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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Ponzi, Valerio Puglisi, Adriano Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The use of autonomous systems in Intensive Care Units (ICUs) has become incredibly important, especially during the COVID-19 pandemic. This period has overwhelmed both ICUs and hospitals, halting many other medical activities and causing significant challenges. This project aims to develop a navigation system tailored specifically for the ICU environment, adapting it to the unique procedures and regulations of that setting. Due to the critical conditions of ICU patients, strict rules dictate precise requirements for navigation, necessitating a context-specific approach. This work will propose a comprehensive navigation system capable of safely guiding from point A to point B within an ICU while addressing the critical issues present in such environments. Unlike traditional Nav2 systems, it will feature specialized collision avoidance components designed specifically for ICU settings, taking into account both contextual demands and the chosen approach. This will involve implementing a multilayered protection technique and employing active movements to prevent collisions with dynamic obstacles. © 2023 Copyright for this paper by its authors.

关键词： Reinforcement learning

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Exploring Brain Tumor Segmentation and Patient Survival: An Interpretable Model Approach 8

Exploring Brain Tumor Segmentation and Patient Survival: An ...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Ponzi, Valerio De Magistris, Giorgio Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

Detecting and delineating brain tumors from MRI images using artificial intelligence presents a complex challenge in medical AI. Recent progress has seen a variety of techniques employed to assist medical professionals in this task. Despite the effectiveness of machine learning algorithms in segmenting tumors, their lack of transparency in decision-making hinders trust and validation. In our project, we constructed an interpretable U-Net Model specifically tailored for brain tumor segmentation, leveraging both the Gradient-weighted Class Activation Mapping (Grad-CAM) Algorithm and the SHapley Additive exPlanations (SHAP) library. We relied on the BraTS2020 benchmark dataset for training and evaluation purposes. The U-Net model we employed yielded promising results. We then utilized Grad-CAM to visualize the crucial features attended to by the model within an image. Additionally, we enhanced interpretability by utilizing the SHAP library to elucidate the predictions made by various models (including Random Forest, KNN, SVC, and MLP) utilized for predicting patient survival days. © 2023 Copyright for this paper by its authors.

关键词： Decision making

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Distance Estimation of Fixed Objects in Driving Environments 9

Distance Estimation of Fixed Objects in Driving Environments

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

作者： Leporoni, Giorgio Ponzi, Valerio Pro, Francesco Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

Autonomous driving is a highly relevant topic today, particularly among major car manufacturers attempting to lead in technological innovation and enhance driving safety. An autonomous vehicle must possess the capability to sense its environment and navigate without human intervention. Thus, it serves as both a driver support system and, in some cases, a substitute. A crucial aspect involves identifying the positions of pedestrians, traffic signs, traffic lights, and other vehicles while computing distances from them. This enables the vehicle to emit alerts to the driver in potentially dangerous situations, such as impending obstacles due to external factors or driver distraction. In this paper, we introduce an approach for identifying traffic signs and determining the distance from them. Our method utilizes the YOLOv4 network for identification and a customized network for distance computation. This integration of AI technologies facilitates the timely detection of hazards and enables proactive alert mechanisms, thereby advancing the capabilities of autonomous vehicles and enhancing driving safety. © 2023 Copyright for this paper by its authors.

关键词： Autonomous vehicles

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Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data

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computers, Materials & Continua 2023年第4期75卷 1883-1900页

作者： Ghada Atteia El-Sayed M.El-kenawy Nagwan Abdel Samee Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Ahmad Taher Azar Nima Khodadadi Reham A.Ghanem Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Civil Engineering University of Science and TechnologyMiami33101USA Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Oral Biology Department Faculty of Oral and Dental MedicineDelta University for Science and TechnologyGamasaEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

The rapid population growth results in a crucial problem in the early detection of diseases inmedical *** all the cancers unveiled,breast cancer is considered the second most severe ***,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical *** recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death *** this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated *** proposed algorithm is evaluated using four publicly available breast cancer *** evaluation results show the effectiveness of the proposed approach from the accuracy and speed *** prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted *** addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed *** best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.

关键词： Medical dataset breast cancer guided whale optimizer dipper throated optimizer feature selection meta-heuristics

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High-Capacity DWDM Transmission System for Free-Space Optical Network

High-Capacity DWDM Transmission System for Free-Space Optica...

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International Conference on Electrical, control and Instrumentation engineering (ICECIE)

作者： M.T Rahman M.A Jahid S.Y Anha Md. Shohanur Rahman F. Azmi Orpy A. S. M. Bakibillah Department of Computer Science and Engineering University Asia Pacific (UAP) Bangladesh Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798350380040

ISBN: (纸本)9798350380057

This study presents the architecture and performance evaluation of a high-capacity free-space optical (FSO) communication system that makes use of dense wavelength division multiplexing (DWDM) and a 1.28 Tb/s link. The system under consideration comprises 64 individual channels, each of which is capable of transferring data streams at a speed of 20 Gbps. A return-to-zero (RZ) line coding algorithm is incorporated to maximize performance across 2.0 km of FSO link. Important parameters that affect the system's performance, like beam divergence and atmosphere attenuation, are taken into account in the analysis. The signals' bit-error-rate (BER), quality factor (Q), and eye diagrams are calculated and examined to assess the system's performance. For future optical-wireless networks that require significant bandwidth capabilities, cost-effective implementation, and increased flexibility, this novel high-capacity design offers a promising solution. The results aid in the development of dependable and useful high-speed FSO communication systems.

关键词： Q-factor Wireless communication Performance evaluation Free-space optical communication System performance Bit error rate Optical fiber networks Wavelength division multiplexing Encoding Streams

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Explaining complex systems: a tutorial on transparency and interpretability in machine learning models (part II)

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IFAC-PapersOnLine 2024年第15期58卷 497-501页

作者： Donatello Materassi Sean Warnick Cristian Rojas Maarten Schoukens Elizabeth Cross Department of Electrical and Computer Engineering University of Minnesota US Department of Homeland Security Science and Technology Directorate EECS/Division of Decision and Control Systems KTH Royal Institute of Technology Control Systems Group Eindhoven University of Technology Eindhoven The Netherlands Department of Mechanical Engineering University of Sheffield

In the second segment of the tutorial, we transition from the granularity of local interpretability to a broader exploration of eXplainable AI (XAI) methods. Building on the specific focus of the first part, which delved into Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), this section takes a more expansive approach. We will navigate through various XAI techniques of more global nature, covering counterfactual explanations, equation discovery, and the integration of physics-informed AI. Unlike the initial part, which concentrated on two specific methods, this section offers a general overview of these broader classes of techniques for explanation. The objective is to provide participants with a comprehensive understanding of the diverse strategies available for making complex machine learning models interpretable on a more global scale.

关键词： eXplainable AI

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A Cloud-Based Trust Computing Model for the Social Internet of Things

A Cloud-Based Trust Computing Model for the Social Internet ...

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2021 International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2021

作者： Ali-Eldin, Amr M.T. Mansoura University Faculty of Engineering Computer Engineering and Control Systems Department Mansoura Egypt

ISBN: (纸本)9781665412438

As IoT systems would have an economic impact, they are gaining growing interest. Millions of IoT devices are expected to join the internet of things, which will carny both major benefits and significant security threats to consumers. For IoT systems that secure data and preserve privacy of users, trust management is an essential component. IoT objects carry on the ownership settings of their owners, allowing them to interact with each other. Social relationships are believed to be important in confidence building. In this paper, we explain how to compute trust in social IoT environments using a cloud-based approach. © 2021 IEEE.

关键词： Internet of things

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Robust control of Feeding Speed in Coal Seam Drilling Process Considering Borehole Friction

Robust Control of Feeding Speed in Coal Seam Drilling Proces...

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2024 China Automation Congress, CAC 2024

作者： Liu, Xiao Chen, Luefeng Lu, Chengda Fan, Haipeng Wu, Min Pedrycz, Witold School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan430074 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Department of Computer Engineering Istinye University Sariyer Istanbul34396 Turkey

ISBN: (纸本)9798350368604

During the coal seam drilling process, the drill string is subject to compressive deformation, compounded by unpredictable variations in formation hardness and borehole wall friction, leading to challenges in maintaining a stable feeding speed. This paper presents a novel approach by introducing uncertain parameters to describe the effects of formation hardness and borehole wall friction. Drill string axial movement model is modeled as a polyhedral system based on a lumped parameter representation. To meet industrial performance requirements, we design a robust H∞ controller to achieve consistent feeding speed control. Our simulation results demonstrate the controller’s effectiveness in ensuring system stability despite fluctuations in formation hardness and drill string friction. © 2024 IEEE.

关键词： Coal deposits

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Numerical approach to design of low frequency magnetic field irradiation system for lab on chip experiments 14

Numerical approach to design of low frequency magnetic field...

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14th International Conference ELEKTRO, ELEKTRO 2022

作者： Radil, Roman Bajtos, Marek Carnecka, Lucia Hargasova, Klaudia Kamencay, Patrik University of Zilina Department of Electromagnetic and Biomedical Engineering Zilina Slovakia Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Systems Biology and Engineering Gliwice Poland

ISBN: (纸本)9781665467261

The article is focused on design of specific electromagnetic coil system using numerical modelling and simulation methods. The proposed solution would be capable of delivering magnetic field of desired strength/ flux density and homogeneity within the specific area to affect the cultivation process of single cells in lab on chip experiments. © 2022 IEEE.

关键词： Numerical models

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Construction and optimization of a computational holographic lithography system 8

Construction and optimization of a computational holographic...

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8th International Conference on Speckle Metrology, Speckle 2023

作者： Chen, Rui Lin, Angyi You, Jianfeng Lin, Huixiang Zhao, Wenqing Zhang, Fucai School of Control and Computer Engineering North China Electric Power University Baoding071003 China The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China Engineering Research Center of Intelligent Computing for Complex Energy Systems Ministry of Education North China Electric Power University China

ISBN: (数字)9781510674592

ISBN: (纸本)9781510674585

Traditional lithography techniques are currently facing challenges, including high costs and the susceptibility of mask plates to damage. This research aims to elucidate the feasibility and technical constraints of a novel diffraction lithography approach. This method can reduce the size and cost of lithography machines, break through existing technological barriers, and offer new possibilities for the future development of lithography machines. We have built a basic computational holographic lithography system utilizing a 405nm laser and validated the system using holograms obtained with both classical GS algorithm and a newly proposed method. With the GS holograms, patterns with 700nm features have been successfully exposed on the photoresist. With our new algorithms, multiple holograms for the same target pattern can be generated with different settings in the algorithm. Sequential exposure of those holograms resulted in continuous lines 500 nm wide with reduced speckle artifacts, which was about a 1.4-time improvement over the result from the GS algorithm. Future research will focus on optimizing and enhancing the experimental system and the hologram calculation methods to reduce the line widths further and improve the exposure pattern quality. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Holograms

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