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6th EAI international conference on Green Energy and Networking, GreeNets 2019

6th EAI International Conference on Green Energy and Network...

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6th EAI international conference on Green Energy and Networking, GreeNets 2019

ISBN: (纸本)9783030217297

the proceedings contain 27 papers. the special focus in this conference is on Green Energy and Networking. the topics include: A Multi-local World Network Model;Channel Estimation in Massive MIMO TDD systems;A Improved AOMDV Routing Protocol Based on Load Balancing with Energy Constraining for Ad Hoc Network;automatic Parking Guidance System Based on Ultraviolet Communication;a Novel Spectral Matching Algorithm to Application Environment Fitness Evaluation Method;Performance Analysis of 40Â GB/s DWDM School LAN Modulation Mode;design of Intelligent Home Lighting Control System Based on Speech Recognition;dynamics and Synchronization Analysis of Chaotic Characteristic Interconnected Electrical Power System;dynamical Analysis of Nose-Hoover Continuous Chaotic System Based on Gingerbreadman Discrete Chaotic Sequence;Cooperative NOMA-Based DCO-OFDM VLC System;dynamical Analysis of the Fractional-Order Memristive Band Pass Filter Chaotic Circuit;a New Pseudo-random Sequence Generator Based on a Discrete Hyperchaotic System;a Trademark Graphic Encryption Algorithm Based on Discrete Chaotic System and Its Performance Analysis;research on the Emotional Response Level of Museum Visitors Based on Lighting Design Methods and Parameters;design and Implementation of Intelligent Car for Light Environment Detection Based on Data Analysis;LED Floodlight Optical System Design;emotional Feedback Lighting Control System Based on Face Recognition;mapping Research on 1931 Chromaticity Diagram and Fengshui Five Elements theory;robust Power Control Algorithm Based on Probabilistic Constraints in Cognitive Radio Networks;dual Optimal Robust Power Control Algorithm Based on Channel Uncertainty;High-Performance LED Light Source Mixed Optical System Design;a Framework for Classification of Data Stream Application in Vehicular Network computing.

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Toward An Experimental Federated 6G Testbed: A Federated Leaning Approach

Toward An Experimental Federated 6G Testbed: A Federated Lea...

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ACS/IEEE international conference on Computer systems and Applications

作者： Hisham A. Kholidy Salim Hariri Dept. of Networks and Computer Security College of Engineering SUNY Polytechnic Institute Utica NY USA Dept. of Electrical and Computer Engineering Univ. of Arizona Tucson AZ

ISBN: (纸本)9798350310092

With the development of the smart systems such as smart city, smart buildings, smart industries, the need for a highly reliable, scalable, and secure communications using high-data-rate and low latency networks such as 6G networks is increased. the artificial intelligence and machine learning (AI/ML) will be pervasive and of key relevance across the security technology stack and architecture in the 6G networks. Despite the advantages of the 6G networks, sophisticated cyberattacks can disrupt the operation of 6G critical infrastructures and associated services. In this paper, we present a novel methodology to create a federated cyber testbed as a service (FCTaaS) that can be offered as a ubiquitous cloud service. Due to the widespread usage of Machine Learning (ML) in critical decision processes of 5G/6G resource management and their applications, there is an exponential growth in cyberattacks to maliciously manipulate the ML algorithms and consequently influence their decision process in favor of the attackers. Currently, there are many isolated cyber testbeds; however, little research has focused on methods to automatically build a federated cyber testbed in general and especially in 6G testbeds. In this paper, we show how to use the FCTaaS services can be used to seamlessly compose a federated cyber testbed that allows researchers to experiment with and evaluate different algorithms to implement different algorithms to conduct data analytics, cybersecurity, and resilient algorithms. In particular, we will show how the FCTaaS can be used to develop highly efficient and accurate federated learning algorithms that can tolerate a wide range of attacks against ML algorithms such as data poisoning and ML model attacks.

关键词： 6G mobile communication Cloud computing Smart buildings Federated learning Smart cities Reliability engineering Data models

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Mechanism Analysis of Injury Tools Based on Fracture Mechanics Model and Collision Dynamics Model 23

Mechanism Analysis of Injury Tools Based on Fracture Mechani...

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proceedings of the 6th international conference on Information Technologies and Electrical Engineering

作者： Yiyang Chen School of International Education Wuhan University of Technology China

ISBN: (纸本)9798400708299

In the forensic injury identification work, the inference of the injury tool is very important, which directly affects the efficiency of tracking the murder weapon at the scene and the possibility of case detection. this article conducts an in-depth analysis of the inference process of injury tools based on fracture mechanics, elastic mechanics, and system control discipline models. First, for the wound conditions caused by sharp tools in different human body parts, a model based on the finite difference method was constructed to model the wound shape in detail. through the analysis of wound boundary stress and related reasonable assumptions, the basic model of the wound formation process was determined. Secondly, the mechanism of collision between blunt injury tools and the human body was studied, a physical collision dynamics model was established, and the Laplace transform and finite difference method were used to solve the model. Finally, based on the aforementioned simulation results, an inversion model of the injury tool was constructed. In summary, this study uses the Westergard complex function method to quantify the lower boundary boundary conditions of the model. Calling the unit step function to simulate the actual force effect is beneficial to simplification of the model. Using multidisciplinary models and methods, through mathematical modeling and simulation analysis, the key issues regarding injury tools in forensic injury identification are solved, providing strong support for case detection.

关键词： Complex function methods Convection diffusion equations Fracture mechanics Spring oscillator systems Westergard

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Multi-Parametric Characterization of Partial Discharges for Typical Defects within Low-Carbon Combination Appliances 23

Multi-Parametric Characterization of Partial Discharges for ...

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proceedings of the 6th international conference on Information Technologies and Electrical Engineering

作者： Zhaoyu Peng Fangrong Zhou Zhou Yang Xiaohui He Yan Tang Research Institute of Electric Power Science of Yunnan Power Grid Co. China Nujiang Power Supply Yunnan Power Grid Co. China Lijiang Power Supply of Yunnan Power Grid Co. China

ISBN: (纸本)9798400708299

With the surge of building green and environmentally friendly new power systems mainly based on new energy sources, SF6/N2 gas mixture combination electrical appliances have taken shape and have a broad application prospect. However, SF6/N2 gas mixture shows large differences with SF6 gas insulation in physical and chemical properties, electrical characteristics and insulation failure mechanisms, while the operation and maintenance techniques and experiences of traditional power equipment are hardly applicable to low-carbon combined electrical appliances. In this paper, a SF6/N2 partial discharge test platform with typical discharge defects is constructed to study the spectral characteristics of typical discharges and their evolution based on multi-physical parameters. the results show that the discharge maximum of the pulse current signal and the repetition rate of the optical signal can be used to classify the discharge along the surface defects into three stages: the onset stage, the development stage and the near breakdown stage. the research results provide a basis for the formation of operation monitoring means and partial discharge diagnostic evaluation standards applicable to environmentally friendly GIS equipment.

关键词： GIS Multi-parameter Partial discharge SF6/N2 Typical defects

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Power Curve Estimation of a Wind Turbine Considering Different Weather Conditions using Machine Learning algorithms

Power Curve Estimation of a Wind Turbine Considering Differe...

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international Middle-East Power System conference, MEPCON

作者： Mostafa Al-Gabalawy Haitham S. Ramadan Mohammed A. Mostafa Shimaa A. Hussien Electronics Communications Computers Engineering Department El-Safawa High Institute of Engineering Cairo Egypt Electrical Power and Machines Dept Faculty of Engineering Zagazig University Zagazig Egypt Electrical Power and Machines Dept Faculty of Engineering Must University 6th October Cairo Egypt Electrical Power and Control Department Pyramids Higher Institute for Engineering and Technology 6th October Cairo Egypt

ISBN: (纸本)9781665463645

theoretically, the output of the wind turbine might be estimated based the most known power equation that depends mainly in the wind speed. there are many issues appeared in the phase of the estimating and control while applying this equation due to ignoring many weather conditions. this paper introduces a multivariate estimation for the power curve of the wind turbine considering the weather conditions such as wind speed, air density, wind turbulence, and wind share. there variables are termed features, and a lot of measurement has been occurred to collect all possible data for these features, where measurements (system data) exceed 47,000 points. this data is proceeded mainly by three steps of the data sciences; exploratory data analysis (EDA), data processing, and building the model, using Python programming language, where it gives more flexibility more than the other languages. the power curve estimation is executed using different machine learning tools such as linear regression, polynomial regression, random forest regression, gradient boost (G Boost), and extreme gradient regression (XGBoost). A comparative study is introduced considering the R-square and the root mean square error. From the results, XGBoost learning tool provides the best performance in terms of root mean square error (RMSE). the RMSE value decreases to 6.404 while using the proposed algorithm compared to (6.631, 6.6721, and 9.072) attained through the alternative G Boost, forest random, and 4th-degree polynomial respectively.

关键词： Machine learning algorithms Wind energy Wind speed Estimation Mathematical models Wind turbines Power systems

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A Machine Learning Based Novel Approach of Predicting international Roughness Index(IRI) from Traffic Characteristics using Random Forest Regression 23

A Machine Learning Based Novel Approach of Predicting Intern...

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proceedings of the 2023 6th Artificial Intelligence and Cloud computing conference

作者： Abrar Rahman Abir Computer Science and Engineering Bangladesh University of Engineering and Technology Bangladesh

ISBN: (纸本)9798400716225

international Roughness Index (IRI) stands as a well-established metric for assessing pavement roughness and overall condition. Predicting IRI is crucial for maintaining pavement infrastructure. In this study, we present a novel approach to predict IRI using Random Forest regression, focusing exclusively on traffic characteristics as predictive variables. Existing studies considered a wide range of factors, including pavement materials, climate, structural attributes, and various pavement distress indicators alongside traffic data where we developed our model using only traffic characteristics. We have used Long-Term Pavement Performance Program (LTPP) dataset for training our models. We have compared our Random forest model with three other models (XGBoost,SVM regression,Gradient Boosting). R squared value and Mean Squared Error (MSE) were taken as performance evaluation metrics. Random forest showed R squared value of 0.70623 and MSE of 8.22 × 10−6 where Gradient Boosting, XGBoost and SVM had R squared value of 0.5737, 0.497, and 0.3455 *** also compared between two hyperparameter tuning methods(Random Search and Grid Search) used in our models and found Random search to perform better. We have also presented a comparative analysis of existing IRI prediction models with our model. Finally we present a SHAP(SHapley Additive exPlanations) analysis to interpret our model and find the contribution of each input feature on our model. We found Annual ESAL (Equivalent Single Axle Load) to be the most dominant factor to predict IRI from traffic characteristics.

关键词： Additional Key Words and Phrases: Machine Learning Pavement Roughness Random Forest Regression

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A propose model analysis of investor factors on interests and decisions of using online investment applications 6

A propose model analysis of investor factors on interests an...

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6th international conference on Information Management and Technology, ICIMTech 2021

作者： Fernando, Erick Angelia, Verent Meiryani Pradipta, Indry Aristianto Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Economics Communication Accounting Department Jakarta11480 Indonesia Bina Nusantara University BINUS Business School Undergraduate Program International Marketing Program Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Online investment applications are digital applications that support investors in investing, allowing investors to obtain information, sell and buy the investment products they want. In 2020, there will be a COVID-19 pandemic that requires all people to stay at home, and increasingly massive technological developments have increased internet users and users of online investment applications. the problem is that there is a loss from 2009 to 2019, reaching 29 trillion rupiahs caused by various problems, one of which is a fraudulent investment. this study uses literature studies from various journals. this study results from the factors of investor acceptance of online investment applications using the UTAUT2 method as the basis for the theory. the variables used are Facilitating Conditions, Hedonic Motivation, Performance Expectancy, Effort Expectancy, Social Influence, Price Value, Habit, and adding additional variables Perceived Risk, Trust. as well as Awareness-Knowledge. © 2021 IEEE.

关键词： Investments

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Suitability of School Furniture and Correlation with Pain Prevalence Among Slovenian 6th to 9th Graders 8th

Suitability of School Furniture and Correlation with Pain Pr...

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8th international Ergonomics conference, ERGONOMICS 2020

作者： Podrekar, N. Kastelic, K. Burnard, M. Šarabon, Nejc InnoRenew CoE Izola Slovenia Faculty of Health Sciences University of Primorska Izola Slovenia Andrej Marušič Institute University of Primorska Koper Slovenia

ISBN: (纸本)9783030669362

School furniture has been recognised as a potential risk factor for musculoskeletal pain among primary school pupils. the aim of this study was to assess student-furniture mismatch and to determine the association between unsuitable school furniture and musculoskeletal pain among Slovenian pupils. Firstly, pupils’ body dimensions were measured, and student-furniture mismatch was calculated. Secondly, pain prevalence was assessed using adjusted Nordic questionnaire. A total of 68 pupils from 6th to 9th grade (age 12 ± 1, 44% males) participated in the study. Seat height was unsuitable for 90% of pupils and desk height for 82% of pupils. One third of pupils reported knee pain and more than one fifth of pupils reported neck, shoulder, upper, and lower back pain. Logistic regression revealed no significant impact of school furniture on pain occurrence among pupils. However, the data indicated upper back pain might be associated with unsuitable backrest. the results showed that school furniture in Slovenian primary school is unsuitable for most pupils. the association between furniture and pupils’ musculoskeletal pain needs further investigation. Redesign of school furniture is desired, and the anthropometric data obtained could be considered when designing new school furniture. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

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Application of boruta feature selection in enhancing financial distress prediction performance of hybrid MLP_GA 22

Application of boruta feature selection in enhancing financi...

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proceedings of the 6th international conference on Future Networks & Distributed systems

作者： Ahmed Khedr Magdi Bannany Sakeena Kanakkayil Maqsudjon Yuldashev Department of Computer Science University of Sharjah UAE College of Business Administration Umm Al Quwain University UAE Department of International Business Management Tashkent State University of Economics Uzbekistan

ISBN: (纸本)9781450399050

Financial distress prediction (FDP) has been a subject of extensive and ongoing research because of its significance in both internal and external components of enterprises including investors and creditors. Financial institutions must to be able to foresee financial difficulty in order to allow them for evaluating the financial health of businesses and individuals. Data pre-processing techniques have been found to increase the efficacy of prediction models, and many research consider feature selection as a pre-processing step before creating the models. the creation of efficient feature selection algorithms is one of the main challenges facing FDP. In this study, we present a hybrid methodology for predicting financial distress using a Multi-Layer Perceptron and Genetic Algorithm (MLP_GA) model with boruta automated feature selection. the proposed model is designed on genetic algorithm- based tuning of the crucial MLP hyperparameters, including Network depth, Dense layer activation function, Network width, and Network optimizer for a reliable prediction. this paper investigates how boruta algorithm based feature selection method improve the accuracy of our MLP_GA algorithm. We access the FDP performance utilizing samples of enterprises based in MENA area. Resampling with k-fold evaluation metrics is employed in the experiments. the experimental results indicate that the adoption of the boruta automated feature selection method has significantly enhanced the prediction performance and accuracy of the FDP model.

关键词： Boruta feature selection

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6th international conference on Positive systems, POSTA 2018

6th International Conference on Positive Systems, POSTA 2018

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6th international conference on Positive systems, POSTA 2018

ISBN: (纸本)9783030043261

the proceedings contain 27 papers. the special focus in this conference is on Positive systems. the topics include: Travel Behavior Analysis for Free-Floating Bike Sharing systems Based on Markov-Chain Models;positive L1 Controller Design for Positive Piecewise Homogeneous Markovian Jump systems;stabilization Design for Uncertain Linear Positive systems with Time-Delay;stability Analysis of Discrete-Time Periodic Positive systems with Delays;extension of Lossless Negative Imaginary Lemmas to systems with Poles at the Origin;SDA-RVM Based Approach for Surge Fault Detection and Diagnosis During Aero-Engine Take-Off Process;closed-Loop Nash Games for Interconnected Positive Nonlinear systems with H∞ Constraint;note on Interval Observer Design Based on Positive System theory;a New Interval Observer Design Method with Application to Fault Detection;novel Approaches to Stability and Stabilization of Positive Switched systems with Unstable Subsystems;A Left Eigenvector Producing a Smooth Lyapunov Function of ISS Networks;pinning Control Design for Stabilization of Boolean Networks From Constructed Boolean Control Networks;improved Multiple-State Observer Design for Boolean Control Networks;identifiability of Boolean Networks via Output Data and Initial States;oscillation Analysis of Abscisic Acid Signal Transduction Network: A Semi-tensor Product Approach;a New Energy-Efficient Flooding Broadcast Time Synchronization for Wireless Sensor Networks;bounded Synchronization for a Class of Coupled Complex Networks with Nonidentical Nodes;on the Common Linear Copositive Lyapunov Functions for Compartmental Switched systems;robust Stabilization for Constrained Switched Positive Linear systems via Output-Feedback;stability Analysis of Discrete-Time Switching Positive System with Delays: Homogeneous Polynomial Copositive Lyapunov Function Method.

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