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Ensemble unit and AI techniques for prediction of rock strain

Pradeep T; Pijush SAMUI; Navid KARDANI; Panagiotis G ASTERIS

《结构与土木工程前沿（英文）》 2022年 第16卷 第7期   页码 858-870 doi: 10.1007/s11709-022-0831-3

摘要： The behavior of rock masses is influenced by a variety of forces, with measurement of stress and strain playing the most critical roles in assessing deformation. The laboratory test for determining strain at each location within rock samples is expensive and difficult but rock strain data are important for predicting failure of rock material. Many researchers employ AI technology in order to solve these difficulties. AI algorithms such as gradient boosting machine (GBM), support vector regression (SVR), random forest (RF), and group method of data handling (GMDH) are used to efficiently estimate the strain at every point within a rock sample. Additionally, the ensemble unit (EnU) may be utilized to evaluate rock strain. In this study, 3000 experimental data are used for the purpose of prediction. The obtained strain values are then evaluated using various statistical parameters and compared to each other using EnU. Ranking analysis, stress-strain curve, Young’s modulus, Poisson’s ratio, actual vs. predicted curve, error matrix and the Akaike’s information criterion (AIC) values are used for comparing models. The GBM model achieved 98.16% and 99.98% prediction accuracy (in terms of values of R2) in the longitudinal and lateral dimensions, respectively, during the testing phase. The GBM model, based on the experimental data, has the potential to be a new option for engineers to use when assessing rock strain.

关键词： prediction     strain     ensemble unit     rank analysis     error matrix    

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Robust ensemble of metamodels based on the hybrid error measure

《机械工程前沿（英文）》 2021年 第16卷 第3期   页码 623-634 doi: 10.1007/s11465-021-0641-7

摘要： Metamodels have been widely used as an alternative for expensive physical experiments or complex, time-consuming computational simulations to provide a fast but accurate analysis. However, challenge remains in the prior determination of the most suitable metamodel for a particular case because of the lack of information about the actual behavior of a system. In addition, existing studies on metamodels have largely restricted on solving deterministic problems (e.g., data from finite element models), whereas some real-life engineering problems (e.g., data from physical experiment) are stochastic problems with noisy data. In this work, a robust ensemble of metamodels (EMs) is proposed by combining three regression stand-alone metamodels in a weighted sum form. The weight factor is adaptively determined according to the hybrid error metric, which combines global and local error measures to improve the accuracy of the EMs. Furthermore, three typical individual metamodels that can filter noise are selected to construct the EMs to extend their application in practical engineering problems. Three well-known benchmark problems with different levels of noise and three engineering problems are used to verify the effectiveness of the proposed EMs. Results show that the proposed EMs have higher accuracy and robustness than the individual metamodels and other typical EMs in major cases.

关键词： metamodel     ensemble of metamodels     hybrid error measure     stochastic problem    

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Processing parameter optimization of fiber laser beam welding using an ensemble of metamodels and MOABC

《机械工程前沿（英文）》 2022年 第17卷 第4期 doi: 10.1007/s11465-022-0703-5

摘要： In fiber laser beam welding (LBW), the selection of optimal processing parameters is challenging and plays a key role in improving the bead geometry and welding quality. This study proposes a multi-objective optimization framework by combining an ensemble of metamodels (EMs) with the multi-objective artificial bee colony algorithm (MOABC) to identify the optimal welding parameters. An inverse proportional weighting method that considers the leave-one-out prediction error is presented to construct EM, which incorporates the competitive strengths of three metamodels. EM constructs the correlation between processing parameters (laser power, welding speed, and distance defocus) and bead geometries (bead width, depth of penetration, neck width, and neck depth) with average errors of 10.95%, 7.04%, 7.63%, and 8.62%, respectively. On the basis of EM, MOABC is employed to approximate the Pareto front, and verification experiments show that the relative errors are less than 14.67%. Furthermore, the main effect and the interaction effect of processing parameters on bead geometries are studied. Results demonstrate that the proposed EM-MOABC is effective in guiding actual fiber LBW applications.

关键词： laser beam welding     parameter optimization     metamodel     multi-objective    

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Efficient Identification of water conveyance tunnels siltation based on ensemble deep learning

Xinbin WU; Junjie LI; Linlin WANG

《结构与土木工程前沿（英文）》 2022年 第16卷 第5期   页码 564-575 doi: 10.1007/s11709-022-0829-x

摘要： The inspection of water conveyance tunnels plays an important role in water diversion projects. Siltation is an essential factor threatening the safety of water conveyance tunnels. Accurate and efficient identification of such siltation can reduce risks and enhance safety and reliability of these projects. The remotely operated vehicle (ROV) can detect such siltation. However, it needs to improve its intelligent recognition of image data it obtains. This paper introduces the idea of ensemble deep learning. Based on the VGG16 network, a compact convolutional neural network (CNN) is designed as a primary learner, called Silt-net, which is used to identify the siltation images. At the same time, the fully-connected network is applied as the meta-learner, and stacking ensemble learning is combined with the outputs of the primary classifiers to obtain satisfactory classification results. Finally, several evaluation metrics are used to measure the performance of the proposed method. The experimental results on the siltation dataset show that the classification accuracy of the proposed method reaches 97.2%, which is far better than the accuracy of other classifiers. Furthermore, the proposed method can weigh the accuracy and model complexity on a platform with limited computing resources.

关键词： water conveyance tunnels     siltation images     remotely operated vehicles     deep learning     ensemble learning     computer vision    

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A solution to stochastic unit commitment problem for a wind-thermal system coordination

B. SARAVANAN,Shreya MISHRA,Debrupa NAG

《能源前沿（英文）》 2014年 第8卷 第2期   页码 192-200 doi: 10.1007/s11708-014-0306-x

摘要： Unit commitment (UC) problem is one of the most important decision making problems in power system. In this paper the UC problem is solved by considering it as a real time problem by adding stochasticity in the generation side because of wind-thermal co-ordination system as well as stochasticity in the load side by incorporating the randomness of the load. The most important issue that needs to be addressed is the achievement of an economic unit commitment solution after solving UC as a real time problem. This paper proposes a hybrid approach to solve the stochastic unit commitment problem considering the volatile nature of wind and formulating the UC problem as a chance constrained problem in which the load is met with high probability over the entire time period.

关键词： unit commitment (UC)     randomness     wind generation     univariate     chance constrained    

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Solving unit commitment problem using a novel version of harmony search algorithm

Roozbeh MORSALI,Tohid JAFARI,Amirhossein GHODS,Mohammad KARIMI

《能源前沿（英文）》 2014年 第8卷 第3期   页码 297-304 doi: 10.1007/s11708-014-0309-7

摘要： In this context, a novel structure was proposed for improving harmony search (HS) algorithm to solve the unit comment (UC) problem. The HS algorithm obtained optimal solution for defined objective function by improvising, updating and checking operators. In the proposed improved self-adaptive HS (SGHS) algorithm, two important control parameters were adjusted to reach better solution from the simple HS algorithm. The objective function of this study consisted of operation, start-up and shut-down costs. To confirm the effectiveness, the SGHS algorithm was tested on systems with 10, 20, 40 and 60 generating units, and the obtained results were compared with those of the simple HS algorithm and other related works.

关键词： generation scheduling     harmony search (HS) algorithm     intelligent technique     unit commitment    

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A solution to the unit commitment problem—a review

B. SARAVANAN, Siddharth DAS, Surbhi SIKRI, D. P. KOTHARI

《能源前沿（英文）》 2013年 第7卷 第2期   页码 223-236 doi: 10.1007/s11708-013-0240-3

摘要： Unit commitment (UC) is an optimization problem used to determine the operation schedule of the generating units at every hour interval with varying loads under different constraints and environments. Many algorithms have been invented in the past five decades for optimization of the UC problem, but still researchers are working in this field to find new hybrid algorithms to make the problem more realistic. The importance of UC is increasing with the constantly varying demands. Therefore, there is an urgent need in the power sector to keep track of the latest methodologies to further optimize the working criterions of the generating units. This paper focuses on providing a clear review of the latest techniques employed in optimizing UC problems for both stochastic and deterministic loads, which has been acquired from many peer reviewed published papers. It has been divided into many sections which include various constraints based on profit, security, emission and time. It emphasizes not only on deregulated and regulated environments but also on renewable energy and distributed generating systems. In terms of contributions, the detailed analysis of all the UC algorithms has been discussed for the benefit of new researchers interested in working in this field.

关键词： unit commitment (UC)     optimization     deterministic load     stochastic load     evolutionary programming (EP)     hybrid    

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A novel ensemble model for predicting the performance of a novel vertical slot fishway

Aydin SHISHEGARAN, Mohammad SHOKROLLAHI, Ali MIRNOROLLAHI, Arshia SHISHEGARAN, Mohammadreza MOHAMMAD KHANI

《结构与土木工程前沿（英文）》 2020年 第14卷 第6期   页码 1418-1444 doi: 10.1007/s11709-020-0664-x

摘要： We investigate the performance of a novel vertical slot fishway by employing finite volume and surrogate models. Multiple linear regression, multiple log equation regression, gene expression programming, and combinations of these models are employed to predict the maximum turbulence, maximum velocity, resting area, and water depth of the middle pool in the fishway. The statistical parameters and error terms, including the coefficient of determination, root mean square error, normalized square error, maximum positive and negative errors, and mean absolute percentage error were employed to evaluate and compare the accuracy of the models. We also conducted a parametric study. The independent variables include the opening between baffles ( ), the ratio of the length of the large and small baffles, the volume flow rate, and the angle of the large baffle. The results show that the key parameters of the maximum turbulence and velocity are the volume flow rate and .

关键词： novel vertical slot fishway     parametric study     finite volume method     ensemble model     gene expression programming    

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Energy saving design of the machining unit of hobbing machine tool with integrated optimization

《机械工程前沿（英文）》 2022年 第17卷 第3期 doi: 10.1007/s11465-022-0694-2

摘要： The machining unit of hobbing machine tool accounts for a large portion of the energy consumption during the operating phase. The optimization design is a practical means of energy saving and can reduce energy consumption essentially. However, this issue has rarely been discussed in depth in previous research. A comprehensive function of energy consumption of the machining unit is built to address this problem. Surrogate models are established by using effective fitting methods. An integrated optimization model for reducing tool displacement and energy consumption is developed on the basis of the energy consumption function and surrogate models, and the parameters of the motor and structure are considered simultaneously. Results show that the energy consumption and tool displacement of the machining unit are reduced, indicating that energy saving is achieved and the machining accuracy is guaranteed. The influence of optimization variables on the objectives is analyzed to inform the design.

关键词： energy saving design     energy consumption     machining unit     integrated optimization     machine tool    

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Low crosstalk switch unit for dense piezoelectric sensor networks

Lei QIU, Shenfang YUAN,

《机械工程前沿（英文）》 2009年 第4卷 第4期   页码 401-406 doi: 10.1007/s11465-009-0047-4

摘要： Structural health monitoring (SHM), on the basis of piezoelectric (PZT) sensors and lamb wave method, is efficient in estimating the state of monitored structures. Furthermore, to monitor large-scale structures, dense piezoelectric sensor networks are required, which usually contain many piezoelectric sensor pairs called actuator-sensor channels. In that case, considering the few data acquisition channels especially in the data acquisition board with a high sampling rate and limited quantity of signal amplifiers used in an integrated computer system, a switch unit is adopted to switch to different channels. Because of the high frequency and power of the lamb wave excitation signal, there exists a crosstalk signal in the switch unit. A large crosstalk signal is mixed into the response signal so that the on/off-line signal processing task is difficult to achieve. This paper first analyzes the crosstalk signal phenomenon, describes its production mechanism, and proposes a method to reduce it. Then a 24-switch channel low crosstalk switch unit based on a digital I/O board PCI7248 produced by Adlink technology is developed. An experiment is implemented to validate it. Its low crosstalk characteristics make it promote the real application of the SHM based active lamb wave method. Finally, a general software program based on LabVIEW software platform is developed to control this switch unit.

关键词： structural health monitoring (SHM)     piezoelectric (PZT) sensor networks     switch unit     crosstalk signal    

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集成增强主动学习混合判别分析模型及其在半监督故障分类中的应用 Research Article

王伟俊1,王云2,王君1,方信昀3,何雨辰1

《信息与电子工程前沿（英文）》 2022年 第23卷 第12期   页码 1814-1827 doi: 10.1631/FITEE.2200053

摘要： 故障分类作为过程监控中不可缺少的部分，其性能高度依赖于过程知识的充分性。然而，由于采样条件有限及实验室分析昂贵，数据标签总是难以获取，这可能导致分类性能下降。为了解决这个难题，本文提出一种新的半监督故障分类方法，其中每个未标记样本相对于特定标记数据集的价值采用增强的主动学习来评估。具有高价值的未标记样本将作为训练数据集的补充信息。此外，引入了几个合理的指标和准则大大降低了人工标注的干扰。最后，通过数值例子和田纳西伊士曼过程（TEP）评估了该方法的故障分类有效性。

关键词： 半监督；主动学习；集成学习；混合判别分析；故障分类    

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Performance design of a cryogenic air separation unit for variable working conditions using the lumped

Jinghua XU, Tiantian WANG, Qianyong CHEN, Shuyou ZHANG, Jianrong TAN

《机械工程前沿（英文）》 2020年 第15卷 第1期   页码 24-42 doi: 10.1007/s11465-019-0558-6

摘要： Large-scale cryogenic air separation units (ASUs), which are widely used in global petrochemical and semiconductor industries, are being developed with high operating elasticity under variable working conditions. Different from discrete processes in traditional machinery manufacturing, the ASU process is continuous and involves the compression, adsorption, cooling, condensation, liquefaction, evaporation, and distillation of multiple streams. This feature indicates that thousands of technical parameters in adsorption, heat transfer, and distillation processes are correlated and merged into a large-scale complex system. A lumped parameter model (LPM) of ASU is proposed by lumping the main factors together and simplifying the secondary ones to achieve accurate and fast performance design. On the basis of material and energy conservation laws, the piecewise-lumped parameters are extracted under variable working conditions by using LPM. Takagi–Sugeno (T–S) fuzzy interval detection is recursively utilized to determine whether the critical point is detected or not by using different thresholds. Compared with the traditional method, LPM is particularly suitable for “rough first then precise” modeling by expanding the feasible domain using fuzzy intervals. With LPM, the performance of the air compressor, molecular sieve adsorber, turbo expander, main plate-fin heat exchangers, and packing column of a 100000 Nm O /h large-scale ASU is enhanced to adapt to variable working conditions. The designed value of net power consumption per unit of oxygen production (kW/(Nm O )) is reduced by 6.45%.

关键词： performance design     air separation unit (ASU)     lumped parameter model (LPM)     variable working conditions     T–S fuzzy interval detection    

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基于Spark面向分布式EEMDN-SABiGRU模型的乘客热点预测

夏大文,耿建,黄瑞曦,申冰琪,胡杨,李艳涛,李华青

《信息与电子工程前沿（英文）》 2023年 第24卷 第9期   页码 1316-1331 doi: 10.1631/FITEE.2200621

摘要： 针对出租车与乘客之间的供需不平衡问题，本文提出一种基于Spark的分布式归一化集合经验模态分解和面向空间注意力机制的双向门控循环单元（EEMDN-SABiGRU）模型，实现乘客热点的精准预测，旨在于降低盲目巡航开支、提高载客效率和实现收益最大化。首先，提出一种归一化的集合经验模态分解方法（EEMDN），处理网格中乘客热点数据，解决非平稳序列问题和数值差异过大造成的预测精度下降问题，避免EMD本征模态函数（IMF）存在的模态混叠现象。其次，构建一种基于乘客上下车热点的权重和乘客的空间规律性的空间注意力机制，捕捉每个网格中的乘客热点特征。再次，融合一种双向门控循环单元（GRU）算法，解决GRU仅能获取前向信息而忽略后向信息问题，提高特征提取的准确性。最后，在Spark并行计算框架下，采用真实的出租车GPS轨迹数据，基于EEMDN-SABiGRU模型实现了乘客热点的准确预测。实验结果表明，在00网格4个数据集上，与LSTM、EMDL-STM、EEMD-LSTM、GRU、EMD-GRU、EEMD-GRU、EMDN-GRU、CNN和BP相比，EEMDN-SABiGRU的平均绝对百分比误差、平均绝对误差、均方根误差和最大误差值分别降低了43.18%、44.91%、55.04%和39.33%。

关键词： 乘客热点预测     集合经验模态分解（EEMD）     空间注意力机制     双向门控循环单元（BiGRU）     GPS轨迹     Spark    

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Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG

《机械工程前沿（英文）》 2021年 第16卷 第2期   页码 340-352 doi: 10.1007/s11465-021-0629-3

摘要： Deep learning has achieved much success in mechanical intelligent fault diagnosis in recent years. However, many deep learning methods cannot fully extract fault information to recognize mechanical health states when processing high-dimensional samples. Therefore, a multi-model ensemble deep learning method based on deep convolutional neural network (DCNN) is proposed in this study to accomplish fault recognition of high-dimensional samples. First, several 1D DCNN models with different activation functions are trained through dimension reduction learning to obtain different fault features from high-dimensional samples. Second, the obtained features are constructed into 2D images with multiple channels through a conversion method. The integrated 2D feature images can effectively represent the fault characteristic contained in raw high-dimension vibration signals. Lastly, a 2D DCNN model with multi-layer convolution and pooling is used to automatically learn features from the 2D images and identify the fault mode of the mechanical equipment by adopting a softmax classifier. The proposed method, which is validated using the bearing public dataset of Case Western Reserve University, USA and a one-stage reduction gearbox dataset, has high recognition accuracy. Compared with other classical deep learning methods, the proposed fault diagnosis method has considerable improvements.

关键词： fault intelligent diagnosis     deep learning     deep convolutional neural network     high-dimensional samples    

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Variation characteristics of atmospheric methane and carbon dioxide in summertime at a coastal site in the South China Sea

《环境科学与工程前沿（英文）》 2022年 第16卷 第11期 doi: 10.1007/s11783-022-1574-z

摘要：

● Diurnal patterns of CH4 and CO2 are clearly extracted using EEMD.

关键词： Methane     Carbon dioxide     Diurnal pattern     Ensemble empirical mode decomposition     South China Sea     Sea breeze    

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标题 作者 时间 类型 操作

Ensemble unit and AI techniques for prediction of rock strain

Pradeep T; Pijush SAMUI; Navid KARDANI; Panagiotis G ASTERIS

期刊论文

Robust ensemble of metamodels based on the hybrid error measure

期刊论文

Processing parameter optimization of fiber laser beam welding using an ensemble of metamodels and MOABC

期刊论文

Efficient Identification of water conveyance tunnels siltation based on ensemble deep learning

Xinbin WU; Junjie LI; Linlin WANG

期刊论文

A solution to stochastic unit commitment problem for a wind-thermal system coordination

B. SARAVANAN,Shreya MISHRA,Debrupa NAG

期刊论文

Solving unit commitment problem using a novel version of harmony search algorithm

Roozbeh MORSALI,Tohid JAFARI,Amirhossein GHODS,Mohammad KARIMI

期刊论文

A solution to the unit commitment problem—a review

B. SARAVANAN, Siddharth DAS, Surbhi SIKRI, D. P. KOTHARI

期刊论文

A novel ensemble model for predicting the performance of a novel vertical slot fishway

Aydin SHISHEGARAN, Mohammad SHOKROLLAHI, Ali MIRNOROLLAHI, Arshia SHISHEGARAN, Mohammadreza MOHAMMAD KHANI

期刊论文

Energy saving design of the machining unit of hobbing machine tool with integrated optimization

期刊论文

Low crosstalk switch unit for dense piezoelectric sensor networks

Lei QIU, Shenfang YUAN,

期刊论文

集成增强主动学习混合判别分析模型及其在半监督故障分类中的应用

王伟俊1,王云2,王君1,方信昀3,何雨辰1

期刊论文

Performance design of a cryogenic air separation unit for variable working conditions using the lumped

Jinghua XU, Tiantian WANG, Qianyong CHEN, Shuyou ZHANG, Jianrong TAN

期刊论文

基于Spark面向分布式EEMDN-SABiGRU模型的乘客热点预测

夏大文,耿建,黄瑞曦,申冰琪,胡杨,李艳涛,李华青

期刊论文

Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples

Xin ZHANG, Tao HUANG, Bo WU, Youmin HU, Shuai HUANG, Quan ZHOU, Xi ZHANG

期刊论文

Variation characteristics of atmospheric methane and carbon dioxide in summertime at a coastal site in the South China Sea

期刊论文