资源类型

期刊论文 1096

年份

2023 75

2022 83

2021 65

2020 79

2019 60

2018 48

2017 54

2016 45

2015 56

2014 46

2013 41

2012 43

2011 42

2010 53

2009 45

2008 42

2007 36

2006 30

2005 30

2004 26

展开 ︾

关键词

数学模型 13

模型试验 9

数值模拟 8

模型 7

COVID-19 4

不确定性 4

决策 4

GM(1 3

计算机模拟 3

1)模型 2

DX桩 2

D区 2

Preissmann格式 2

SARS 2

TRIZ 2

Weibull分布 2

k-ε模型 2

三峡工程 2

临震信号 2

展开 ︾

检索范围：

排序： 展示方式：

Heuristic solution using decision tree model for enhanced XML schema matching of bridge structural calculation

Sang I. PARK, Sang-Ho LEE

《结构与土木工程前沿（英文）》 2020年 第14卷 第6期   页码 1403-1417 doi: 10.1007/s11709-020-0666-8

摘要： Research on the quality of data in a structural calculation document (SCD) is lacking, although the SCD of a bridge is used as an essential reference during the entire lifecycle of the facility. XML Schema matching enables qualitative improvement of the stored data. This study aimed to enhance the applicability of XML Schema matching, which improves the speed and quality of information stored in bridge SCDs. First, the authors proposed a method of reducing the computing time for the schema matching of bridge SCDs. The computing speed of schema matching was increased by 13 to 1800 times by reducing the checking process of the correlations. Second, the authors developed a heuristic solution for selecting the optimal weight factors used in the matching process to maintain a high accuracy by introducing a decision tree. The decision tree model was built using the content elements stored in the SCD, design companies, bridge types, and weight factors as input variables, and the matching accuracy as the target variable. The inverse-calculation method was applied to extract the weight factors from the decision tree model for high-accuracy schema matching results.

关键词： structural calculation document     bridge structure     XML Schema matching     weight factor     data mining     decision tree model    

HTML PDF 收藏

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

《机械工程前沿（英文）》 2021年 第16卷 第4期   页码 814-828 doi: 10.1007/s11465-021-0650-6

摘要： The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery. Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge. However, the inexplicability and low generalization ability of fault diagnosis models still bar them from the application. To address this issue, this paper explores a decision-tree-structured neural network, that is, the deep convolutional tree-inspired network (DCTN), for the hierarchical fault diagnosis of bearings. The proposed model effectively integrates the advantages of convolutional neural network (CNN) and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree, which is by no means a simple combination of the two models. The proposed DCTN model has unique advantages in 1) the hierarchical structure that can support more accuracy and comprehensive fault diagnosis, 2) the better interpretability of the model output with hierarchical decision making, and 3) more powerful generalization capabilities for the samples across fault severities. The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig. Experimental results can fully demonstrate the feasibility and superiority of the proposed method.

关键词： bearing     cross-severity fault diagnosis     hierarchical fault diagnosis     convolutional neural network     decision tree    

HTML PDF 收藏

Four-protein model for predicting prognostic risk of lung cancer

《医学前沿（英文）》 2022年 第16卷 第4期   页码 618-626 doi: 10.1007/s11684-021-0867-0

摘要： Patients with lung cancer at the same stage may have markedly different overall outcome and a lack of specific biomarker to predict lung cancer outcome. Heat-shock protein 90 β (HSP90β) is overexpressed in various tumor cells. In this study, the ELISA results of HSP90β combined with CEA, CA125, and CYFRA21-1 were used to construct a recursive partitioning decision tree model to establish a four-protein diagnostic model and predict the survival of patients with lung cancer. Survival analysis showed that the recursive partitioning decision tree could distinguish the prognosis between high- and low-risk groups. Results suggested that the joint detection of HSP90β, CEA, CA125, and CYFRA21-1 in the peripheral blood of patients with lung cancer is plausible for early diagnosis and prognosis prediction of lung cancer.

关键词： lung cancer     HSP90β     decision tree model     prognosis    

HTML PDF 收藏

决策树技术在电缆绝缘状态评估中的应用

孙秋野,张化光,张铁岩

《中国工程科学》 2010年 第12卷 第2期   页码 90-94

摘要：

电缆的绝缘状态通常可以分为良好、不好、差和故障等几种，以电缆的日常检修数据、试验数据和在线监测数据为基础，对电缆的状态进行判断是一个非常有意义的课题。采用决策树分类技术来对电缆的绝缘状态进行分类，分别对各种类型数据形成子树，然后通过子树合成技术形成最终的决策树，从而对电缆的绝缘状态进行判断。通过一个实际电缆的各种数据，采用SPSS软件进行实际应用，最终的仿真结果说明决策树技术是一种非常有效的电缆绝缘状态分类技术。

关键词： 决策树     分类     数据挖掘     电缆绝缘    

HTML PDF 收藏

An innovative model for predicting the displacement and rotation of column-tree moment connection under

Mohammad Ali NAGHSH, Aydin SHISHEGARAN, Behnam KARAMI, Timon RABCZUK, Arshia SHISHEGARAN, Hamed TAGHAVIZADEH, Mehdi MORADI

《结构与土木工程前沿（英文）》 2021年 第15卷 第1期   页码 194-212 doi: 10.1007/s11709-020-0688-2

摘要： In this study, we carried out nonlinear finite element simulations to predict the performance of a column-tree moment connection (CTMC) under fire and static loads. We also conducted a detailed parameter study based on five input variables, including the applied temperature, number of flange bolts, number of web bolts, length of the beam, and applied static loads. The first variable is changed among seven levels, whereas the other variables are changed among three levels. Employing the Taguchi method for variables 2–5 and their levels, 9 samples were designed for the parameter study, where each sample was exposed to 7 different temperatures yielding 63 outputs. The related variables for each output are imported for the training and testing of different surrogate models. These surrogate models include a multiple linear regression (MLR), multiple Ln equation regression (MLnER), an adaptive network-based fuzzy inference system (ANFIS), and gene expression programming (GEP). 44 samples were used for training randomly while the remaining samples were employed for testing. We show that GEP outperforms MLR, MLnER, and ANFIS. The results indicate that the rotation and deflection of the CTMC depend on the temperature. In addition, the fire resistance increases with a decrease in the beam length; thus, a shorter beam can increase the fire resistance of the building. The numbers of flanges and web bolts slightly affect the rotation and displacement of the CTMCs at temperatures of above 400°C.

关键词： column-tree moment connection     Finite element model     parametric study     fire     regression models     gene expression programming    

HTML PDF 收藏

Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete

《结构与土木工程前沿（英文）》 2023年 第17卷 第2期   页码 284-305 doi: 10.1007/s11709-022-0901-6

摘要： Fiber-reinforced self-compacting concrete (FRSCC) is a typical construction material, and its compressive strength (CS) is a critical mechanical property that must be adequately determined. In the machine learning (ML) approach to estimating the CS of FRSCC, the current research gaps include the limitations of samples in databases, the applicability constraints of models owing to limited mixture components, and the possibility of applying recently proposed models. This study developed different ML models for predicting the CS of FRSCC to address these limitations. Artificial neural network, random forest, and categorical gradient boosting (CatBoost) models were optimized to derive the best predictive model with the aid of a 10-fold cross-validation technique. A database of 381 samples was created, representing the most significant FRSCC dataset compared with previous studies, and it was used for model development. The findings indicated that CatBoost outperformed the other two models with excellent predictive abilities (root mean square error of 2.639 MPa, mean absolute error of 1.669 MPa, and coefficient of determination of 0.986 for the test dataset). Finally, a sensitivity analysis using a partial dependence plot was conducted to obtain a thorough understanding of the effect of each input variable on the predicted CS of FRSCC. The results showed that the cement content, testing age, and superplasticizer content are the most critical factors affecting the CS.

关键词： compressive strength     self-compacting concrete     artificial neural network     decision tree     CatBoost    

HTML PDF 收藏

A consensus model for group decision making under interval type-2 fuzzy environment

Xiao-xiong ZHANG,Bing-feng GE,Yue-jin TAN

《信息与电子工程前沿（英文）》 2016年 第17卷 第3期   页码 237-249 doi: 10.1631/FITEE.1500198

摘要： We propose a new consensus model for group decision making (GDM) problems, using an interval type-2 fuzzy environment. In our model, experts are asked to express their preferences using linguistic terms characterized by interval type-2 fuzzy sets (IT2 FSs), because these can provide decision makers with greater freedom to express the vagueness in real-life situations. Consensus and proximity measures based on the arithmetic operations of IT2 FSs are used simultaneously to guide the decision-making process. The majority of previous studies have taken into account only the importance of the experts in the aggregation process, which may give unreasonable results. Thus, we propose a new feedback mechanism that generates different advice strategies for experts according to their levels of importance. In general, experts with a lower level of importance require a larger number of suggestions to change their initial preferences. Finally, we investigate a numerical example and execute comparable models and ours, to demonstrate the performance of our proposed model. The results indicate that the proposed model provides greater insight into the GDM process.

关键词： Group decision making (GDM)     Interval type-2 fuzzy sets (IT2 FSs)     Feedback mechanism    

HTML PDF 收藏

Financing Model Decision of Inter-basin Water Transfer Projects

Ji-wei Zhu,Li-nan Zhou,Zhao Zhai,Cong Wang

《工程管理前沿（英文）》 2016年 第3卷 第4期   页码 396-403 doi: 10.15302/J-FEM-2016060

摘要： Inter-basin Water Transfer Projects require the appropriate financing model to attract large amounts of social investment. Therefore, financing model decision becomes the key of engineering construction. In three aspects, such as the subject, the object and the target of the financing model, Grey Target Model is established in this paper. First, the complex financing mode decision problems of Inter-basin Water Transfer Projects are decomposed by using hierarchical decomposition method. Then Analytical Hierarchy Process (AHP) method is used to calculate the comprehensive weight of evaluation index. Experts’ opinions financing model are transformed into the evaluation matrix based on the Dephi method. The Weighted Grey Target Model is used to calculate the approaching degree of financing model and assists financing mode decision. In addition, this paper takes the water diversion project from the Han to the Wei River of Shaanxi Province as a verification example for the model. For other water diversion projects, the evaluation results are also reliable and provide theoretical references for the financing model decision of Inter-basin Water Transfer Projects.

关键词： Inter-basin Water Transfer Projects     financing model     Weighted Grey Target Model     water diversion     Han River     Wei River    

HTML PDF 收藏

Development of machine learning multi-city model for municipal solid waste generation prediction

《环境科学与工程前沿（英文）》 2022年 第16卷 第9期 doi: 10.1007/s11783-022-1551-6

摘要：

● A database of municipal solid waste (MSW) generation in China was established.

关键词： Municipal solid waste     Machine learning     Multi-cities     Gradient boost regression tree    

HTML PDF 收藏

Kd-tree and quad-tree decompositions for declustering of 2D range queries over uncertain space

Ahmet SAYAR,Süleyman EKEN,Okan ÖZTÜRK

《信息与电子工程前沿（英文）》 2015年 第16卷 第2期   页码 98-108 doi: 10.1631/FITEE.1400165

摘要： We present a study to show the possibility of using two well-known space partitioning and indexing techniques, kd trees and quad trees, in declustering applications to increase input/output (I/O) parallelization and reduce spatial data processing times. This parallelization enables time-consuming computational geometry algorithms to be applied efficiently to big spatial data rendering and querying. The key challenge is how to balance the spatial processing load across a large number of worker nodes, given significant performance heterogeneity in nodes and processing skews in the workload.

关键词： Kd tree     Quad tree     Space partitioning     Spatial indexing     Range queries     Query optimization    

HTML PDF 收藏

基于动态多指标灰色关联决策模型的研究

党耀国,刘思峰,刘斌,陶勇

《中国工程科学》 2005年 第7卷 第2期   页码 69-72

摘要：

针对动态多指标系统的决策特点，对指标数据初始化处理时，利用“奖优罚劣”原则，提出了一种易于计算且实用的［-1，1］线性变换算子，用此方法寻求各时段的正、负理想方案，建立一种新的基于动态多指标灰色关联分析决策模型，在模型中充分考虑了各指标在系统中的成长特性，将此特性用于灰色关联分析，为动态多目标决策问题提供了一种科学、实用的方法，并利用现有的实例来证实此方法的科学性与可行性。

关键词： 灰色关联分析     多指标决策     模型    

HTML PDF 收藏

民航视情维修决策优化模型发展

张海军,左洪福,梁剑,戎翔

《中国工程科学》 2005年 第7卷 第11期   页码 17-20

摘要：

目前视情维修是民航业采用的主要维修方式，可以使维修的有效性、经济性大幅度提高。维修决策优化模型对民航维修成本的降低和利润的提升具有显著的指导作用；归纳了视情维修的时间延迟模型、冲击模型、比例危险模型、马尔可夫决策模型等优化模型的建模方法；分析了该类模型在应用中存在的不足，并展望了其发展前景。

关键词： 视情维修     优化模型     决策     成本    

HTML PDF 收藏

双枝模糊决策与决策识别问题

史开泉,李歧强

《中国工程科学》 2001年 第3卷 第1期   页码 71-77

摘要：

文章提出具有中性域X*(X*≠{x})X的上的双枝模糊决策的概念和决策优化分析模型、决策判定定理、决策识别定理、决策去余定理和决策因素域X上的挖洞原理。双枝模糊决策具有下列特征：决策结论的双向依赖特性，决策结论的叠加合成特性，决策结论的枝-分离特性，决策结论的枝-退化特性，决策结论的非失误特性。研究结果得到了应用。

关键词： 双枝模糊决策     决策模型     决策判定定理     决策识别定理     决策去余定理     挖洞原理    

HTML PDF 收藏

基于区间数的多指标灰靶决策模型的研究

党耀国,刘思峰,刘斌

《中国工程科学》 2005年 第7卷 第8期   页码 31-35

摘要：

现实生活中遇到的许多问题都具有不确定性，使得在对系统进行决策评估时，指标值难以精确化。在此情形下，人们常常对指标值给出一个区间，到目前为止， 尚未有人研究区间数灰靶决策。首先定义了区间数、m维区间数的距离及其距离性质，并证明了当区间数为实数时，区间数距离就是实数距离的推广；提出了区间数规范化方法，在此基础上, 建立了基于区间数的灰靶决策模型，从而把灰靶决策模型由实数序列拓展到区间数序列，使灰靶决策理论得到发展，同时为扩大灰靶决策的应用领域提供了理论根据。最后以实例验证了该模型的有效性与实用性。

关键词： 区间数     灰靶决策     模型     应用    

HTML PDF 收藏

Decision support for the development, simulation and optimization of dynamic process models

Norbert Asprion, Roger Böttcher, Jan Schwientek, Johannes Höller, Patrick Schwartz, Charlie Vanaret, Michael Bortz

《化学科学与工程前沿（英文）》 2022年 第16卷 第2期   页码 210-220 doi: 10.1007/s11705-021-2046-x

摘要： Simulation is besides experimentation the major method for designing, analyzing and optimizing chemical processes. The ability of simulations to reflect real process behavior strongly depends on model quality. Validation and adaption of process models are usually based on available plant data. Using such a model in various simulation and optimization studies can support the process designer in his task. Beneath steady state models there is also a growing demand for dynamic models either to adapt faster to changing conditions or to reflect batch operation. In this contribution challenges of extending an existing decision support framework for steady state models to dynamic models will be discussed and the resulting opportunities will be demonstrated for distillation and reactor examples.

关键词： decision support     multicriteria optimization     model validation     dynamic model     sensitivity analysis    

HTML PDF 收藏

标题 作者 时间 类型 操作

Heuristic solution using decision tree model for enhanced XML schema matching of bridge structural calculation

Sang I. PARK, Sang-Ho LEE

期刊论文

Deep convolutional tree-inspired network: a decision-tree-structured neural network for hierarchical

期刊论文

Four-protein model for predicting prognostic risk of lung cancer

期刊论文

决策树技术在电缆绝缘状态评估中的应用

孙秋野,张化光,张铁岩

期刊论文

An innovative model for predicting the displacement and rotation of column-tree moment connection under

Mohammad Ali NAGHSH, Aydin SHISHEGARAN, Behnam KARAMI, Timon RABCZUK, Arshia SHISHEGARAN, Hamed TAGHAVIZADEH, Mehdi MORADI

期刊论文

Optimization of machine learning models for predicting the compressive strength of fiber-reinforced self-compacting concrete

期刊论文

A consensus model for group decision making under interval type-2 fuzzy environment

Xiao-xiong ZHANG,Bing-feng GE,Yue-jin TAN

期刊论文

Financing Model Decision of Inter-basin Water Transfer Projects

Ji-wei Zhu,Li-nan Zhou,Zhao Zhai,Cong Wang

期刊论文

Development of machine learning multi-city model for municipal solid waste generation prediction

期刊论文

Kd-tree and quad-tree decompositions for declustering of 2D range queries over uncertain space

Ahmet SAYAR,Süleyman EKEN,Okan ÖZTÜRK

期刊论文

基于动态多指标灰色关联决策模型的研究

党耀国,刘思峰,刘斌,陶勇

期刊论文

民航视情维修决策优化模型发展

张海军,左洪福,梁剑,戎翔

期刊论文

双枝模糊决策与决策识别问题

史开泉,李歧强

期刊论文

基于区间数的多指标灰靶决策模型的研究

党耀国,刘思峰,刘斌

期刊论文

Decision support for the development, simulation and optimization of dynamic process models

Norbert Asprion, Roger Böttcher, Jan Schwientek, Johannes Höller, Patrick Schwartz, Charlie Vanaret, Michael Bortz

期刊论文