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7th International Conference on Computational Intelligence and Intelligent systems, CIIS 2024

作者： Humpert, Lynn Zagatta, Kristin Anacker, Harald Dumitrescu, Roman Department of Systems Engineering Fraunhofer Institute for Mechatronic Systems Design North Rhine Westphalia Paderborn Germany Bl Advanced Systems Engineering Fraunhofer Institute for Mechatronic Systems Design North Rhine Westphalia Paderborn Germany

ISBN: (纸本)9798400717437

The European Green Deal is one of the key milestones in achieving the European Commission's sustainability goals. A key objective is to reduce the use of resources in order to reduce global emissions. Early validation of products is one way to reduce the need for late rework or modification of equipment, which in turn reduces the use of resources. It also has the benefit of reducing costs and the time-to-market, as late product changes require more resources and time. This is especially challenging for complex systems, where interconnects are often difficult to identify. For this reason, many companies use systems engineering as a design method to collaborate on products across departments and disciplines. Early validation and systems engineering are usually used separately, but research shows that the combination can provide an advantage in development, so a basis for linking them and further research needs to be identified. In this paper, a systematic literature review was conducted to identify approaches for early validation and systems engineering. It also examined the suitability of using these approaches in combination for product development and design of intelligent systems. This resulted in 49 potential methods being identified and evaluated according to requirements of the industry. They are discussed based on a concrete example in special purpose machinery in the production context of complex intelligent systems. © 2024 Copyright held by the owner/author(s).

关键词： Large scale systems

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Nanoimprint lithography-assisted block copolymer self-assembly for hyperfine fabrication of magnetic patterns based on L10-FePt nanoparticles

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Science China Chemistry 2025年第5期68卷 2027-2034页

作者： Jie Zhang Yi Chen Guijun Li Zelin Sun Zhengong Meng Wai-Yeung Wong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University The Hong Kong Polytechnic University Shenzhen Research Institute Division of Integrative Systems and Design and Department of Electronic and Computer EngineeringThe Hong Kong University of Science and Technology School of Flexible Electronics (Future Technology) and Institute of Advanced Materials (IAM) Nanjing Tech University

L10-FePt-type bit-patterned media has provided a promising alternative for ultrahigh-density magnetic recording systems in the current digital era, but rapid fabrication of magnetic patterns with hyperfine bit islands is still challenging, especially with the target for miniaturization and scalable production simultaneously. Herein, Fe,Pt-containing block copolymers were utilized as single-source precursors for solution-processable patterning and subsequent generation of the demanding magnetic FePt dots by in situ pyrolysis. High-throughput nanoimprint lithography was initially employed to fabricate the predefined bit cells precisely,and then the intrinsic self-assembly of phase-separated block copolymers further drove the formation of accurate bit *** from the synergistic effect of top-down lithographic approach and bottom-up self-assembly, the customizable patterns could be achieved for large-scale mass production in targeted areas, but high-density isolated dots could also be accurately aligned along the patterned features after subsequent self-assembly. This reliable strategy would provide a good avenue to precisely construct ultrahigh-density magnetic data storage devices.

关键词： organometallic precursor magnetic nanoparticles block copolymer self-assembly nanoimprint lithography

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Modulated Structure Formation in Dislocation Cells in 316L Stainless Steel Fabricated by Laser Powder Bed Fusion

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Nippon Kinzoku Gakkaishi/Journal of the Japan Institute of Metals 2025年第5期89卷 175-181页

作者： Sun, Fei Ogawa, Toshio Adachi, Yoshitaka Sato, Kazuhisa Takagi, Shunya Miyamoto, Goro Suzuki, Asuka Yamanaka, Akinori Nakada, Nobuo Ishimoto, Takuya Nakano, Takayoshi Koizumi, Yuichiro Department of Material Design Innovation Engineering Nagoya University Nagoya464-8603 Japan Research Center for Ultra-High Voltage Electron Microscopy Osaka University Ibaraki Osaka567-0047 Japan Tohoku University Sendai980-8577 Japan Tsukuba305-0047 Japan Department of Materials Process Engineering Graduate School of Engineering Nagoya University Nagoya464-8603 Japan Division of Advanced Mechanical Systems Engineering Institute of Engineering Tokyo University of Agriculture and Technology Koganei184-8588 Japan Department of Materials Science and Engineering Tokyo Institute of Technology Yokohama226-8502 Japan Aluminium Research Center University of Toyama Toyama930-8555 Japan Division of Materials and Manufacturing Science Graduate School of Engineering Osaka University Suita565-0871 Japan Anisotropic Design & Additive Manufacturing Research Center Osaka University Suita565-0871 Japan

Metal additive manufacturing enables producing complex geometric structures with high accuracy and breaks the design constraints of traditional manufacturing methods. Laser powder bed fusion, a typical additive manufacturing process, presents a challenge in experimentally understanding the nano-scaled microstructure-process relationship regarding the wide range of process parameters. In this study, we aim to reveal the novel nanoscale structural features by advanced scanning transmission electron microscopy to clarify the formation mechanisms in 316L stainless steel by laser powder bed fusion. Here we show that the slender columnar grains were confined to the centreline of the melt pool along the build direction, and the columnar cell structure at the side branching of the melt pool grew along orthogonal directions to follow drastic changes in thermal gradient across adjacent melt pools. Novel nano-scaled modulated structures have been observed in the dislocation cells parallel to the laser scan direction, which were mainly caused by the elastic strain involving the thermal gradient inside the melt pool and across adjacent melt pools as well as the effective strain field in the dislocation cell interiors. An in-depth understanding of microstructure developments is worthy of fabricating high-performance materials by controlling the additive manufacturing process. ©2025 The Japan Institute of Metals and Materials.

关键词： Refining

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Multi-Stage Model Predictive Control with Enhanced Discrete-Time Models for Multilevel Inverters

Multi-Stage Model Predictive Control with Enhanced Discrete-...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Hoang Le Apparao Dekka Deepak Ronanki Abdul R. Beig Department of Electrical and Computer Engineering Lakehead University Thunder Bay Canada Department of Engineering Design Indian Institute of Technology Madras Chennai India Advanced Power and Energy Systems Electrical Engineering Department Khalifa University Abu Dhabi UAE

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

Model accuracy, computational complexity, and weighting factor dependency are three primary challenges associated with multi-stage model predictive control (MS-MPC) of multilevel inverters (MLIs). Typically, existing MS-MPC employs the forward Euler integration method to discretize continuous-time models, due to the ease of implementation in real-time controllers. This approach faces a significant deterioration in computational accuracy as sampling period increases. To address this issue, this paper proposes a new formulation of MS-MPC for MLIs. Also, an enhanced discrete-time model of MLI by using Heun’s integration method is proposed to implement the proposed MS-MPC, resulting in a more precise formulation and indirect minimization of common-mode voltage (CMV). Unlike the existing methods, the proposed method does not employ a cost function with weighting factor or offline selection of voltage vectors (VVs) to minimize the CMV. Furthermore, it does not increase computational complexity compared to the existing MS-MPCs. Simulation studies are conducted on a four-level MLI system to validate the efficacy of the proposed MS-MPC, and its performance is compared with the existing methods.

关键词： Accuracy Computational modeling Predictive models Multilevel inverters Mathematical models Vectors Real-time systems Power electronics Computational complexity Predictive control

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Core-shell structured BN/SiO_(2)nanofiber membrane featuring with dual-effect thermal management and flame retardancy for extreme space thermal protection

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Science Bulletin 2025年第5期70卷 722-732页

作者： Duo Pan Ziyuan Han Junting Lei Yutao Niu Hu Liu Sunmi Shin Chuntai Liu Zhanhu Guo State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou 450001China Key Laboratory of Multifunctional Nanomaterials and Smart Systems Advanced Materials DivisionSuzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhou 215123China Department of Mechanical Engineering College of Design and EngineeringNational University of SingaporeSingapore 117575Singapore Mechanical and Construction Engineering Faculty of Engineering and EnvironmentNorthumbria UniversityNewcastle Upon TyneNE18STUK

With the rapid progress of aerospace frontier engineering,the extreme space thermal environment has brought severe challenges to astronauts’space suits,putting forward higher requirements for thermal protection *** this basis,a unique core-shell structured hexagonal boron nitride(h-BN)/silicon dioxide(SiO_(2))nanofiber membrane(HS)was prepared using the coaxial electrospinning method,of which both the thermal insulation SiO_(2)nanofiber cortex and the passive radiation cooling(PRC)h-BN nanofiber core make it a promising dual-effect thermal management ***,when the amount of h-BN is 0.9 g,the resultant HS(HS0.9)exhibits astonishing low thermal conductivity of 0.026 W m−1 K−1 and high reflectivity and emissivity of exceeding 90%over an extremely wide *** expected dual-effect thermal management performance enables the HS to have an ideal cooling effect under both high sunlight intensity and strong light *** addition,HS also shows excellent flame retardant performance arising from the excellent high-temperature stability of h-BN and SiO_(2).What is more,the tensile strength of HS0.9 was also significantly increased from 0.42 to 7.2 MPa by encapsulating polyimide through vacuum ***,the research results of this work provide innovative highlights for high-temperature protection in daily life and even extreme space environments.

关键词： h-BN/SiO_(2)nanofiber membrane Core-shell structure Passive radiative cooling Thermal insulation Flame retardancy

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Toward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Control

arXiv

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arXiv 2025年

作者： Zhang, Yuli Wang, Shangbo Jia, Dongyao Fan, Pengfei Jiang, Ruiyuan Gu, Hankang Chow, Andy H.F. School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou215123 China Department of Systems Engineering University of Sussex BrightonBN1 9RH United Kingdom Department of Engineering and Design City University of Hong Kong Hong Kong

Reinforcement learning (RL) emerges as a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, with deep neural networks substantially augmenting its learning capabilities. However, centralized RL becomes impractical for ATSC involving multiple agents due to the exceedingly high dimensionality of the joint action space. Multi-agent RL (MARL) mitigates this scalability issue by decentralizing control to local RL agents. Nevertheless, this decentralized method introduces new challenges: the environment becomes partially observable from the perspective of each local agent due to constrained inter-agent communication. Both centralized RL and MARL exhibit distinct strengths and weaknesses, particularly under heavy intersectional traffic conditions. In this paper, we justify that MARL can achieve the optimal global Q-value by separating into multiple IRL (Independent Reinforcement Learning) processes when no spill-back congestion occurs (no agent dependency) among agents (intersections). In the presence of spill-back congestion (with agent dependency), the maximum global Q-value can be achieved by using centralized RL. Building upon the conclusions, we propose a novel Dynamic Parameter Update Strategy for Deep Q-Network (DQN-DPUS), which updates the weights and bias based on the dependency dynamics among agents, i.e. updating only the diagonal sub-matrices for the scenario without spill-back congestion. We validate the DQN-DPUS in a simple network with two intersections under varying traffic, and show that the proposed strategy can speed up the convergence rate without sacrificing optimal exploration. The results corroborate our theoretical findings, demonstrating the efficacy of DQN-DPUS in optimizing traffic signal control. We applied the proposed method to a dual-intersection, and the results indicate that our approach performs effectively under various traffic conditions. These findings confirm the robustness and

关键词： Traffic signals

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Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion

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IEEE Wireless Communications Letters 2025年

作者： Guo, Lei Chen, Wei Sun, Yuxuan Ai, Bo Pappas, Nikolaos Quek, Tony Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China Linköping University Department of Computer and Information Science Linköping Sweden Singapore University of Technology and Design Information Systems Technology and Design Singapore

Obtaining high-resolution hyperspectral images (HR-HSI) is costly and data-intensive, making it necessary to fuse low-resolution hyperspectral images (LR-HSI) with high-resolution RGB images (HR-RGB) for practical applications. However, traditional fusion techniques, which integrate detailed information into the reconstruction, significantly increase bandwidth consumption compared to directly transmitting raw data. To overcome these challenges, we propose a hierarchy-aware and channel-adaptive semantic communication approach for bandwidth-limited data fusion. A hierarchical correlation module is proposed to preserve both the overall structural information and the details of the image required for super-resolution. This module efficiently combines deep semantic and shallow features from LR-HSI and HR-RGB. To further reduce bandwidth usage while preserving reconstruction quality, a channel-adaptive attention mechanism based on Transformer is proposed to dynamically integrate and transmit the deep and shallow features, enabling efficient data transmission and high-quality HR-HSI reconstruction. Experimental results on the CAVE and Washington DC Mall datasets demonstrate that our method outperforms single-source transmission, achieving up to a 2 dB improvement in peak signal-to-noise ratio (PSNR). Additionally, it reduces bandwidth consumption by two-thirds, confirming its effectiveness in bandwidth-constrained environments for HR-HSI reconstruction tasks. © 2012 IEEE.

关键词： Bandwidth

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An Adaptive Prediction Model for Randomly Distributed Traffic Data in Urban Road Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Jiang, Ruiyuan Wang, Shangbo Jia, Dongyao Mao, Guoqiang Lim, Eng Gee Xi'an Jiaotong-Liverpool University School of Advanced Technology Suzhou215000 China University of Sussex Department of Engineering and Design BrightonBN1 9RH United Kingdom University of Hong Kong Department of Civil Engineering Hong Kong Xidian University Communication and Information Systems Xi'an710126 China

Effective and efficient traffic prediction can provide a reliable data basis for traffic management in Intelligent Transportation systems (ITS). While various machine learning methods have been proposed to enhance prediction accuracy in recent decades, there remain potential issues to be further addressed. Firstly, the inherent randomness of traffic dynamics usually leads to some outliers in historical observations, which may deviate the model parameter estimation when utilizing deep learning-based models to learn data distribution. Secondly, the spatial correlation among the road sections may dynamically change over time, posing challenges for modeling. In addition, due to the complexity of urban traffic networks, capturing such non-linear spatial dependencies based on the global road structure may consume huge computational resources. To address these issues, this paper proposes an adaptive temporal graph attention network (ATGAN), which is implemented in two steps: (1) An outlier time series filter (OTSF) technique is introduced to mitigate the adverse impact of outlier points and to adaptively learn the distribution of fluctuations of traffic data;(2) We design a group attention temporal graph convolutional network (GA-TGCN) to model the spatiotemporal features among neighboring road sections, which is achieved by adjusting the spatial correlation matrix dynamically in each training epoch with attention mechanism. We evaluate the prediction performance of ATGAN on two real-world datasets and the results show that our model can achieve higher prediction accuracy in less computational time compared with baseline methods. © 1967-2012 IEEE.

关键词： Prediction models

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Probabilistic Health Monitoring and Maintenance Scheduling for Nuclear Power systems

Probabilistic Health Monitoring and Maintenance Scheduling f...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Amer, Ahmad Tsilifis, Panagiotis Saxena, Abhinav Baglietto, Emilio Khan, Genghis Probabilistic Design & Material Informatics GE Aerospace Research Niskayuna NY12309 United States Robotics AI & Computer Vision GE Vernova Advanced Research Niskayuna NY12309 United States AI & Computer Vision GE Aerospace Research San RamonCA94583 United States Department of Nuclear Science & Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Sustainable Systems GE Aerospace Research Niskayuna NY12309 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

Operations and Maintenance (O&M) cost optimization in the nuclear energy industry is an imperative task for developing sustainable systems and efficient renewable technologies. We present a modular probabilistic framework for addressing maintenance scheduling challenges for small modular nuclear reactors using a Dynamic Bayesian Network (DBN) approach that monitors fatigue and performance in multiple subsystems of the reactors and provides a probabilistic assessment for potential failure events. The DBN framework fuses information from potential digital twins that assimilate real time data from several sensors with different time granularities in order to monitor and update thecurrentstate of knowledge aboutthestatus of several fatigue or performance criteria. Comprised of multi-modal digital twins (empirical, data- and physics-based twins), this DBN serves as a predictive framework forecasting the future state of specific reactor subsystems and enhancing decision-making capabilities associated with preventing system failure and minimizing the time and cost of maintenance events. The work presented in this paper is a holistic approach for monitoring and safeguarding complex, multi-component engineering systems where maintenance scheduling is critical and does not restrict itself to nuclear environments. However, our goal is to illustrate how such a framework can be adjusted to small modular water reactors. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Nuclear energy

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Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion

arXiv

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arXiv 2025年

作者： Guo, Lei Chen, Wei Sun, Yuxuan Ai, Bo Pappas, Nikolaos Quek, Tony State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Department of Computer and Information Science Linköping University Linköping Sweden Information Systems Technology and Design Singapore University of Technology and Design Singapore

关键词： Bandwidth

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