Accurate recognition of low-contrast targets in complex visual environments is essential for advanced intelligent machine vision systems.Conventional photodetectors often suffer from a weak photoresponse and a linear ...Accurate recognition of low-contrast targets in complex visual environments is essential for advanced intelligent machine vision systems.Conventional photodetectors often suffer from a weak photoresponse and a linear dependence of photocurrent on light intensity,which restricts their ability to capture low-contrast features and makes them susceptible to noise.Inspired by the adaptive mechanisms of the human visual system,we present a molybdenum disulfide(MoS_(2))phototransistor with tunable sensitivity,in which the gate stack incorporates a heterostructure diode—composed of O-plasma-treated MoS_(2) and pristine MoS_(2)—that serves as the photosensitive layer.This configuration enables light-intensity-dependent modulation of the diode’s conductance,which dynamically in turn alters the voltage distribution across the gate dielectric and transistor channel,leading to a significant photoresponse.By modulating the gate voltage,the light response range can be finely tuned,maintaining high sensitivity to low-contrast targets while suppressing noise interference.Compared to conventional photodetectors,the proposed device achieves a 1000-fold improvement in sensitivity for low-contrast signal detection and exhibits significantly enhanced noise immunity.The intelligent machine vision system built on this device demonstrates exceptional performance in detecting low-contrast targets,underscoring its promise for next-generation machine vision applications.展开更多
The design of stabilizing controllers for general nonlinear systems remains a challenging task due to their inherent complexities and nonconvexities.In this paper,we consider the problem of designing an asymptotically...The design of stabilizing controllers for general nonlinear systems remains a challenging task due to their inherent complexities and nonconvexities.In this paper,we consider the problem of designing an asymptotically stable controller of a nonlinear dynamic system.We begin by framing the problem as an inverse optimal control problem,aiming to design a pair of cost functions that ensure asymptotic stability for the nonlinear model predictive control closed-loop system.By leveraging the relaxed dynamic programming inequality,a machine learning based algorithm is proposed to learn the cost functions.Finally,we demonstrate the effectiveness of the proposed method through illustrative examples.展开更多
基金supported by the National Key Research and Development Program of China(2021YFA1200801)the National Natural Science Foundation of China(No.62304226,52188101,62450124,62125406)+9 种基金the China Postdoctoral Science Foundation(2024T170946,2023M733574)the Excellent Youth Fund Project of Liaoning Province(2023JH3/10200003)the Outstanding Youth Fund Project of Liaoning Province(2025JH6/101100015)the Special Projects of the Central Government in Guidance of Local Science and Technology Development(2024010859-JH6/1006)the Special Research Assistantship Project of the Chinese Academy of Sciences(E455L502)the China Postdoctoral Science Foundation under Grant Number GZB20230776the Liaoning Provincial Key Laboratory of Public Opinion and Network Security Information System(d252453002)the Artificial Intelligence Technology Innovation Project of Liaoning Province(Grant No.2023JH26/10300019)the Young Top-notch Talents of the National High-level Talent Special Support Program,the basic scientific research project of universities funded by the Liaoning Provincial Department of Education(LJ212510140016)the Liaoning Province High-quality Industry-University Cooperation and Collaborative Education Project(241201160090747)。
文摘Accurate recognition of low-contrast targets in complex visual environments is essential for advanced intelligent machine vision systems.Conventional photodetectors often suffer from a weak photoresponse and a linear dependence of photocurrent on light intensity,which restricts their ability to capture low-contrast features and makes them susceptible to noise.Inspired by the adaptive mechanisms of the human visual system,we present a molybdenum disulfide(MoS_(2))phototransistor with tunable sensitivity,in which the gate stack incorporates a heterostructure diode—composed of O-plasma-treated MoS_(2) and pristine MoS_(2)—that serves as the photosensitive layer.This configuration enables light-intensity-dependent modulation of the diode’s conductance,which dynamically in turn alters the voltage distribution across the gate dielectric and transistor channel,leading to a significant photoresponse.By modulating the gate voltage,the light response range can be finely tuned,maintaining high sensitivity to low-contrast targets while suppressing noise interference.Compared to conventional photodetectors,the proposed device achieves a 1000-fold improvement in sensitivity for low-contrast signal detection and exhibits significantly enhanced noise immunity.The intelligent machine vision system built on this device demonstrates exceptional performance in detecting low-contrast targets,underscoring its promise for next-generation machine vision applications.
文摘The design of stabilizing controllers for general nonlinear systems remains a challenging task due to their inherent complexities and nonconvexities.In this paper,we consider the problem of designing an asymptotically stable controller of a nonlinear dynamic system.We begin by framing the problem as an inverse optimal control problem,aiming to design a pair of cost functions that ensure asymptotic stability for the nonlinear model predictive control closed-loop system.By leveraging the relaxed dynamic programming inequality,a machine learning based algorithm is proposed to learn the cost functions.Finally,we demonstrate the effectiveness of the proposed method through illustrative examples.
