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A New Elimination Based Multi-Material Reconstruction for Spectral CT Imaging 3

A New Elimination Based Multi-Material Reconstruction for Sp...

引用

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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An Edge-Guided Network for Building Footprint Extraction from Optical Remote Sensing Images 12

An Edge-Guided Network for Building Footprint Extraction fro...

引用

SPIE 12th International Symposium on Multispectral Image processing and Pattern Recognition, MIPPR 2023

作者： Zhou, Yuhang Tan, Yihua National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

Accurate building footprint extraction from optical remote sensing images remains challenging due to the diverse appearance and complex scenarios. Although recent deep learning-based methods have been shown to greatly improve the accuracy of building footprint extraction, vanilla deep networks still suffer from ambiguous predictions of edge pixels. The building edge contains abundant location and shape information, which is important for downstream applications such as building positioning and area measurement. Therefore, the problem of inaccurate edge prediction needs to be resolved urgently. To this end, we propose a novel edge-guided network (EGNet) that makes ample use of the edge prior in an end-to-end manner. First, an edge extraction module (EEM) is proposed to extract the building edge map. Then, an edge-guidance module (EGM) is designed to utilize the edge map to guide each encoder block in extracting edge-related features. Furthermore, a multi-scale context aggregation module (MCAM) is built to enhance the feature representation by aggregating contextual semantics with different receptive fields. EGNet can effectively mine edge semantics and guide the representation learning of boundaries, achieving 75.21% and 91.16% IoU on the Massachusetts and WHU datasets, respectively. Experimental results demonstrate that the proposed EGNet has a certain superiority in both accuracy and efficiency compared with current state-of-the-art (SOTA) methods. © 2024 SPIE.

关键词： Extraction

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Time-varying Formation-containment Tracking control of Heterogeneous Multi-agent System under Denial of Service Attack and Sensor Fault

Time-varying Formation-containment Tracking Control of Heter...

引用

2024 China Automation Congress, CAC 2024

作者： Niu, Baojian Fan, Huijin Liu, Lei Wang, Bo National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798350368604

In this paper, the problem of output-feedback time-varying formation-containment tracking control for heterogeneous multi-agent systems with sensor fault under distributed denial of service (DoS) attacks is studied. The leaders are non-cooperative, which are embedded with unknown input. Firstly, in order to estimate the sensor fault and the states of the followers, a distributed fault-detecting observer is designed. Secondly, in order to deal with distributed DoS attacks and the unknown input of the leaders, a leader state estimator is designed. Finally, a distributed output-feedback containment control law is proposed to realize the time-varying formation tracking of the convex hull of multiple leaders. It is proved that the proposed control scheme guarantees the exponential convergence of all errors of the multi-agent system, and the effectiveness and superiority of the proposed scheme are verified by simulation experiments. © 2024 IEEE.

关键词： Denial-of-service attack

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Fault diagnosis for small samples based on VMR

Fault diagnosis for small samples based on VMR

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2024 China Automation Congress, CAC 2024

作者： Wu, Xuejin Wu, Yali Hu, Yanghu Department of Information and Control Engineering Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an710048 China State Key Laboratoiy of Metal Extrusion and Forging Equipment Technology China National Heavy Machinery Research Institute Co. Ltd Xi'an710016 China

ISBN: (纸本)9798350368604

The scarcity of fault samples presents a significant challenge for fault diagnosis in industrial processes. Conventional methods often fail to deliver satisfactory results when dealing with small sample sizes. In addressing the issue of small sample fault diagnosis in industrial processes, this paper utilizes the Variational Mode Reconstruction(VMR) algorithm to screen the best components decomposed by the Variational Mode Decomposition(VMD) through the envelope entropy then random weighting them and signal reconstruction to achieve data enhancement, to enrich the sample size and to prevent the model overfitting phenomenon due to the scarcity of samples. Meanwhile, this paper integrates Convolutional Neural Network (CNN) and Long Short-term Memory Neural Network (LSTM) to propose a network model of spatio-temporal feature fusion for feature extraction and classification. The experiments demonstrate that this model not only enhances data diversity but also ensures consistency before and after enhancement, effectively mitigating overfitting. Furthermore, it guarantees high recognition and generalization rates in fault diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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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 ...

引用

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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Research on Waxiang Dialect Recognition Based on Wav2vec2.0 Pre-Training and Transfer Learning

Research on Waxiang Dialect Recognition Based on Wav2vec2.0 ...

引用

2024 International Conference on Computing, Robotics and System Sciences, ICRSS 2024

作者： Deng, Mixuan Tang, Pengju Gao, Yanxia Li, Huixuan Zhen, Guodong Fu, Kaifeng Wu, Xiaotian Huaihua University Huaihua China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application in Hunan Province Universities China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua China

ISBN: (纸本)9798331511296

As a unique local dialect, Waxiang dialect carries rich regional culture and historical information. However, with the acceleration of modernization and the increase of population mobility, the inheritance and protection of Waxiang dialect are facing severe challenges. By constructing the 'Xuewatong' APP- Waxiang dialect digital protection platform, based on cloud storage technology, digital resource display, multimedia integration and other technologies, and providing learning resources through big data analysis of user needs, it can effectively promote the digital recording and preservation of Waxiang dialect language resources, and significantly improve the dissemination and educational effect of Waxiang dialect culture, showing the great potential of digital technology in dialect protection. © 2024 IEEE.

关键词： Linguistics

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Discussion on application of FLIC-Fluent coupled simulation technology on medium-scale and large-scale municipal solid waste incinerators 6

Discussion on application of FLIC-Fluent coupled simulation ...

引用

6th International Conference on intelligent Computing, Communication, and Devices, ICCD 2023

作者： Zhang, Hongze Guangxi Key Laboratory of Machine Vision and Intelligent Control Guangxi Wuzhou China Guangxi Colleges and Universities Key Lab. of Image Processing and Intelligent Information System Guangxi Wuzhou China School of Electronics and Information Engineering Wuzhou University Guangxi Wuzhou China

ISBN: (数字)9781510666269

ISBN: (纸本)9781510666252

Today, the world economy is in the stage of rapid development, followed by the rising quantity, the decreasing moisture content, and the increasing heat value of Municipal Solid Waste (MSW). Since waste incineration has the advantages of excellent volume reduction effect, high resource utilization efficiency, and less secondary pollution to the environment, waste incineration disposal technology has received extensive attention worldwide. With the continuous increase of the urban population, the amount of sewage sludge is growing year by year. The process of the sludge incineration system is highly complicated, and the cost of construction and operation is high. The waste incinerator can process not only MSW but also sewage sludge. Therefore, the co-incineration of sewage sludge with MSW in the existing waste incinerator is a safe and effective way to dispose of sewage sludge. In this paper, the simulation results of Computational Fluid Dynamics (CFD) are analyzed for medium-scale and large-scale waste incinerators, characterized by high moisture content, high ash content, low heat value of sewage sludge, and incomplete combustion and high pollutant emissions in waste incinerators. With FLIC-Fluent coupled simulation method, the critical information such as temperature field, velocity field, concentration field of principal components, and the pollutant emissions can be predicted. Based on the CFD simulation results, the performance and structure of waste incinerators can be optimized, and the new products can be designed and developed. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Waste incineration

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Adaptive Tracking of Uncertain Markovian Jump Nonlinear Systems With Random Actuator Faults

Adaptive Tracking of Uncertain Markovian Jump Nonlinear Syst...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Jiao-Yang Fan, Huijin Liu, Lei Wang, Bo National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798350368604

This article deals with the adaptive fault-tolerant tracking problem for uncertain Markovian jump nonlinear systems (MJNSs) in a stochastic framework. A joint and a scalar Markovian process are employed to describe behaviors of the unknown parameters and actuator faults, respectively, thereby characterizing uncertainties in a random manner. With the aid of the backstepping technique, an adaptive tracking controller as well as the corresponding parameter update laws is constructed by treating the regimes of all Markovian variables as constants. It is shown that with appropriate design parameters, the output tracking error can converge into a small adjustable residual set in the sense of mean quadratic value while all closed-loop signals are bounded in probability, regardless of infinite number of random abrupt jumps in both uncertain parameters and the control input. An illustrative example is provided to show the effectiveness of the theoretical results. © 2024 IEEE.

关键词： Markov processes

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A Convolutional Knowledge Tracking Model with Forgetting Behavior and Graph Embedding 3

A Convolutional Knowledge Tracking Model with Forgetting Beh...

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3rd International Conference on Electronics and information Technology, EIT 2024

作者： Huang, Guimin Huang, Zhiqing Guilin University of Electronic Technology School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798350369151

With the prevalence of various intelligent educational systems, it is imperative to uncover learners' proficiency in mastering knowledge points through their prior practice circumstances. Knowledge tracking is an extremely beneficial instrument in this regard. Among them, Convolutional Knowledge Tracking (CKT) has demonstrated excellent performance in numerous KT tasks, while it does not take into account the phenomenon of forgetting and the interconnection between knowledge points. In order to tackle these problems, we propose a model called FGE-CKT, which integrates forgetting behavior and graph embedding with CKT. Our model addresses the drawbacks of forgetting characteristics by modelling three key factors affecting students' forgetting behaviors through a fully connected neural net: repeated knowledge learning times, adjacent learning interval and interval between visits to repeat knowledge. At the same time, convolutional neural networks are used to simultaneously extract and incorporate connections between knowledge points into the model's input portion. It was confirmed by experiments on two well-established datasets that FGE-CKT achieved significant improvements in AUC performance. © 2024 IEEE.

关键词： Graph embeddings

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