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2nd international Conference on Forthcoming Networks and Sustainability in the AIoT Era, FoNeS-AIoT 2024

2nd International Conference on Forthcoming Networks and Sus...

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2nd international Conference on Forthcoming Networks and Sustainability in the AIoT Era, FoNeS-AIoT 2024

ISBN: (纸本)9783031628702

The proceedings contain 69 papers. The special focus in this conference is on Forthcoming Networks and Sustainability in the AIoT Era. The topics include: Finding the Flies in the Sky Using Advanced Deep Learning algorithms;selective Harmonic Elimination in multilevel Inverters Using the Bonobo optimization Algorithm;optimizing Solar Energy Efficiency Through Automatic Solar Tracking Systems;instant High Starting Current Protection System for Induction Motor Based Reducing the Starting Speed;robot Hand-Controlled by Gyroscope Sensor Using Arduino;human Activity Recognition Using Convolutional Neural Networks;cyberbullying Detection for Urdu Language Using Machine Learning;A Cybersecurity Procedure to Vulnerabilities Classification of Windows OS Based on Feature Selection and Machine Learning;a Survey of Next Words Prediction Models;blockchain-Based Secured Estate Property Registration System: Baghdad Estate Registration as a Case Study;a Mobile Robot with an Autonomous and Custom-Designed Control System;A Web-Based Disease Prediction System Using Machine Learning algorithms and PCA;enhancing IoT Device Security: A Comparative Analysis of Machine Learning algorithms for Attack Detection;Enhancing Facial Recognition Accuracy and Efficiency Through Integrated CNN, PCA, and SVM Techniques;a Survey of Machine Learning Assistance in Seismic Interpretation;designing and Simulation of Three Phase Grid-Connected Photovoltaic System;Creating Synthetic Test Data by Generative Adversarial Networks (GANs) for Mobile Health (mHealth) applications;deep Neural Networks for Fetal Health Monitoring Through Cardiography Data Analysis;Detecting BGP Routing Anomalies Using Machine Learning: A Review;calculation of Bit-Error Probability for Direct-Sequence Spread-Spectrum Communications with Multiple-Access Interference of Rayleigh Distribution;determining the Digits of Turkish Sign Languages Using Deep Learning Techniques.

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Construction of digital twin workshop integrated with edge computing and deep learning 4

Construction of digital twin workshop integrated with edge c...

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2024 4th international Conference on Artificial Intelligence and Industrial Technology applications, AIITA 2024

作者： Liu, Xinning Xie, Tianxia Wu, Bo Li, Mingjun Li, Haotian School of Computer and Software Engineering Dalian Neusoft University of Information Dalian116023 China

This study addresses the challenges of real-time data synchronization and big data processing in the construction of digital twin workshops under the background of intelligent manufacturing. A solution that integrates edge computing with deep learning technology is proposed. Through a multi-layered, modular framework structure, this solution achieves real-time synchronous mapping and interaction between physical workshops and virtual workshops, effectively improving production efficiency, reducing maintenance costs, and ensuring product quality. The edge computing layer in the research is responsible for preprocessing heterogeneous data from multiple sources and making rapid response decisions. Simultaneously, deep learning algorithms are employed to conduct in-depth analysis, forecasting, and optimization of the production process. Case studies demonstrate that this solution can significantly improve equipment utilization and optimize workpiece scheduling, validating its application value in actual manufacturing environments. © Published under licence by IOP Publishing Ltd.

关键词： Smart manufacturing

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Adaptive Simulated Binary Crossover with Bayesian optimization for Industrial applications

Adaptive Simulated Binary Crossover with Bayesian Optimizati...

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international Russian Smart Industry Conference (SmartIndustryCon)

作者： Ali Deeb Vladimir Khokhlovskiy Viacheslav Shkodyrev Higher School of Cyberphysical Systems and Control Peter the Great St. Petersburg Polytechnic University Saint Petersburg Russian Federation

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

Genetic algorithms are widely used as global optimization solvers for complex and non-convex problems in industrial applications (resource and load planning, workshop logistics, process control, etc.). Simulated Binary Crossover (SBX) is a common operator in real-coded Genetic algorithms for continuous optimization, but it typically relies on a fixed distribution index parameter that may not stay optimal throughout the evolutionary process. To address this, we propose a Transfer Learning-Based Adaptive Simulated Binary Crossover approach, which employs Bayesian optimization to dynamically update the parameter during the Genetic Algorithm run. The parameter is periodically re-optimized based on the population's current dynamics. Tested on benchmark functions including Rosenbrock, Rastrigin, Ackley, and Griewank, our method achieves faster convergence and higher solution quality than standard Genetic algorithms with a fixed parameter.

关键词： Industries Adaptive systems Benchmark testing Bayes methods Indexes optimization Tuning Standards Genetic algorithms Convergence

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optimization of 360-based photogrammetric pipeline for narrow and dark indoor surveys 8

Optimization of 360-based photogrammetric pipeline for narro...

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8th international ISPRS workshop LowCost 3D - Sensors, algorithms, applications

作者： Pamart, Anthony Maillard, Nicolas Comte, Florent Bergerot, Laurent MAP Modèles et Simulations pour l'Architecture et le Patrimoine UPR 2002 CNRS Marseille13009 France

Photogrammetry has been commonly used for surveying architectural structures over the past two decades;however, this technique faces limitations, particularly in complex, narrow, or dark spaces due to optical constraints. Recently, advanced technologies such as 360° cameras have started to influence established photogrammetric protocols. Capturing an entire scene in a single spherical shot offers significant value for the rapid and efficient surveying of indoor spaces, although new methodologies are needed for data management, storage, and computational optimization. Building on previous work conducted within the SESAMES Project, a validated pyramidal scheme highlights the limitations of a single-camera linear sequence and emphasizes the importance of orienting "omnidirectional" sensors. This paper introduces SQUILLIDAE (Spherical acQUisition Instrument for Low-Light and InDoor nArrow spacEs), a cost-efficient and versatile multi-camera system designed to enhance photogrammetric techniques derived from spherical imagery. It details our approach from initial investigations to final design choices and includes a comparative analysis of results, as well as several perspectives for future technical improvements. Copyright: © 2024 Anthony Pamart et al.

关键词： Information management

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Bits to BNNs: Reconstructing FPGA ML-IP with Joint Bitstream and Side-Channel Analysis

Bits to BNNs: Reconstructing FPGA ML-IP with Joint Bitstream...

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IEEE international Symposium on Hardware Oriented Security and Trust (HOST)

作者： Olney, Brooks Karam, Robert Univ S Florida Tampa FL 33620 USA

ISBN: (纸本)9798350300628

Energy-efficient hardware acceleration platforms for edge deployment of artificial intelligence (AI) and machine learning (ML) applications has been an ongoing research endeavor. Many efforts have focused on optimizing the algorithms and compute structures for use in resource-constrained hardware such as field-programmable gate arrays (FPGAs). Indeed, the difficult nature of crafting the best model makes the ML model itself a valuable intellectual property (IP) asset. This can be problematic, as the IP can now be exposed to an attacker through physical interfaces, enabling threats from side-channel analysis (SCA) attacks. One of the more devastating attacks is the model extraction attack, which threatens piracy and cloning of the valuable IP. While the problem of SCA-based model extraction on FPGA-deployed neural networks has been well-studied, it does not capture the full picture of what vulnerabilities may be present in those platforms. In this paper, we demonstrate how bitstream analysis can be used to obtain neural network parameters and connectivity information from block RAMs (BRAMs). We leverage the knowledge gleaned from the bitstream to mount a power SCA attack to further refine the network reconstruction effort. This is the first method that has approached the problem of ML-IP theft from the angle of FPGA bitstream analysis and suggests that further work is needed to improve security assurance for edge intelligence.

关键词： Constrained optimization

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Prediction and optimization of Electric Resistance of Printed Graphene Nanosheet Based on Artificial Intelligence

Prediction and Optimization of Electric Resistance of Printe...

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IEEE MTT-S international Microwave workshop Series on Advanced Materials and Processes for RF and THz applications (IMWS-AMP)

作者： Chen, Hao Huang, Tian-Yu Zhang, Yi-Yang Liu, En-Cheng Yang, Wu Lu, Wei-Bing Southeast Univ Sch Informat Sci & Engn State Key Lab Millimeter Waves Nanjing Peoples R China Southeast Univ Chien Shiung Wu Coll Nanjing Peoples R China

ISBN: (纸本)9781665478342

Graphene has attracted wide attention in the field of electromagnetism and has become a front runner in fabricating electronic devices, especially flexible ones. However, currently, there's no report on the prediction of its electric resistance, which is critical to an electric component. In this work, several machine learning (ML) and artificial neural network (ANN) models are trained to predict the electric resistance(R) of printed graphene nanosheets with only the knowledge of the concentration of graphene ink, the width, and the length of the printed nanosheet. Three different ML algorithms, i.e., Kriging, Efficient Global optimization (EGO), and Support Vector regression (SVR) are exploited, together with ANNs with different sets of parameters. Harboring high accuracy and yielding satisfactory results, the models can be applied to predict the resistance of graphene nanosheets in real-time working conditions. It paves the way for the wide application of printable graphene nanosheets in electric devices.

关键词： Graphene resistance machine learning artificial neural network

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Integrated Sensing System for Automotive applications 14

Integrated Sensing System for Automotive Applications

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14th international workshop on Structural Health Monitoring: Designing SHM for Sustainability, Maintainability, and Reliability, IWSHM 2023

作者： Ghannam, Mahmoud Farooq, Iskander Joseph, Roshan Gray, Cody Franklin, L.I. Kumar, Amrita Acellent Technologies Inc. United States Ford

ISBN: (纸本)9781605956930

The Department of Energy (DOE) Vehicles Group has identified a need for novel multifunctional composite materials and structures for the automotive industry that have the capability to reduce weight and volume as well as costs of "conventional" structural components by performing engineering functions beyond load carrying. As part of this project, Acellent in collaboration with Ford Motor Company is developing a sensing system for use in automotive applications. An advanced sensing system is needed to effectively distinguish impacts between different objects. Information received from the first contact between object and automobile bumper can be used to identify the object for vehicle applications. This paper will discuss the need and requirements for such a system and provide an overview of the development of a piezoelectric sensor system including the design, development and initial testing of a complete prototype of the system through various tests on bumpers. Acellent's SMART layer sensor design for the bumpers will be discussed along with their manufacturing and installation on the car bumper. The paper will also discuss potential optimization of the sensor locations. Detection algorithms were developed to distinguish between various objects that were used in the impact on the bumper. Additionally key parameters were considered when building the algorithms: • impact speed, • bumper stiffness and dimensions, • object stiffness, mass and size Testing was conducted using, for example, simulated small animal, stone, wooden board etc. Results from the testing of the complete prototype system for the impact detection for automobile applications will be presented. © 2023 by DEStech Publi cations, Inc. All rights reserved

关键词： Stiffness

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jSO and GWO algorithms Optimize Together 22

jSO and GWO Algorithms Optimize Together

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22nd Conference Information Technologies - applications and Theory, ITAT 2022

作者： Poláková, Radka Valenta, Daniel Silesian University in Opava Faculty of Philosophy and Science in Opava The Institute of Computer Science Opava Czech Republic

This paper deals with global optimization. There are many algorithms for global optimization in the literature. In this text, we focus on two effective optimizers. The first one is an adaptive version of Differential evolution algorithm which was the most successful version of the algorithm on CEC 2014 congress and is called the jSO algorithm. The second one is the Grey wolf optimizer which was introduced in 2014. We propose new algorithm cooperation in which both of these algorithms are used together to get better results when optimization problems are being solved. In our attempt, both algorithms take turns making the optimization process. We tested the proposed algorithm on four multi-modal functions on two levels of dimension. The results are quite promising. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Global optimization

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multilevel Load Balancing Algorithm for Domestic Heterogeneous Manycore Architecture

Multilevel Load Balancing Algorithm for Domestic Heterogeneo...

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international Symposium on Parallel and Distributed Processing with applications, ISPA

作者： Yi Ma Xin Chen Heng Guo Fang Li Xin Liu Tsinghua University Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Load imbalance often occurs in particle-in-cell simulations on parallel computing, which seriously affects the efficiency of applications. Due to the characteristics of multilevel parallelism and communication asymmetry of compute nodes in domestic heterogeneous manycore architecture, the impact of load imbalance is more prominent. The paper proposes a multilevel load-balancing algorithm for domestic heterogeneous manycore architecture. Inside the supernode, computing tasks are redivided based on manycore acceleration. Between the supernodes, a greedy-based communication mode is designed to minimize communication across supernodes. The experimental results show that the proposed algorithm achieves almost ideal dynamic load balance, and improves the performance of the evaporation module in two-phase flow simulation by 10.9-19.7 times for the 50 million-sized grid.

关键词： Heuristic algorithms Computational modeling Computer architecture Parallel processing Load management Nonhomogeneous media Supercomputers Evaporation optimization Load modeling

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Computational optimization of a vehicle dynamic simulation model

Computational optimization of a vehicle dynamic simulation m...

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international workshop/Symposium on Distributed Simulation and Real-Time applications (DS-RT)

作者： Ruslan Shaiakhmetov Department of Computer Science and Engineering Alma Mater Studiorum—Università di Bologna Cesena Italy

ISBN: (数字)9798331527211

ISBN: (纸本)9798331527228

The importance of simulation in motor-sport racing has steadily increased. Fine-tuning these simulators is complex and traditionally manual, relying on engineers’ *** study develops a virtual driver and automates a parameter tuning pipeline for a race car simulator. The virtual driver based on the Data-enabled Predictive Control (DeePC) algorithm outperforms traditional Proportional Integral Derivative (PID) and Model Predictive Control (MPC) controllers and can potentially be extended to compute an optimal trajectory.A YAML-configured optimization pipeline with a custom Domain-Specific Language (DSL) was developed to accelerate parameter tuning. However, parameter interdependency is problematic for traditional optimization algorithms. Bayesian optimization can potentially find a global minimum despite interdependencies and converge to the optimum faster than traditional optimization methods.

关键词： Heuristic algorithms Pipelines Prediction algorithms Trajectory Bayes methods Automobiles optimization Tuning Vehicles Predictive control

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