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RISS 인기검색어
- 검색키워드
- 저자 : Jiahang Li (검색결과 4 건)
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Jiahang Lu,Xiuying Li 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.11
In this paper, model free adaptive control algorithms are proposed based on ten improved gradient descent methods which are commonly used as optimization algorithms in deep learning. For the designed control scheme, the modelling, control and optimization can be integrated in a unified framework. The effects of ten algorithms on the consensus tracking performance in multi-agent systems are studied and compared. In order to get a more universal conclusion, systems with fixed and switching topology are considered respectively. Simulation results show that the model free adaptive control algorithm based on adaptive momentum estimation method with decoupled weight decay (AdamW) has optimal performance.
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Zhang Bin,Li Zhuoran,Xia Yuanchen,Shi Jihao,Zhang Jinnan,Wang Boqiao,Yu Jiahang,Qu Yanxu,Chen Li,Lin Yejin,Wu Wanqing 대한조선학회 2022 International Journal of Naval Architecture and Oc Vol.14 No.1
A variety of experimental configurations including different water mist obstacles, are used to investigate the combined effects of obstacles and water mist upon the gas explosion. The results demonstrate the 8 mm water mist can significantly inhibit the deflagration, while both 45 mm and 80 mm water mists exhibit the opposite effect under all the locally distributed positions of water spray nozzle without obstacles inside the vessel. When considering the obstacles, the 45 mm water mist starts to mitigate the deflagration and its mitigation effect is more significant than that induced by the 8 mm water mist. What's more, the 80 mm water mist can slow down the flame propagation speed while it would still lead to the gas explosion. Additionally, there remains unchanged about the effect of locally spraying 8 mm water mist upon deflagration at the initial stage of flame development as varying the position of obstacles, while the effects of mitigating deflagration by 45 mm and 80 mm water mist are decreased. The results will make contributions to design the arrangement of equipment and water mist configuration on the offshore platform or NG-fueled ship's engine room so as to mitigate the gas explosion accident.
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Axial behavior of RC column strengthened with SM-CFST
Haibo Jiang,Jiahang Li,Quan Cheng,Jie Xiao,Zhenkan Chen 국제구조공학회 2022 Steel and Composite Structures, An International J Vol.43 No.6
This paper aims to investigate the axial compressive behavior of reinforced concrete (RC) columns strengthened with self-compacting and micro-expanding (SM) concrete-filled steel tubes (SM-CFSTs). Nine specimens were tested in total under the local axial compression. The test parameters included steel tube thickness, filling concrete strength, filling concrete type and initial axial preloading. The test results demonstrated that the initial stiffness, ultimate bearing capacity and ductility of original RC columns were improved after being strengthened by SM-CFSTs. The ultimate bearing capacity of the SM-CFST strengthened RC columns was significantly enhanced with the increase of steel tube thickness. The initial stiffness and ultimate bearing capacity of the SM-CFST strengthened RC columns were slightly enhanced with the increase of filling concrete strength. However, the effect of filling concrete type and initial axial preloading of the SM-CFST strengthened RC columns were negligible. Three equations for predicting the ultimate bearing capacity of the SM-CFST strengthened RC columns were compared, and the modified equation based on Chinese code (GB 50936-2014) was more precise.
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An integrated method of flammable cloud size prediction for offshore platforms
Zhang Bin,Zhang Jinnan,Yu Jiahang,Wang Boqiao,Li Zhuoran,Xia Yuanchen,Chen Li 대한조선학회 2021 International Journal of Naval Architecture and Oc Vol.13 No.1
Response Surface Method (RSM) has been widely used for flammable cloud size prediction as it can reduce computational intensity for further Explosion Risk Analysis (ERA) especially during the early design phase of offshore platforms. However, RSM encounters the overfitting problem under very limited simulations. In order to overcome the disadvantage of RSM, Bayesian Regularization Artificial Neural (BRANN)-based model has been recently developed and its robustness and efficiency have been widely verified. However, for ERA during the early design phase, there seems to be room to further reduce the computational intensity while ensuring the model's acceptable accuracy. This study aims to develop an integrated method, namely the combination of Center Composite Design (CCD) method with Bayesian Regularization Artificial Neural Network (BRANN), for flammable cloud size prediction. A case study with constant and transient leakages is conducted to illustrate the feasibility and advantage of this hybrid method. Additionally, the performance of CCD-BRANN is compared with that of RSM. It is concluded that the newly developed hybrid method is more robust and computational efficient for ERAs during early design phase.
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