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14th International Conference on the Properties and Applications of Dielectric Materials, ICPADM 2024

作者： Cheypoca, Thepjit Promphanich, Wiboon Thway, Aung Ye Hankae, Angelina Phimpissada Jeenmuang, Siwakorn Pattanadech, Norasage School of Engineering King Mongkut's Institute of Technology Ladkrabang Instrumentation and Control Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Computer Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Electrical Engineering Dept. Bangkok Thailand School of Engineering King Mongkut's Institute of Technology Ladkrabang Mechatronics Engineering Dept. Bangkok Thailand

ISBN: (纸本)9798350374605

This paper introduces an approach with the Transformer Neural Networks model for partial discharge patterns classification, that consists of corona discharge, internal discharge and surface discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The phase value will be encoded into the charge array and Transformer Neural Network is constructed using Positional Embedding and Transformer Encoder Layer. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed © 2024 IEEE.

关键词： Electric corona

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Forecasting of Monkeypox Cases Using Optimized SARIMAX Based Model 3

Forecasting of Monkeypox Cases Using Optimized SARIMAX Based...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Elshabrawy, Mahmoud Eid, Marwa M. Abdelhamid, Abdelaziz A. El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed Delta Higher Institute of Engineering and Technology Dept. of Communications & Electronics Mansoura Egypt Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Ain Shams University Faculty of Computer and Information Sciences Dep. of Computer Science Cairo Egypt University Mansoura Faculty of Engineering Computer Engineering and Control Systems Dept. Egypt

ISBN: (纸本)9798350323511

This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases. The work optimizes the SARIMAX model using grid search cross-validation and fine-tunes its hyperparameters using DTACO to improve prediction accuracy. The suggested model's consistency and accuracy are considerable compared to previous studies. Comparisons with state-of-the-art models validate the proposed model's predictions. DTACO+SARIMAX can be used to control disease and monitor monkeypox. Healthcare organizations and governments can better manage and track the pandemic's course by offering accurate predictions, reducing public panic, and enabling effective pandemic planning. The Analysis of Variance (ANOVA) and Wilcoxon signed-rank tests are conducted on the proposed DTACO-SARIMAX model and compared models. © 2023 IEEE.

关键词： Ant colony optimization

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Dynamic Topology Optimization for Efficient and Decentralised Federated Learning

Dynamic Topology Optimization for Efficient and Decentralise...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Menegatti, Danilo Giuseppi, Alessandro Poli, Cecilia Pietrabissa, Antonio Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy Istituto Superiore di Sanità National Centre for Innovative Technologies in Public Health Department Rome Italy

ISBN: (纸本)9798350362480

Topology optimization in decentralised federated learning settings enables the design of policies aimed at minimizing the number of communication rounds needed to reach algorithmic convergence. Given a federation of autonomous agents, finding the optimal topology which guarantees that the underlying graph is connected is still an open issue. This paper proposes a novel energy-aware topology optimization algorithm with the goal to derive an optimal topology which maximizes the algebraic connectivity of the corresponding graph in presence of energy and communication constraints. The effectiveness of the proposed approach is validated in the context of a consensus-based federated learning algorithm over an e-Health scenario. © 2024 IEEE.

关键词： Federated learning

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Predicting the Time from Diagnosis to Treatment in Breast Cancer

Predicting the Time from Diagnosis to Treatment in Breast Ca...

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E-Health and Bioengineering Conference (EHB)

作者： Sergiu Ceică Ştefan-Daniel Achirei Dept. of Computer Engineering Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iasi Iasi Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

Recent advancements in machine learning (ML) have significantly impacted the medical field, particularly in diagnosing and treating breast cancer. This study models the time from diagnosis to treatment for breast cancer patients, focusing on delay factors and ML-based solutions. The goal is to develop precise ML models that predict treatment delays, thereby improving the quality and efficiency of medical services. We investigated socio-economic disparities affecting treatment access and developed models to predict these delays. The predictive models used include K-Nearest Neighbors, Decision Trees, Linear Regression, and Boosting Algorithms such as AdaBoost and Gradient Boosting Regressor. By accurately predicting the interval from diagnosis to the first treatment, our work aims to promote equity in healthcare, ensuring timely treatment for all patients. Our findings highlight the potential of ML in optimizing treatment timelines and resource allocation, contributing to improved patient outcomes and advancing the medical system.

关键词： Medical services Predictive models Boosting Prediction algorithms Breast cancer Delays Resource management Socioeconomics Medical diagnostic imaging Regression tree analysis

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Identification of Cancer Biomarkers for Multi-class Diagnostics through Network Analysis of RNAseq Data of Tumor-Educated Platelets

Identification of Cancer Biomarkers for Multi-class Diagnost...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Toccacieli, Ali Petti, Manuela Sapienza University of Rome MSc Program in Engineering in Computer Science Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9781665468190

Tumor-educated platelets (TEPs) are circulating blood cells with a distinct tumor-driven phenotype and act as carriers and protectors of metastases. To date, some studies have shown that the TEPs transcriptome can be used for cancer diagnostics. The objective of this study is to propose a procedure based on differential gene expression and differential gene co-expression analyses to identify a set of key genes for multi-class cancer diagnostics. To reach this aim, we analyzed RNA-seq data (57736 genes) of 130 subjects, of whom 40 patients with glioblastoma multiforme (GBM), 35 patients with pancreatic adenocarcinoma (PAAD), and 55 healthy donors (HC). We focused our analysis on the subset of differentially expressed genes (DEGs), and we used these genes to build and analyze the differential co-expression networks, identifying the hub genes. With this procedure, we obtained a restricted set of DEGs that maximize the accuracy in classifying patients according to their conditions (GBM, PAAD, or HC). Indeed, we validated our results by comparing the achieved classification accuracy with that resulting from random selections of DEGs and we obtained that genes selected by differential co-expression (DCE) network analysis have greater predictive power than any other set of differentially expressed genes, including using all of them. © 2022 IEEE.

关键词： Platelets

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MINOS: A Tool To Analyze HTTP Requests for compliance to GDPR

MINOS: A Tool To Analyze HTTP Requests for compliance to GDP...

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Lorenzo Laudadio Riccardo Coppola Antonio Vetrò Marco Torchiano Juan Carlos De Martin Dept. of Control and Computer Engineering Politecnico di Torino Turin Italy

ISBN: (数字)9798350387537

ISBN: (纸本)9798350387544

Many of the currently available services and commodities extensively share personal data and digital identities, raising privacy, security and ethical *** this paper, we present a novel software, MINOS, designed to enhance users’ privacy awareness while browsing the web. Our approach combines a user-friendly browser with a backend tailored to record and analyze HTTP requests directed to countries outside the European Economic Area (EEA).As opposed to other available tools, our solution uniquely addresses the issue of data transfers to third *** simulations have yielded promising results and clear directions for further research and development of the tool.

关键词： Economics Data privacy Ethics Europe Data transfer HTTP Software Information and communication technology Security Research and development

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Smart Healthy Schools: An IoT-enabled concept for multi-room dynamic air quality control

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 24-31页

作者： Zivelonghi, Alessandro Giuseppi, Alessandro Dep. of Applied Mathematical Science IIS "Calabrese-Levi" VR San Pietro in Cariano Italy PowerConsulting Studio San Pietro in Cariano Verona Italy Dept. of Computer Control and Management Engineering University of Rome "La Sapienza" Via Ariosto 25 Rome00185 Italy

Smart Healthy Schools (SHS) are a new paradigm in building engineering and infection risk control in school buildings where the disciplines of Indoor Air Quality (IAQ), IoT (Internet of Things) and Artificial Intelligence (AI) merge together. In the post-pandemic era, equipping schools with a network of smart IoT sensors has become critical to aspire for the optimal control of the IAQ and lowering the airborne infection risk of several pathogens, indirectly related to cumulated human emitted CO2 levels over time. Thermal energy waste in winter due to improved air renewal remains of major concern but can be well monitored within a SHS monitoring architecture thanks to the flexibility of the LoRaWAN protocol able to process also a large amount of energy and climatic data at room and building scale. In this work, we report the design of the AulaSicura platform, an IoT control system co-designed by the main author and Gizero Energie to implement the SHS paradigm via clearly visible (and audible) alarm signalling in existing and new school buildings. The cloud-based LoRa system is capable of continuous and simultaneous monitoring of a variety of sensors and IAQ parameters including indoor/oudoor temperatures, rel. humidities and human-emitted excess CO2. The multi-room monitoring concept of indoor-CO2 levels allows centralized control of natural ventilation levels in individual classrooms and can handle (quasi)-real-time data, relevant for data post-processing and future developments in (quasi)-real-rime assessment of IAQ and infection risk levels at single room scale. The sensor network is also extensible to up to one thousand of classrooms per LoRa-node allowing centralized control of entire school districts at an urban scale. Moreover, through Modbus-LoRa I/O converters, AulaSicura can also control the same amount of mechanical ventilation units per node either in pure or hybrid mechanical ventilation modes. © 2023 The Authors

关键词： Internet of things

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Automated Optical Inspection for Quality control in PCBA assembly lines: a case study for Point of Sale Devices Production Lines 6

Automated Optical Inspection for Quality Control in PCBA ass...

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6th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Aras, Kerem Saif, Syed Saad Giuseppi, Alessandro Coskun, Vedat R&D Center Token Financial Technologies Istanbul Turkey Dept. of Computer Control and Management of Rome La Sapienza Rome Italy Dept. of Software Engineering Istanbul Atlas University Istanbul Turkey

ISBN: (纸本)9798350394634

This paper reports on the design and development of a customized Automated Optical Inspection (AOI) solution aimed at detecting defects in a production line related to the correct mounting of integrated circuits. Contrary to most solutions avail- able on the market, the developed system relies on deep learning to be able to perform detailed real-time visual inspections of components without the need to compare the captured photos with any reference images/golden sample. The proposed solution was designed to also provide good generalization capabilities, accommodating visual changes in the environment and in the structure of the component being produced. A custom testing machine was built in order to perform real-time inferences and validate the simulation results in a real-world setting. © 2024 IEEE.

关键词： Assembly

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Process-Model-Free Design of MPC via a Reference-Model-Based PI controller for the Gas Turbine 9

Process-Model-Free Design of MPC via a Reference-Model-Based...

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9th International Conference on Electrical engineering, control and Robotics, EECR 2023

作者： Shi, Gengjin Dai, Chengbo Ma, Miaomiao Li, Donghai Ding, Yanjun Tsinghua University State Key Lab of Power Systems Dept. of Energy & Power Engineering Beijing China North China Electric Power University School of Control & Computer Engineering Beijing China

ISBN: (纸本)9781665491204

Model predictive control (MPC) is a modern advanced control strategy which has great reputation because of its excellent reference tracking performance and the ability to deal with process constraints, time delay and multivariable problems. However, the gas turbine is difficult to model accurately and unmeasurable disturbances are unavoidable, which limits the application of MPC to the gas turbine. To solve these problems, a process-model-free design method for MPC via a reference-model (RM)-based proportional-integral (PI) controller-desired dynamic equational (DDE)-PI controller (MPC-DDE) is proposed in this paper. The DDE-PI provides the design model for the MPC and handle with unmeasurable disturbances while MPC provides a rolling optimized set point for DDE-PI. Simulation results illustrate the superiorities of the proposed hierarchical control strategy in reference tracking, disturbance rejection and robustness, which indicates the promising prospect of practical application of MPC-DDE to the gas turbine. © 2023 IEEE.

关键词： Model predictive control

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Physics-Informed Neural Networks: A Step Towards Data-Driven Optimization of Additive Manufacturing 29

Physics-Informed Neural Networks: A Step Towards Data-Driven...

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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024

作者： Depaoli, Fabio Felicioni, Stefano Ponzio, Francesco Aliberti, Alessandro MacIi, Enrico Bondioli, Federica Padovano, Elisa Di Cataldo, Santa Dept. of Control and Computer Engineering Politecnico di Torino Italy Dept. of Applied Science and Technology Politecnico di Torino Italy Interuniv. Dept. of Reg. and Urban Studies and Planning Politecnico di Torino Italy

ISBN: (纸本)9798350361230

Laser powder bed fusion (L-PBF) is the most popular Additive Manufacturing (AM) process for metals. It builds a 3D object layer-by-layer, by spreading metal powder on top of the previous layer and selectively melting it with a laser. Despite its many advantages, large-scale production may be hampered by the large number of process parameters and the challenges associated with their optimization. We propose an automated parameter selection approach based on process signatures extracted from a parameterized simulation of the process. Specifically, we outline a rapid data-driven simulation method based on Physics-Informed Neural Network (PINN). This approach involves training a neural network to solve the partial differential equation describing the process at varying values of a parameter of interest (for example, the laser power), thus eliminating the need for repeated Finite Elements Method (FEM) simulations. Our preliminary experiments demonstrate the feasibility of our approach. © 2024 IEEE.

关键词： Smart manufacturing

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