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3rd International Conference on Optics and Image processing, ICOIP 2023

作者： Yu, Xiaohuan Cai, Ailong Li, Lei Yan, Bin Henan Key Laboratory of Imaging and Intelligent Processing PLA Strategic Support Force Information Engineering University Zhengzhou China

ISBN: (纸本)9781510667426

Spectral computed tomography (spectral CT) is an emerging imaging technology that is capable of distinguishing material properties. However, the difficulty of decomposition process is intensified by the nonlinearity of the measurements and ill-conditioned problem, particularly when the number of materials and energies do not match. Therefore, the key issue in spectral CT is to design an improved algorithm in the accurate material decomposition. This study proposes a one-step multi-material algorithm that combines a statistical reconstruction model with a gradient-sparsity based prior. In our approach, the elimination based method under volume conservation constrained condition is designed for the inconsistent scanning of number of materials and energies. Newton descent method is adopted to efficiently solve the optimization problem based on a simple surrogate function. Through simulated experiments, the proposed method achieves a significantly higher peak signal-to-noise ratio (PSNR) compared to other algorithms, with an increase of approximately 23.988 dB and 23.462 dB. Numerical experiments have confirmed the efficiency of our proposed method in reconstructing the material distributions while reducing noise compared to state-of-the-art methods. © 2023 SPIE. All rights reserved.

关键词： Signal to noise ratio

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Network link weight optimization based on antisymmetric deep graph networks and reinforcement learning 6

Network link weight optimization based on antisymmetric deep...

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6th International Conference on Next Generation Data-Driven Networks, NGDN 2024

作者： Chen, Yuhan Guo, Yingya Department of Computer and Data Science Fuzhou University Fuzhou China Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9798350388374

Route optimization is a key core technology to optimize network traffic distribution, achieve network load balancing, and improve network performance. Traditional distributed networks widely run shortest-path based routing protocols, and the path of traffic is determined by the link weights of the distributed network, so routing optimization methods are usually optimized around the link weights of the network. Heuristics-based link weight optimization methods are widely used. However, heuristic methods rely on manually set rules, which are poorly generalized and cannot adapt well to dynamically changing traffic demands. Compared to heuristics, deep reinforcement learning (DRL) has the advantage of extracting more accurate feature representations in addition to its ability to handle high-dimensional state and action spaces. We propose a network link weight optimization method based on anti-symmetric deep graph networks (A-DGN) and reinforcement learning using a novel GNN framework anti-symmetric deep graph networks, where link weights are adjusted to reduce the network link utilization with the optimization objective of minimizing the maximum link utilization in the network. Experimental results show that the proposed method achieves significant performance improvements in the link weight optimization problem in four real-world network topology scenarios. © 2024 IEEE.

关键词： Deep reinforcement learning

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Two-stage stochastic planning for distribution networks considering uncertainties of electric vehicles and distributed generators 3

Two-stage stochastic planning for distribution networks cons...

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2024 3rd International Conference on Smart Grid and Green Energy, ICSGGE 2024

作者： Zheng, Huhu Ye, Jianhua Tianjin Key Laboratory of Information Sensing & Intelligent Control Tianjin University of Technology and Education Tianjin300222 China

A significant quantity of electric vehicles and distributed generator (DG) sources are connected to the distribution network (DN), resulting in the formation of a new power-transportation network (PTN). This integration introduces source-load uncertainty to the system. This research presents a stochastic planning model for PTN that focuses on making decisions on charging station locations. Initially, a model for the distribution network is created, which incorporates energy storage devices, demand response devices, and DG devices. The transportation network utilizes a P-median siting determination model. Ultimately, a two-stage stochastic planning approach is employed to address system uncertainty. The primary goal is to minimize both the investment cost and operation cost of the system while also considering several common situations. The efficacy of the proposed approach has been proven using a combined system consisting of a 33-node DN and a 25-node TN. © Published under licence by IOP Publishing Ltd.

关键词： Stochastic programming

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Polynomial stacked-attention network for nationality classification

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Frontiers of Computer Science 2022年第4期16卷 215-217页

作者： Kunyan Li Jie Zhang Shiguang Shan Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing100190China University of Chinese Academy of Sciences Beijing100049China

1Introduction In recent years,nationality and region inference has atracted more and_more *** anthropological researches indicate that human faces are influenced by genes,and other external factors,e.g-,geographical environment[1].This relevance provides possible solutions for geographical area inference by ***,it remains a challenging task due to the tiny differences on faces for various countries,which is even hard for human beings to tell.

关键词： Polynomial s. network

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Polyphonic sound event localization and detection based on Multiple Attention Fusion ResNet

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Mathematical Biosciences and Engineering 2024年第2期21卷 2004-2023页

作者： Zhang, Shouming Zhang, Yaling Liao, Yixiao Pang, Kunkun Wan, Zhiyong Zhou, Songbin Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Institute of Intelligent Manufacturing Guangdong Academy of Science Guangdong Key Laboratory of Modern Control Technology Guangzhou510030 China

Sound event localization and detection have been applied in various fields. Due to the polyphony and noise interference, it becomes challenging to accurately predict the sound event and their occurrence locations. Aiming at this problem, we propose a Multiple Attention Fusion ResNet, which uses ResNet34 as the base network. Given the situation that the sound duration is not fixed, and there are multiple polyphonic and noise, we introduce the Gated Channel Transform to enhance the residual basic block. This enables the model to capture contextual information, evaluate channel weights, and reduce the interference caused by polyphony and noise. Furthermore, Split Attention is introduced to the model for capturing cross-channel information, which enhances the ability to distinguish the polyphony. Finally, Coordinate Attention is introduced to the model so that the model can focus on both the channel information and spatial location information of sound events. Experiments were conducted on two different datasets, TAU-NIGENS Spatial Sound Events 2020, and TAU-NIGENS Spatial Sound Events 2021. The results demonstrate that the proposed model significantly outperforms state-of-the-art methods under multiple polyphonic and noise-directional interference environments and it achieves competitive performance under a single polyphonic environment. ©2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Deep learning

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Acoustic Analysis of Long and Short Vowels of Eastern Yugur Language for Low Resource Speech Synthesis

Acoustic Analysis of Long and Short Vowels of Eastern Yugur ...

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2024 International Conference on Language technology and Digital Humanities, LTDH 2024

作者： Lyu, Shiliang Qi, Hanyun Min, Yuwen Key Laboratory of Minzu Language and Cultures Intelligent Information Processing Northwest Minzu University Gansu Province China Key Laboratory of Linguistic and Cultural Computing Ministry of Education Northwest Minzu University China

ISBN: (纸本)9798350380187

The articulation of long and short vowels is not only a change in duration, but also includes changes in the shape of the oral cavity to comprehensively affect the timbre. Since the low-resource phonetic data is very limited, especially the pronunciation of long and short vowels will be affected by the surrounding phonemes or syllables, which makes it difficult for the speech synthesis model to adequately learn and capture the differences of vowels, thus affecting the accuracy and naturalness of the synthesis of long and short vowels. The eastern Yugur language in China belongs to the Mongolian group of the Altaic language family, with an endangered language and limited phonetic resource data. If this endangered language is extinct, it can be restored through speech synthesis. In this article, we analyze the acoustics of long and short vowels in the Eastern Yugur language, and find out the differences in the distribution of acoustic parameters, as well as the differences in the parameters of males and females, so as to provide a reference for the synthesis of the Eastern Yugur language. ©2024 IEEE.

关键词： Speech synthesis

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Improving high-field strain and temperature stability on KNN-based ceramics sintered in reducing atmosphere via defect engineering

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Journal of Materiomics 2024年第6期10卷 1165-1175页

作者： Zhenyong Cen Meiyi Feng Fuzhi Cao Yuqi Jiang Ze Xu Changrong Zhou Ke Wang Nengneng Luo State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures MOE Key Laboratory of New Proce sing Technology for Nonferrous Metals and MaterialsSchool of ResourcesEnvironment and MaterialsGuangxi Univers ityNanning530004China Guangexi Key Laboratory of Information Materials Guilin University of Electronic TechnologyGuilin541004China State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua UniversityBeijing 100084China

High-field strain and its temperature stability of(1-x)K_(0.48)Na_(0.52)Nb_(0.96)Ta_(0.04)O_(3)-xBaZrO_(3)+8%MnO+3%ZrO_(2)(in mole,KNNT-xBZ+8Mn)ceramics sintered in reducing atmosphere are improved simultaneously via defect design in A and B *** is a conducting type transition from n-type to p-type at x=*** BaZrO_(3) dopant not only induces the increase of defects(Zr′_(Nb),Ba_(Na),Ba_(K))concentration,but also results in the increase of defect(Mn″_(Nb))concentration,because more Mn ions as+2 oxidation state in ceramics is triggered by BaZrO3 doping *** dipoles(Mn″_(Nb)-V_(o),Zr′_(Nb)-V_(o),Ba_(Na)-V′_(Na),Ba_(Na)-V′_(Na))in poled and aged ceramics enhance the reversibility of the non-180°domains switching,which increases the high-field strain of KNNT-xBZ+8Mn *** reversibility of non-180°domain switching can be preserved to high temperature due to stable defect dipoles(Ba_(Na)-V′_(Na),Ba_(K)-V′_(K))in *** KNNT-xBZ+8Mn ceramics at x=0.07 show the largest high-field strain coefficient(543 pm/V@20 kV/cm)and the highest temperature stability(125℃).The KNNT-xBZ+8Mn ceramic is a lead-free material with great potential to be applied in the fabrication of multilayer ceramic actuators with Ni inner electrodes in the future.

关键词： Lead-free ceramics Piezoelectric materials Reducing atmosphere Defects

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Revealing the atomic mechanism of diamond–iron interfacial reaction

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Carbon Energy 2024年第3期6卷 255-263页

作者： Yalun Ku Kun Xu Longbin Yan Kuikui Zhang Dongsheng Song Xing Li Shunfang Li Shaobo Cheng Chongxin Shan Henan Key Laboratory of Diamond Optoelectronic Materials and Devices Key Laboratory of Material PhysicsMinistry of EducationSchool of Physics and MicroelectronicsZhengzhou UniversityZhengzhouChina Department of Mechanical Engineering Stanford UniversityStanfordCaliforniaUSA Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of EducationInstitutes of Physical Science and Information TechnologyAnhui UniversityHefeiChina Institute of Quantum Materials and Physics Henan Academy of SciencesZhengzhouChina

Diamond,with ultrahigh hardness,high wear resistance,high thermal conductivity,and so forth,has attracted worldwide ***,researchers found emergent reactions at the interfaces between diamond and ferrous materials,which significantly affects the performance of diamond-based ***,combing experiments and theoretical calculations,taking diamond–iron(Fe)interface as a prototype,the counter-diffusion mechanism of Fe/carbon atoms has been ***,it is identified that Fe and diamond first form a coherent interface,and then Fe atoms diffuse into diamond and prefer the carbon vacancies ***,the relaxed carbon atoms diffuse into the Fe lattice,forming Fe_(3)***,graphite is observed at the Fe_(3)C surface when Fe_(3)C is over-saturated by carbon *** present findings are expected to offer new insights into the atomic mechanism for diamondferrous material's interfacial reactions,benefiting diamond-based device applications.

关键词： coherent interface counter-diffusion diamond iron phase transition

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Semisupervised Transfer Boosting (SS-TrBoosting)

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3431-3444页

作者： Deng, Lingfei Zhao, Changming Du, Zhenbang Xia, Kun Wu, Dongrui Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China and also with Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen 518063 China

Semisupervised domain adaptation (SSDA) aims at training a high-performance model for a target domain using few labeled target data, many unlabeled target data, and plenty of auxiliary data from a source domain. Previous works in SSDA mainly focused on learning transferable representations across domains. However, it is difficult to find a feature space where the source and target domains share the same conditional probability distribution. Additionally, there is no flexible and effective strategy extending existing unsupervised domain adaptation (UDA) approaches to SSDA settings. In order to solve the above two challenges, we propose a novel fine-tuning framework, semisupervised transfer boosting (SS-TrBoosting). Given a well-trained deep learning-based UDA or SSDA model, we use it as the initial model, generate additional base learners by boosting, and then use all of them as an ensemble. More specifically, half of the base learners are generated by supervised domain adaptation, and half by semisupervised learning. Furthermore, for more efficient data transmission and better data privacy protection, we propose a source data generation approach to extend SS-TrBoosting to semisupervised source-free domain adaptation (SS-SFDA). Extensive experiments showed that SS-TrBoosting can be applied to a variety of existing UDA, SSDA, and SFDA approaches to further improve their performance. © 2020 IEEE.

关键词： Supervised learning

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Fractional core-based collapse mechanism and structural optimization in complex systems

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Science China(information Sciences) 2023年第9期66卷 168-183页

作者： Shubin SI Changchun LV Zhiqiang CAI Dongli DUAN Jurgen KURTHS Zhen WANG School of Mechanical Engineering Northwestern Polytechnical University Key Laboratory of Industrial Engineering and Intelligent Manufacturing(Ministry of Industry and Information Technology) School of Information and Control Engineering Xi'an University of Architecture and Technology Instiute of Physics Humboldt-University Potsdam Institute for Climate Impact Research

Catastrophic and major disasters in real-world systems ranging from financial markets and ecosystems, often show generic early-warning signals that may indicate a collapse. Hence, understanding the collapse mechanism of a complex network and predicting its process are of uttermost importance. However, these challenges are often hindered by the extremely high dimensionality of the underlying *** present here the concept of the fractional core(F-core) that considers the contribution of the network topology and dynamics to systematically analyze the collapse process in such networks, and encompass a broad range of dynamical systems, from mutualistic ecosystems to regulatory dynamics. We offer testable predictions on the tipping point, and, in particular, prove that the extinction of the maximum F-core of a network is an efficient indicator of whether a system completely collapses. The results show that the death of species or cells in a low-order F-core may improve the average density and have little influence on the tipping point. Generally, the principle of the F-core demonstrates how complex systems collapse and opens an innovative optimization strategy to uncover the optimal structure of systems.

关键词： collapse mechanism tipping point structural optimization complex systems phase transition

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