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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Maffettone, Gian Carlo Bernardo, Mario Di Porfiri, Maurizio Scuola Superiore Meridionale Naples Italy University of Naples Federico Ii Department of Electrical Engineering and Information Technology Naples Italy Tandon School of Engineering New York University Center for Urban Science and Progress Department of Biomedical Engineering Department of Mechanical and Aerospace Engineering New York United States

ISBN: (纸本)9798350316339

This paper investigates the robustness of a novel high-dimensional continuification control method for complex multi-agent systems. We begin by formulating a partial differential equation describing the spatio-temporal density dynamics of swarming agents. A stable control action for the density is then derived and validated under nominal conditions. Subsequently, we discretize this macroscopic strategy into actionable velocity inputs for the system's agents. Our analysis demonstrates the robustness of the approach beyond idealized assumptions of unlimited sensing and absence of perturbations. © 2024 IEEE.

关键词： Robustness (control systems)

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Small Signal Modeling of Fractional-Order Boost Converter with Non-Singular Fractional Derivative 9

Small Signal Modeling of Fractional-Order Boost Converter wi...

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Yu, Donghui Liao, Xiaozhong Wang, Yong Ran, Manjie Lin, Da School of Automation Beijing Insititute of Technology Beijing China Shanghai Institute of Mechanical and Electrical Engineering China Aerospace Science and Technology Corporation Shanghai China

ISBN: (纸本)9798350311402

The fractional-order models have received extensive attention as they could describe the system characteristics of the Boost converter more accurately compared with integer-order models. Due to the singular kernels of traditional definitions that would reduce the modeling accuracy, this paper proposes a Caputo-Fabrizio (C-F) definition-based fractional-order Boost converter model using the small signal modeling method by employing equivalent state variables instead of traditional ones. The proposed model has high accuracy, which is verified by scanning the frequency of the circuit model and comparing it with the mathematical model. Additionally, the influence of fractional order on the system characteristics of the model in the frequency domain is analyzed, providing theoretical support for further studies on fractional-order boost converters with non-singular kernels. © 2023 IEEE.

关键词： Boost converter

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Overcoming challenges:advancements in cutting techniques for high strength-toughness alloys in aero-engines

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International Journal of Extreme Manufacturing 2024年第6期6卷 379-447页

作者： Biao Zhao Yufeng Wang Jianhao Peng Xin Wang Wenfeng Ding Xiaofei Lei Bangfu Wu Minxiu Zhang Jiuhua Xu Liangchi Zhang Raj Das College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016JiangsuPeople's Republic of China Department of Mechanics and Aerospace Engineering Southern University of Science and TechnologyShenzhen 518055GuangdongPeople's Republic of China Department of Aerospace Engineering RMIT UniversityMelboure 3000Australia

Aero-engines,the core of air travel,rely on advanced high strength-toughness alloys(THSAs)such as titanium alloys,nickel-based superalloys,intermetallics,and ultra-high strength *** precision of cutting techniques is crucial for the manufacture of key components,including blades,discs,shafts,and ***,machining THSAs pose significant challenges,including high cutting forces and temperatures,which lead to rapid tool wear,reduced efficiency,and compromised surface *** review thoroughly explores the current landscape and future directions of cutting techniques for THSAs in *** examines the principles,mechanisms,and benefits of energy-assisted cutting technologies like laser-assisted machining and cryogenic *** review assesses various tool preparation methods,their effects on tool performance,and strategies for precise shape and surface integrity *** also outlines intelligent monitoring technologies for machining process status,covering aspects such as tool wear,surface roughness,and chatter,contributing to intelligent ***,it highlights emerging trends and potential future developments,including multi-energy assisted cutting mechanisms,advanced cutting tools,and collaborative control of structure shape and surface integrity,alongside intelligent monitoring software and *** review serves as a reference for achieving efficient and high-quality manufacturing of THSAs in aero-engines.

关键词： energy-assisted machining technology tool preparation technology controlling the shape and surface integrity technology intelligent monitoring technology

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Free vibration analysis of functionally graded porous curved nanobeams on elastic foundation in hygro-thermo-magnetic environment

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Frontiers of Structural and Civil engineering 2023年第4期17卷 584-605页

作者： Quoc-Hoa PHAM Parviz MALEKZADEH Van Ke TRAN Trung NGUYEN-THOI Faculty of Engineering and Technology Nguyen Tat Thanh UniversityHo Chi Minh City 700000Vietnam Department of Mechanical Engineering Persian Gulf UniversityBushehr ***Iran Faculty of Mechanical Engineering Le Quy Don Technical UniversityHanoi 100000Vietnam Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 700000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 700000Vietnam Bualuang ASEAN Chair Professor Thammasat School of EngineeringThammasat UniversityPathumtani 12120Thailand

Herein,a two-node beam element enriched based on the Lagrange and Hermite interpolation function is proposed to solve the governing equation of a functionally graded porous(FGP)curved nanobeam on an elastic foundation in a hygro–thermo–magnetic *** material properties of curved nanobeams change continuously along the thickness via a power-law distribution,and the porosity distributions are described by an uneven porosity *** effects of magnetic fields,temperature,and moisture on the curved nanobeam are assumed to result in axial loads and not affect the mechanical properties of the *** equilibrium equations of the curved nanobeam are derived using Hamilton’s principle based on various beam theories,including the classical theory,first-order shear deformation theory,and higher-order shear deformation theory,and the nonlocal elasticity *** accuracy of the proposed method is verified by comparing the results obtained with those of previous reliable ***,the effects of different parameters on the free vibration behavior of the FGP curved nanobeams are investigated comprehensively.

关键词： functionally graded porous material curved nanobeam hygro-thermo-magnetic enriched finite element method

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Modified U-Net with attention gate for enhanced automated brain tumor segmentation

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Neural Computing and Applications 2025年第7期37卷 5521-5558页

作者： Saifullah, Shoffan Dreżewski, Rafal Yudhana, Anton Wielgosz, Maciej Caesarendra, Wahyu Faculty of Computer Science AGH University of Krakow Krakow30-059 Poland Department of Informatics Universitas Pembangunan Nasional Veteran Yogyakarta Yogyakarta55281 Indonesia Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia Faculty of Computer Science Electronics and Telecommunications AGH University of Krakow Krakow30-059 Poland Faculty of Mechanical Engineering Opole University of Technology 76 Proszkowska St. Opole45-758 Poland

This study addresses the formidable challenges encountered in automated brain tumor segmentation, including the complexities of irregular shapes, ambiguous boundaries, and intensity variations across MRI modalities. Manual segmentation, plagued by subjectivity and time constraints, further exacerbates the problem. To address these issues, we propose a modified U-Net architecture with an integrated attention gate. The proposed model demonstrates high performance, with notable Dice Similarity Coefficient (DSC) and Jaccard Index (JI) values across various tumor classes, consistently exceeding 0.93 and 0.87, respectively. Incorporating Contrast-Limited Adaptive Histogram Equalization and Histogram Equalization improves segmentation accuracy, particularly in cases of Meningioma. Comparative analyses against established models reveal a DSC of 0.9521 and a JI of 0.9093, underscoring the superiority of our method. Validation in the BraTS 2021 dataset underscores the robustness of the method, achieving high DSC and JI scores in four MRI modalities, with the T2 modality demonstrating the highest performance (DSC: 0.9216, JI: 0.8556). While acknowledging these achievements, we recognize challenges related to dataset specificity and computational intensity associated with the attention gate. Future research efforts should address these issues to improve the generalizability and applicability of the method in real-world scenarios. In addition to presenting a novel automated brain tumor segmentation method, this study contributes comprehensive result values and comparative analyses with previous research, providing valuable insights into the evolving landscape of medical image analysis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image segmentation

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Modelling and analysis of green hydrogen production by solar energy

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Energy Harvesting and Systems 2023年第2期10卷 229-245页

作者： Hassan, Qusay Abbas, Majid K. Tabar, Vahid Sohrabi Tohidi, Sajjad Jaszczur, Marek Abdulrahman, Imad Saeed Salman, Hayder M. Department of Mechanical Engineering University of Diyala Diyala Iraq Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Faculty of Energy and Fuels AGH University of Science and Technology Krakow Poland College of Technical Engineering Al-Farahidi University Baghdad Iraq Al-Turath University College Baghdad Iraq

In the article, the viability of adopting photovoltaic energy systems to convert solar energy into hydrogen in Iraqi four main cities are examined. A 22 kWp off-grid solar system, an 8 kW alkaline electrolyzer, a hydrogen compressor, and a hydrogen tank were modeled for an entire year in order to produce hydrogen. Using hourly experimental weather data from 2021 to 2030, MATLAB/Simulink is used to create a mathematical model of the recommended system behavior. The results revealed a range of annual hydrogen production from 1713.92 to 1891.12 kg, annual oxygen production from 1199.74 to 1323.78 kg, and annual water consumption from 7139.91 to 7877.29 L. Each kilogram of hydrogen costs & amp;dollar;3.79. The results indicate that the optimal location for solar hydrogen production systems might be constructed in the central region of Iraq and in other regions with comparable climatic characteristics, particularly those with high radiation levels. & amp;copy;2022 Walter de Gruyter GmbH, Berlin/Boston. © 2023 Walter de Gruyter GmbH. All rights reserved.

关键词： Solar energy

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Adversarial Attacks on Influence Maximization in Homogeneous Hypergraphs 10

Adversarial Attacks on Influence Maximization in Homogeneous...

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10th IEEE Smart World Congress, SWC 2024

作者： Lin, Tianwei Sun, Yanhao Wang, Zeyu Song, Nuan Xie, Kaige Wu, Jie Li, Dong University of Michigan Electrical Engineering and Computer Science Department Ann ArborMI48109 United States Shenzhen Research Institute of Shandong University ShenZhen518000 China Shandong University School of Mechanical Electrical & Information Engineering WeiHai264209 China

ISBN: (纸本)9798331520861

Adversarial strategies targeting influence maximization within homogeneous hypergraphs is an essential area with applications in viral marketing, rumor control, and epidemic prevention. We define the Adversarial Influence Maximization Problem (AdvIMH) and propose a Deep Reinforcement Learning-based algorithm ADV-DRL for selecting attack nodes. Additionally, we introduce a hypergraph embedding algorithm HyperVec to obtain vector representations of hypergraphs. By modeling real-world high-order relationships in datasets such as Music and Restaurant hypergraphs, we demonstrate the effectiveness of ADV-DRL through extensive experiments. Results show that ADV-DRL significantly outperforms baseline algorithms in reducing influence spread, highlighting its potential to identify vulnerable nodes and strategically diminish information propagation in hypergraphs. This work not only broadens the scope of influence maximization applications but also offers an innovative approach for tackling adversarial scenarios in networked systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Construction of Wind Farm Load Combination Forecasting Model Based on GBDT, LightGBM and RF 6

Construction of Wind Farm Load Combination Forecasting Model...

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6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Xian, Qinyuan Feng, Shengjie Yang, Yajin Liu, Juan School of Aerospace Engineering Xiamen University Xiamen China School of Electrical and Electronic Engineering Chongqing University of Technology Chongqing China School of Mechanical and Electrical Engineering China University of Mining and Technology-Beijing Beijing China Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen University Xiamen China

ISBN: (纸本)9798350374315

To enhance the accuracy and effectiveness of wind farm load forecasting results, this study investigates the development of a machine learning-based wind farm load combination forecasting model. Firstly, three types of machine learning regression prediction models, namely Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF), are established. Secondly, particle swarm optimization (PSO) is employed to optimize the weighted coefficients of the combination prediction model. Finally, a GBDT-LightGBM-RF combination prediction model is constructed. The empirical analysis demonstrates that the mean square error (MSE) value of the combination forecasting model is 0.216, which is lower than that of individual forecasting models. The machine learning-based combination forecasting model significantly enhances wind power generation forecast accuracy and holds valuable implications for wind power generation planning in diverse regions. © 2024 IEEE.

关键词： Wind forecasting

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Soft Artificial Synapse Electronics

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Research 2024年第4期8卷 101-118页

作者： Md.Rayid Hasan Mojumder Seongchan Kim Cunjiang Yu Department of Electrical Engineering The Pennsylvania State UniversityUniversity ParkPA 16802USA Department of Electrical and Systems Engineering University of PennsylvaniaPhiladelphiaPA 19104USA Department of Electrical and Computer Engineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Department of Materials Science and Engineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Department of Mechanical Science and Engineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Department of Bioengineering University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Materials Research Laboratory University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Beckman Institute for Advanced Science and Technology University of IllinoisUrbana-ChampaignUrbanaIL 61801USA Nick Holonyak Micro and Nanotechnology Laboratory University of IllinoisUrbana-ChampaignUrbanaIL 61801USA

Soft electronics,known for their bendable,stretchable,and flexible properties,are revolutionizing fields such as biomedical sensing,consumer electronics,and robotics.A primary challenge in this domain is achieving low power consumption,often hampered by the limitations of the conventional von Neumann *** response,the development of soft artificial synapses(SASs)has gained substantial *** synapses seek to replicate the signal transmission properties of biological synapses,offering an innovative solution to this *** review explores the materials and device architectures integral to SAS fabrication,emphasizing flexibility and stability under mechanical *** architectures,including floating-gate dielectric,ferroelectric-gate dielectric,and electrolyte-gate dielectric,are analyzed for effective weight control in *** utilization of organic and low-dimensional materials is highlighted,showcasing their plasticity and energy-efficient ***,the paper investigates the integration of functionality into SASs,particularly focusing on devices that autonomously sense external *** SASs,capable of recognizing optical,mechanical,chemical,olfactory,and auditory cues,demonstrate promising applications in computing and sensing.A detailed examination of photo-functionalized,tactile-functionalized,and chemoreception-functionalized SASs reveals their potential in image recognition,tactile sensing,and chemosensory applications,*** study highlights that SASs and functionalized SAS devices hold transformative potential for bioelectronics and sensing for soft-robotics applications;however,further research is necessary to address scalability,longtime stability,and utilizing functionalized SASs for prosthetics and in vivo applications through clinical *** providing a comprehensive overview,this paper contributes to the understanding of SASs,bridging research gaps and paving the way tow

关键词： dielectric utilizing gained

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AI-Driven Clinical Decision Support Systems: Revolutionizing Healthcare with Predictive Models

AI-Driven Clinical Decision Support Systems: Revolutionizing...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Kukreti, Sanjeev Shrivastava, Anurag Chandrashekar, Rakesh Rani, K. Pushpa Badhoutiya, Arti Lakhanpal, Sorabh Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Electrical Engineering Mathura India Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798331512965

The integration of artificial intelligence (AI) into Clinical Decision Support Systems (CDSS) is changing the face of healthcare by allowing AI powered predictive, data driven insights, enabling medical practitioners to prolong their diagnosis and treatment planning. To make better and faster clinical judgements, CDSS based on AI using machine learning techniques such as ensemble methods and deep learning is discussed in this paper. He said most of the focus is on models that can predict future events, diagnose plagues and deliver tailored treatment programs sifting through mountains of health care data, such as genetics, medical imaging and EHRs. Some of the main problems we work with as we implement AI driven CDSS is its data privacy, model interpretability and integration to current healthcare processes. In addition this study summarizes recent advances in artificial intelligence (AI), enabling real time analysis and making real time decisions to improve patient outcomes and reduce workloads of healthcare personnel. Case examples that employ AI-CDSS to identify diseases, optimize treatments and predict risk are provided to show how they can change the face of clinical practice. Finally, we explore where AI healthcare may be going from here with a focus on the importance of deploying these systems with patient well being in mind and with full compliance to the relevant regulations. © 2025 IEEE.

关键词： AI-Driven CDSS Clinical Decision Support Healthcare Machine Learning Predictive Models

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