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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Youssef Aboudorra Aaron Saini Antonio Franchi Robotics and Mechatronics Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede The Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This work presents techniques for scheduling the position controller gains for a class of fully-actuated morphing multi-rotor UAVs that use synchronized tilting to change their actuation capabilities. The feasible set of forces and torques that can be produced by the platform changes with the tilting angle, thus the tracking and disturbance rejection capabilities also change. To exploit the platform limits, two methods are proposed for gain scheduling using a simplified example, then one method is tested in simulation with an omnidirectional morphing multi-rotor (OmniMorph). The simulation results show that the developed techniques achieve consistent position tracking performance along the range of tilting angles when rejecting step disturbance forces of values close to the maximum force capabilities. The proposed methods offer a trade-off between simplicity and accuracy, that could be potentially applied for any multi-rotor with synchronized tilting capabilities. A video summary can be found in: https://***/kH-rrO8gWeU

关键词： Accuracy Processor scheduling Attitude control Simulation Force Rotors Position control

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Pneumonia Detection from X-ray Images Using Convolutional Neural Networks

Pneumonia Detection from X-ray Images Using Convolutional Ne...

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International Conference on control Systems and computer science (CSCS)

作者： Viorel Dediu Andreea Udrea Department of Automatic Control and Systems Engineering Faculty of Automatic Control and Computer Science Univesity Politehnica of Bucharest Bucharest Romania

Pneumonia is one of the top causes of death in Romania and early detection of this disease improves the recovery chances and shortens the length of hospitalization. In this work, we develop a solution for automatic pneumonia detection based on convolutional neural networks. Four network models are investigated. They are trained on 4.163 images from a public dataset and tested on 530 images. The best results are obtained by one of the proposed models conducting to a sensitivity of 98.72%, an accuracy of 89.81%, and ROC 93.46%. Thus, this research proposes a lightweight screening tool that can help triaging the patients with pneumonia.

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Cardiac Clarity: Harnessing Machine Learning for Accurate Heart-Disease Prediction

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IEEE Access 2025年 13卷 97529-97544页

作者： Sujith Santhosh Krishnaraj Chadaga R.Vijaya Arjunan Sandra D'Souza Department of Instrumentation and Control Engineering Manipal Academy of Higher Education Manipal Institute of Technology Manipal Karnataka India Department of Computer Science and Engineering Manipal Academy of Higher Education Manipal Manipal Institute of Technology Manipal Karnataka India

The major contributor to global mortality is cardiovascular disease, posing a formidable challenge to the global healthcare system. Heart disease often develops and progresses without noticeable symptoms, emphasizing the need for earlier detection to prevent severe outcomes. AI models provide tools for accurate diagnosis, data analysis, and predictive diagnosis of people who may be suffering from heart disease. But increasingly, solutions like Explainable AI (XAI) are gaining traction. XAI demystifies the decisions of AI models, making them transparent and understandable to the point where they boost confidence in the context of AI diagnostics by healthcare professionals; the AI then provides support to professionals to decide the best course of action for each patient based on the analysis. XAI techniques such as SHAP, LIME, QLattice and Anchor are popular, and have been used in this study. We analysed a heart disease dataset collected from a multispecialty hospital in India, which is publicly available on Mendeley. We used ML models, including RF, Logistic Regression, KNN, XGBoost, DT and SVM. These models were optimized and stack-ensembled to form the ‘CARDIACX’ model to predict the risk of heart disease. The models were fine-tuned using Grid Search, Random Search, and Bayesian Optimization methods, achieving promising results with Random Forest achieving an AUC =0.99 and accuracy of 98.5%, outperforming all the other models and demonstrating robust performance on various metrics such as AUC, PR Curve, Log Loss, Jaccard Score, and MCC. This study provides a reliable and transparent framework for early detection of heart disease, providing useful information and enabling patients to receive timely and personalized care.

关键词： Heart Diseases Accuracy Machine learning Predictive models Medical services Explainable AI Tuning Support vector machines Random forests

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Optimizing Membrane Distillation Configuration for Superior Heat and Mass Transfer

Optimizing Membrane Distillation Configuration for Superior ...

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2024 International Conference on Emerging Trends in Smart Technologies, ICETST 2024

作者： Arslan, Muhammad Haizhen, Xian Nouman Khalid Khan, M. Ali, Kashif Baig, Zaid Bin Tariq North China Electric Power University School Of Energy Power And Mechanical Engineering Beijing China University Of Engineering & Technology School Of Mechanical Engineering Lahore Pakistan North China Electric Power University School Of Renewable And Clean Energy Beijing China North China Electric Power University Department Of Control And Computer Science Beijing China

ISBN: (纸本)9798331515850

Membrane distillation (MD) has emerged as a promising technique for desalination and water purification, offering potential advantages in terms of energy efficiency and operational flexibility. However, challenges related to heat and mass transfer inefficiencies limit its widespread adoption. This study explores the use of innovative materials and optimized configurations to enhance heat and mass transfer in direct contact membrane distillation (DCMD) systems. By integrating advanced hybrid membranes with nano-coating techniques and evaluating their performance through simulation-based methodologies, we demonstrate significant improvements in thermal conductivity, mass transfer efficiency, and fouling resistance. The results indicate that the newly developed hybrid membranes not only enhance heat and mass transfer rates but also maintain high separation efficiency, ultimately leading to improved overall system performance. These findings provide a foundation for the further development of MD technologies and suggest pathways for overcoming existing limitations, paving the way for more sustainable and efficient water treatment solutions. © 2024 IEEE.

关键词： Desalination

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Transforming stroop task cognitive assessments with multimodal inverse reinforcement learning

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Smart Health 2025年 36卷

作者： Abbasi, Ali Gong, Jiaqi Korivand, Soroush Department of Control and Computer Engineering Polytechnic University of Turin Italy Department of Computer Science The University of Alabama United States Department of Mechanical Engineering Mississippi State University United States

Stroop tasks, recognized for their cognitively demanding nature, hold promise for diagnosing and monitoring neurodegenerative diseases. Understanding how humans allocate attention and resolve interference in the Stroop test remains a challenge;yet addressing this gap could reveal key opportunities for early-stage detection. Traditional approaches overlook the interplay between overt behavior and underlying neural processes, limiting insights into the complex color-word associations at play. To tackle this, we propose a framework that applies Inverse Reinforcement Learning (IRL) to fuse electroencephalography (EEG) signals with eye-tracking data, bridging the gap between neural and behavioral markers of cognition. We designed a Stroop experiment featuring congruent and incongruent conditions to evaluate attention allocation under varying levels of interference. By framing gaze as actions guided by an internally derived reward, IRL uncovers hidden motivations behind scanning patterns, while EEG data — processed with advanced feature extraction — reveals task-specific neural dynamics under high conflict. We validate our approach by measuring Probability Mismatch, Target Fixation Probability-Area Under the Curve, Sequence Score, and MultiMatch metrics. Results show that the IRL-EEG model outperforms an IRL-Image baseline, demonstrating improved alignment with human scanpaths and heightened sensitivity to attentional shifts in incongruent trials. These findings highlight the value of integrating neural data into computational models of cognition and illuminate possibilities for early detection of neurodegenerative disorders, where subclinical deficits may first emerge. Our IRL-based integration of EEG and eye-tracking further supports personalized cognitive assessments and adaptive user interfaces. © 2025 The Authors

关键词： Cognitive assessment EEG Eye-tracking Inverse reinforcement learning Neurodegenerative diseases

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Towards Responsible AI Music: an Investigation of Trustworthy Features for Creative Systems

arXiv

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arXiv 2025年

作者： de Berardinis, Jacopo Porcaro, Lorenzo Meroño-Peñuela, Albert Cangelosi, Angelo Buckley, Tess Department of Computer Science University of Liverpool United Kingdom Department of Computer Control and Management Engineering Sapienza University of Rome Italy Department of Informatics King’s College London United Kingdom Department of Computer Science University of Manchester United Kingdom Association of AI Ethicists

Generative AI is radically changing the creative arts, by fundamentally transforming the way we create and interact with cultural artefacts. While offering unprecedented opportunities for artistic expression and commercialisation, this technology also raises ethical, societal, and legal concerns. Key among these are the potential displacement of human creativity, copyright infringement stemming from vast training datasets, and the lack of transparency, explainability, and fairness mechanisms. As generative systems become pervasive in this domain, responsible design is crucial. Whilst previous work has tackled isolated aspects of generative systems (e.g., transparency, evaluation, data), we take a comprehensive approach, grounding these efforts within the Ethics Guidelines for Trustworthy Artificial Intelligence produced by the High-Level Expert Group on AI appointed by the European Commission – a framework for designing responsible AI systems across seven macro requirements. Focusing on generative music AI, we illustrate how these requirements can be contextualised for the field, addressing trustworthiness across multiple dimensions and integrating insights from the existing literature. We further propose a roadmap for operationalising these contextualised requirements, emphasising interdisciplinary collaboration and stakeholder engagement. Our work provides a foundation for designing and evaluating responsible music generation systems, calling for collaboration among AI experts, ethicists, legal scholars, and artists. This manuscript is accompanied by a website: https://***/raim-framework/. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A Machine Learning based Real-Time Application for Engagement Detection 9

A Machine Learning based Real-Time Application for Engagemen...

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9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Iacobelli, Emanuele Russo, Samuele Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Department of Psychology Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

The study of human engagement has significantly grown in recent years, particularly accelerated by the interaction with a growing number of smart computing machines [1, 2, 3]. Engagement estimation has significant importance across various domains of study, including advertising, marketing, human-computer interaction, and healthcare [4, 5, 6]. In this paper, we propose a real-time application that leverages a single RGB camera to capture user behavior. Our approach implements a novel method for estimating human engagement in real-world scenarios by extracting valuable information from the combination of facial expressions and gaze direction analysis. To acquire this data, we employed fast and accurate machine learning algorithms from the external library dlib, along with custom versions of Residual Neural Networks implemented from scratch. For training our models, we used a modified version of the DAiSEE dataset, a multi-label user affective states classification dataset that collects frontal videos of 112 different people recorded in real-world scenarios. In the absence of a baseline for comparing the results obtained by our application, we conducted experiments to assess its robustness in estimating engagement levels, leading to very encouraging results. © 2023 Copyright for this paper by its authors.

关键词： Eye tracking

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Minimization of Trim Loss During Reel Cutting at Paper Mill by Using Different Optimization Algorithms 2nd

Minimization of Trim Loss During Reel Cutting at Paper Mill ...

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2nd International Congress on Advances in Mechanical and Systems engineering, CAMSE 2021

作者： Srivastava, Manish Pati, Smitarani Verma, Om Prakash Sharma, Tarun Kumar Gupta, Himanshu Arya, Raj Kumar Tiwari, Anurag Kumar Sahu, Deepak Department of Instrumentation and Control Engineering Dr. B. R. Ambedkar National Institute of Technology Punjab Jalandhar144011 India Department of Computer Science and Engineering Shobhit University Saharanpur India Department of Chemical Engineering Dr. B. R. Ambedkar National Institute of Technology Punjab Jalandhar144011 India

ISBN: (纸本)9789811921872

Trim loss is one of the major issues in the paper industry and occurs during the cutting of master and stocked reels to fulfill the ordered demands of reels with smaller widths. The trims (paper waste) produced during this reel cutting need to be reprocessed and follow a heavy chemical process that causes a significant amount of effluent discharge to the water surface which is hazardous as per environmental concern. Hence, this work aims to reduce the trim loss problem by using the flexible width master reels and stocked over-product and usable leftover reels. The minimization is performed with a linear single objective optimization problem with consideration of different operational and technological constraints. Three different solution approaches named simplex method, particle swarm optimization, and genetic algorithm have been proposed to optimize this trim loss problem employed for solving a real-world industrial problem. Again, the obtained solutions are analyzed in contrast with some reported results to prove the validity of the model and check the efficiency of the algorithms. The optimized result produces minimization in trim production due to which a lesser reproduction required that depletes the chemical use and tends to economic saving and reduction in environmental pollution. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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On the Existence of Static Equilibria of a Cable-Suspended Load with Non-stopping Flying Carriers

On the Existence of Static Equilibria of a Cable-Suspended L...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Chiara Gabellieri Antonio Franchi Robotics and Mechatronics Department Electrical Engineering Mathematics and Computer Science (EEMCS) Faculty University of Twente Enschede AE The Netherlands Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This work answers positively the question whether non-stop flights are possible for maintaining constant the pose of cable-suspended objects. Such a counterintuitive answer paves the way for a paradigm shift where energetically efficient fixed-wing flying carriers can replace the inefficient multirotor carriers that have been used so far in precise cooperative cable-suspended aerial manipulation. First, we show that one or two flying carriers alone cannot perform non-stop flights while maintaining a constant pose of the suspended object. Instead, we prove that three flying carriers can achieve this task provided that the orientation of the load at the equilibrium is such that the components of the cable forces that balance the external force (typically gravity) do not belong to the plane of the cable anchoring points on the load. Numerical tests are presented in support of the analytical results.

关键词： Task analysis Aircraft Gravity

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Contrast enhancement of optical videoscope images

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Iran Journal of computer science 2023年第3期6卷 261-275页

作者： Ammar, Reda Aboshosha, Sahar El-Hag, Noha A. Saifeldeen, Reda El-Shafai, Walid El-Banby, Ghada M. Ashiba, Huda I. Khalaf, Ashraf A. M. Elazm, Atef Abou El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. El-Safrawey, Amir Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Comm. Faculty of Engineering Minia University Minya Egypt Department of Management of Information Systems Higher Institute of Commercial Science El-Mahala El-Kobra Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Communications Engineering Bilbis Higher Institute of Engineering Sharqia Bilbis Egypt Security Engineering Laboratory Computer Science Department Prince Sultan University Riyadh 11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia

The videoscope (VS) images have poor quality and low contrast. Hence, in this paper, three proposed frameworks to improve the quality of VS images are presented. The first framework depends on contrast-limited adaptive histogram equalization (CLAHE) with adaptive gamma correction. The second framework depends on homomorphic processing. The third framework is based on the homomorphic enhancement with histogram equalization. The first one is superior from the entropy perspective, while the third one is superior from the contrast perspective. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Fuzzy enhancement Histogram equalization Homomorphic enhancement Spectral entropy

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