Shaoxiong Wu¹, Ruoxin Li¹, Xiaofeng Tao¹, Hailong Wu^1,*, Ping Miao¹, Yang Lu¹, Yanyan Lu¹, Qi Liu², Li Pan²

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3063-3077, 2024, DOI:10.32604/cmc.2024.055381 - 18 November 2024

Abstract Time series prediction has always been an important problem in the field of machine learning. Among them, power load forecasting plays a crucial role in identifying the behavior of photovoltaic power plants and regulating their control strategies. Traditional power load forecasting often has poor feature extraction performance for long time series. In this paper, a new deep learning framework Residual Stacked Temporal Long Short-Term Memory (RST-LSTM) is proposed, which combines wavelet decomposition and time convolutional memory network to solve the problem of feature extraction for long sequences. The network framework of RST-LSTM consists of two More >

Jiachang Liu^1,*, Zhengwei Huang², Junfeng Xiang¹, Lu Liu¹, Manlin Hu¹

Energy Engineering, Vol.121, No.11, pp. 3461-3486, 2024, DOI:10.32604/ee.2024.054514 - 21 October 2024

Abstract To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance, this paper proposes a seasonal short-term load combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model. Specifically, the characteristics of load components are analyzed for different seasons, and the corresponding models are established. First, the improved complete ensemble empirical modal decomposition with adaptive noise (ICEEMDAN) method is employed to decompose the system load for all four seasons, and the new sequence is obtained through reconstruction based on the… More >

Yingnan Zhao^*, Yuyuan Ruan, Zhen Peng

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 549-566, 2024, DOI:10.32604/cmc.2024.056240 - 15 October 2024

Abstract As the penetration ratio of wind power in active distribution networks continues to increase, the system exhibits some characteristics such as randomness and volatility. Fast and accurate short-term wind power prediction is essential for algorithms like scheduling and optimization control. Based on the spatio-temporal features of Numerical Weather Prediction (NWP) data, it proposes the WVMD_DSN (Whale Optimization Algorithm, Variational Mode Decomposition, Dual Stream Network) model. The model first applies Pearson correlation coefficient (PCC) to choose some NWP features with strong correlation to wind power to form the feature set. Then, it decomposes the feature set More >

Xinyi Guan¹, Shaoqiang Tang^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.010869

Abstract Inheriting a convergence difficulty explained by the Kolmogorov N-width [1], the advection-diffusion equation is not effectively solved by the Proper Generalized Decomposition [2] (PGD) method. In this paper, we propose a new strategy: Proper Generalized Decomposition with Coordinate Transformation (CT-PGD). Converting the mixed hyperbolic-parabolic equation to a parabolic one, it resumes the efficiency of convergence for advection-dominant problems. Combining PGD with CT-PGD, we solve advection-diffusion equation by much fewer degrees of freedom, hence improve the efficiency. The advection-dominant regime and diffusion-dominant regime are quantitatively classified by a threshold, computed numerically. Moreover, we find that appropriate More >

Dijun Rao^1,2,3,4, Min Huang^1,2,3,5, Xiuzhi Shi⁴, Zhi Yu^6,*, Zhengxiang He⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 187-217, 2024, DOI:10.32604/cmes.2024.051402 - 20 August 2024

Abstract The denoising of microseismic signals is a prerequisite for subsequent analysis and research. In this research, a new microseismic signal denoising algorithm called the Black Widow Optimization Algorithm (BWOA) optimized Variational Mode Decomposition (VMD) joint Wavelet Threshold Denoising (WTD) algorithm (BVW) is proposed. The BVW algorithm integrates VMD and WTD, both of which are optimized by BWOA. Specifically, this algorithm utilizes VMD to decompose the microseismic signal to be denoised into several Band-Limited Intrinsic Mode Functions (BLIMFs). Subsequently, these BLIMFs whose correlation coefficients with the microseismic signal to be denoised are higher than a threshold… More >

Zhongyang Wang, Hu Zhu, Feng Liu^*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 605-623, 2024, DOI:10.32604/cmc.2024.050959 - 18 July 2024

Abstract Visual object tracking plays a crucial role in computer vision. In recent years, researchers have proposed various methods to achieve high-performance object tracking. Among these, methods based on Transformers have become a research hotspot due to their ability to globally model and contextualize information. However, current Transformer-based object tracking methods still face challenges such as low tracking accuracy and the presence of redundant feature information. In this paper, we introduce self-calibration multi-head self-attention Transformer (SMSTracker) as a solution to these challenges. It employs a hybrid tensor decomposition self-organizing multi-head self-attention transformer mechanism, which not only… More >

Guangfei Jia^*, Yanchao Meng, Zhiying Qin

Structural Durability & Health Monitoring, Vol.18, No.4, pp. 445-463, 2024, DOI:10.32604/sdhm.2024.049298 - 05 June 2024

Abstract The vibration signals of rolling bearings exhibit nonlinear and non-stationary characteristics under the influence of noise. In intelligent fault diagnosis, unprocessed signals will lead to weak fault characteristics and low diagnostic accuracy. To solve the above problem, a fault diagnosis method based on parameter optimization feature mode decomposition and improved deep belief networks is proposed. The feature mode decomposition is used to decompose the vibration signals. The parameter adaptation of feature mode decomposition is implemented by improved whale optimization algorithm including Levy flight strategy and adaptive weight. The selection of activation function and parameters is More > Graphic Abstract

Zhang Zhi¹, Haiyu Huang², Wei Xiong², Yijia Zhou³, Mingyu Yan^3,*, Shaolian Xia², Baofeng Jiang², Renbin Su², Xichen Tian⁴

Energy Engineering, Vol.121, No.6, pp. 1557-1576, 2024, DOI:10.32604/ee.2024.047401 - 21 May 2024

Abstract Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existing scenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios, which threatens the robustness of stochastic unit commitment and hinders its application. This paper provides a stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming and Benders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouples the primal problem into the master problem and two types of subproblems. In the master problem, the committed generator is determined, while the feasibility and… More >

Jianfeng He^1,2, Haowei Ye¹, Jie Ning¹, Hui Zhou^1,2,*, Bo She^3,*

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3355-3372, 2024, DOI:10.32604/cmc.2024.048706 - 15 May 2024

Abstract In addressing the challenge of motion artifacts in Positron Emission Tomography (PET) lung scans, our study introduces the Triple Equivariant Motion Transformer (TEMT), an innovative, unsupervised, deep-learning-based framework for efficient respiratory motion correction in PET imaging. Unlike traditional techniques, which segment PET data into bins throughout a respiratory cycle and often face issues such as inefficiency and overemphasis on certain artifacts, TEMT employs Convolutional Neural Networks (CNNs) for effective feature extraction and motion decomposition.TEMT’s unique approach involves transforming motion sequences into Lie group domains to highlight fundamental motion patterns, coupled with employing competitive weighting for More >

Bin Chen¹, Ziyang Li¹, Shipeng Li¹, Qingzhou Zhao¹, Xingdou Liu^2,*

Energy Engineering, Vol.121, No.5, pp. 1291-1307, 2024, DOI:10.32604/ee.2024.046374 - 30 April 2024

Abstract To reduce carbon emissions, clean energy is being integrated into the power system. Wind power is connected to the grid in a distributed form, but its high variability poses a challenge to grid stability. This article combines wind turbine monitoring data with numerical weather prediction (NWP) data to create a suitable wind power prediction framework for distributed grids. First, high-precision NWP of the turbine range is achieved using weather research and forecasting models (WRF), and Kriging interpolation locates predicted meteorological data at the turbine site. Then, a preliminary predicted power series is obtained based on More >