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Profile analysis with reconstruction robustness for measurement data subject to outliers

APPLIED OPTICS 2022年第13期61卷 3777-3785页

作者： Gu, Tianqi Xiong, Cui Tang, Dawei Chen, Jianxiong lin, shuwen Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350108 Peoples R China Univ Huddersfield Ctr Precis Technol Huddersfield HD1 3DH W Yorkshire England

In the surface profile analysis, there are often a few observations that contain outliers. Due to the existence of outliers, the application of non-robust reconstruction algorithms for measurement data will become a huge problem because these methods are often sensitive to outliers and the approximation effectiveness will be greatly aggravated. In view of this, this paper presents a novel angle-based moving total least squares reconstruction method, to the best of our knowledge, that applies two-step pre-treatment to handle outliers. The first step is an abnormal point detection process that characterizes the geometric features of discrete points in the support domain through a new angle-based parameter constructed by total least square. Then, the point with the largest anomaly degree is removed, and a relevant weight function is defined to adjust the weights of the remaining points. After pretreatment, the final estimates are calculated by weighted total least squares (WTLS) based on the compact weight function. The detection and removal of outliers are automatic, and there is no need to set a threshold value artificially, which effectively avoids the adverse impacts of human operation. Numerical simulations and experiments verify the applicability of the proposed algorithm as well as its accuracy and robustness. (C) 2022 Optica Publishing Group

关键词： Interpolation Numerical simulation Reconstruction algorithms Surfaces White light White light interferometry

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Surface reconstruction method for measurement data with outlier detection by using improved RANSAC and correction parameter

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2022年第12期236卷 1589-1600页

作者： Gu, Tianqi Luo, Zude Tang, Dawei Chen, Jianxiong lin, shuwen Fuzhou Univ Sch Mech Engn & Automat 2 Xueyuan Rd Fuzhou 350108 Peoples R China Univ Huddersfield Ctr Precis Technol Huddersfield W Yorkshire England

The moving least squares (MLS) and moving total least squares (MTLS) are two of the most popular methods used for reconstructing measurement data, on account of their good local approximation accuracy. However, their reconstruction accuracy and robustness will be greatly reduced when there are outliers in measurement data. This article proposes an improved MTLS method (IMTLS), which introduces an improved random sample consensus (RANSAC) algorithm and a correction parameter in the support domain, to deal with the outliers and random errors. Based on the nodes within the support domain, firstly the improved RANSAC is used to generate a model for establishing the group of pre-interpolation and calculating the residual of each node. Subsequently, the abnormal degree of the node with the largest residual is evaluated by the correction parameter associated with the node residual and random errors. The node with certain abnormal degree will be eliminated and the remaining nodes are used to obtain the approximation coefficients. The correction parameter can be used for data reconstruction without insufficient or excessive elimination. The results of numerical simulation and measurement experiment show that the reconstruction accuracy and robustness of the IMTLS method is superior to the MLS and MTLS method.

关键词： Measurement data outliers moving total least squares random sample consensus surface reconstruction

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Geometric error identification for machine tools using a novel 1D probe system

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2021年第11-12期114卷 3475-3487页

作者： Chen, Jianxiong lin, shuwen Gu, Tianqi Fuzhou Univ Sch Mech Engn & Automat Fuzhou Fujian Peoples R China

Measuring and evaluating the geometric error of a linear axis periodically, is an essential operation in the day-to-day usage of a machine tool. In this paper, a system consisting of a novel one-dimension probe and a ball array is developed to fast estimate the geometric error a linear axis from the ball center deviations in three dimensions. The proposed 1D probe is assembled by an inductance micrometer and a simple fixture. Five measuring positions on the ball surface are selected to recognize the ball center offset caused by the geometric error. Then, an identification model is established to recognize the error at the ball center in the array. Moreover, a correction method is proposed to eliminate the installation error. It applies the least square method to form the virtual baseline by the measured ball centers, in order to eliminate the effect that resulted from the inaccuracy and the misalignment of the ball array during the manufacturing and setting, respectively. Then, the remaining part of the measured results is applied to evaluate the geometric error of the measured linear axis, including one positioning error and two straightness errors. Finally, a prototype system is developed to verify the correctness of the proposed 1D probe, while a measurement experiment is conducted on a machining center to verify the validity of the proposed method. The results indicate that the maximum absolute error among one positioning error and two straightness errors reach to 2.1 mu m, 2.3 mu m, and 1.6 mu m, respectively, while the root mean square error, and the average absolute error are no more than 2.0 mu m, when comparing with the results from the laser interferometer.

关键词： Geometric error linear axis 1D ball array 1D probe Error correction

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Sensitive pH-responsive point-of-care electrochemical immunoassay for influenza A (H1N1) virus using glucose oxidase-functionalized Ti₃C₂-MXene nanosheets

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ANALYST 2021年第13期146卷 4391-4399页

作者： lin, shuwen Hu, Xiaomei lin, Jiayi Wang, Shunqin Xu, Jin Cai, Fan lin, Jiancheng Fudan Univ Xiamen Childrens Hosp Dept Clin Lab Childrens Hosp Xiamen Xiamen 361006 Peoples R China Anhui Med Univ Hefei 230032 Peoples R China Xiamen Radit Chinese Med Hosp Xiamen Key Lab Neonatal Dis Xiamen 361001 Peoples R China Fudan Univ Dept Clin Lab Childrens Hosp Shanghai 201102 Peoples R China Fujian Normal Univ Coll Life Sci Fuzhou 350117 Peoples R China

Influenza A (H1N1) virus is a serious health threat and potential leading cause of death around the world during the processes of immunity and inflammation. Herein a sensitive pH-responsive point-of-care (POC) electrochemical immunoassay was designed for the quantitative monitoring of H1N1 influenza virus using glucose oxidase (GOx) and secondary antibody-functionalized Ti3C2-MXene nanosheets. The assay was carried out on the basis of the sandwich-type immunoreaction in the capture antibody-coated microplate. Two-dimensional (2D) Ti3C2-MXene nanosheets with a large surface area could efficiently enhance the loading amount of GOx molecules, thereby resulting in the signal amplification. Accompanying the formed immunocomplexes, labeled GOx molecules catalyzed glucose into gluconic acid and hydrogen peroxide. The generated gluconic acid caused a pH change of the detection solution, which was quantitatively determined on a handheld pH meter. Two labeling strategies with and without Ti3C2-MXene nanosheets were investigated to determine the target H1N1 influenza virus, and improved properties were acquired with the Ti3C2-MXene-labeled system. Under optimum conditions, the Ti3C2-MXene-based immunoassay gave good dynamic responses toward the target H1N1 influenza virus from 0.01 mu g mL(-1) to 100 mu g mL(-1) with a detection limit of 1.3 ng mL(-1). Good reproducibility, high specificity, and acceptable stability were also achieved in the analysis of the target H1N1 influenza virus. Significantly, measurements of the H1N1 influenza virus from clinical human samples were demonstrated to further confirm the method reliability and accuracy of the Ti3C2-MXene-based electrochemical immunoassay. Importantly, such a pH-meter-based immunoassay can be suitable for use in point-of-care applications and opens new opportunities for diagnostics.

关键词： Glucose oxidase

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A robust reconstruction method based on local Bayesian estimation combined with CURE clustering

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INFORMATION SCIENCES 2024年 680卷

作者： Gu, Tianqi Kang, Cheng Tang, Dawei lin, shuwen Luo, Tianzhi Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350108 Peoples R China Univ Huddersfield Ctr Precis Technol Huddersfield HD1 3DH England Univ Sci & Technol China Dept Modern Mech CAS Key Lab Mech Behav & Design Mat Hefei 230022 Peoples R China

Due to their good approximation accuracy and local fitting characteristics, the moving least squares (MLS) and moving total least squares (MTLS) methods are widely used in various engineering fields. However, neither of these two methods is robust and they cannot effectively deal with outliers in measurement data. To eliminate the negative influence of outliers and achieve robust reconstruction, a novel MTLS method is proposed in this paper, which introduces local Bayesian estimation combined with clustering using representatives (CURE) algorithm. In the support domain, this method adopts a two-step process to remove the abnormal points and adjust the weights of discrete points through compound weighting. Bayesian estimation is first performed on discrete points to derive the reference model, and the residuals are calculated as the input of CURE clustering. The points with large residuals are classified into one cluster and removed. The remaining points undergo repeated processing until the iteration concludes. A gradient weight function based on the residuals and a compact support weight function are combined to determine the final estimated value using weighted Bayesian estimation. The simulations and experiments demonstrate that the proposed reconstruction method achieves excellent accuracy and robustness, surpassing several existing methods when handling highly contaminated datasets.

关键词： Moving total least squares Measurement data Outliers Bayesian estimation Clustering using representatives

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Anα-moving total least squares fitting method for measurement data

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2021年第1-2期235卷 65-72页

作者： Gu, Tianqi Hu, Chenjie Tang, Dawei lin, shuwen Luo, Tianzhi Fuzhou Univ Sch Mech Engn & Automat Fuzhou Peoples R China Univ Huddersfield Ctr Precis Technol Huddersfield HD1 3DH W Yorkshire England Univ Sci & Technol China Dept Modern Mech CAS Key Lab Mech Behav & Design Mat Hefei Peoples R China

The Moving Least Squares (MLS) and Moving Total Least Squares (MTLS) method are widely used for approximating discrete data in many areas such as surface reconstruction. One of the disadvantages of MLS is that it only considers the random errors in the dependent variables. The MTLS method achieves a better fitting accuracy by taking into account the errors of both dependent and independent variables. However, both MLS and MTLS suffer from a low fitting accuracy when applied to the measurement data with outliers. In this work, an improved method named as alpha-MTLS method is proposed, which uses the Total Least Square (TLS) method based on singular value decomposition (SVD) to fit the nodes in the influence domain and introduces a geometric characteristic parameter alpha to associated with the abnormal degree of nodes. The generated fitting points are used to construct the parameter and quantify the abnormal degree of the nodes. The node with the largest parameter value is eliminated and the remaining nodes are used to determine the local coefficients. By trimming only one node per influence domain, multiple outliers of measurement data can be effectively handled. There is no need to set threshold values subjectively or assign weights which avoids the negative influence of manual operation. The performance of the improved method is demonstrated by numerical simulations and measurement experiment. It is shown that the alpha-MTLS method can effectively reduce the influence of the outliers and thus has higher fitting accuracy and greater robustness than that of the MLS and MTLS method.

关键词： Moving least squares local approximants reconstruction outliers

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A Multi-Objective Collaborative Optimization Method for the Excavator Working Device to Support Energy Consumption Reduction

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ENERGIES 2023年第20期16卷 7056页

作者： Lu, Zhe lin, shuwen Chen, Jianxiong Gu, Tianqi Xie, Yu Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350108 Peoples R China

To address the limitation of existing excavator optimization methods, which primarily focus on the force performance while neglecting energy consumption and fail to realize environmentally friendly and low-carbon designs, this paper proposes a new multi-objective collaborative optimization method for an excavator to reduce energy consumption during the working process while maintaining optimal performance. By formulating a mathematical model that quantifies the energy consumption during the working process, this paper optimizes the working conditions by analyzing the energy consumption characteristics under typical working conditions. To overcome the limitation of existing linear weighting methods in multi-objective optimization, such as imbalanced optimization quality among sub-objectives, this paper proposes a new modeling approach based on the loss degree of sub-objectives. A multi-objective collaborative optimization model for the excavator with reduced energy consumption is established, and a corresponding multi-objective collaborative optimization algorithm is developed and applied to achieve optimal solutions for sub-objectives. The optimization results demonstrate that applying the new multi-objective collaborative optimization method to the excavator achieves better optimization quality than traditional methods. It also provides a more balanced improvement in the optimization values of each sub-objective, resulting in a significant reduction in the energy consumption of the excavator during operation.

关键词： excavator energy consumption modeling energy-saving excavator working performance multiobjective optimization

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Robust moving total least squares: A technique for the reconstruction of measurement data in the presence of multiple outliers

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2022年第PartA期167卷 108542-108542页

作者： Gu, Tianqi lin, Hongxin Tang, Dawei lin, shuwen Luo, Tianzhi Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350108 Peoples R China Univ Huddersfield Ctr Precis Technol Huddersfield HD1 3DH W Yorkshire England Univ Sci & Technol China Dept Modern Mech CAS Key Lab Mech Behav & Design Mat Hefei 230022 England Taizhou Luohua Biotechnol Ltd Taizhou 225306 Peoples R China

This article is concerned with the reconstruction of contaminated measurement data based on the moving total least squares (MTLS) method, which is extensively applied to many engineering and scientific fields. Traditional MTLS method is lack of robustness and sensitive to the outliers in measurement data. Based on the framework of MTLS method, we proposed a robust MTLS method called RMTLS method by introducing a two-step pre-process to detect and remove the anomalous nodes in the support domain. The first step is an iterative regression procedure that combines with k-medoids clustering to automatically reduce the weight of anomalous node for a regressionbased reference (curve or surface). Based on the distances between reference and discrete points, the second step adopts a density function defined by a sorted distance sequence to select the normal points without setting a threshold artificially. After the two-step pre-process, weighted total least square is performed on the selected point set to obtain the estimation value. By disposing of the anomalous nodes in each independent support domain, multiple outliers can be suppressed within the whole domain. Furthermore, the suppression of multiple continual outliers is possible by adopting asymmetric support domain and introducing previous estimation points. The proposed method shows great robustness and accuracy in reconstructing the simulation and experiment data.

关键词： Measurement data Moving least squares K-medoids clustering Outlier

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Comparative Venous and Arterial Thromboembolism Profiles Associated with Different Cyclin-Dependent Kinase 4/6 Inhibitors: A Real-World Study

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BLOOD 2024年 144卷 5084-5085页

作者： Chiang, Cho Han lin, shuwen Chang, Yu-Cheng Song, Junmin Chi, Kuan-Yu Xanthavanij, Nutchapon Chang, Yu Chiang, Cho-Hung Xu, Xiaocao Haze Mt Auburn Hosp Dept Med Cambridge MA USA Montefiore Einstein Canc Ctr Montefiore Med Ctr Bronx NY USA Danbury Hosp Dept Med Danbury CT USA Albert Einstein Coll Med Jacobi Med Ctr Dept Med Bronx NY USA Mt Auburn Hosp Cambridge MA USA Natl Cheng Kung Univ Natl Cheng Kung Univ Hosp Coll Med Dept SurgSect Neurosurg Tainan Taiwan Natl Taiwan Univ Hosp Taipei Taiwan Univ Vermont Med Ctr Burlington VT USA

Introduction: Cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors are used in conjunction with endocrine therapy in hormone receptor-positive breast cancer. Recent data from clinical trials and real-world observational studies have reported an increased venous and arterial thromboembolism rates associated with CDK4/6 inhibitors. However, there has been limited data on the comparative thromboembolism rates among different types of CDK4/6 inhibitors. To address this knowledge gap, we leveraged a large global database to compare the thromboembolism profiles among different CDK4/6 inhibitors. Methods: We performed a retrospective, propensity score-matched cohort study using the TriNetX Analytics Network database, which includes de-identified electronic health records from over 70 healthcare organizations and 101 million individuals. We included adult female breast cancer patients who received an aromatase inhibitor (AI) and a CDK4/6 inhibitor from February 2015 to June 2023. We excluded patients with a prior history of venous thromboembolism (VTE), arterial thromboembolism (ATE), atrial fibrillation, and those who received oral anticoagulation. The index date was determined as the start date of AI and CDK4/6 inhibitor therapy. The primary outcomes were VTE, which was defined as a composite of pulmonary embolism (PE) and deep venous thrombosis (DVT), and ATE, which was defined as a composite of myocardial infarction and ischemic stroke. The secondary outcomes were individual components of VTE and ATE. All outcomes were captured within 1 year of the index date. Cohorts were matched using a propensity score that incorporated variables such as age, sex, race, underlying comorbidities, components of the Khorana score, and breast cancer-directed therapy. Results: We identified a total of 8912 patients eligible for inclusion, among which 6632 received palbociclib, 790 received ribociclib, and 1490 received abemaciclib. After propensity score matching, cohorts were well-balance

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A multi-objective collaborative optimization method for excavator working devices based on knowledge engineering

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ADVANCES IN MECHANICAL ENGINEERING 2024年第1期16卷

作者： Lu, Zhe lin, shuwen Chen, Jianxiong Gu, Tianqi Xie, Yu Zhao, Zihao Fuzhou Univ Sch Mech Engn & Automat Fuzhou Peoples R China Fuzhou Univ Sch Mech Engn & Automat 2 Wulongjiang North Rd Minhou 350108 Fujian Peoples R China

To enhance the excavator performance considering the digging force and boom lift force under typical working conditions, this paper aims to solve the complex multiobjective optimization of the excavator by proposing a new knowledge-based method. The digging force at multiple key points is utilized to characterize the excavator's performance during the working process. Then, a new optimization model is developed to address the imbalanced optimization quality among subobjectives obtained from the ordinary linear weighted model. The new model incorporates the loss degree relative to the optimal solution of each subobjective, aiming to achieve a more balanced optimization. Knowledge engineering is integrated into the optimization process to improve the optimization quality, utilizing a knowledge base incorporating seven different types of knowledge to store and reuse the information related to optimization. Furthermore, a knowledge-based multiobjective algorithm is proposed to perform the knowledge-guided optimization. Experimental results demonstrate that the proposed knowledge-based method outperforms existing methods, resulting in an average increase of 15.1% in subobjective values.

关键词： Optimization design knowledge engineering multiobjective optimization excavator multiobjective evolutionary algorithm

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