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Data driven models for compressive strength prediction of concrete at high temperatures

Mahmood AKBARI, Vahid JAFARI DELIGANI

《结构与土木工程前沿（英文）》 2020年第14卷第2期页码 311-321 doi: 10.1007/s11709-019-0593-8

摘要： The use of data driven models has been shown to be useful for simulating complex engineering processes, when the only information available consists of the data of the process. In this study, four data-driven models, namely multiple linear regression, artificial neural network, adaptive neural fuzzy inference system, and nearest neighbor models based on collection of 207 laboratory tests, are investigated for compressive strength prediction of concrete at high temperature. In addition for each model, two different sets of input variables are examined: a complete set and a parsimonious set of involved variables. The results obtained are compared with each other and also to the equations of NIST Technical Note standard and demonstrate the suitability of using the data driven models to predict the compressive strength at high temperature. In addition, the results show employing the parsimonious set of input variables is sufficient for the data driven models to make satisfactory results.

关键词： data driven model compressive strength concrete high temperature

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Decomposition and decoupling analysis of electricity consumption carbon emissions in China

《工程管理前沿（英文）》页码 486-498 doi: 10.1007/s42524-022-0215-3

摘要： Electricity consumption is one of the major contributors to greenhouse gas emissions. In this study, we build a power consumption carbon emission measurement model based on the operating margin factor. We use the decomposition and decoupling technology of logarithmic mean Divisia index method to quantify six effects (emission intensity, power generation structure, consumption electricity intensity, economic scale, population structure, and population scale) and comprehensively reflect the degree of dependence of electricity consumption carbon emissions on China’s economic development and population changes. Moreover, we utilize the decoupling model to analyze the decoupling state between carbon emissions and economic growth and identify corresponding energy efficiency policies. The results of this study provide a new perspective to understand carbon emission reduction potentials in the electricity use of China.

关键词： electricity consumption carbon emission measurement LMDI model decoupling model data driven

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An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings

Yanfeng PENG, Junsheng CHENG, Yanfei LIU, Xuejun LI, Zhihua PENG

《机械工程前沿（英文）》 2018年第13卷第2期页码 301-310 doi: 10.1007/s11465-017-0449-7

摘要：

A novel data-driven method based on Gaussian mixture model (GMM) and distance evaluation technique (DET) is proposed to predict the remaining useful life (RUL) of rolling bearings. The data sets are clustered by GMM to divide all data sets into several health states adaptively and reasonably. The number of clusters is determined by the minimum description length principle. Thus, either the health state of the data sets or the number of the states is obtained automatically. Meanwhile, the abnormal data sets can be recognized during the clustering process and removed from the training data sets. After obtaining the health states, appropriate features are selected by DET for increasing the classification and prediction accuracy. In the prediction process, each vibration signal is decomposed into several components by empirical mode decomposition. Some common statistical parameters of the components are calculated first and then the features are clustered using GMM to divide the data sets into several health states and remove the abnormal data sets. Thereafter, appropriate statistical parameters of the generated components are selected using DET. Finally, least squares support vector machine is utilized to predict the RUL of rolling bearings. Experimental results indicate that the proposed method reliably predicts the RUL of rolling bearings.

关键词： Gaussian mixture model distance evaluation technique health state remaining useful life rolling bearing

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使用数据驱动模型优化抗体纯化策略 Article

刘松崧, Lazaros G. Papageorgiou

《工程（英文）》 2019年第5卷第6期页码 1077-1092 doi: 10.1016/j.eng.2019.10.011

摘要：

本工作致力于抗体片段纯化过程的多尺度优化。优化了生产过程中的色谱决策，包括色谱柱的数量及其大小，每批的循环数以及操作流速。使用基于微型实验数据的制造规模模拟数据集，建立了以负载质量、流速和柱床高度为输入的色谱通量数据驱动模型。与其他方法相比，分段线性回归建模方法具有简单、预测精度高的优点。提出了两种混合整数非线性规划（MINLP）模型，结合数据驱动模型，以最小化每克抗体纯化过程的总成本。然后，使用线性化技术和多参数分解将这些MINLP模型重新构造为混合整数线性规划（MILP）模型。研究了两个具有不同色谱柱尺寸替代品的工业相关案例，以证明所提出模型的适用性。

关键词：抗体纯化多尺度优化抗原结合片段混合整数规划数据驱动模型分段线性回归

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Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

《化学科学与工程前沿（英文）》 2023年第17卷第6期页码 759-771 doi: 10.1007/s11705-022-2269-5

摘要： This work introduces a deep-learning network, i.e., multi-input self-organizing-map ResNet (MISR), for modeling refining units comprised of two reactors and a separation train. The model is comprised of self-organizing-map and the neural network parts. The self-organizing-map part maps the input data into multiple two-dimensional planes and sends them to the neural network part. In the neural network part, residual blocks enhance the convergence and accuracy, ensuring that the structure will not be overfitted easily. Development of the MISR model of hydrocracking unit also benefits from the utilization of prior knowledge of the importance of the input variables for predicting properties of the products. The results show that the proposed MISR structure predicts more accurately the product yields and properties than the previously introduced self-organizing-map convolutional neural network model, thus leading to more accurate optimization of the hydrocracker operation. Moreover, the MISR model has smoother error convergence than the previous model. Optimal operating conditions have been determined via multi-round-particle-swarm and differential evolution algorithms. Numerical experiments show that the MISR model is suitable for modeling nonlinear conversion units which are often encountered in refining and petrochemical plants.

关键词： hydrocracking convolutional neural networks self-organizing map deep learning data-driven optimization

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多尺度材料与过程设计的数据驱动和机理混合建模方法 Perspective

周腾, Rafiqul Gani, Kai Sundmacher

《工程（英文）》 2021年第7卷第9期页码 1231-1238 doi: 10.1016/j.eng.2020.12.022

摘要：

世界人口的不断增长要求加工业以更高效和更可持续的方式生产食品、燃料、化学品和消费品。功能性过程材料是这一挑战的核心。传统上，人们根据经验或者通过反复试验的方法来发现新型先进材料。随着理论方法和相关工具的不断改进和计算机能力的提高，现在流行使用计算方法来指导材料选择和设计，这种方法也非常有效。由于材料选择与材料使用的过程操作之间存在很强的相互作用，必须同时进行材料设计和过程设计。尽管有这种重要联系，但由于通常需要使用不同规模的多个模型，材料和过程的集成设计并不容易。混合建模为解决此类复杂的设计问题提供了一个有前景的选择。在混合建模中，用数据驱动模型描述原本计算成本高昂的材料特性，而用机理模型表示众所周知的过程相关原理。本文重点介绍了混合建模在多尺度材料和过程设计中的重要性。首先介绍通用设计方法，然后选择了六个重要的应用领域：四个来自化学工程领域，两个来自能源系统工程领域。对于选定的每个领域，讨论了使用混合建模进行多尺度材料和过程设计的最新研究。最后，本文给出了结论，指出当前研究的局限性和未来的发展空间。

关键词：数据驱动代理模型机器学习混合建模材料设计过程优化

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Prediction of hydro-suction dredging depth using data-driven methods

《结构与土木工程前沿（英文）》 2021年第15卷第3期页码 652-664 doi: 10.1007/s11709-021-0719-7

摘要： In this study, data-driven methods (DDMs) including different kinds of group method of data handling (GMDH) hybrid models with particle swarm optimization (PSO) and Henry gas solubility optimization (HGSO) methods, and simple equations methods were applied to simulate the maximum hydro-suction dredging depth (h_s). Sixty-seven experiments were conducted under different hydraulic conditions to measure the h_s. Also, 33 data samples from three previous studies were used. The model input variables consisted of pipeline diameter (d), the distance between the pipe inlet and sediment level (Z), the velocity of flow passing through the pipeline (u₀), the water head (H), and the medium size of particles (D₅₀). Data-driven simulation results indicated that the HGSO algorithm accurately trains the GMDH methods better than the PSO algorithm, whereas the PSO algorithm trained simple simulation equations more precisely. Among all used DDMs, the integrative GMDH-HGSO algorithm provided the highest accuracy (RMSE = 7.086 mm). The results also showed that the integrative GMDHs enhance the accuracy of polynomial GMDHs by ~14.65% (based on the RMSE).

关键词： sedimentation water resources dam engineering machine learning heuristic

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基于混合驱动高斯过程学习的强机动多目标跟踪方法 Research Article

国强1,滕龙1,2,尹天祥3,郭云飞3,吴新良2,宋文明2

《信息与电子工程前沿（英文）》 2023年第24卷第11期页码 1647-1656 doi: 10.1631/FITEE.2300348

摘要：现有机动目标跟踪方法在杂波环境中强机动目标的跟踪性能并不令人满意。本文提出一种混合驱动方法，利用数据驱动和基于模型算法的优点跟踪多个高机动目标。将时变恒速（CV）模型集成到在线学习的高斯过程（GP）中，提高高斯过程的预测性能。进一步与广义概率数据关联（GPDA）算法相结合，实现多目标跟踪。通过仿真实验可知，与广泛使用的机动目标跟踪算法如交互式多模型（IMM）和数据驱动的高斯过程运动跟踪器（GPMT）相比，提出的混合驱动方法具有显著的性能优势。

关键词：目标跟踪；高斯过程；数据驱动；在线学习；模型驱动；概率数据关联

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Data-driven distribution network topology identification considering correlated generation power of distributed

《能源前沿（英文）》 2022年第16卷第1期页码 121-129 doi: 10.1007/s11708-021-0780-x

摘要： This paper proposes a data-driven topology identification method for distribution systems with distributed energy resources (DERs). First, a neural network is trained to depict the relationship between nodal power injections and voltage magnitude measurements, and then it is used to generate synthetic measurements under independent nodal power injections, thus eliminating the influence of correlated nodal power injections on topology identification. Second, a maximal information coefficient-based maximum spanning tree algorithm is developed to obtain the network topology by evaluating the dependence among the synthetic measurements. The proposed method is tested on different distribution networks and the simulation results are compared with those of other methods to validate the effectiveness of the proposed method.

关键词： power distribution network data-driven topology identification distributed energy resource maximal information coefficient

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Test-driven verification/validation of model transformations

László LENGYEL,Hassan CHARAF

《信息与电子工程前沿（英文）》 2015年第16卷第2期页码 85-97 doi: 10.1631/FITEE.1400111

摘要： Why is it important to verify/validate model transformations? The motivation is to improve the quality of the transformations, and therefore the quality of the generated software artifacts. Verified/validated model transformations make it possible to ensure certain properties of the generated software artifacts. In this way, verification/validation methods can guarantee different requirements stated by the actual domain against the generated/modified/optimized software products. For example, a verified/validated model transformation can ensure the preservation of certain properties during the model-to-model transformation. This paper emphasizes the necessity of methods that make model transformation verified/validated, discusses the different scenarios of model transformation verification and validation, and introduces the principles of a novel test-driven method for verifying/validating model transformations. We provide a solution that makes it possible to automatically generate test input models for model transformations. Furthermore, we collect and discuss the actual open issues in the field of verification/validation of model transformations.

关键词： Graph rewriting based model transformations Verification/validation Test-driven verification

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A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting

《环境科学与工程前沿（英文）》 2023年第17卷第2期 doi: 10.1007/s11783-023-1622-3

摘要：

● A novel deep learning framework for short-term water demand forecasting.

关键词： Short-term water demand forecasting Long-short term memory neural network Convolutional Neural Network Wavelet multi-resolution analysis Data-driven models

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A real-life stability model for a large shield-driven tunnel in heterogeneous soft soils

Xinyu HU, Zixin ZHANG, Scott KIEFFER

《结构与土木工程前沿（英文）》 2012年第6卷第2期页码 176-187 doi: 10.1007/s11709-012-0149-7

摘要： The current models that have been published to date only allow for homogeneous soil at the tunnel face. This paper presents a real-life face stability model to determine the minimal pressure needed at the tunnel face for a large shield-driven tunnel in heterogeneous soft soils. It is found that the influence of multilayered soil boundaries is significant, especially for the mixed-layer (e.g., sand and clay) soils. The suggested M-M model is developed by considering the influence of the heterogeneity of the soil on the angle of slip and the minimal support pressure. Comparisons of the solutions in mixed-layer soils are conducted, and the effects of the involved parameters for a large, multilayered, shield-driven tunnel are also investigated.

关键词： analytical solution shield-driven tunnel multilayered soil face stability

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Evaluation of the k-nearest neighbor method for forecasting the influent characteristics of wastewater treatment plant

Minsoo KIM,Yejin KIM,Hyosoo KIM,Wenhua PIAO,Changwon KIM

《环境科学与工程前沿（英文）》 2016年第10卷第2期页码 299-310 doi: 10.1007/s11783-015-0825-7

摘要： The k-nearest neighbor (k-NN) method was evaluated to predict the influent flow rate and four water qualities, namely chemical oxygen demand (COD), suspended solid (SS), total nitrogen (T-N) and total phosphorus (T-P) at a wastewater treatment plant (WWTP). The search range and approach for determining the number of nearest neighbors (NNs) under dry and wet weather conditions were initially optimized based on the root mean square error (RMSE). The optimum search range for considering data size was one year. The square root-based (SR) approach was superior to the distance factor-based (DF) approach in determining the appropriate number of NNs. However, the results for both approaches varied slightly depending on the water quality and the weather conditions. The influent flow rate was accurately predicted within one standard deviation of measured values. Influent water qualities were well predicted with the mean absolute percentage error (MAPE) under both wet and dry weather conditions. For the seven-day prediction, the difference in predictive accuracy was less than 5% in dry weather conditions and slightly worse in wet weather conditions. Overall, the k-NN method was verified to be useful for predicting WWTP influent characteristics.

关键词： influent wastewater prediction data-driven model k-nearest neighbor method (k-NN)

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数据驱动的加工过程异常诊断 Article

Y.C. Liang, S. Wang, W.D. Li, X. Lu

《工程（英文）》 2019年第5卷第4期页码 646-652 doi: 10.1016/j.eng.2019.03.012

摘要：

为了在计算机数控（CNC）加工过程中实现零缺陷生产，开发有效的异常检测诊断系统势在必行。然而，由于加工过程中机床和工装的动态条件限制，目前在工业生产中采用的相关诊断系统所能发挥的作用往往非常有限。为了解决这个问题，本文提出了一种全新的异常数据驱动的诊断系统。在该系统之中，我们持续收集随动态加工过程而产生的状态监测功率数据，并以此支持在线诊断分析。为了便于分析，我们设计了预处理机制对所监视的数据进行去噪、标准化以及校准。随后我们即从监控数据中提取关键特征，并定义阈值以识别异常。考虑到加工过程中机床和工装的动态条件，用于识别异常的阈值可以调整。我们还可以基于历史数据利用果蝇优化（FFO）算法优化阈值，以实现更准确的检测。通过实践验证，我们证明了该系统在工业应用中的有效性和巨大前景。

关键词：计算机数控加工异常检测果蝇优化算法数据驱动方法

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机器学习和数据驱动算法在智慧发电系统中的应用——一种不确定性处理的视角 Review

孙立, Fengqi You

《工程（英文）》 2021年第7卷第9期页码 1239-1247 doi: 10.1016/j.eng.2021.04.020

摘要：

由于人们对气候变化和环境保护的日益关注，智慧发电已成为常规火力发电厂和可再生能源系统经济安全运行的关键。面对日益增长的系统规模及其各种不确定性，传统的基于模型的第一定律方法已难以满足系统控制的要求。机器学习（ML）和数据驱动控制（DDC）技术的蓬勃发展为这些传统方法提供了一种替代方案。本文回顾了机器学习和数据驱动控制技术在发电系统监测、控制、优化和故障检测方面的典型应用，特别着重于揭示这些方法在评价、消除或耐受相关不确定性影响方面的作用。本文为智慧发电控制技术提供了一个从调节层到规划层的总体视角，分别从可见性、机动性、灵活性、经济性和安全性（简称“五性”）方面对机器学习和数据驱动控制技术的优势进行阐释。最后，对未来研究和应用进行了展望。

关键词：智慧发电机器学习数据驱动控制系统工程