折弯件模型参数优化研究 被引量:2
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作者 徐承亮 曹志勇 +1 位作者 王大军 胡吉全 《机床与液压》 北大核心 2018年第20期29-32,58,共5页
影响高强度U形折弯件回弹的因素众多,比如工件尺寸、力学性能和负载条件等,使得高强度折弯件的弯曲回弹难以控制。把回弹角α和最小弯曲回弹半径R作为双目标函数,首先利用监督学习-高斯过程隐变量模型(SGPLVM)进行变量筛选和降维,构建U... 影响高强度U形折弯件回弹的因素众多,比如工件尺寸、力学性能和负载条件等,使得高强度折弯件的弯曲回弹难以控制。把回弹角α和最小弯曲回弹半径R作为双目标函数,首先利用监督学习-高斯过程隐变量模型(SGPLVM)进行变量筛选和降维,构建U形折弯件的最小二乘支持向量机模型(LSSVM);再把SGPLVM-LSSVM实验结果分别与SVM、FEM、实际零件进行比较,验证了此算法模型的可行性。 展开更多
关键词 U形折弯件 支持向量机模型 监督学习-高斯过程隐变量模型
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快速凸包算法在发射车状态监控中的应用
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作者 余彦 白鹏英 张雪峰 《现代防御技术》 北大核心 2024年第3期143-150,共8页
针对当前复杂工业系统运行状态监控策略普遍存在误报警数目过多的问题,提出了一种基于快速凸包算法的发射车状态监控方法。该方法利用快速凸包算法从给定的正常历史数据中估计发射车的正常工作空间,对于新采集的发射车运行状态监控数据... 针对当前复杂工业系统运行状态监控策略普遍存在误报警数目过多的问题,提出了一种基于快速凸包算法的发射车状态监控方法。该方法利用快速凸包算法从给定的正常历史数据中估计发射车的正常工作空间,对于新采集的发射车运行状态监控数据,如果由它们构成的工作点位于发射车的正常工作空间内,就认为发射车的状态是正常的,否则就是异常的。与基于静态阈值的方法相比,提出的方法降低了误报警数目;相较于基于隐变量的方法,提出的方法具有良好的可解释性。通过数值仿真技术,分别分析了一个2维和3维案例来评估提出的方法的性能表现。仿真结果表明,提出的方法物理意义明确,产生的误报警数目少。 展开更多
关键词 快速凸包算法 状态监控 误报警 正常工作空间 静态阈值 隐变量
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