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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Yulin Yang Yinhao Li Qingqing Chen Xian-Hua Han Jing Liu Lanfen Lin Hongjie Hu Yen-Wei Chen Gradate School of Information Science and Engineering Ritsumeikan University Shiga Japan College of Information Science and Engineering Ritsumeikan University Shiga Japan Department of Radiology Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China Artificial Intelligence Research Center Yamaguchi University Japan Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

Compared to non-contrast computed tomography (NC-CT) scans, contrast-enhanced (CE) CT scans provide more abundant information about focal liver lesions (FLLs), which play a crucial role in the FLLs diagnosis. However, CE-CT scans require patient to inject contrast agent into the body, which increase the physical and economic burden of the patient. In this paper, we propose a spatial attention-guided generative adversarial network (SAG-GAN), which can directly obtain corresponding CE-CT images from the patient’s NC-CT images. In the SAG-GAN, we devise a spatial attention-guided generator, which utilize a lightweight spatial attention module to highlight synthesis task-related areas in NC-CT image and neglect unrelated areas. To assess the performance of our approach, we test it on two tasks: synthesizing CE-CT images in arterial phase and portal venous phase. Both qualitative and quantitative results demonstrate that SAG-GAN is superior to existing GANs-based image synthesis methods.

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Fault diagnosis method of rolling bearing based on Variational Modal Decomposition Multisynchrosqueezing Transform Combined with Long Short-term Memory Networks

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IEEE Access 2022年 1-1页

作者： Liang, Tao Lv, Qingzhao Tan, Jianxin Jing, Yanwei Sun, Hexu School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin 300401 China Hebei Jiantou Energy Investment Co. Ltd. Shijiazhuang 050011 China Xintian Green Energy Co. Ltd. Shijiazhuang 050011 China School of Electrical Engineering Hebei University of Science and Technology Shijiazhuang 050018 China

Aiming at the characteristics of weak vibration signal, strong interference, unevenness and nonlinearity in rolling bearing faults, this paper proposes a bearing fault intelligent diagnosis method based on variational modal decomposition (VMD) and Multisynchrosqueezing transform (MSST). First, the original vibration signal of the bearing is decomposed into multiple intrinsic mode functions (IMF) through the optimal parameter VMD, and then an effective IMF is selected for signal reconstruction according to the kurtosis value and mutual information. Secondly, MSST is applied to the reconstructed signal to obtain a time-frequency (TF) images with high energy concentration, and then extract the time-frequency and time-amplitude signals of the vibration signal in the TF images according to the ridge detection algorithm. Finally, these time series are used as input, using Long short-term memory (LSTM) network is trained to complete the intelligent classification and diagnosis of bearing failure. This method is compared with other fault diagnosis methods through test data sets. The results show that the method proposed in this paper is superior to other methods in recognition accuracy and recognition type, can accurately identify and classify bearing faults, and has strong generalization. Ability to better meet the needs of actual engineering. Author

关键词： Failure analysis

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Federated Learning for 6G Communications:Challenges,Methods,and Future Directions

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China Communications 2020年第9期17卷 105-118页

作者： Yi Liu Xingliang Yuan Zehui Xiong Jiawen Kang Xiaofei Wang Dusit Niyato School of Data Science of Technology Heilongjiang UniversityHarbinChina Faculty of Information Technology Monash UniversityAustralia Alibaba-NTU Joint Research Institute and also School of Computer Science and Engineering NTUSingapore Energy Research Institute Nanyang Technological UniversitySingapore College of Intelligence and Computing Tianjin UniversityTianjinChina School of Computer Science and Engineering NTUSingapore

As the 5G communication networks are being widely deployed worldwide,both industry and academia have started to move beyond 5G and explore 6G *** is generally believed that 6G will be established on ubiquitous artificial intelligence(AI)to achieve data-driven Machine Learning(ML)solutions in heterogeneous and massive-scale ***,traditional ML techniques require centralized data collection and processing by a central server,which is becoming a bottleneck of large-scale implementation in daily life due to significantly increasing privacy *** learning,as an emerging distributed AI approach with privacy preservation nature,is particularly attractive for various wireless applications,especially being treated as one of the vital solutions to achieve ubiquitous AI in *** this article,we first introduce the integration of 6G and federated learning and provide potential federated learning applications for *** then describe key technical challenges,the corresponding federated learning methods,and open problems for future research on federated learning in the context of 6G communications.

关键词： 6G communication federated learning security and privacy protection

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Multi-view clustering guided by unconstrained non-negative matrix factorization

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Knowledge-Based Systems 2023年 266卷

作者： Deng, Ping Li, Tianrui Wang, Dexian Wang, Hongjun Peng, Hong Horng, Shi-Jinn School of Computer and Software Engineering Xihua University Chengdu610039 China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Chengdu611756 China Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

Multi-view clustering based on non-negative matrix factorization (NMFMvC) is a well-known method for handling high-dimensional multi-view data. To satisfy the non-negativity constraint of the matrix, NMFMvC is usually solved using the Karush–Kuhn–Tucker (KKT) conditions. However, this optimization method is poorly scalable. To this end, we propose an unconstrained non-negative matrix factorization multi-view clustering (uNMFMvC) model. First, the objective function was constructed by decoupling the elements of the matrix and combining the elements with a non-linear mapping function in a non-negative value domain. The objective function was then optimized using the stochastic gradient descent (SGD) algorithm. Subsequently, three uNMFMvC methods were constructed based on different mapping functions and detailed reasoning was provided. Finally, experiments were conducted on eight public datasets and compared with cutting-edge multi-view clustering methods. The experimental results demonstrate that the proposed model has significant advantages. © 2023 Elsevier B.V.

关键词： Non-negative matrix factorization

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PID Controller Tuning Using Multi-Objective Ant Colony Optimization for Blood Glucose Level of a Diabetic Patient

PID Controller Tuning Using Multi-Objective Ant Colony Optim...

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Cyber Resilience (ICCR), International Conference on

作者： Mohanad A. Dief Waleed Alomoush Hani Atta Osama A. Khashan Ahmed Solyman Rania ELGohary Department of Artificial intelligence College of Information Technology Misr University for Science & Technology (MUST) 6th of October City Egypt School of Computing Skyline University College Sharjah United Arab Emirates Faculty of Engineering Zarqa University Zarqa Jordan Research and Innovation Centres Rabdan Academy Abu Dhabi United Arab Emirates School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow UK

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

In this research, a nonlinear suboptimal controller was provided for type 1 diabetes patients' blood glucose level regulation based on a Proportional-Integral-Derivative (PID) controller with Ant Colony Optimization (ACO). The ACO approach is used to determine the ideal PID controller gain settings within a stability area. This study simulates three cases of a physiological model of glucose regulation in people with type 1 diabetes (No insulin treatment, fixed insulin injection, and Controlled blood glucose concentration using a PID controller) by using the Bergman Model in the context of meal-induced disturbances. In terms of dynamic performance, such as a reduction in maximum overshoot, settling time, and rising time, simulation findings suggest that the proposed technique for PID tuning using the ACO algorithm is more adaptable and cost-effective.

关键词： Ant colony optimization Adaptation models Heuristic algorithms Regulation Stability analysis Glucose Diabetes

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Unsupervised Behaviour Analysis of News Consumption in Turkish Media

arXiv

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arXiv 2022年

作者： Makaroğlu, Didem Cakir, Altan Töreyin, Behçet Uğur Demirören Teknoloji A.S. İstanbul Turkey Signal Processing for Computational Intelligence Group Informatics Institute Istanbul Technical University Istanbul Turkey Physics Engineering Faculty of Science and Letters Istanbul Technical University Istanbul Turkey Istanbul Technical University Artificial Intelligence Data Science Research and Application Center Istanbul Turkey

Clickstream data, which come with a massive volume generated by human activities on websites, have become a prominent feature for identifying readers’ characteristics by newsrooms after the digitization of news outlets. It is essential to have elastic architectures to process the streaming data, particularly for unprecedented traffic, enabling conducting more comprehensive analyses, such as recommending mostly related articles to readers. Although the nature of clickstream data has a similar logic within websites, it has inherent limitations to recognize human behaviours when looking from a broad perspective, which brings the need to limit the problem in niche areas. This study investigates the anonymized readers’ click activities on the organizations’ websites to identify news consumption patterns following referrals from Twitter, who incidentally reach but propensity is mainly routed news content. The investigation is widened to a broad perspective by linking the log data with news content to enrich the insights rather than sticking into the web journey. Methodologies for ensemble cluster analysis with mixed-type embedding strategies are applied and compared to find similar reader groups and interests independent of time. Various internal validation perspectives are used to determine the optimality of the quality of clusters, where the Calinski Harabasz Index (CHI) is found to give a generalizable result. Our findings demonstrate that clustering a mixed-type dataset approaches the optimal internal validation scores, which we define to discriminate the clusters and algorithms considering applied strategies, when embedded by Uniform Manifold Approximation and Projection (UMAP) and using a consensus function as a key to access the most applicable hyper parameter configurations in the given ensemble rather than using consensus function results directly. Evaluation of the resulting clusters highlights specific clusters repeatedly present in the separated monthly sample

关键词： Digital storage

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Reconstructing suspended sediment concentrations in the Mekong River Basin via semi-supervised-based deep neural networks

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science of the Total Environment 2024年 955卷 176758页

作者： Nguyen, Thi Thu Ha Vu, Duc Quang Doan, Ngoc Phu Chi, Huynh Thi Khanh Li, Peixin Binh, Doan Van An, Yimeng Dung, Pham Tuan Hoang, Tuan A. Son, Mai Thai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat City Viet Nam

The Mekong River Basin (MRB) is crucial for the livelihoods of over 60 million people across six Southeast Asian countries. Understanding long-term sediment changes is crucial for management and contingency plans, but the sediment concentration data in the MRB are extremely sporadic, making analysis challenging. This study focuses on reconstructing long-term suspended sediment concentration (SSC) data using a novel semi-supervised machine learning (ML) model. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on daily hydroclimate and SSC data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the superior performance of the proposed method compared to the state-of-the-art supervised techniques (i.e., Random Forest, XGBoost, CatBoost, MLP, CNN, and LSTM), and surpasses existing semi-supervised methods (i.e., CoReg, ⊓ Model, ICT, and Mean Teacher). This approach is the first semi-supervised method to reconstruct sediment data in the field and has the potential for broader application in other river systems. © 2024 The Author(s)

关键词： Self-supervised learning

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Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis

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Computer Modeling in engineering & sciences 2023年第12期137卷 2129-2147页

作者： Qiankun Zuo Junhua Hu Yudong Zhang Junren Pan Changhong Jing Xuhang Chen Xiaobo Meng Jin Hong School of Information Engineering Hubei University of EconomicsWuhan430205China State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin China Institute of Water Resources and Hydropower ResearchBeijing100038China School of Computing and Mathematic Sciences University of LeicesterLeicesterLE17RHUK Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518055China Faculty of Science and Technology University of MacaoMacao999078China School of Geophysics Chengdu University of TechnologyChengdu610059China Laboratory of Artificial Intelligence and 3D Technologies for Cardiovascular Diseases Guangdong Provincial Key Laboratory of South China Structural Heart DiseaseGuangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhou519041China Medical Research Institute Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhou519041China

The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia *** brain functional network is suitable to bridge the correlation between abnormal connectivities and dementia ***,it is challenging to access considerable amounts of brain functional network data,which hinders the widespread application of data-driven models in dementia *** this study,a novel distribution-regularized adversarial graph auto-Encoder(DAGAE)with transformer is proposed to generate new fake brain functional networks to augment the brain functional network dataset,improving the dementia diagnosis accuracy of data-driven ***,the label distribution is estimated to regularize the latent space learned by the graph encoder,which canmake the learning process stable and the learned representation ***,the transformer generator is devised to map the node representations into node-to-node connections by exploring the long-term dependence of highly-correlated distant brain *** typical topological properties and discriminative features can be preserved ***,the generated brain functional networks improve the prediction performance using different classifiers,which can be applied to analyze other cognitive *** on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that the proposed model can generate good brain functional *** classification results show adding generated data can achieve the best accuracy value of 85.33%,sensitivity value of 84.00%,specificity value of 86.67%.The proposed model also achieves superior performance compared with other related ***,the proposedmodel effectively improves cognitive disease diagnosis by generating diverse brain functional networks.

关键词： Adversarial graph encoder label distribution generative transformer functional brain connectivity graph convolutional network dementia

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LEARNING LOCAL EQUIVARIANT REPRESENTATIONS FOR QUANTUM OPERATORS

arXiv

引用

arXiv 2024年

作者： Zhouyin, Zhanghao Gan, Zixi Liu, MingKang Pandey, Shishir Kumar Zhang, Linfeng Gu, Qiangqiang AI for Science Institute Beijing100080 China Department of Physics McGill University MontrealQCH3A2T8 Canada Department of Chemistry Zhejiang University Hangzhou310027 China Department of Mechanical Engineering National University of Singapore Singapore117575 Singapore Birla Institute of Technology & Science Pilani-Dubai Campus Dubai345055 United Arab Emirates DP Technology Beijing100080 China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research China University of Science and Technology of China Suzhou215123 China

Predicting quantum operator matrices such as Hamiltonian, overlap, and density matrices in the density functional theory (DFT) framework is crucial for material science. Current methods often focus on individual operators and struggle with efficiency and scalability for large systems. Here we introduce a novel deep learning model, SLEM (strictly localized equivariant message-passing) for predicting multiple quantum operators, that achieves state-of-the-art accuracy while dramatically improving computational efficiency. SLEM’s key innovation is its strict locality-based design for equivariant representations of quantum tensors while preserving physical symmetries. This enables complex many-body dependency without expanding the effective receptive field, leading to superior data efficiency and transferability. Using an innovative SO(2) convolution and invariant overlap parameterization, SLEM reduces the computational complexity of high-order tensor products and is therefore capable of handling systems requiring the f and g orbitals in their basis sets. We demonstrate SLEM’s capabilities across diverse 2D and 3D materials, achieving high accuracy even with limited training data. SLEM’s design facilitates efficient parallelization, potentially extending DFT simulations to systems with device-level sizes, opening new possibilities for large-scale quantum simulations and high-throughput materials discovery. © 2024, CC BY.

关键词： Tensors

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SMART2: Multi-library Statistical Mitogenome Assembly with Repeats 1

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9th International Conference on Computational Advances in Bio and Medical sciences, ICCABS 2019

作者： Alqahtani, Fahad Măndoiu, Ion Computer Science and Engineering Department University of Connecticut Storrs MansfieldCT United States National Center for Artificial Intelligence and Big Data Technology King Abdulaziz City for Science and Technology Riyadh Saudi Arabia

ISBN: (数字)9783030461652

ISBN: (纸本)9783030461645

SMART2 is an enhanced version of the SMART pipeline for mitogenome assembly from low-coverage whole-genome sequencing (WGS) data. Novel features include automatic selection of the optimal number of read pairs used for assembly and the ability to assemble multiple sequencing libraries when available. SMART2 succeeded in generating mitochondrial sequences for 26 metazoan species with WGS data but no previously published mitogenomes in NCBI databases. The SMART2 pipeline is publicly available via a user-friendly Galaxy interface at https://***/?tool_id=SMART2. © 2020, Springer Nature Switzerland AG.

关键词： Pipelines

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