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2020 International Online Conference on engineering Response to COVID-19, IOCER-COVID-19 2020

作者： Majumder, Annwesha Banerjee Gupta, Somsubhra Singh, Dharmpal Majumder, Sourav Dept. of Information Technology JIS College of Engineering Kalyani Nadia West Bengal India Amity University Kolkata WestBengal India Dept. of Computer Science and Engineering JIS College of Engineering Kalyani Nadia West Bengal India Wipro Technologies Kolkata West Bengal India

World is now at a critical condition. Covid-19 is the disease caused by corona virus newly identified [1], the pandemic affecting the human being a lot. As on date the total active cases in India are 3.23 Million and death cases are 59,449. Many scientists and medical practitioners are working hard to fight against this, in search of proper medicine and vaccine. Research is also going on in the field of machine learning and AI to predict the spread of disease and also in identification of the presence of the virus in human body, which will help the field of medical science. In this paper we have proposed a method to identify whether a patient has risk of COVID-19 using Logistic Regression model, considering multiple symptoms. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： COVID-19

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Parameter fault diagnosis in heat exchange networks with distributed time delay

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IFAC-PapersOnLine 2022年第18期55卷 39-44页

作者： Wijaya Kurniawan Katalin M. Hangos Lőrinc Márton Dept. of Electrical Engineering and Information Systems University of Pannonia Veszprém Hungary Systems and Control Laboratory Institute for Computer Science and Control P.O. Box 63 H-1518 Budapest Hungary Dept. of Electrical Engineering Sapientia Hungarian University of Transylvania Tirgu Mures Romania

This paper deals with parameter fault diagnosis in heat exchange networks (HENs) with joining and splitting connections where the change in the heat transfer coefficient is considered as fault. The fault diagnosis oriented model of the HEN elements was developed based on the equivalent LTI realization of distributed delay models. The Signed Directed Graph (SDG) method is used to derive the fault observability conditions. The presence of faults induces bilinear fault-input terms into the system model. Thus, a nonlinear adaptive observer was proposed for fault diagnosis. To verify and validate the proposed method, a case study is presented. The simulation results show that the observers are successfully detecting and estimating the faults and unknown system states.

关键词： Fault diagnosis Heat Exchange Networks, Distributed delay, Process systems

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2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring System using Mobile Application

2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring Sy...

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International Electrical engineering Congress (iEECON)

作者： Pruek Vanna-Iampikul Visutr Boonnateephisit Somphop Limsoontharakul Jednipat Moonrinta Matthew Dailey Mongkol Ekpanyapong Department of Electrical Engineering Burapha University Chonburi Thailand School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Dept. Computational Science Prince of Songkla University Songkhla Thailand Computer Science Field of Study School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Information & Communication Tech. School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Dept. of Industrial Systems Engineering School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

LiDAR technology has rapidly advanced, offering highly accurate solutions for tree geometry estimation, which is particularly valuable in agriculture and forestry. Its ability to capture precise 2D and 3D data has revolutionized tree detection and measurement. In Eucalyptus plantations, continuous monitoring is critical to maximizing growth, enhancing paper production, and mitigating environmental damage. However, existing solutions face challenges in real-time monitoring due to factors such as parasitic interference, low-light conditions, and uneven terrain. In this work, we present a novel, end-to-end framework that enhances Eucalyptus tree measurement and monitoring. Our framework significantly improves data collection efficiency by allowing measurements at walking speed, reducing measurement distance by 50% compared to traditional manual approaches. This system integrates 2D-LiDAR with additional environmental sensors to extract key tree parameters, including circumference, position, and tree count. The collected data is stored in real-time and uploaded to a centralized server for further analysis. Experimental evaluations across 160 trees, under five distinct farm conditions, demonstrate that our system achieves 89% accuracy in tree diameter estimation and 98.5% accuracy in tree counting, significantly outperforming traditional methods. These results establish our framework as a highly effective solution for improving the accuracy and efficiency of tree monitoring in challenging environments.

关键词： Geometry Accuracy Laser radar Plantations Estimation Vegetation Production Real-time systems Time measurement Monitoring

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2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring System using Mobile Application

2D LiDAR-Based Eucalyptus Tree Measurement and Monitoring Sy...

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13th International Electrical engineering Congress, iEECON 2025

作者： Vanna-Iampikul, Pruek Boonnateephisit, Visutr Limsoontharakul, Somphop Moonrinta, Jednipat Dailey, Matthew Ekpanyapong, Mongkol Burapha University Department of Electrical Engineering Chonburi Thailand School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Prince of Songkla University Dept. Computational Science Songkhla Thailand Computer Science Field of Study School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand Information & Communication Tech. School of Engineering and Technology Asian Institute of Technology Pathumthani Thailand School of Engineering and Technology Asian Institute of Technology Dept. of Industrial Systems Engineering Pathumthani Thailand

ISBN: (纸本)9798331543952

关键词： Deforestation

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Pre-Decision-Making-empowered Double Auction for Data Services over Vehicles

Pre-Decision-Making-empowered Double Auction for Data Servic...

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IEEE International Conference on Communications in China (ICCC)

作者： Sicheng Wu Minghui Liwang Wei Gong Zhipeng Cheng Xiaoyu Xia Xianbin Wang School of Informatics Xiamen University Xiamen China College of Electronic and Information Engineering Tongji University Shanghai China School of Future Science and Engineering Soochow University Suzhou China School of Computing Technologies RMIT University Melbourne Australia Dept. of Electrical and Computer Engineering Western University Ontario Canada

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Double auction has been emerged as a popular mechanism to enable dynamic data sharing among moving vehicles for critical applications such as autonomous driving. This paper proposes a novel pre-decision-making-empowered double auction mechanism for data provisioning among moving vehicles (which are classified into data buyers and sellers), who may pass a considered road intersection. To help buyers obtain their required data (e.g., different data types and qualities), while supporting a responsive auction process between vehicles, the proposed mechanism features an unique pre-decision-making empowered two-stages auction to reduce the data trading latency. We further investigate DBSCAN-based clustering algorithm and VCG-based pricing rule for the first stage, to facilitate long-term contracts between buyers and sellers, as well as preference lists for buyers, in advance to practical transactions. Then, the second stage will follow the pre-decisions in the former stage, thus enabling a time-efficient data trading process. Simulations validate our superior performance in social welfare, time efficiency, and maintaining crucial auction properties such as truthfulness and individual rationality.

关键词： Solution design Roads Smart contracts Clustering algorithms Pricing Mathematical models Data models

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An Artificial Neural Network Model to Forecast Grain Crops in North-Kazakstan Agricultural Experimental Station

An Artificial Neural Network Model to Forecast Grain Crops i...

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Communications, information, Electronic and Energy systems.(CIEES), International Conference on

作者： Aigul Mimenbayeva Maylen Omirtay Rozamgul Niyazova Gulmira Bekmagambetova Raya Suleimenova Ainur Tursumbayeva Dept. of Computational and Data Sciences Astana IT University Astana Kazakstan Specialty Smart Technologies Astana IT University Astana Kazakstan Dept. of Artificial Intelligence Technologies L. Gumilyov Eurasian National University Astana Kazakhstan Dept. of Computer Science S. Seifullin Kazakh Agrotechnical Research University Astana Kazakstan School of Engineering and Information Technology Eurasian Technological University Almaty Kazakstan Dept. of Information Systems S. Seifullin Kazakh Agrotechnical Research University Astana Kazakstan

The paper analyzes and predicts the yield of grain crops of LLP “North-Kazakhstan Agricultural Experimental Station” for the last 30 years. The class of artificial neural networks-multilayer perceptron (MLP) was used as a research tool. The process of training of a model of a neural network consisting of 30 networks was performed on the basis of 70% training and 30% control samples. Based on the analysis of residuals histogram and scatter plot of the target and output function, the best neural network models MLP-1-7-1 (p=0.92), MLP-1-8-1 (p=0.97), MLP-1-5-1 (p=0.82) were selected. Next, using the best MLP 1-8-1 network, the model was tested the given data. Using time series projection, a graph was illustrated and a table of predicted grain yield values for the coming years was tabulated. The absolute error showed the high accuracy of the obtained best MLP 1-8-1 network for forecasting the amount of grain crop yield. The obtained model of artificial network can be applied in research and monitoring of agricultural production development.

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Client-server Mobile System for Diagnosis of Asynchronous Motors 4

Client-server Mobile System for Diagnosis of Asynchronous Mo...

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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Chorna, Olha Kurliak, Petro Busher, Victor Chornyi, Oleksii Tytiuk, Valerii Zachepa, Iurii Institute of Education and Science in Electrical Engineering and Information Technologies Kremenchuk Mykhailo Ostrogradskyi National University Kremenchuk Ukraine Department of Electrical Power Engineering Electrical Engineering And Electromechanics Ivano-Frankivsk National Technical University of Oil and Gas Ivano-Frankivsk Ukraine Dept. of Electrical Engineering and Electronics National University "Odessa Maritime Academy" Odessa Ukraine Department of Electromechanics Kryvyi Rih National University Kryvyi Rih Ukraine Department of Systems of Automatic Control and Electric Drive Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine

ISBN: (纸本)9798350395532

This paper presents client-server mobile system for diagnosis of asynchronous motors. The system is created as a mobile application for smartphones and tablets. The system provides processing of the measured values of the currents and voltages of the stator phases of the asynchronous motor. According to the processing results, the results of diagnostics and forecasting of the current state of asynchronous motors are calculated. The software runs on the Android OS. The main purpose is to predict the current state of AD, which has a parametric electromagnetic asymmetry of the stator phases. © 2023 IEEE.

关键词： Induction motors

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Blockchain Anchored Federated Learning and Tokenized Traceability for Sustainable Food Supply Chains

Blockchain Anchored Federated Learning and Tokenized Traceab...

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Ubiquitous Computing and Intelligent information systems.(ICUIS), International Conference on

作者： Koushik Reddy Chaganti B. Naveen Kumar Pruthvi Krishna Gutta Sivananda Lahari Reddy Elicherla C. Nagesh K Raghavendar Dept. of Computer and Information Systems University of the Cumberlands KY USA Department of Information Technology Vignan Institute of Technology and Science Hyderabad Dept of IT Tesla Austin Texas United States Dept of CSE Dayananda Sagar University Bangalore Dept of CSE Srinivasa Ramanujan Institute of Technology Anantapur Dept of CSE Teegala Krishna Reddy Engineering College Hyderabad

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

The management of food supply chains relates to just about the most imperative issues concerning sustainability, quality, and traceability, where existing methodologies usually fail to meet the demands caused by data privacy, lack of transparency, and inefficiencies concerning collaboration among stakeholders. This compromises data security, and thus there arises single points of failure. Conversely, traditional traceability frameworks fail to incite wide-scale participation and lack real-time accuracy. Having these limitations in mind, we introduce a hybrid framework consisting of three advanced methodologies: Decentralized Federated Learning with Blockchain Anchoring (DFLBA), Dynamic Tokenized Traceability Framework (DTTF), and Adaptive Supply Chain Graph Neural Networks (ASCGNN) process. DFLBA would support privacy-preserving machine learning as such that collaborative training of yield prediction and crop monitoring models by stakeholders without revealing raw data will be feasible. Blockchain anchoring ensures logs of training updates are tamper-proof, thus increasing trust and transparency. DTTF introduces tokenized incentives that enhance traceability in real-time: every product has a digital twin represented by blockchain-anchored tokens updated by stakeholders. Smart contracts enforce the compliance that will improve participation and lifecycle data accuracy. ASCGNN models the food supply chain as a dynamic graph, leveraging graph neural networks to capture stakeholder interdependencies, optimize operations, and detect fraud. Blockchain integration ensures reliability and transparency by securing edge weights. The suggested framework has demonstrated good impact, like ~95% model accuracy for yield prediction, 99% traceability coverage, and ~92% prediction accuracy for bottlenecks of the supply chain. It provides a scalable, privacy-preserving, and transparent solution that enhances the efficiency of food supply chains, and positively impacts stakeholders in

关键词： Training Accuracy Federated learning Supply chains Collaboration Predictive models Real-time systems Graph neural networks Blockchains Stakeholders

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On the Trade-Offs Between Accuracy, Privacy, and Resilience in Average Consensus Algorithms

On the Trade-Offs Between Accuracy, Privacy, and Resilience ...

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IEEE Conference on Decision and Control

作者： Guilherme Ramos André M. H. Teixeira Sérgio Pequito Dept. of Computer Science and Engineering Instituto Superior Técnico University of Lisbon Portugal and Instituto de Telecomunicações Lisbon Portugal Department of Information Technology Division of Systems and Control Uppsala University Sweden

There can be none. In this paper, we address the problem of a set of discrete-time networked agents reaching average consensus privately and resiliently in the presence of a subset of attacked agents. Existing approaches to the problem rely on trade-offs between accuracy, privacy, and resilience, sacrificing one for the others. We show that a separation-like principle for privacy-preserving and resilient discrete-time average consensus is possible. Specifically, we propose a scheme that combines strategies from resilient average consensus and private average consensus, which yields both desired properties. The proposed scheme has polynomial time-complexity on the number of agents and the maximum number of attacked agents. In other words, each agent that is not under attack is able to detect and discard the values of the attacked agents, reaching the average consensus of non-attacked agents while keeping each agent's initial state private. Finally, we demonstrate the effectiveness of the proposed method with numerical results.

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Semantics-Enhanced Temporal Graph Networks for Content Caching and Energy Saving

Semantics-Enhanced Temporal Graph Networks for Content Cachi...

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IEEE International Conference on Communications (ICC)

作者： Jianhang Zhu Rongpeng Li Xianfu Chen Shiwen Mao Jianjun Wu Zhifeng Zhao Dept. of Information Science and Electronic Engineering Zhejiang University Hangzhou China VTT Technical Research Centre of Finland Oulu Finland Dept. of Electrical and Computer Engineering Auburn University Auburn AL USA Huawei Technologies Company Ltd. Shanghai China Zhejiang Lab Hangzhou China

The enormous amount of network equipment and users implies a tremendous growth of Internet traffic for multi-media services. To mitigate the traffic pressure, architectures with in-network storage have been proposed to cache popular content at devices in close proximity to users in order to decrease the number of backhaul hops. Meanwhile, the reduced transmission distance also contributes to energy saving. However, due to limited storage, only a fraction of the content can be cached, while caching the most popular content is cost-effective. Correspondingly, it becomes essential to devise an effective popularity prediction method. In this regard, some existing efforts manifest the effectiveness of dynamic graph neural network (DGNN) models, but it remains challenging to tackle sparse datasets. Herein, we first propose a reformative temporal graph network, named STGN, to address the challenge and improve prediction performance. Specifically, the STGN model leverages extra semantic messages to help establish implicit paths within the sparse interaction graph and enhance the temporal and structural learning of a DGNN model. Furthermore, we devise a user-specific attention mechanism to aggregate various semantics in a fine-grained manner. Finally, extensive simulations verify the superiority of our STGN models and demonstrate the potential in terms of energy-saving.

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