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  1. Article ; Online: Test Strategy Optimization Based on Soft Sensing and Ensemble Belief Measurement.

    Mei, Wenjuan / Liu, Zhen / Tang, Lei / Su, Yuanzhang

    Sensors (Basel, Switzerland)

    2022  Volume 22, Issue 6

    Abstract: Resulting from the short production cycle and rapid design technology development, traditional prognostic and health management (PHM) approaches become impractical and fail to match the requirement of systems with structural and functional complexity. ... ...

    Abstract Resulting from the short production cycle and rapid design technology development, traditional prognostic and health management (PHM) approaches become impractical and fail to match the requirement of systems with structural and functional complexity. Among all PHM designs, testability design and maintainability design face critical difficulties. First, testability design requires much labor and knowledge preparation, and wastes the sensor recording information. Second, maintainability design suffers bad influences by improper testability design. We proposed a test strategy optimization based on soft-sensing and ensemble belief measurements to overcome these problems. Instead of serial PHM design, the proposed method constructs a closed loop between testability and maintenance to generate an adaptive fault diagnostic tree with soft-sensor nodes. The diagnostic tree generated ensures high efficiency and flexibility, taking advantage of extreme learning machine (ELM) and affinity propagation (AP). The experiment results show that our method receives the highest performance with state-of-art methods. Additionally, the proposed method enlarges the diagnostic flexibility and saves much human labor on testability design.
    MeSH term(s) Humans ; Learning ; Machine Learning ; Prognosis
    Language English
    Publishing date 2022-03-10
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s22062138
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Effect of 3-mercapto-1-propane sulfonate sulfonic acid and polyvinylpyrrolidone on the growth of cobalt pillar by electrodeposition

    Ni Xiuren / Wang Chong / Su Yuanzhang / Luo Yuyao / Ye Yilin / Su Xinhong / He Wei / Wang Shouxu / Hong Yan / Chen Yuanming / Zhou Guoyun / Liu Bingyun

    Nanotechnology Reviews, Vol 11, Iss 1, Pp 1209-

    2022  Volume 1218

    Abstract: Cobalt is a promising material for electronic interconnections in the post-Moore law period. However, the vertical cobalt pillar is not fully compatible with the current electroplating-involved manufacturing process due to hydrogen evolution at the ... ...

    Abstract Cobalt is a promising material for electronic interconnections in the post-Moore law period. However, the vertical cobalt pillar is not fully compatible with the current electroplating-involved manufacturing process due to hydrogen evolution at the cathode and poor throwing power of the products. In this article, electrodeposition with multiple organic additives was employed to realize the fabrication of cobalt pillars. Electrochemical measurements were used to investigate the depolarization of 3-mercapto-1-propane sulfonate sulfonic acid (MPS) and the polarization of the polyvinylpyrrolidone (PVP) during cobalt electrodeposition. Notably, the competitive adsorption between MPS and PVP was verified and discussed in cobalt electrodeposition. In order to understand the adsorption and functional groups of the additives, quantum chemical calculations were performed to simulate the distribution of electrostatic potential and molecular orbital energy of the additives. Accordingly, the thiol group of MPS and the amide group of PVP were speculated to be the molecular adsorption sites in cobalt electrodeposition. The mechanism including three stages was proposed for cobalt pillar electrodeposition in solution with MPS and PVP. The electrodeposition of practical cobalt pillars with a depth of 50 µm and diameters of 60, 80, and 100 µm was successfully achieved by electroplating experiments, thereby promoting the application of metal cobalt for electronic packaging.
    Keywords cobalt electrodeposition ; additive ; cobalt pillar ; adsorption ; Technology ; T ; Chemical technology ; TP1-1185 ; Physical and theoretical chemistry ; QD450-801
    Subject code 660
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher De Gruyter
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: Latent Multi-view Semi-Supervised Classification

    Bo, Xiaofan / Kang, Zhao / Zhao, Zhitong / Su, Yuanzhang / Chen, Wenyu

    2019  

    Abstract: To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method. Unlike most existing multi-view semi-supervised classification methods that learn the ... ...

    Abstract To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method. Unlike most existing multi-view semi-supervised classification methods that learn the graph using original features, our method seeks an underlying latent representation and performs graph learning and label propagation based on the learned latent representation. With the complementarity of multiple views, the latent representation could depict the data more comprehensively than every single view individually, accordingly making the graph more accurate and robust as well. Finally, LMSSC integrates latent representation learning, graph construction, and label propagation into a unified framework, which makes each subtask optimized. Experimental results on real-world benchmark datasets validate the effectiveness of our proposed method.

    Comment: ACML 2019
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2019-09-09
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Similarity Learning via Kernel Preserving Embedding

    Kang, Zhao / Lu, Yiwei / Su, Yuanzhang / Li, Changsheng / Xu, Zenglin

    2019  

    Abstract: Data similarity is a key concept in many data-driven applications. Many algorithms are sensitive to similarity measures. To tackle this fundamental problem, automatically learning of similarity information from data via self-expression has been developed ...

    Abstract Data similarity is a key concept in many data-driven applications. Many algorithms are sensitive to similarity measures. To tackle this fundamental problem, automatically learning of similarity information from data via self-expression has been developed and successfully applied in various models, such as low-rank representation, sparse subspace learning, semi-supervised learning. However, it just tries to reconstruct the original data and some valuable information, e.g., the manifold structure, is largely ignored. In this paper, we argue that it is beneficial to preserve the overall relations when we extract similarity information. Specifically, we propose a novel similarity learning framework by minimizing the reconstruction error of kernel matrices, rather than the reconstruction error of original data adopted by existing work. Taking the clustering task as an example to evaluate our method, we observe considerable improvements compared to other state-of-the-art methods. More importantly, our proposed framework is very general and provides a novel and fundamental building block for many other similarity-based tasks. Besides, our proposed kernel preserving opens up a large number of possibilities to embed high-dimensional data into low-dimensional space.

    Comment: Published in AAAI 2019
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Multimedia ; Statistics - Machine Learning
    Subject code 006 ; 004
    Publishing date 2019-03-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Fabrication of optoplasmonic particles through electroless deposition and the application in SERS-based screening of nodule-involved lung cancer.

    Wang, Zehua / Hong, Yan / Yan, Huan / Luo, Huaichao / Zhang, Yating / Li, Lintao / Lu, Shun / Chen, Yuanming / Wang, Dongsheng / Su, Yuanzhang / Yin, Gang

    Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

    2022  Volume 279, Page(s) 121483

    Abstract: In this work, a core-satellite optoplasmonic particle containing a silica microsphere covered with gold nanoparticles (AuNPs) was developed through wet chemistry synthesis in aqueous phase. The electroless deposition and galvanic replacement were ... ...

    Abstract In this work, a core-satellite optoplasmonic particle containing a silica microsphere covered with gold nanoparticles (AuNPs) was developed through wet chemistry synthesis in aqueous phase. The electroless deposition and galvanic replacement were employed to anchor AuNPs onto silica sphere surface. The escalated as well as expanded electric field enhancement within the dielectric-metallic interface was analyzed through finite difference time domain (FDTD) simulation. The numerical models and the surface-enhancement Raman spectroscopy (SERS) measurements over blood serum both support that the equatorial plane is the preferred collecting plane for improved signal intensity and stability. The nanocomposite emerged lower relative standard deviation (RSD) in repetitive measurement compared to AuNPs. In practice, this hybrid structure was applied for lung cancer diagnosis based on serum SERS spectra analysis of the patients diagnosed with nodules. The prediction with the aid of principal component analysis (PCA) and support-vector machine (SVM) was attempted for the classification of healthy, benign and relatively malignant sample groups. The accuracy of distinguish benign samples from malignant ones reaches over 90%. These advantages make the structure a promising SERS substrate for the early screening of cancer based on the non-invasive biological samples.
    MeSH term(s) Early Detection of Cancer ; Gold/chemistry ; Humans ; Lung Neoplasms/diagnosis ; Metal Nanoparticles/chemistry ; Silicon Dioxide/chemistry ; Spectrum Analysis, Raman/methods
    Chemical Substances Gold (7440-57-5) ; Silicon Dioxide (7631-86-9)
    Language English
    Publishing date 2022-06-06
    Publishing country England
    Document type Journal Article
    ZDB-ID 210413-1
    ISSN 1873-3557 ; 0370-8322 ; 0584-8539 ; 1386-1425
    ISSN (online) 1873-3557
    ISSN 0370-8322 ; 0584-8539 ; 1386-1425
    DOI 10.1016/j.saa.2022.121483
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Bonn / Germany

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  6. Article: An efficient primary screening of COVID-19 by serum Raman spectroscopy.

    Yin, Gang / Li, Lintao / Lu, Shun / Yin, Yu / Su, Yuanzhang / Zeng, Yilan / Luo, Mei / Ma, Maohua / Zhou, Hongyan / Orlandini, Lucia / Yao, Dezhong / Liu, Gang / Lang, Jinyi

    Journal of Raman spectroscopy : JRS

    2021  Volume 52, Issue 5, Page(s) 949–958

    Abstract: The outbreak of COVID-19 coronavirus disease around the end of 2019 has become a pandemic. The preferred method for COVID-19 detection is the real-time polymerase chain reaction (RT-PCR)-based technique; however, it also has certain limitations, such as ... ...

    Abstract The outbreak of COVID-19 coronavirus disease around the end of 2019 has become a pandemic. The preferred method for COVID-19 detection is the real-time polymerase chain reaction (RT-PCR)-based technique; however, it also has certain limitations, such as sample-dependent procedures with a relatively high false negative ratio. We propose a safe and efficient method for screening COVID-19 based on Raman spectroscopy. A total of 177 serum samples are collected from 63 confirmed COVID-19 patients, 59 suspected cases, and 55 healthy individuals as a control group. Raman spectroscopy is adopted to analyze these samples, and a machine learning support-vector machine (SVM) method is applied to the spectrum dataset to build a diagnostic algorithm. Furthermore, 20 independent individuals, including 5 asymptomatic COVID-19 patients and 5 symptomatic COVID-19 patients, 5 suspected patients, and 5 healthy patients, were sampled for external validation. In these three groups-confirmed COVID-19, suspected, and healthy individuals-the distribution of statistically significant points of difference showed highly consistency for intergroups after repeated sampling processes. The classification accuracy between the COVID-19 cases and the suspected cases is 0.87 (95% confidence interval [CI]: 0.85-0.88), and the accuracy between the COVID-19 and the healthy controls is 0.90 (95% CI: 0.89-0.91), while the accuracy between the suspected cases and the healthy control group is 0.68 (95% CI: 0.67-0.73). For the independent test dataset, we apply the obtained SVM model to the classification of the independent test dataset to have all the results correctly classified. Our model showed that the serum-level classification results were all correct for independent test dataset. Our results suggest that Raman spectroscopy could be a safe and efficient technique for COVID-19 screening.
    Language English
    Publishing date 2021-02-19
    Publishing country England
    Document type Journal Article
    ZDB-ID 1481008-6
    ISSN 1097-4555 ; 0377-0486
    ISSN (online) 1097-4555
    ISSN 0377-0486
    DOI 10.1002/jrs.6080
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Book ; Online: Multiple Partitions Aligned Clustering

    Kang, Zhao / Guo, Zipeng / Huang, Shudong / Wang, Siying / Chen, Wenyu / Su, Yuanzhang / Xu, Zenglin

    2019  

    Abstract: Multi-view clustering is an important yet challenging task due to the difficulty of integrating the information from multiple representations. Most existing multi-view clustering methods explore the heterogeneous information in the space where the data ... ...

    Abstract Multi-view clustering is an important yet challenging task due to the difficulty of integrating the information from multiple representations. Most existing multi-view clustering methods explore the heterogeneous information in the space where the data points lie. Such common practice may cause significant information loss because of unavoidable noise or inconsistency among views. Since different views admit the same cluster structure, the natural space should be all partitions. Orthogonal to existing techniques, in this paper, we propose to leverage the multi-view information by fusing partitions. Specifically, we align each partition to form a consensus cluster indicator matrix through a distinct rotation matrix. Moreover, a weight is assigned for each view to account for the clustering capacity differences of views. Finally, the basic partitions, weights, and consensus clustering are jointly learned in a unified framework. We demonstrate the effectiveness of our approach on several real datasets, where significant improvement is found over other state-of-the-art multi-view clustering methods.

    Comment: IJCAI 2019
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2019-09-12
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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