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arXiv 2024年

作者： Cueto-López, Nahúm García-Ordás, Maria Teresa Dávila-Batista, Veronica Moreno, Víctor Aragonés, Nuria Alaiz-Rodríguez, Rocío Department of Electrical Systems and Automatic Engineering Universidad of León Campus de Vegazana s/n León24071 Spain Spain Unit of Biomarkers and Susceptibility Cancer Prevention and Control Programme Catalan Institute ofOncology-IDIBELL L’Hospitalet de Llobregat Spain Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain Madrid Spain Madrid Spain

Background and objective: Risk prediction models aim at identifyingpeople at higher risk of developing a target disease. Feature selection is particularly important to improve the prediction model performance avoiding overfitting and to identify the leading cancer risk (and protective) factors. Assessing the stability of feature selection/ranking algorithms becomes an important issue when the aim is to analyze the features with more prediction power. Methods: This work is focused on colorectal cancer, assessing several feature ranking algorithms in terms of performance for a set of risk prediction models (Neural Networks, Support Vector Machines (SVM), Logistic Regression, k-Nearest Neighbors and Boosted Trees). Additionally, their robustness is evaluated following a conventional approach with scalar stability metrics and a visual approach proposed in this work to study both similarity among feature ranking techniques as well as their individual stability. A comparative analysis is carried out between the most relevant features found out in this study and features provided by the experts according to the state-of-the-art knowledge. Results: The two best performance results in terms of Area Under the ROC Curve (AUC) are achieved with a SVM classifier using the top-41 features selected by the SVM wrapper approach (AUC=0.693) and Logistic Regression with the top-40 features selected by the Pearson (AUC=0.689). Experiments showed that performing feature selection contributes to classification performance with a 3.9% and 1.9% improvement in AUC for the SVM and Logistic Regression classifier, respectively, with respect to the results using the full feature set. The visual approach proposed in this work allows to see that the Neural Network-based wrapper ranking is the most unstable while the Random Forest is the most stable. Conclusions: This study demonstrates that stability and model performance should be studied jointly as Random Forest turned out to be the most stable

关键词： Stability

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A Distributed control framework for the optimal operation of DC microgrids

arXiv

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arXiv 2022年

作者： Fu, Zao Cucuzzella, Michele Cenedese, Carlo Yu, Wenwu Scherpen, Jacquelien M.A. School of Cyber Science and Engineering Southeast University Nanjing211189 China Faculty of Science and Engineering University of Groningen Groningen9747 AG Netherlands Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia27100 Italy Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zürich Physikstrasse 3 Zürich8092 Switzerland

In this paper we propose an original distributed control framework for DC mcirogrids. We first formulate the (optimal) control objectives as an aggregative game suitable for the energy trading market. Then, based on the dual theory, we analyze the equivalent distributed optimal condition for the proposed aggregative game and design a distributed control scheme to solve it. By interconnecting the DC mcirogrid and the designed distributed control system in a power preserving way, we steer the DC microgrid's state to the desired optimal equilibrium, satisfying a predefined set of local and coupling constraints. Finally, based on the singular perturbation system theory, we analyze the convergence of the closed-loop system. The simulation results show excellent performance of the proposed control framework. © 2022, CC BY-NC-SA.

关键词： Perturbation techniques

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Routing on the Shortest Pairs of Disjoint Paths

Routing on the Shortest Pairs of Disjoint Paths

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IFIP Networking Conference

作者： Pé ter Babarczi Gá bor Ré tvá ri Lajos Ró nyai Janos Tapolcai Department of Telecommunications and Media Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Department of Algebra Institute for Computer Science and Control Institute of Mathematics Budapest University of Technology and Economics E&#x00F6 tv&#x00F6 s Lorand Research Network Budapest Hungary

Recent trends point towards communication networks will be multi-path in nature to increase failure resilience, support load-balancing and provide alternate paths for congestion avoidance. We argue that the transition from single-path to multi-path routing should be as seamless as possible in order to lower the deployability barrier for network operators. Therefore, in this paper we are focusing on the problem of routing along the shortest pairs of disjoint paths between each source-destination pair over the currently deployed link-state routing architecture. We show that the union of disjoint path-pairs towards a given destination has a special structure, and we propose an efficient tag encoding scheme which requires only one extra forwarding table entry per router per destination. Our numerical evaluations demonstrate that in real-world topologies usually only 4 bit tags are sufficient in the packet headers to route on the disjoint path-pairs. Finally, we show that our tags automatically encode additional paths beyond the shortest pair of disjoint paths, including the shortest paths themselves, which enables incremental deployment of the proposed method.

关键词： Focusing Tagging Routing Data structures Market research Load management Encoding

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Fabrication of UV Photodetectors Based on Photoelectrochemically Etched Nanoporous Silicon: Effect of Etchants Ratio

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Journal of Nanomaterials 2023年第1期2023卷

作者： Thahe, Asad A. Qaeed, Motahher A. Abdullah, Suhail Najm Badawy, Ibrahim M. Alqaraghuli, Hasan Alajerami, Yasser Saleh Mustafa Mindil, A. Al-Farga, Ammar Department of Medical Physics College of Applied Science University of Fallujah Baghdad Iraq Faculty of Science University of Jeddah Jeddah Saudi Arabia Department of Medical Equipment Technology Engineering Al-Rasheed University College Baghdad Iraq Energy Materials Laboratory School of Sciences and Engineering The American University in Cairo New Cairo11835 Egypt Faculty of Electrical Engineering Mechatronics and Automatic Control Department University of Technology Malaysia Johor Bahru81310 Malaysia Faculty of Applied Medical Sciences Medical Imaging Department Al-Azhar University-Gaza Gaza Palestine Faculty of Science Department of Biochemistry University of Jeddah Jeddah Saudi Arabia

Despite several attempts to enhance the electrical and charge carrier transport characteristics of porous silicon (PSi), the requisite conditions for optimally synthesizing n-PSi with appealing optoelectronic properties are yet to be achieved. Therefore, this research explores the effect of the chemical ratio of precursor materials (HF:C2H6O:H2O2) on the surface morphology, crystalline structure, and optical and electric properties of PSi. The PSi was produced by photoelectrochemical etching followed by anodization of the n-type Si under light illumination. The properties of the as-prepared PSi were studied by means of microscopic and spectroscopic techniques. The HF:C2H6O:H2O2 chemical ratio was optimized at 2: 1: 1. A metal-semiconductor-metal (MSM) ultraviolet photodetector (Pt/n-PSi/Pt) was fabricated, which exhibited high performances under UV light (365 nm) illumination. The photodetector was shown to be highly stable and reliable with a rapid rise time of 0.56 s at a bias voltage of +5 V. The MSM photodetector displayed responsivity (Rp) of 9.17 A/m at 365 nm, which significantly exceeds the values reported for TiC/porous Si/Si in some contemporary research. The photodetector fabricated from n-PSi, synthesized at an optimum chemical ratio (2: 1: 1) exhibited the best photodetection performance, possibly due to the high porosity and defect-free state of the n-PSi thin films. © 2023 Asad A. Thahe et al.

关键词： Fabrication

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Mobile Application for Visualizing the Measurements

Mobile Application for Visualizing the Measurements

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International Conference on Modern electrical and Energy Systems (MEES)

作者： Simona Kirešová Milan Guzan Branislav Sobota Viacheslav Melnykov Rostyslav Yatsiuk Bohdan Peryvertailo Dept. of Theoretical and Industrial Electrical Engineering Technical University of Košice Košice Slovakia Dept. of Computers and Informatics Technical University of Košice Košice Slovakia Dept. of Systems of Automatic Control and Electric Drive Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine Dept. of Computer Engineering and Electronics Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine

This paper deals with the design of an Android mobile application and visualization of the measured values of particulate matter and meteorological factors from the measurement stations. The application can in principle be effectively used in any field of data visualization. The architecture of the mobile application, the security, the use of AWS services to access the data in the InfluxDB databases, as well as the user interface and graphical visualizations of the measured data are described and illustrated. The application is user tested and the paper documents their first experiences using the mobile application.

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on Computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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Semi-supervised active transfer learning for fetal ECG arrhythmia detection

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Computer Methods and Programs in Biomedicine Update 2023年 3卷

作者： Mohebbian, Mohammad Reza Marateb, Hamid Reza Wahid, Khan A. Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon S7N 5A9 SK Canada Biomedical Engineering Department Engineering Faculty University of Isfahan Isfahan Iran Biomedical Engineering Research Centre (CREB) Automatic Control Department (ESAII) Universitat Politècnica de Catalunya-Barcelona Tech (UPC) Barcelona Spain

Deep learning has demonstrated excellent results for ECG anomaly detection, wherein most approaches used supervised learning. The requirement of thousands of manually annotated samples is a concern for state-of-the-art anomaly detection systems, especially for fetal ECG (FECG), and currently, there is not a publicly available FECG dataset annotated for each FECG beat. In this paper, we offer a modified active learning technique based on transfer learning, calibration probability, and autoencoder-based sampling to reduce number of samples requires to annotate. In this regard, we used 25,000 s of recording from 47 patients from the MIT-BIH Arrhythmia Database to train a deep learning model to detect anomalies in non-fetus subjects. Then we used the unlabeled Non-Invasive Fetal ECG Arrhythmia Database (NIFEA DB) of 26 subjects to fine-tune the trained model to fine-tune the trained model based on active learning to detect anomalies in binary form for fetal. A variational autoencoder is trained on all data (adult and fetal ECG), and clustering is applied to latent features extracted from data after dimension reduction. Then, the sampling process of active learning selected samples from different clusters with low confidence to cover all data distribution. Moreover, a probability calibration based on mc-dropout and isotonic regression is used to calibrate confidences, helping to select reliable low-confidence samples. Various ablation studies were performed to show the influence of autoencoder-based sampling, calibration, and transfer learning, which showed that the proposed method could achieve 92% accuracy using 399 training samples. In contrast, other methods required more training samples to reach the same level of accuracy without calibration or an autoencoder and clustering approach or training without active learning. The study also found that transfer learning significantly impacted faster convergence and that the proposed active learning approach was more effect

关键词： Active learning Anomaly Arrhythmia FECG Transfer learning

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Distributed Optimal Power Flow for VSC-MTDC Meshed AC/DC Grids Using ALADIN

Distributed Optimal Power Flow for VSC-MTDC Meshed AC/DC Gri...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Junyi Zhai Xinliang Dai Yuning Jiang Ying Xue Veit Hagenmeyer Colin N. Jones Xiao-Ping Zhang College of New Energy China University of Petroleum (UPC) China Karlsruhe Institute of Technology (KIT) Institute for Automation and Applied Informatics Germany Automatic Control Laboratory École Polytechnique Fédérale de Lausanne (EPFL) Switzerland School of Electric Power Engineering South China University of Technology (SCUT) China Department of Electronic Electrical and Systems Engineering University of Birmingham (UBir) Birmingham United Kingdom

The increasing application of voltage source converter (VSC) based high voltage direct current (VSC-HVDC) technology in power grids has raised the importance of incorporating DC grids and converters into the existing transmission network. This poses significant challenges in dealing with the resulting optimal power flow (OPF) problem. In this paper, a recently proposed nonconvex distributed optimization algorithm — Augmented Lagrangian based Alternating Direction Inexact Newton method (ALADIN), is tailored to solve the nonconvex AC/DC OPF problem for emerging voltage source converter (VSC) based multiterminal high voltage direct current (VSC-MTDC) meshed AC/DC hybrid systems. The proposed scheme decomposes this AC/DC hybrid OPF problem and handles it in a fully distributed way. Compared to the existing state-of-art Alternating Direction Method of Multipliers (ADMM), which is in general, not applicable for nonconvex problems, ALADIN has a theoretical convergence guarantee. Applying these two approaches to VSC-MTDC coupled with an IEEE benchmark AC power system illustrates that the tailored ALADIN outperforms ADMM in convergence speed and numerical robustness.

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Comparison of Gated Recurrent Unit vs. Mixture Density Network in Insulin Sensitivity Prediction

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IFAC-PapersOnLine 2022年第15期55卷 180-185页

作者： Bálint Szabó Ákos Szlávecz Béla Paláncz Geoffrey Chase Balázs Benyó Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. H-1111 Budapest Hungary Department of Geodesy and Surveying Faculty of Civil Engineering Budapest University of Technology and Economics Műegyetem rkp. 3. H-1111 Budapest Hungary Center of Bioengineering University of Canterbury Christchurch New Zealand

There are several methods for the prediction of future values of a time series, or in general, for the probability density prediction problem. In this paper two neural network prediction models are compared: Mixture Density Network (MDN) and Gated Recurrent Unit (GRU) in the prediction of the insulin sensitivity (SI) of a patient under intensive care. The basic difference between the two methods is that while MDN is a stateless network model the GRU is a stateful model. In the study, both of the models predict the probability density function of the future SI value, but the MDN considers the physiological process like a Markov-chain: the future SI value depends lowly on the previous value. In contrast, the GRU model attempts to model the longer-term dependencies between subsequent SI values in its internal states and improve the prediction accuracy based on the information derived from the previous SI values. In the study we have found that the GRU implemented stateful prediction improved the prediction accuracy by 10 percent, showing the prediction power of the GRU model in physiological problems and proving the existence of longer-term dependencies in the SI time series. These outcomes confirm previously published results showing that the insulin sensitivity prediction can be improved considering the longer-term history of the patients.

关键词： Insulin sensitivity Time series prediction Machine learning Neural networks Gated Recurrent Unit Mixture Density Network Gaussian distributions Process control

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Multi-Objective Scheduling of Single Mobile Robot Based on Grey Wolf Optimization Algorithm

SSRN

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SSRN 2022年

作者： Petrović, Milica Jokić, Aleksandar Miljković, Zoran Kulesza, Zbigniew University of Belgrade Faculty of Mechanical Engineering Department of Production Engineering Laboratory for Industrial Robotics and Artificial Intelligence Kraljice Marije 16 35 Belgrade11120 Serbia University of Belgrade Faculty of Mechanical Engineering Department of Production Engineering Kraljice Marije 16 35 Belgrade11120 Serbia Bialystok University of Technology Faculty of Electrical Engineering Department of Automatic Control and Robotics Wiejska 45D Bialystok15-351 Poland

During the last decades, intelligent mobile robots have been recognized as one of the most promising and emerging solutions used to fulfill material transport demands in intelligent manufacturing systems. One of the most significant characteristics of those demands is their multi-objectivity, where identified objectives might usually be in conflict. Therefore, obtaining the optimally scheduled robotic-based material transport system that is simultaneously faced with several conflicting objectives is a highly challenging task. In order to address such a challenge, this paper proposes a novel multi-objective Grey Wolf Optimizer (MOGWO) methodology to efficiently schedule material transport systems based on an intelligent single mobile robot. The proposed optimization methodology includes the comprehensive analysis and the mathematical formulation for 13 novel fitness functions, which are combined to form a Pareto front of the multi-objective optimization problem, as well as a novel strategy for optimal exploration of multi-objective search space. Moreover, four metrics, i.e., Generational Distance (GD), Inverted Generational Distance (IGD), Spacing (SP), and Maximum Spread (MS), are employed to quantitively evaluate and compare the effectiveness of the proposed enhanced MOGWO algorithm with two state-of-the-art metaheuristic methods (MOGA, MOAOA) on 25 benchmark problems. The obtained results achieved through two experimental scenarios indicate that the enhanced MOGWO algorithm outperforms other algorithms in terms of convergence, coverage, and robust optimal Pareto solution obtained. Finally, transportation paths based on obtained scheduling plans are experimentally validated by mobile robot RAICO (Robot with Artificial Intelligence based Cognition) within a physical model of the intelligent manufacturing environment. The achieved experimental results successfully demonstrate the efficiency of the proposed methodology for optimal multi-objective scheduling of materia

关键词： Mobile robots

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