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IEEE International Conference on Cognitive Machine Intelligence (CogMI)

作者： Feng Luo Ling Liu G. Geoff Wang Vijay Kumar Mark S. Ashton Jacob Abernethy Fatemeh Afghah Matthew H. E. M Browning David Coyle Philip Dames Tom O'Halloran James Hays Patrick Heisl Chenfanfu Jiang Puskar Khanal Venkat Narayan Krovi Sara Kuebbing Nianyi Li JingJing Liang Ninghao Liu Steve McNulty Christopher M. Oswalt Neil Pederson Demetri Terzopoulos Christopher W. Woodall Yongkai Wu Jian Yang Yin Yang Liang Zhao School of Computing Clemson University Clemson SC USA College of Computing Georgia Institute of Technology Atlanta GA USA Department of Forestry and Environmental Conservation Clemson University Clemson SC USA School of Engineering and Applied Science University of Pennsylvania Philadelphia PA USA The Forest School Yale University New Haven CT USA Department of Electrical and Computer Engineering Clemson University Clemson SC USA Department of Parks Recreation and Tourism Management Clemson University Clemson SC USA Department of Mechanical Engineering Temple University Philadelphia PA USA Department of Mathematics University of California Los Angeles Los Angeles CA USA Department of Automotive Engineering Clemson University Clemson SC USA Department of Forestry and Natural Resources Purdue University West Lafayette IN USA School of Computing University of Georgia Athens GA USA Northern Research Station USDA Forestry Service Durham NH USA South Research Station USDA Forestry Service Knoxville TN USA The Harvard Forest Harvard University Petersham MA USA Computer Science Department University of California Los Angeles Los Angeles CA USA Department of Forestry and Natural Resources University of Kentucky Lexington KY USA School of Computing University of Utah Salt Lake City UT USA Department of Computer Science Emory University Atlanta GA USA

Forests and forest ecosystems are vital to our social, economic, and environmental well-being. However, climate change and climate-driven disturbances (CDDs) are undermining the health and resilience of forests worldwide and pose significant uncertainty to sustainable forest management. Climate-smart forestry (CSF) remains a grand challenge in practice due to our limited knowledge of how forests respond to climate change and our abilities to collect related information to empower decision making. Rapid advances in artificial intelligence (AI) can offer a timely opportunity to address the challenges in CSF. We argue that the AI-enabled, next-generation CSF can be achievable through synergistically coordinated and transdisciplinary efforts that develop and advance foundational and use-inspired AI technologies that can lead to building next-generation forest decision support systems.

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Reinforcement learning based distributed control of dissipative networked systems

arXiv

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arXiv 2020年

作者： Kosaraju, Krishna Chaitanaya Sivaranjani, S. Suttle, W. Gupta, V. Liu, J. Department of Electrical Engineering University of Notre Dame Notre DameIN46556 United States Department of Electrical and Computer Engineering Texas A&M University College StationTX77843 United States Department of Applied Mathematics and Statistics Department of Electrical and Computer Engineering Stony Brook University Stony BrookNY11794 United States

We consider the problem of designing distributed controllers to stabilize a class of networked systems, where each subsystem is dissipative and designs a reinforcement learning based local controller to maximize an individual cumulative reward function. We develop an approach that enforces dissipativity conditions on these local controllers at each subsystem to guarantee stability of the entire networked system. The proposed approach is illustrated on a DC microgrid example, where the objective is maintain voltage stability of the network using local distributed controllers at each generation unit. © 2020, CC-BY.

关键词： Reinforcement learning

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HUSBoost: A Hubness-Aware Boosting for High-Dimensional Imbalanced Data Classification

HUSBoost: A Hubness-Aware Boosting for High-Dimensional Imba...

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International Conference on Machine Learning and Data engineering (iCMLDE)

作者： Qin Wu Yaping Lin Tuanfei Zhu Jianhao Wei College of Information Science and Engineering Hunan University Changsha China College of Computer Engineering and Applied Mathematics Changsha University Changsha China

Learning from high-dimensional imbalanced data is a challenging research problem in machine learning, due to the curse of dimensionality caused by high dimension and the learning bias resulted from class imbalance. The existing works generally apply dimension reduction methods to reduce the dimensionality of features first, and then deal with the class imbalance problem by traditional imbalanced learning technologies. However, dimensionality reduction may cause the loss of useful information and cannot effectively address the problem of hubness which is an important aspect of the curse of dimensionality. In this paper, we present a hubness-aware cluster-based ensemble algorithm, HUSBoost, for learning high- dimensional imbalanced data. For hubs induced by high dimensionality, HUSBoost introduces discount factors to slow down the excessive growth of their weights, so as to alleviate the negative impacts of "bad" hubs on the classification decisions of component classifiers. To address the class imbalance problem, HUSBoost utilizes a cluster-based majority undersampling method to correct imbalanced class distribution. Specifically, k- hubs clustering technology is used to divide the majority samples into multiple clusters, and then the representative majority samples are selected from each cluster so as to form the balanced class distribution. Experimental results based on sixteen high-dimensional imbalanced data sets show the effectiveness of HUSBoost.

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Corrigendum to “Szeged and Mostar root-indices of graphs”

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applied mathematics and Computation 2025年 499卷

作者： Simon Brezovnik Matthias Dehmer Niko Tratnik Petra Žigert Pleteršek University of Ljubljana Faculty of Mechanical Engineering Slovenia Institute of Mathematics Physics and Mechanics Ljubljana Slovenia Swiss Distance University of Applied Sciences Brig Switzerland Department for Biomedical Computer Science and Mechatronics UMIT-Private University for Health Sciences Medical Informatics and Technology Eduard-Wallnfer-Zentrum 1 6060 Hall in Tyrol Tyrol Austria College of Artificial Intelligence Nankai University Tianjin China School of Science Xian Technological University Xian Shaanxi 710021 China University of Maribor Faculty of Natural Sciences and Mathematics Slovenia University of Maribor Faculty of Chemistry and Chemical Engineering Slovenia

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2024年 1057 LNNS卷 xiii-xiv页

作者： Bhateja, Vikrant Tang, Jinshan Sharma, Dilip Kumar Polkowski, Zdzislaw Ahmad, Afaq Department of Electronics Engineering Veer Bahadur Singh Purvanchal University Uttar Pradesh Jaunpur India Department of Health Administration and Policy College of Public Health George Mason University FairfaxVA United States GLA University Uttar Pradesh Mathura India Department of Humanities and Social Sciences The Karkonosze University of Applied Sciences Jelenia Góra Poland Department of Mathematics and Computer Science Modern College of Business and Science Muscat Oman

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Study on stability of roll over control of aircraft

Study on stability of roll over control of aircraft

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第二届电气、控制与自动化国际会议

作者： Jun Liu Chunyan Zhu Institute of Applied Mathematics Qujing Normal University College of Computer Science and Engineering Qujing Normal University

In this paper, studies the problem of rolling out of flight in air flight that may be out of control. First, the corresponding mathematical model is established through dynamic analysis. Secondly, the mathematical model is studied by using Lyapunov function method. Finally, the stability of the model is demonstrated in theory. The effectiveness and safety of the controller are designed.

关键词： Study on stability of roll over control of aircraft

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Load balancing for multi-physics simulations 21

Load balancing for multi-physics simulations

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Chen, Chunsen Li, Kenli Liu, Xu Li, Keqin College of Information Science and Engineering Hunan University Changsha China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing China Department of Computer Science State University of New York New York United States

ISBN: (纸本)9781728120584

Multi-physics simulations are usually essential to simplify researches on complex physical phenomena. In this paper, we extend the rectangular partitioning from single-physics simulations to multi-physics simulations. This work presents the Single-rectangle Tightly-coupled algorithm (ST) and Multirectangle Tightly-coupled algorithm (MT) based on rectangular partitioning. The MT algorithm is more flexible and achieves higher load balancing efficiency than the ST algorithm by greedily assigning the remaining unallocated most loaded rectangle to the least loaded processor and assigning k rectangles to each of the processors. To study the effect of machine characteristics on load balancing, the experiments use two types of machine parameters based on Tianhe-2 supercomputer. Inspired by the roofline model, this paper analyzes the relationship among the choice of the load balancing algorithms, the load characteristics of multi-physics application and the features of high performance parallel computer through the comparison experiment of looselycoupled algorithm and tightly-coupled algorithm. In the case of LARED-P, MT algorithm can further reduce the total simulation time compared with the ST algorithm. Through experiments, we show that whether the load balancing algorithm is effective depends on both the load characteristics of multi-physics simulation and the features of high performance parallel computer. © 2019 IEEE.

关键词： Supercomputers

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A novel deep learning framework with artificial protozoa optimization-based adaptive environmental response for wind power prediction

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Scientific Reports 2025年第1期15卷 1-34页

作者： Lee, Sangkeum Almomani, Mohammad H. Alomari, Saleh Ali Saleem, Kashif Smerat, Aseel Snasel, Vaclav Gandomi, Amir H. Abualigah, Laith Department of Computer Engineering Hanbat National University 125 Dongseo-daero Yuseong-gu Daejeon South Korea Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa 13133 Jordan Faculty of Science and Information Technology Jadara University Irbid 21110 Jordan Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh 11362 Saudi Arabia Faculty of Educational Sciences Al-Ahliyya Amman University Amman 19328 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Poruba-Ostrava 70800 Czech Republic Faculty of Engineering and Information Technology University of Technology Sydney Ultimo 2007 NSW Australia Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Computer Technologies Engineering Mazaya University College Nasiriyah Iraq University Research and Innovation Center (EKIK) Óbuda University Budapest 1034 Hungary Department of Computer Science Khazar University Baku Azerbaijan

Accurate very short-term wind power forecasting is critical for the reliable integration of renewable energy into modern power systems. However, the inherent variability and non-linearity of wind power data pose significant challenges. To address these, this study proposes a novel hybrid deep learning framework, IAPO-LSTM, which combines Convolutional Neural Networks (CNNs) for spatial feature extraction and Gated Recurrent Units (GRUs) for temporal sequence modeling. The model is optimized using an enhanced Artificial Protozoa Optimizer (IAPO) augmented with an Adaptive Environmental Response Mechanism (AERM), which dynamically adjusts exploration and exploitation strategies based on the problem landscape to improve convergence and hyperparameter tuning efficiency. The proposed IAPO-LSTM model was evaluated on four real-world datasets—NREL WIND, EMD WIND, WWSIS, and ERCOT GRID—and benchmarked against six state-of-the-art forecasting models. Results demonstrate that IAPO-LSTM achieved the lowest forecasting errors across all datasets, with Mean Absolute Error (MAE) as low as 2.78, Root Mean Square Error (RMSE) of 4.50, and Theil’s Inequality Coefficient (TIC) of 0.0292 on the ERCOT dataset. Additionally, the model demonstrated faster inference times and better statistical significance (p < 0.005) compared to baseline methods. These outcomes confirm that IAPO-LSTM is not only highly accurate but also efficient and robust for real-time wind power forecasting applications. © The Author(s) 2025.

关键词： Adaptive environmental response mechanism Artificial Protozoa optimizer Hybrid deep learning Renewable energy integration Time series prediction Wind power forecasting

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Corrections to “Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications”

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IEEE Access 2024年 12卷 135103-135103页

作者： Mohammed A. El-Shorbagy Anas Bouaouda Hossam A. Nabwey Laith Abualigah Fatma A. Hashim Department of Mathematics College of Science and Humanities in Al-Kharj Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Faculty of Science and Technology Hassan II University of Casablanca Mohammedia Morocco Department of Basic Engineering Science Faculty of Engineering Menoufia University Shebin El-Kom Egypt Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Faculty of Information Technology Isra University Amman Jordan College of Engineering Yuan Ze University Taoyuan Taiwan Applied Science Research Center Applied Science Private University Amman Jordan Faculty of Engineering Helwan University Cairo Egypt

Presents corrections to the paper, Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications.

关键词： Metaheuristics

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Eighth power residue double circulant self-dual codes

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International Journal of Network Security 2020年第5期22卷 736-742页

作者： Jiang, Changsong Sun, Yuhua Liang, Xueting College of Science China University of Petroleum Qingdao Shandong266580 China Provincial Key Laboratory of Applied Mathematics Putian University Putian Fujian351100 China School of Computer Science and Engineering University of Electronic Science and Technology of China China

Self-dual codes are one of the most important classes of linear codes. Power residue classes are widely used in the constructions of linear codes and pseudo-random sequences. In this paper, we give new constructions of self-dual codes over GF(2) and GF(4) by eighth power residues. We get multiple pure double circulant codes and bordered double circulant codes. Some of these new self-dual codes have large minimum distances. © Femto Technique Co., Ltd.

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