The stabilization of the Timoshenko equation of a nonuniform beam with locally distributed feedbacks is considered.It is proved that the system is exponentially stabilizable.The frequency domain method and the multipl...The stabilization of the Timoshenko equation of a nonuniform beam with locally distributed feedbacks is considered.It is proved that the system is exponentially stabilizable.The frequency domain method and the multiplier technique are applied.展开更多
In this paper,in order to stabilize the position and angle of the light source point,a new photon beam position feedback system based on the Photon Beam Position Monitors was developed on Hefei Light Source,and used t...In this paper,in order to stabilize the position and angle of the light source point,a new photon beam position feedback system based on the Photon Beam Position Monitors was developed on Hefei Light Source,and used to correct the position drift and angle variation of the light source at the same time.On introducing the feedback principle,the transfer function matrix is calibrated,indicating that the new system is workable and effective.展开更多
In this paper, we define the ({A,E},B)-invariant subspace pair contained in Ker C for singular systems, rigorously justifying the name and demonstrating the existence of the supremal ({A,E},B)-invariant;subspace pair ...In this paper, we define the ({A,E},B)-invariant subspace pair contained in Ker C for singular systems, rigorously justifying the name and demonstrating the existence of the supremal ({A,E},B)-invariant;subspace pair contained in Ker C, we show how the supremal ({A,E},B)-invariant subspace pair contained in Ker C can be computed via some subspace recursions, We provide necessary and sufficient condition for the existence of a state feedback that achieves disturbance localization in a linear time-invariant singular system.展开更多
Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examini...Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examining both its advantages and disadvantages. Positive impacts of AI are evident in communication, feedback systems, tracking mechanisms, and decision-making processes within organizations. AI-powered communication tools, as exemplified by Slack, facilitate seamless collaboration, transcending geographical barriers. Feedback systems, like Adobe’s Performance Management System, employ AI algorithms to provide personalized development opportunities, enhancing employee growth. AI-based tracking systems optimize resource allocation, as exemplified by studies like “AI-Based Tracking Systems: Enhancing Efficiency and Accountability.” Additionally, AI-powered decision support, demonstrated during the COVID-19 pandemic, showcases the capability to navigate complex challenges and maintain resilience. However, AI adoption poses challenges in human resources, potentially leading to job displacement and necessitating upskilling efforts. Managing AI errors becomes crucial, as illustrated by instances like Amazon’s biased recruiting tool. Data privacy concerns also arise, emphasizing the need for robust security measures. The proposed solution suggests leveraging Local Machine Learning Models (LLMs) to address data privacy issues. Approaches such as federated learning, on-device learning, differential privacy, and homomorphic encryption offer promising strategies. By exploring the evolving dynamics of AI and leadership, this research advocates for responsible AI adoption and proposes LLMs as a potential solution, fostering a balanced integration of AI benefits while mitigating associated risks in corporate settings.展开更多
基金Supported partially by the NSFC and the Science Foundation of China State Education Commission.
文摘The stabilization of the Timoshenko equation of a nonuniform beam with locally distributed feedbacks is considered.It is proved that the system is exponentially stabilizable.The frequency domain method and the multiplier technique are applied.
基金Supported by National Natural Science Foundation of China(No.10675118,and No.11175173)
文摘In this paper,in order to stabilize the position and angle of the light source point,a new photon beam position feedback system based on the Photon Beam Position Monitors was developed on Hefei Light Source,and used to correct the position drift and angle variation of the light source at the same time.On introducing the feedback principle,the transfer function matrix is calibrated,indicating that the new system is workable and effective.
文摘In this paper, we define the ({A,E},B)-invariant subspace pair contained in Ker C for singular systems, rigorously justifying the name and demonstrating the existence of the supremal ({A,E},B)-invariant;subspace pair contained in Ker C, we show how the supremal ({A,E},B)-invariant subspace pair contained in Ker C can be computed via some subspace recursions, We provide necessary and sufficient condition for the existence of a state feedback that achieves disturbance localization in a linear time-invariant singular system.
文摘Artificial Intelligence (AI) is transforming organizational dynamics, and revolutionizing corporate leadership practices. This research paper delves into the question of how AI influences corporate leadership, examining both its advantages and disadvantages. Positive impacts of AI are evident in communication, feedback systems, tracking mechanisms, and decision-making processes within organizations. AI-powered communication tools, as exemplified by Slack, facilitate seamless collaboration, transcending geographical barriers. Feedback systems, like Adobe’s Performance Management System, employ AI algorithms to provide personalized development opportunities, enhancing employee growth. AI-based tracking systems optimize resource allocation, as exemplified by studies like “AI-Based Tracking Systems: Enhancing Efficiency and Accountability.” Additionally, AI-powered decision support, demonstrated during the COVID-19 pandemic, showcases the capability to navigate complex challenges and maintain resilience. However, AI adoption poses challenges in human resources, potentially leading to job displacement and necessitating upskilling efforts. Managing AI errors becomes crucial, as illustrated by instances like Amazon’s biased recruiting tool. Data privacy concerns also arise, emphasizing the need for robust security measures. The proposed solution suggests leveraging Local Machine Learning Models (LLMs) to address data privacy issues. Approaches such as federated learning, on-device learning, differential privacy, and homomorphic encryption offer promising strategies. By exploring the evolving dynamics of AI and leadership, this research advocates for responsible AI adoption and proposes LLMs as a potential solution, fostering a balanced integration of AI benefits while mitigating associated risks in corporate settings.
