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Comparative analyses of multi-criteria methods in supplier selection problem

Procedia computer science 2022年 207卷 4593-4602页

作者： Bartosz Paradowski Zdzisław Szyjewski Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Żołnierska 49 71-210 Szczecin Poland Institute of Management University of Szczecin ul. Cukrowa 8 71-004 Szczecin Poland

The ever-growing interest in multi-criteria decision-making methods requires research that would compare them with each other as the decision-makers would need help choosing the appropriate method. It is important to highlight the differences between them, because using different methods may result in discrepancies in the obtained rankings. Additionally, it is worthwhile for the research to take into account the problems whose solution is extremely valuable. To this end, we present a comparison of five multi-criteria decision-making methods: TOPSIS, VIKOR, COMET, SPOTIS, and MARCOS in the supplier selection problem. The conducted research showed significant differences in the evaluation of the preferences of the alternatives and some discrepancies in the obtained rankings.

关键词： MCDM MCDA supplier selection comparison rank similarity

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Practical Study of Selected Multi-Criteria Methods Comparison

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Procedia computer science 2022年 207卷 4565-4573页

作者： Jakub Więckowski Zdzisław Szyjewski Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Żołnierska 49 71-210 Szczecin Poland Institute of Management University of Szczecin ul. Cukrowa 8 71-004 Szczecin Poland

Multi-Criteria Decision Analysis (MCDA) methods enable a comprehensive analysis of decision options to identify the most preferred values. However, the abundance of these techniques, using different transformations in the calculations, can cause a divergence of results. However, the resulting rankings should be as consistent and reliable as possible. In this paper, we used the TOPSIS, COPRAS, SPOTIS, and COMET methods to examine the consistency of their results in the practical problem of laptop evaluation. The rankings were then compared with the reference ranking. The research showed that among the methods listed, the COPRAS method's results differed the most from the others. In turn, this technique ensured a ranking in line with the reference ranking. Based on the results, it can be established that the methods used are characterized by high consistency of results.

关键词： MCDA TOPSIS COPRAS SPOTIS COMET Comparison Laptops assessment

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3d-Swim: 3d Vision Based Seam Width Measurement for Industrial Composite Fiber Layup In-Situ Inspection

SSRN

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SSRN 2022年

作者： Zhang, Yinlong Yuan, Libiao Liang, Wei Xia, Xiaofang Pang, Zhibo State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China Shenyang Pharmaceutical University China School of Computer Science and Technology Xidian University Xian710071 China Department of Automation Technology ABB Corporate Research Sweden Vasteras72178 Sweden Stockholm11428 Sweden

Composite fiber layup in-situ inspection plays an important role in the advanced manufacturing. An accurate seam width measurement is an indispensable procedure during the layup inspection. However, the traditional seam measurements may fail in presence of surface curvature variance, curved edges between sheets, to name a few. This paper intentionally designs a novel 3D vision based Seam Width Measurement for Industrial Composite Fiber Layup In-situ inspection (called 3D-SWiM). It aims to address three major challenges in measuring the seam width between the composite fiber sheets, namely, seam region over-segmentation, failures on region of interests (ROI) seam detection, seam width measurement inconsistency. Firstly, the region growing with semantic refinement is designed for 3D point cloud clustering, to overcome the over-segmentation on seam regions. Afterwards, the gridding model with edge discrimination is applied to extract the 3D potential candidates in the edge areas. Eventually, we use the cubic B-spline fitting model to describe the seam region curves and the improved particle swarm optimization model to estimate the minimum and maximum distance between the seam edges. Our method has been evaluated extensively on the developed composite fiber layup platform. The experimental results prove the competitive accuracy against the state-of-the-art methods in seam width measurement. © 2022, The Authors. All rights reserved.

关键词： Inspection

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Efficient factored gradient descent algorithm for quantum state tomography

arXiv

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arXiv 2022年

作者： Wang, Yong Liu, Lijun Cheng, Shuming Li, Li Chen, Jie College of Electronics and Information Engineering Tongji University Shanghai201804 China Department of Mathematics and Computer Science Shanxi Normal University Taiyuan030006 China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201203 China Institute for Advanced Study Tongji University Shanghai200092 China

Reconstructing the state of quantum many-body systems is of fundamental importance in quantum information tasks, but extremely challenging due to the curse of dimensionality. In this work, we present an efficient quantum tomography protocol that combines the state-factored with eigenvalue mapping to address the rank-deficient issue and incorporates a momentum-accelerated gradient descent algorithm to speed up the optimization process. We implement extensive numerical experiments to demonstrate that our factored gradient descent algorithm efficiently mitigates the rank-deficient problem and admits orders of magnitude better tomography accuracy and faster convergence. We also find that our method can accomplish the full-state tomography of random 11-qubit mixed states within one minute. © 2022, CC BY.

关键词： Eigenvalues and eigenfunctions

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Unsupervised Multilayer Fuzzy Neural Networks for Image Clustering

SSRN

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SSRN 2022年

作者： Wang, Yifan Ishibuchi, Hisao Er, Meng Joo Zhu, Jihua School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation China College of Marine Electrical Engineering Dalian Maritime University Dalian116026 China Institute of Artificial Intelligence Marine Robotics China

Nowadays, labeling a huge number of images is still a very challenging task. To tackle the problem of unlabeled data, unsupervised learning has been proposed. Among many unsupervised learning algorithms, K-means is the most popular algorithm. However, in a low-dimensional space, fuzzy c-means, which is more robust and less sensitive to initialization, has several advantages over K-means clustering. On the other hand, stacked convolutional pooling structures and manifold representation play pivotal roles in image clustering. In this paper, we proposed an unsupervised multilayer fuzzy neural network for image clustering, which unifies fuzzy systems, multilayer convolutional structures and manifold representation. The main contributions of this work are: First, we extend fuzzy systems to unsupervised tasks by introducing the manifold representation. Next, we proposed the idea of using only a small number of attributes with the largest variances to compute firing strengths. This is to prevent the firing strengths from falling to zero in clustering high-dimensional data. Then, randomly generated convolutional weights are used to extract features, which is a good choice for data without labels. It is demonstrated on a wide range of image datasets that the proposed approach is superior to existing fuzzy and non-fuzzy clustering algorithms. © 2022, The Authors. All rights reserved.

关键词： Fuzzy neural networks

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Role of substrate clamping on anisotropy and domain structure in the canted antiferromagnet α−Fe2O3

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Physical Review B 2022年第22期106卷 224419-224419页

作者： Angela Wittmann Olena Gomonay Kai Litzius Allison Kaczmarek Alexander E. Kossak Daniel Wolf Axel Lubk Tyler N. Johnson Elizaveta A. Tremsina Alexandra Churikova Felix Büttner Sebastian Wintz Mohamad-Assaad Mawass Markus Weigand Florian Kronast Larry Scipioni Adam Shepard Ty Newhouse-Illige James A. Greer Gisela Schütz Norman O. Birge Geoffrey S. D. Beach Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Institute of Physics Johannes Gutenberg-University Mainz 55128 Mainz Germany Max Planck Institute for Intelligent Systems 70569 Stuttgart Germany Leibniz Institute for Solid State and Materials Research IFW Dresden 01069 Dresden Germany Department of Chemical Engineering and Materials Science Michigan State University East Lansing Michigan 48824 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Helmholtz-Zentrum Berlin für Materialien und Energie GmbH 14109 Berlin Germany PVD Products Wilmington Massachusetts 01887 USA Department of Physics and Astronomy Michigan State University East Lansing Michigan 48824 USA

Antiferromagnets have recently been propelled to the forefront of spintronics by their high potential for revolutionizing memory technologies. For this, understanding the formation and driving mechanisms of the domain structure is paramount. In this work, we investigate the domain structure in a thin-film canted antiferromagnet α−Fe2O3. We find that the internal destressing fields driving the formation of domains do not follow the crystal symmetry of α−Fe2O3, but fluctuate due to substrate clamping. This leads to an overall isotropic distribution of the Néel order with locally varying effective anisotropy in antiferromagnetic thin films. Furthermore, we show that the weak ferromagnetic nature of α−Fe2O3 leads to a qualitatively different dependence on the magnetic field compared to collinear antiferromagnets such as NiO. The insights gained from our work serve as a foundation for further studies of electrical and optical manipulation of the domain structure of antiferromagnetic thin films.

关键词： Antiferromagnetism Magnetic domains Magnetic texture Spin Hall magnetoresistance

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Community Detection for Access-Control Decisions: Analysing the Role of Homophily and Information Diffusion in Online Social Networks

arXiv

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arXiv 2021年

作者： Díaz Ferreyra, Nicolás E. Hecking, Tobias Aïmeur, Esma Heisel, Maritta Hoppe, H. Ulrich University of Duisburg-Essen Department of Computer Science and Applied Cognitive Science Duisburg47057 Germany Institute for Software Technology Intelligent and Distributed Systems German Aerospace Center Köln51147 Germany Department of Computer Science and Operations Research University of Montréal Canada

Access-Control Lists (ACLs) (a.k.a. "friend lists") are one of the most important privacy features of Online Social Networks (OSNs) as they allow users to restrict the audience of their publications. Nevertheless, creating and maintaining custom ACLs can introduce a high cognitive burden on average OSNs users since it normally requires assessing the trustworthiness of a large number of contacts. In principle, community detection algorithms can be leveraged to support the generation of ACLs by mapping a set of examples (i.e. contacts labelled as "untrusted") to the emerging communities inside the user’s ego-network. However, unlike users’ access-control preferences, traditional community-detection algorithms do not take the homophily characteristics of such communities into account (i.e. attributes shared among members). Consequently, this strategy may lead to inaccurate ACL configurations and privacy breaches under certain homophily scenarios. This work investigates the use of community-detection algorithms for the automatic generation of ACLs in OSNs. Particularly, it analyses the performance of the aforementioned approach under different homophily conditions through a simulation model. Furthermore, since private information may reach the scope of untrusted recipients through the re-sharing affordances of OSNs, information diffusion processes are also modelled and taken explicitly into account. Altogether, the removal of gatekeeper nodes is further explored as a strategy to counteract unwanted data dissemination. Copyright © 2021, The Authors. All rights reserved.

关键词： Access control

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Multi-agent systems with memories under DoS attacks

Multi-agent systems with memories under DoS attacks

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International Conference on Control, Automation, robotics and Vision (ICARCV)

作者： Ricardo Almeida Ewa Girejko Luís Machado Agnieszka B. Malinowska Natália Martins Department of Mathematics Center for Research and Development in Mathematics and Applications University of Aveiro Portugal Faculty of Computer Science Bialystok University of Technology Białystok Poland Institute of Systems and Robotics - University of Coimbra Coimbra Portugal Department of Mathematics University of Trás-os-Montes e Alto Douro (UTAD) Vila Real Portugal

ISBN: (纸本)9781665476881

The paper is devoted to multi-agent systems with memories under denial-of-service (DoS) attacks. The memory is represented by the fractional derivative that appears in the dynamics of the considered systems. The problem that we aim to address is, under which conditions the system continues to be stable or is in consensus mode, despite DoS attacks. Solutions to these problems are proposed in the presented theorems. We strengthen the theoretical considerations with numerical simulations.

关键词： Linear systems Sufficient conditions Automation Denial-of-service attack Numerical simulation Stability analysis Robots

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Development of a VS ultrasound diagnostic system with image evaluation functions

Development of a VS ultrasound diagnostic system with image ...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Jiayi Zhou Norihiro Koizumi Yu Nishiyama Ryosuke Tsumura Hiroyuki Tsukihara Naoki Matsumoto Department of Mechanical and Intelligent Systems The University of Electro-Communications Tokyo Japan Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Health and Medical Research Institute National Institute of Advanced Industrial Science and Technology Tokyo Japan Graduate School of Engineering The University of Tokyo Tokyo Japan Department of Gastroenterology and Hepatology Nihon University Tokyo Japan

ISBN: (纸本)9781665492331

An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross-section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Two different image processing methods (BiSeNet V2) were used to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50) in developed system.

关键词： Deep learning Visualization Ultrasonic imaging Image processing Medical services Probes Servomotors

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Synthesizing skeletal motion and physiological signals as a function of a virtual human’s actions and emotions

Synthesizing skeletal motion and physiological signals as a ...

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2021 SIAM International Conference on Data Mining, SDM 2021

作者： Banerjee, Bonny Kapourchali, Masoumeh Heidari Baruah, Murchana Deb, Mousumi Sakauye, Kenneth Olufsen, Mette Institute for Intelligent Systems Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Texas A&M AgriLife Research and Extension Center TempleTX76502 United States Department of Psychiatry University of Tennessee Health Science Center MemphisTN38163 United States Department of Mathematics North Carolina State University RaleighNC27695 United States

ISBN: (纸本)9781611976700

Round-the-clock monitoring of human behavior and emotions is required in many healthcare applications which is very expensive but can be automated using machine learning (ML) and sensor technologies. Unfortunately, the lack of infrastructure for collection and sharing of such data is a bottleneck for ML research applied to healthcare. Our goal is to circumvent this bottleneck by simulating a human body in virtual environment. This will allow generation of potentially infinite amounts of shareable data from an individual as a function of his actions, interactions and emotions in a care facility or at home, with no risk of confidentiality breach or privacy invasion. In this paper, we develop for the first time a system consisting of computational models for synchronously synthesizing skeletal motion, electrocardiogram, blood pressure, respiration, and skin conductance signals as a function of an open-ended set of actions and emotions. Our experimental evaluations, involving user studies, benchmark datasets and comparison to findings in the literature, show that our models can generate skeletal motion and physiological signals with high fidelity. The proposed framework is modular and allows the flexibility to experiment with different models. In addition to facilitating ML research for round-the-clock monitoring at a reduced cost, the proposed framework will allow reusability of code and data, and may be used as a training tool for ML practitioners and healthcare professionals. © 2021 by SIAM.

关键词： Actions Electrocardiogram Emotions Signal synthesis Skeletal motion Skin conductance

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