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Quality-guaranteed Dubins Path Planning for USV Based on Mixed-integer Piecewise linear Programming for Addressing the Extended Minimum-time Intercept Problem 认领 引用 被引量：1
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作者 Xing Zhou Kelin Zhu +3 位作者 Shuang Liu Zhaoqing Li Wenxin Zhang Kang Du 《哈尔滨工程大学学报（英文版）》 CSCD 2026年第1期216-227,共12页
During the use of robotics in applications such as antiterrorism or combat,a motion-constrained pursuer vehicle,such as a Dubins unmanned surface vehicle(USV),must get close enough(within a prescribed zero or positive... During the use of robotics in applications such as antiterrorism or combat,a motion-constrained pursuer vehicle,such as a Dubins unmanned surface vehicle(USV),must get close enough(within a prescribed zero or positive distance)to a moving target as quickly as possible,resulting in the extended minimum-time intercept problem(EMTIP).Existing research has primarily focused on the zero-distance intercept problem,MTIP,establishing the necessary or sufficient conditions for MTIP optimality,and utilizing analytic algorithms,such as root-finding algorithms,to calculate the optimal solutions.However,these approaches depend heavily on the properties of the analytic algorithm,making them inapplicable when problem settings change,such as in the case of a positive effective range or complicated target motions outside uniform rectilinear motion.In this study,an approach employing a high-accuracy and quality-guaranteed mixed-integer piecewise-linear program(QG-PWL)is proposed for the EMTIP.This program can accommodate different effective interception ranges and complicated target motions(variable velocity or complicated trajectories).The high accuracy and quality guarantees of QG-PWL originate from elegant strategies such as piecewise linearization and other developed operation strategies.The approximate error in the intercept path length is proved to be bounded to h2/(4√2),where h is the piecewise length. 展开更多
关键词 Minimum-time intercept problem Dubins vehicle Mixed-integer piecewise-linear program Linearization Approximate error trigonometric function USV
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Reprogramming induced neurons from olfactory ensheathing glial cells: A feasible approach for spinal cord injury repair 认领 引用
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作者 Javier Sierra María Portela-Lomba +1 位作者 Diana Simón M.Teresa Moreno-Flores 《Neural Regeneration Research》 SCIE CAS CSCD 2026年第1期296-297,共2页
Every year, around the world, between 250,000 and 500,000 people suffer a spinal cord injury(SCI). SCI is a devastating medical condition that arises from trauma or disease-induced damage to the spinal cord, disruptin... Every year, around the world, between 250,000 and 500,000 people suffer a spinal cord injury(SCI). SCI is a devastating medical condition that arises from trauma or disease-induced damage to the spinal cord, disrupting the neural connections that allow communication between the brain and the rest of the body, which results in varying degrees of motor and sensory impairment. Disconnection in the spinal tracts is an irreversible condition owing to the poor capacity for spontaneous axonal regeneration in the affected neurons. 展开更多
关键词 injury feasible programming
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Multi-phase trajectory programming with divert capability for lunar lander powered descent 认领 引用
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作者 ZHANG Xiaowen WANG Dayi +2 位作者 HUANG Xiangyu LI Ji WANG Tianshu 《中国空间科学技术（中英文）》 CSCD 北大核心 2026年第3期143-156,共14页
It is important for a lunar lander to possess a large divert capability during the final landing phase,as this can enhance the tolerance for flight deviations in the early phase or improve the obstacle avoidance perfo... It is important for a lunar lander to possess a large divert capability during the final landing phase,as this can enhance the tolerance for flight deviations in the early phase or improve the obstacle avoidance performance.Therefore,when designing the powered descent trajectory,sufficient final phase divert capability should be reserved at the minimum propellant cost.To this end,a multi-phase trajectory programming(MPTP)method for powered descent with approaching phase divert capability is proposed.First,the entire powered descent trajectory is divided into the main braking phase and the approaching phase.The main braking phase is responsible for dissipating the majority of the initial velocity.The approaching phase is responsible for safely and precisely flying toward the landing site.It is nominally a vertical descent trajectory and possesses equal divert capability in all horizontal directions.Then,a constant-thrust linear tangent guidance(LTG)accounting for the lunar curvature is designed for the main braking phase.For the approaching phase,a variable-thrust lossless convex programming(LCP)guidance considering the constraints of tilt angle and glide-slope angle is developed.Subsequently,to connect the two phases and further optimize the propellant consumption throughout the entire trajectory,a method for determining the phase switching condition is proposed.The originally difficult-to-solve two-parameter optimization problem is decomposed into two more easily solvable subproblems,which are solved iteratively via a bilevel optimization framework.Finally,the divert capability of the proposed method is verified through numerical simulation.The programmed trajectory is basically consistent with the results of the pseudospectral method,with the difference in propellant consumption being only 0.006%.This method is suitable for the rapid iterative design of nominal trajectories for lunar lander powered descent in engineering applications. 展开更多
关键词 lunar landing powered descent trajectory programming multi-phase divert capability
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H∞Optimal Output Regulation of Unknown Linear Systems via an Adaptive Dynamic Programming and Internal Model 认领 引用
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作者 Yong-Sheng Ma Jian Sun Yong Xu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第6期1314-1324,共11页
This paper delves into the H∞optimal output regulation problem for continuous-time linear systems with an unknown system model.By integrating the internal model principle with optimal control,we derive an optimal con... This paper delves into the H∞optimal output regulation problem for continuous-time linear systems with an unknown system model.By integrating the internal model principle with optimal control,we derive an optimal control policy and a worst-case disturbance policy through the formulation and solution of a zero-sum game problem.Subsequently,leveraging adaptive dynamic programming,we propose a policy iteration learning algorithm capable of learning both the optimal control policy and the worst-case disturbance policy directly from system data.The existing algorithms necessitate an initial stabilizing policy,a full-rank condition,and the storage of historical data to guarantee algorithm convergence.In contrast,we design a dual policy iteration algorithm equipped with an online learning mechanism,thereby eliminating these additional prerequisites.Simulation results with an antonomous ground vehicle underscore the effectiveness of our proposed algorithm,and its superiority is further demonstrated through comparisons with existing methodologies. 展开更多
关键词 Adaptive dynamic programming internal model principle optimal control output regulation
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Open-loop control of combustion instabilities in a full-scale annular ramjet combustor using linear genetic programming 认领 引用
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作者 Jianguo TAN Zheng XU +2 位作者 Yao LIU Dongdong ZHANG Yi HOU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期20-28,共9页
The operational demands of a wide range significantly exacerbate combustion instability issues within ramjet combustor.To suppress combustion oscillations,an open-loop control system utilizing Linear Genetic Programmi... The operational demands of a wide range significantly exacerbate combustion instability issues within ramjet combustor.To suppress combustion oscillations,an open-loop control system utilizing Linear Genetic Programming(LGP)has been developed for a full-scale annular ramjet combustor.The LGP is used to generate control laws that include multi-frequency forcing.These laws are then transformed into square waves to actuate the solenoid valve,which modulates the kerosene supply for open-loop control.The results show that the duty cycle has little effect on instability amplitude,whereas an increase in frequency leads to a remarked reduction in combustion amplitude.After five generations evolvements,the pressure amplitude is reduced by 40.6% under the optimal control law generated by LGP.Furthermore,the machine learning process is depicted using a proximity map of control law similarity,with the search pathway visualized by the steepest descent.All individuals go forward to the upper left corner of the map with the evolution process,terminating at the optimal individual of the fifth generation. 展开更多
关键词 Annular ramjet combustor Combustion instabilities Linear genetic programming Machine learning Open-loop control
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Thermal performance optimization in CuO-water nanofluid enclosure with sinusoidal heating using deep learning and multi-expression programming 认领 引用
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作者 Naeem Ullah Aneela Bibi +1 位作者 Yufeng Nie Dianchen Lu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期24-48,共25页
Natural convection in enclosures containing nanofluids has attracted significant attention due to its relevance in thermal management systems.In this context,this study presents a comprehensive numerical investigation... Natural convection in enclosures containing nanofluids has attracted significant attention due to its relevance in thermal management systems.In this context,this study presents a comprehensive numerical investigation of flow and heat transfer in a square cavity saturated with water-based CuO nanofluid having a centrally placed sinusoidal-shaped heated element.All the enclosure walls satisfy the no-slip velocity condition.Thermally,the vertical walls are kept at a cold reference temperature,the lower wall is partially heated at its center,and the remaining portions of the lower and entire upper walls are adiabatic.The internal sinusoidal element is also uniformly heated.The flow dynamics and thermal fields are governed by the two-dimensional steady-state Navier-Stokes and energy equations,solved using the Galerkin finite element method.Additionally,a novel hybrid approach integrating multi-expression programming(MEP)technique with a convolutional neural network bidirectional gated recurrent unit(CNN-BiGRU)deep learning network is also applied to enhance flow and thermal prediction accuracy.This hybrid approach enables precise evaluation of how heater waviness,magnetic field orientation,and nanoparticle dispersion influence flow structure and heat transfer.Results reveal stronger convection at high Rayleigh numbers,magnetic damping at increased Hartmann numbers,and higher temperatures with reduced velocity at greater nanoparticle concentrations.Among the analyzed situations,increasing heater waviness improves heat-transfer performance.Both the MEP and CNN-BiGRU models accurately capture the key features of flow and heat transport trends,indicating that the hybrid approach provides enhanced predictive capability for complex convection-driven nanofluid systems. 展开更多
关键词 Magnetohydrodynamic convection Nanofluid flow simulation Sinusoidal heated source Deep learning Multi-expression programming
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Entry trajectory optimization for cross-domain morphing vehicles by hybrid sequential second-order cone programming 认领 引用
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作者 Zheng Li Zhenyue Jia +2 位作者 Zheng Fang Hongmiao Zhou Jianqiao Yu 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第4期149-175,共27页
This paper proposes a hybrid sequential second-order cone programming(HSSOCP)method with a three-layer scheme for the entry trajectory optimization of the cross-domain morphing vehicles(CDMVs).By defining the new morp... This paper proposes a hybrid sequential second-order cone programming(HSSOCP)method with a three-layer scheme for the entry trajectory optimization of the cross-domain morphing vehicles(CDMVs).By defining the new morphing rate control variable and using relaxation techniques to relax the bank angle constraint,the SOCP-based entry problem is constructed.A dynamic relaxation penal-ization technique is developed in the first layer to overcome artificial infeasibility and significantly enhance initialization robustness.A novel standard oscillation identification(SOI)method is proposed to precisely identify the iteration oscillations of basic SSOCP in the second layer,which can significantly improve the solution accuracy.A soft-trust-region strategy is applied in the third layer to eliminate oscillations and accelerate convergence.Simulation results of two scenarios demonstrate that the proposed SOI method effectively avoids non-standard oscillation interference versus traditional methods.The morphing aircraft can complete tasks better with a 7.01%and 10.43%reduction in heat load respectively compared to fixed-wing aircraft.The HSSOCP method can maintain accuracy while reducing computation time by 63.47%and 73.86%versus VATSSOCP.Monte Carlo simulations further validate the robustness. 展开更多
关键词 Cross-domain vehicle Morphing vehicle Entry trajectory optimization Sequential second-order cone programming Iteration oscillation Hybrid scheme
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Entry trajectory optimization for cross-domain morphing vehicles using oscillation-avoidance-based multistage trust-region sequential convex programming 认领 引用
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作者 Zheng LI Zheng FANG +1 位作者 Zhenyue JIA Jianqiao YU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第4期310-333,共24页
This paper develops an Oscillation-avoidance-based Multistage Trust-region Sequential Convex Programming(OMTSCP)method for the highly nonlinear entry trajectory optimization problem of Cross-Domain Morphing Vehicles(C... This paper develops an Oscillation-avoidance-based Multistage Trust-region Sequential Convex Programming(OMTSCP)method for the highly nonlinear entry trajectory optimization problem of Cross-Domain Morphing Vehicles(CDMVs).The decoupling of states and controls for complex nonlinear dynamics is achieved by defining new control and state variables.A series of sub convex problems is formulated by successive linearization and discretization of the constraints.The proposed Trust-region Sequential Convex Programming(TSCP)scheme consists of three stages:an initial guess generation stage,a basic solution stage,and an optimal solution stage.An approach to penalize the dynamic relaxation is firstly developed to obtain an initial guess with considerable accuracy and significantly improve the robustness of the algorithm by overcoming the drawbacks of potential artificial infeasibility.The oscillation phenomenon of the TSCP method under rectangular trust region is then investigated,and a novel N-shape-based oscillation identification method is proposed to identify the oscillation accurately.Finally,an oscillation-avoidance method based on the sort trust-region is proposed to improve the convergence of the TSCP algorithm.Numerical comparisons of the proposed method and a typical TSCP method,as well as the morphing and fixed-morphing vehicles are provided to demonstrate the effectiveness and efficiency of the proposed method and the performance advantages of the morphing vehicle.The robustness of the method is further verified by Monte Carlo simulation. 展开更多
关键词 Convex optimization Cross-domain vehicle Entry trajectory optimization Morphing vehicle Multistage Oscillation phenomenon Trust-region sequential convex programming
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Laser-induced in situ reprogramming of magnetic shape memory composites for adaptive devices 认领 引用
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作者 Qianqian Zhang Rui Li +5 位作者 Yuan Tao Yiyu Chen Yanlei Hu Dong Wu Jiaru Chu Jiawen Li 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第1期767-779,共13页
Programmableeprogrammable magneto-responsive composites(MRCs)are highly desirable for applications in soft robotics,morphable actuators,and biomedical devices due to their capabilities of undergoing reversible,complex... Programmableeprogrammable magneto-responsive composites(MRCs)are highly desirable for applications in soft robotics,morphable actuators,and biomedical devices due to their capabilities of undergoing reversible,complex,untethered,and rapid deformations.However,current MRC-based devices primarily rely on soft matrices,which revert to their original shapes and cease functioning when external magnetic fields are removed.Moreover,their magnetization programming,deformations,and functioning need to alternate between encoding and actuation platforms,limiting the adaptability and efficiency.Here,we present a reprogrammable magnetic shape-memory composite(RM-SMC)integrating a shape-memory polymer(SMP)skeleton with phase-transition magnetic microcapsules.High-intensity laser melts microcapsules for magnetic realignment under programmed fields,while low-intensity laser softens SMP for structural reconfiguration without compromising integrity.This dual-laser strategy facilitates in situ magnetization programming,shape morphing,and function execution within a single material system.Our innovative approach enables unique applications,including omnidirectional multi-degree-of-freedom actuators that can activate light switches,solar trackers that optimize energy capture,and adaptive impellers that modulate fluid pumping.By eliminating platform alternation and enabling shape/function retention post-actuation,the RM-SMC platform overcomes critical limitations in conventional MRCs,establishing a paradigm for multifunctional devices requiring persistent configuration control and field-independent operation. 展开更多
关键词 magneto-responsive composites shape memory polymer in situ programming and deformation omnidirectional multi-degree of freedom actuator adjustable fluid pumping
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Genetic programming-driven damage state prediction for slender rectangular reinforced concrete structural walls 认领 引用
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作者 Nabajit Sarkar Kaustubh Dasgupta 《Earthquake Engineering and Engineering Vibration》 SCIE EI CSCD 2026年第2期431-447,共17页
Structures located in high seismic zones often utilize reinforced concrete(RC)frame-wall systems for improved lateral strength and stiffness,whereby the structural walls serve as a critical component of the lateral lo... Structures located in high seismic zones often utilize reinforced concrete(RC)frame-wall systems for improved lateral strength and stiffness,whereby the structural walls serve as a critical component of the lateral load resisting system.To effectively assess the potential vulnerability of structural systems across different levels of seismic demands,it is important to establish clear,quantitative thresholds for specific damage states,especially for the critical structural components within a building system.The currently available damage state definitions for RC structural walls are based on empirical limits and do not provide predictions for damage thresholds based on key design characteristics of a wall.To address this challenge,the present study employs genetic programming(GP),a form of artificial intelligence,to formulate accurate expressions for drift prediction for various damage states,using a dataset of 8,125 analytically studied specimens of RC structural walls.These expressions take into account the effects of various design characteristics,such as wall aspect ratio,axial load ratio,boundary element longitudinal reinforcement ratio,web longitudinal reinforcement ratio,and ratio of boundary element length to wall length in determining deformation limits.The developed prediction models have been evaluated for accuracy and validity using various statistical measures.In addition,the proposed equations have been compared with other available deformation limits in relevant design standards and the available literature to predict experimental results of RC wall components.The findings of these analyses indicate that the developed expressions provide significantly higher accuracy and superior predictions compared to existing empirical damage state definitions. 展开更多
关键词 RC structural wall damage state threshold genetic programming drift level parameterized damage state expression
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Insights into Student Perceptions of Error Feedback and Improvement Preferences in Online Programming Education 认领 引用
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作者 Li Zhang Tianze Wang +1 位作者 Jing Jiang Yufei Zhou 《计算机教育》 2026年第3期176-189,共14页
Online programming platforms are popular in programming education.However,there has been no research investigating students’real opinions and expectations of the error feedback mechanisms,leaving educators without a ... Online programming platforms are popular in programming education.However,there has been no research investigating students’real opinions and expectations of the error feedback mechanisms,leaving educators without a solid data foundation when attempting to improve the error feedback mechanisms.This paper makes a survey of 834 students across various programming courses and investigates student perceptions of error feedback mechanisms on online programming platforms.It explores the effectiveness of existing feedback,student satisfaction,and preferences for potential improvements,focusing on automatic error localization and program repair mechanisms.Results reveal a significant portion of students are dissatisfied with current feedback due to its limited informativeness.Students also express a clear demand for stronger feedback mechanisms,such as error localization and repair hints.Nevertheless,they prefer feedback that subtly guides them toward solutions,rather than providing direct and explicit answers,valuing the opportunity to enhance their debugging skills.The findings suggest a need for balanced,educational-focused feedback mechanisms that aid learning while promoting independent problem-solving. 展开更多
关键词 Error feedback Online programming education Program error localization Automated program repair
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From recursion to tabulation:Rethinking how we teach dynamic programming 认领 引用
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作者 Jiang Long 《计算机教育》 2026年第6期302-311,共10页
This paper proposes an intuitive,modular teaching framework for dynamic programming(DP).Beginning with the fundamental concept of recursion,the framework leverages dependency graphs to visually illustrate structural r... This paper proposes an intuitive,modular teaching framework for dynamic programming(DP).Beginning with the fundamental concept of recursion,the framework leverages dependency graphs to visually illustrate structural relationships among subproblems.This visual approach helps students intuitively grasp the underlying logic of tabulation,enabling them to independently determine correct tabulation orders.The plug-and-play design significantly reduces the barrier to learning,while strengthening students'analytical thinking and problem-solving skills.The effectiveness of this method is demonstrated through classroom application using classic examples such as the longest common subsequence(LCS),matrix chain multiplication(MCM),and rod cutting problems. 展开更多
关键词 Dynamic programming Plug-and-play framework Algorithm pedagogy Computer science education
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Global Optimization Algorithm for Minimizing Linear Fractional Programming 认领 引用
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作者 ZHAO Peng SHEN Pei-ping ZHONG Zhe-wei 《Chinese Quarterly Journal of Mathematics》 2026年第1期50-59,共10页
In this paper,we study a class of Linear Fractional Programming on a nonempty bounded set,called the Problem(LFP),and design a branch and bound algorithm to find the global optimal solution of the problem(LFP).First,w... In this paper,we study a class of Linear Fractional Programming on a nonempty bounded set,called the Problem(LFP),and design a branch and bound algorithm to find the global optimal solution of the problem(LFP).First,we convert the problem(LFP)to the equivalent problem(EP2).Secondly,by applying the linear relaxation technique to the problem(EP2),the linear relaxation programming problem(LRP2Y)was obtained.Then,the overall framework of the algorithm is given,and the convergence and complexity of the algorithm are analyzed.Finally,experimental results are listed to illustrate the effectiveness of the algorithm. 展开更多
关键词 Global optimization Linear Fractional Programming Branch and bound algorithm Linear relaxation
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Exploration of Blended Teaching Practice in the Course of Fundamentals of Programming 认领 引用
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作者 Yongrui Cui 《Journal of Contemporary Educational Research》 2026年第2期198-205,共8页
The“Fundamentals of Programming”course employs a blended learning model that integrates online resources with offline instruction to enhance students’self-directed learning and practical skills.Powered by knowledge... The“Fundamentals of Programming”course employs a blended learning model that integrates online resources with offline instruction to enhance students’self-directed learning and practical skills.Powered by knowledge graphs,the curriculum enables personalized learning paths where students autonomously design their study plans,while instructors dynamically adjust teaching strategies based on learning data to achieve precision education.The gamified practice platform transforms grammar training into immersive gaming experiences,significantly boosting learning engagement and practical retention.The teaching strategy adopts tiered cultivation,combining macro-level projects with micro-level knowledge points to strengthen computational thinking and practical abilities.Implementation results demonstrate improved mastery of knowledge points,continuous enhancement of coding and debugging skills,and markedly strengthened learning motivation.This model creates a closed-loop system for knowledge transfer and competency development,laying a solid foundation for subsequent specialized courses.It embodies innovative concepts of knowledge graph-driven learning,gamified practice,and tiered cultivation,effectively fostering students’self-directed learning drive and critical thinking. 展开更多
关键词 Blended learning Fundamentals of Programming Knowledge graph Gamified learning Computational thinking
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Design of a Programming Trainer Based on Narrative Theory 认领 引用
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作者 Huitong Zeng 《Journal of Electronic Research and Application》 2026年第4期114-123,共10页
Addressing the high cognitive barriers and abstract nature of early programming for children aged 5-8,this study integrates narrative theory into the design of Tangible User Interfaces(TUIs).We developed a five-dimens... Addressing the high cognitive barriers and abstract nature of early programming for children aged 5-8,this study integrates narrative theory into the design of Tangible User Interfaces(TUIs).We developed a five-dimensional narrative model encompassing themes,characters,actions,scenes,and props to mitigate learners’cognitive load through contextualized representation.A one-week comparative experiment demonstrated that children in the narrative tangible programming group significantly outperformed those in traditional computer programming and abstract tangible programming groups in terms of core concept comprehension,task efficiency,and self-correction proficiency.The findings suggest that narrative design achieves the“de-abstraction”of programming logic by embedding it into concrete storylines,fostering deep logical understanding in autonomous learning environments.This research provides valuable insights and design pathways for the development of early programming educational tools. 展开更多
关键词 Narrative theory Tangible user interfaces Programming trainer Children aged 5-8
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Magneto-soft robots based on multi-materials optimizing and heat-assisted in-situ magnetic domains programming 认领 引用 被引量：5
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作者 Fuzhou Niu Quhao Xue +9 位作者 Qing Cao Xinyang He Taolei Wang HaoChen Wang Chonglei Hao Xiaojian Li Ying Li Hao Yang Huayong Yang Dong Han 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2025年第5期447-462,共16页
Soft robots, inspired by the flexibility and versatility of biological organisms, have potential in a variety of applications. Recent advancements in magneto-soft robots have demonstrated their abilities to achieve pr... Soft robots, inspired by the flexibility and versatility of biological organisms, have potential in a variety of applications. Recent advancements in magneto-soft robots have demonstrated their abilities to achieve precise remote control through magnetic fields, enabling multi-modal locomotion and complex manipulation tasks. Nonetheless, two main hurdles must be overcome to advance the field: developing a multi-component substrate with embedded magnetic particles to ensure the requisite flexibility and responsiveness, and devising a cost-effective,straightforward method to program three-dimensional distributed magnetic domains without complex processing and expensive machinery. Here, we introduce a cost-effective and simple heat-assisted in-situ integrated molding fabrication method for creating magnetically driven soft robots with three-dimensional programmable magnetic domains. By synthesizing a composite material with neodymium-iron-boron(NdFeB) particles embedded in a polydimethylsiloxane(PDMS) and Ecoflex matrix(PDMS:Ecoflex = 1:2 mass ratio, 50% magnetic particle concentration), we achieved an optimized balance of flexibility, strength, and magnetic responsiveness. The proposed heat-assisted in-situ magnetic domains programming technique,performed at an experimentally optimized temperature of 120℃, resulted in a 2 times magnetization strength(9.5 mT) compared to that at 20℃(4.8 m T), reaching a saturation level comparable to a commercial magnetizer. We demonstrated the versatility of our approach through the fabrication of six kinds of robots, including two kinds of two-dimensional patterned soft robots(2D-PSR), a circular six-pole domain distribution magnetic robot(2D-CSPDMR), a quadrupedal walking magnetic soft robot(QWMSR), an object manipulation robot(OMR), and a hollow thin-walled spherical magneto-soft robot(HTWSMSR). The proposed method provides a practical solution to create highly responsive and adaptable magneto-soft robots. 展开更多
关键词 magneto-soft robots multi-materials optimizing three-dimensional magnetic domains programming
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A 28 nm 576K RRAM-based computing-in-memory macro featuring hybrid programming with area efficiency of 2.82 TOPS/mm2 认领 引用 被引量：1
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作者 Siqi Liu Songtao Wei +7 位作者 Peng Yao Dong Wu Lu Jie Sining Pan Jianshi Tang Bin Gao He Qian Huaqiang Wu 《Journal of Semiconductors》 EI CAS CSCD 2025年第6期112-119,共8页
Computing-in-memory(CIM)has been a promising candidate for artificial-intelligent applications thanks to the absence of data transfer between computation and storage blocks.Resistive random access memory(RRAM)based CI... Computing-in-memory(CIM)has been a promising candidate for artificial-intelligent applications thanks to the absence of data transfer between computation and storage blocks.Resistive random access memory(RRAM)based CIM has the advantage of high computing density,non-volatility as well as high energy efficiency.However,previous CIM research has predominantly focused on realizing high energy efficiency and high area efficiency for inference,while little attention has been devoted to addressing the challenges of on-chip programming speed,power consumption,and accuracy.In this paper,a fabri-cated 28 nm 576K RRAM-based CIM macro featuring optimized on-chip programming schemes is proposed to address the issues mentioned above.Different strategies of mapping weights to RRAM arrays are compared,and a novel direct-current ADC design is designed for both programming and inference stages.Utilizing the optimized hybrid programming scheme,4.67×programming speed,0.15×power saving and 4.31×compact weight distribution are realized.Besides,this macro achieves a normalized area efficiency of 2.82 TOPS/mm2 and a normalized energy efficiency of 35.6 TOPS/W. 展开更多
关键词 computing-in-memory on-chip programming scheme hybrid programming resistive random access memory matrix-vector-multiplication acceleration
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Safe flight corridor constrained sequential convex programming for efficient trajectory generation of fixed-wing UAVs 认领 引用 被引量：5
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作者 Jing SUN Guangtong XU +2 位作者 Zhu WANG Teng LONG Jingliang SUN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2025年第1期537-550,共14页
Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequent... Generating dynamically feasible trajectory for fixed-wing Unmanned Aerial Vehicles(UAVs)in dense obstacle environments remains computationally intractable.This paper proposes a Safe Flight Corridor constrained Sequential Convex Programming(SFC-SCP)to improve the computation efficiency and reliability of trajectory generation.SFC-SCP combines the front-end convex polyhedron SFC construction and back-end SCP-based trajectory optimization.A Sparse A*Search(SAS)driven SFC construction method is designed to efficiently generate polyhedron SFC according to the geometric relation among obstacles and collision-free waypoints.Via transforming the nonconvex obstacle-avoidance constraints to linear inequality constraints,SFC can mitigate infeasibility of trajectory planning and reduce computation complexity.Then,SCP casts the nonlinear trajectory optimization subject to SFC into convex programming subproblems to decrease the problem complexity.In addition,a convex optimizer based on interior point method is customized,where the search direction is calculated via successive elimination to further improve efficiency.Simulation experiments on dense obstacle scenarios show that SFC-SCP can generate dynamically feasible safe trajectory rapidly.Comparative studies with state-of-the-art SCP-based methods demonstrate the efficiency and reliability merits of SFC-SCP.Besides,the customized convex optimizer outperforms off-the-shelf optimizers in terms of computation time. 展开更多
关键词 Fixed-wing unmanned aerial vehicle Efficient trajectory planning Safe flight corridor Sequential convex programming Customized convex optimizer
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Glucocorticoid-insulin like growth factor 1 axis programming might be involved in pancreaticβ-cell dysplasia and dysfunction in female offspring rats exposed to caffeine prenatally 认领 引用 被引量：1
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作者 Shuxia Gui Xiaoling Guo +2 位作者 Yongguo Dai Hao Kou Hui Wang 《Food Science and Human Wellness》 SCIE CAS CSCD 2025年第10期4262-4277,共16页
Prenatal caffeine exposure(PCE)leads to intrauterine growth retardation and altered glucose homeostasis after birth,but the underlying mechanism remains unclear.This study aims to investigate the alteration of pancrea... Prenatal caffeine exposure(PCE)leads to intrauterine growth retardation and altered glucose homeostasis after birth,but the underlying mechanism remains unclear.This study aims to investigate the alteration of pancreatic development and insulin biosynthesis in the PCE female offspring and explore the intrauterine programming mechanism.Pregnant rats were orally treated with 120 mg/(kg·day)of caffeine from gestational day(GD)9 to 20.Results showed that fetal pancreaticβ-cells in the PCE group exhibited reduced mass and impaired insulin synthesis function,as evidenced by decreased expression of developmental and functional genes and reduced pancreatic insulin content.At postnatal week(PW)12,the PCE offspring exhibited glucose intolerance,diminishedβ-cell mass,and lower blood insulin levels.However,by PW28,glucose tolerance showed some improvement.Both in vivo and in vitro findings collectively indicated that excessive serum corticosterone(CORT)levels of the PCE fetuses may act through the activation of the pancreatic glucocorticoid receptor(GR)and recruitment of histone deacetylase 9(HDAC9),leading to H3K9 deacetylation in promoter and downregulation of insulin-like growth factor 1(IGF1),thereby inhibiting pancreatic islet morphogenesis and insulin synthesis in fetal rats.Furthermore,the PCE offspring after birth exhibited decreased blood CORT levels,increased H3K9 acetylation in promoter and upregulated gene expression of the pancreatic IGF1 promoter region,accompanied by elevated insulin biosynthesis.However,when exposed to chronic stress,the above changes were totally reversed.Conclusively,“glucocorticoid-insulin like growth factor 1(GC-IGF1)axis”programming may be involved in pancreaticβ-cell dysplasia and dysfunction in the PCE female offspring. 展开更多
关键词 Prenatal caffeine exposure Pancreatic development Glucocorticoids Insulin-like growth factor 1 Intrauterine programming
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Cat Swarm Algorithm Generated Based on Genetic Programming Framework Applied in Digital Watermarking 认领 引用 被引量：1
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作者 Shu-Chuan Chu Libin Fu +2 位作者 Jeng-Shyang Pan Xingsi Xue Min Liu 《Computers, Materials & Continua》 SCIE EI 2025年第5期3135-3163,共29页
Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programm... Evolutionary algorithms have been extensively utilized in practical applications.However,manually designed population updating formulas are inherently prone to the subjective influence of the designer.Genetic programming(GP),characterized by its tree-based solution structure,is a widely adopted technique for optimizing the structure of mathematical models tailored to real-world problems.This paper introduces a GP-based framework(GPEAs)for the autonomous generation of update formulas,aiming to reduce human intervention.Partial modifications to tree-based GP have been instigated,encompassing adjustments to its initialization process and fundamental update operations such as crossover and mutation within the algorithm.By designing suitable function sets and terminal sets tailored to the selected evolutionary algorithm,and ultimately derive an improved update formula.The Cat Swarm Optimization Algorithm(CSO)is chosen as a case study,and the GP-EAs is employed to regenerate the speed update formulas of the CSO.To validate the feasibility of the GP-EAs,the comprehensive performance of the enhanced algorithm(GP-CSO)was evaluated on the CEC2017 benchmark suite.Furthermore,GP-CSO is applied to deduce suitable embedding factors,thereby improving the robustness of the digital watermarking process.The experimental results indicate that the update formulas generated through training with GP-EAs possess excellent performance scalability and practical application proficiency. 展开更多
关键词 Cat swarm algorithm genetic programming digital watermarking update mode mode generation framework
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