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34th International Scientific Symposium Metrology and Metrology Assurance, MMA 2024

作者： Miroshnyk, Maryna Zaichenko, Olga Miroshnyk, Anatolii Hapon, Nataliia Pakhomov, Yurii Klymenko, Liubov V. N. Karazin Kharkiv National University Dept. of Theoretical and Applied System Engineering Kharkov Ukraine Kharkiv National University of Radioelectronics Dept. of Design and Operation of Electronic Devices Kharkiv Ukraine Kharkiv National University of Radioelectronics Dept. of Design Automation Kharkiv Ukraine O.M. Beketov National University of Urban Economy in Kharkiv Dept. of Computer Science and Information Technologies Kharkiv Ukraine Ukrainian State University of Railway Transport Dept. of Specialized Computer Systems Kharkiv Ukraine

ISBN: (纸本)9798350352832

The research aim is to derive connection between the parameters of dielectric resonators frequency shift and the warp defect of 3D printing filament. Object of investigation are the processes of interaction of electromagnetic wave of a dielectric resonator with filament for 3D printing. The proposed model describes the connection between the frequency shift and defect warp of filament frequency pulling depending on the load. © 2024 IEEE.

关键词： Q factor measurement

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Proposal for a Job Management Tool for Deep Learning in a Mixed Environment of Heterogeneous GPUs

Proposal for a Job Management Tool for Deep Learning in a Mi...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Nobuyuki Ishii Aoi Kondo Hiroaki Koga Dept. Control and Information Systems Engineering National Institute of Technology Kurume College Fukuoka Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

In recent years, the use of GPUs for computation has become commonplace in deep learning. It is not uncommon for deep learning labs to procure GPU servers for computation without standardising specifications such as GPU architecture and VRAM capacity, resulting in a heterogeneous GPU server environment. Container technology is also widely used as a method of isolating software execution environments. Among them, Docker is the de facto standard for container platforms. In this paper, we have developed a management system that can allocate deep learning training jobs to GPU servers (workers) using container technology to absorb differences in execution environments in heterogeneous GPU server environments. In experiments, we confirmed that the proposed system can estimate the completion time of training for each worker and can detect Out-Of-Memory (OOM) of GPUs in advance. We were able to confirm in advance the “inference time” and the “possibility of OOM”, which are difficult to predict when performing deep learning. We have succeeded in obtaining the “predicted value of learning time” and “possibility of OOM” that engineers need to know in advance, and have shown that the proposed system is effective in deep learning tests.

关键词： Deep learning Training Graphics processing units Containers Software Servers Resource management Proposals Standards Testing

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Verification of the Effect of Label Modification on Neural Network Learning

Verification of the Effect of Label Modification on Neural N...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Tsubasa Oka Hiroaki Koga Dept. Control and Information Systems Engineering National Institute of Technology Kurume College Fukuoka Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

This study focuses on the problem of subjective labeling that arises in the application of machine learning with neural networks to the medical field. The first problem is that of subjectivity, which arises because the labeling of medical data relies on expert knowledge and the process can be subjective. The second problem is the reduced information content of labels caused by discrete and quantitative labeling. If the intermediate values between the discrete values also have evaluative meaning, the detailed information is lost due to the discrete numerical labeling. We focus on such instability in labels and propose an approach to label modification using values inferred during neural network training. Specifically, we consider labels that are discrete, quantitative numbers as continuous numbers, compare the inferred value with the label value at each step in the learning phase, and compare the label value with the inferred value at each step in the learning phase. The update method is to give the label a sign-responsive variation. The goal is to modify each label using the value inferred by the inference made during learning in this way. As an experiment, we conducted an experiment on label modification under pseudo-conditions to verify its effectiveness. The results showed that when the amount of label modification was controlled by a constant, the generalization performance and the nature of label modification differed depending on the constant. In particular, generalization performance was improved when the constant of the label modification amount was determined so that the label value was separated from the inferred value at each step.

关键词： Training Neural networks Machine learning Labeling

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Dynamic Adaptation Gain Design and Tuning for Threat Discrimination 63

Dynamic Adaptation Gain Design and Tuning for Threat Discrim...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Zhang, Kangkang Chen, Kaiwen Polycarpou, Marios M. Parisini, Thomas University of Cyprus Kios Research and Innovation Center of Excellence Dept. of Electrical and Computer Engineering Nicosia1678 Cyprus Imperial College London Dept. of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Italy

ISBN: (纸本)9798350316339

Considering potential threats to cyber-physical systems such as component faults and stealthy cyber-attacks, an adaptive observer-based threat discrimination method is proposed for identifying the occurring threat type. Typically, stealthy attacks have only weak effects easily obscured by disturbances on the system outputs. To solve this problem, a parameter adaptation algorithm based on a newly designed dynamic adaptive gain generator is proposed, aiming at improving the sensitivity of the adaptive threat discrimination scheme to potential threats. Only the strictly positive real condition of the proposed gain generator sufficiently ensures the stability of the adaptive observer error system. A moment-matching method is then developed to determine the proper parameters of the gain generator, allowing for the improvement of the sensitivity of the threat discriminators. A numerical example to demonstrate the effectiveness of the proposed methodology is presented. © 2024 IEEE.

关键词： Cyber attacks

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NIS Test for Selecting the Order of Local Polynomial Model 3

NIS Test for Selecting the Order of Local Polynomial Model

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3rd International Conference on Communications, Information, Electronic and Energy systems, CIEES 2022

作者： Nikovski, Plamen Titova, Tanya Mihov, Vasil University of Food Technologies Dept. of Electronics And Electrical Engineering Plovdiv Bulgaria University of Food Technologies Dept. of Automation Information And Control Systems Plovdiv Bulgaria

ISBN: (数字)9781665491495

ISBN: (纸本)9781665491495

This work presents an approach that uses normalized innovations squared (NIS) test to determine the order of a local polynomial trend model of a humidity sensor signal. Compared to the classical Akaike information criterion which balances model fit and model complexity NIS test allows to determine the lowest order of the model at which a consistent Bayesian estimation is possible. This approach is useful when signal processing algorithms, such as those for data fusion in multi-sensor systems, contain a Kalman filter, because a successful NIS test is a prerequisite for normal filter operation. The results of the study show that the response of the SHT31-DIS sensor under a step change in humidity is adequately described by a second-order local polynomial model, and this implies a consistent estimation for other input variations. © 2022 IEEE.

关键词： Polynomials

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Impact of Rain Attenuation on Short-Range Frequency Operations in 5G Networks

Impact of Rain Attenuation on Short-Range Frequency Operatio...

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International Conference on Microwaves, Radar and Wireless Communications

作者： Sandra Bazebo Matondo Pius Adewale Owolawi Dept. Computer Systems engineering Tshwane University of Technology Pretoria South Africa

ISBN: (数字)9788396972613

ISBN: (纸本)9798350371611

Attenuation caused by rain poses significant challenges for millimeter wave communication systems, especially in regions with heavy rainfall. Accurate attenuation prediction is required to set realistic performance expectations and ensure robust millimeter-wave communications links for 5G networks even in challenging environments. The aim of this work is to model and optimize rain attenuation in terrestrial 5G using evolutionary algorithms. The attenuation is modelled using symbolic regression that takes rain rate, path length, and frequencies between 24.25 GHz and 86 GHz as input. Next, differential evolution is used to determine the optimal parameters that best fit the model. The model is found to predict the attenuation with minimal errors. The proposed model’s performance is evaluated against the ITU-R, Crane, Da Silva and Melo models. The proposed model’s accuracy and performance are further validated based on the obtained MSE, MAE and R-squared values.

关键词： Rain Accuracy Cranes 5G mobile communication Evolutionary computation Predictive models Millimeter wave radar

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Enhancing Train Transportation in Sri Lanka: A Smart IOT based Multi-Subsystem Approach using MQTT

arXiv

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arXiv 2025年

作者： Balasingham, Dhanushka Samarathunga, Sadeesha Bandara, Anuththara Arachchige, Gayantha Godakanda Gamage, Narmada Wijayaraja, Jaliya L. Dept. of Computer Systems Engineering Sri Lanka Institute of Information Technology Sri Lanka

This research proposes a system as a solution for the challenges faced by Sri Lanka’s historic railway system, such as scheduling delays, overcrowding, manual ticketing, and management inefficiencies. It proposes a multi-subsystem approach, incorporating GPS tracking, RFID-based e-ticketing, seat reservation, and vision-based people counting. The GPS-based real-time train tracking system performs accurately within 24 meters, with the MQTT protocol showing twice the speed of the HTTP-based system. All subsystems use the MQTT protocol to enhance efficiency, reliability, and passenger experience. The study’s data and methodology demonstrate the effectiveness of these innovations in improving scheduling, passenger flow, and overall system performance, offering promising solutions for modernizing Sri Lanka’s railway infrastructure. © 2025, CC BY.

关键词： Global positioning system

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Enacting Rural Empowerment: Implementing Cooperative Commerce in Communities using Machine Learning

Enacting Rural Empowerment: Implementing Cooperative Commerc...

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Networking and Communications (ICNWC), International Conference on

作者： Skandarsini R R Sangavi M S Ravitla Harika Varun Cm Dept. of Computer Science and Business Systems RMK Engineering College Thiruvallur India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

In order to revitalize rural economies through the empowerment of self-help organizations and the promotion of economic growth, this study offers a revolutionary cooperative commerce platform. The platform integrates cutting-edge features such as digital ledger and inventory management for sellers, demand forecasting, and dynamic pricing, thereby fostering optimized production and efficient marketplaces. Central to the platform's efficacy is the implementation of user segmentation, which categorizes users based on income and spending behaviors, enabling targeted marketing strategies and continuous application improvements to enhance user engagement and profitability. Leveraging machine learning algorithms, personalized user experiences are cultivated, elevating buyer satisfaction and overall engagement. Additionally, administrative tools provide valuable insights for informed decision-making. Preliminary results demonstrate improved financial management for self-help groups, heightened economic activities, and an enriched user-centric experience. Identified challenges and proposed future enhancements underscore the platform's commitment to adaptability and responsiveness to the evolving needs of rural communities, ensuring sustained relevance and impact.

关键词： Profitability Decision making Demand forecasting Pricing Machine learning Production Organizations

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Predicting Lifespan of Ground-to-Air Multipath Components in mmWave UAV Channels

Predicting Lifespan of Ground-to-Air Multipath Components in...

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IEEE Conference on Wireless Communications and Networking

作者： Wahab Khawaja Rune H. Jacobsen Sajid Hussain Ismail Guvenc Dept. Electrical and Computer Engineering Aarhus University Aarhus Denmark Dept. Computer Systems Engineering Mirpur Univ. Science and Technology Mirpur AJK Pakistan Dept. Electrical and Computer Engineering North Carolina State University Raleigh NC USA

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In mobile ground-to-air (GA) propagation channels, the birth and death of multipath components (MPCs) are frequently observed, and the wide-sense stationary uncorrelated scattering (WSSUS) assumption does not always hold. Several methods exist for tracking the birth and death of MPCs, however, to the best of knowledge of authors, there is no existing literature that addresses the prediction of the lifespan of the MPCs in non-WSSUS GA propagation channels. In this work, we consider the GA channel as non-WSSUS and individual MPCs across receiver positions are represented as time series based on the Euclidean distance between channel parameters of the MPCs. These time series representations, referred to as path bins, are analyzed using a semi-Markov chain model. The channel parameter variations and dependencies between path bins are used to predict the lifespan of path bins using weighted sum method, machine learning classifiers, and deep neural networks. For comparison, the birth and death of path bins are also modeled using a Poisson distribution and a Markov chain. Simulation results demonstrate that deep neural networks offer highly accurate predictions for the lifespan (including death) of MPC path bins in the considered GA propagation scenario.

关键词： Analytical models Simulation Time series analysis Scattering Artificial neural networks Machine learning Receivers Euclidean distance Autonomous aerial vehicles Millimeter wave communication

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Vyoma-ADS : An AI-Driven Tool for Adaptive Monitoring and Anomaly Detection for Space Agriculture

Vyoma-ADS : An AI-Driven Tool for Adaptive Monitoring and An...

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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： Rajdeep Das Vaishnavi Sharma Priyanka Kumari Abhishek Raj Sudipta Das Indrajit De Dept. of Computer Science and Business Systems Institute of Engineering and Management Kolkata India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

As humanity ventures further into space exploration, securing sustainable resources becomes paramount. The success of space farming depends not only on the ability to cultivate crops but also on the effective management of environmental conditions. Introducing Vyoma-ADS: an innovative system that integrates IoT technology with an adaptive Anomaly Detection System, specifically designed for space agriculture. Our study underscores the crucial role of environmental monitoring within space shuttles, where even minor fluctuations can have a significant impact on plant growth and productivity. To tackle these challenges, we designed a compact IoT-enabled system to monitor the moisture content in the soil, the temperature and humidity of the surrounding environment, and the ppm value of unwanted gases in the atmosphere. Through rigorous testing and analysis, we established optimal thresholds conducive to plant growth in the unique conditions of space. Recognizing the inherent variability and occasional anomalies in environmental data, we investigated three distinct machine learning models for anomaly detection: Isolation Forest, One-Class Support Vector Machines (OCSVM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Our findings reveal that DBSCAN outperforms other techniques in identifying anomalies amidst fluctuating data. Additionally, we detail our approach to data visualization using a SaaS cloud platform, which enhances the comprehensibility of the study and facilitates better decision-making processes. This paper demonstrates how the integration of IoT monitoring and advanced anomaly detection methods can significantly improve the reliability and efficiency of space agriculture. By leveraging these technologies, we aim to create a robust system capable of maintaining optimal growing conditions for plants in space.

关键词： Temperature sensors Cloud computing Atmospheric modeling Space shuttles Data visualization Machine learning Humidity Agriculture Sensors Anomaly detection

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