A Bi-Level Capacity Optimization Method for Hybrid Energy Storage Systems Combining the IBWO and MVMD Algorithms by Qiaoqiao Xing, Shidong Li, Da Qiu, Yang Long, Qinyi Liao, Xiangjin Yin, Yunxiang Li, Kai Qian

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Meta model construction mechanism in SDN architecture by Bin ZHUGE, Hua ZHU, Baoxia WANG, Weiming WANG, Julong LAN

“…Micro service is a new method of modern application architecture,its characteristic is that decomposing the complex applications into discrete,independent service,so that decoupling of applications scheme can be achieved,and the individual service could be developed,deployed and extensioned independently.The change of the application must drive on the change of the underlying network,the architecture of reconfigurable fundamental information communication network aimd to compensate for the network business requirements and network transmission capacity through the network reconfiguration mechanism.A meta model construction mechanism with the SDN architecture for fusion the reconfiguration and software defined network technology was proposed,it aimed to present the services of each layer in the SDN architecture by the form of meta model.And after referencing the AKF scale cube,a kind of meta model building method based on orthogonal decomposition was proposed.Finally,the feasibility of meta model building method based on orthogonal decomposition from the theoretical analysis and development of example was verified.…”
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A Short-Term Wind Speed Forecasting Hybrid Model Based on Empirical Mode Decomposition and Multiple Kernel Learning by Yuanyuan Xu, Genke Yang

“…Extensive experimental results show that, compared with existing machine learning methods, the EMD-MKL model proposed in this paper has better performance in terms of the prediction accuracy evaluation indexes and confidence intervals and shows a better ability to generalize.…”
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Photovoltaic solar energy prediction using the seasonal-trend decomposition layer and ASOA optimized LSTM neural network model by Venkatachalam Mohanasundaram, Balamurugan Rangaswamy

“…Parameter standardization employing ASOA, the RSTL decomposition approach, and the conceptual model of LSTM networks are all presented in this research work. …”
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Traffic congestion forecasting using machine learning methods by Ramil R. Zagidullin, Almaz N. Khaybullin

“…Materials and methods. To identify patterns in the data, additive time series decomposition, spectral analysis based on the fast Fourier transform, and autocorrelation analysis were applied. …”
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Systematic Literature Review for Robust Mixed-Integer Linear Programming Models Using Benders Decomposition in Facility Location Problems by Fadiah Hasna Nadiatul Haq, Diah Chaerani, Anita Triska

“…The robust Mixed-Integer Linear Programming (MILP) model is an approach to address uncertainty in linear optimization involving integer and continuous variables, which can be solved using the Benders Decomposition method. …”
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Quantum chemistry modeling of the unimolecular decomposition mechanism and substituent effects of pyrazolo-tetrazine: A case study of BITE-101 by Zi-xuan Yang, Shuang-fei Zhu, Hai Duan, Rui-jun Gou, Yang Liu, Shu-hai Zhang

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DEVELOPMENT OF THE METHODS FOR RESOURCE REALLOCATION IN CLOUD COMPUTING SYSTEMS by Viacheslav Davydov, Daryna Hrebeniuk

“…The subject matter of the article is development of the models and methods of load and resource balancing and reallocation in cloud computing systems based on the infrastructure as a service model. …”
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A multi-channel spatiotemporal SegNet model for short term wind power prediction with sequence decomposition and reconstruction by Xingdou Liu, Liang Zou, Li Zhang, Jiangong Wang, Zhiyun Han, Yong Li

“…The experimental results show that the proposed method outperforms multiple classical and advanced benchmarks in spatiotemporal joint training.…”
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Two-step hybrid model for monthly runoff prediction utilizing integrated machine learning algorithms and dual signal decompositions by Shujun Wu, Zengchuan Dong, Sandra M. Guzmán, Gregory Conde, Wenzhuo Wang, Shengnan Zhu, Yiqing Shao, Jinyu Meng

“…The dual decomposition method, integrating seasonal-trend decomposition based on loess (STL) and successive variational mode decomposition (SVMD), demonstrated exceptional efficacy in addressing the complexities of hydrometeorological time series. …”
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Domain knowledge-driven decomposition-based large-scale optimization for ship cabin structures by Puyu JIANG, Jun LIU, Qiangjun LUO, Yuansheng CHENG

“…ObjectivesThis paper proposes a domain knowledge-driven large-scale optimization algorithm for ship cabin structures based on a decomposition optimization framework. MethodsThe proposed algorithm combines domain mechanical knowledge with a general black box optimization algorithm, groups the design variables into location variables and size variables, and decomposes the original problem into a series of low-dimensional subproblems. …”
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Water quality prediction model based on improved long short-term memory neural network and empirical mode decomposition by Feng Lin, Xu Li, Yang Su, Jun Yan

“…This study proposes an improved long short-term memory neural network model for complex time-series water quality data. …”
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A novel deep learning model for air quality index prediction integrating time series decomposition and intelligent optimization by Guangyao Ma, Kai Xu, Yue Zhang, Lanhe Zhang, Zicheng Chen

“…We then leverage the TimesNet model to capture long-term and periodic patterns within the trend and seasonal series, while employing an improved Transformer (iTransformer) to model the short-term, high-frequency residuals. …”
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A novel method based on the SCNGO‐ICEEMDAN and MCNN‐BiLSTM model for fault diagnosis of motor bearings for more electric aircraft by Dongsheng Yuan, Feng Liu, Zhonggang Yin, Yanqing Zhang, Yanping Zhang, Peien Luo

“…In this article, a Northern Goshawk algorithm using a fusion subtraction optimiser and Cauchy strategy (SCNGO) is proposed to optimise the number of white noise additions and amplitude weights in the improved full set empirical mode decomposition method based on adaptive noise (ICEEMDAN). …”
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Resampling Multi-Resolution Signals Using the Bag of Functions Framework: Addressing Variable Sampling Rates in Time Series Data by David Orlando Salazar Torres, Diyar Altinses, Andreas Schwung

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Novel Control Method for the MMC Connected to Wind Farms by Xiangyu Pei, Caoyang Jia, Feng Ji, Hui Pang, Guangfu Tang

“…Finally, an electromagnetic transient simulation model is built with PSCADIEMTDC, and the efficacy and practicality of the proposed method are demonstrated through extensive simulation experiments.…”
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Single-channel cooling system design by using perforated porous insert and modeling with POD for double conductive panel by Selimefendigil Fatih, Benabdallah Faiza, Ghachem Kaouther, Albalawi Hind, Alshammari Badr M., Kolsi Lioua

“…For panel surface temperature, a proper orthogonal decomposition (POD)-based reconstruction model with 12 POD modes is used. …”
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Implementing Astronomical Potential and Wavelet Analysis to Improve Regional Tide Modeling by Jihene Abdennadher, Moncef Boukthir

“…Such understanding is crucial for improving the representation of oceanic energy transfer and mixing processes in numerical models. To this end, the astronomical potential, load tide effects, and a wavelet-based analysis method were implemented in the three-dimensional ROMS model. …”
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Blood-based tri-hybrid nanofluid flow through a porous channel with the impact of thermal radiation used in drug administration by Subhalaxmi Dey, Surender Ontela, P.K. Pattnaik, S.R. Mishra

“…Further, a semi-analytical approach Adomian Decomposition Method (ADM) is proposed for the solution of the model. …”
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Hybrid framework for improved PM2.5 prediction based on seasonal-trend decomposition and tailored component processing by Dongbao Jia, Wenjun Ruan, Rui Ma, Shiwei Zhao, Yichen Wang, Wei Xu, Weijie Zhou, Xinxin Ge, Zhongxun Xu

“…However, accurately forecasting PM2.5 levels presents significant challenges due to the complex, non-linear, and non-stationary characteristics inherent in the PM2.5 concentration data, which are influenced by diverse meteorological conditions, emission sources, and atmospheric transport dynamics. Existing prediction methods often struggle to adequately capture these multifaceted patterns simultaneously; single models may fail to address both long-term trends and seasonal variations as well as short-term stochastic fluctuations, while many hybrid decomposition approaches may not optimally utilize techniques suited to the distinct nature of each decomposed component, thereby limiting potential accuracy improvements. …”
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