Rong Wang¹, Xinxin Xu², Zijia Wang^3,*, Fei Ji¹, Nankun Mu⁴

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2363-2385, 2024, DOI:10.32604/cmc.2024.053564 - 15 August 2024

Abstract Marine container terminal (MCT) plays a key role in the marine intelligent transportation system and international logistics system. However, the efficiency of resource scheduling significantly influences the operation performance of MCT. To solve the practical resource scheduling problem (RSP) in MCT efficiently, this paper has contributions to both the problem model and the algorithm design. Firstly, in the problem model, different from most of the existing studies that only consider scheduling part of the resources in MCT, we propose a unified mathematical model for formulating an integrated RSP. The new integrated RSP model allocates and… More >

Xue Zhao, Shaojun Tao, Hongying Tang, Jiang Wang^*, Baoqing Li^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1335-1351, 2024, DOI:10.32604/cmc.2024.047996 - 25 April 2024

Abstract In recent years, target tracking has been considered one of the most important applications of wireless sensor network (WSN). Optimizing target tracking performance and prolonging network lifetime are two equally critical objectives in this scenario. The existing mechanisms still have weaknesses in balancing the two demands. The proposed heuristic multi-node collaborative scheduling mechanism (HMNCS) comprises cluster head (CH) election, pre-selection, and task set selection mechanisms, where the latter two kinds of selections form a two-layer selection mechanism. The CH election innovatively introduces the movement trend of the target and establishes a scoring mechanism to determine More >

Hongqiao Wang^1,*, Yanning Cai²

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 187-203, 2022, DOI:10.32604/cmes.2022.018000 - 29 November 2021

Abstract Inertial system platforms are a kind of important precision devices, which have the characteristics of difficult acquisition for state data and small sample scale. Focusing on the model optimization for data-driven fault state prediction and quantitative degree measurement, a fast small-sample supersphere one-class SVM modeling method using support vectors pre-selection is systematically studied in this paper. By theorem-proving the irrelevance between the model's learning result and the non-support vectors (NSVs), the distribution characters of the support vectors are analyzed. On this basis, a modeling method with selected samples having specific geometry character from the training More >