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3rd International Conference on Sustainable Buildings and Structures, ICSBS 2023

作者： Liang, Jiaxin Xiang, Changying Division of Integrative Systems and Design School of Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9789819979646

The use of adaptive photovoltaic (PV) facades holds great promise in reducing energy consumption, harvesting clean solar energy on site, and optimizing indoor climate. To improve building facade performance, various design methods have been employed at both the city and building scale to optimize PV facade parameters and geometry. However, while prior studies have focused mainly on technical aspects such as PV energy performance and adaptive technologies, few have systematically investigated design methods of adaptive PV facades, particularly from an architectural or city design perspective. This literature review aims to address this gap by qualitatively analyzing applications of adaptive PV facades at both scales, discussing prevalent design methods and their effectiveness, and analyzing cutting-edge research in related fields. The review emphasizes three major aspects of adaptive PV facade design: energy efficiency, human comfort, and aesthetics. Additionally, the study evaluates the contributions and limitations of optimized design methods in selected papers, identifies research gaps in adaptive PV facades, and provides guidance for future research in this area. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Energy efficiency

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Guidance on a Digital Factory Twin: Proposal for a Reference Architecture

Guidance on a Digital Factory Twin: Proposal for a Reference...

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2024 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2024

作者： Jonas, Lick Fiona, Kattenstroth Malte, Trienens Jan-Philipp, Disselkamp Arno, Kuhn Roman, Dumitrescu Fraunhofer Institute for Mechatronic Systems Design Department for Digital Transformation Paderborn Germany Fraunhofer Institute for Mechatronic Systems Design Department for Digital Engineering Paderborn Germany Fraunhofer Institute for Mechatronic Systems Design Department for Systems Lifecycle Management Paderborn Germany Fraunhofer Institute for Mechatronic Systems Design Division for Product Development Paderborn Germany University of Paderborn - Heinz Nixdorf Institute Professorship Advanced Systems Engineering Paderborn Germany

ISBN: (纸本)9798350367355

The digital twin in manufacturing, also called digital factory twin (DFT), is not only a recent technology but also a polymorphic concept which generates a profusion of state-of-the-art work from industry and research without real coherence. The work relates to very different realities including various definitions, shapes, models and applications. A wide variety of architectures are proposed and there is a significant lack of information on how to deploy a digital twin efficiently and how to define an appropriate architecture. The DFT is often implemented use-case-driven and in various ways concerning IT architectures. The paper proposes a reference architecture based on enterprise architecture modelling language ArchiMate and TOGAF principles, which allows to model business architectures and IT architectures. We make use of that and model a DFT business architecture based on the lifecycle of a factory and a DFT IT architecture based on IT applications and IT functions. © 2024 IEEE.

关键词： Smart manufacturing

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Fundamental Limits of Two-Hop MIMO Channels: An Asymptotic Approach

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IEEE Transactions on Information Theory 2025年第6期71卷 4069-4111页

作者： Zhuang, Zeyan Zhang, Xin Xu, Dongfang Song, Shenghui The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong The Hong Kong University of Science and Technology Division of Integrative Systems and Design Hong Kong Hong Kong

Multi-antenna relays and intelligent reflecting surfaces (IRSs) have been utilized to construct favorable channels to improve the performance of wireless systems. A common feature between relay systems and IRS-aided systems is the two-hop multiple-input multiple-output (MIMO) channel. As a result, the mutual information (MI) of two-hop MIMO channels has been widely investigated with very engaging results. However, a rigorous investigation on the fundamental limits of two-hop MIMO channels, i.e., the first and second-order analysis, is not yet available in the literature, due to the difficulties caused by the two-hop (product) channel and the noise introduced by the relay (active IRS). In this paper, we employ large random matrix theory, specifically Gaussian tools, to derive the closed form deterministic approximation for the mean and variance of the MI. Additionally, we determine the convergence rate for the mean, variance and the characteristic function of the MI, and prove the asymptotic Gaussianity. Furthermore, we also investigate the analytical properties of the fundamental equations that describe the closed-form approximation and prove the existence and uniqueness of the solution. An iterative algorithm is then proposed to obtain the solutions for the fundamental equations. Numerical results validate the accuracy of the theoretical analysis. © 1963-2012 IEEE.

关键词： Iterative methods

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Fracture Properties and Strength Evaluation of Carbon Fiber under Tension-Torsion Loading

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Zairyo/Journal of the Society of Materials Science, Japan 2024年第5期73卷 379-385页

作者： Hashimoto, Mikiyasu Farhanah, Siti Macadre, Arnaud Goda, Koichi Division of Systems and Design Engineering Graduate School of Sciences and Technology for Innovation Yamaguchi University Tokiwadai Ube755-8611 Japan Department of Mechanical Engineering Faculty of Engineering Yamaguchi University Tokiwadai Ube755-8611 Japan Division of Mechanical Engineering Graduate School of Sciences and Technology for Innovation Yamaguchi University Tokiwadai Ube755-8611 Japan

Recently, carbon fiber reinforced plastics (CFRPs) have been used as high-pressure container materials for fuel cell vehicles and other applications. When used as structural components, carbon fibers in a CFRP are subjected to multiaxial loading. As is well known, multiaxial strength and fracture behavior are significantly different from those under uniaxial loading. However, research on the multiaxial strength evaluation of carbon fibers is insufficient. To date, there have been a few reports on studies under tension-torsion loading, but these have hardly clarified the fracture criterion. In this study, the tensile-torsional fracture strengths of PAN-based carbon fibers were obtained by proportional loading tests. As a result, it was found that the tensile strength along the fiber axis decreased largely with increasing shear stress at the fiber surface under tension-torsion loading. The changes in the experimental results were well explained by adding a correction term to Tsai-Hill criterion, the theory for anisotropic materials. Furthermore, since PAN-based carbon fibers showed extreme differences in strength between uniaxial tension and pure torsion, it was finally concluded that the carbon fibers follow the maximum principal stress theory when using normalized stresses. ©2024 The Society of Materials Science, Japan.

关键词： Tensile strength

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Neuro-Cognitive Inspired Deep Learning for Enhanced Navigation in No-Till Agriculture Using Multisensory Data 23

Neuro-Cognitive Inspired Deep Learning for Enhanced Navigati...

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23rd International Conference on Machine Learning and Cybernetics, ICMLC 2024

作者： Wan, Anhar Risnuma Ardilla, Fernando Nakamura, Yoshimasa Kubota, Naoyuki Graduate School of Systems Design Tokyo Metropolitan University Department of Mechanical Systems Engineering Tokyo Hino191-0065 Japan Politeknik Elektronika Negeri Mechatronics Engineering Division Surabaya Indonesia

ISBN: (纸本)9798331528041

No-till farming, an approach to combating climate change, poses obstacles to autonomous agricultural navigation. This approach often leads to uneven fields and varying crop densities, making navigation challenging and sometimes reducing crop yields compared to conventional tillage. However, when no-till practices are combined with conservation methods such as residue retention and crop rotation, it can mitigate these drawbacks and even improve productivity, especially in dry climates. These complex conditions require advanced autonomous mobile robots capable of effectively navigating uneven terrains, adjusting to fluctuating crop densities, and quickly responding to unexpected obstacles such as fallen branches. Drawing inspiration from neurocognitive science, this study introduces a novel deep learning methodology that focuses on immediate adaptability through sensory-steering coordination and multimodal sensory integration. By leveraging convolutional neural networks and a fusion mechanism for navigational commands, the proposed model skillfully navigates the dynamic and unstructured conditions of no-till agriculture, showcasing dependable capabilities in simulated 3D environments. © 2024 IEEE.

关键词： Crop rotation

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Towards Holistic Cyber-Physical Production systems in Existing Production Environment: Challenges from a Case Study

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Procedia CIRP 2025年 134卷 115-120页

作者： Tobias Englund Jessica Bruch Koteshwar Chirumalla Mohammad Ashjaei Mälardalen University Division of Product Realization School of Innovation Design and Engineering Eskilstuna Sweden Mälardalen University Division of Networked and Embedded Systems School of Innovation Design and Engineering Eskilstuna Sweden

Cyber-physical production systems (CPPS) are the backbone of Smart Production. Digital transformation often starts from a hierarchical systems landscape based on the automation pyramid with legacy systems and low data availability. CPPS’s are characterized by a decentralized structure with the ability to share data and services across the value chain, to gain benefits in line with Industry 5.0 demands. Literature proposes different models, which in theory should enable companies with individual starting points and legacy systems to implement a holistic CPPS architecture. Nevertheless, companies struggle with its implementation. Therefore, the purpose of this article is to analyze challenges when implementing a holistic CPPS architecture. A case study assesses the transformation of a global manufacturing company towards a holistic CPPS architecture with input from stakeholders at management level to operators. This paper provides examples and insights into current challenges and gives recommendations for next steps towards a holistic CPPS architecture.

关键词： Cyber-Physical Production System Smart Production Digital Transformation

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Enhanced Prognostics and Health Management in Automated Driving systems: Using Graph Neural Networks to Recognize Operational Contexts

Enhanced Prognostics and Health Management in Automated Driv...

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2024 Prognostics and System Health Management Conference, PHM 2024

作者： Su, Peng Wang, Yeqi Xiang, Conglei Wendel, Erik Mishra, Madhav Chen, Dejiu KTH Royal Institute of Technology Mechatronics Division Department of Engineering Design Stockholm Sweden School KTH Royal Institute of Technology Stockholm Sweden RISE Research Institutes of Sweden Digital Systems Division Gothenburg Sweden

ISBN: (纸本)9798350360585

Prognostics and health management (PHM) is an engineering discipline that aims to maintain system behaviour and function and ensure mission success, safety and effectiveness. Addressing the challenges in prognostics and health management for modern intelligent systems, especially automated driving systems, is complex due to the contextual nature of faults. This complexity necessitates a thorough understanding of spatial, and temporal conditions, and relationships within operational scenarios and life-cycle stages. This paper introduces a framework designed to automatically recognize driving scenarios in automated driving systems using graph neural networks (GNNs). The framework extracts relational data from image frames, constructing graph-based models and transforming unstructured sensory data into structured data with diverse node types and relationships. A specific graph neural network processes the graph model to reveal and detect operational conditions and relationships. The proposed framework is evaluated using the KITTI dataset, demonstrating superior performance compared to conventional feed-forward networks such as MLP, particularly in handling relational data. © 2024 IEEE.

关键词： Graph neural networks

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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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Preemies: Anthropometry and Body Shape Models

Preemies: Anthropometry and Body Shape Models

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2024 International Research Council on the Biomechanics of Injury, IRCOBI 2024

作者： Brolin, Karin Brooks, Sarah Alvarez, Victor Chen, Siyuan Zimmer, Pia Brandberg, August Huber-Dangl, Florentine Li, Xiaogai Kleiven, Svein Visvikis, Costandinos Lightness by Design Stockholm Sweden Northern Neonatal Network United Kingdom CYBEX GmbH London United Kingdom Vienna Austria Division of Neuronic Engineering Department of Biomedical Engineering and Health Systems KTH Stockholm Sweden

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Enhancing Drone Surveillance with NeRF: Real-World Applications and Simulated Environments 43

Enhancing Drone Surveillance with NeRF: Real-World Applicati...

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43rd AIAA DATC/IEEE Digital Avionics systems Conference, DASC 2024

作者： Lindén, Joakim Burresi, Giovanni Forsberg, Håkan Daneshtalab, Masoud Söderquis, Ingemar School of Innovation Design and Engineering Mälardalen University Division of Intelligent Future Technologies Västerås Sweden Saab Aeronautics Stockholm Sweden School of Electrical Engineering and Computer Science Royal Institute of Technology Division of Electronics and Embedded Systems Stockholm Sweden

ISBN: (纸本)9798350349610

Machine Learning (ML) systems require representative and diverse datasets to accurately learn the objective task. In supervised learning data needs to be accurately annotated, which is an expensive and error-prone process. We present a method for generating synthetic data tailored to the use-case achieving excellent performance in a real-world usecase. We provide a method for producing automatically annotated synthetic visual data of multi rotor unmanned aerial vehicles (UAV) and other airborne objects in a simulated environment with a high degree of scene diversity, from collection of 3D models to generation of annotated synthetic datasets (synthsets). In our data generation framework SynRender we introduce a novel method of using Neural Radiance Field (NeRF) methods to capture photo-realistic high-fidelity 3D-models of multirotor UAVs in order to automate data generation for an object detection task in diverse environments. By producing data tailored to the real-world setting, our NeRF-derived results show an advantage over generic 3D asset collection-based methods where the domain gap between the simulated and real-world is unacceptably large. In the spirit of keeping research open and accessible to the research community we release our dataset VISER DroneDiversity used in this project, where visual images, annotated boxes, instance segmentation and depth maps are all generated for each image sample. © 2024 IEEE.

关键词： Drones

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