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A Smart, Data-Driven Approach to Qualify Additively Manufactured Steel Samples for Print-Parameter-Based Imperfections

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MATERIALS 2024年第11期17卷 2513页

作者： Alaparthi, Suresh Subadra, Sharath P. Sheikhi, Shahram Univ Appl Sci Hamburg Inst Mat Sci & Joining Technol Berliner Tor 13 D-20099 Hamburg Germany Univ Appl Sci Hamburg Forschungs & Transferzentrum 3i Berliner Tor 13 D-20099 Hamburg Germany

With additive manufacturing (AM) processes such as Wire Arc Additive Manufacturing (WAAM), components with complex shapes or with functional properties can be produced, with advantages in the areas of resource conservation, lightweight construction, and load-optimized production. However, proving component quality is a challenge because it is not possible to produce 100% defect-free components. In addition to this, statistically determined fluctuations in the wire quality, gas flow, and their interaction with process parameters result in a quality of the components that is not 100% reproducible. Complex testing procedures are therefore required to demonstrate the quality of the components, which are not cost-effective and lead to less efficiency. As part of the project "3DPrintFEM", a sound emission analysis is used to evaluate the quality of AM components. Within the scope of the project, an approach was being developed to determine the quality of an AM part dependent not necessarily on its geometry. Samples were produced from WAAM, which were later cut and milled to precision. To determine the frequencies, the samples were put through a resonant frequency test (RFM). The unwanted modes were then removed from the spectrum produced by the experiments by comparing it with FEM simulations. Later, defects were introduced in experimental samples in compliance with the ISO 5817 guidelines. In order to create a database of frequencies related to the degree of the sample defect, they were subjected to RFM. The database was further augmented through frequencies from simulations performed on samples with similar geometries, and, hence, a training set was generated for an algorithm. A machine-learning algorithm based on regression modelling was trained based on the database to sort samples according to the degree of flaws in them. The algorithm's detectability was evaluated using samples that had a known level of flaws which forms the test dataset. Based on the outcome, the a

关键词： additive manufacturing gas metal arc welding microstructure porosity defects quality non-destructive testing acoustics data acquisition sorting algorithm

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Computing and visually analyzing mutual information in molecular co-evolution

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BMC BIOINFORMATICS 2010年第1期11卷 330-330页

作者： Bremm, Sebastian Schreck, Tobias Boba, Patrick Held, Stephanie Hamacher, Kay Tech Univ Darmstadt Dept Comp Sci Darmstadt Germany Tech Univ Darmstadt Dept Biol Darmstadt Germany

Background: Selective pressure in molecular evolution leads to uneven distributions of amino acids and nucleotides. In fact one observes correlations among such constituents due to a large number of biophysical mechanisms (folding properties, electrostatics, ...). To quantify these correlations the mutual information -after proper normalization - has proven most effective. The challenge is to navigate the large amount of data, which in a study for a typical protein cannot simply be plotted. Results: To visually analyze mutual information we developed a matrix visualization tool that allows different views on the mutual information matrix: filtering, sorting, and weighting are among them. The user can interactively navigate a huge matrix in real-time and search e. g., for patterns and unusual high or low values. A computation of the mutual information matrix for a sequence alignment in FASTA-format is possible. The respective stand-alone program computes in addition proper normalizations for a null model of neutral evolution and maps the mutual information to Z-scores with respect to the null model. Conclusions: The new tool allows to compute and visually analyze sequence data for possible co-evolutionary signals. The tool has already been successfully employed in evolutionary studies on HIV1 protease and acetylcholinesterase. The functionality of the tool was defined by users using the tool in real-world research. The software can also be used for visual analysis of other matrix-like data, such as information obtained by DNA microarray experiments. The package is platform-independently implemented in Java and free for academic use under a GPL license.

关键词： Mutual Information Null Model sorting algorithm Sequence Entropy Peripheral Anionic Site

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An optimized sweeping solution method for the three-dimensional Sn equations of neutron transport on hexahedral meshes

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JOURNAL OF COMPUTATIONAL PHYSICS 2022年 455卷

作者： Gao, Yanni Hang, Xudeng Yuan, Guangwei Inst Appl Phys & Computat Math Fenghaodong Rd Beijing 100094 Peoples R China

We propose a new sweeping solution method for the three-dimensional (3D) discrete ordinates (Sn) equations of neutron transport on general hexahedral meshes, with particular focus on handling the surface integrals and sweeping deadlocks. The main contributions of this paper include three aspects. Firstly, the surface integrals on the non-planar cell faces are well discretized by virtue of the effective face method, which successfully addresses the reentrance problem without the decomposition of the cell faces. Secondly, based on the effective face method, we devise a new sorting algorithm which works for any hexahedral meshes. In this algorithm, the identification for dependent cycles is avoided and the physical characteristics of transport problems are taken into account in the choice of lagged cells used to decouple the sweeping deadlocks. Combining the above advancements, a splitting sweeping iterative method is proposed for the solution of the Sn equations on general hexahedral meshes. Finally, we prove theoretically that this sweeping iterative method always converges, and the decoupling method affects little on iterative convergence rate. Numerical experiments are presented to demonstrate the effectiveness of the proposed methods on both cubic and spherical domains. The ideas presented in this paper are also applicable to the polyhedral meshes or two-dimensional polygonal meshes. (C) 2022 Elsevier Inc. All rights reserved.

关键词： Distorted hexahedral meshes Neutron transport Sn equations sorting algorithm Reentrance problem Finite volume method Effective face method

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Experimental, algorithmic, and theoretical analyses for selecting an optimal laboratory method to evaluate working fluid damage in coal bed methane reservoirs

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FUEL 2020年 282卷 118513-118513页

作者： Okere, Chinedu J. Su, Guandong Zheng, Lihui Cai, Yuzhe Li, Zhaochuan Liu, Hao China Univ Petr Coll Petr Engn Beijing 102249 Peoples R China Beijing LihuiLab Energy Technol Co Ltd Beijing 102200 Peoples R China Penn State Univ John & Willie Leone Family Dept Energy & Mineral University Pk PA 16802 USA

The existing laboratory methods have limitations that make it very difficult to select the most suitable method for obtaining precise permeability measurements for evaluating reservoir formation damage. To date, there has been no research critically addressing this issue. In this study, the permeabilities of core or cuttings samples taken from No.15 Reservoir in the Qinshui Basin before and after damage caused by drilling and fracturing fluids were randomly measured using six general laboratory methods. Their average permeability values, as well as the extent of absolute and relative formation damage, were calculated and analyzed using a simple ranking method, statistical screening algorithm, and theoretical method. The results show that, first, there is a regular distribution of absolute reservoir damage measured by the laboratory methods;and second, the application priority of permeability measurement methods for formation damage evaluation in coal bed methane reservoirs is as follows: cuttings pulse decay method > constant flow rate method > nuclear magnetic resonance method > plunger pulse decay method = pressure oscillation method = constant pressure method. The cuttings pulse decay method is therefore recommended for measuring core matrix damage, while the constant flow rate method is suggested for measuring overall damage to core samples.

关键词： Coal bed methane Formation damage Permeability measurement Working fluids sorting algorithm Statistical screening algorithm

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Suffix Array Construction in External Memory Using D-Critical Substrings

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ACM TRANSACTIONS ON INFORMATION SYSTEMS 2014年第1期32卷 1-1页

作者： Nong, Ge Chan, Wai Hong Zhang, Sen Guan, Xiao Feng Sun Yat Sen Univ Dept Comp Sci Guangzhou 510275 Guangdong Peoples R China Hong Kong Inst Educ Dept Math & Informat Technol Kowloon Hong Kong Peoples R China SUNY Coll Oneonta Dept Math Comp Sci & Stat Oneonta NY 13820 USA

We present a new suffix array construction algorithm that aims to build, in external memory, the suffix array for an input string of length n measured in the magnitude of tens of Giga characters over a constant or integer alphabet. The core of this algorithm is adapted from the framework of the original internal memory SA-DS algorithm that samples fixed-size d-critical substrings. This new external-memory algorithm, called EM-SA-DS, uses novel cache data structures to construct a suffix array in a sequential scanning manner with good data spatial locality: data is read from or written to disk sequentially. On the assumed external-memory model with RAM capacity Omega ((nB)(0.5)), disk capacity O(n), and size of each I/O block B, all measured in log n-bit words, the I/O complexity of EM-SA-DS is O(n/B). This work provides a general cache-based solution that could be further exploited to develop external-memory solutions for other suffix-array-related problems, for example, computing the longest-common-prefix array, using a modern personal computer with a typical memory configuration of 4GB RAM and a single disk.

关键词： Suffix array sorting algorithm external memory algorithms Performance

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EBOC: Ensemble-Based Ordinal Classification in Transportation

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JOURNAL OF ADVANCED TRANSPORTATION 2019年第Pt.2期000卷 1-17页

作者： Yildirim, Pelin Birant, Ulas K. Birant, Derya Manisa Celal Bayar Univ Dept Software Engn TR-45400 Manisa Turkey Dokuz Eylul Univ Dept Comp Engn TR-35390 Izmir Turkey

Learning the latent patterns of historical data in an efficient way to model the behaviour of a system is a major need for making right decisions. For this purpose, machine learning solution has already begun its promising marks in transportation as well as in many areas such as marketing, finance, education, and health. However, many classification algorithms in the literature assume that the target attribute values in the datasets are unordered, so they lose inherent order between the class values. To overcome the problem, this study proposes a novel ensemble-based ordinal classification (EBOC) approach which suggests bagging and boosting (AdaBoost algorithm) methods as a solution for ordinal classification problem in transportation sector. This article also compares the proposed EBOC approach with ordinal class classifier and traditional tree-based classification algorithms (i.e., C4.5 decision tree, RandomTree, and REPTree) in terms of accuracy. The results indicate that the proposed EBOC approach achieves better classification performance than the conventional solutions.

关键词： target attributes transport system parameter bagging Ordinal Dataset historical data optical coherence tomography angiography device sorting algorithm

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Feature Reduction Based on Genetic algorithm and Hybrid Model for Opinion Mining

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SCIENTIFIC PROGRAMMING 2015年第1期2015卷 15-15页

作者： Kalaivani, P. Shunmuganathan, K. L. Sathyabama Univ St Josephs Coll Engn Dept Comp Sci & Engn Madras 600119 Tamil Nadu India RMK Engn Coll Dept Comp Sci & Engn Madras Tamil Nadu India

With the rapid growth of websites and web form the number of product reviews is available on the sites. An opinion mining system is needed to help the people to evaluate emotions, opinions, attitude, and behavior of others, which is used to make decisions based on the user preference. In this paper, we proposed an optimized feature reduction that incorporates an ensemble method of machine learning approaches that uses information gain and genetic algorithm as feature reduction techniques. We conducted comparative study experiments on multidomain review dataset and movie review dataset in opinion mining. The effectiveness of single classifiers Naive Bayes, logistic regression, support vector machine, and ensemble technique for opinion mining are compared on five datasets. The proposed hybrid method is evaluated and experimental results using information gain and genetic algorithm with ensemble technique perform better in terms of various measures for multidomain review and movie reviews. Classification algorithms are evaluated using McNemar's test to compare the level of significance of the classifiers.

关键词： Hybrid modes sorting algorithm Support Vector Network Dataset opinion Cinema User preferences mining information gain Genetic algorithm (GA) Classifiers

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An Artificial Immune System for Clustering Amino Acids in the Context of Protein Function Classification

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Journal of mathematical modelling and alogrithms 2009年第1/4期8卷 103-123页

作者： A. Secker M. N. Davies A. A. Freitas J. Timmis E. Clark D. R. Flower Computing Laboratory and Centre for BioMedical Informatics University of Kent Canterbury CT2 7NF UK The Jenner Institute University of Oxford Compton Newbury Berkshire RG20 7NN UK Departments of Computer Science and Electronics University of York York Y010 5DD UK

This paper addresses the classification task of data mining (a form of supervised learning) in the context of an important bioinformatics problem, namelythe prediction of protein functions. This problem is cast as a hierarchical classificationproblem. The protein functions to be predicted correspond to classes that are ar-ranged in a hierarchical structure (this takes the form of a class tree). The main con-tribution of this paper is to propose a new Artificial Immune System that creates anew representation for proteins, in order to maximize the predictive accuracy of ahierarchical classification algorithm applied to the corresponding protein functionprediction problem.

关键词： Artificial immune systems Optimisation Bioinformatics Classification Clustering protein structure function sorting algorithm artificial immune systems Hierarchical Semi-supervised Learning Bioinformatics Cluster Analysis

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Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles

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BMC BIOINFORMATICS 2008年第1期9卷 155-155页

作者： Tien, Yin-Jing Lee, Yun-Shien Wu, Han-Ming Chen, Chun-Houh Acad Sinica Inst Stat Sci Taipei 11529 Taiwan Natl Cent Univ Inst Stat Tao Yuan 32001 Taiwan Chang Gung Mem Hosp Genom Med Res Core Lab Tao Yuan 33305 Taiwan Ming Chuan Univ Dept Biotechnol Tao Yuan 33348 Taiwan Tamkang Univ Dept Math Tamsui 25137 Taiwan

Background: The hierarchical clustering tree (HCT) with a dendrogram [1] and the singular value decomposition (SVD) with a dimension-reduced representative map [2] are popular methods for two-way sorting the gene-by-array matrix map employed in gene expression profiling. While HCT dendrograms tend to optimize local coherent clustering patterns, SVD leading eigenvectors usually identify better global grouping and transitional structures. Results: This study proposes a flipping mechanism for a conventional agglomerative HCT using a rank-two ellipse (R2E, an improved SVD algorithm for sorting purpose) seriation by Chen [3] as an external reference. While HCTs always produce permutations with good local behaviour, the rank-two ellipse seriation gives the best global grouping patterns and smooth transitional trends. The resulting algorithm automatically integrates the desirable properties of each method so that users have access to a clustering and visualization environment for gene expression profiles that preserves coherent local clusters and identifies global grouping trends. Conclusion: We demonstrate, through four examples, that the proposed method not only possesses better numerical and statistical properties, it also provides more meaningful biomedical insights than other sorting algorithms. We suggest that sorted proximity matrices for genes and arrays, in addition to the gene-by-array expression matrix, can greatly aid in the search for comprehensive understanding of gene expression structures. Software for the proposed methods can be obtained at http://***/Software/GAP.

关键词： Singular Value Decomposition Severe Acute Respiratory Syndrome Severe Acute Respiratory Syndrome sorting algorithm Severe Acute Respiratory Syndrome Patient

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Granular Computing Classification algorithms Based on Distance Measures between Granules from the View of Set

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2014年第unknown期2014卷 656790-656798页

作者： Liu, Hongbing Liu, Chunhua Wu, Chang-an Xinyang Normal Univ Sch Comp & Informat Technol Xinyang 464000 Peoples R China

Granular computing classification algorithms are proposed based on distance measures between two granules from the view of set. Firstly, granules are represented as the forms of hyperdiamond, hypersphere, hypercube, and hyperbox. Secondly, the distance measure between two granules is defined from the view of set, and the union operator between two granules is formed to obtain the granule set including the granules with different granularity. Thirdly the threshold of granularity determines the union between two granules and is used to form the granular computing classification algorithms based on distance measures (DGrC). The benchmark datasets in UCI Machine Learning Repository are used to verify the performance of DGrC, and experimental results show that DGrC improved the testing accuracies.

关键词： sorting algorithm View distance measurement

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