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10. Robust Matrix Factorization with Grouping Effect

Author

Jiang, Haiyan, Li, Shuyu, Zhang, Luwei, Xiong, Haoyi, and Dou, Dejing

Subjects
Computer Science - Machine Learning, Statistics - Methodology, Statistics - Machine Learning
Abstract

Although many techniques have been applied to matrix factorization (MF), they may not fully exploit the feature structure. In this paper, we incorporate the grouping effect into MF and propose a novel method called Robust Matrix Factorization with Grouping effect (GRMF). The grouping effect is a generalization of the sparsity effect, which conducts denoising by clustering similar values around multiple centers instead of just around 0. Compared with existing algorithms, the proposed GRMF can automatically learn the grouping structure and sparsity in MF without prior knowledge, by introducing a naturally adjustable non-convex regularization to achieve simultaneous sparsity and grouping effect. Specifically, GRMF uses an efficient alternating minimization framework to perform MF, in which the original non-convex problem is first converted into a convex problem through Difference-of-Convex (DC) programming, and then solved by Alternating Direction Method of Multipliers (ADMM). In addition, GRMF can be easily extended to the Non-negative Matrix Factorization (NMF) settings. Extensive experiments have been conducted using real-world data sets with outliers and contaminated noise, where the experimental results show that GRMF has promoted performance and robustness, compared to five benchmark algorithms., Comment: 22 pages, 5 figures, 4 tables

Published
2021

20. Abnormal phenotypic defects detection of jujube using explainable machine learning enhanced computer vision.

Author

Zhang, Luwei, Chen, Yan, Guo, Xiangyun, Li, You, Chen, Chao, Zou, Yu, Zhang, Xiaoshuan, and Hu, Jinyou

Subjects
*BACK propagation, *COMPUTER vision, *DATA augmentation, *JUJUBE (Plant), *IMAGE processing
Abstract

Jujube is susceptible to biotic and abiotic adversity stresses resulting in abnormal phenotypic defects. Therefore, abnormal phenotype fruits should be removed during postharvest sorting to increase added value. An improved maximum horizontal diameter linear regression (MHD‐LR) method for size grading of jujube prior to detection of abnormal phenotypic defects was developed. The accuracy of the MHD‐LR model is 95%, with an error of only 0.95 mm. In addition, a method for detecting abnormal phenotypic defects in jujube was established. It can effectively and accurately classify seven kinds of jujube phenotypes (regular, irregular, wrinkled, moldy, hole‐broken, skin‐broken, and scarred). The data augmentation method based on linear interpolation can effectively expand the dataset with a variance of only 0.0006. Support vector machine‐decision tree (SVMDT), logistic regression, back propagation neural network, and long short‐term memory network models were established to classify jujube samples with different phenotypes, with accuracies of 99.57%, 99.00%, 99.14%, and 99.29%, respectively. The results showed that the SVMDT model had higher accuracy and explainability. This research is expected to provide a new method to improve the precise classification of abnormal phenotypic defects in postharvest jujube. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Amperometric Highly Sensitive Phosphate Ion Sensor Based on the Electrochemically Modified Ni Electrode

Author

Li, Yinpeng, Liu, Jinjian, Zhang, Luwei, Yang, Qiaozhi, Chen, Weiyun, Wu, Jie, Zhang, Lifeng, Li, Xin, and Xu, Kebin

Abstract

We present a study of high-performance electrochemical phosphate sensors, which are exquisitely designed and easy to operate. We innovatively utilized the insolubility of nickel phosphate and developed a new type of sensor through electrochemical methods. The experiment first used cyclic voltammetry to determine −0.4 V as the optimal electrochemical modification potential and used constant potential electrodeposition technology to form a nickel oxide layer on the surface of the nickel electrode, which serves as the active layer in response to phosphate ions. The changes in the surface structure and chemical composition of the electrode before and after modification were thoroughly characterized by scanning electron microscopy and energy scattering spectroscopy analysis. The performance evaluation of the sensor shows that the modified nickel electrode has excellent responsiveness to phosphate ions in the concentration range of 10–7to 10–10mol/L, with a detection lower limit of 10–10mol/L. As the concentration decreases, a shoulder peak appears at ∼0.63 V and the current change shows a regular increase. Compared with traditional detection methods, this sensor exhibits higher stability and practicality and is suitable for the rapid identification of phosphates in real samples. In summary, this study successfully developed a fast, sensitive, and wide response range current type electrochemical phosphate sensor, which has broad application prospects in environmental monitoring, water quality analysis, and biomedical fields.

Published
2024
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45. Adaptive Interfacial Contact between Copper Nanoparticles and Triazine Functionalized Graphdiyne Substrate for Improved Lithium/Sodium Storage.

Author

Liu, Jingyi, Zhang, Luwei, Wang, Kaihang, Jiang, Chao, Zhang, Chunfang, and Wang, Ning

Subjects
*COPPER, *SODIUM, *NANOPARTICLES, *ENERGY storage, *CHARGE transfer
Abstract

The high activity of nano‐sized metal particles (NMPs) makes it easy to appear uncontrolled aggregation, which seriously affects Li/Na storage in electrode materials. Introducing adaptive substrates with proper affinity to NMPs is an effective strategy that optimizes the stability and capacity of the related electrodes. Herein, a comprehensive strategy for the fabrication of adaptive interfacial contacts between metallic Cu nanoparticles (NPs) and triphenyl‐substituted triazine graphdiyne (TPTG) substrates is reported. The sp C in the acetylenic linkers and N heteroatoms in the triazine groups synergistically stabilized the Cu NPs loaded onto the TPTG substrates. The stabilizing effect of the TPTG substrate induces a reversible lattice change of the Cu NPs during the charge–discharge process, thus efficiently facilitating the stable transfer of Li+/Na+. Intrinsic mechanism analysis indicates that the heterojunction contact interface of Cu NPs/TPTG provides branched charge transfer pathways from Li/Na to the Cu NPs and TPTG substrates, which synergistically adjusts the affinity to Li/Na atoms and ultimately improves the electrochemical performance in Li/Na storage. The investigation of the structure–property relationship deepens the understanding of the function of heterointerfaces, which is essential for optimizing the performance of energy storage devices. [ABSTRACT FROM AUTHOR]

Published
2023
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49. Diffusion Kinetics Study of Lithium Ion in the Graphdiyne Based Electrode

Author

Zhang, Luwei, Liu, Jingyi, Bai, Ling, and Wang, Ning

Abstract

Herein, we report a comparative investigation of the electrochemical lithium diffusion within graphidyne(GDY) based electrodes. The transfer kinetic behaviors of lithium ions during the insertion/extraction process are analyzed through different methods including the galvanostatic intermittent titration technique(GITT) and the electrochemical impedance spectroscopy (EIS). GDY with the morphology of nanosheets(GDY NS) shows lithium diffusion coefficients in the orders range of 10−12–10−13cm2/s through the GITT method. Meanwhile, EIS indicates quite a lower value of lithium diffusion coefficients between 10−13and 10−15cm2/s, which indicates that the analysis technique has an influence on the evaluation of GDY-based electrodes. In addition, under the same measurement condition of GITT, GDY nanoparticles(GDY NP) exhibit a lower value of Li+diffusion coefficient(10−14–10−16cm2/s) during the charge-discharge process compared to those of GDY NS, which can be ascribed to the wide distributing range of particle size in GDY NP based electrodes. The analysis results in this work reveal that the aggregating forms of GDY electrode material have an important effect on the diffusion process of lithium ions, which provides a pathway to optimize the performance of GDY-based energy storage devices.

Published
2024
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