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27th IEEE International Scientific Conference Electronics, ET 2018

作者： Radkov, Rosen Stefanov Dimitrov, Ivan Dimitrov Department of Software and Internet Technologies Faculty of Computer Sciences and Automation Technical University of Varna 1 Studentska Str. Varna9010 Bulgaria Department of Computer Science and Engineering Faculty of Computer Sciences and Automation Technical University of Varna 1 Studentska Str. Varna9010 Bulgaria

ISBN: (纸本)9781538666920

It is of utmost importance for each organization to ensure the continuity of its work and the rapid recovery after an *** are classifications to assess the level of IT infrastructures according to their disaster recovery capabilities. In this paper, the authors make a critical analysis of these classifications and prove that they are obsolete and insufficient to determine that the risks associated with the operation of an IT infrastructures are covered. It is proposed to include additional indicators in assessing the sustainability of IT infrastructures against disasters that take into account not only the technical solutions applied in IT infrastructures but also the organization of its operation as well as the more frequent updating of the assessment approach in order to take into account new technologies and the current threats to the operation of an IT infrastructure. © 2018 IEEE.

关键词： Disasters

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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OTHR multitarget tracking with a GMRF model of ionospheric parameters

arXiv

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arXiv 2020年

作者： Guo, Zhen Wang, Zengfu Lan, Hua Pan, Quan Lu, Kun School of Automation Northwestern Polytechnical University Xi'an Shaanxi710072 China Key Laboratory of Information Fusion Technology Ministry of Education Xi'an Shaanxi710072 China Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology DelftCD2826 Netherlands Nanjing Research Institute of Electronics Technology Nanjing Jiangsu210039 China

The ionosphere is the propagation medium for radio waves transmitted by an over-The-horizon radar (OTHR). Ionospheric parameters, typically, virtual ionospheric heights (VIHs), are required to perform coordinate registration for OTHR multitarget tracking and localization. The inaccuracy of ionospheric parameters has a significant deleterious effect on the target localization of OTHR. Therefore, to improve the localization accuracy of OTHR, it is important to develop accurate models and estimation methods of ionospheric parameters and the corresponding target tracking algorithms. In this paper, we consider the variation of the ionosphere with location and the spatial correlation of the ionosphere in OTHR target tracking. We use a Gaussian Markov random field (GMRF) to model the VIHs, providing a more accurate representation of the VIHs for OTHR target tracking. Based on expectation-conditional maximization and GMRF modeling of the VIHs, we propose a novel joint optimization solution, called ECM-GMRF, to perform target state estimation, multipath data association and VIHs estimation simultaneously. In ECM-GMRF, the measurements from both ionosondes and OTHR are exploited to estimate the VIHs, leading to a better estimation of the VIHs which improves the accuracy of data association and target state estimation, and vice versa. The simulation indicates the effectiveness of the proposed algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Target tracking

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Corrigendum to “Simultaneously enhanced hardness and toughness of normalized graphite ductile irons by TiC-TiB 2 nanoparticles” [Mater. Lett. 291 (2021) 129597]

引用

Materials Letters 2021年 298卷

作者： Bing-Xu Wang Yu Zhang Feng Qiu Ming Hu Wei-Wei Cui Zi-Rui Hu Gary C. Barber Faculty of Mechanical Engineering and Automation Zhejiang Sci-Tech University Hangzhou Zhejiang 310018 PR China Automotive Tribology Center Department of Mechanical Engineering School of Engineering and Computer Science Oakland University Rochester MI 48309 USA Key Laboratory of Automobile Materials Department of Materials Science and Engineering Jilin University Changchun Jilin 130025 PR China

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The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

arXiv

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arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Stochastic integer waste management problem solved by a modified progressive hedging algorithm

Stochastic integer waste management problem solved by a modi...

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作者： Málek, Martin Šomplák, Radovan Popela, Pavel Kůdela, Jakub Institute of Mathematics Brno University of Technology Faculty of Mechanical Engineering Czech Republic Institute of Process Engineering Brno University of Technology Faculty of Mechanical Engineering Czech Republic Institute of Automation and Computer Science Brno University of Technology Faculty of Mechanical Engineering Czech Republic

In this paper we describe a real-world large-scale stochastic integer waste-management decision making problem. The problem consists of choosing the optimal locations and capacities of new incineration plants, that will be used for the disposal of waste. To solve this problem, we implement a modified version of the progressive hedging algorithm. The presented case study with real-world data concerns the situation in the Czech Republic. © 2018, Brno University of Technology. All rights reserved.

关键词： Stochastic programming

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A block sparsity based estimator for mmwave massive MIMO channels with beam squint

arXiv

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arXiv 2019年

作者： Wang, Mingjin Gao, Feifei Flanagan, Mark F. Shlezinger, Nir Eldar, Yonina C. Department of Automation Tsinghua University Beijing China School of Electrical and Electronic Engineering University College Dublin Belfield Dublin 4 Ireland Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel

Multiple-input multiple-output (MIMO) millimeter wave (mmWave) communication is a key technology for next generation wireless networks. One of the consequences of utilizing a large number of antennas with an increased bandwidth is that array steering vectors vary among different subcarriers. Due to this effect, known as beam squint, the conventional channel model is no longer applicable for mmWave massive MIMO systems. In this paper, we study channel estimation under the resulting non-standard model. To that aim, we first analyze the beam squint effect from an array signal processing perspective, resulting in a model which sheds light on the angle-delay sparsity of mmWave transmission. We next design a compressive sensing based channel estimation algorithm which utilizes the shift-invariant block-sparsity of this channel model. The proposed algorithm jointly computes the off-grid angles, the off-grid delays, and the complex gains of the multi-path channel. We show that the newly proposed scheme reflects the mmWave channel more accurately and results in improved performance compared to traditional approaches. We then demonstrate how this approach can be applied to recover both the uplink as well as the downlink channel in frequency division duplex (FDD) systems, by exploiting the angle-delay reciprocity of mmWave channels. Copyright © 2019, The Authors. All rights reserved.

关键词： MIMO systems

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Predicting protein functions through non-negative matrix factorization regularized by protein-protein interaction network and gene functional information

Predicting protein functions through non-negative matrix fac...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wei Peng Lun Li Wei Dai Jielin Du Wei Lan Faculty of Information Engineering and Automation Kunming University of Science and Technology Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Kunming China School of Computer Electronic and Information Guangxi University Guangxi University Nanning China

Protein function prediction is necessary for understanding life and is valuable for application on drug design and health care. It is still a big challenge to predict protein function correctly by integrating multi-biological information. In this work, we propose a novel non-Negative Matrix Factorization (NMF) based method regularized by PPI network and GO similarity network, namely PONMF, for protein function prediction, which decomposes the known GO-protein association matrix into two low-rank matrixes for proteins and GO terms. In the course of factorization, PONMF incorporates the label matrix factorization term, an additional network regularization term and a GO similarity regularization term into the objective function. Finally, the potential protein functions are predicted by referring to the product of the two low-rank matrixes. PONMF not only successfully integrates diverse biological information to predict protein functions, but also naturally partitions proteins into different modules and infers functions from the proteins in the same modules. Our methods as well as the other two state-of-the-art methods (UBiRW and NMFGO) are applied to predict functions for protein of S. cerevisiae and H. sapiens. The prediction results show that PONMF outperforms the other two existing methods.

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PID2018 Benchmark Challenge: learning feedforward control

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IFAC-PapersOnLine 2018年第4期51卷 663-668页

作者： Zhao, Yang Dehghan, Sina Ates, Abdullah Yuan, Jie Zhou, Fengyu Li, Yan Chen, YangQuan School of Control Science and Engineering Center for Robotics Shandong University JinanShandong250061 China University of California Merced MercedCA95340 United States School of Automation Southeast University Nanjing210096 China Engineering Faculty Computer Engineering Department Inonu University Malatya44280 Turkey

The design and application of learning feedforward controllers (LFFC) for the one-staged refrigeration cycle model described in the PID2018 Benchmark Challenge is presented, and its effectiveness is evaluated. The control system consists of two components: 1) a preset PID component and 2) a learning feedforward component which is a function approximator that is adapted on the basis of the feedback signal. A B-spline network based LFFC and a low-pass filter based LFFC are designed to track the desired outlet temperature of evaporator secondary flux and the superheating degree of refrigerant at evaporator outlet. Encouraging simulation results are included. Qualitative and quantitative comparison results evaluations show that, with little effort, a high-performance control system can be obtained with this approach. Our initial simple attempt of low-pass filter based LFFC and B-spline network based LFFC give J=0.4902 and J=0.6536 relative to the decentralized PID controller, respectively. Besides, the initial attempt of a combination controller of our optimized PI controller and low-pass filter LFFC gives J=0.6947 relative to the multi-variable PID controller. © 2018

关键词： Controllers Benchmarking Electric control equipment Evaporators Feedforward control Interpolation Learning systems Low pass filters Proportional control systems Refrigeration Three term control systems Combination controllers Conditional integrations Decentralized PID controller Feed forward controllers High performance control systems PID 2018 Benchmark Challenge Quantitative comparison Vapour compression refrigeration systems

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Multi-criteria decision model for multicircular flight control of unmanned aerial vehicles through a hybrid approach

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Scientific reports 2025年第1期15卷 18962页

作者： Noorulden Basil Hamzah M Marhoon Bayan Mahdi Sabbar Abdullah Fadhil Mohammed Osamah Albahri Ahmed Albahri Abdullah Alamoodi Iman Mohamad Sharaf Amare Merfo Amsal Mahrous Ahmed Enas Ali Sherif S M Ghoneim Department of Electrical Engineering College of Engineering Mustansiriyah University Baghdad Iraq. noorulden@uomustansiriyah.edu.iq. Department of Automation Engineering and Artificial Intelligence College of Information Engineering Al-Nahrain University Jadriya Baghdad Iraq. College of Engineering and Engineering Techniques Al-Mustaqbal University Babylon Iraq. Department of Electrical Engineering College of Engineering Mustansiriyah University Baghdad Iraq. Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq. Victorian Institute of Technology Melbourne Australia. Technical Engineering College Imam Ja'afar Al-Sadiq University (IJSU) Baghdad Iraq. University of Information Technology and Communications (UOITC) Baghdad Iraq. Applied Science Research Center Applied Science Private University Amman Jordan. GUST Engineering and Applied Innovation Research Center (GEAR) Gulf University for Scienceand Technology Mishref Kuwait. Department of Basic Sciences Higher Technological Institute 10th of Ramadan City Egypt. Department of Mechanical Engineering Faculty of Technology Debre Markos University P. O. Box 269 Debre Markos Ethiopia. amare_merfo@dmu.edu.et. Department of Electrical Engineering College of Engineering Taif University 21944 Taif Saudi Arabia. Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab 140401 India.

This study presents a novel approach for optimizing UAV (unmanned aerial vehicle) Multicircular flight control by developing a fractional order proportional integral derivative (FOPID)-based hybrid Eagle strategy particle swarm optimization ant lion optimizer (HESPSOALO). The proposed algorithm combines the strengths of particle swarm optimization (PSO) and the ant lion optimizer (ALO), which are enhanced by the Eagle strategy to systematically fine-tune the FOPID controller parameters. This hybrid optimization method aims to improve system stability, responsiveness, and disturbance rejection in UAVs, particularly in challenging dynamic flight conditions. The proposed approach was validated against traditional control methods that utilize FOPID (Base), the Base HESPSOALO algorithm, the FOPID-based HPSOGWO (Hybrid Particle Swarm Optimization-Gray Wolf Optimizer), and the FOPID-based HGWOALO (Hybrid Gray Wolf Optimization-Ant Lion Optimizer) with a set of benchmark functions used in the analysis. The results demonstrate a minimization of position and angular errors, reduced oscillations, and overall improved control stability for the FOPID-based HESPSOALO compared with the other methods. Furthermore, a multicriteria decision-making (MCDM) framework is applied to evaluate the overall performance of alternative control strategies utilizing the CRiteria importance through intercriteria correlation (CRITIC) and technique of order preference by similarity to ideal solution (TOPSIS) techniques. The MCDM analysis demonstrates that among the evaluated criteria, [Formula: see text] has the highest importance, with a weight of 0.244019, whereas [Formula: see text] is deemed the least significant, with a weight of 0.161023. The ranking results reveal that the HESPSOALO algorithm (Base) is the best-performing controller method, with a ranking score of 0.571161, indicating its superior control performance across major metrics. In contrast, the FOPID + HPSOGWO controller method ran

关键词： CRITIC FOPID Hybrid optimization TOPSIS UAV multicircular flight control

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