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Research of spatial context convolutional neural networks for early diagnosis of Alzheimer's disease

JOURNAL OF SUPERCOMPUTING 2024年第4期80卷 5279-5297页

作者： Tong, Yinsheng Li, Zuoyong Huang, Hui Gao, Libin Xu, Minghai Hu, Zhongyi Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Zhejiang Peoples R China Key Lab Intelligent Image Proc & Anal Wenzhou 325035 Zhejiang Peoples R China Minjiang Univ Coll Comp & Control Engn Fuzhou 350108 Fujian Peoples R China Wenzhou Univ Technol Coll Intelligent Mfg & Elect Engn Wenzhou 325035 Zhejiang Peoples R China

The early and effective diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) has received increasing attention in recent years. However, currently available deep learning methods often ignore the contextual spatial information contained in structural MRI images used for early diagnosis and classification of Alzheimer's disease. This may lead us to miss important structural details by failing to adequately capture the potential connections between each slice and its neighboring slices. This lack of contextual information may cause the accuracy of the network model to suffer, which in turn affects its generalization ability and application in real-life scenarios. To explore deeper the connection between spatial context slices, this research is designed to develop a new network model to effectively detect or predict AD by digging into the deeper spatial contextual structural information. In this paper, we design a spatial context network based on 3D convolutional neural network to learn the multi-level structural features of brain MRI images for AD classification. The experimental results show that the model has good stability, accuracy and generalization ability. Our experimental method had a classification accuracy of 92.6% in the AD/CN comparison, 74.9% in the AD/MCI comparison, and 76.3% in the MCI/CN comparison. In addition, this paper demonstrates the effectiveness of the proposed network model through ablation experiments.

关键词： Alzheimer's disease Structural magnetic resonance image Spatial context Classification

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Disassembly of DNA origami dimers controlled by programmable polymerase primers

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CHEMICAL COMMUNICATIONS 2022年第92期58卷 12879-12882页

作者： Liao, Kangchao Chen, Kuiting Xie, Chun Chen, Zhekun Pan, Linqiang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Hubei Peoples R China

Dynamic regulation of DNA origami nanostructures is important for the fabrication of intelligent DNA nanodevices. Toehold-mediated strand displacement is a common regulation strategy, which utilizes trigger strands to assemble and disassemble nanostructures. Such trigger strands are required to be completely complementary to the corresponding substrate strands, which strictly demands orthogonality and accuracy of the sequence design. Herein, we present a disassembly strategy of DNA origami dimers based on polymerase-triggered strand displacement, where the polymerase primers, as the trigger strands, were only partially complementary to the toehold region of the substrate strands. To demonstrate the programmability of trigger strands, we utilized primers with different sequence combination patterns to disassemble DNA origami dimers. The statistical summary of AFM images and fluorescence curves proved the feasibility of the new strategy. The utilization of polymerase-triggered strand displacement on the disassembly of DNA origami structures enriches the toolbox for the dynamic regulation of DNA nanostructures.

关键词： DNA

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MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action Recognition

MoLo: Motion-augmented Long-short Contrastive Learning for F...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wang, Xiang Zhang, Shiwei Qing, Zhiwu Gao, Changxin Zhang, Yingya Zhao, Deli Sang, Nong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Current state-of-the-art approaches for few-shot action recognition achieve promising performance by conducting frame-level matching on learned visual features. However, they generally suffer from two limitations: i) the matching procedure between local frames tends to be inaccurate due to the lack of guidance to force long-range temporal perception;ii) explicit motion learning is usually ignored, leading to partial information loss. To address these issues, we develop a Motion-augmented Long-short Contrastive Learning (MoLo) method that contains two crucial components, including a long-short contrastive objective and a motion autodecoder. Specifically, the long-short contrastive objective is to endow local frame features with long-form temporal awareness by maximizing their agreement with the global token of videos belonging to the same class. The motion autodecoder is a lightweight architecture to reconstruct pixel motions from the differential features, which explicitly embeds the network with motion dynamics. By this means, MoLo can simultaneously learn long-range temporal context and motion cues for comprehensive few-shot matching. To demonstrate the effectiveness, we evaluate MoLo on five standard benchmarks, and the results show that MoLo favorably outperforms recent advanced methods. The source code is available at https://***/alibaba-mmai-research/MoLo.

关键词： Video: Action and event understanding

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A Novel Data-Driven LSTM-SAF Model for Power Systems Transient Stability Assessment

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第7期20卷 9083-9097页

作者： Shao, Zonghe Wang, Qichao Cao, Yuzhe Cai, Defu You, Yang Lu, Renzhi Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China State Grid Hubei Elect Power Res Inst Wuhan 430079 Peoples R China Huazhong Univ Sci & Technol Engn Res Ctr Autonomous Intelligent Unmanned Syst Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Transient stability is an important metric for assessing the operational state of a power system. However, due to the inherent complexity of the power systems, it is difficult to achieve stable and precise transient stability assessment (TSA). This article proposes a novel data-driven long short-term memory with self-attention mechanism and focal loss function (LSTM-SAF) model to achieve a rapid and reliable TSA scheme. First, an improved wrapper approach involving a genetic algorithm is established to obtain concise and effective input features, which can enhance model performance and efficiency. Then, an LSTM network combined with a self-attention mechanism is developed to learn reliable TSA paradigms, in which the self-attention mechanism can further explore the information relationships of temporal features extracted from the LSTM, thereby significantly improving TSA accuracy. In addition, to resolve the lack of insufficient training related to sample imbalance, a new focal loss function is designed to guide model training. This article provides a complete TSA scheme (including offline training and online execution) that considers both assessment performance and response speed. The effectiveness of the proposed model is verified by the numerical testing results on IEEE 39 bus system, NPCC 140 bus system, IEEE 145 bus system and IEEE 300 bus system.

关键词： Power system stability Feature extraction Long short term memory Data models Numerical stability Stability criteria Numerical models Focal loss long short-term memory (LSTM) network self-attention transient stability wrapper approach

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Frequency Information Matters for image Matting 1

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Luo, Rongsheng Wei, Rukai Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory For Optoelectronics Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9783031476341

ISBN: (纸本)9783031476334

image matting aims to estimate the opacity of foreground objects in order to accurately extract them from the background. Existing methods are only concerned with RGB features to obtain alpha mattes, limiting the perception of local tiny details. To address this issue, we introduce frequency information as an auxiliary clue to accurately distinguish foreground boundaries and propose the Frequency Matting Network (FMN). Specifically, we deploy a Frequency Boosting Module (FBM) in addition to the Discrete Cosine Transform (DCT) to extract frequency information from input images. The proposed FBM is a learnable component that empowers the model to adapt to complex scenarios. Furthermore, we design a Domain Aggregation Module (DAM) to effectively fuse frequency features with RGB features. With the assistance of frequency clues, our proposed FMN achieves significant improvements in matting accuracy and visual quality compared with state-of-the-art methods. Extensive experiments on Composition-1k and Distinctions-646 datasets demonstrate the superiority of introducing frequency information for image matting. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Discrete cosine transforms

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Multi-scale convolutional neural networks for brain disease diagnosis

Multi-scale convolutional neural networks for brain disease ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Hu, Zhongyi Gao, Libin Tong, Yinsheng Lu, Xingjin Li, Rui Xiao, Lei Li, Zuoyong Wenzhou University College of Computer Science and Aritificial Intelligence Wenzhou325035 China Key Laboratory of Intelligent Image Processing and Analysis Wenzhou China Minjiang University College of Computer and Control Engineering Fuzhou China

ISBN: (纸本)9798350337488

The brain is the most sophisticated and complex organ in the human body. Nowadays, diagnosing complex and diverse brain diseases is a hot topic. Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), and others are common brain diseases. With the increased usage of Artificial Intelligence (AI) in medical image analysis, the endeavor to make AI comprehend brain images for assisting doctors in making objective diagnoses of brain diseases has gained considerable attention. Resting-state functional magnetic resonance imaging (rs-fMRI) is a widely used tool for diagnosing and analyzing brain ***, the Pearson correlation coefficient (PCC) method constructs a dynamic functional connectivity (dFC) network using a fixed window size. However, this method has limitations as it is challenging to extract potential high-level features from the dFC and determine local feature relevance. This paper presents a method for constructing dFC based on multi-scale sliding windows and proposes Multi-scale Convolutional Neural Networks (MsCNN) for learning and analyzing dFC at various scales. Finally, a deep fusion of features learned at different scales is employed for the diagnostic classification of brain *** to the rs-fMRI dataset from ADNI, the classification accuracy was 84.0% for eMCI/lMCI, 84.4% for lMCI/AD, and 58.4% for NC/eMCI/lMCI/AD. Applied to rs-fMRI data from ABIDE, the accuracy was 74.7% for ASD/NC. The proposed method exhibits robust classification performance and introduces a new approach to diagnosing other brain diseases. © 2023 IEEE.

关键词： Brain diseases Classification Diagnostic Multi-scale convolutional neural networks (MsCNN) Resting-state functional magnetic resonance imaging (rs-fMRI)

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Cooperative online schedule of interconnected data center microgrids with shared energy storage

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ENERGY 2023年 285卷

作者： Xiao, Jiang-Wen Yang, Yan-Bing Cui, Shichang Wang, Yan-Wu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Technol Wuhan 430074 Peoples R China

The rapid increasing of data centers calls for an efficient method to reduce high operation costs and carbon emissions. This paper proposes a cooperative online schedule framework for local interconnected data centers considering shared energy storage. A time-average optimization problem is built to reduce the overall operation cost with essential operational constraints. Multiple randomness and the temporal coupling constraints of energy system levels make it challenging to solve the stochastic problem. Adapting the Lyapunov optimization theory with relaxation and perturbation technique, an online algorithm is proposed to find an acceptable solution to the problem to achieve both good service coordination and efficient energy coordination. The algorithm does not require prediction of random parameters, and has a fast convergence rate. Especially, the theoretical conditions for convergence and the distance between the acceptable and real optimal solution are derived in a closed form. Simulation studies show that the algorithm can effectively reduce operation cost of data centers, carbon emissions, and dependence on the main grid.

关键词： Data center Service coordination Energy coordination Energy storage sharing Online optimization

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controllability of Multi-Agent Systems With Cartesian Product Networks

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2024年第2期71卷 838-850页

作者： Liu, Bo Hu, Mengjie Huang, Junjie Chen, Yin Su, Housheng Minzu Univ China Sch Informat Engn Minist Educ Key Lab Intelligent Anal & Secur Governance Ethn Beijing 100081 Peoples R China Inner Mongolia Univ Sch Math Sci Hohhot 010021 Peoples R China Univ Strathclyde Dept Elect & Elect Engn Glasgow G1 1XW Scotland Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Autonomous Intelligent Unmanned Syst Engn Res Ctr Image Proc & Intelligent Control Key LabEduc Min Wuhan 430074 Peoples R China

This paper investigates the controllability of Cartesian product networks consisting of smaller factor networks via Cartesian product of graphs, reveals the controllability relationship between the Cartesian product network and its factor networks, and presents a theoretic analysis framework for a Cartesian product network by the eigenvalue-eigenvector pairs of factor networks. A sufficient and necessary condition for the controllability of Cartesian product networks is established based on the PBH test conjoining with the properties of Kronecker products, which is an important theoretical analysis tool to analyze the controllability of product networks. This work will help us to understand the cooperative mechanisms of the system controllability, network topology and nodal dynamics, and provide a theory and technical guidance to build a more reasonable large-scale network model and design an effective control scheme for a real networked system.

关键词： controllability Network topology Topology Laplace equations Multi-agent systems Analytical models Eigenvalues and eigenfunctions multi-agent systems Cartesian product networks eigenvalue-eigenvector pair

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Elevating the acceptable cost threshold for solid oxide cells: A case study on refinery decarbonization

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APPLIED ENERGY 2024年 373卷

作者： Chi, Yingtian Lin, Jin Li, Peiyang Yu, Zhipeng Mu, Shujun Li, Xi Song, Yonghua Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Operat & Control Beijing 10084 Peoples R China Tsinghua Sichuan Energy Internet Res Inst Chengdu 610213 Peoples R China Natl Inst Clean & Low Carbon Energy Beijing 102211 Peoples R China Huazhong Univ Sci & Technol Educ Minist Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Univ Macau State Key Lab Internet Things Smart City Zhuhai 999078 Macao Peoples R China

Solid oxide cells (SOCs) have multiple advantages, including high efficiency and reversibility, but they are still characterized by low maturity and high investment costs. Their cost-effectiveness remains to be evaluated from an investment viewpoint for their ability to compete with mature and low-cost alkaline electrolysis (ALK). To this end, this study considers the application of SOC in the production of green hydrogen and electricity to decarbonize a refinery plant, a potential application scenario favoring the efficient SOC technology due to its requirement for stable operation and lack of inexpensive renewable electricity. A planning model is proposed to optimize the system configuration and minimize the system cost, based on which the acceptable cost threshold enabling SOC to compete with ALK is determined, defined as the unit investment cost of SOC at which substituting ALK with SOC will not increase the system cost. The results show that the high efficiency of SOC reduces the investment in renewable generation to fulfill the hydrogen demand, and its reversibility eliminates additional hydrogen-to-electricity and ammonia-to-electricity investments to realize long-duration energy storage. These factors enable SOC to compete with ALK at an investment cost of 11.6 CNY/W, three times the investment cost of ALK. Moreover, when SOC and ALK are combined, SOC can operate at a capacity factor above 75%, 1.5 times the capacity factor of ALK. This further boosts the threshold to 20.4 CNY/W, revealing that SOC and ALK, instead of competing, can work together to lower the system cost and drive commercialization of the emerging SOC technology.

关键词： Solid oxide cell Acceptable cost threshold Cost effectiveness Refinery decarbonization Long-duration energy storage Ammonia

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Adaptive multiobjective evolutionary algorithm for large-scale transformer ratio error estimation

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MEMETIC COMPUTING 2022年第2期14卷 237-251页

作者： Huang, Changwu Li, Lianghao He, Cheng Cheng, Ran Yao, Xin Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Wuhan 430074 Peoples R China

As a typical large-scale multiobjective optimization problem extracted from real-world applications, the voltage transformer ratio error estimation (TREE) problem is challenging for existing evolutionary algorithms (EAs). Due to the large number of decision variables in the problems, existing algorithms cannot solve TREE problems efficiently. Besides, most EAs may fail to balance the convergence enhancement and diversity maintenance, leading to the trap in local optima even at the early stage of the evolution. This work proposes an adaptive large-scale multiobjective EA (LSMOEA) to handle the TREE problems with thousands of decision variables. Generally, multiple efficient offspring generation and environmental selection strategies selected from some representative LSMOEAs are included. Then an adaptive selection strategy is used to determine which offspring generation and environmental selection operators are used in each generation of the evolution. Thus, the search behavior of the proposed algorithm evolves along with the evolution process, the balance between convergence and diversity is maintained, and the proposed algorithm is expected to solve TREE problems effectively and efficiently. Experimental results show that the proposed algorithm achieves significant performance improvement due to the adaptive selection of different operators, providing an effective and efficient approach for large-scale optimization problems.

关键词： Large-scale optimization Multiobjective optimization Adaptive operator selection Voltage transformer ratio error estimation

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