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Optimal Placement of Programmable Tooling Machines Considering Hierarchical Structure via sparse learning for Multistage Assembly Processes

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 9970-9982页

作者： Tao, Chengyu Du, Juan Liu, Jian Hong Kong Univ Sci & Technol Acad Interdisciplinary Studies Hong Kong 999077 Peoples R China Hong Kong Univ Sci & Technol Guangzhou Smart Mfg Thrust & Data Sci & Analyt Thrust Guangzhou 511453 Peoples R China Guangzhou HKUST Fok Ying Tung Res Inst Guangzhou 511453 Peoples R China Univ Arizona Dept Syst & Ind Engn Tucson AZ 85721 USA

End-of-line product dimensional quality assurance is crucial in multistage assembly processes (MAPs). Active control strategies involve the deployment of controllable, programmable tooling machines (PTs) to adjust part positions for in-process dimensional error compensation. In MAPs, multiple PTs may be deployed within a single stage or across various stages. To enable the normal operation of deployed PTs within a specific stage, an associated supporting platform (SP) is necessary. Consequently, the lower PT-level and the upper platform-level define a two-level hierarchical structure in MAPs. This paper aims to propose an optimal placement strategy of PTs, focusing on the final dimensional quality of the product and the total cost related to the number of accommodated PTs and required SPs. Based on the stream-of-variation (SOV) model of MAPs, we develop a novel sparse learning framework along with the corresponding parameter estimation algorithm to achieve the optimal placement. The case study demonstrates the effectiveness of our proposed method for the optimal placement of PTs in reducing dimensional variation in MAPs.

关键词： Assembly Fixtures Costs Process control Standards Error compensation Bibliographies Actuators Semiconductor device modeling Quality control Multistage assembly process dimensional variation reduction in-process active control optimal placement programmable tooling machines supporting platform sparse learning

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PointSGRADE: sparse learning with graph representation for anomaly detection by using unstructured 3D point cloud data

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IISE TRANSACTIONS 2025年第2期57卷 131-144页

作者： Tao, Chengyu Du, Juan Hong Kong Univ Sci & Technol Guangzhou Smart Mfg Thrust Guangzhou Peoples R China Hong Kong Univ Sci & Technol Dept Mech & Aerosp Engn Hong Kong Peoples R China Hong Kong Univ Sci & Technol Acad Interdisciplinary Studies Hong Kong Peoples R China Guangzhou HKUST Fok Ying Tung Res Inst Guangzhou Peoples R China

Surface anomaly detection by using 3D point cloud data has recently received significant attention. To completely measure the common free-form surfaces without loss of details, advanced 3D scanning technologies, such as 3D laser scanners, can be applied and will produce an unstructured point cloud. However, this irregular data structure poses challenges to anomaly detection, in that the existing methods based on regular data, e.g., 2D image, cannot be directly applied. This article proposes a sparse learning framework with a graph representation of the unstructured point cloud for anomaly detection (PointSGRADE). Specifically, the free-form surface is assumed to be smooth. Then, the associated point cloud can be represented as a graph. Subsequently, considering the sparse anomalies, we propose a sparse learning framework and formulate the anomaly detection problem as a penalized optimization problem, which is further solved by a computationally efficient majorization-minimization framework. Case studies demonstrate the accuracy and robustness of the proposed method. This article proposes a novel methodology for sparse anomaly detection on smooth free-form surfaces represented by unstructured point cloud, which is critical for quality inspection in manufacturing and other application areas.

关键词： Anomaly detection unstructured 3D point cloud data graph representation sparse learning majorization-minimization free-form surface smooth surface

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Multi-level sparse network lasso: Locally sparse learning with flexible sample clusters

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NEUROCOMPUTING 2025年 635卷

作者： Fei, Luhuan Wang, Xinyi Wang, Jiankun Sun, Lu Zhang, Yuyao ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Michigan State Univ Coll Engn Dept Comp Sci & Engn E Lansing MI USA

Traditional learning usually assumes that all samples share the same global model, which fails to preserve critical local information for heterogeneous data. It can be tackled by detecting sample clusters and learning sample-specific models but is limited to sample-level clustering and sample-specific feature selection. In this paper, we propose multi-level sparse network lasso (MSN Lasso) for flexible local learning. It multiplicatively decomposes model parameters into two components: One component is for coarse-grained group-level, and another is for fine-grained entry-level. At the clustering stage, MSN Lasso simultaneously groups samples (group-level) and clusters specific features across samples (entry-level). At the feature selection stage, it enables both across-sample (group-level) and sample-specific (entry-level) feature selection. Theoretical analysis reveals a potential equivalence to a jointly regularized local model, which informs the development of an efficient algorithm. A divide-and-conquer optimization strategy is further introduced to enhance the algorithm's efficiency. Extensive experiments across diverse datasets demonstrate that MSN Lasso outperforms existing methods and exhibits greater flexibility.

关键词： sparse learning Feature selection Sample clustering Multi-level network lasso

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Feature selection through adaptive sparse learning for scene recognition

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APPLIED SOFT COMPUTING 2025年 169卷

作者： Sun, Yunyun Li, Peng Sun, Hang Xu, He Wang, Ruchuan Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210023 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing 210023 Jiangsu Peoples R China Jiangsu High Technol Res Key Lab Wireless Sensor N Nanjing 210023 Jiangsu Peoples R China

Scene recognition is an important and challenging task in the field of computer vision. Current research typically focuses on local features in scene images by utilizing pretrained convolutional neural networks (CNN) to obtain a feature dictionary. However, these local features often contain similar features to other scene categories, leading to feature confusion and subsequently low accuracy in scene recognition. Therefore, focusing on local features for scene recognition is controversial. In contrast to existing works, we consider extracting common features within the same category from scene images and using them as discriminative features between different categories. We propose a scene recognition method based on adaptive sparse learning feature selection. This method leverages sparse learning to distinguish the contribution of each feature in the deep features to the classification task, aiming to construct a salient feature dictionary that describes the importance of features in scene images. Subsequently, an adaptive weight optimization method is employed through alternating updates to automatically adjust feature weights, enabling the selection of common features within the same scene category from the compact dictionary. Experimental results on multiple benchmark scene recognition datasets demonstrate that our proposed method is superior to state-of-the-art methods.

关键词： sparse learning Feature selection Weight variable Adaptive optimization Common features

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sparse Homogeneous learning: A New Approach for sparse learning

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CHINESE JOURNAL OF ELECTRONICS 2025年第2期34卷 712-721页

作者： Shi, Jiajie Yang, Zhi Liu, Jiafeng Shi, Hongli Capital Med Univ Coll Biomed Engn Beijing 100069 Peoples R China Capital Med Univ Beijing Key Lab Fundamental Res Biomech Clin Appli Beijing 100069 Peoples R China Beijing Informat Sci & Technol Univ Sch Instrumentat Sci & Optoelect Engn Beijing 100192 Peoples R China

Many sparse representation problems boil down to address the underdetermined systems of linear equations subject to solution sparsity restriction. Many approaches have been proposed such as sparse Bayesian learning. In order to improve solution sparsity and effectiveness in a more intuitive way, a new approach is proposed, which starts from the general solution of the linear equation system. The general solution is decomposed into the particular and homogeneous solutions, where the homogeneous solution is designed to counteract as many elements of particular solution as possible to make the general solution sparse. First, construct a special system of linear equations to link the homogeneous solution with particular solution, which typically is an inconsistent system. Second, the largest consistent sub-system are extracted from the system so that as many corresponding elements of two solutions as possible cancel each other out. By improving implementation efficiency, the procedure can be accomplished with moderate computational time. The results of extensive experiments for sparse signal recovery and image reconstruction demonstrate the superiority of the proposed approach in terms of sparseness or recovery accuracy with acceptable computational burden.

关键词： sparse learning sparse signal recovery Compressed sensing sparse Bayesian learning Particular solution Homogeneous solution

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A context constraint and sparse learning based on correlation filter for high-confidence coarse-to-fine visual tracking

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 268卷

作者： Su, Yinqiang Xu, Fang Wang, Zhongshi Sun, Mingchao Zhao, Hui Chinese Acad Sci Xian Inst Opt & Precis Mech Xian 710119 Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys State Key Lab Dynam Opt Imaging & Measurement Changchun 130033 Peoples R China

Discriminative Correlation Filters (DCFs) have recently garnered significant considerable in the field of visual single tracking. However, existing trackers frequently struggle to fully mine the structural complementarity and diversity among various features, resulting in a decline in discriminability in complex scenarios. To address these challenges, we explore a novel context-aware sparse learning (CCSL) framework based on DCFs and hierarchically infer the maximum response represented by each class filter to locate the target confidently. Guided by sparse learning principles, our tracker collaboratively and bidirectionally selects effective spatial elements that encode target appearance through lasso regression and sparse response estimate. Additionally, the target and its surroundings are jointly incorporated into the learning framework, thereby bolstering the discriminability of the learned model. To tackle the optimization problem, we leverage the Alternating Direction Method of Multipliers (ADMM) in the Fourier domain. Furthermore, we introduce a hierarchical inference scheme for target localization that harnesses complementary cues from different features, which leverages historical displacement and multi-modal detection to reveal the tracking state respectively. Extensive experiments are conducted on renowned benchmarks, including OTB-100, UAV123, UAV20L, and TC128, to demonstrate the efficiency and effectiveness of the proposed tracker. These results underscore the tracker's ability to perform robustly in complex scenarios, showcasing its superior discriminative power and accuracy.

关键词： Visual tracking Discriminative Correlation Filters sparse learning Lasso regression Context constraints

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sparse learning of Higher-Order Statistics for Communications and Sensing

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2020年第1期4卷 13-22页

作者： Sun, Zhuo Kong, Song Wang, Wenbo Beijing Univ Posts & Telecommun Key Lab Universal Wireless Commun Beijing 100876 Peoples R China China Acad Informat & Commun Technol Beijing 100191 Peoples R China

Signal processing based higher-order statistics (HOS) has been acting as a potential important tool on variety of target identification and information sensing fields. While a concise or compact expression of HOS is needed to ease the burden of data acquisition and computational complexity, sparse representation of HOS could be the optimum solution to this problem. In this paper, we formulate the issue of sparse representation of HOS by categorizing them into three cases according to the discriminative sparsity: strictly sparse, structure-based sparse and structure-based compressible. The corresponding algorithms of sparse representation for the three types of HOS are designed individually. For strictly sparse HOS, we mainly address on how to build the linear relationship between one-dimensional time domain samples and high-dimensional HOS and reduce the computational complexity of equivalent sensing matrix. Autocorrelation and four-order statistic are taken as examples to illustrate proposed sparse decomposing method for structure-based sparse HOS by exploiting their intra-structure properties. The sparse representation of structure-based compressible HOS are approximated with a joint decomposing algorithm using eigenvalue and single-value decomposition approaches. In addition, we have integrate our proposed sparse representation of HOS into compressive sensing framework to verify the feasibility and performance of sparse representation solutions.

关键词： Higher-order statistics sparse representation sparse learning compressive sensing structure-based sparse

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sparse learning-Based Correlation Filter for Robust Tracking

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2021年 30卷 878-891页

作者： Zhang, Wenhua Jiao, Licheng Li, Yuxuan Liu, Jia Xidian Univ Int Res Ctr Intelligent Percept & Computat Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China Xidian Univ Joint Int Res Lab Intelligent Percept & Computat Sch Artificial Intelligence Xian 710071 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

Many objective tracking methods are based on the framework of correlation filtering (CF) due to its high efficiency. In this paper, we propose a l(2)-norm based sparse response regularization term to restrain unexpected crests in response for CF framework. CF trackers learn online to regress the region of interest into a Gaussian response. However, due to the uncertain transformations of tracked object, there are many unexpected crests in the response map. When the response of tracked object is corrupted by other crests, the tracker will lost the object. Therefore, the sparse response is used to increase the robustness to transformations of tracked object. Since the novel term is directly incorporated into the objective function of the CF framework, it can be used to improve the performance of many methods which are based on this framework. Moreover, from the solutions we derive, the new method will not increase the computational complexity. Through the experiments on benchmarks of OTB-100, TempleColor, VOT2016 and VOT2017, the proposed regularization term can improve the tracking performance of various CF trackers, including those based on standard discriminative CF framework and those based on context-aware CF framework. We also embed the sparse response regularization term in the state-of-the-art integrated tracker MCCT to test its generalization performance. Although MCCT is an expert integrated tracker and owns an exquisite algorithm for selecting experts, the experimental results show that our method can still improve its long-term tracking performance without increasing computational complexity.

关键词： Object tracking correlation filtering sparse learning context-aware correlation filtering

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sparse learning Method With Feature Selection for Sensor Placement and Response Prediction

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第6期60卷 8022-8033页

作者： Zhang, Minzhao Ding, Junliang Li, Bin Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China Chinese Flight Test Estab Xian 710089 Peoples R China

Monitoring the vibration responses of structures accurately and efficiently is the key point of structural health monitoring (SHM). The monitoring of structural vibration responses depends on the sensor system. Therefore, discovering critical sensor positions is a challenging but beneficial task for SHM. Unfortunately, the predominant approaches only focus on predicting the vibration responses and lack the capability to identify meaningful sensor positions, which inevitably restricts their predictive capabilities. Furthermore, the strong linear correlation between sensors results in instability when selecting sensor locations, which leads to the difficulty of sensor optimization. To bridge this gap, we propose a sparse learning method with feature selection (SLMFS) to identify meaningful and interpretable sensor positions. In this method, to facilitate interpretation and stability, we use sparsity-inducing penalties to select the important sensors at both individual and group levels. In addition, we introduce independent regularization for stable and consistent feature selection. We also present an efficient iterative optimization algorithm to address the SLMFS, which is guaranteed to converge to the global optimum. The synthetic data, simulation data, and laboratory data are used to validate the effectiveness and accuracy of SLMFS. The results show that sensor selection and response prediction mutually reinforce each other. On the one hand, the guidance from sensors further ensures that the response prediction produces a good representation. On the other hand, the good representation enables more precise selection of target response-related sensors. Therefore, our learning method could improve the efficiency and accuracy of response prediction.

关键词： Sensors Feature extraction Vibrations Sensor phenomena and characterization Optimization Vectors Accuracy Feature selection independent regularization (IR) response prediction sensor placement sparse learning

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sparse learning model with embedded RIP conditions for turbulence super-resolution reconstruction

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2024年 425卷

作者： Huang, Qinyi Zhu, Wei Ma, Feng Liu, Qiang Wen, Jun Chen, Lei Beijing Inst Technol Beijing 100081 Peoples R China

In practical engineering scenarios, constraints arising from sensor placement, quantity, and the limitations of current testing technologies often lead to turbulence data characterized by low resolution and irregular structures. Turbulence super-resolution reconstruction is crucial for extracting finer details from irregularly structured, low-resolution measurement data, thereby facilitating comprehensive flow field analyses. This article introduces a sparse learning model (Embedding Restricted Isometry Property Autoencoder (RIP-AE)) for achieving super-resolution reconstruction of flow fields by obtaining compressed representations and sparse transform domain information. In this model, we embed the Restricted Isometry Property (RIP) condition to ensure the effectiveness of compressed representations and sparse transform domains, thereby enhancing the super-resolution reconstruction accuracy of the model. To assess the performance of the RIP-AE model, we utilized data sets of flow around a cylinder at different Reynolds numbers generated by unsteady Reynolds-Averaged Navier-Stokes simulations. We sequentially validate the effectiveness of the RIP condition at different Reynolds numbers (ranging from 1,000 to 500,000) and compare the reconstruction results of the RIP-AE model with those of the downsampled skip-connection/multi-scale (DSC/MS) and MS-AE models under different sampling ratios. The results indicate that the RIP-AE model excels in terms of L2 relative error and demonstrates the capability to achieve high-precision flow field reconstruction even under high sampling ratios.

关键词： Super-resolution Deep learning sparse learning RIP Condition Turbulence

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