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学者姓名：林述温
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Abstract ：
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.
Keyword ：
excavator excavator knowledge engineering knowledge engineering multiobjective evolutionary algorithm multiobjective evolutionary algorithm multiobjective optimization multiobjective optimization Optimization design Optimization design
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| GB/T 7714 | Lu, Zhe , Lin, Shuwen , Chen, Jianxiong et al. A multi-objective collaborative optimization method for excavator working devices based on knowledge engineering [J]. | ADVANCES IN MECHANICAL ENGINEERING , 2024 , 16 (1) . |
| MLA | Lu, Zhe et al. "A multi-objective collaborative optimization method for excavator working devices based on knowledge engineering" . | ADVANCES IN MECHANICAL ENGINEERING 16 . 1 (2024) . |
| APA | Lu, Zhe , Lin, Shuwen , Chen, Jianxiong , Gu, Tianqi , Xie, Yu , Zhao, Zihao . A multi-objective collaborative optimization method for excavator working devices based on knowledge engineering . | ADVANCES IN MECHANICAL ENGINEERING , 2024 , 16 (1) . |
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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.
Keyword ：
energy consumption modeling energy consumption modeling energy-saving energy-saving excavator excavator excavator working performance excavator working performance multiobjective optimization multiobjective optimization
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| GB/T 7714 | Lu, Zhe , Lin, Shuwen , Chen, Jianxiong et al. A Multi-Objective Collaborative Optimization Method for the Excavator Working Device to Support Energy Consumption Reduction [J]. | ENERGIES , 2023 , 16 (20) . |
| MLA | Lu, Zhe et al. "A Multi-Objective Collaborative Optimization Method for the Excavator Working Device to Support Energy Consumption Reduction" . | ENERGIES 16 . 20 (2023) . |
| APA | Lu, Zhe , Lin, Shuwen , Chen, Jianxiong , Gu, Tianqi , Xie, Yu . A Multi-Objective Collaborative Optimization Method for the Excavator Working Device to Support Energy Consumption Reduction . | ENERGIES , 2023 , 16 (20) . |
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In the past few decades, there has been significant progress made in metasurfaces and integrated and miniatur-ized optical devices. As one of the most prominent applications of metasurfaces, the metalens is the subject of significant research. In this paper, for achieving better focusing performance of the initial metalens designed by the Pancharatnam-Berry (PB) phase, a concept of micro-dimensional oscillation is proposed to optimize the geomet-ric parameters of nanopillars. A strategy of grouping iteration is proposed to reduce the loss rate and computational effort in a holistic way. Its essence is to divide an extremely large-scale optimization space into many overlapping groups. Meanwhile, an improved genetic-simulated annealing (IGSA) algorithm is presented for the optimal solution of each group. By introducing the adaptive crossover and mutation probabilities in traditional genetic algorithms, the IGSA algorithm has both strong global searching capability and excellent local searching capability. After optimization, the maximum field intensity of the central hot spot can be increased by about 8% compared to the initial metalens. Moreover, the field intensity of the side lobes around the hot spot is almost constant, and the central hot spot increases, which provides a potential for the realization of high imaging contrast.(c) 2022 Optica Publishing Group
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| GB/T 7714 | Gu, T. I. A. N. Q. I. , Gao, X. I. A. N. G. , Tang, D. A. W. E., I et al. Micro-dimensional oscillation-based optimization for a dielectric metalens in the mid-infrared [J]. | APPLIED OPTICS , 2022 , 61 (32) : 9324-9333 . |
| MLA | Gu, T. I. A. N. Q. I. et al. "Micro-dimensional oscillation-based optimization for a dielectric metalens in the mid-infrared" . | APPLIED OPTICS 61 . 32 (2022) : 9324-9333 . |
| APA | Gu, T. I. A. N. Q. I. , Gao, X. I. A. N. G. , Tang, D. A. W. E., I , Lin, S. H. U. W. E. N. , Fang, B. I. N. G. . Micro-dimensional oscillation-based optimization for a dielectric metalens in the mid-infrared . | APPLIED OPTICS , 2022 , 61 (32) , 9324-9333 . |
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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.
Keyword ：
Measurement data Measurement data moving total least squares moving total least squares outliers outliers random sample consensus random sample consensus surface reconstruction surface reconstruction
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| GB/T 7714 | Gu, Tianqi , Luo, Zude , Tang, Dawei et al. Surface reconstruction method for measurement data with outlier detection by using improved RANSAC and correction parameter [J]. | PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE , 2022 , 236 (12) : 1589-1600 . |
| MLA | Gu, Tianqi et al. "Surface reconstruction method for measurement data with outlier detection by using improved RANSAC and correction parameter" . | PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 236 . 12 (2022) : 1589-1600 . |
| APA | Gu, Tianqi , Luo, Zude , Tang, Dawei , Chen, Jianxiong , Lin, Shuwen . Surface reconstruction method for measurement data with outlier detection by using improved RANSAC and correction parameter . | PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE , 2022 , 236 (12) , 1589-1600 . |
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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
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| GB/T 7714 | Gu, Tianqi , Xiong, Cui , Tang, Dawei et al. Profile analysis with reconstruction robustness for measurement data subject to outliers [J]. | APPLIED OPTICS , 2022 , 61 (13) : 3777-3785 . |
| MLA | Gu, Tianqi et al. "Profile analysis with reconstruction robustness for measurement data subject to outliers" . | APPLIED OPTICS 61 . 13 (2022) : 3777-3785 . |
| APA | Gu, Tianqi , Xiong, Cui , Tang, Dawei , Chen, Jianxiong , Lin, Shuwen . Profile analysis with reconstruction robustness for measurement data subject to outliers . | APPLIED OPTICS , 2022 , 61 (13) , 3777-3785 . |
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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.
Keyword ：
K-medoids clustering K-medoids clustering Measurement data Measurement data Moving least squares Moving least squares Outlier Outlier
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| GB/T 7714 | Gu, Tianqi , Lin, Hongxin , Tang, Dawei et al. Robust moving total least squares: A technique for the reconstruction of measurement data in the presence of multiple outliers [J]. | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2022 , 167 . |
| MLA | Gu, Tianqi et al. "Robust moving total least squares: A technique for the reconstruction of measurement data in the presence of multiple outliers" . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING 167 (2022) . |
| APA | Gu, Tianqi , Lin, Hongxin , Tang, Dawei , Lin, Shuwen , Luo, Tianzhi . Robust moving total least squares: A technique for the reconstruction of measurement data in the presence of multiple outliers . | MECHANICAL SYSTEMS AND SIGNAL PROCESSING , 2022 , 167 . |
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针对现有挖掘机铲斗结构参数优化设计中，优化变量主要凭设计者经验选取导致优化效果欠佳，优化设计过程中需反复调用有限元软件进行结构应力分析，致使优化效率低等问题，提出一种基于知识引导的铲斗结构参数优化设计方法。以综合考虑铲斗结构轻量化和综合多工况下结构等强度最大化为优化目标，利用结构参数对铲斗结构件体积和综合多工况下最大应力值的灵敏度知识，指导优化变量的选择，采用基于样本的应力普查方法确定铲斗应力特征截面，并建立铲斗结构神经网络应力预测模型；在此基础上，针对现有遗传算法的局限性，构建优化过程知识引导的遗传寻优算法，并通过实例验证该方法的可行性。实例验证优化结果表明，与优化前相比，铲斗体积下降了14...
Keyword ：
代理模型 代理模型 挖掘机铲斗结构 挖掘机铲斗结构 智能优化设计 智能优化设计 知识引导 知识引导
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| GB/T 7714 | 赵子豪 , 林述温 , 赵国朋 . 基于知识引导的液压挖掘机铲斗结构智能优化设计 [J]. | 工程机械 , 2022 , 53 (07) : 74-83,11 . |
| MLA | 赵子豪 et al. "基于知识引导的液压挖掘机铲斗结构智能优化设计" . | 工程机械 53 . 07 (2022) : 74-83,11 . |
| APA | 赵子豪 , 林述温 , 赵国朋 . 基于知识引导的液压挖掘机铲斗结构智能优化设计 . | 工程机械 , 2022 , 53 (07) , 74-83,11 . |
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在实际工程问题中,由于人为或环境等外界因素的影响,通过仪器测量获得的数据,不可避免地会存在粗大误差,以某种方式偏离测量数据,导致数据重构的精度不稳定.针对包含粗大误差的测量数据,本文提出一种基于移动最小二乘法的稳健重构方法,该方法对支持域内节点采用最小二乘法进行拟合,将生成的拟合点根据引入的几何特征参数a,量化各节点的异常程度并剔除异常值.对支持域内的剩余节点采用加权最小二乘法确定局部拟合系数,移动支持域完成全域的曲线曲面重构.在每个支持域内仅剔除一个点,就能有效地处理多个粗大误差,且剔除过程无需主观地设定阈值或分配权重.数值模拟与测量实验结果表明:本文方法可有效剔除测量数据中的粗大误差,与传统移动最小二乘法相比,本文数值案例精度能提高60％以上,具有良好的重构稳健性.
Keyword ：
曲线曲面重构 曲线曲面重构 移动最小二乘法 移动最小二乘法 稳健性 稳健性 粗大误差 粗大误差 计算机应用 计算机应用
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| GB/T 7714 | 顾天奇 , 胡晨捷 , 涂毅 et al. 基于移动最小二乘法的稳健重构方法 [J]. | 吉林大学学报（工学版） , 2021 , 51 (2) : 685-691 . |
| MLA | 顾天奇 et al. "基于移动最小二乘法的稳健重构方法" . | 吉林大学学报（工学版） 51 . 2 (2021) : 685-691 . |
| APA | 顾天奇 , 胡晨捷 , 涂毅 , 林述温 . 基于移动最小二乘法的稳健重构方法 . | 吉林大学学报（工学版） , 2021 , 51 (2) , 685-691 . |
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Curve and surface reconstruction methods play an important role in many research and engineering fields. It is an imperative procedure to carry out surface reconstruction from measurement data in reverse engineering, which is complicated with the presence of outliers. To achieve better accuracy and robustness of reconstruction, an improved moving total least squares (MTLS) algorithm based on k-means clustering called a KMTLS method is proposed in this article. Based on MTLS, KMTLS adjusts the weight of discrete points within the support domain by adopting a two-step fitting procedure. Firstly, an ordinary least squares (OLS) method is adopted to obtain the pre-fitting result and calculate the residuals as the input of k-means clustering. In k-means clustering, abnormal nodes are classified into one cluster and a weight function based on clustering information is introduced to deal with these nodes. Secondly, based on the compact weight function in MTLS and the weight obtained in the prefitting procedure, a weighted total least squares method is conducted to determine the final estimated value. The process of detecting outliers is automatic without setting threshold artificially. The simulation and experiment show that KMTLS has great robustness and accuracy.
Keyword ：
K-means clustering K-means clustering Moving least squares Moving least squares Outliers Outliers Surface reconstruction Surface reconstruction
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| GB/T 7714 | Gu, Tianqi , Lin, Hongxin , Tang, Dawei et al. Curve and surface reconstruction based on MTLS algorithm combined with k-means clustering [J]. | MEASUREMENT , 2021 , 182 . |
| MLA | Gu, Tianqi et al. "Curve and surface reconstruction based on MTLS algorithm combined with k-means clustering" . | MEASUREMENT 182 (2021) . |
| APA | Gu, Tianqi , Lin, Hongxin , Tang, Dawei , Lin, Shuwen , Luo, Tianzhi . Curve and surface reconstruction based on MTLS algorithm combined with k-means clustering . | MEASUREMENT , 2021 , 182 . |
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Abstract ：
在实际工程问题中,由于人为或环境等外界因素的影响,通过仪器测量获得的数据,不可避免地会存在粗大误差,以某种方式偏离测量数据,导致数据重构的精度不稳定。针对包含粗大误差的测量数据,本文提出一种基于移动最小二乘法的稳健重构方法,该方法对支持域内节点采用最小二乘法进行拟合,将生成的拟合点根据引入的几何特征参数α,量化各节点的异常程度并剔除异常值。对支持域内的剩余节点采用加权最小二乘法确定局部拟合系数,移动支持域完成全域的曲线曲面重构。在每个支持域内仅剔除一个点,就能有效地处理多个粗大误差,且剔除过程无需主观地设定阈值或分配权重。数值模拟与测量实验结果表明:本文方法可有效剔除测量数据中的粗大误差,与传...
Keyword ：
曲线曲面重构 曲线曲面重构 移动最小二乘法 移动最小二乘法 稳健性 稳健性 粗大误差 粗大误差 计算机应用 计算机应用
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| GB/T 7714 | 顾天奇 , 胡晨捷 , 涂毅 et al. 基于移动最小二乘法的稳健重构方法 [J]. | 吉林大学学报(工学版) , 2021 , 51 (02) : 685-691 . |
| MLA | 顾天奇 et al. "基于移动最小二乘法的稳健重构方法" . | 吉林大学学报(工学版) 51 . 02 (2021) : 685-691 . |
| APA | 顾天奇 , 胡晨捷 , 涂毅 , 林述温 . 基于移动最小二乘法的稳健重构方法 . | 吉林大学学报(工学版) , 2021 , 51 (02) , 685-691 . |
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