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2021第五届教育与创新国际会议

作者： Yang Qian Gao Jinzhu School of Mechanical and Electrical Engineering Xi'an University of Architecture and Technology Department of Computer Science University of the Pacific

Different modulation modes of PWM technology and inverters can be selected for different types of the motors, a variety of AC variable frequency variable speed system structures can also be derived based on the same control principle. So it brings a great challenge to teaching AC variable speed systems. We have identified four factors that hinder students' learning in this topic: 1) the abstraction of the mathematical logic behind the control theory;2) the complexity of system structures;3) the diversity of implementation methods;4) lack of motivation to explore more engineering applications due to boring textbook contents. To facilitate and inspire student learning, we developed an innovative way to teach the AC Variable Speed Systems course using the simulation models library. The library was implemented based on MATLAB/Simulink to assist students in studying five different types of AC variable speed systems. We assessed the effectiveness of our new teaching practice by evaluating students' cognitive and affective behavior under both the traditional lecture-based teaching environment and the new simulation-assisted teaching environments. The practice assessment tools that take into accounts the in-class performance(attendance accounts for 10% and the experimental reports account for 20%), the lab assignments computer operation score(30%) includes software operation(10%) and system constitution and design(20%), and the final exam(40%) are designed to assess the students' understanding of AC variable speed systems. The student group learning under the simulation-assisted teaching environment delivered a superior performance in all measures including the noticeable improved scores on the final exam. We also developed a survey to evaluate the students' affective behavior by measuring their motivation for learning and their perceptions of effectiveness of practice. Ninety percent of students reported that the new teaching practice using the simulation models library is

关键词： AC Variable Speed Systems Simulation-assisted Teaching Methodology MATLAB/GUI Simulation Models Library Vector Control Practice Training

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Deploying Fuzzy Rough Set and Artificial Immune System Algorithms for Air Quality Prediction 11th

Deploying Fuzzy Rough Set and Artificial Immune System Algor...

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11th International Conference on Robotics, Vision, Signal Processing and Power Applications, RoViSP 2021

作者： Lasisi, Ayodele Ghazali, Rozaida Ismail, Lokman Hakim Husaini, Noor Aida Department of Mathematical Sciences Faculty of Science Augustine University Ilara-Epe Lagos106101 Nigeria Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat Johor Parit Raja86400 Malaysia Department of Design Engineering and Architecture Faculty of Civil and Environmental Engineering Universiti Tun Hussein Onn Malaysia Batu Pahat Johor Parit Raja86400 Malaysia

ISBN: (纸本)9789811681288

The widespread of air pollution due to emissions of highly detrimental concentrates on human life needs paramount attention. Devising air prediction strategy that accurately analyze air quality level through gathering of useful information allow for relevant organizations to disseminate promptly control measures. This paper proposes the use of artificial immune system (AIS) algorithms consisting of Immunos algorithms (Immunos-1, Immunos-2, Immunos-99), CLONal selection ALGorithm (CLONALG), clonal selection classification algorithm (CSCA), artificial immune recognition system algorithms (AIRS1, AIRS2, Parallel AIRS2) for air quality prediction. The fuzzy rough set selects pertinent data features summarizing interpretations of the data. Comparative simulations reveal that the Parallel AIRS2 produced superlative results to other algorithms with detection rate of 96.40%. Effective prediction performance can be generated with AIS algorithms having highest detection rates and lowest false alarm rates. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Classification of Malicious Code Based on Grayscale Texture Fingerprint 7

Classification of Malicious Code Based on Grayscale Texture ...

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7th International Conference on Information Science and Control Engineering, ICISCE 2020

作者： Li, Jun Liu, Shengao Niu, Yan Zhang, Hui Hubei University of Technology Department of Computer Science Wuhan China Hubei University of Technology School of Civil Engineering Architecture and Environment Wuhan China

ISBN: (纸本)9781728164069

Traditional malicious code classification technology faces a challenge to do with increasingly deformed malicious code, whose problems include having low detection rate and being time-consuming. In order to solve the above-mentioned problems, the traditional malicious code classification technology combines feature extraction of texture fingerprints on the image with the deep learning neural network classification method and proposes a malicious code classification method based on gray texture features. This method first converts the binary file of the malicious code into a grayscale image. It uses the grayscale co-occurrence matrix to extract the features in the malicious code, and the extracted features are in turn substituted into an improved neural network model. Classification training is then performed to reduce the probability of feature loss in the pooling layer. The experimental results indicate that the malicious code classification based on the above method have higher accuracy. Through the use of features extracted by the gray level co-occurrence matrix as output of the pooling layer, the learning time can also be effectively shortened hence making this method best for classification detection usage. © 2020 IEEE.

关键词： Textures

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Steps: A development of students' employability prediction system using logistic regression model based on principal component analysis 10

Steps: A development of students' employability prediction s...

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10th IEEE International Conference on System Engineering and technology, ICSET 2020

作者： Casuat, Cherry D. Castro, Julius C. Evangelista, Deanne Cameren P. Merencilla, Niño E. Atal, Christina P. Department of Computer Engineering Technological Institute of the Philippines Manila Philippines College of Engineering and Architecture Bohol Island State University Bohol Philippines Department of Computer Engineering FEU Institute of Technology Manila Philippines Department of Computer Engineering Universidad De Manila Manila Philippines

ISBN: (纸本)9781728199108

Predicting students' employability prior to graduation can be a great tool for every HEI's career center to intervene timely and to take steps on how to improve the weaknesses of the students to become more employable. At present, there is no tool that can be used to determine undergraduate students who are at risk of unemployment or becoming disadvantaged because vulnerabilities are not detected early. In this study the principal component analysis (PCA) and logistic regression were used to determine the most predictive features in the students' employability prediction system (STEPS). The Dataset used consist of 1000 information of engineering students who took their on-the Job training from School year 2017- 2018 to School year 2019. The features used were professionalism and branding, confidence, comprehension, communication skills, growth potential, student performance rating. Upon using PCA,the experiments resulted to communication skills growth potential and student performance rating obtained the most predictive attributes that affects the employability prediction. © 2020 IEEE

关键词： Principal component analysis

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SSI meets Metaverse for Industry 4.0 and Beyond

TechRxiv

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TechRxiv 2022年

作者： Cali, Umit Ferdous, Md Sadek Karaarslan, Enis Gourisetti, Nikhil Gupta Mylrea, Michael Department of Electric Power Engineering Norwegian University of Science and Technology Trondheim Norway Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Department of Computer Engineering Muğla Sıtkı Koçman University Muğla Turkey Department of Security Architecture National Resilience La Jolla CA United States

As the global industrial complex gears toward fulfilling the tenets of Industry 4.0 and beyond, technologies such as distributed ledger technologies, digital twins, and artificial intelligence become pivotal enablers. In the last decade, metaverse as a concept and technology found its place among crucial enablers for technology and digital advancement across several engineering domains. Metaverse has the potential to combine the elements from distributed computing platforms, the digital evolution of physical systems, and advanced learning systems to unearth a fully digitized world of comparative properties of the real world. We should ensure the privacy, integrity, and confidentiality of personal data. These requirements will lead to proper identity management in the metaverse. Given the complex nature of the metaverse, traditional centralized systems may not offer a viable identity management solution. Therefore, this study explores a decentralized identity management system called the Self-sovereign Identity (SSI) as a logical alternative to traditional centralized identity management systems. The proposed holistic framework aims to ignite new ideas and discussions related to the combined deployment of DLT, SSI, and metaverse to inspire new implementation areas within the Industry 4.0 environment. The paper also discusses various opportunities, enablers, technical & privacy aspects, legislation requirements, and other barriers related to SSI implementation. © 2022, CC BY.

关键词： Industry 4.0

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Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy system model

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International Journal of Sediment Research 2022年第3期37卷 383-398页

作者： Rana Muhammad Adnan Zaher Mundher Yaseen Salim Heddam Shamsuddin Shahid Aboalghasem Sadeghi-Niaraki Ozgur Kisi State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State University New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Faculty of Science Agronomy Department Hydraulics Division University School of Civil Engineering Faculty of Engineering Universiti Teknologi Malaysia (UTM) Geoinformation Tech.Center of Excellence Faculty of Geomatics Engineering K.N.Toosi University of Technology Department of Computer Science and Engineering Sejong University Civil Engineering Department Ilia State University

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm（ANFIS-FCM-PSOGSA） in improving the estimation accuracy of river suspended sediment loads（SSLs） is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization（ANFIS-FCM-PSO）, ANFIS-FCM, and sediment rating curve（SRC） models. Various input combinations involving lagged river flow（Q） and suspended sediment（S） values were used for model development. The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error（RMSE）, mean absolute error（MAE）, Nash-Sutcliffe Efficiency（NSE）, and coefficient of determination（R2） and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO（or ANFIS-FCM） models by 8.14%（1.72%）, 14.7%（5.71%）, 12.5%（2.27%）, and 25.6%（1.86%）,in terms of the RMSE, MAE, NSE and R2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification.

关键词： Suspended sediment load Adaptive neuro-fuzzy system Particle swarm optimization Gravitational search algorithm

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Designing EV charging stations deployment through holistic simulations: the SANEVEC project

Designing EV charging stations deployment through holistic s...

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EVI: Charging Ahead (EVI 2023)

作者： J.-L. Guisado-Lizar G. Jimenez-Moreno F. Diaz-del-Rio J. L. Sevillano-Ramos D. Cagigas-Muñiz M. J. Morón-Fernández D. Cascado-Caballero F. Jimenéz-Morales M. Cárdenas-Montes G. A. Wainer D. Ragel-Díaz-Jara J. Rodríguez-Leal E. Cerezuela-Escudero J. M. Montes-Sánchez D. Casanueva-Morato A. Ayuso-Martínez Research Institute of Computer Engineering (I3US) Universidad de Sevilla Avenida Reina Mercedes s/n 41012 Sevilla Spain Department of Condensed Matter Physics Universidad de Sevilla Avenida Reina Mercedes s/n 41012 Sevilla Spain Department of Basic Research Centro de Investigaciones Energeticas Medioambientales y Tecnologicas Madrid Spain Department of Systems and Computer Engineering Carleton University Ottawa Canada Department of Computer Architecture and Technology Universidad de Sevilla Avenida Reina Mercedes s/n 41012 Sevilla Spain

To determine the optimal location of the large number of urban public charging stations that will have to be deployed in the following years to transition to electric vehicles (EVs) is a challenging task, as it is intimately interconnected with the complexity of urban traffic and electric grid operation. The city is a complex system in which emerging patterns such as traffic jams, and instabilities or even outages in the electric grid are difficult to predict. The layout of charging stations can alter those patterns in traffic and electric grid operation, with consequences to mobility, air quality, and electric system. The present article presents the concept under the research project SANEVEC. It will build a framework for the optimal determination of the location of EV charging stations in a city based on simulation, which will be able to reproduce the feedback effects between all the variables involved: locations of the stations, traffic patterns, characteristics, and operation of the electricity grid, charging fees and air quality in the city. It will employ artificial intelligence methods to find high-quality station location solutions from the simulations and to predict the air quality in the city.

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SSI meets Metaverse for Industry 4.0 and Beyond

SSI meets Metaverse for Industry 4.0 and Beyond

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Global Emerging technology Blockchain Forum: Blockchain & Beyond (iGETblockchain), IEEE

作者： Umit Cali Md Sadek Ferdous Enis Karaarslan Sri Nikhil Gupta Gourisetti Michael Mylrea Department of Electric Power Engineering Norwegian University of Science and Technology Trondheim Norway Department of Computer Science and Engineering BRAC University Dhaka Bangladesh Department of Computer Engineering Muğla Sıtkı Koçman University Muğla Türkiye Department of Security Architecture National Resilience La Jolla CA USA

As the global industrial complex gears toward fulfilling the tenets of Industry 4.0 and beyond, technologies such as distributed ledger technologies, digital twins, and artificial intelligence become pivotal enablers. In the last decade, metaverse as a concept and technology found its place among crucial enablers for technology and digital advancement across several engineering domains. Metaverse has the potential to combine the elements from distributed computing platforms, the digital evolution of physical systems, and advanced learning systems to unearth a fully digitized world of comparative properties of the real world. We should ensure the privacy, integrity, and confidentiality of personal data. These requirements will lead to proper identity management in the metaverse. Given the complex nature of the metaverse, traditional centralized systems may not offer a viable identity management solution. Therefore, this study explores a decentralized identity management system called the Self-sovereign Identity (SSI) as a logical alternative to traditional centralized identity management systems. The proposed holistic framework aims to ignite new ideas and discussions related to the combined deployment of DLT (Distributed Ledger technology), SSI, and metaverse to inspire new implementation areas within the Industry 4.0 environment. The paper also discusses various opportunities, enablers, technical & privacy aspects, legislation requirements, and other barriers related to SSI implementation.

关键词： Privacy Metaverse Distributed ledger Shape Supply chains Transportation Market research

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Forty Years of Event Graphs in Research and Education

Forty Years of Event Graphs in Research and Education

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2023 Winter Simulation Conference, WSC 2023

作者： Gunal, Murat M. Osais, Yahya Ismail Schruben, Lee Wagner, Gerd Yücesan, Enver Fenerbahce University Faculty of Engineering and Architecture Atatürk Mah. Ataşehir Bulvari Istanbul Ataşehir34758 Turkey King Fahd University of Petroleum and Minerals Center for Intelligent Secure Systems Computer Engineering Department P.O. Box 2244 Dhahran31261 Saudi Arabia University of California Berkeley Industrial Engineering and Operations Research BerkeleyCA94720 United States Brandenburg University of Technology Department of Informatics Konrad-Wachsmann-Allee 5 Cottbus03046 Germany Insead Technology and Operations Management Area Ayer Rajah Avenue 138676 Singapore

ISBN: (纸本)9798350369663

Forty years ago, in 1983, Lee Schruben proposed the Event Graph formalism and modeling language, subsequently defining the paradigm of Event-Based Simulation, in a precise way, which had been pioneered 20 years before by SIMSCRIPT. The purpose of this panel is for a group of Event Graph researchers both from Operations Research and computer Science, including the inventor of Event Graphs and one of his former PhD students who has made essential contributions to their theory, to discuss their views on the history and potential of Event Graph modeling and simulation. In particular, the adoption of Event Graphs as a discrete process modeling language in Discrete Event Simulation and in computer Science, and their potential as a foundation for the entire field of Discrete Event Simulation and for the fields of process modeling and AI in computer Science is debated. © 2023 IEEE.

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Trikarenos: Design and Experimental Characterization of a Fault-Tolerant 28nm RISC-V-based SoC

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IEEE Transactions on Nuclear Science 2025年

作者： Rogenmoser, Michael Wiese, Philip Forlin, Bruno Endres Gurkaynak, Frank K. Rech, Paolo Menicucci, Alessandra Ottavi, Marco Benini, Luca Integrated Systems Laboratory ETH Zurich Switzerland Computer Architecture for Embedded Systems University of Twente Netherlands Department of Industrial Engineering University of Trento Italy Delft University of Technology Department of Space Engineering Netherlands University of Rome Department of Electronic Engineering Tor Vergata Italy Information Engineering at the University of Bologna Department of Electrical Electronic Italy

RISC-V-based fault-tolerant system-on-chip (SoC) designs are critical for the new generation of automotive and space SoC architectures. However, reliability assessment requires characterization under controlled radiation doses to accurately quantify the fault tolerance of the fabricated designs. This work analyzes the Trikarenos design, a SoC implemented in TSMC 28nm, for single event upset (SEU) vulnerability under atmospheric neutron and 200MeV proton radiation, comparing these results to simulation-based fault injection. All faults in error correction codes (ECC) protected memory are corrected by a scrubber, showing an estimated cross-section per bit of up to 1.09 × 10−14 cm2 bit−1. Furthermore, the triple-core lockstep (TCLS) mechanism implemented in Trikarenos is validated and is shown to correct errors affecting a cross-section up to 3.23 × 10−11 cm2, with the remaining uncorrectable vulnerability below 5.36 × 10−12 cm2. When augmenting the experimental analysis of fabricated chips with gate-level fault injection in simulation, 99.10% of injections into the SoC produced correct results, while 100% of injections in the TCLS-protected cores were handled *** 12.28%of all injected faults leading to a TCLS recovery, this indicates an approximate effective flip-flop cross-section of up to 1.28 × 10−14 cm2/FF. © 1963-2012 IEEE.

关键词： Radiation damage

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