Wuyang Fan, Shisheng Zhong^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2525-2555, 2024, DOI:10.32604/cmes.2023.046951

Abstract The dynamic balance assessment during the assembly of the coordinator gyroscope significantly impacts the guidance accuracy of precision-guided equipment. In dynamic balance debugging, reliance on rudimentary counterweight empirical formulas persists, resulting in suboptimal debugging accuracy and an increased repetition rate. To mitigate this challenge, we present a multi-head residual graph attention network (ResGAT) model, designed to predict dynamic balance counterweights with high precision. In this research, we employ graph neural networks for interaction feature extraction from assembly graph data. An SDAE-GPC model is designed for the assembly condition classification to derive graph data inputs for the ResGAT regression model, which… More >

Xin Fan^1,2, Shuqing Zhang^1,2,*, Kaisheng Wu^1,2, Wei Zheng^1,2, Yu Ge^1,2

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1687-1711, 2024, DOI:10.32604/cmc.2023.046187

Abstract Cross-Project Defect Prediction (CPDP) is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project. However, existing CPDP methods only consider linear correlations between features (indicators) of the source and target projects. These models are not capable of evaluating non-linear correlations between features when they exist, for example, when there are differences in data distributions between the source and target projects. As a result, the performance of such CPDP models is compromised. In this paper, this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique (SMOTE)… More >

Dongyang Ou, Yongjian Ren, Congfeng Jiang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2043-2067, 2024, DOI:10.32604/cmes.2023.029987

Abstract Cloud service providers generally co-locate online services and batch jobs onto the same computer cluster, where the resources can be pooled in order to maximize data center resource utilization. Due to resource competition between batch jobs and online services, co-location frequently impairs the performance of online services. This study presents a quality of service (QoS) prediction-based scheduling model (QPSM) for co-located workloads. The performance prediction of QPSM consists of two parts: the prediction of an online service’s QoS anomaly based on XGBoost and the prediction of the completion time of an offline batch job based on random forest. On-line service… More >

Yifan Zhang¹, Qiancheng Yu^1,2,*, Lisi Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 661-677, 2024, DOI:10.32604/cmes.2023.044255

Abstract Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection. To address this issue, based on the deep forest algorithm and further integrating evolutionary ensemble learning methods, this paper proposes a novel Deep Adaptive Evolutionary Ensemble (DAEE) model. This model introduces model diversity into the cascade layer, allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior patterns. Moreover, this paper optimizes the methods of obtaining feature vectors, enhancement vectors, and prediction results within the deep forest algorithm to enhance the… More >

Shumin Sun¹, Peng Yu¹, Jiawei Xing¹, Yan Cheng¹, Song Yang¹, Qian Ai^2,*

Energy Engineering, Vol.120, No.12, pp. 2761-2782, 2023, DOI:10.32604/ee.2023.042635

Abstract Wind power prediction is very important for the economic dispatching of power systems containing wind power. In this work, a novel short-term wind power prediction method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and (long short-term memory) LSTM neural network is proposed and studied. First, the original data is prepossessed including removing outliers and filling in the gaps. Then, the random forest algorithm is used to sort the importance of each meteorological factor and determine the input climate characteristics of the forecast model. In addition, this study conducts seasonal classification of the annual data where… More >

XIANBAO FANG^1,#, MINGYAN TANG^1,#, ZIYANG YU^1,#, JIAQI DING¹, CHONG CUI², HONG ZHANG^1,*

BIOCELL, Vol.47, No.9, pp. 2059-2068, 2023, DOI:10.32604/biocell.2023.030565

Abstract Objective: Key genes were screened to analyze molecular mechanisms and their drug targets of endometriosis by applying a bioinformatics approach. Methods: Gene expression profiles of endometriosis and healthy controls were obtained from the Gene Expression Omnibus database. Significant differentially expressed genes were screened using the limma package. Correlation pathways were screened by Spearman correlation analysis on the echinoderm microtubule-associated protein-like 4 (EML4) and enrichment in endometriosis pathways and estimated by the GSVA package. Immune characteristics were assessed by the “ESTIMATE” R package. Potential regulatory pathways were determined by enrichment analysis. The SWISS-MODE website was used in homology modeling with EML4… More > Graphic Abstract

Wangxin Yu^1,*, XiaoLiang Wang¹, Qingquan Liu¹, Huaning Wang²

The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.1, pp. 1-1, 2023, DOI:10.32604/icces.2023.09825

Abstract Landslide, debris flow and other large-scale natural disasters have a great threat to human life and property safety. The accuracy of prediction and calculation of large-scale disasters still needs great improvement, so as the study of prevention and interaction. In this paper, the depth-integrated shallow water flow model is adopted, and the numerical method of Kurganov developed in recent years is used to develop a highresolution algorithm which can capture shock waves and satisfy the hydrodynamic conditions. In order to make it adapt to the granular flow, appropriate adjustment is made distinct from the original aerodynamic problem, and it can… More >

XUEFENG WANG^1,#, SICONG JIANG^2,#, XINHONG ZHOU³, XIAOFENG WANG⁴, LAN LI⁵, JIANJUN TANG^1,*

Oncology Research, Vol.31, No.5, pp. 697-714, 2023, DOI:10.32604/or.2023.029458

Abstract Background: Pancreatic cancer is associated with high mortality and is one of the most aggressive of malignancies, but studies have not fully evaluated its molecular subtypes, prognosis and response to immunotherapy of different subtypes. The purpose of this study was to explore the molecular subtypes and the key genes associated with the prognosis of pancreas cancer patients and study the clinical phenotype, prognosis and response to immunotherapy using single-cell seq data and bulk RNA seq data, and data retrieved from GEO and TCGA databases. Methods: Single-cell seq data and bioinformatics methods were used in this study. Pancreatic cancer data were… More >

Tusongjiang Kari¹, Sun Guoliang², Lei Kesong¹, Ma Xiaojing^1,*, Wu Xian¹

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1437-1452, 2023, DOI:10.32604/iasc.2023.037012

Abstract Wind power volatility not only limits the large-scale grid connection but also poses many challenges to safe grid operation. Accurate wind power prediction can mitigate the adverse effects of wind power volatility on wind power grid connections. For the characteristics of wind power antecedent data and precedent data jointly to determine the prediction accuracy of the prediction model, the short-term prediction of wind power based on a combined neural network is proposed. First, the Bi-directional Long Short Term Memory (BiLSTM) network prediction model is constructed, and the bi-directional nature of the BiLSTM network is used to deeply mine the wind… More >

Fulu Wei¹, Xin Li¹, Yongqing Guo^1,*, Zhenyu Wang², Qingyin Li¹, Xueshi Ma³

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2001-2018, 2023, DOI:10.32604/iasc.2023.035799

Abstract Traffic flow prediction plays an important role in intelligent transportation systems and is of great significance in the applications of traffic control and urban planning. Due to the complexity of road traffic flow data, traffic flow prediction has been one of the challenging tasks to fully exploit the spatiotemporal characteristics of roads to improve prediction accuracy. In this study, a combined flow direction level traffic flow prediction graph convolutional network (GCN) and long short-term memory (LSTM) model based on spatiotemporal characteristics is proposed. First, a GCN model is employed to capture the topological structure of the data graph and extract… More >