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International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Zhang, Zhichao Zhang, Yuan Jin, Wei Kos, Anton Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan Shandong Peoples R China Shandong Acad Chinese Med Jinan Shandong Peoples R China Univ Ljubljana Fac Elect Engn Ljubljana Slovenia

ISBN: (纸本)9781509059522

In the traditional Chinese medicine (TCM) wrist pulse diagnosis plays a major role in detecting the health status of an individual. It depends strongly on the doctors' long-term experience as well as on their different inferences. Being subjective and based on long-term experience, pulse detection methods are difficult to standardize. Kim pulse diagnosis (KPD), established by Wei Jin, is an efficient method validated by both traditional Chinese medicine and in recent years also by western medicine. The key step to automatic implementation of KPD, using signal processing and analysis, is the developed KPD signal acquisition device. However, the raw wrist pulse signal acquired from KPD device includes a significant amount of noise. This paper proposes several preprocessing algorithms for pulse diagnosis, that includes wavelet transform and Gaussian filter to remove noise and the iterative sliding window (ISW) algorithm to remove the baseline wander and split the continuous signal into single periods. Experimental results show, that the algorithm for baseline wander removal is efficient and that the segmented signal matches the signal described in KPD.

关键词： preprocessing algorithm Kim pulse diagnosis (KPD) de-noising iterative sliding window (ISW)

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preprocessing method for robust topography reconstruction of surfaces of metal additive manufactured parts based on focus variation microscopy

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TM-TECHNISCHES MESSEN 2024年第5期91卷 233-242页

作者： Xu, Xin Pahl, Tobias Serbes, Hueseyin Krooss, Philipp Niendorf, Thomas Lehmann, Peter Univ Kassel Fachgebiet Messtechn FB16 Elektrotech Informat Kassel Germany Univ Kassel Inst Werkstofftechn Fachgebiet Met Werkstoffe FB15 Maschinenbau Kassel Germany

When using an areal measuring optical instrument to measure rough surfaces, especially surfaces generated by metal additive manufacturing (e.g. laser and electron beam powder bed fusion), topographical artifacts such as spikes on a reconstructed surface are nearly unavoidable. These artifacts may affect the determination of surface roughness parameters and lead to erroneous surface features. This paper proposes a new preprocessing method to eliminate most artifacts before extracting surface heights of rough surfaces measured by focus variation microscopy. In this method, the axial region where a surface height value is located with the highest probability is estimated, based on datasets of planes parallel to the axial scanning direction. Results regarding height measurements with and without the preprocessing method are compared by measuring a Rubert Microsurf 329 comparator test panel for reference and workpieces produced by metal additive manufacturing.

关键词： focus variation microscopy outlier elimination preprocessing algorithm 3D profilometry roughness measurement metal additive manufacturing

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Computational enhancements of continuous production scheduling MILPs using tightening constraints

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COMPUTERS & CHEMICAL ENGINEERING 2024年 184卷

作者： Samadi, Amin Maravelias, Christos T. Princeton Univ Dept Chem & Biol Engn Princeton NJ 08540 USA Princeton Univ Andlinger Ctr Energy & Environm Princeton NJ 08540 USA

In this paper, we propose employing tightening constraints, based on known system information, into discretetime continuous production scheduling models to enhance their computational performance. We first establish a model with transient operations such as startups, shutdowns, and direct transitions. We propose a demand propagation algorithm (DPA), implemented as a preprocessing step, that utilizes demand information and other known system parameters to calculate parameters which are later used in a series of constraints to tighten the feasible space of the linear programming (LP) relaxation of the mixed-integer linear programming (MILP) problem. This reduces the branching required to close the optimality gap which consequently decreases solution times. We present computational results that show our proposed method can lead to over an order of magnitude reduction in solution times.

关键词： Discrete -time Continuous processes preprocessing algorithm Bound propagation

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Item Response Theory in Sample Reweighting to Build Fairer Classifiers 1

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10th Annual International Conference on Information Management and Big Data (SIMBig)

作者： Minatel, Diego dos Santos, Nicolas Roque da Silva, Vinicius Ferreira Curi, Mariana Lopes, Alneu de Andrade Univ Sao Paulo Inst Math & Comp Sci Sao Carlos Brazil

ISBN: (数字)9783031636165

ISBN: (纸本)9783031636158;9783031636165

Currently, one of the biggest challenges of Machine Learning (ML) is to develop fairer models that do not propagate prejudices, stereotypes, social inequalities, and other types of discrimination in their decisions. Before ML faced the problem of unfair decision-making, the field of educational testing developed several mathematical tools to decrease bias in selections made by tests. Thus, the Item Response Theory is one of these main tools, and its great power of evaluation helps make fairer selections. Therefore, in this paper, we use the concepts of Item Response Theory to propose a novel sample reweighting method named IRT-SR. The IRT-SR method aims to assign weights to the most important instances to minimize discriminatory effects in binary classification tasks. According to our results, IRT-SR guides classification algorithms to fit fairer models, improving the main group fairness notions such as demographic parity, equal opportunity, and equalized odds without significant performance loss.

关键词： Data Bias Fairness IRT Machine Learning preprocessing algorithm

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Enhancing Fairness in Classification Tasks with Multiple Variables: A Data- and Model-Agnostic Approach 1

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3rd International Workshop on algorithmic Bias in Search and Recommendation (BIAS) held as part of the 43rd European Conference on Information Retrieval (ECIR)

作者： d'Aloisio, Giordano Stilo, Giovanni Di Marco, Antinisca D'Angelo, Andrea Univ Aquila Dept Engn & Informat Sci & Math Laquila Italy

ISBN: (数字)9783031093166

ISBN: (纸本)9783031093166;9783031093159

Nowadays assuring that search and recommendation systems are fair and do not apply discrimination among any kind of population has become of paramount importance. Those systems typically rely on machine learning algorithms that solve the classification task. Although the problem of fairness has been widely addressed in binary classification, unfortunately, the fairness of multi-class classification problem needs to be further investigated lacking well-established solutions. For the aforementioned reasons, in this paper, we present the Debiaser for Multiple Variables, a novel approach able to enhance fairness in both binary and multi-class classification problems. The proposed method is compared, under several conditions, with the well-established baseline. We evaluate our method on a heterogeneous data set and prove how it overcomes the established algorithms in the multi-classification setting, while maintaining good performances in binary classification. Finally, we present some limitations and future improvements.

关键词： Machine learning Bias and fairness Multi-class classification preprocessing algorithm

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A pre-processing technique to decrease inspection time in glass tube production lines

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IET IMAGE PROCESSING 2021年第10期15卷 2179-2191页

作者： De Vitis, Gabriele Antonio Foglia, Pierfrancesco Prete, Cosimo Antonio Univ Pisa Dipartimento Ingn Informaz Via Diotisalvi 2 I-56100 Pisa Italy

In case of glass tube for pharmaceutical applications, high-quality defect detection is made via inspection systems based on computer vision. The processing must guarantee real-time inspection and meet increasing rate and quality requirements. Defect detection in glass tubes is complicated by aspects that hamper the efficiency of state-of-the-art techniques. This paper presents a pre-processing algorithm which excludes portions of the image where defects are surely absent. The approach decreases the time for defect detection and classification phases (any detection algorithm can be applied), as they are applied only in high-probability candidate sub-image. We derive a methodology to get robust values of algorithm's parameters during production. The algorithm relies on detrended standard deviation and double threshold hysteresis, which solve issues related to the misalignment between illuminator and acquisition camera, and enable a robust detection despite rotation, vibration, and irregularities of tubes. We consider Canny, MAGDDA, and Niblack algorithms. The solution keeps the detection quality of such algorithms and reaches a 4.69x throughput gain. It represents a methodology to obtain defect detection in time-constrained environments through a software-only approach, and can be exploited in parallel/accelerated solutions and in contexts where a linear camera is utilized on both flat and uneven surfaces.

关键词： robust detection detection algorithm time constrained environments probability quality requirements high probability candidate subimage automatic optical inspection preprocessing technique image sensors Niblack algorithms glass products inspection systems production engineering computing high quality defect detection preprocessing algorithm computer vision inspection time glass tube production lines image coding detection quality quality control real-time inspection

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Debiaser for Multiple Variables to enhance fairness in classification tasks

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INFORMATION PROCESSING & MANAGEMENT 2023年第2期60卷

作者： d'Aloisio, Giordano D'Angelo, Andrea Di Marco, Antinisca Stilo, Giovanni Univ Laquila Dept Informat Engn & Informat Sci & Math Laquila Italy

Nowadays assuring that search and recommendation systems are fair and do not apply dis-crimination among any kind of population has become of paramount importance. This is also highlighted by some of the sustainable development goals proposed by the United Nations. Those systems typically rely on machine learning algorithms that solve the classification task. Although the problem of fairness has been widely addressed in binary classification, unfortunately, the fairness of multi-class classification problem needs to be further investigated lacking well-established solutions. For the aforementioned reasons, in this paper, we present the Debiaser for Multiple Variables (DEMV), an approach able to mitigate unbalanced groups bias (i.e., bias caused by an unequal distribution of instances in the population) in both binary and multi-class classification problems with multiple sensitive variables. The proposed method is compared, under several conditions, with a set of well-established baselines using different categories of classifiers. At first we conduct a specific study to understand which is the best generation strategies and their impact on DEMV's ability to improve fairness. Then, we evaluate our method on a heterogeneous set of datasets and we show how it overcomes the established algorithms of the literature in the multi-class classification setting and in the binary classification setting when more than two sensitive variables are involved. Finally, based on the conducted experiments, we discuss strengths and weaknesses of our method and of the other baselines.

关键词： Machine learning Bias and Fairness Multi-class classification preprocessing algorithm

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Preconditioned structure-preserving Jacobi algorithms for computing the SVD of a quaternion matrix

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PHYSICA SCRIPTA 2025年第6期100卷

作者： Ma, Ru-Ru Ma, Zhicai Suzhou Univ Sci & Technol Sch Math Sci Suzhou 215009 Peoples R China Lanzhou Univ Technol Sch Sci Lanzhou 730050 Peoples R China

The singular value decomposition (SVD) of quaternion matrices is a cornerstone of color image compression. However, existing computational methods for quaternion SVD remain inefficient, often lacking the speed and scalability required for real-world applications. To address this challenge, we introduce two novel preconditioned structure-preserving Jacobi algorithms designed to compute the SVD of quaternion matrices efficiently. Central to our approach is the use of quaternion rank-revealing QR (QRRQR) factorization as a preprocessing step, which serves as a robust dimension reduction strategy for both one-sided and two-sided Jacobi methods. Notably, we rigorously analyze the off-diagonal structure of the upper triangular matrix generated by QRRQR, a key innovation that enables significant acceleration of the two-sided Jacobi algorithm. By exploiting this structural property, we effectively reduce computational overhead while preserving numerical stability. Extensive numerical experiments validate the superior efficiency, accuracy, and scalability of our algorithms compared to some conventional approaches, demonstrating their potential for practical deployment in large-scale color image processing tasks.

关键词： quaternion SVD structure-preserving Jacobi method rank revealing QR preprocessing algorithm

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A Data preprocessing algorithm Based on Rough Set for SVM Classifier

A Data Preprocessing Algorithm Based on Rough Set for SVM Cl...

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IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

作者： Huang, Zhiqi Guo, Jun E China Normal Univ Ctr Comp Shanghai 200062 Peoples R China

ISBN: (纸本)9781479915064;9781479915088

Support vector machine (SVM) is now widely applied in various areas for its excellent performances. For a data set, usually we use normalization method to deal with the features. However, in many cases, the value of each feature is different. Thus, SVM can't work very well. In this paper, we propose a preprocessing algorithm based on rough set (RS) theory to give different weights on each feature, which can well reflect the value of each feature. The experimental results on real data show that the proposed approach can achieve a fairly improvement of classification accuracy.

关键词： SVM preprocessing algorithm rough set feature

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An image preprocessing method for kidney stone segmentation in CT scan images

An image preprocessing method for kidney stone segmentation ...

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Joint International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM) / 11th AUN/SEED-Net Regional Conference on Computer and Information Engineering (RCCIE)

作者： Thein, Nilar Nugroho, Hanung Adi Adji, Teguh Bharata Hamamoto, Kazuhiko Univ Gadjah Mada Dept Elect Engn & Informat Technol Yogyakarta Indonesia Tokai Univ Dept Informat Media Technol Tokyo Japan

ISBN: (纸本)9781538675090

In 3D medical imaging, anatomical and other structures such as kidney stones are often identified and extracted with the aid of diagnosis and assessment of disease. Automatic kidney stone segmentation from abdominal CT images is challenging on the aspects of segmentation accuracy due to its variety of size, shape and location. The performance of 3D organ segmentation algorithm is also degraded by the image complexity containing multiple organs and because of their huge size. The current need is a preprocessing algorithm to assist the segmentation process. The objective of the present study was to develop reader independent preprocessing algorithm for kidney stone detection and segmentation in CT images. Three thresholding algorithms based on intensity, size and location are applied for unwanted regions removing such as soft-organ removing, bony skeleton removing and bed-mat removing. The digitized transverse abdomen CT scans images from 30 patients with kidney stone cases were included in statistical analysis and validation. As validation data for analysis, the estimation of coordinate points in stone region was measured independently by expert radiology. Experimental results prove that the proposed preprocessing algorithm has 95.24% sensitivity as the evaluation parameter. So, it can reduce the noise and unwanted regions in each procedure with good detection.

关键词： preprocessing algorithm kidney stone detection unwanted regions removing

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