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Predicting beach profile evolution with group method data handling-type neural networks on beaches with

M. A. LASHTEH NESHAEI, M. A. MEHRDAD, N. ABEDIMAHZOON, N. ASADOLLAHI

《结构与土木工程前沿（英文）》 2013年 第7卷 第2期   页码 117-126 doi: 10.1007/s11709-013-0205-y

摘要： A major goal of coastal engineering is to develop models for the reliable prediction of short- and long-term near shore evolution. The most successful coastal models are numerical models, which allow flexibility in the choice of initial and boundary conditions. In the present study, evolutionary algorithms (EAs) are employed for multi-objective Pareto optimum design of group method data handling (GMDH)-type neural networks that have been used for bed evolution modeling in the surf zone for reflective beaches, based on the irregular wave experiments performed at the Hydraulic Laboratory of Imperial College (London, UK). The input parameters used for such modeling are significant wave height, wave period, wave action duration, reflection coefficient, distance from shoreline and sand size. In this way, EAs with an encoding scheme are presented for evolutionary design of the generalized GMDH-type neural networks, in which the connectivity configurations in such networks are not limited to adjacent layers. Also, multi-objective EAs with a diversity preserving mechanism are used for Pareto optimization of such GMDH-type neural networks. The most important objectives of GMDH-type neural networks that are considered in this study are training error (TE), prediction error (PE), and number of neurons ( ). Different pairs of these objective functions are selected for two-objective optimization processes. Therefore, optimal Pareto fronts of such models are obtained in each case, which exhibit the trade-offs between the corresponding pair of the objectives and, thus, provide different non-dominated optimal choices of GMDH-type neural network model for beach profile evolution. The results showed that the present model has been successfully used to optimally prediction of beach profile evolution on beaches with seawalls.

关键词： beach profile evolution     genetic algorithms     group method of data handling     Pareto     reflective beaches    

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An efficient two-stage approach for structural damage detection using meta-heuristic algorithms and groupmethod of data handling surrogate model

Hamed FATHNEJAT, Behrouz AHMADI-NEDUSHAN

《结构与土木工程前沿（英文）》 2020年 第14卷 第4期   页码 907-929 doi: 10.1007/s11709-020-0628-1

摘要： In this study, the performance of an efficient two-stage methodology which is applied in a damage detection system using a surrogate model of the structure has been investigated. In the first stage, in order to locate the damage accurately, the performance of the modal strain energy based index for using different numbers of natural mode shapes has been evaluated using the confusion matrix. In the second stage, to estimate the damage extent, the sensitivity of most used modal properties due to damage, such as natural frequency and flexibility matrix is compared with the mean normalized modal strain energy (MNMSE) of suspected damaged elements. Moreover, a modal property change vector is evaluated using the group method of data handling (GMDH) network as a surrogate model during damage extent estimation by optimization algorithm; in this part of methodology, the performance of the three popular optimization algorithms including particle swarm optimization (PSO), bat algorithm (BA), and colliding bodies optimization (CBO) is examined and in this regard, root mean square deviation ( ) based on the modal property change vector has been proposed as an objective function. Furthermore, the effect of noise in the measurement of structural responses by the sensors has also been studied. Finally, in order to achieve the most generalized neural network as a surrogate model, GMDH performance is compared with a properly trained cascade feed-forward neural network (CFNN) with log-sigmoid hidden layer transfer function. The results indicate that the accuracy of damage extent estimation is acceptable in the case of integration of PSO and MNMSE. Moreover, the GMDH model is also more efficient and mimics the behavior of the structure slightly better than CFNN model.

关键词： two-stage method     modal strain energy     surrogate model     GMDH     optimization damage detection    

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Lignin-based polymer with high phenolic hydroxyl group content prepared by the alkyl chain bridging method

《化学科学与工程前沿（英文）》 2023年 第17卷 第8期   页码 1075-1084 doi: 10.1007/s11705-022-2272-x

摘要： Inspired by the importance of the phenolic group to the electron transporting property of hole transport materials, phenolic hydroxyl groups were introduced in lignosulfonate (LS) via the alkyl chain bridging method to prepare phenolated-lignosulfonate (PLS). The results showed that the phenolic group was boosted from 0.81 mmol∙g–1 of LS to 1.19 mmol∙g–1 of PLS. The electrochemical property results showed two oxidation peaks in the cyclic voltammogram (CV) curve of PLS, and the oxidation potential of the PLS-modified electrode decreased by 0.5 eV compared with that of LS. This result indicates that PLS is more easily oxidized than LS. Based on the excellent electron transporting property of PLS, PLS was applied as a dopant in poly(3,4-ethylenedioxythiophene) (PEDOT, called PEDOT:PLSs). PLS showed excellent dispersion properties for PEDOT. Moreover, the transmittance measurement results showed that the transmittance of PEDOT:PLSs exceeded 85% in the range of 300–800 nm. The CV results showed that the energy levels of PEDOT:PLSs could be flexibly adjusted by PLS amounts. The results indicate that the phenolic hydroxyl group of lignin can be easily boosted by the alkyl chain bridging method, and phenolated lignin-based polymers may have promising potential as dopants of PEDOT to produce hole transporting materials for different organic photovoltaic devices.

关键词： lignosulfonate     phenolic group     PEDOT:PLS     hole extract layer     energy level    

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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    

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Anensemble method for data stream classification in the presence of concept drift

Omid ABBASZADEH,Ali AMIRI,Ali Reza KHANTEYMOORI

《信息与电子工程前沿（英文）》 2015年 第16卷 第12期   页码 1059-1068 doi: 10.1631/FITEE.1400398

摘要： One recent area of interest in computer science is data stream management and processing. By ‘data stream’, we refer to continuous and rapidly generated packages of data. Specific features of data streams are immense volume, high production rate, limited data processing time, and data concept drift; these features differentiate the data stream from standard types of data. An issue for the data stream is classification of input data. A novel ensemble classifier is proposed in this paper. The classifier uses base classifiers of two weighting functions under different data input conditions. In addition, a new method is used to determine drift, which emphasizes the precision of the algorithm. Another characteristic of the proposed method is removal of different numbers of the base classifiers based on their quality. Implementation of a weighting mechanism to the base classifiers at the decision-making stage is another advantage of the algorithm. This facilitates adaptability when drifts take place, which leads to classifiers with higher efficiency. Furthermore, the proposed method is tested on a set of standard data and the results confirm higher accuracy compared to available ensemble classifiers and single classifiers. In addition, in some cases the proposed classifier is faster and needs less storage space.

关键词： Data stream     Classificaion     Ensemble classifiers     Concept drift    

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Prediction of the flash points of alkanes by group bond contribution method using artificial neural networks

PAN Yong, JIANG Juncheng, WANG Zhirong

《化学科学与工程前沿（英文）》 2007年 第1卷 第4期   页码 390-394 doi: 10.1007/s11705-007-0071-z

摘要： A group bond contribution model using artificial neural networks, which had the high ability of nonlinear of prediction, was established to predict the flash points of alkanes. This model contained not only the information of group property but also connectivity in molecules. A set of 16 group bonds were used as input parameters of neural networks to study the correlation of molecular structures with flash points of 44 alkanes. The results showed that the predicted flash points were in good agreement with the experimental data that the absolute mean absolute error was 6.9 K and the absolute mean relative error was 2.29%, which were superior to those of traditional group contribution methods. The method can be used not only to reveal the quantitative correlation between flash points and molecular structures of alkanes but also to predict the flash points of organic compounds for chemical engineering.

关键词： information     nonlinear     quantitative correlation     superior     molecular    

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Numerical evaluation of group-pile foundation subjected to cyclic horizontal load

Youngji JIN, Xiaohua BAO, Yoshimitsu KONDO, Feng ZHANG,

《结构与土木工程前沿（英文）》 2010年 第4卷 第2期   页码 196-207 doi: 10.1007/s11709-010-0021-6

摘要： In this paper, three-dimensional (3D) finite element analyses of a real-scale group-pile foundation subjected to horizontal cyclic loading are conducted using a program named DBLEAVES. In the simulations, nonlinear behaviors of ground and piles are described by subloading model and the axial-force dependent model (AFD model) which considered the axial-force dependency in the nonlinear moment-curvature relations. In order to consider the influence of an effective stress path on the prediction of the group-pile foundation, the analyses are conducted within the framework of the soil-water coupling method with finite-difference and finite-element regime. The material parameters of soils are determined based on conventional triaxial drained compression tests on undisturbed and remolded specimens. The applicability of the proposed numerical method is encouraging, and therefore, it is quite confident to say that the method can be used to predict the mechanical behaviors of group-pile foundation to a satisfactory accuracy, particularly with the effective stress analysis.

关键词： group-pile foundation     real-scale cyclic loading test     three-dimensional finite element method (3D-FEM)     soil-water coupling analysis     undisturbed and remolded specimens    

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Group-based multiple pipe routing method for aero-engine focusing on parallel layout

《机械工程前沿（英文）》 2021年 第16卷 第4期   页码 798-813 doi: 10.1007/s11465-021-0645-3

摘要： External pipe routing for aero-engine in limited three-dimensional space is a typical nondeterministic polynomial hard problem, where the parallel layout of pipes plays an important role in improving the utilization of layout space, facilitating pipe assembly, and maintenance. This paper presents an automatic multiple pipe routing method for aero-engine that focuses on parallel layout. The compressed visibility graph construction algorithm is proposed first to determine rapidly the rough path and interference relationship of the pipes to be routed. Based on these rough paths, the information of pipe grouping and sequencing are obtained according to the difference degree and interference degree, respectively. Subsequently, a coevolutionary improved differential evolution algorithm, which adopts the coevolutionary strategy, is used to solve multiple pipe layout optimization problem. By using this algorithm, pipes in the same group share the layout space information with one another, and the optimal layout solution of pipes in this group can be obtained in the same evolutionary progress. Furthermore, to eliminate the minor angle deviation of parallel pipes that would cause assembly stress in actual assembly, an accurate parallelization processing method based on the simulated annealing algorithm is proposed. Finally, the simulation results on an aero-engine demonstrate the feasibility and effectiveness of the proposed method.

关键词： multiple pipe routing     optimization algorithm     aero-engine     pipe grouping     parallel layout    

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Module-based method for design and analysis of reconfigurable parallel robots

Fengfeng XI, Yuwen LI, Hongbo WANG

《机械工程前沿（英文）》 2011年 第6卷 第2期   页码 151-159 doi: 10.1007/s11465-011-0121-6

摘要：

This paper presents a method for the design and analysis of reconfigurable parallel robots. The inherent modularity in a parallel robot lends itself as a natural candidate for reconfiguration. By taking the branches as building blocks, many modular parallel robots can be constructed, from which a reconfigurable parallel robot can be realized. Among three types of reconfigurations, namely, geometry morphing, topology morphing, and group morphing, the method presented here is for the last two reconfigurations, thereby advancing the current research that is mainly limited to geometry morphing. It is shown that the module-based method not only provides a systematic way of designing a reconfigurable parallel robot, but also offers a unified modeling for robot analysis. Two examples are provided, one showing the topology morphing and the other showing the group morphing.

关键词： reconfigurable parallel robot     topology morphing     group morphing    

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基于μ综合鲁棒控制的四轮转向车辆操纵稳定性研究

殷国栋,陈南,李普

《中国工程科学》 2005年 第7卷 第4期   页码 54-58

摘要：

车辆总是承担不同的载荷，车辆建模亦存在着误差，传统四轮转向控制器难以达到原有的性能指标；针对外界干扰，采用μ综合鲁棒控制方法，构造横摆角速度跟踪综合控制系统设计框架，选取了不同环节的权函数。仿真结果表明，四轮转向车辆控制系统具有良好的动态性能、鲁棒稳定性和鲁棒性能，有效地提高了车辆操纵稳定性和安全性。

关键词： 四轮转向     鲁棒控制     操纵稳定性     μ综合    

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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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A review of intelligent optimization for group scheduling problems in cellular manufacturing

《工程管理前沿（英文）》   页码 406-426 doi: 10.1007/s42524-022-0242-0

摘要： Given that group technology can reduce the changeover time of equipment, broaden the productivity, and enhance the flexibility of manufacturing, especially cellular manufacturing, group scheduling problems (GSPs) have elicited considerable attention in the academic and industry practical literature. There are two issues to be solved in GSPs: One is how to allocate groups into the production cells in view of major setup times between groups and the other is how to schedule jobs in each group. Although a number of studies on GSPs have been published, few integrated reviews have been conducted so far on considered problems with different constraints and their optimization methods. To this end, this study hopes to shorten the gap by reviewing the development of research and analyzing these problems. All literature is classified according to the number of objective functions, number of machines, and optimization algorithms. The classical mathematical models of single-machine, permutation, and distributed flowshop GSPs based on adjacent and position-based modeling methods, respectively, are also formulated. Last but not least, outlooks are given for outspread problems and problem algorithms for future research in the fields of group scheduling.

关键词： cellular manufacturing     group scheduling     flowshop     literature review    

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Slope stability analysis based on big data and convolutional neural network

Yangpan FU; Mansheng LIN; You ZHANG; Gongfa CHEN; Yongjian LIU

《结构与土木工程前沿（英文）》 2022年 第16卷 第7期   页码 882-895 doi: 10.1007/s11709-022-0859-4

摘要： The Limit Equilibrium Method (LEM) is commonly used in traditional slope stability analyses, but it is time-consuming and complicated. Due to its complexity and nonlinearity involved in the evaluation process, it cannot provide a quick stability estimation when facing a large number of slopes. In this case, the convolutional neural network (CNN) provides a better alternative. A CNN model can process data quickly and complete a large amount of data analysis in a specific situation, while it needs a large number of training samples. It is difficult to get enough slope data samples in practical engineering. This study proposes a slope database generation method based on the LEM. Samples were amplified from 40 typical slopes, and a sample database consisting of 20000 slope samples was established. The sample database for slopes covered a wide range of slope geometries and soil layers’ physical and mechanical properties. The CNN trained with this sample database was then applied to the stability prediction of 15 real slopes to test the accuracy of the CNN model. The results show that the slope stability prediction method based on the CNN does not need complex calculation but only needs to provide the slope coordinate information and physical and mechanical parameters of the soil layers, and it can quickly obtain the safety factor and stability state of the slopes. Moreover, the prediction accuracy of the CNN trained by the sample database for slope stability analysis reaches more than 99%, and the comparisons with the BP neural network show that the CNN has significant superiority in slope stability evaluation. Therefore, the CNN can predict the safety factor of real slopes. In particular, the combination of typical actual slopes and generated slope data provides enough training and testing samples for the CNN, which improves the prediction speed and practicability of the CNN-based evaluation method in engineering practice.

关键词： slope stability     limit equilibrium method     convolutional neural network     database for slopes     big data    

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基于数据互联服务的隧道新奥法施工构想与初探 Article

杜博文, 杜彦良, 徐飞, 贺鹏

《工程（英文）》 2018年 第4卷 第1期   页码 123-130 doi: 10.1016/j.eng.2017.07.002

摘要：
新奥法（NATM）广泛应用于山岭隧道、城市地铁、地下贮库、地下厂房、矿山巷道等地下工程，掌子面前方地质、围岩变形、支护结构受力状态等在施工过程中的动态变化情况，是评价结构稳定程度、优化施工方案，确保隧道施工安全与质量的必要信息。施工过程中获取的大量动态监测信息的不确定性与离散性，给施工方案的选择及灾害事故与险情的准确预测带来了巨大挑战，增加了隧道安全隐患。针对上述问题，本文提出了一种基于互联网大数据支持环境下的隧道施工数据服务系统，通过对已施工案例中各检测器结果进行标记，建立同场景下施工相关参数的关联，利用工程案例的积累不断补充和完善，实现相似环境下的参数提取，为同类场景下施工方案设计、施工资源的合理分配提供数据支撑，为后续工程设计、施工提供依据。

关键词： 新奥法     大数据环境     数据服务     隧道施工    

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Emergence mechanisms of group consensus in social networks

《工程管理前沿（英文）》 doi: 10.1007/s42524-023-0277-x

摘要： Reaching consensus within larger social network groups has emerged as a pivotal concern in the digital age of connectivity. This article redefines group consensus as the emergence of collective intelligence resulting from self-organizing actions and interactions of individuals within a social network group. In our exploration of extant research on group consensus, we illuminate two frequently underestimated, yet noteworthy facets: Dynamism and emergence. In contrast to the conventional perspective of consensus as a mere outcome, we perceive it as an ongoing, dynamic process. This process encompasses self-organized communication and interaction among group members, collectively guiding the group towards cognitive convergence and viewpoint integration. Consequently, it is imperative to redirect our focus from the outcomes of group interactions to an examination of the relationships and processes underpinning consensus formation, thus elucidating the mechanisms responsible for the generation of group consensus. The amalgamation of cognitive contexts and accurate simplification of real-world scenarios for simulation and experimental analysis offers a pragmatic operational approach. This study contributes novel theoretical underpinnings and quantitative insights for establishing and sustaining group consensus within the realm of engineering management practices. Concurrently, it holds substantial importance for advancing the broader research landscape pertaining to social consensus.

关键词： group consensus     social network     collective intelligence    

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标题 作者 时间 类型 操作

Predicting beach profile evolution with group method data handling-type neural networks on beaches with

M. A. LASHTEH NESHAEI, M. A. MEHRDAD, N. ABEDIMAHZOON, N. ASADOLLAHI

期刊论文

An efficient two-stage approach for structural damage detection using meta-heuristic algorithms and groupmethod of data handling surrogate model

Hamed FATHNEJAT, Behrouz AHMADI-NEDUSHAN

期刊论文

Lignin-based polymer with high phenolic hydroxyl group content prepared by the alkyl chain bridging method

期刊论文

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

孙立, Fengqi You

期刊论文

Anensemble method for data stream classification in the presence of concept drift

Omid ABBASZADEH,Ali AMIRI,Ali Reza KHANTEYMOORI

期刊论文

Prediction of the flash points of alkanes by group bond contribution method using artificial neural networks

PAN Yong, JIANG Juncheng, WANG Zhirong

期刊论文

Numerical evaluation of group-pile foundation subjected to cyclic horizontal load

Youngji JIN, Xiaohua BAO, Yoshimitsu KONDO, Feng ZHANG,

期刊论文

Group-based multiple pipe routing method for aero-engine focusing on parallel layout

期刊论文

Module-based method for design and analysis of reconfigurable parallel robots

Fengfeng XI, Yuwen LI, Hongbo WANG

期刊论文

基于μ综合鲁棒控制的四轮转向车辆操纵稳定性研究

殷国栋,陈南,李普

期刊论文

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

期刊论文

A review of intelligent optimization for group scheduling problems in cellular manufacturing

期刊论文

Slope stability analysis based on big data and convolutional neural network

Yangpan FU; Mansheng LIN; You ZHANG; Gongfa CHEN; Yongjian LIU

期刊论文

基于数据互联服务的隧道新奥法施工构想与初探

杜博文, 杜彦良, 徐飞, 贺鹏

期刊论文

Emergence mechanisms of group consensus in social networks

期刊论文