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Timing decision-making method of engine blades for predecisional remanufacturing based on reliability analysis

Le CHEN, Xianlin WANG, Hua ZHANG, Xugang ZHANG, Binbin DAN

《机械工程前沿（英文）》 2019年 第14卷 第4期   页码 412-421 doi: 10.1007/s11465-019-0551-0

摘要： A timing decision-making method for predecisional remanufacturing is presented. The method can effectively solve the uncertainty problem of remanufacturing blanks. From the perspective of reliability, this study analyzes the timing decision-making interval for predecisional remanufacturing of mechanical products during the service period and constructs an optimal timing model based on energy consumption and cost. The mapping relationships between time and energy consumption are predicted by using the characteristic values of performance degradation of products combined with the least squares support vector regression algorithm. Application of game theory reveals that when the energy consumption and cost are comprehensively optimal, this moment is the best time for predecisional remanufacturing. Used engine blades are utilized as an example to demonstrate the validity and effectiveness of the proposed method.

关键词： predecisional remanufacturing     reliability     least squares support vector regression (LS-SVR)     game theory    

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Identification of thermal error in a feed system based on multi-class LS-SVM

Chao JIN, Bo WU, Youmin HU, Yao CHENG

《机械工程前沿（英文）》 2012年 第7卷 第1期   页码 47-54 doi: 10.1007/s11465-012-0307-6

摘要：

Research of thermal characteristics has been a key issue in the development of high-speed feed system. The thermal positioning error of a ball-screw is one of the most important objects to consider for high-accuracy and high-speed machine tools. The research work undertaken herein ultimately aims at the development of a comprehensive thermal error identification model with high accuracy and robust. Using multi-class least squares support vector machines (LS-SVM), the thermal positioning error of the feed system is identified with the variance and mean square value of the temperatures of supporting bearings and screw-nut as feature vector. A series of experiments were carried out on a self-made quasi high-speed feed system experimental bench HUST-FS-001 to verify the identification capacity of the presented method. The results show that the recommended model can be used to predict the thermal error of a feed system with good accuracy, which is better than the ordinary BP and RBF neural network. The work described in this paper lays a solid foundation of thermal error prediction and compensation in a feed system.

关键词： least squares support vector machine (LS-SVM)     feed system     thermal error     precision machining    

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Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

Gurmanik KAUR,Ajat Shatru ARORA,Vijender Kumar JAIN

《信息与电子工程前沿（英文）》 2015年 第16卷 第6期   页码 474-485 doi: 10.1631/FITEE.1400295

摘要： Accurate blood pressure (BP) measurement is essential in epidemiological studies, screening programmes, and research studies as well as in clinical practice for the early detection and prevention of high BP-related risks such as coronary heart disease, stroke, and kidney failure. Posture of the participant plays a vital role in accurate measurement of BP. Guidelines on measurement of BP contain recommendations on the position of the back of the participants by advising that they should sit with supported back to avoid spuriously high readings. In this work, principal component analysis (PCA) is fused with forward stepwise regression (SWR), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and the least squares support vector machine (LS-SVM) model for the prediction of BP reactivity to an unsupported back in normotensive and hypertensive participants. PCA is used to remove multi-collinearity among anthropometric predictor variables and to select a subset of components, termed ‘principal components’ (PCs), from the original dataset. The selected PCs are fed into the proposed models for modeling and testing. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that a PCA-based LS-SVM (PCA-LS-SVM) model is a promising approach for the prediction of BP reactivity in comparison to others. This assessment demonstrates the importance and advantages posed by hybrid models for the prediction of variables in biomedical research studies.

关键词： Blood pressure (BP)     Principal component analysis (PCA)     Forward stepwise regression     Artificial neural network (ANN)     Adaptive neuro-fuzzy inference system (ANFIS)     Least squares support vector machine (LS-SVM)    

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基于PSO优化LS-SVM算法的水电站厂房结构振动响应预测

练继建,何龙军,王海军

《中国工程科学》 2011年 第13卷 第12期   页码 45-50

摘要：

依据二滩水电站地下厂房和机组的原型观测数据对机组和厂房结构振动的相关性进行分析，据此建立基于粒子群优化最小二乘支持向量计算法的厂房振动响应预测模型，预测结果与实测资料吻合。在此基础上将运行水头作为输入因子引入到智能预测模型中，扩大了该智能预测模型的适用范围，取得了很好的效果。

关键词： 水电站厂房     耦联振动     粒子群优化算法     最小二乘支持向量机     响应预测    

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Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model

《环境科学与工程前沿（英文）》 2023年 第17卷 第8期 doi: 10.1007/s11783-023-1698-9

摘要：

● Data acquisition and pre-processing for wastewater treatment were summarized.

关键词： Chemical oxygen demand     Mining-beneficiation wastewater treatment     Particle swarm optimization     Support vector regression     Artificial neural network    

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最小二乘支持向量机的扩展及其在时间序列预测中的应用

向小东

《中国工程科学》 2008年 第10卷 第11期   页码 89-92

摘要：

根据时间序列近期数据较远期数据包含有更多未来信息的思想，对最小二乘支持向量机预测方法进行了扩展，得到了更具一般性的最小二乘支持向量机预测模型，给出了扩展后的预测模型具体算法。两个时间序列的预测实例表明，扩展后的预测方法获得了更好的预测效果，提升了最小二乘支持向量机预测方法的价值。

关键词： 最小二乘支持向量机     扩展     时间序列     预测    

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Unconfined compressive strength prediction of soils stabilized using artificial neural networks and supportvector machines

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

《结构与土木工程前沿（英文）》 2021年 第15卷 第2期   页码 520-536 doi: 10.1007/s11709-021-0689-9

摘要： This study aims to improve the unconfined compressive strength of soils using additives as well as by predicting the strength behavior of stabilized soils using two artificial-intelligence-based models. The soils used in this study are stabilized using various combinations of cement, lime, and rice husk ash. To predict the results of unconfined compressive strength tests conducted on soils, a comprehensive laboratory dataset comprising 137 soil specimens treated with different combinations of cement, lime, and rice husk ash is used. Two artificial-intelligence-based models including artificial neural networks and support vector machines are used comparatively to predict the strength characteristics of soils treated with cement, lime, and rice husk ash under different conditions. The suggested models predicted the unconfined compressive strength of soils accurately and can be introduced as reliable predictive models in geotechnical engineering. This study demonstrates the better performance of support vector machines in predicting the strength of the investigated soils compared with artificial neural networks. The type of kernel function used in support vector machine models contributed positively to the performance of the proposed models. Moreover, based on sensitivity analysis results, it is discovered that cement and lime contents impose more prominent effects on the unconfined compressive strength values of the investigated soils compared with the other parameters.

关键词： unconfined compressive strength     artificial neural network     support vector machine     predictive models     regression    

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基于回归预测集成学习的交互式图像分割 Article

Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU

《信息与电子工程前沿（英文）》 2017年 第18卷 第7期   页码 1002-1020 doi: 10.1631/FITEE.1601401

摘要： 首先，基于已标记样本训练出两个在属性上互补的多元自适应回归样条学习器（multivariate adaptive regression splines, MARS）和薄板样条回归学习器（thin platespline regression, TPSR）；接着，提出一种基于聚类假设和半监督学习的回归器增强算法，该算法从未标记样本中抽选部分样本辅助训练MARS和TPSR；然后，引入支持向量回归方法（supportvector regression, SVR）集成MARS和TPSR的预测结果；最后，对SVR集成结果进行GraphCut图像分割。

关键词： 交互式图像分割；多元自适应回归样条；集成学习；薄板样条回归；半监督学习；支持向量回归    

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LSSVM-based approach for refining soil failure criteria and calculating safety factor of slopes

Shiguo XIAO; Shaohong LI

《结构与土木工程前沿（英文）》 2022年 第16卷 第7期   页码 871-881 doi: 10.1007/s11709-022-0863-8

摘要： The failure criteria of practical soil mass are very complex, and have significant influence on the safety factor of slope stability. The Coulomb strength criterion and the power-law failure criterion are classically simplified. Each one has limited applicability owing to the noticeable difference between calculated predictions and actual results in some cases. In the work reported here, an analysis method based on the least square support vector machine (LSSVM), a machine learning model, is purposefully provided to establish a complex nonlinear failure criterion via iteration computation based on strength test data of the soil, which is of more extensive applicability to many problems of slope stability. In particular, three evaluation indexes including coefficient of determination, mean absolute percentage error, and mean square error indicate that fitting precision of the machine learning-based failure criterion is better than those of the linear Coulomb criterion and nonlinear power-law criterion. Based on the proposed LSSVM approach to determine the failure criterion, the limit equilibrium method can be used to calculate the safety factor of three-dimensional slope stability. Analysis of results of the safety factor of two three-dimensional homogeneous slopes shows that the maximum relative errors between the proposed approach and the linear failure criterion-based method and the power-law failure criterion-based method are about 12% and 7%, respectively.

关键词： slope stability     safety factor     failure criterion     least square support vector machine    

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electroencephalogram signals using spatial constraint independent component analysis based recursive leastsquares in brain-computer interface

Bang-hua YANG,Liang-fei HE,Lin LIN,Qian WANG

《信息与电子工程前沿（英文）》 2015年 第16卷 第6期   页码 486-496 doi: 10.1631/FITEE.1400299

摘要： Ocular artifacts cause the main interfering signals within electroencephalogram (EEG) signal measurements. An adaptive filter based on reference signals from an electrooculogram (EOG) can reduce ocular interference, but collecting EOG signals during a long-term EEG recording is inconvenient and uncomfortable for the subject. To remove ocular artifacts from EEG in brain-computer interfaces (BCIs), a method named spatial constraint independent component analysis based recursive least squares (SCICA-RLS) is proposed. The method consists of two stages. In the first stage, independent component analysis (ICA) is used to decompose multiple EEG channels into an equal number of independent components (ICs). Ocular ICs are identified by an automatic artifact detection method based on kurtosis. Then empirical mode decomposition (EMD) is employed to remove any cerebral activity from the identified ocular ICs to obtain exact artifact ICs. In the second stage, first, SCICA applies exact artifact ICs obtained in the first stage as a constraint to extract artifact ICs from the given EEG signal. These extracted ICs are called spatial constraint ICs (SC-ICs). Then the RLS based adaptive filter uses SC-ICs as reference signals to reduce interference, which avoids the need for parallel EOG recordings. In addition, the proposed method has the ability of fast computation as it is not necessary for SCICA to identify all ICs like ICA. Based on the EEG data recorded from seven subjects, the new approach can lead to average classification accuracies of 3.3% and 12.6% higher than those of the standard ICA and raw EEG, respectively. In addition, the proposed method has 83.5% and 83.8% reduction in time-consumption compared with the standard ICA and ICA-RLS, respectively, which demonstrates a better and faster OA reduction.

关键词： Ocular artifacts     Electroencephalogram (EEG)     Electrooculogram (EOG)     Brain-computer interface (BCI)     Spatial constraint independent component analysis based recursive least squares (SCICA-RLS)    

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Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

《结构与土木工程前沿（英文）》 2013年 第7卷 第1期   页码 72-82 doi: 10.1007/s11709-013-0185-y

摘要： A support vector machine (SVM) model has been developed for the prediction of liquefaction susceptibility as a classification problem, which is an imperative task in earthquake engineering. This paper examines the potential of SVM model in prediction of liquefaction using actual field cone penetration test (CPT) data from the 1999 Chi-Chi, Taiwan earthquake. The SVM, a novel learning machine based on statistical theory, uses structural risk minimization (SRM) induction principle to minimize the error. Using cone resistance ( ) and cyclic stress ratio ( ), model has been developed for prediction of liquefaction using SVM. Further an attempt has been made to simplify the model, requiring only two parameters ( and maximum horizontal acceleration ), for prediction of liquefaction. Further, developed SVM model has been applied to different case histories available globally and the results obtained confirm the capability of SVM model. For Chi-Chi earthquake, the model predicts with accuracy of 100%, and in the case of global data, SVM model predicts with accuracy of 89%. The effect of capacity factor ( ) on number of support vector and model accuracy has also been investigated. The study shows that SVM can be used as a practical tool for prediction of liquefaction potential, based on field CPT data.

关键词： earthquake     cone penetration test     liquefaction     support vector machine (SVM)     prediction    

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Estimation of photolysis half-lives of dyes in a continuous-flow system with the aid of quantitative structure-property relationship

Davoud BEIKNEJAD,Mohammad Javad CHAICHI

《环境科学与工程前沿（英文）》 2014年 第8卷 第5期   页码 683-692 doi: 10.1007/s11783-014-0680-y

摘要： In this paper the photolysis half-lives of the model dyes in water solutions and under ultraviolet (UV) radiation were determined by using a continuous-flow spectrophotometric method. A quantitative structure-property relationship (QSPR) study was carried out using 21 descriptors based on different chemometric tools including stepwise multiple linear regression (MLR) and partial least squares (PLS) for the prediction of the photolysis half-life ( ) of dyes. For the selection of test set compounds, a K-means clustering technique was used to classify the entire data set, so that all clusters were properly represented in both training and test sets. The QSPR results obtained with these models show that in MLR-derived model, photolysis half-lives of dyes depended strongly on energy of the highest occupied molecular orbital ( ), largest electron density of an atom in the molecule ( ) and lipophilicity (log ). While in the model derived from PLS, besides aforementioned and descriptors, the molecular surface area ( ), molecular weight ( ), electronegativity ( ), energy of the second highest occupied molecular orbital ( ) and dipole moment ( ) had dominant effects on logt values of dyes. These were applicable for all classes of studied dyes (including monoazo, disazo, oxazine, sulfonephthaleins and derivatives of fluorescein). The results were also assessed for their consistency with findings from other similar studies.

关键词： dye     photolysis half-life     quantitative structure-property relationship     continuous-flow     stepwise multiple linear regression     partial least squares    

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用于电网节点重要度评估的一种基于网络嵌入和支持向量回归的人工智能方法 Research Papers

Hui-fang WANG, Chen-yu ZHANG, Dong-yang LIN, Ben-teng HE

《信息与电子工程前沿（英文）》 2019年 第20卷 第6期   页码 816-828 doi: 10.1631/FITEE.1800146

摘要： 重要节点识别对电网安全意义重大。但电网在规模、结构等方面差异较大，评价指标难以涵盖电网不同状态下所有信息，因此基于指标构建的传统评估方法，其效果视情况而定，通用性不足。由此，本文提出基于人工智能的电网节点重要度评估法。首先利用网络嵌入，提出综合考虑电网结构与电气量的电网节点特征选择法。然后对具体电网，进行各类运行方式下的稳态与节点故障暂态仿真，构建能反映节点特征与节点重要度内在关系的样本集。最后，根据优化后的样本集训练支持向量回归模型，模型成熟后可用于电网节点重要度在线评估。结果表明，本方法能根据从样本中学到的信息有效评估电网节点重要度。相比传统指标构建法，本方法规避了片面性和主观性。此外，基于该人工智能框架，本方法可针对每个具体电网建立特定样本集，具有通用性。

关键词： 电网；人工智能；节点重要度；TADW法；网络嵌入；支持向量回归    

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Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and supportvector machine

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

《结构与土木工程前沿（英文）》 2019年 第13卷 第1期   页码 215-239 doi: 10.1007/s11709-018-0489-z

摘要： Plastic concrete is an engineering material, which is commonly used for construction of cut-off walls to prevent water seepage under the dam. This paper aims to explore two machine learning algorithms including artificial neural network (ANN) and support vector machine (SVM) to predict the compressive strength of bentonite/sepiolite plastic concretes. For this purpose, two unique sets of 72 data for compressive strength of bentonite and sepiolite plastic concrete samples (totally 144 data) were prepared by conducting an experimental study. The results confirm the ability of ANN and SVM models in prediction processes. Also, Sensitivity analysis of the best obtained model indicated that cement and silty clay have the maximum and minimum influences on the compressive strength, respectively. In addition, investigation of the effect of measurement error of input variables showed that change in the sand content (amount) and curing time will have the maximum and minimum effects on the output mean absolute percent error (MAPE) of model, respectively. Finally, the influence of different variables on the plastic concrete compressive strength values was evaluated by conducting parametric studies.

关键词： bentonite/sepiolite plastic concrete     compressive strength     artificial neural network     support vector machine     parametric analysis    

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Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in

Xiangyang Ye, Jian’e Zuo, Ruohan Li, Yajiao Wang, Lili Gan, Zhonghan Yu, Xiaoqing Hu

《环境科学与工程前沿（英文）》 2019年 第13卷 第2期 doi: 10.1007/s11783-019-1102-y

摘要：

An image-recognition-based diagnosis system of pipe defect types was established.

1043 practical pipe images were gathered by CCTV robot in a southern Chinese city.

The overall accuracy of the system is 84% and the highest accuracy is 99.3%.

The accuracy shows positive correlation to the number of training samples.

关键词： Sewer pipe defects     Defect diagnosing     Image recognition     Multi-features extraction     Support vector machine    

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标题 作者 时间 类型 操作

Timing decision-making method of engine blades for predecisional remanufacturing based on reliability analysis

Le CHEN, Xianlin WANG, Hua ZHANG, Xugang ZHANG, Binbin DAN

期刊论文

Identification of thermal error in a feed system based on multi-class LS-SVM

Chao JIN, Bo WU, Youmin HU, Yao CHENG

期刊论文

Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

Gurmanik KAUR,Ajat Shatru ARORA,Vijender Kumar JAIN

期刊论文

基于PSO优化LS-SVM算法的水电站厂房结构振动响应预测

练继建,何龙军,王海军

期刊论文

Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model

期刊论文

最小二乘支持向量机的扩展及其在时间序列预测中的应用

向小东

期刊论文

Unconfined compressive strength prediction of soils stabilized using artificial neural networks and supportvector machines

Alireza TABARSA, Nima LATIFI, Abdolreza OSOULI, Younes BAGHERI

期刊论文

基于回归预测集成学习的交互式图像分割

Jin ZHANG, Zhao-hui TANG, Wei-hua GUI, Qing CHEN, Jin-ping LIU

期刊论文

LSSVM-based approach for refining soil failure criteria and calculating safety factor of slopes

Shiguo XIAO; Shaohong LI

期刊论文

electroencephalogram signals using spatial constraint independent component analysis based recursive leastsquares in brain-computer interface

Bang-hua YANG,Liang-fei HE,Lin LIN,Qian WANG

期刊论文

Liquefaction prediction using support vector machine model based on cone penetration data

Pijush SAMUI

期刊论文

Estimation of photolysis half-lives of dyes in a continuous-flow system with the aid of quantitative structure-property relationship

Davoud BEIKNEJAD,Mohammad Javad CHAICHI

期刊论文

用于电网节点重要度评估的一种基于网络嵌入和支持向量回归的人工智能方法

Hui-fang WANG, Chen-yu ZHANG, Dong-yang LIN, Ben-teng HE

期刊论文

Modeling of bentonite/sepiolite plastic concrete compressive strength using artificial neural network and supportvector machine

Ali Reza GHANIZADEH, Hakime ABBASLOU, Amir Tavana AMLASHI, Pourya ALIDOUST

期刊论文

Diagnosis of sewer pipe defects on image recognition of multi-features and support vector machine in

Xiangyang Ye, Jian’e Zuo, Ruohan Li, Yajiao Wang, Lili Gan, Zhonghan Yu, Xiaoqing Hu

期刊论文