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Corporate Governance Reform and State Ownership: Evidence from China
Yao Lu,Xinzheng Shi 한국증권학회 2012 Asia-Pacific Journal of Financial Studies Vol.41 No.6
Using a sample of propensity-score matched overseas and domestically listed firms, we examine whether the effect of corporate governance reform (CGR) in 2001 in China varies among firms with different ownership structures. The positive effect of the CGR is weaker for firms with more state-owned shares, and product market competition increases the effect of the CGR on such firms. These findings suggest that government regulations on corporate governance and market competition can serve as complementary solutions to agency problems that arise from state ownership.
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Dynamic Event-triggered Quantitative Feedback Control for Switched Affine Systems
Xiang Lu,Hongyu Sun,Xinzheng Lyu,Anhao Wen,Yinjing Guo,Gang Jing,Qunxian Zheng 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.6
This paper focuses on the design of a dynamic output feedback controller for switched affine systems under limited communication resources. Since the system states information is difficult to obtain, the dynamic output feedback switching function is considered to stabilize the switched affine system. Quantized output measurements are transmitted to the dynamic output feedback controller to reduce the communication load. In order to significantly reduce the sampling frequency, an event-triggering mechanism is introduced to detect the event periodically. By using Lyapunov stability theory and linear matrix inequality (LMI) technique, a set of dynamic output feedback gains together with a switching rule are designed assuring the global asymptotic stability of the desired equilibrium point. More specifically, the design conditions do not require that there exist a stable convex combination of the subsystems state-space matrix. Finally, a numerical example show the validity of the obtained results of this paper.
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Xu Yongjia,Lu Xinzheng,Fei Yifan,Huang Yuli 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.5
There are numerous advantages of deep neural network surrogate modeling for response time-history prediction. However, due to the high cost of refined numerical simulations and actual experiments, the lack of data has become an unavoidable bottleneck in practical applications. An iterative self-transfer learning method for training neural networks based on small datasets is proposed in this study. A new mapping-based transfer learning network, named as deep adaptation network with three branches for regression (DAN-TR), is proposed. A general iterative network training strategy is developed by coupling DAN-TR and the pseudo-label strategy, and the establishment of corresponding datasets is also discussed. Finally, a complex component is selected as a case study. The results show that the proposed method can improve the model performance by near an order of magnitude on small datasets without the need of external labeled samples, well behaved pre-trained models, additional artificial labeling, and complex physical/mathematical analysis.
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Generative Artificial Intelligence for Structural Design of Tall Buildings
Wenjie Liao,Xinzheng Lu,Yifan Fei Council on Tall Building and Urban Habitat Korea 2023 International journal of high-rise buildings Vol.12 No.3
The implementation of artificial intelligence (AI) design for tall building structures is an essential solution for addressing critical challenges in the current structural design industry. Generative AI technology is a crucial technical aid because it can acquire knowledge of design principles from multiple sources, such as architectural and structural design data, empirical knowledge, and mechanical principles. This paper presents a set of AI design techniques for building structures based on two types of generative AI: generative adversarial networks and graph neural networks. Specifically, these techniques effectively master the design of vertical and horizontal component layouts as well as the cross-sectional size of components in reinforced concrete shear walls and frame structures of tall buildings. Consequently, these approaches enable the development of high-quality and high-efficiency AI designs for building structures.
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