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Towards carbon Neutrality: Prediction of wave energy based on improved GRU in Maritime transportation

APPLIED ENERGY 2023年 331卷

作者： Lv, Zhihan Wang, Nana Lou, Ranran Tian, Yajun Guizani, Mohsen Chinese Acad Sci Qingdao Inst Bioenergy & Bioproc Technol Extended Energy Big Data & Strategy Res Ctr Qingdao 266101 Peoples R China Uppsala Univ Fac Arts Dept Game Design S-75375 Uppsala Sweden State Key Lab Media Convergence Prod Technol & Sys Beijing 100803 Peoples R China Shandong Energy Inst Qingdao 266101 Peoples R China Qingdao New Energy Shandong Lab Qingdao 266101 Peoples R China Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence MBZ Machine Learning Dept Abu Dhabi U Arab Emirates

Efficient use of renewable energy is one of the critical measures to achieve carbon neutrality. Countries have introduced policies to put carbon neutrality on the agenda to achieve relatively zero emissions of greenhouse gases and to cope with the crisis brought about by global warming. This work analyzes the wave energy with high energy density and wide distribution based on understanding of various renewable energy sources. This study provides a wave energy prediction model for energy harvesting. At the same time, the Gated Recurrent Unit network (GRU), Bayesian optimization algorithm, and attention mechanism are introduced to improve the model's performance. Bayesian optimization methods are used to optimize hyperparameters throughout the model training, and attention mechanisms are used to assign different weights to features to increase the prediction accuracy. Finally, the 1-hour and 6-hour forecasts are made using the data from China's NJI and BSG observatories, and the system performance is analyzed. The results show that, compared with mainstream prediction algorithms, GRU based on Bayesian optimization and attention mechanism has the highest prediction accuracy, with the lowest MAE of 0.3686 and 0.8204, and the highest R2 of 0.9127 and 0.6436, respectively. Therefore, the prediction model proposed here can provide support and reference for the navigation of ships powered by wave energy.

关键词： Carbon Neutrality Maritime Transportation Wave Energy Prediction Bayesian optimization algorithm Gated Recurrent Unit Network

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Method for persistent topological features extraction of schizophrenia patients' electroencephalography signal based on persistent homology

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FRONTIERS IN COMPUTATIONAL NEUROSCIENCE 2022年 16卷 1024205页

作者： Guo, Guangxing Zhao, Yanli Liu, Chenxu Fu, Yongcan Xi, Xinhua Jin, Lizhong Shi, Dongli Wang, Lin Duan, Yonghong Huang, Jie Tan, Shuping Yin, Guimei Taiyuan Normal Univ Coll Geog Sci Jinzhong Peoples R China Taiyuan Normal Univ Inst Big Data Anal Technol & Applicat Jinzhong Peoples R China Shanxi Agr Univ Coll Resource & Environm Taigu Peoples R China Peking Univ Huilongguan Clin Beijing Huilongguan Hosp Med Sch Psychiat Res Ctr Beijing Peoples R China Taiyuan Normal Univ Coll Comp Sci & Technol Lab Data Min & Machine Learning Jinzhong Peoples R China Taiyuan Univ Sci & Technol Coll Appl Sci Taiyuan Peoples R China

With the development of network science and graph theory, brain network research has unique advantages in explaining those mental diseases, the neural mechanism of which is unclear. Additionally, it can provide a new perspective in revealing the pathophysiological mechanism of brain diseases from the system level. The selection of threshold plays an important role in brain networks construction. There are no generally accepted criteria for determining the proper threshold. Therefore, based on the topological data analysis of persistent homology theory, this study developed a multi-scale brain network modeling analysis method, which enables us to quantify various persistent topological features at different scales in a coherent manner. In this method, the Vietoris-Rips filtering algorithm is used to extract dynamic persistent topological features by gradually increasing the threshold in the range of full-scale distances. Subsequently, the persistent topological features are visualized using barcodes and persistence diagrams. Finally, the stability of persistent topological features is analyzed by calculating the Bottleneck distances and Wasserstein distances between the persistence diagrams. Experimental results show that compared with the existing methods, this method can extract the topological features of brain networks more accurately and improves the accuracy of diagnostic and classification. This work not only lays a foundation for exploring the higher-order topology of brain functional networks in schizophrenia patients, but also enhances the modeling ability of complex brain systems to better understand, analyze, and predict their dynamic behaviors.

关键词： topological data analysis persistent homology complex brain networks schizophrenia patients persistent topological features EEG signal

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BEAN: Brain Extraction and Alignment Network for 3D Fetal Neurosonography

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NEUROIMAGE 2022年 258卷 119341-119341页

作者： Moser, Felipe Huang, Ruobing Papiez, Bartlomiej W. Namburete, Ana I. L. Univ Oxford Dept Comp Sci Oxford Machine Learning Neuroimaging Lab OMNI Oxford England Univ Oxford Inst Biomed Engn Dept Engn Sci Oxford England Univ Oxford John Radcliffe Hosp Nuffield Dept Womens & Reprod Hlth Oxford England Univ Oxford Li Ka Shing Ctr Hlth Informat & Discovery Big Data Inst Oxford England Univ Oxford Wellcome Ctr Integrat Neuroimaging Nuffield Dept Clin Neurosci FMRIB Oxford England

Brain extraction (masking of extra-cerebral tissues) and alignment are fundamental first steps of most neuroimage analysis pipelines. The lack of automated solutions for 3D ultrasound (US) has therefore limited its potential as a neuroimaging modality for studying fetal brain development using routinely acquired scans. In this work, we propose a convolutional neural network (CNN) that accurately and consistently aligns and extracts the fetal brain from minimally pre-processed 3D US scans. Our multi-task CNN, Brain Extraction and Alignment Network (BEAN), consists of two independent branches: 1) a fully-convolutional encoder-decoder branch for brain extraction of unaligned scans, and 2) a two-step regression-based branch for similarity alignment of the brain to a common coordinate space. BEAN was tested on 356 fetal head 3D scans spanning the gestational range of 14 to 30 weeks, significantly outperforming all current alternatives for fetal brain extraction and alignment. BEAN achieved state-of-the-art performance for both tasks, with a mean Dice Similarity Coefficient (DSC) of 0.94 for the brain extraction masks, and a mean DSC of 0.93 for the alignment of the target brain masks. The presented experimental results show that brain structures such as the thalamus, choroid plexus, cavum septum pellucidum, and Sylvian fissure, are consistently aligned throughout the dataset and remain clearly visible when the scans are averaged together.

关键词： Fetal brain development Convolutional neural network Deep learning 3D ultrasound Brain extraction Brain alignment

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Voting-Based Multiagent Reinforcement learning for Intelligent IoT

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IEEE INTERNET OF THINGS JOURNAL 2021年第4期8卷 2681-2693页

作者： Xu, Yue Deng, Zengde Wang, Mengdi Xu, Wenjun So, Anthony Man-Cho Cui, Shuguang Beijing Univ Posts & Telecommun Minist Educ Key Lab Universal Wireless Commun Beijing 100876 Peoples R China Chinese Univ Hong Kong Shenzhen Res Inst Big Data Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen 518172 Peoples R China Chinese Univ Hong Kong Dept Syst Engn & Engn Management Hong Kong Peoples R China Princeton Univ Dept Elect Engn Ctr Stat & Machine Learning Dept Operat Res & Financial Engn Princeton NJ 08544 USA Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Chinese Univ Hong Kong Future Network Intelligence Inst Shenzhen 518172 Peoples R China

The recent success of single-agent reinforcement learning (RL) in Internet of Things (IoT) systems motivates the study of multiagent RL (MARL), which is more challenging but more useful in large-scale IoT. In this article, we consider a voting-based MARL problem, in which the agents vote to make group decisions and the goal is to maximize the globally averaged returns. To this end, we formulate the MARL problem based on the linear programming form of the policy optimization problem and propose a primal-dual algorithm to obtain the optimal solution. We also propose a voting mechanism through which the distributed learning achieves the same sublinear convergence rate as centralized learning. In other words, the distributed decision making does not slow down the process of achieving global consensus on optimality. Finally, we verify the convergence of our proposed algorithm with numerical simulations and conduct case studies in practical multiagent IoT systems.

关键词： Convergence Internet of Things Optimization Collaboration Task analysis learning (artificial intelligence) Games Multiagent reinforcement learning (MARL) primal– dual algorithm voting mechanism

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Inconsistency Distillation For Consistency:Enhancing Multi-View Clustering via Mutual Contrastive Teacher-Student Leaning

Inconsistency Distillation For Consistency:Enhancing Multi-V...

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IEEE International Conference on data Mining (ICDM)

作者： Dunqiang Liu Shu-Juan Peng Xin Liu Lei Zhu Zhen Cui Taihao Li Dept. of Comput. Sci. & Fujian Key Lab. of Big Data Intelligence and Security Huaqiao University Xiamen China Zhejiang Lab Hangzhou China Xiamen Key Lab. of Computer Vision and Pattern Recognition Huaqiao University Xiamen China Key Lab. of Computer Vision and Machine Learning (Huaqiao University) Fujian Province University Xiamen China School of Information Sci. and Eng. Shandong Normal University Jinan China School of Computer Sci. and Eng. Nanjing University of Science and Technology Nanjing China

Multi-view clustering has attracted more attention recently since many real-world data are comprised of different representations or views. Recent multi-view clustering works mainly exploit the instance consistency to obtain the shared representations across different views, and apply a single-view clustering method to perform data partitions. However, these existing methods often ignore the inconsistency of instance associations within the views, which may enlarge the intra-class diversity among the views and therefore degrade the clustering performance. To address this issue, this paper proposes an efficient mutual contrastive teacher-student leaning (MC-TSL) model to enhance the multi-view clustering, which is the first attempt to study the inconsistency distillation for consistency learning. First, the proposed MC-TSL approach exploits a view-specific encoder with two heads, an instance encoding head and a semantic distillation head, respectively, for capturing the consistent and discriminative feature representations. To be specific, the former head exploits a cross-view contrastive learning method to obtain a redundancy-free consistent representation at the instance level, while the latter head designs a mutual teacher-student learning module to capture the intra-view information at semantic level. By training these two heads in an end-to-end manner, the discriminative multi-view embeddings are efficiently obtained and refined by minimizing the weighted sum of the reconstruction loss, contrastive loss and contrast distillation loss. Extensive experiments verify the superiorities of the proposed MC-TSL framework and show its competitive clustering performances.

关键词： Training learning systems Clustering methods Semantics Encoding data mining

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Visit-to-visit HbA1c variability, dementia, and hippocampal atrophy among adults without diabetes

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EXPERIMENTAL GERONTOLOGY 2023年第1期178卷 112225页

作者： Li, Chenxi Guo, Junyan Zhao, Yining Sun, Kaili Abdelrahman, Zeinab Cao, Xingqi Zhang, Jingyun Zheng, Zhoutao Yuan, Changzheng Huang, Huiqian Chen, Yaojing Liu, Zuyun Chen, Zuobing Zhejiang Univ Affiliated Hosp 2 Sch Publ Hlth Key Lab Intelligent Prevent Med Zhejiang ProvSch Hangzhou 310058 Zhejiang Peoples R China Zhejiang Univ Affiliated Hosp 2 Dept Neurobiol Dept OrthopedSch Med Hangzhou 310009 Zhejiang Peoples R China Zhejiang Univ NHC Hangzhou 310058 Zhejiang Peoples R China Zhejiang Univ MOE Frontier Sci Ctr Brain Res & Brain Machine Int Sch Brain Sci & Brain Med CAMS Key Lab Med Neurobiol Hangzhou 310058 Zhejiang Peoples R China Zhejiang Univ Affiliated Hosp 1 Dept Rehabil Med Sch Med Hangzhou 310003 Zhejiang Peoples R China Zhejiang Univ Dept Big Data Hlth Sci Key Lab Intelligent Prevent Med Zhejiang Prov Sch MedSch Publ Hlth Hangzhou 310058 Zhejiang Peoples R China Zhejiang Univ Affiliated Hosp 2 Clin Res Ctr Sch Med Hangzhou 310009 Zhejiang Peoples R China Beijing Normal Univ State Key Lab Cognit Neurosci & Learning Beijing 100875 Peoples R China Zhejiang Univ Affiliated Hosp 2 Ctr Clin Big Data & AnalytSch Med Dept Big Data Hlth SciSch Publ HlthKey Lab Inte 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China Zhejiang Univ Affiliated Hosp 1 Coll Med Dept Rehabil Med Hangzhou 310003 Zhejiang Peoples R China

Objectives: Adults without diabetes are not completely healthy;they are probably heterogeneous with several potential health problems. The management of hemoglobin A1c (HbA1c) is crucial among patients with diabetes;but whether similar management strategy is needed for adults without diabetes is unclear. Thus, this study aimed to investigate the associations of visit-to-visit HbA1c variability with incident dementia and hippocampal volume among middle-aged and older adults without diabetes, providing potential insights into this question. Methods: We conducted a prospective analysis for incident dementia in 10,792 participants (mean age 58.9 years, 47.8 % men) from the UK Biobank. A subgroup of 3793 participants (mean age 57.8 years, 48.6 % men) was included in the analysis for hippocampal volume. We defined HbA1c variability as the difference in HbA1c divided by the mean HbA1c over the 2 sequential visits ([latter - former]/mean). Dementia was identified using hospital inpatient records with ICD-9 codes. T1-structural brain magnetic resonance imaging was conducted to derive hippocampal volume (normalized for head size). The nonlinear and linear associations were examined using restricted cubic spline (RCS) models, Cox regression models, and multiple linear regression models. Results: During a mean follow-up (since the second round) of 8.4 years, 90 (0.8 %) participants developed dementia. The RCS models suggested no significant nonlinear associations of HbA1c variability with incident dementia and hippocampal volume, respectively (All P > 0.05). Above an optimal cutoff of HbA1c variability at 0.08, high HbA1c variability (increment in HbA1c) was associated with an increased risk of dementia (Hazard Ratio, 1.88;95 % Confidence Interval, 1.13 to 3.14, P = 0.015), and lower hippocampal volume (coefficient, -96.84 mm3, P = 0.037), respectively, in models with adjustment of covariates including age, sex, etc. Similar results were found for a different cut-off of 0. A se

关键词： HbA1c variability Dementia Hippocampal volume Middle-aged and older adults Diabetes

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Combined compression and encryption of linear wireless sensor network data using autoencoders

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Scientific Reports 2025年第1期15卷 1-16页

作者： Shylashree, N. Kumar, Sachin Min, Hong Department of Electronics and Communication RV College of Engineering Affiliated to VTU Belagavi Bengaluru 560059 India Big Data and Machine Learning Lab South Ural State University Chelyabinsk Russian Federation Akian College of Science and Engineering American University of Armenia Yerevan 0019 Armenia School of Computing Gachon University Seongnam South Korea

In a linear wireless sensor network (LWSN), sensor nodes are deployed in a linear fashion to monitor and gather data along a linear path or route. Generally, the base station collects the data from the contiguously placed nodes in the same order. When the sensors are deployed closely and with a gradual variation of sensor data along the route, a high degree of correlation exists among the sensed data. The sensed data sequence can be compressed with very low loss in such a situation. In this paper, a joint compression and encryption method for LWSN data is presented. The method is based on the dimension reduction property of an autoencoder at the bottleneck section. The Encoder part of the trained Autoencoder, housed at the Base Station (BS), reduces the number of data samples at the encoded output. Hence, the data gets compressed at the output of the Encoder. The output of the Encoder is encrypted using an asymmetric encryption that provides immunity to the Chosen Plaintext Attack. Thus, both data compression and encryption are achieved together at the BS. Therefore, the procedure at the BS is denoted as joint compression and encryption. The encrypted data is sent to the Cloud Server for secured storage and further distribution to the End User, where it is decrypted and subsequently decompressed by the Decoder part of the trained Autoencoder. The decompressed data sequence is very nearly equal to the original data sequence. The proposed lossy compression has a mean square reconstruction error of less than 0.5 for compression ratios in the range of 5 to 10. The compression time taken is short even though the Autoencoder training process, which occurs once in a while, takes a relatively long time. © The Author(s) 2025.

关键词： Autoencoder with supervised learning Chosen plaintext attack Integer matrix keys Joint compression and encryption Linear wireless sensor network Secured cloud storage

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Adversarial learning with Cost-Sensitive Classes

arXiv

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arXiv 2021年

作者： Shen, Haojing Chen, Sihong Wang, Ran Wang, Xizhao Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China The College of Mathematics and Statistics Shenzhen University Shenzhen518060 China The Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

It is necessary to improve the performance of some special classes or to particularly protect them from attacks in adversarial learning. This paper proposes a framework combining cost-sensitive classification and adversarial learning together to train a model that can distinguish between protected and unprotected classes, such that the protected classes are less vulnerable to adversarial examples. We find in this framework an interesting phenomenon during the training of deep neural networks, called Min-Max property, that is, the absolute values of most parameters in the convolutional layer approach zero while the absolute values of a few parameters are significantly larger becoming bigger. Based on this Min-Max property which is formulated and analyzed in a view of random distribution, we further build a new defense model against adversarial examples for adversarial robustness improvement. An advantage of the built model is that it performs better than the standard one and can combine with adversarial training to achieve an improved performance. It is experimentally confirmed that, regarding the average accuracy of all classes, our model is almost as same as the existing models when an attack does not occur and is better than the existing models when an attack occurs. Specifically, regarding the accuracy of protected classes, the proposed model is much better than the existing models when an attack occurs. © 2021, CC BY.

关键词： Deep neural networks

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Deep learning model based on multi-lesion and time series CT images for predicting the benefits from anti-HER2 targeted therapy in stage IV gastric cancer

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INSIGHTS INTO IMAGING 2024年第1期15卷 59页

作者： He, Meng Chen, Zi-fan Liu, Song Chen, Yang Zhang, Huan Zhang, Li Zhao, Jie Yang, Jie Zhang, Xiao-tian Shen, Lin Gao, Jian-bo Dong, Bin Tang, Lei Peking Univ Canc Hosp & Inst Dept Radiol Key Lab Carcinogenesis & Translat Res Minist Educ Beijing Peoples R China Peking Univ Ctr Data Sci Beijing Peoples R China Affiliated Hosp Nanjing Univ Nanjing Drum Tower Hosp Dept Radiol Med Sch 321Zhongshan Rd Nanjing 210008 Jiangsu Peoples R China Peking Univ Canc Hosp & Inst Dept Gastrointestinal Oncol Key Lab Carcinogenesis & Translat Res Minist Educ Beijing Beijing Peoples R China Shanghai Jiao Tong Univ Ruijin Hosp Dept Radiol Sch Med Shanghai 200025 Peoples R China Peking Univ Natl Engn Lab Big Data Anal & Applicat Beijing Peoples R China Peking Univ Changsha Inst Comp & Digital Econ Changsha Peoples R China Zhengzhou Univ Dept Radiol Affiliated Hosp 1 Zhengzhou Peoples R China Peking Univ Beijing Int Ctr Math Res Beijing Peoples R China Peking Univ Ctr Machine Learning Res Beijing Peoples R China

Objective To develop and validate a deep learning model based on multi-lesion and time series CT images in predicting overall survival (OS) in patients with stage IV gastric cancer (GC) receiving anti-HER2 targeted therapy. Methods A total of 207 patients were enrolled in this multicenter study, with 137 patients for retrospective training and internal validation, 33 patients for prospective validation, and 37 patients for external validation. All patients received anti-HER2 targeted therapy and underwent pre- and post-treatment CT scans (baseline and at least one follow-up). The proposed deep learning model evaluated the multiple lesions in time series CT images to predict risk probabilities. We further evaluated and validated the risk score of the nomogram combining a two-follow-up lesion-based deep learning model (LDLM-2F), tumor markers, and clinical information for predicting the benefits from treatment (Nomo-LDLM-2F). Results In the internal validation and prospective cohorts, the one-year AUCs for Nomo-LDLM-2F using the time series medical images and tumor markers were 0.894 (0.728-1.000) and 0.809 (0.561-1.000), respectively. In the external validation cohort, the one-year AUC of Nomo-LDLM-2F without tumor markers was 0.771 (0.510-1.000). Patients with a low Nomo-LDLM-2F score derived survival benefits from anti-HER2 targeted therapy significantly compared to those with a high Nomo-LDLM-2F score (all p < 0.05). Conclusion The Nomo-LDLM-2F score derived from multi-lesion and time series CT images holds promise for the effective readout of OS probability in patients with HER2-positive stage IV GC receiving anti-HER2 therapy.

关键词： Human epidermal growth factor receptor 2 Deep learning Gastric cancer Computed tomography (X-ray) Treatment outcome

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Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox

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IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE 2020年第4期8卷 60-88页

作者： Rasti, Behnood Hong, Danfeng Hang, Renlong Ghamisi, Pedram Kang, Xudong Chanussot, Jocelyn Benediktsson, Jon Atli Helmholtz Zentrum Dresden Rossendorf Helmholtz Inst Freiberg Resource Technol Explorat Div Machine Learning Grp Dresden Germany Univ Grenoble Alpes CNRS Grenoble Inst Technol Grenoble Images Parole Signal Automat Lab Grenoble France German Aerosp Ctr Remote Sensing Technol Inst Wessling Germany Nanjing Univ Key Lab Big Data Anal Technol Sch Automat Nanjing Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha Peoples R China Key Lab Visual Percept & Artificial Intelligence Changsha Peoples R China Univ Grenoble Alpes INRIA CNRS Grenoble Inst TechnolLab Jean Kuntzmann Grenoble France Univ Iceland Fac Elect & Comp Engn Reykjavik Iceland

Hyperspectral images (HSIs) provide detailed spectral information through hundreds of (narrow) spectral channels (also known as dimensionality or bands), which can be used to accurately classify diverse materials of interest. The increased dimensionality of such data makes it possible to significantly improve data information content but provides a challenge to conventional techniques (the so-called curse of dimensionality) for accurate analysis of HSIs.

关键词： Training data Hyperspectral imaging Feature extraction machine learning data mining

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