检索结果-内蒙古大学图书馆

Identification of Cognitive Dysfunction in Patients with T2DM Using Whole Brain Functional Connectivity

学校读者我要写书评

暂无评论

Genomics, Proteomics & Bioinformatics 2019年第4期17卷 441-452页

作者： Zhenyu Liu Jiangang Liu Huijuan Yuan Taiyuan Liu Xingwei Cui Zhenchao Tang Yang Du Meiyun Wang Yusong Lin Jie Tian CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China Department of Endocrinology and Metabolism Henan Provincial People’s Hospital&the People’s Hospital of Zhengzhou UniversityZhengzhou 450003China Department of Radiology Henan Provincial People’s Hospital&the People’s Hospital of Zhengzhou UniversityZhengzhou 450003China Cooperative Innovation Center for Internet Healthcare&School of Software Zhengzhou UniversityZhengzhou 450003China School of Mechanical Electrical&Information EngineeringShandong University(Weihai)Weihai 264209China University of Chinese Academy of Sciences Beijing 100080China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of MedicineBeihang UniversityBeijing 100191China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’an 710126China

Majority of type 2 diabetes mellitus(T2DM)patients are highly susceptible to several forms of cognitive impairments,particularly ***,the underlying neural mechanism of these cognitive impairments remains *** aimed to investigate the correlation between whole brain resting state functional connections(RSFCs)and the cognitive status in 95 patients with *** constructed an elastic net model to estimate the Montreal Cognitive Assessment(MoCA)scores,which served as an index of the cognitive status of the patients,and to select the RSFCs for further ***,we utilized a machine learning technique to evaluate the discriminative ability of the connectivity pattern associated with the selected *** estimated and chronological MoCA scores were significantly correlated with R=0.81 and the mean absolute error(MAE)=***,cognitive impairments of patients with T2DM can be identified using the RSFC pattern with classification accuracy of 90.54%and the area under the receiver operating characteristic(ROC)curve(AUC)of *** connectivity pattern not only included the connections between regions within the default mode network(DMN),but also the functional connectivity between the task-positive networks and the DMN,as well as those within the task-positive *** results suggest that an RSFC pattern could be regarded as a potential biomarker to identify the cognitive status of patients with T2DM.

关键词： Type 2 diabetes mellitus Resting state functional connectivity Elastic net Support vector machines MoCA

O-PRESS: Boosting OCT axial resolution with Prior guidance, Recurrence, and Equivariant Self-Supervision

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Li, Kaiyan Yang, Jingyuan Liang, Wenxuan Li, Xingde Zhang, Chenxi Chen, Lulu Wu, Chan Zhang, Xiao Xu, Zhiyan Wang, Yuelin Meng, Lihui Zhang, Yue Chen, Youxin Kevin Zhou, S. School of Biomedical Engineering University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Department of Ophthalmology Peking Union Medical College Hospital Chinese Academy of Medical Sciences Beijing100730 China Key Laboratory of Ocular Fundus Diseases Chinese Academy of Medical Sciences Beijing100730 China Department of Biomedical Engineering Johns Hopkins Univesity Baltimore21287 United States Institute of Computing Technology CAS Beijing100190 China School of Physical Sciences University of Science and Technology of China Anhui Hefei230026 China

Optical coherence tomography (OCT) is a noninvasive technology that enables real-time imaging of tissue microanatomies. The axial resolution of OCT is intrinsically constrained by the spectral bandwidth of the employed light source while maintaining a fixed center wavelength for a specific application. Physically extending this bandwidth faces strong limitations and requires a substantial cost. We present a novel computational approach, called as O-PRESS, for boosting the axial resolution of OCT with Prior Guidance, a Recurrent mechanism, and Equivariant Self-Supervision. Diverging from conventional superresolution methods that rely on physical models or data-driven techniques, our method seamlessly integrates OCT modeling and deep learning, enabling us to achieve real-time axial-resolution enhancement exclusively from measurements without a need for paired images. Our approach solves two primary tasks of resolution enhancement and noise reduction with one treatment. Both tasks are executed in a self-supervised manner, with equivariance imaging and free space priors guiding their respective processes. Experimental evaluations, encompassing both quantitative metrics and visual assessments, consistently verify the efficacy and superiority of our approach, which exhibits performance on par with fully supervised methods. Importantly, the robustness of our model is affirmed, showcasing its dual capability to enhance axial resolution while concurrently improving the signal-to-noise ratio. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical tomography

A Hybrid Fault Diagnosis Method for Rolling Bearing Based on Attention and CNN 5

学校读者我要写书评

暂无评论

A Hybrid Fault Diagnosis Method for Rolling Bearing Based on...

5th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Wang, Songtao Liu, Qianlong Li, Jun Liang, Kun Cai, Hui Tian, Hangwei Shandong Hi-Speed Construction Management Group Co. Ltd. Jinan China Shandong Scicom Information and Economy Research Institute Co. Ltd. Jinan China Shandong Hi-Speed Group Linteng Highway Co. Ltd. Jinan China School of Qilu Transportation Shandong University Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350357950

In the field of mechanical manufacturing, rolling bearings are important core components. To achieve stable rotational operation, improving diagnostic accuracy has become an urgent issue. In this paper, a hybrid fault diagnosis method is proposed, which combines attention and convolutional neural network and introduces Savitzky-Golay filter, called Savitzky-Golay Convolutional Attention network (SGCA-Net). The method adopts a dual-branch structure for rolling bearing fault diagnosis, which can effectively eliminate noise, and combines attention mechanism and convolutional neural network to perform feature learning on data. The experimental results show that the proposed method can accurately identify various faults, and has high accuracy and robustness. © 2023 IEEE.

关键词： Artificial Intelligence Attention component Convolutional Neural network Fault Diagnosis

A DoS-attack Resilient Control Approach for AIOT-enabled Systems

学校读者我要写书评

暂无评论

A DoS-attack Resilient Control Approach for AIOT-enabled Sys...

Artificial Intelligence of Things and Systems (AIoTSys), International Conference on

作者： Xiaoya Cao Zhenya Chen Weisheng Liu Yuanlong Liu Wenting Wang Xin Wang Ming Yang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Grid Shandong Electric Power Company Jinan China State Grid Shandong Electric Power Research Institute Jinan China

This paper presents a novel controller design for dealing with the challenges posed by Denial-of-Service (DoS) attacks in the context of Artificial Intelligence of Things (AIOT). The proposed design employs a prediction approach that utilizes uniform quantization for both encoder and decoder, aiming to effectively mitigate data loss during data transmission in scenarios involving DoS attacks. Furthermore, the study investigates the application of dynamic system design as a means to enhance the security defense mechanisms of AIOT, consequently bolstering the overall resilience of the system. The effectiveness and practicality of the proposed approach are validated through comprehensive simulations, providing empirical evidence of its efficacy.

关键词：

Generic detection-based error-mitigation using quantum autoencoders

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Zhang, Xiao-Ming Kong, Weicheng Farooq, Muhammad Usman Yung, Man-Hong Guo, Guoping Wang, Xin Department of Physics City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong Origin Quantum Computing Company Limited Hefei Anhui230088 China Department of Mathematics City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong Shenzhen Institute for Quantum Science and Engineering Department of Physics Southern University of Science and Technology Shenzhen518055 China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen518055 China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei Anhui230026 China City University of Hong Kong Shenzhen Research Institute Shenzhen Guangdong518057 China

Efficient error-mitigation techniques demanding minimal resources is key to quantum information processing. We propose a generic protocol to mitigate quantum errors using detection-based quantum autoencoders. In our protocol, the quantum data are compressed into a latent subspace while leaving errors outside, the latter of which is then removed by a measurement and post-selection. Compared to previously developed methods, our protocol on the one hand requires no extra qubits, and on the other hand has a near-optimal denoising power, in which under reasonable requirements all errors detected outside of the latent subspace can be removed, while those inside the subspace cannot be removed by any means. Our detection-based quantum autoencoders are therefore particularly useful for near-term quantum devices in which controllable qubits are limited while noise reduction is important. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

Hierarchical deep CNN feature set-based representation learning for robust cross-resolution face recognition

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Gao, Guangwei Yu, Yi Yang, Jian Qi, Guo-Jun Yang, Meng Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo101-8430 Japan School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China Department of Computer Science University of Central Florida OrlandoFL32816 United States School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University Guangzhou510006 China

Cross-resolution face recognition (CRFR), which is important in intelligent surveillance and biometric forensics, refers to the problem of matching a low-resolution (LR) probe face image against high-resolution (HR) gallery face images. Existing shallow learning-based and deep learning-based methods focus on mapping the HR-LR face pairs into a joint feature space where the resolution discrepancy is mitigated. However, little works consider how to extract and utilize the intermediate discriminative features from the noisy LR query faces to further mitigate the resolution discrepancy due to the resolution limitations. In this study, we desire to fully exploit the multilevel deep convolutional neural network (CNN) feature set for robust CRFR. In particular, our contributions are threefold. (i) To learn more robust and discriminative features, we desire to adaptively fuse the contextual features from different layers. (ii) To fully exploit these contextual features, we design a feature set-based representation learning (FSRL) scheme to collaboratively represent the hierarchical features for more accurate recognition. Moreover, FSRL utilizes the primitive form of feature maps to keep the latent structural information, especially in noisy cases. (iii) To further promote the recognition performance, we desire to fuse the hierarchical recognition outputs from different stages. Meanwhile, the discriminability from different scales can also be fully integrated. By exploiting these advantages, the efficiency of the proposed method can be delivered. Experimental results on several face datasets have verified the superiority of the presented algorithm to the other competitive CRFR approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

Explicit Linear Dual-Multistep Methods Applied to ZNN Illustrated via Discrete Time-Dependent Linear and Nonlinear Inequalities System Solving

学校读者我要写书评

暂无评论

Explicit Linear Dual-Multistep Methods Applied to ZNN Illust...

International Joint Conference on Neural networks (IJCNN)

作者： Jinjin Guo Binbin Qiu Liangjie Ming Yunong Zhang School of Data and Computer Science Sun Yat-sen University Guangzhou China Research Institute of Sun Yat-sen University in Shenzhen Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China School of Electronics and Information Technology Sun Yat-sen University Guangzhou China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

In this work, time-dependent linear and nonlinear inequalities system (TDLNIS) is studied and solved. First, using zeroing neural network (ZNN) method twice, a continuous time-dependent ZNN (CTDZNN) model is proposed to solve the continuous TDLNIS. Subsequently, explicit linear dual-multistep methods, i.e., explicit linear dual-4-step, dual-3-step, and dual-2-step methods, are presented and studied. Afterwards, by applying the explicit linear dual-4-step method to the proposed CTDZNN model, a 4-step discrete time-dependent ZNN (4S-DTDZNN) model is proposed to solve the discrete TDLNIS. For comparison, 3-step discrete time-dependent ZNN (3S-DTDZNN) and 2-step discrete time-dependent ZNN (2S-DTDZNN) models are also developed for solving the discrete TDLNIS. In addition, theoretical analyses and results indicate the effectiveness and superiority of the proposed 4S-DTDZNN model. Finally, numerical experimental results further substantiate the effectiveness and superiority of the proposed 4S-DTDZNN model.

关键词： Numerical models Mathematical model Trajectory Neural networks Analytical models Computational modeling Finite wordlength effects

Rapid Maize Seedling Detection Based on Receptive-Field and Cross-Dimensional Information Interaction

学校读者我要写书评

暂无评论

Rapid Maize Seedling Detection Based on Receptive-Field and ...

IEEE International Conference on Systems, Man and Cybernetics

作者： Fengqi Hao Shulei Zhu Dexin Ma Xiangjun Dong Hoiio Kong Xia Liu Chunhua Mu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Data Science City University of Macau Macau China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Communication College Qingdao Agricultural University Qingdao China National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture Maize Research Institute Shandong Academy of Agricultural Sciences Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The seedling stage is important in the growth and development of maize, and it is also a critical period that affects maize yield and quality. Accurately recognizing this stage of maize is challenging, as current maize seedling detection methods struggle with small sizes and complex environments. This paper proposes a rapid maize seedling detection method that emphasizes spatial feature extraction in the receptive field and cross-dimensional interaction—receptive field triplet attention YOLO (RFT-YOLO). First, we design a convolutional module for cross-dimensional information interaction within the receptive field. Second, we introduce an advanced selective fusion network, which boosts the multi-scale fusion capability of small object features while reducing the GFLOPs of the model. Additionally, we employ Inner-IoU to diminish sensitivity to positional biases of small objects and expedite the convergence of the loss function. Finally, we create a dataset of maize seedlings to conduct our experiments. Experimental results demonstrate that the RFT-YOLO significantly enhances performance, reducing the number of parameters by 35.3% and GFLOPs by 14.24%, compared to the baseline model. Moreover, the mean average precision (mAP) improves from 89.6% to 91.5%. These improvements confirm the model's effectiveness in detecting maize seedlings.

关键词： YOLO Sensitivity Accuracy Semantics Feature extraction Complexity theory data mining Cybernetics Convergence

Audio-Visual Target Speaker Extraction with Reverse Selective Auditory Attention

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Tao, Ruijie Qian, Xinyuan Jiang, Yidi Li, Junjie Wang, Jiadong Li, Haizhou Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Computer Science and Technology University of Science and Technology Beijing Beijing100083 China Chinese University of Hong Kong Shenzhen518172 China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big data Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore University of Bremen 28359 Germany

Audio-visual target speaker extraction (AV-TSE) aims to extract the specific person’s speech from the audio mixture given auxiliary visual cues. Previous methods usually search for the target voice through speech-lip synchronization. However, this strategy mainly focuses on the existence of target speech, while ignoring the variations of the noise characteristics, i.e., interference speaker and the background noise. That may result in extracting noisy signals from the incorrect sound source in challenging acoustic situations. To this end, we propose a novel selective auditory attention mechanism, which can suppress interference speakers and non-speech signals to avoid incorrect speaker extraction. By estimating and utilizing the undesired noisy signal through this mechanism, we design an AV-TSE framework named Subtraction-and-ExtrAction network (SEANet) to suppress the noisy signals. We conduct abundant experiments by re-implementing three popular AV-TSE methods as the baselines and involving nine metrics for evaluation. The experimental results show that our proposed SEANet achieves state-of-the-art results and performs well for all five datasets. The code can be found in: https://***/TaoRuijie/*** © 2024, CC BY.

关键词： Background noise