首页|Studies from Chongqing Jiaotong University Update Current Data on Support Vector Machines (Particle Swarm Optimization of Support Vector Machine Inversion Model for Overhead Upright Piers Damage-Inducing Factor)
Studies from Chongqing Jiaotong University Update Current Data on Support Vector Machines (Particle Swarm Optimization of Support Vector Machine Inversion Model for Overhead Upright Piers Damage-Inducing Factor)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Research findings on are discussed in a new report. According to news reporting out ofChongqing, People’s Republic of China, by NewsRx editors, research stated, “In the Three Gorges reservoirarea, the overhead upright pier is the primary structural form.”Financial supporters for this research include Chongqing Science And Technology Foundation.Our news editors obtained a quote from the research from Chongqing Jiaotong University: “For intelligentmonitoring of existing terminals, this research chooses Chongqing Xintian Port as the study objectand proposes a support vector machine (SVM) damage-inducing factor (DIF) inversion model based onparticle swarm optimization (PSO). To apply the finite element method to analyze the stress distributioncharacteristics of quay pile groups under three main DIFs, including the stacking effect, ship impact loadeffect, and bank slope effect. After characterizing the stress data, it becomes evident that there exists acorrelation between stress and each DIF parameter. Before generating the training sample set, principalcomponent analysis is employed to reduce dimensionality and eliminate a substantial amount of redundantdata. The model has an accuracy of 0.999 for the identification of the type of DIF and 0.975 for theidentification of the location of the action of the DIF with F1 coefficients of 0.999 and 0.978, respectively.For the strength of DIF predictions, MAE and MSE were 4.871 and 1.202, respectively, R2 was 0.986,NSE was 0.986, WI was 0.996, and PBIAS was 0.095.”
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