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An Eigenvalue-Based Approach for structure classification in Polarimetric SAR Images

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2020年第6期17卷 1003-1007页

作者： Biondi, Filippo Clemente, Carmine Orlando, Danilo Italian Minist Def I-00187 Rome Italy Univ Strathclyde Dept Elect & Elect Engn Ctr Signal & Image Proc Glasgow G1 1XW Lanark Scotland Univ Niccolo Cusano I-00166 Rome Italy

In this letter, we design a novel unsupervised architecture for automatic classification of the dominant polarization in polarimetric SAR images. To this end, we leverage the ideas developed in [1] and suitably exploit them to build a decision logic capable of recognizing the dominant scattering mechanism which characterizes the pixel under test. Specifically, we combine the original data to generate three different sets of reduced-size vectors, which feed dominant eigenvalues classifier based upon the model order selection rules. Then, the outputs of the latter classification schemes are exploited to infer, according to a specific criterion, the dominant polarization. The performance analysis is conducted on the measured data and points out the effectiveness of the newly proposed classification architecture also showing that information about the dominant polarization can be representative of the type of structure which gives raise to the dominant backscattering mechanism.

关键词： Eigenvalues and eigenfunctions Synthetic aperture radar Scattering Radar polarimetry Monitoring Covariance matrices Integrated circuits Covariance matrix eigenvalues decomposition model order selection (MOS) rules polarimetric SAR image classification structure classification

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Source attribution and structure classification-assisted strategy for comprehensively profiling Chinese herbal formula: Ganmaoling granule as a case

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JOURNAL OF CHROMATOGRAPHY A 2016年第0期1464卷 102-114页

作者： Chen, Jinfeng Shi, Ziyi Song, Yuelin Guo, Xiaoyu Zhao, Mingbo Tu, Pengfei Jiang, Yong Peking Univ Sch Pharmaceut Sci State Key Lab Nat & Biomimet Drugs Beijing 100191 Peoples R China Beijing Univ Chinese Med Modern Res Ctr Tradit Chinese Med Beijing 100029 Peoples R China

Chinese herbal formula (CHF) has extremely complex chemical composition, Herein, a source attribution and structure classification-assisted strategy was established based on reductionism for rapidly and comprehensively profiling CHF, and Ganmaoling granule (GMLG) was selected as a representative case to illustrate such a strategy and to confirm its applicability. Firstly, comprehensive data acquisition was achieved using neutral losses along with full scan on a liquid chromatography coupled with hybrid ion trap-time of flight mass spectrometer (LC-IT-TOF-MS). Then, the detected precursor and product ions were paired to construct a list of ion transitions for profiling GMLG and its constituent herbs using the scheduled multiple reaction monitoring (sMRM) mode on a LC coupled with hybrid triple quadrupole linear ion trap mass spectrometer (LC-Q-Trap-MS). The mass parameters of sMRM were optimized using an online optimization strategy to achieve the highest sensitivity, and the automated source attribution was performed with the assistant of the "Quantitate" function of Analyst software. The target peaks were then structurally classified into seven classes through integrating the mass defect filtering (MDF) and diagnostic fragment ion filtering (DFIF), and identified by combination of the mass fragmentation rules and a 'structure extension' approach. Using this strategy, 261 components, including 148 trace ones (with the intensity lower than 100,000 cps), were tentatively characterized. The findings demonstrated that such a comprehensive source attribution and structure classification-assisted strategy is qualified to be an efficient approach for rapidly and globally characterizing the chemical profile of CHF. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Chinese herbal formula Source attribution structure classification Ganmaoling granule Scheduled multiple reaction monitoring Online parameter optimization

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Loess structure: Evolution and a scale-based classification

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EARTH-SCIENCE REVIEWS 2024年 249卷

作者： Li, Yanrong Wang, Yuanyuan Aydin, Adnan Taiyuan Univ Technol Dept Earth Sci & Engn Taiyuan 030024 Peoples R China Univ Mississippi Dept Geol & Geol Engn Oxford MS 38677 USA

Loess structure plays a vital role in the evolution of loess landforms, which mainly involves water erosion and slope collapse processes. The existing literature on loess structure lacks a comprehensive and systematic overview covering structural characteristics and features at all relevant spatial scales and explaining how they underlie these processes. This paper presents a detailed discussion of loess structure from this perspective and proposes a structural classification over three contiguous ranges of spatial scales (micro-, meso- and macro-) in terms of nature of particles and pore patterns. Then it details how the initial loess structure is modified by various environmental processes, and outlines cross-scale relations of inheritance among the solid structural elements (microscopic particle assemblies, mesoscopic fragments and macroscopic blocks) and weak segments (pores, channels, pipes, biological pores, abiogenic joints and cracks) making up the present-day loess structure. The ascending impact of ubiquitous micro-scale structure is evidenced by similarity in shapes and spatial arrangement patterns of all features across the scale ranges. Vertically aligned strong units (particle assemblies) coupled with vertically aligned weak segments (pores) defining micro-scale loess structure set the stage for the formation of the structural elements at larger scales. This structure, named as "vertiloess", extends to field-scale landforms (>10 m) bounded and shaped with vertical features throughout the loess plateau.

关键词： Loessification structure classification structure inheritance Vertiloess

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Crystal structure map for materials classification and modeling

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SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS-METHODS 2024年第1期4卷

作者： Oguchi, Tamio Osaka Univ Ctr Spintron Res Network Grad Sch Engn Sci Osaka 5608531 Japan Osaka Univ Inst Open & Transdisciplinary Res Initiat Spintron Res Network Div Osaka Japan

For classifying and modeling properties of crystalline materials in terms of structure, a three-step workflow with (1) generation of structure feature vectors, (2) evaluation of distances among the feature vectors as a measure of similarity in structure, and (3) mapping of each structure in a low-dimensional space with principal components using dimension reduction is proposed. The obtained distance and resulting principal components are useful for classifying similar crystal structures for a given set of materials systems and for constructing descriptors in machine-learning analyses of properties. The eigenvector of the principal components indicates which part of the original structure feature vector is contained as important information. Examples are demonstrated for classification and property modeling of Al O-2 polymorphs including amorphous structures and of the alloy configurations of Si-doped LaFe( 13)compounds.

关键词： structure classification multidimensional scaling DFT calculations

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A Systematic classification and Typological Assessment Method for Mortise and Tenon Joints

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BIORESOURCES 2024年第3期19卷 4918页

作者： Shang, Bin Chen, Zhe Lin, Yuxi Chang, Hong Wei, Jianing China Univ Min & Technol Sch Architecture & Design Xuzhou 221116 Peoples R China Shandong Jiaotong Univ Jinan 250357 Peoples R China China Univ Min & Technol Sch Mech & Civil Engn Xuzhou 221116 Peoples R China

The classification of Mortise and Tenon (MT) joints is vital, as it enables standardized terminology, facilitates comparative analysis, and enhances understanding of construction techniques across a variety of applications including the design, manufacturing, and management of wood products. Although the classification of MT joints is crucial, current research in this area lacks a systematic approach. The study adopts a morphological composition paradigm to investigate MT joints. This study introduces a 6level classification index hierarchy for MT morphology, employing methods from biological classification and arithmetic cross -method coding. By encoding joint features and morphological composition, the study delineates 352 possible joint types and 1056 theoretical compositions across dimensions, elucidating diverse structural logics and aiding comprehension. Next, a feasibility typicality assessment identifies 198 typical and 310 atypical morphological types, presented clearly in graphical form. Validations are conducted through analysis of 2654 research cases, which are encoded according to the index hierarchy, thereby affirming the scientific validity and practical utility of the classification system.

关键词： Mortise and Tenon joint structure classification Typicality assessment

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Automated identification of stacking faults and twin boundaries in face-centered cubic crystal

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COMPUTATIONAL MATERIALS SCIENCE 2024年 235卷

作者： Bai, Zhiwen Fu, Tao Hu, Hao Yu, Wenshan Peng, Xianghe Xi An Jiao Tong Univ Sch Aerosp Xian 710049 Peoples R China Chongqing Univ Dept Engn Mech Chongqing 400044 Peoples R China

The primary objective of atomic-scale simulations is to meticulously monitor the formation and evolution of microstructures and defects, and quantitatively analyze their effects on mechanical and physical properties. In face-centered cubic (FCC) metals, planar defects such as stacking faults (SFs), twin boundaries (TBs), and local hexagonal close-packed (HCP) structures have a significant influence on mechanical properties. However, the existing methods for identifying microstructures classify SF, TB, and local HCP phase transitions as HCP structures, without further differentiation. To address this issue, an approach is proposed that can further identify the defect structures by meticulously examining the local structural environment and neighbors of atoms. Based on a density-based clustering algorithm in the particle-position datasets, this method can not only clearly distinguish these planar defects but also provide more quantitative information about them, such as their quantity, area, and number of atoms involved, which is crucial for establishing a quantitative relationship between different planar defects and their contents within FCC structural materials, and their mechanical properties.

关键词： structure classification Atomic-scale simulations Identification of planar defects Stacking fault Twin boundary

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Discovery, biosynthesis, organic synthesis, and bioactivities of meroterpenoids from Rhododendron species

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PHYTOCHEMISTRY 2024年 222卷 114089页

作者： Deng, Yi Lu, Gui-Hu Xu, Jing-Yang Luo, Qi Du, Qing-Feng Southern Med Univ Sch Tradit Chinese Med Guangdong Prov Key Lab Chinese Med Pharmaceut Guangzhou 510515 Peoples R China Southern Med Univ Guangdong Basic Res Ctr Excellence Integrated Trad Guangzhou 510515 Peoples R China Southern Med Univ Hosp Integrated Tradit Chinese & Western Med Guangzhou 510315 Peoples R China

Meroterpenoids discovered in Rhododendrons species possess unique chemical structures and biological activities and are expected to become new drug targets for Alzheimer's disease, metabolic disorders, and chronic kidney disease, and these compounds have attracted increasing attention in recent years. In this study, Rhododendron meroterpenoids and their structures, classifications, racemate distribution, biosynthetic pathways, chemical synthesis, and bioactivities are reviewed prior to 2023.

关键词： Rhododendron Ericaceae Meroterpenoids structure classification Bioactivities Synthesis Biosynthesis

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Defect inspection in semiconductor images using FAST-MCD method and neural network

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第3-4期129卷 1547-1565页

作者： Yu, Jinkyu Han, Songhee Lee, Chang-Ock Korea Adv Inst Sci & Technol Dept Math Sci Daejeon 34141 South Korea Samsung Elect Yongin 17113 Gyeonggi Do South Korea

Most defect inspection methods used in semiconductor manufacturing require design layout or golden die images. Unlike methods that require such additional information, this paper presents a method for automatic inspection of defects in semiconductor images with a single image. First, we devise a method to classify images into four types: flat, linear, patterned, and complex using a cosine similarity. For linear and patterned images, we obtain defect-free images that retain the structure. A flat image is then obtained by subtracting the defect-free image from the input image. The FAST-MCD method then estimates the parameters of the inlier distribution of the flat image and uses them to detect defects. A segmentation neural network is used to detect defects in complex images. Unlike conventional methods that only work on a specific structure, our method classifies structures and finds defects in each structure. We use 16 defective images in our experiments, where our method detects all 16 defective images, while the conventional methods detect fewer defective images.

关键词： Defect inspection Semiconductor image FAST-MCD method Neural network structure classification

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Charting Nanocluster structures via Convolutional Neural Networks

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ACS NANO 2023年第21期17卷 21287-21296页

作者： Telari, Emanuele Tinti, Antonio Settem, Manoj Maragliano, Luca Ferrando, Riccardo Giacomello, Alberto Sapienza Univ Roma Dipartimento Ingn Meccan & Aerosp I-00184 Rome Italy Univ Politecn Marche Dipartimento Sci Vita & Ambiente I-60131 Ancona Italy Ist Italiano Tecnol Ctr Synapt Neurosci & Technol I-16132 Genoa Italy Univ Genoa Dipartimento Fis I-16146 Genoa Italy

A general method to obtain a representation of the structural landscape of nanoparticles in terms of a limited number of variables is proposed. The method is applied to a large data set of parallel tempering molecular dynamics simulations of gold clusters of 90 and 147 atoms, silver clusters of 147 atoms, and copper clusters of 147 atoms, covering a plethora of structures and temperatures. The method leverages convolutional neural networks to learn the radial distribution functions of the nanoclusters and distills a low-dimensional chart of the structural landscape. This strategy is found to give rise to a physically meaningful and differentiable mapping of the atom positions to a low-dimensional manifold in which the main structural motifs are clearly discriminated and meaningfully ordered. Furthermore, unsupervised clustering on the low-dimensional data proved effective at further splitting the motifs into structural subfamilies characterized by very fine and physically relevant differences such as the presence of specific punctual or planar defects or of atoms with particular coordination features. Owing to these peculiarities, the chart also enabled tracking of the complex structural evolution in a reactive trajectory. In addition to visualization and analysis of complex structural landscapes, the presented approach offers a general, low-dimensional set of differentiable variables that has the potential to be used for exploration and enhanced sampling purposes.

关键词： machine learning metal nanoclusters collectivevariables molecular dynamics structure classification

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Let's Vibrate with Vibration: Augmenting Structural Engineering with Low-Cost Vibration Sensing 20th

Let's Vibrate with Vibration: Augmenting Structural Engineer...

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20th EAI International Conference on Mobile and Ubiquitous Systems - Computing Networking and Services (EAI MobiQuitous)

作者： Rahaman, Masfiqur Sakib, Md Nazmul Hasan Islam, Nafisa Salim, Saiful Islam Kamal, Uday Rasheed, Raihan Al Islam, A. B. M. Alim Bangladesh Univ Engn & Technol Dept Comp Sci & Engn Dhaka Bangladesh Bangladesh Univ Engn & Technol Dept Civil Engn Dhaka Bangladesh Bangladesh Univ Engn & Technol Dept Elect & Elect Engn Dhaka Bangladesh

ISBN: (纸本)9783031639883;9783031639890

Using low-cost piezoelectric sensors to sense real structural vibration exhibits great potential in augmenting structural engineering, which is yet to be explored in the literature to the best of our knowledge. An example of such unexplored augmentation includes classifying diverse structures (such as buildings, flyovers, foot over-bridge, etc.). To explore these aspects, we develop a low-cost piezoelectric sensor-based vibration sensing system aiming to collect real vibration data from diversified civil structures remotely. We dig into our collected sensed data to classify five different types of structures through rigorous statistical and machine learning-based analyses. Furthermore, we design a lightweight Convolutional Neural Network architecture and perform necessary hyperparameter tuning to achieve better accuracy in classification. Our analyses achieve a classification accuracy of up to 97% with an F1 score of 0.97.

关键词： structure classification Structural vibration Piezoelectric sensor Time domain signal Sensor systems

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