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10th Convention of the European Acoustics Association, EAA 2023

作者： Xia, Yuanxin Engsig-Karup, Allan P. Jeong, Cheol-Ho Acoustic Technology Department of Electrical and Photonics Engineering Technical University of Denmark Kgs. Lyngby2800 Denmark Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby2800 Denmark

ISBN: (纸本)9788888942674

The estimation of the absorption coefficients of the boundary surfaces in a room is important in room acoustic engineering. This research presents a machine learning method learns from simulated data to estimate the room dimensions and frequency-dependent absorption coefficients. We employ multi-task convolutional neural networks for inferring the frequency-dependent absorption coefficients and the dimensions of the room from transfer functions calculated by wave-based room acoustic methods. The proposed method provides reasonably accurate estimation of the boundary conditions and dimensions. © 2023 Xia et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

关键词： Boundary conditions

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Self-Assembled Porous-Reinforcement Microstructure-Based Flexible Triboelectric Patch for Remote Healthcare

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Nano-Micro Letters 2023年第7期15卷 324-336页

作者： Hao Lei Haifeng Ji Xiaohan Liu Bohan Lu Linjie Xie Eng Gee Lim Xin Tu Yina Liu Peixuan Zhang Chun Zhao Xuhui Sun Zhen Wen Institute of Functional Nano and Soft Materials(FUNSOM) Joint International Research Laboratory of Carbon-Based Functional Materials and DevicesSoochow UniversitySuzhou 215123People’s Republic of China Department of Electrical and Electronic Engineering School of Advanced TechnologyXi’an Jiaotong-Liverpool UniversitySuzhou 215123People’s Republic of China Department of Applied Mathematics School of Mathematics and PhysicsXi’an Jiaotong-Liverpool UniversitySuzhou 215123People’s Republic of China Department of Electrical and Electronic Engineering University of LiverpoolLiverpool L693GJUK Jiangsu Key Laboratory for Carbon-Based Functional Materials and Devices Soochow UniversitySuzhou 215123JiangsuPeople’s Republic of China

Realizing real-time monitoring of physiological signals is vital for preventing and treating chronic diseases in elderly individuals. However,wearable sensors with low power consumption and high sensitivity to both weak physiological signals and large mechanical stimuli remain ***, a flexible triboelectric patch(FTEP) based on porous-reinforcement microstructures for remote health monitoring has been reported. The porousreinforcement microstructure is constructed by the self-assembly of silicone rubber adhering to the porous framework of the PU sponge. The mechanical properties of the FTEP can be regulated by the concentrations of silicone rubber dilution. For pressure sensing, its sensitivity can be effectively improved fivefold compared to the device with a solid dielectric layer, reaching 5.93 kPa^(-1) under a pressure range of 0–5 kPa. In addition, the FTEP has a wide detection range up to 50 kPa with a sensitivity of 0.21 kPa^(-1). The porous microstructure makes the FTEP ultra-sensitive to external pressure, and the reinforcements endow the device with a greater deformation limit in a wide detection range. Finally, a novel concept of the wearable Internet of Healthcare(Io H) system for real-time physiological signal monitoring has been proposed, which could provide real-time physiological information for ambulatory personalized healthcare monitoring.

关键词： Pressure sensor Triboelectric nanogenerator Porous dielectric layer Physiological signals Internet of Healthcare

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Multivariable Decoupling Control of Civil Turbofan Engines Based on Fully Actuated System Approach

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Journal of Systems Science & Complexity 2023年第3期36卷 947-959页

作者： PENG Kai WANG Hongxia ZHANG Huanshui ZHANG Zhaorong YANG Fan School of Power and Energy Northwestern Polytechnical UniversityXi’an 710072China School of Electrical and Automation Engineering Shandong University of Science and TechnologyQingdao 266590China Department of Applied Mathematics The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina. Xi’an Aeronautics Computing Technique Research Institute AVICXi’an 710068China

Gas turbine engines must be operated by means of control,and how to achieve multivariable control decoupling with aero-engine control constraints is an open thorny issue attracting increasingly more *** paper considers the multivariable decoupling problems of aero-engines by using a compound controller,which originates from the fact that it is impossible to eliminate all the nonlinear dynamics of system to obtain desired constant linear closed-loop system by using full actuated control because of modeling errors and some physical *** controllers are involved in the compound *** is a fully actuated controller and the other is classical feedback *** order to use fully actuated control and maintain the accuracy of engine model,a full state scheduling linear parameter-varying(LPV)modeling method is proposed based on fuzzy neural network *** a general input matrix of the system,its generalized inverse is applied to design fully actuated controller to result in a pseudolinear *** with a feedback controller and control limiter,the control synthesis is *** simulation shows that the proposed method is possessed of a better decoupling and tracking effect compared with traditional control approach.

关键词： Compound control decoupling control fully actuated control turbofan engine

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A Novel Nash-Based Matching Approach for Multirobot Task Allocation in Distributed Robotic Networks 7th

A Novel Nash-Based Matching Approach for Multirobot Task All...

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7th EAI International Conference on Robotics and Networks, ROSENET 2023

作者： Hamidoğlu, Ali Gül, Ömer Melih Gültekin, Gökhan Koray Kadry, Seifedine Nimer Department of Mathematics Bahcesehir University İstanbul Turkey Department of Computer Engineering Bahçeşehir University İstanbul Turkey Department of Electrical-Electronics Engineering Ankara Yıldırım Beyazıt University Ankara Turkey Department of Applied Data Science Noroff College Kristiansand Norway Ajman University Ajman United Arab Emirates

ISBN: (纸本)9783031644948

Efficiency is paramount in distributed robotic networks, where multiple autonomous robots collaborate to perform complex tasks. In this context, the identification of the most efficient path for robots, considering both distance and cost, plays a crucial role in the development of an effective matching algorithm for addressing multirobot task allocation (MRTA) challenges. This study presents a novel cooperative Nash game framework that serves as a distributed matching method for addressing the task allocation problem in a distributed robotic network consisting of robots and tasks. A particular MRTA problem is investigated where each robot moves at a constant speed determined by maximizing energy harvesting while minimizing energy consumption during the motion. In this framework, Nash equilibrium is established as a near-optimal approach for matching based on distance. In the numerical experiments, the performances are assessed for various scenarios involving 10 robots and 20 robots with the same number of tasks. Here, the Hungarian algorithm is used as an optimal benchmark algorithm to demonstrate the reliability of the theoretical findings and the robustness of the proposed model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Distributed systems Energy harvesting Game theory Multirobot systems Task allocation

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Sub-GMN: The Neural Subgraph Matching Network Model 16

Sub-GMN: The Neural Subgraph Matching Network Model

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16th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2023

作者： Lan, Zixun Yu, Limin Yuan, Linglong Wu, Zili Niu, Qiang Ma, Fei Xi'an Jiaotong-Liverpool University School of Science Department of Applied Mathematics Suzhou215123 China Xi'an Jiaotong-Liverpool University Department of Electrical and Electronic Engineering Suzhou China The University of Liverpool Department of Mathematical Sciences United Kingdom

ISBN: (纸本)9798350330755

As one of the most fundamental tasks in graph theory, subgraph matching is a crucial task in many fields, ranging from information retrieval, computer vision, biology, chemistry and natural language processing. Yet subgraph matching problem remains to be an NP-complete problem. This study proposes an end-to-end learning-based approximate method for subgraph matching task, called subgraph matching network (Sub-GMN). The proposed Sub-GMN firstly uses graph representation learning to map nodes to node-level embedding. It then combines metric learning and attention mechanisms to model the relationship between matched nodes in the data graph and query graph. To test the performance of the proposed method, we applied our method on two databases. We used two existing methods, GNN and FGNN as baseline for comparison. Our experiment shows that, on dataset 1, on average the accuracy of Sub-GMN are 12.21% and 3.2% higher than that of GNN and FGNN respectively. On average running time Sub-GMN runs 20-40 times faster than FGNN. In addition, the average F1-score of Sub-GMN on all experiments with dataset 2 reached 0.95, which demonstrates that Sub-GMN outputs more correct node-to-node *** with the previous GNNs-based methods for subgraph matching task, our proposed Sub-GMN allows varying query and data graphes in the test/application stage, while most previous GNNs-based methods can only find a matched subgraph in the data graph during the test/application for the same query graph used in the training stage. Another advantage of our proposed Sub-GMN is that it can output a list of node-to-node matches, while most existing end-to-end GNNs based methods cannot provide the matched node pairs. © 2023 IEEE.

关键词： Graph theory

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Smart visual sensors for real time weather and ground conditions recognition for agricultural robotics

Smart visual sensors for real time weather and ground condit...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Diego Gragnaniello Antonio Greco Carlo Sansone Bruno Vento Department of Information and Electrical Engineering and Applied Mathematics (DIEM) University of Salerno Italy Department of Electrical Engineering and Information Technology (DIETI) University of Napoli Federico II Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

The dynamic and unpredictable nature of environmental elements, such as weather and ground conditions, profoundly influences the efficiency of agricultural robots. Real-time environmental perception is an enabling technology for several agricultural automatic systems, facilitating precise scheduling of tasks like planting, harvesting, and irrigation. Drawing inspiration from successful applications of computer vision in crop health monitoring and AI-driven weather impact prediction, in this paper we propose a system for simultaneous recognition of weather and ground conditions based on computer vision techniques. The system can run in real-time onboard smart cameras, making it affordable at low cost. After providing a detailed description of its architecture and methodology, we conduct extensive experiments to demonstrate the effectiveness of the system in real-world conditions and its significance in advancing agricultural robotics, fostering sustainable farming practices by optimizing resource utilization, and mitigating environmental impact.

关键词： Visualization Smart cameras Robot sensing systems Real-time systems Resource management Sustainable development Intelligent sensors Remote control Meteorology Farming

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Hybrid Models for Recognizing Indonesian Textual Entailment 7

Hybrid Models for Recognizing Indonesian Textual Entailment

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7th International Conference on Informatics and Computational Sciences, ICICoS 2024

作者： Ahmadian, Hendri Abidin, Taufik Fuadi Riza, Hammam Muchtar, Kahlil Universitas Syiah Kuala Graduate School of Mathematics and Applied Sciences Banda Aceh Indonesia Universitas Syiah Kuala Faculty of Mathematics and Natural Sciences Department of Informatics Banda Aceh Indonesia National Research and Innovation Agency Research Center for Artificial Intelligence and Cyber Security Jakarta Indonesia Universitas Syiah Kuala Faculty of Engineering Department of Electrical and Computer Engineering Banda Aceh Indonesia

ISBN: (纸本)9798350375886

A paragraph raises pertinent issues about linguistic disparities in understanding the semantic relationships between sentences within the natural language processing domain (NLP) Addressing this challenge necessitates the development of robust NLP models, with a specific emphasis on text entailment. This research endeavors to enhance the performance of Indonesian NLP benchmarks in text entailment by harnessing a variant of the Bidirectional Encoder Representations from Transformer (BERT) model tailored explicitly for the Indonesian language, known as IndoBERT. The approach in this study adopts a hybrid methodology, amalgamating the IndoBERT layer modification model by summing the last four hidden layers with neural networks. The performance evaluation of the hybrid models, combining the IndoBERT layer modification model with the Bidirectional Gated Recurrent Unit (BiGRU) model and the IndoBERT layer modification model with the Bidirectional Long Short-Term Memory (BiLSTM) model, yields the F1-scores of 0.87 and 0.84, respectively. © 2024 IEEE.

关键词： Semantics

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Direct System Identification of Dynamical Networks with Partial Measurements: A Maximum Likelihood Approach

Direct System Identification of Dynamical Networks with Part...

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European Control Conference (ECC)

作者： João Victor Galvão Da Mata Anders Hansson Martin S. Andersen Department of Applied Mathematics and Computer Science Technical University of Denmark Department of Electrical Engineering Linköping University.

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper introduces a novel direct approach to system identification of dynamic networks with missing data based on maximum likelihood estimation. Dynamic networks generally present a singular probability density function, which poses a challenge in the estimation of their parameters. By leveraging knowledge about the network's interconnections, we show that it is possible to transform the problem into a more tractable form by applying linear transformations. This results in a nonsingular probability density function, enabling the application of maximum likelihood estimation techniques. Our preliminary numerical results suggest that when combined with global optimization algorithms or a suitable initialization strategy, we are able to obtain a good estimate of the dynamics of the internal systems.

关键词： Maximum likelihood estimation Sensitivity Heuristic algorithms Transfer functions Optimization methods Transforms Probability density function

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Image Reconstruction for Proton Therapy Range Verification via U-NETs 28th

Image Reconstruction for Proton Therapy Range Verification ...

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28th Annual Conference on Medical Image Understanding and Analysis, MIUA 2024

作者： Setterdahl, Lena M. Lionheart, William R. B. Holman, Sean Skjerdal, Kyrre Ratliff, Hunter N. Ytre-Hauge, Kristian Smeland Lathouwers, Danny Meric, Ilker Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Department of Mathematics University of Manchester Manchester United Kingdom Department of Physics and Technology University of Bergen Bergen Norway Department of Radiation Science and Technology Delft University of Technology Delft Netherlands

ISBN: (纸本)9783031669545

This study aims to investigate the capability of U-Nets in improving image reconstruction accuracy for proton range verification within the framework of the NOVO (Next generation imaging for real-time dose verification enabling adaptive proton therapy) project. NOVO aims to enhance the accuracy of proton range verification by imaging the distribution of prompt gamma-rays (PGs) and fast neutrons (FNs) produced by nuclear interactions within tissue. In this work, focus lies on FNs, leaving PGs as future work. A dataset consisting of Monte Carlo-based simple back-projection and ground truth images of FN production distributions in a homogeneous water phantom was utilized. Various U-Net models were trained to predict FN distributions, and a set of range landmark (RL) metrics were computed for evaluation. Linear regression models were established to correlate shifts in mean RL with true range shift magnitudes. Our findings demonstrate a strong linear correlation between the shifts in mean RL in U-Net predictions and the true range shift magnitudes. Multiple RL metrics, including weighted average, inflection point, edge, and peak, were explored. This study highlights the potential utility of U-Nets in enhancing image reconstruction accuracy for proton range verification. The observed correlations between RL shifts and true range shifts provide evidence of the ability of U-Nets to accurately predict images containing range information. Future research will focus on generating more realistic training data incorporating more clinically relevant phantoms, including tissue heterogeneities. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Tissue

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Super-Mossian Dielectrics for Nanophotonics

Super-Mossian Dielectrics for Nanophotonics

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Wang, Xielin Doiron, Chloe F. Li, Zhichao Khurgin, Jacob B. Naik, Gururaj V. Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States

Searching for high-index dielectrics, we identify materials that break the index upper bound set by Moss' rule. We highlight the promise of such super-Mossian materials by demonstrating nanophotonic devices made o... 详细信息

ISBN: (纸本)9781957171258

关键词： Layered semiconductors

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