Peiyan Wang^1,*, Haoyu Wang¹, Minghui Liu², Fuchao Liu¹, Zhufeng Yue¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.3, pp. 1-1, 2024, DOI:10.32604/icces.2024.011318

Abstract The virtual test is supported by the physical test data, and a high-precision simulation model needs to be established to maximize the alignment between the simulation prediction results and the physical test data. It can replace other physical tests and achieve the goal of reducing the design cycle time and cost. However, due to the errors caused by the position and angle deviation of the strain gauge paste, as well as the sensitivity coefficient of the strain gauge and the wire, it is difficult for the simulation results to correspond to the test results in… More >

Gang Wang^*, Yuedong Wu, Xiaoyi Qian, Yi Zhao

Energy Engineering, Vol.121, No.11, pp. 3417-3435, 2024, DOI:10.32604/ee.2024.052268 - 21 October 2024

Abstract A robust scheduling optimization method for wind–fire storage system distribution based on the mixed carbon trading mechanism is proposed to improve the rationality of carbon emission quota allocation while reducing the instability of large-scale wind power access systems. A hybrid carbon trading mechanism that combines short-term and long-term carbon trading is constructed, and a fuzzy set based on Wasserstein measurement is proposed to address the uncertainty of wind power access. Moreover, a robust scheduling optimization method for wind–fire storage systems is formed. Results of the multi scenario comparative analysis of practical cases show that the More >

Yujian Wang¹, Jianxun Zhang^1,*, Renhao Sun²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 533-548, 2024, DOI:10.32604/cmc.2024.054644 - 15 October 2024

Abstract The effectiveness of facial expression recognition (FER) algorithms hinges on the model’s quality and the availability of a substantial amount of labeled expression data. However, labeling large datasets demands significant human, time, and financial resources. Although active learning methods have mitigated the dependency on extensive labeled data, a cold-start problem persists in small to medium-sized expression recognition datasets. This issue arises because the initial labeled data often fails to represent the full spectrum of facial expression characteristics. This paper introduces an active learning approach that integrates uncertainty estimation, aiming to improve the precision of facial… More >

Changtao Wang¹, Honghua Dai^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.3, pp. 1-2, 2024, DOI:10.32604/icces.2024.010943

Abstract Orbital uncertainty propagation is fundamental in space situational awareness-related missions such as orbit prediction and tracking. Linear models and full nonlinear Monte Carlo simulations were primarily used to propagate uncertainties [1]. However, these methods hampered the application due to low precision and intensive computation. Over the past two decades, numerous nonlinear uncertainty propagators have been proposed. Among these methods, the state transition tensor (STT) method has been widely used due to its controllable accuracy and high efficiency [2]. However, this method has two drawbacks. First, its semi-analytical formulation is too intricate to implement, which hinders… More >

Xinxin Yue¹, Jian Zhang^2,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.4, pp. 1-2, 2024, DOI:10.32604/icces.2024.011344

Abstract The "curse of dimensionality" faced by high-dimensional complex engineering problems can be tackled by a set of quantitative model evaluation and analysis tools named high-dimensional model representation (HDMR) [1,2], which has attracted much attention from researchers in various fields, such as global sensitivity analysis (GSA) [3], structural reliability analysis (SRA) [4], CFD uncertainty quantification [5] and so on [6]. In this paper, a new method for uncertainty quantification is proposed. Firstly, PCE-HDMR for SRA is developed by taking advantage of the accuracy and efficiency of PCE-HDMR for modeling high-dimensional problems [7]. Secondly, the formulas for… More >

Zhenshuai Wan^1,2,*, Longwang Yue², Yanfeng Wang², Pu Zhao²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1047-1065, 2024, DOI:10.32604/cmes.2024.053773 - 27 September 2024

Abstract Hydraulic actuators are highly nonlinear when they are subjected to different types of model uncertainties and dynamic disturbances. These unfavorable factors adversely affect the control performance of the hydraulic actuator. Although various control methods have been employed to improve the tracking precision of the dynamic system, optimizing and adjusting control gain to mitigate the hydraulic actuator model uncertainties remains elusive. This study presents an adaptive back-stepping sliding mode controller (ABSMC) to enhance the trajectory tracking precision, where the virtual control law is constructed to replace the position error. The adaptive control theory is introduced in More >

Rajan Aravind^1,2, Sundararajan Natarajan¹, Krishnankutty Jayakumar², Ratna Kumar Annabattula^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 997-1032, 2024, DOI:10.32604/cmes.2024.053047 - 27 September 2024

Abstract Understanding the probabilistic nature of brittle materials due to inherent dispersions in their mechanical properties is important to assess their reliability and safety for sensitive engineering applications. This is all the more important when elements composed of brittle materials are exposed to dynamic environments, resulting in catastrophic fatigue failures. The authors propose the application of a non-intrusive polynomial chaos expansion method for probabilistic studies on brittle materials undergoing fatigue fracture when geometrical parameters and material properties are random independent variables. Understanding the probabilistic nature of fatigue fracture in brittle materials is crucial for ensuring the… More >

Khalilallah Memarzadeh¹, Hamed Kazemipoor^1,*, Mohammad Fallah¹, Babak Farhang Moghaddam²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1275-1304, 2024, DOI:10.32604/cmes.2024.050306 - 27 September 2024

Abstract Motivated by a critical issue of airline planning process, this paper addresses a new two-stage scenario-based robust optimization in operational airline planning to cope with uncertainty and possible flight disruptions. Following the route network scheme and generated flight timetables, aircraft maintenance routing and crew scheduling are critical factors in airline planning and operations cost management. This study considers the simultaneous assignment of aircraft fleet and crew to the scheduled flight while satisfying a set of operational constraints, rules, and regulations. Considering multiple locations for airline maintenance and crew bases, we solve the problem of integrated… More > Graphic Abstract

Tianfei Yang¹, Xianyi Long^2,*

International Journal of Mental Health Promotion, Vol.26, No.9, pp. 719-730, 2024, DOI:10.32604/ijmhp.2024.053705 - 20 September 2024

Abstract Background: Though the COVID-19 pandemic recedes, and our society gradually returns to normal, Chinese people’s work and lifestyles are still influenced by the “pandemic aftermath”. In the post-pandemic era, employees may feel uncertainty at work due to the changed organizational operations and management and perceive the external environment to be more dynamic. Both these perceptions may increase employees’ negative emotions and contribute to conflicts between work and life. Drawing from the ego depletion theory, this study aimed to examine the impact of job insecurity during the post-pandemic era on employees’ work-life conflicts, and the mediating… More >

Zixuan Wu¹, Ye Wang^1,*, Lifeng Shen², Feng Hu¹, Hong Yu^1,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4111-4127, 2024, DOI:10.32604/cmc.2024.054581 - 12 September 2024

Abstract Hierarchical Text Classification (HTC) aims to match text to hierarchical labels. Existing methods overlook two critical issues: first, some texts cannot be fully matched to leaf node labels and need to be classified to the correct parent node instead of treating leaf nodes as the final classification target. Second, error propagation occurs when a misclassification at a parent node propagates down the hierarchy, ultimately leading to inaccurate predictions at the leaf nodes. To address these limitations, we propose an uncertainty-guided HTC depth-aware model called DepthMatch. Specifically, we design an early stopping strategy with uncertainty to More >