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3rd Conference on Language, Data and Knowledge, LDK 2021

作者： Armaselu, Florentina Apostol, Elena-Simona Khan, Anas Fahad Liebeskind, Chaya McGillivray, Barbara Truică, Ciprian-Octavian Oleškevičiene, Giedre Valūnaite University of Luxembourg Luxembourg Department of Computer Science and Engineering Faculty of Automatic Control and Computer University Politehnica of Bucharest Romania Institute for Computational Linguistics «A. Zampolli» National Research Council of Italy Pisa Italy Department of Computer Science Jerusalem College of Technology Israel Theoretical and Applied Linguistics Faculty of Modern and Medieval Languages and Linguistics University of Cambridge United Kingdom The Alan Turing Institute London United Kingdom Department of Computer Science and Engineering Faculty of Automatic Control and ComputerUniversity Politehnica of Bucharest Romania Institute of Humanities Mykolas Romeris University Vilnius Lithuania

ISBN: (纸本)9783959771993

The paper proposes an interdisciplinary approach including methods from disciplines such as history of concepts, linguistics, natural language processing (NLP) and Semantic Web, to create a comparative framework for detecting semantic change in multilingual historical corpora and generating diachronic ontologies as linguistic linked open data (LLOD). Initiated as a use case (UC4.2.1) within the COST Action Nexus Linguarum, European network for Web-centred linguistic data science, the study will explore emerging trends in knowledge extraction, analysis and representation from linguistic data science, and apply the devised methodology to datasets in the humanities to trace the evolution of concepts from the domain of socio-cultural transformation. The paper will describe the main elements of the methodological framework and preliminary planning of the intended workflow. © Florentina Armaselu, Elena-Simona Apostol, Anas Fahad Khan, Chaya Liebeskind, Barbara McGillivray, Ciprian-Octavian Truică, and Giedre Valūnaite Oleškevičien e;licensed under Creative Commons License CC-BY 4.0

关键词： Linguistics

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Simulation of Conducted Emissions From Power Converters Using Leading Switching Technologies

Simulation of Conducted Emissions From Power Converters Usin...

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IEEE Global Electromagnetic Compatibility Conference (GEMCCON)

作者： Amr Madi Francinei Vieira Piotr Lezynski Sebastian Koj Robert Smolenski Electrical Engineering Mathematics & Computer Science (EEMCS) University of Twente Enschede Netherlands Institute of Electrical Engineering and Measurement Technology Leibniz University Hannover Hannover Germany Institute of Automatic Control Electronics and Electrical Engineering University of Zielona Gora Zielona Gora Poland Department of Engineering Sciences Jade University of Applied Sciences Wilhelmshaven Germany

This paper presents a comparative study between the commonly used switches (IGBT, MOSFET, SiC and GaN) for power inverters of electric vehicles. A 400 V, three-phase inverter system simulation test is implemented using LTspice and the conducted emissions are compared to variant CISPR standards and switching frequencies in order to show the possible interferences with the other car equipment.

关键词： MOSFET Silicon carbide Switching frequency Interference Predictive models SPICE Inverters

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A Review of RPL Based Routing Protocols in Vehicular Ad Hoc Networks (VANET)

A Review of RPL Based Routing Protocols in Vehicular Ad Hoc ...

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Mechatronic Systems Supervision (IW_MSS), IEEE International Workshop on

作者： Wafa Akermi Nouha Alyaoui Awatef Benfradj Guiloufi Karim Chabir Automatic Industrial Systems Department Higher Institute of Applied Sciences and Technology Gafsa University Gafsa Tunisia Laboratory of Modeling Analysis and Control of Systems (MACS) National Engineering School of Gabes (ENIG) Gabes University Gabes Tunisia Computer Science and Telecommunications Department Higher Institute of Applied Sciences and Technology Gafsa University Gafsa Tunisia Preparatory Institute for Engineering Studies of Gabes Laboratory of Modeling Analysis and Control of Systems (MACS) National Engineering School of Gabes (ENIG) Gabes University Gabes Tunisia

In the present day, the Internet of Things is becoming more and more popular. Enabling continuous connectivity and packet routing in this kind of network is difficult because of the resource limitations that are a feature of IoT. Reduce end-to-end latency, conserve energy, and balance network load are the three basic objectives of lightweight routing protocol. In this paper, we give a quick review of the IPv6 Routing Protocol for Low-Power and Lossy Networks-based routing protocols of VANET already in use and discuss their advantages and disadvantages. More specifically, we examine a few chosen VANET routing protocols and contrast their performance based on metrics like packet delivery ratio, Throughput, End to end delay, Routing- Cost, etc.

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A New Mutimodel approach for modelling of fractional order interval transfer functions

A New Mutimodel approach for modelling of fractional order i...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Samia Talmoudi Moufida Lahmari Ksouri Department of Computer Science Applied College University of Tabuk Tabuk Saudi Arabia University of Tunis El Manar National Engineering School of Tunis LR11ES20 Analysis Design and Control of Systems Laboratory Tunis Tunisia

This paper is focused on uncertain fractional order systems. In this context, a new modelling approach of uncertain fractional order systems represented by an explicit fractional order interval transfer function is proposed. This approach is based, essentially, on multimodel approach. In fact, firstly, two new determination methods of models’ library, inspired from the Kharitonov approach, are exposed. Then, we propose to compute the validity degree of each model of the obtained library, by optimizing a constrained least squares problem. The global model is, finally deduced by fusion of outputs of the different library’ models. To prove its efficiency and its precision, the proposed approach is then compared to the approximation approach of Oustaloup through two simulation examples.

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Automated Leukemia Screening and Sub-types Classification Using Deep Learning

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computer Systems Science & engineering 2023年第9期46卷 3541-3558页

作者： Chaudhary Hassan Abbas Gondal Muhammad Irfan Sarmad Shafique Muhammad Salman Bashir Mansoor Ahmed Osama M.Alshehri Hassan H.Almasoudi Samar M.Alqhtani Mohammed M.Jalal Malik A.Altayar Khalaf F.Alsharif Department of Computer Science COMSATS University Islamabad44000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Control Automotive and Robotics LabNational Center of Robotics and Automation(NCRA HEC)MirpurPakistan Mirpur University of Science and Technology(MUST)MirpurPakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Clinical Laboratory Sciences College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesUniversity of TabukTabukSaudi Arabia Department of Clinical Laboratory Sciences College of Applied Medical SciencesTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human *** produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells(RBCs)and white blood cells(WBC),and *** can be diagnosed manually by taking a complete blood count test of the patient’s blood,from which medical professionals can investigate the signs of leukemia ***,two other methods,microscopic inspection of blood smears and bone marrow aspiration,are also utilized while examining the patient for ***,all these methods are labor-intensive,slow,inaccurate,and require a lot of human experience and *** authors have proposed automated detection systems for leukemia diagnosis to overcome these *** have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast ***,these systems are more efficient,reliable,and fast than previous manual diagnosing ***,more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class *** this paper,we have proposed a robust automated system to diagnose leukemia and its *** have classified ALL into its sub-types based on FAB classification,i.e.,L1,L2,and L3 types with better *** have achieved 96.06%accuracy for subtypes classification,which is better when compared with the state-of-the-art methodologies.

关键词： Healthcare cancer detection deep learning convolutional neural network

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Tripartite entanglement measure under local operations and classical communication

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Physical Review A 2023年第3期107卷 032405-032405页

作者： Xiaozhen Ge Lijun Liu Shuming Cheng Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong China College of Mathematics and Computer Science Shanxi Normal University Linfen 041000 China The Department of Control Science and Engineering Tongji University Shanghai 201804 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai 201804 China Institute for Advanced Study Tongji University Shanghai 200092 China

Multipartite entanglement is an indispensable resource in quantum communication and computation; however, it is a challenging task to faithfully quantify this global property of multipartite quantum systems. In this work we study the concurrence fill, which admits a geometric interpretation to measure genuine tripartite entanglement for the three-qubit system [Xie and Eberly, Phys. Rev. Lett. 127, 040403 (2021)]. First, we use the well-known three-tangle and bipartite concurrence to reformulate this quantifier for all pure states. We then construct an explicit example to conclusively show that the concurrence fill can be increased under local operations and classical communication (LOCC) on average, implying it is not an entanglement monotone. Moreover, we give a simple proof of the LOCC monotonicity of the three-tangle and find that the bipartite concurrence and the squared concurrence can have distinct performances under the same LOCC. Finally, we propose a reliable monotone to quantify genuine tripartite entanglement, which can also be easily generalized to the multipartite system. Our results shed light on the study of genuine entanglement and also reveal the complex structure of multipartite systems.

关键词： Quantum correlations in quantum information Quantum entanglement Quantum networks

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Cnn-Gnn Convolution Bridge: An Effective Algorithmic Migration Strategy from Cnns to Gnns

SSRN

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SSRN 2023年

作者： Zhang, Kuijie Pang, Shanchen Yang, Huahui Lin, Jerry Chun-Wei Zhang, Yuanyuan Wu, Wenhao Li, Hengxiao Shandong 266580 China School of Qilu Transportation Shandong University Shandong 250002 China Department of Computer Science Electrical Engineering and Mathematical Science Western Norway University of Applied Sciences Bergen264200 Norway School of Information and Control Engineering Qingdao University of Technology Shandong 266520 China

Graph neural networks (GNNs), as rising stars in machine learning, are widely used in relational data models and have achieved outstanding performance in graph tasks. GNNs continuously receive mature models from other domains such as computer vision and natural language processing to motivate the development of graph algorithms. However, due to the data structures various from different domains, the cross-domain migration of models has to go through a long period of disassembly and reconstruction, which may not yield the desired results. To preserve the excellent properties of convolution and optimize the migration process from convolutional neural networks (CNNs) to GNNs, we propose a CNN-GNN Convolution Bridge. The CNN-GNN Convolution Bridge realizes the data alignment from CNN to GNN, so that the CNN-based model can be efficiently migrated to the graph structure model. To demonstrate the effectiveness of our migration strategy, we migrated the Inception module and U-Net architecture from CNNs to GNNs, named GraInc and GraU-Net, for the node-level task and the graph-level task, respectively. Experimental results show that GraInc and GraU-Net are highly competitive with the current state-of-the-art models, particularly on dense graph datasets. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Machine Learning and Intelligent Network System based Covid 19 Health Care System

Machine Learning and Intelligent Network System based Covid ...

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International Conference on Power, Energy, control and Transmission Systems (ICPECTS)

作者： Amjath Ali Jakul Basha K. Saravanan T Mangayarkarasi C Balarengadurai RCC - ENGG Department Lecturer Computer Engineering Electrical and Electronic Section Department of Engineering Sultanate of Oman University of Technology and Applied Sciences Ibra Department of Information Technology R.M.D Engineering College Chennai Tamil Nadu India Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru Karnataka India

ISBN: (纸本)9781665462761

COVID-19 has lately infected a big number of people worldwide. Medical service frameworks are strained as a result of the infection. The emergency unit, which is part of the medical services area, has experienced several challenges as a result of the low data quality offered by existing ICU clinical equipment. The Internet of Things has enhanced the capability for essential information mobility in medical services in the twenty-first century. Nonetheless, many of today's ideal models use IoT innovation to assess patients' well-being. As a result, executives lack understanding regarding the most effective method to apply such innovation to ICU clinical equipment. The IoT Based Paradigm for Medical Equipment Management Systems, a breakthrough IoT-based paradigm for successfully administering clinical hardware in ICUs, is introduced in this study. During the COVID-19 episode, IoT technology is used to boost the data stream between clinical hardware, executive frameworks, and ICUs, enabling the maximum level of openness and reasonableness in clinical equipment redistribution. IoT MEMS conceptual and functional features were painstakingly drawn. Using IoT MEMS expands the capacity and limits of emergency clinics, effectively easing COVID-19. It will also have a substantial impact on the nature of the data and will improve the partners' trust and transparency.

关键词： COVID-19 Micromechanical devices Biomedical equipment Technological innovation Medical services Production Hardware

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Shunt Capacitor Bank Fault Detection and Localization Using Sub-Cycle Algorithm

Shunt Capacitor Bank Fault Detection and Localization Using ...

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IEEE Canada Electric Power Conference, EPEC

作者： Mohsen Tajdinian Behzad Behdani Ali Goodarzi Harold R. Chamorro Vijay K. Sood Department of Power and Control Engineering School of Electrical and Computer Engineering Shiraz University Shiraz Iran Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft the Netherlands Fars Electricity Distribution Company Fars Iran School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University Ontario Canada

ISBN: (纸本)9781665463195

Fast internal detection and location in Shunt Ca-pacitor Banks (SCBs) can lead to the prevention of damages to other SCBs' elements and consequently avoid undesirable performance and effects in power system operation. This paper targets the performance of phasor-based algorithms of failure detection and fault location of SCBs. Being dependent on the fundamental phasor components which usually are calculated based on the Discrete Fourier Transform (DFT), the failure detection and fault location algorithms suffer from almost one-cycle delay. This paper provides sub-cycle phasor estimation based on the least-square technique. The proposed algorithm is evaluated for different configurations of SCBs considering different fuse protection designs. The proposed method provides a criterion for relay decision-making in the case of multiple faulty phases condition. The proposed method is designed to monitor and detect consecutive failures based on the existing data of commercial relays. Performance evaluations are conducted under different circumstances namely voltage unbalance conditions and multiple internal fault locations.

关键词： Performance evaluation Fault detection Capacitors Discrete Fourier transforms Estimation Voltage Fault location

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Network Traffic Co-Movement Assessment via Oriented Basis Signal Processing and Ensemble Decision Trees

Network Traffic Co-Movement Assessment via Oriented Basis Si...

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International Conference on Intelligent Transportation

作者： Mohammad Reza Mohebbi Mohammad Javad Hassan Zada Majid Rostami-Shahrbabaki Mario Döller Iuliia Yamnenko Josef Ressel Center Vision2Move project University of Applied Sciences Kufstein Tirol Austria Department of Computer Science University of Passau Germany Chair of Traffic Engineering and Control Technical University of Munich Germany Department of Electronic Devices and Systems National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Accurate short-term network-wide traffic prediction is essential to guarantee high service quality in urban traffic control systems. Nevertheless, traffic state time series represent network-scale spatiotemporal co-movement patterns and location-specific features. Therefore, hybrid statistical machine learning (ml) algorithms could be utilized to accommodate the aforementioned characteristics. In this paper, a hybrid random forest (rf) and extreme gradient boosting tree (XGBoost) model is introduced for network-wide traffic prediction. Moreover, a multi-noise oriented basis wavelet transform (OBT) filter is employed to pre-process the original time series, and improve the predictive accuracy. The RF model captures the traffic co-movements and the XGBoost extracts the local information. Comparative analysis of the hybrid algorithm and deep learning-based benchmarks indicate better performance of the proposed methodology in hourly traffic state prediction of 30 loop detectors, located in the paris city center. Hence, the hybrid decision-tree-based mechanism is a useful framework for real-time network traffic forecasting offering fewer trainable (hyper-)parameters, and thus, lower computational cost.

关键词： Wavelet transforms Radio frequency Accuracy Machine learning algorithms Urban areas Signal processing algorithms Detectors Prediction algorithms Decision trees Random forests

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