Article
Green & Sustainable Science & Technology
Ling Liu, Jujie Wang, Jianping Li, Lu Wei
Summary: To cope with global warming and increasing energy demand, wind power has been rapidly developed around the world. Analyzing the characteristics of wind speed distribution is crucial for improving the development and utilization of wind energy. While many studies have focused on improving the estimation accuracy of wind speed distribution, there is limited research on its variation characteristics and predictability. In this study, a novel horizontal-vertical-integration framework is proposed for predicting wind speed distribution. To address the predictability issue of nonparametric estimation, a data sampling and mapping method is proposed. Furthermore, an improved differential evolution optimization algorithm is designed to indirectly optimize the learning rate of the hybrid neural network. The effectiveness of the proposed methods is validated using data from 9 wind stations and 6 comparison models, and the results demonstrate that the absolute error of the proposed prediction framework is less than 0.0059, outperforming the other comparison models.
Article
Operations Research & Management Science
Wei Liu, Li Yang, Bo Yu
Summary: This paper investigates a KDE-based distributionally robust mean-CVaR portfolio optimization model by using weighted kernel density estimation to approximate the continuous probability density function of the portfolio loss and compute the corresponding approximated CVaR. The distributional uncertainty set is indirectly defined by imposing a constraint on the weights in weighted KDE, converting the infinite-dimensional space of PDF into a finite-dimensional space. The study proves that the optimal value and solution set of the KDE-based DRO problem converge to those of the portfolio optimization problem under the true distribution. Primary empirical test results show the significance of the proposed model.
JOURNAL OF GLOBAL OPTIMIZATION
(2022)
Article
Operations Research & Management Science
Wei Liu, Li Yang, Bo Yu
Summary: This paper proposes a distributionally robust mean-HMCR portfolio optimization model using kernel density estimation and phi-divergence to address the curse of dimensionality. Empirical tests demonstrate that the portfolio strategy obtained by the proposed model outperforms other strategies in most cases, showing higher quality in terms of performance.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Automation & Control Systems
Rachel E. Keil, Alexander T. Miller, Mrinal Kumar, Anil V. Rao
Summary: A numerical method is developed in this paper to solve chance constrained optimal control problems by reformulating the chance constraints as nonlinear constraints and approximating them using kernel density estimators and Markov Chain Monte Carlo sampling. The method is tested on two problems and shown to be reliable and effective in solving chance constrained optimal control problems.
OPTIMAL CONTROL APPLICATIONS & METHODS
(2021)
Article
Engineering, Electrical & Electronic
Linjun Zeng, Jiazhu Xu, Yanbo Wang, Yuxing Liu, Jiachang Tang, Ming Wen, Zhe Chen
Summary: The increasing penetration of renewable energy introduces high uncertainty and complexity in optimal scheduling of power systems. This study proposes a novel improved interval optimization method to enhance the accuracy of interval ranges for renewable energy output. The method utilizes an improved adaptive diffusion kernel density estimation (IADKDE) and data-driven adaptive optimal bandwidth selection. The proposed model considers the driving requirements of electric vehicles (EVs) owners and can be solved using interval linear programming method. The effectiveness and accuracy of the proposed method are validated through comparative analysis with other optimization methods.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
(2023)
Article
Statistics & Probability
D. Scaldelai, L. C. Matioli, S. R. Santos, M. Kleina
Summary: In this paper, the proposed MulticlusterKDE algorithm is used to classify elements of a database based on their similarity. One of the main features of this algorithm is the optional input parameter for the number of clusters. The algorithm is simple, well defined, converges in a finite number of steps, and shows competitive performance compared to other algorithms.
JOURNAL OF APPLIED STATISTICS
(2022)
Article
Computer Science, Artificial Intelligence
Tie-min Ma, Xue Wang, Fu-cai Zhou, Shuang Wang
Summary: With the rapid development of the information industry and the Internet, the use of big data has attracted attention, leading to the emergence of recommendation systems that aim to quickly extract desired information from vast amounts of data. User-based collaborative filtering algorithm has become a research focus in this field. However, existing research mainly focuses on improving collaborative filtering recommendation algorithms using kernel methods, but still face various challenges such as cold start, diversity, data sparsity, and concept drift. To address these challenges, this paper proposes a user-based collaborative filtering algorithm based on kernel methods and multi-objective optimization (MO-KUCF). By introducing kernel density estimation and multi-objective optimization, the proposed algorithm enhances recommendation system diversity, helps deal with concept drift in dynamic data, and improves the accuracy of recommendations. The Netflix dataset is used for empirical analysis, comparing the MO-KUCF algorithm with user-based collaborative filtering (UCF) and user-based collaborative filtering based on kernel methods (KUCF) using mean absolute error (MAE). The results show that MO-KUCF improves accuracy by 5.6% and increases diversity. By combining multi-objective optimization techniques with kernel density estimation methods, the proposed algorithm effectively improves recommendation system diversity and solves the concept drift problem, thereby enhancing system accuracy.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Baigang Du, Shuo Huang, Jun Guo, Hongtao Tang, Lei Wang, Shengwen Zhou
Summary: The current literature focuses on accurate point predictions of water demand, but faces issues with increasing uncertainty. To address this, a hybrid model combining LSTM networks, KDE, and PSO is proposed to acquire water demand prediction intervals and quantify uncertainties. The comprehensive performance of the proposed model surpasses other models, providing reliable decision support for policymakers.
APPLIED SOFT COMPUTING
(2022)
Article
Mathematics
Hui Chen, Kunpeng Xu, Lifei Chen, Qingshan Jiang
Summary: The study introduces a novel self-expressive kernel subspace clustering algorithm, which utilizes self-expressive kernel density estimation and a new feature-weighted non-linear similarity measurement. This algorithm employs an effective non-linear optimization method to solve the clustering objective function, achieving better effectiveness and efficiency in exploring non-linear relationships among attributes.
Article
Computer Science, Hardware & Architecture
Javier Corral-Garcia, Felipe Lemus-Prieto, Miguel-Angel Perez-Toledano
Summary: A transcompiler has been developed to assist researchers and users lacking parallel programming skills in improving the performance of HPC programs and tasks, with current efforts focused on optimizing code fragments to reduce running times by integrating 26 software techniques.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Automation & Control Systems
Mirko Mazzoleni, Gabriele Maroni, Simone Formentin, Fabio Previdi
Summary: In this study, a kernel-based data-driven approach is proposed for optimizing multi-period portfolio control strategies. By minimizing the Lower Partial Moments risk measure, the method provides better trade-offs in terms of risk and investment performance while preserving convexity. Empirical results on real historical financial data demonstrate the effectiveness of the method.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Automation & Control Systems
Qichun Zhang, Hong Wang
Summary: This article introduces a novel data-based approach to address the non-Gaussian stochastic distribution control problem, presenting a new probability density function transformation and two optimization algorithms.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Engineering, Civil
Zhiyuan Liu, Cheng Lyu, Jinbiao Huo, Shuaian Wang, Jun Chen
Summary: Gaussian process regression (GPR) is a promising machine learning model for transportation system estimation and prediction. The radial basis function (RBF) kernel, commonly used in GPR, often faces difficulties in finding the optimal hyperparameter. This paper addresses the issue by promoting the use of the hat kernel and investigating the connection between deformation and the Bayesian generalization error of GPR.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Engineering, Multidisciplinary
Yang Liu, Cheng Yang, Peng Wei, Pingzhang Zhou, Jianbin Du
Summary: This paper discusses a new topology optimization method that combines density-based method with level-set description for efficient structural optimization and topological variation. By using a material interpolation with penalty, the update information becomes more distinguished, leading to stable convergence into solid-void solutions. The method is validated through benchmark examples in 2D and 3D, showing advantageous structural representations and better objective function values compared to the widely accepted SIMP method. Several numerical examples and MATLAB codes are provided to demonstrate the method's characteristics.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Mathematics
Yi Jin, Yulin He, Defa Huang
Summary: The paper proposed an improved variable KDE (IVKDE) method, which can determine the optimal bandwidth for each data point based on the integrated squared error (ISE) criterion. Compared with fixed KDE (FKDE) and variable KDE (VKDE), IVKDE achieved lower estimation errors.
Article
Chemistry, Multidisciplinary
Joshua D. Elliott, Emiliano Poli, Ivan Scivetti, Laura E. Ratcliff, Lampros Andrinopoulos, Jacek Dziedzic, Nicholas D. M. Hine, Arash A. Mostofi, Chris-Kriton Skylaris, Peter D. Haynes, Gilberto Teobaldi
Article
Chemistry, Physical
R. J. Charlton, R. M. Fogarty, S. Bogatko, T. J. Zuehlsdorff, N. D. M. Hine, M. Heeney, A. P. Horsfield, P. D. Haynes
JOURNAL OF CHEMICAL PHYSICS
(2018)
Article
Chemistry, Physical
N. Molinari, A. P. Sutton, A. A. Mostofi
PHYSICAL CHEMISTRY CHEMICAL PHYSICS
(2018)
Article
Multidisciplinary Sciences
Martik Aghajanian, Arash A. Mostofi, Johannes Lischner
SCIENTIFIC REPORTS
(2018)
Article
Chemistry, Physical
Jacek R. Golebiowski, James R. Kermode, Arash A. Mostofi, Peter D. Haynes
JOURNAL OF CHEMICAL PHYSICS
(2018)
Correction
Chemistry, Physical
Chris-Kriton Skylaris, Peter D. Haynes
JOURNAL OF CHEMICAL PHYSICS
(2020)
Correction
Physics, Condensed Matter
P. D. Haynes, C-K Skylaris, A. A. Mostofi, M. C. Payne
JOURNAL OF PHYSICS-CONDENSED MATTER
(2020)
Correction
Physics, Condensed Matter
Chris-Kriton Skylaris, Peter D. Haynes, Arash A. Mostofi, Mike C. Payne
JOURNAL OF PHYSICS-CONDENSED MATTER
(2020)
Correction
Physics, Condensed Matter
Chris-Kriton Skylaris, Peter D. Haynes, Arash A. Mostofi, Mike C. Payne
JOURNAL OF PHYSICS-CONDENSED MATTER
(2020)
Article
Quantum Science & Technology
Chris N. Self, Kiran E. Khosla, Alistair W. R. Smith, Frederic Sauvage, Peter D. Haynes, Johannes Knolle, Florian Mintert, M. S. Kim
Summary: By solving related variational problems in parallel and sharing information between optimizers, the method significantly improves efficiency and is suitable for problems with many physical degrees of freedom, addressing a key challenge in scaling quantum algorithms for real-world problems.
NPJ QUANTUM INFORMATION
(2021)
Article
Chemistry, Multidisciplinary
Marie S. Rider, Maria Sokolikova, Stephen M. Hanham, Miguel Navarro-Cia, Peter D. Haynes, Derek K. K. Lee, Maddalena Daniele, Mariangela Cestelli Guidi, Cecilia Mattevi, Stefano Lupi, Vincenzo Giannini
Article
Materials Science, Multidisciplinary
Zachary A. H. Goodwin, Fabiano Corsetti, Arash A. Mostofi, Johannes Lischner
Article
Chemistry, Multidisciplinary
Wei-Tin Chen, Chris Ablitt, Nicholas C. Bristowe, Arash A. Mostofi, Takashi Saito, Yuichi Shimakawa, Mark S. Senn
CHEMICAL COMMUNICATIONS
(2019)
Article
Materials Science, Multidisciplinary
Dillon Wong, Yang Wang, Wuwei Jin, Hsin-Zon Tsai, Aaron Bostwick, Eli Rotenberg, Roland K. Kawakami, Alex Zettl, Arash A. Mostofi, Johannes Lischner, Michael F. Crommie