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Short-term wind power forecast with turning weather based on DBSCAN-RFE-LightGBM

Renewable Energy 2025年 251卷

作者： Wu, Zhenlong Fan, Xinyu Bian, Guibin Liu, Yanhong Zhang, Xiaoke Chen, YangQuan School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China State Key Laboratory of Intelligent Agricultural Power Equipment Zhengzhou University Luoyang471000 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Electric Power Research Institute of State Grid Henan Electric Power Company Zhengzhou450052 China Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

At present, the wind power prediction technology is becoming more and more mature, however, the turning weather brings a decrease in prediction accuracy due to its suddenness and instability. In this paper, a wind power prediction method considering the turning period and non-turning period respectively is proposed to weaken the adverse effects caused by turning weather. Firstly, the prediction effects of multiple models are compared, density-based spatial clustering of applications with noise (DBSCAN) is selected as the outlier processing method, recursive feature elimination (RFE) is used as the feature selection method, and Light gradient boosting machine (LightGBM) is used for prediction. The combined prediction method based on DBSCAN-RFE-LightGBM can reduce the influence of abnormal data and redundant features and improve the prediction effect. Then, the sliding window is set to detect the turning period. Considering that the turning weather is an emergency and does not happen frequently, the amount of data is small, which leads to the inability to train the model well. Generative adversarial networks (GAN) are applied to expand the turning period data. The LightGBM is trained and predicted by using the expended turning period data. Finally, the time-division prediction results are merged. Using the data collected from wind farms for short-term power prediction experiments, the time-segment prediction method proposed in this paper with GAN reduces MAE by 1.913 and RMSE by 3.351 on a single unit compared with the non-differentiated period. © 2025 Elsevier Ltd

关键词： Merging

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TR-GAN: Multi-Session Future MRI Prediction With Temporal Recurrent Generative Adversarial Network

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2022年第8期41卷 1925-1937页

作者： Fan, Chen-Chen Peng, Liang Wang, Tian Yang, Hongjun Zhou, Xiao-Hu Ni, Zhen-Liang Wang, Guan'an Chen, Sheng Zhou, Yan-Jie Hou, Zeng-Guang Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Commun Univ China Neurosci & Intelligent Media Inst Beijing 100024 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing 100190 Peoples R China Macau Univ Sci & Technol CASIA MUST Joint Lab Intelligence Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

Magnetic Resonance Imaging (MRI) has been proven to be an efficient way to diagnose Alzheimer's disease (AD). Recent dramatic progress on deep learning greatly promotes the MRI analysis based on data-driven CNN methods using a large-scale longitudinal MRI dataset. However, most of the existing MRI datasets are fragmented due to unexpected quits of volunteers. To tackle this problem, we propose a novel Temporal Recurrent Generative Adversarial Network (TR-GAN) to complete missing sessions of MRI datasets. Unlike existing GAN-based methods, which either fail to generate future sessions or only generate fixed-length sessions, TR-GAN takes all past sessions to recurrently and smoothly generate future ones with variant length. Specifically, TR-GAN adopts recurrent connection to deal with variant input sequence length and flexibly generate future variant sessions. Besides, we also design a multiple scale & location (MSL) module and a SWAP module to encourage the model to better focus on detailed information, which helps to generate high-quality MRI data. Compared with other popular GAN architectures, TR-GAN achieved the best performance in all evaluation metrics of two datasets. After expanding the Whole MRI dataset, the balanced accuracy of AD vs. cognitively normal (CN) vs. mild cognitive impairment (MCI) and stable MCI vs. progressive MCI classification can be increased by 3.61% and 4.00%, respectively.

关键词： Magnetic resonance imaging Generative adversarial networks Task analysis Three-dimensional displays Training Generators Data models Alzheimer's disease magnetic resonance imaging generative adversarial network

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Modeling human travel and social contact with multi-layer networks for epidemic prediction 9

Modeling human travel and social contact with multi-layer ne...

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9th IEEE International Conference on Bioinformatics and Computational Biology, ICBCB 2021

作者： Duan, Wei Wang, Tao Wang, Peng Ju, Rusheng Wang, Xiao Yang, Tian College of Systems Engineering National University of Defense Technology Changsha City China State Key Laboratry of Complex Systems Management and Control Institute of Automation Chinese Academy of Sciences Beijing City China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha City China

ISBN: (纸本)9780738132020

It is a key issue to reasonably represent human travel and social contact in epidemic models. Various measures were applied to develop the models of human mobility and contact in a long range or a short range, such as Brown movement, random walks, spatial networks, gravity models, contact networks. We proposed a method of representing human daily movement and social contact by using multi-layer networks with temporal edge weights. We combined bipartite networks with social networks to describe human daily trip and social contact, respectively. Temporal edge weights of multi-layer networks were employed to denote the propensity of individual movement and contact. We also verified our models and parameters by incorporating human daily travel and contact regularities, as well as comparing experimental results with human behavior statistical laws. At last, we applied a Chinese university campus as a case study to investigate students' daily travel and social contact, and studied the transmission and control strategies of COVID-19 virus. We found stricter control strategies are needed to mitigate the transmission of COVID-19 virus in a university. Once a patient case emerges in a university, it is better to close the campus and quarantine all students. Partial control strategies such as quarantining a part of students and buildings cannot achieve a great effect of mitigating the transmission of COVID-19 virus. Our works are beneficial for the practitioners in the field of computational epidemiology. © 2021 IEEE.

关键词： Viruses

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Federated management: Toward Federated Services and Federated Security in Federated Ecology

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2021年第6期8卷 1283-1290页

作者： Wang, Fei-Yue Qin, Rui Li, Juanjuan Wang, Xiao Qi, Hongwei Jia, Xiaofeng Hu, Bin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Datatang Beijing Technol Co Ltd Beijing 100192 Peoples R China Beijing Big Data Ctr Data Management Dept Beijing 100101 Peoples R China Lanzhou Univ Sch Informat Sci & Engn Gansu Prov Key Lab Wearable Comp Gansu 730000 Peoples R China

Welcome to the last issue of IEEE Transactions on Computational Social systems (IEEE TCSS) in 2021. For IEEE TCSS, 2021 is an exciting year. TCSS has been added to the ISI Web of Science Sources Citation Index Expanded (SCIE) database in 2021, and all articles published since 2018 have been indexed by SCIE. This is an important milestone in the development of TCSS. We would like to take this opportunity to thank and congratulate everyone for their great efforts and supports. We are looking forward to working together to further improve the publication quality and speed up the review process of TCSS in the upcoming 2022.

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Optimal control of plant growth in a plant factory using a plant model

Optimal control of plant growth in a plant factory using a p...

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2022 Australian and New Zealand control Conference

作者： Wang, X. J. Kang, M. Z. Lewlomphaisarl, U. Hua, J. Wang, H. Y. Chinese Acad Sci SKL MCCS CASIA Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266000 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Natl Elect & Comp Technol Ctr Pathum Thani 12120 Thailand

ISBN: (数字)9781665498876

ISBN: (纸本)9781665498876

A plant factory (PF) is an environmentally controlled facility that can maintain stable crop cultivation while ensuring rapid production and good crop quality by controlling temperature, humidity, light and other growing factors. Although it has many advantages, PFs have high initial and operating costs, especially the energy for lightening. In this work, we aim to improve the light use efficiency by optimizing the combination of environmental conditions (case of light intensity and CO2 level) in order to fit the best plant's demand. To reach this goal, the effect of environmental conditions on daily assimilation rate is simulated using a process-based plant model 'TomSim'. The daily profit of a PF under different light intensities and CO2 concentrations is evaluated. As a result, the optimal light and CO2 conditions can be obtained for a given leaf area index (LAI) of the lettuce plant. Smaller LAI, which means the early growth stage, requires lower light intensity. A dynamic light control strategy for a PF is expected that can fit plant demand during its growth period while keeping relatively low cost.

关键词： Costs Temperature Crops Optimal control Humidity Production facilities Temperature control

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SST-GAN: Single Sample-based Realistic Traffic Image Generation for Parallel Vision 25

SST-GAN: Single Sample-based Realistic Traffic Image Generat...

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IEEE 25th International Conference on intelligent Transportation systems (ITSC)

作者： Wang, Jiangong Wang, Yutong Tian, Yonglin Wang, Xiao Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266000 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

To improve their adaptability to various kinds of driving situations, deep learning-based vision algorithms need images from rare scenes, such as extreme weather conditions and traffic congestions. However, most datasets collected from physical driving environments are lack of such images, making vision models trained on these datasets do not work well in scarce scenes. Thus, we design an SST-GAN method for controllab.y generating realistic images of scarce driving scenes based on the framework of parallel vision. Trained on only a single sample, SST-GAN can produce hundreds of rare scene images from two directions: style transfer and content generation. Specifically, a transition retraining method is designed to transfer the weather and lighting styles from common scenes to scarce scenes, and a structural similarity index loss is used as reconstruction loss to guarantee the trained network can obtain more realistic content modification and generation during the image reconstruction. Experimental results show that SSTGAN outperforms the state-of-the-art method on expanding the amount of scarce scene images from both style and content. The method is highly adaptable and works flexibly on handling image generation problems for various types of rare scenes.

关键词： Training Adaptation models Image synthesis Design methodology Lighting Indexes Image reconstruction

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Parallel systems Framework: A Safe and Efficient Testbed for Unmanned Ships

Parallel Systems Framework: A Safe and Efficient Testbed for...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Long Fu Zhen Shen Xisong Dong Yunjun Han Shuguang Yang Gang Xiong Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China Schoool of Rail Transportation Shandong Jiaotong University Jinan China State Key Laboratory for Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

In this study, unmanned ships test platform based on the parallel systems framework is proposed to improve the test efficiency and accuracy of unmanned ships in complex ocean environment. The parallel intelligence theory and ACP approach, the efficient interaction between virtual and real environment is achieved, which provides more comprehensive and accurate data for unmanned ships testing. Future research will further expand the scope of virtual environment simulation and improve systems accuracy and response speed to a wider range of unmanned ships test requirements.

关键词： Accuracy Oceans Virtual environments Digital twins Marine vehicles Testing

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Hierarchical Fused Model With Deep Learning and Type-2 Fuzzy Learning for Breast Cancer Diagnosis

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IEEE TRANSACTIONS ON FUZZY systems 2020年第12期28卷 3204-3218页

作者： Shen, Tianyu Wang, Jiangong Gou, Chao Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Social Comp Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Pattern Recognit & Intelligent Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

Breast cancer diagnosis based on medical imaging necessitates both fine-grained lesion segmentation and disease grading. Although deep learning (DL) offers an emerging and powerful paradigm of feature learning for these two tasks, it is hampered from popularizing in practical application due to the lack of interpretability, generalization ability, and large lab.led training sets. In this article, we propose a hierarchical fused model based on DL and fuzzy learning to overcome the drawbacks for pixelwise segmentation and disease grading of mammography breast images. The proposed system consists of a segmentation model (ResU-segNet) and a hierarchical fuzzy classifier (HFC) that is a fusion of interval type-2 possibilistic fuzzy c-means and fuzzy neural network. The ResU-segNet segments the masks of mass regions from the images through convolutional neural networks, while the HFC encodes the features from mass images and masks to obtain the disease grading through fuzzy representation and rule-based learning. Through the integration of feature extraction aided by domain knowledge and fuzzy learning, the system achieves favorable performance in a few-shot learning manner, and the deterioration of cross-dataset generalization ability is alleviated. In addition, the interpretability is further enhanced. The effectiveness of the proposed system is analyzed on the publicly availab.e mammogram database of INbreast and a private database through cross-validation. Thorough comparative experiments are also conducted and demonstrated.

关键词： Image segmentation Biomedical imaging Fuzzy sets Breast cancer Breast cancer deep learning (DL) fuzzy classifier (FC) interval type-2 possibilistic fuzzy c-means (IT2PFCM)

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Human-Guided Functional Connectivity Network Estimation for Chronic Tinnitus Identification: A Modularity View

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第10期26卷 4849-4858页

作者： Li, Wei-Kai Chen, Yu-Chen Xu, Xiao-Wen Wang, Xiao Gao, Xin Chongqing Jiaotong Univ Sch Math & Stat Chongqing 400074 Peoples R China Shanghai Univ Dept PET MR Med Imaging Diagnost Ctr Shanghai 20030 Peoples R China Nanjing Med Univ Nanjing Hosp 1 Dept Radiol Nanjing 211106 Peoples R China Tongji Univ Sch Med Shanghai 200092 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266000 Peoples R China

The functional connectivity network (FCN) has been used to achieve several remarkable advancements in the diagnosis of neuro-degenerative disorders. Therefore, it is imperative to accurately estimate biologically meaningful FCNs. Several efforts have been dedicated to this purpose by encoding biological priors. However, owing to the high complexity of the human brain, the estimation of an 'ideal' FCN remains an open problem. To the best of our knowledge, almost all existing studies lack the integration of domain expert knowledge, which limits their performance. In this study, we focused on incorporating domain expert knowledge into the FCN estimation from a modularity perspective. To achieve this, we presented a human-guided modular representation (MR) FCN estimation framework. Specifically, we designed an adversarial low-rank constraint to describe the module structure of FCNs under the guidance of domain expert knowledge (i.e., a predefined participant index). The chronic tinnitus (TIN) identification task based on the estimated FCNs was conducted to examine the proposed MR methods. Remarkably, MR significantly outperformed the baseline and state-of-the-art(SOTA) methods, achieving an accuracy of 92.11%. Moreover, post-hoc analysis revealed that the FCNs estimated by the proposed MR could highlight more biologically meaningful connections, which is beneficial for exploring the underlying mechanisms of TIN and diagnosing early TIN.

关键词： Estimation Tin Correlation Indexes Support vector machines Biological information theory Task analysis Functional magnetic resonance imaging modularity functional connectivity network chronic tinnitus

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Interval Type-2 Fuzzy Analysis and Comprehensive Evaluation for Neonatal Pathological Jaundice

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL systems 2022年第5期9卷 1542-1551页

作者： Mo, Hong Yang, Chun Wang, Xiao Wang, Fei-Yue Changsha Univ Sci & Technol Coll Elect & Informat Engn Changsha 410114 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Qingdao Acad Intelligent Ind Qingdao 266109 Peoples R China Natl Univ Def Technol Res Ctr Mil Computat Expt & Parallel Syst Changsha 410015 Peoples R China Univ Chinese Acad Sci Ctr China Econ & Social Secur Beijing 100049 Peoples R China

Neonatal pathological jaundice (NPJ) is easy to cause bilirubin encephalopathy, which has high mortality and sequelae rate. Therefore, accurate risk evaluation can help clinicians take appropriate measures to timely intervene in neonatal jaundice level and avoid complications. In this article, five indexes are extracted as the factor set for the risk evaluation of NPJ, and the diagnostic criteria are determined. Then, five index sets are described by interval type-2 fuzzy sets, and the corresponding membership functions and membership function figures are provided. The feasibility of interval type-2 fuzzy comprehensive evaluation in risk evaluation of NPJ is demonstrated through example, and the fuzzy rule bases of prevention and treatment for NPJ are constructed according to the results of risk evaluation. Finally, this article demonstrates that the proposed risk evaluation and treatment process of NPJ is actually a dynamic closed-loop control process, which is consistent with the clinical treatment process. This article provides a new solution for the aided diagnosis and decision-making treatment of NPJ, which is of great significance in reducing neonatal mortality and alleviating the pressure of medical staff.

关键词： Pediatrics Frequency selective surfaces Fuzzy sets Pathology Process control Medical diagnostic imaging Biomedical monitoring Dynamic closed-loop control process fuzzy rule bases interval type-2 fuzzy comprehensive evaluation neonatal pathological jaundice (NPJ)

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