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The three-dimensional meshfree numerical manifold method based on parallel computing 认领 引用
1
作者 Keqin Zhang Wei Wu +2 位作者 Danfeng Zhang Yanfei Kang Hehua Zhu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期360-371,共12页
The numerical manifold method,extensively utilized in numerical computations,faces significant challenges in generating complex manifold elements,particularly for three-dimensional applications.To overcome this challe... The numerical manifold method,extensively utilized in numerical computations,faces significant challenges in generating complex manifold elements,particularly for three-dimensional applications.To overcome this challenge,the meshfree numerical manifold method is developed by integrating the moving least-squares method into the numerical manifold method,effectively bypassing the need for meshing complex geometric objects.However,the implementation of the moving least-squares method introduces computational efficiency issues.To mitigate these,parallel computing methods have been incorporated,resulting in a tenfold increase in the speed of assembling the stiffness matrix with central processing unit parallelism,and a twentyfold increase with graphics processing unit parallelism.The static mechanical system equations for the meshfree numerical manifold method are derived using the Galerkin method.The method’s effectiveness and accuracy are then validated through a series of numerical experiments.The experiments demonstrated that the meshfree numerical manifold method achieves a high precision with minimal nodes and integration points.Additionally,positioning nodes outside the domain significantly improves computational accuracy at the boundaries. 展开更多
关键词 Meshfree numerical manifold method Three-dimensional computation Parallel computation Elastostatics Moving least-squares
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Privacy-Preserving Parallel Non-Negative Matrix Factorization with Edge Computing 认领 引用
2
作者 Wenxuan Yu Wenjing Gao +2 位作者 Jiuru Wang Rong Hao Jia Yu 《Computers, Materials & Continua》 SCIE EI 2026年第6期498-518,共21页
Non-negative Matrix Factorization(NMF)is a computationally intensive matrix operation that resource-constrained clients struggle to complete locally.Privacy-preserving outsourcing allows clients to offload heavy compu... Non-negative Matrix Factorization(NMF)is a computationally intensive matrix operation that resource-constrained clients struggle to complete locally.Privacy-preserving outsourcing allows clients to offload heavy computing tasks to powerful servers,effectively solving the problem of local computing difficulties.However,the existing privacy-preserving NMF outsourcing schemes only allow one server to perform outsourcing computation,resulting in low efficiency on the server side.In order to improve the efficiency of outsourcing computation,we propose a privacy-preserving parallel NMF outsourcing scheme with multiple edge servers.We adopt the matrix blocking technique to divide the computation task into multiple subtasks,and design the NMF parallel computation algorithm based on the multiplication updating rule.The proposed scheme implements the parallel outsourcing of non-negative matrix factorization based on multiple edge servers.We use random permutation matrices to encrypt original matrix,thereby protecting data privacy.In addition,we utilize the iterative nature of the NMF algorithm for result verification.Theoretical analysis and experimental results prove the advantages of the proposed scheme. 展开更多
关键词 Secure outsourcing computation non-negative matrix factorization parallel outsourcing edge computing
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Efficient rock joint detection from large-scale 3D point clouds using vectorization and parallel computing approaches 认领 引用
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作者 Yunfeng Ge Zihao Li +2 位作者 Huiming Tang Qian Chen Zhongxu Wen 《Geoscience Frontiers》 SCIE CAS CSCD 2025年第5期1-15,共15页
The application of three-dimensional(3D)point cloud parametric analyses on exposed rock surfaces,enabled by Light Detection and Ranging(LiDAR)technology,has gained significant popularity due to its efficiency and the ... The application of three-dimensional(3D)point cloud parametric analyses on exposed rock surfaces,enabled by Light Detection and Ranging(LiDAR)technology,has gained significant popularity due to its efficiency and the high quality of data it provides.However,as research extends to address more regional and complex geological challenges,the demand for algorithms that are both robust and highly efficient in processing large datasets continues to grow.This study proposes an advanced rock joint identification algorithm leveraging artificial neural networks(ANNs),incorporating parallel computing and vectorization of high-performance computing.The algorithm utilizes point cloud attributes—specifically point normal and point curvatures-as input parameters for ANNs,which classify data into rock joints and non-rock joints.Subsequently,individual rock joints are extracted using the density-based spatial clustering of applications with noise(DBSCAN)technique.Principal component analysis(PCA)is subsequently employed to calculate their orientations.By fully utilizing the computational power of parallel computing and vectorization,the algorithm increases the running speed by 3–4 times,enabling the processing of large-scale datasets within seconds.This breakthrough maximizes computational efficiency while maintaining high accuracy(compared with manual measurement,the deviation of the automatic measurement is within 2°),making it an effective solution for large-scale rock joint detection challenges.©2025 China University of Geosciences(Beijing)and Peking University. 展开更多
关键词 Rock joints Pointclouds Artificialneuralnetwork High-performance computing Parallel computing Vectorization
FTCSEM—A FORTRAN-based parallelized 1D CSEM forward and inversion program for arbitrary source-receiver geometry 认领 引用
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作者 Wei-ying Chen Si-xu Han +2 位作者 Wan-ting Song Yu-lian Zhu Zheng Liu 《Applied Geophysics》 SCIE CSCD 2026年第2期693-709,870,共17页
This study introduces FTCSEM,a FORTRAN-based,parallelized one-dimensional controlledsource electromagnetic(CSEM)forward modeling and inversion software capable of accommodating arbitrary source-receiver confi guration... This study introduces FTCSEM,a FORTRAN-based,parallelized one-dimensional controlledsource electromagnetic(CSEM)forward modeling and inversion software capable of accommodating arbitrary source-receiver confi gurations.In comparison to existing one-dimensional CSEM tools,FTCSEM incorporates several signifi cant enhancements:it supports transmitters of diverse shapes,quantities,and spatial locations;permits receivers to be positioned flexibly on the surface,subsurface,or in the atmosphere;facilitates simulations and inversions in both frequency and time domains;integrates an adaptive regularized inversion algorithm with multiple model constraints;and leverages GPU-accelerated parallel computing to attain high computational efficiency.Validation through numerical experiments and field data inversion confirms the program’s accuracy and practical applicability.The findings indicate that FTCSEM performs robustly in complex geoelectric environments,multi-source and multi-receiver arrangements,as well as multi-component joint inversion scenarios,thereby offering a versatile and powerful tool for advancing CSEM research and applications. 展开更多
关键词 CSEM forward modeling regularized inversion parallel computing program
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A high-performance parallel algorithm based on problem independent machine learning(PIML)for large-scale topology optimization 认领 引用
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作者 Xinyu Ma Mengcheng Huang +4 位作者 Zongliang Du Yilin Guo Chang Liu Yue Mei Xu Guo 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第3期197-213,共17页
Although supplying extensive design space,the curse of dimensionality restricts the widespread application of largescale topology optimization in practical engineering.Various acceleration techniques have been integra... Although supplying extensive design space,the curse of dimensionality restricts the widespread application of largescale topology optimization in practical engineering.Various acceleration techniques have been integrated with topology optimization,achieving significant attention and progress in large-scale problems.This work aims to investigate how much benefit can be obtained by combining parallel computing and machine learning techniques to enhance the efficiency of large-scale topology optimization algorithms.Accordingly,a parallel problem independent machine learning(PIML)-enhanced topology optimization method is proposed.The PIML model substantially reduces the dimension of the condensed stiffness matrix and its computational cost,and parallel computing reduces the workload per process and enables the application of a parallel multigrid solver.Besides,several techniques,such as matrix-free implementation,direct condensation of uniform coarse elements,and adjusting computational resource limits,have been developed to enhance computational efficiency.The weak scaling efficiency,strong scaling speedup,and maximum achievable efficiency of the proposed method are validated across multiple numerical examples,showing significant improvement in the tractable problem size and solution efficiency compared to traditional topology optimization algorithms. 展开更多
关键词 Topology optimization Large-scale Problem independent machine learning(PIML) Parallel computing
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Parallel divergence with shared barriers,and niche separation between two sympatric avian species groups 认领 引用 被引量：1
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作者 Lei Wu Huan Wang +10 位作者 Yanzhu Ji Ali Haghani Yan Hao Dezhi Zhang Gang Song Yalin Cheng Martin Päckert Jochen Martens Chenxi Jia Per Alström Fumin Lei 《Journal of Systematics and Evolution》 SCIE CSCD 2026年第1期77-94,共18页
Geographic barriers and geological historical events may play pivotal roles in driving allopatric divergence among closely related species.Here,we investigate the genomic divergence patterns and ecological niche separ... Geographic barriers and geological historical events may play pivotal roles in driving allopatric divergence among closely related species.Here,we investigate the genomic divergence patterns and ecological niche separation of the Willow Tit Poecile montanus and the Marsh Tit P.palustris species groups in China,and their ecological niche separation across East Asia.Through comprehensive genomic sequencing,population genomic analysis,and integration of public occurrence data,we unveil striking parallels in the geographic divergence patterns between these two species groups.Notably,both species exhibit multiple divergent lineages in China,with similar spatial distributions of geneflow barriers.Furthermore,our analysis reveals unique evolutionary histories in the southwestern clades of both species groups,highlighting the intricate interplay between historical distribution dynamics,ecological preferences,and genetic divergence.Our study significantly enhances our understanding of the processes underlying the diversification of closely related widespread species within the framework of shared geographical constraints,and stresses the need for a taxonomic revision. 展开更多
关键词 biogeography East Asia Marsh Tit parallel divergence Willow Tit
Memristor devices for next-generation computing:from performance optimization to application-specific co-design 认领 引用 被引量：1
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作者 Zhaorui Liu Caifang Gao +5 位作者 Jingbo Yang Zuxin Chen Enlong Li Jun Li Mengjiao Li Jianhua Zhang 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第1期119-146,共28页
Memristors have emerged as a transformative technology in the realm of electronic devices,offering unique advantages such as fast switching speeds,low power consumption,and the ability to sensor-memory-compute.The app... Memristors have emerged as a transformative technology in the realm of electronic devices,offering unique advantages such as fast switching speeds,low power consumption,and the ability to sensor-memory-compute.The applications span across non-volatile memory,neuromorphic computing,hardware security,and beyond,prompting memristors to become a versatile solution for next-generation computing and data storage systems.Despite enormous potential of memristors,the transition from laboratory prototypes to large-scale applications is challenging in terms of material stability,device reproducibility,and array scalability.This review systematically explores recent advancements in high-performance memristor technologies,focusing on performance enhancement strategies through material engineering,structural design,pulse protocol optimization,and algorithm control.We provide an in-depth analysis of key performance metrics tailored to specific applications,including non-volatile memory,neuromorphic computing,and hardware security.Furthermore,we propose a co-design framework that integrates device-level optimizations with operational-level improvements,aiming to bridge the gap between theoretical models and practical implementations. 展开更多
关键词 memristor performance optimization device design neuromorphic computing
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Quantum computing-enhanced topology optimization with stress constraints for truss structures 认领 引用 被引量：2
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作者 Yan Wang Dixiong Yang +1 位作者 Zhenzeng Lei Guohai Chen 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第6期41-57,共17页
Quantum computing,leveraging the properties of quantum physics such as quantum superposition and entanglement,possesses the potential for exponential acceleration compared to classical computing.It can significantly e... Quantum computing,leveraging the properties of quantum physics such as quantum superposition and entanglement,possesses the potential for exponential acceleration compared to classical computing.It can significantly enhance solution efficiency in topology optimization and effectively avoid the entrapment in local optima.This paper proposes a hybrid classical-quantum computing framework to solve the stress-constrained topology optimization problem for truss structures.Initially,structural analyses are performed on a classical computer to determine the stresses of truss members.Then,the optimization problem is formulated through incremental updates of member cross-sectional areas to make it compatible with a quantum annealer.The update strategy consists of a directional-control function and a magnitude-control function.By embedding stress constraints directly into the directional-control function,the original optimization problem is reformulated as a quadratic unconstrained binary optimization model suitable for quantum annealing.To realize a balance between solution accuracy and iteration efficiency,a dynamic strategy for adjusting the magnitude of area increments is proposed.Thus,the quantum annealer can effectively achieve the optimal solutions.When only the access time of the quantum processing unit is considered,the results from 2D and 3D examples of truss topology optimization validate the effectiveness of the proposed framework,and demonstrate the great potential of quantum computing in structural optimization. 展开更多
关键词 Topology optimization Truss structures Quantum computing Quantum annealing algorithm Quadratic unconstrained binary optimization problem
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In-Sensor-Memory Computing for Post-Von Neumann Intelligence:A Perspective 认领 引用 被引量：1
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作者 Hongyu Tang Ninghai Yu +2 位作者 Pengsheng Min Ruiqian Guo Guoqi Zhang 《Nano-Micro Letters》 SCIE EI CAS CSCD 2026年第10期36-67,共32页
The rapid growth of artificial intelligence,ubiquitous sensing,and edge computing is exposing fundamental limitations of conventional von Neumann architectures,in which the physical separation of sensing,memory,and co... The rapid growth of artificial intelligence,ubiquitous sensing,and edge computing is exposing fundamental limitations of conventional von Neumann architectures,in which the physical separation of sensing,memory,and computation leads to excessive data movement,high energy consumption,and latency.As transistor scaling slows in the post-Moore era,architectural innovation has become essential to sustain progress in intelligent systems.In-sensor-memory computing(ISMC)addresses these challenges by co-locating perception,storage,and computation within unified device and system architectures,enabling in situ signal processing,mixed-signal computation,and event-driven intelligence at the data source.Recent advances in memristive and ferroelectric devices,low-dimensional and multifunctional materials,three-dimensional heterogeneous integration,and neuromorphic architectures have significantly expanded the functional scope of ISMC platforms.In parallel,the co-evolution of algorithms—including spiking neural networks,reservoir computing,and neuromorphic compilers—has facilitated the translation of device-level advantages into system-level performance.This perspective surveys the technological foundations,architectural trends,and emerging applications of ISMC,examines global industry-academia-research(IAR)collaboration,and outlines key challenges related to variability,reliability,scalability,and benchmarking.Collectively,ISMC is positioned as a post-von Neumann hardware paradigm for energy-efficient,distributed intelligence. 展开更多
关键词 In-sensor-memory computing(ISMC) Post-von Neumann intelligence Neuromorphic hardware Industry-academia-research(IAR)
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Data-driven computing ligament loading mechanisms:integration of the computational ligament mechanics models with deep learning 认领 引用 被引量：1
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作者 Datao Xu Huiyu Zhou +7 位作者 Yi Yuan Zanni Zhang Tianle Jie Zhifeng Zhou Zixiang Gao Liangliang Xiang Meizi Wang Yaodong Gu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期628-672,共45页
Ankle injuries account for more than 25%of sports-related injuries.However,there is a lack of computational mechanics modeling and assessment tools for the ligament loading mechanism(LLM)caused by ankle injury.This st... Ankle injuries account for more than 25%of sports-related injuries.However,there is a lack of computational mechanics modeling and assessment tools for the ligament loading mechanism(LLM)caused by ankle injury.This study combines medical imaging data to construct the subject-specific ankle musculoskeletal model,which considers the subject's individualized characteristics and ligamentous attributes.Furthermore,we developed the structural constitutive model to restore the nonlinear short-term viscoelastic properties of the ligament-dense connective tissue,which can more realistically revert the LLM and reveal the mechanical properties of ankle injury.Based on the computational ligament mechanics(CLM)model,we developed a deep learning-based prediction model to predict LLM by CLM data-driven modeling.The modeling simulation results are highly consistent with the calculation results from the dual fluoroscopic imaging system,which demonstrated that the CLM model has high accuracy.The data-driven modeling performs exceptionally well in predicting ligament loading forces.The findings indicate that the constructed CLM data-driven model has the potential to enhance the accuracy and safety of ankle rehabilitation robots,while also providing personalized,dynamically adjusted rehabilitation training programs.The proposed comprehensive solutions would bring benefits to more patients with sports injuries and the general rehabilitation population,and promote the development and advancement of the research field of CLM and biomechanical variable prediction. 展开更多
关键词 Computational ligament mechanics Subject-specific musculoskeletal model Structural constitutive model Ankle ligament injury mechanisms Biomechanical variable prediction
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Task Offloading and Edge Computing in IoT-Gaps, Challenges and Future Directions 认领 引用 被引量：1
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作者 Hitesh Mohapatra 《Computers, Materials & Continua》 SCIE EI 2026年第6期268-296,共29页
This review examines current approaches to real-time decision-making and task optimization in Internet of Things systems through the application of machine learning models deployed at the network edge.Existing literat... This review examines current approaches to real-time decision-making and task optimization in Internet of Things systems through the application of machine learning models deployed at the network edge.Existing literature shows that edge-based distributed intelligence reduces cloud dependency.It addresses transmission latency,device energy use,and bandwidth limits.Recent optimization strategies employ dynamic task offloading mechanisms to determine optimal workload placement across local devices and edge servers without centralized coordination.Empirical findings from the literature indicate performance improvements with latency reductions of approximately 32.8%and energy efficiency gains of 27.4%compared to conventional cloud-centric models.However,critical gaps remain in current methodologies.Most studies focus on static network topologies and do not adequately address load balancing across multiple edge nodes.Security vulnerabilities during task transmission are underexplored,and privacy considerations for sensitive data remain insufficiently integrated into existing frameworks.Task caching strategies and fault tolerance mechanisms require further investigation in highly dynamic environments.The ability of existing approaches to handle large-scale deployments and complex edge-cloud collaborative scenarios has not been thoroughly validated.This review synthesizes current progress while identifying fundamental challenges that must be resolved for practical deployment in time-sensitive applications spanning smart manufacturing,autonomous systems,and healthcare monitoring.Future work should prioritize robust security integration,efficient load distribution,and scalability across heterogeneous edge infrastructures. 展开更多
关键词 Edge computing task offloading deep reinforcement learning mobile edge computing IoT performance optimization methodological rigor assessment
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Computing power networks for unmanned aerial vehicles:a hierarchical resources trading market 认领 引用
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作者 Xiaofei Wang Hui Deng +3 位作者 Chao Qiu Zheyuan Chen Tao Luo Zhao Ming 《Digital Communications and Networks》 SCIE EI CSCD 2026年第4期584-593,共10页
Unmanned Aerial Vehicles(UAVs)are increasingly deployed across military and civilian domains due to their operational flexibility,low maintenance costs,and high mobility.With the growing complexity of UAV applications... Unmanned Aerial Vehicles(UAVs)are increasingly deployed across military and civilian domains due to their operational flexibility,low maintenance costs,and high mobility.With the growing complexity of UAV applications and tasks,robust support from computing power networks is essential.These networks,acting as resource integration paradigms,furnish UAVs with pooled resources to tackle extensive computational demands.In this paper,we develop a framework for trading computing power resources,modeling the transaction process through a three-stage Stackelberg game to facilitate sequential decision-making.We theoretically demonstrate the existence of a Nash equilibrium and introduce a Dynamic Game Reinforcement algorithm to identify optimal strategies.Our experimental results affirm the framework's efficacy and the superior performance of our algorithm.Additionally,we explore how variables like UAV quantity and network congestion influence the market dynamics of the computing power network. 展开更多
关键词 UAVs Cloud computing Computing power network Resource trading Stackelberg game
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Back-gate-tuned organic electrochemical transistor with temporal dynamic modulation for reservoir computing 认领 引用
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作者 Qian Xu Jie Qiu +6 位作者 Mengyang Liu Dongzi Yang Tingpan Lan Jie Cao Yingfen Wei Hao Jiang Ming Wang 《Journal of Semiconductors》 EI CAS CSCD 2026年第1期118-123,共6页
Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal sca... Organic electrochemical transistor(OECT)devices demonstrate great promising potential for reservoir computing(RC)systems,but their lack of tunable dynamic characteristics limits their application in multi-temporal scale tasks.In this study,we report an OECT-based neuromorphic device with tunable relaxation time(τ)by introducing an additional vertical back-gate electrode into a planar structure.The dual-gate design enablesτreconfiguration from 93 to 541 ms.The tunable relaxation behaviors can be attributed to the combined effects of planar-gate induced electrochemical doping and back-gateinduced electrostatic coupling,as verified by electrochemical impedance spectroscopy analysis.Furthermore,we used theτ-tunable OECT devices as physical reservoirs in the RC system for intelligent driving trajectory prediction,achieving a significant improvement in prediction accuracy from below 69%to 99%.The results demonstrate that theτ-tunable OECT shows a promising candidate for multi-temporal scale neuromorphic computing applications. 展开更多
关键词 neuromorphic computing reservoir computing OECT tunable dynamics trajectory prediction
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Advancements and theoretical foundations of cable-driven parallel robots:A comprehensive overview 认领 引用
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作者 Kamran Joyo Han Yuan +2 位作者 Jun Wu Wenfu Xu Honghao Yue 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第3期280-305,共26页
Parallel robotic mechanisms using cables instead of rigid limbs are termed cable-driven parallel robots(CDPRs).Electric motors and pulley mechanisms actuate cables to provide motion for an end-effector in a cable robo... Parallel robotic mechanisms using cables instead of rigid limbs are termed cable-driven parallel robots(CDPRs).Electric motors and pulley mechanisms actuate cables to provide motion for an end-effector in a cable robot.Consequently,CDPRs have emerged as indispensable tools across a spectrum of industrial and technological domains,including astronomy,aerospace,logistics,simulators,and rehabilitation.Their inherent compatibility with the evolving concept of rigid-flexible fusion places CDPRs at the forefront of cutting-edge robotics research.This comprehensive paper aims to consolidate the core theories and advancements underpinning CDPRs,en-compassing key aspects such as configuration design,cable-force distribution,workspace and stiffness analysis,performance evaluation,optimisation techniques,and motion control.We provide in-depth insights into kine-matic modelling,workspace exploration,and cable-force solutions.Furthermore,the paper delves into the in-tricacies of stiffness and dynamic modelling,presenting a range of analytical methods to elucidate their effects on CDPR performance.Addressing reliability concerns and developing a unified control framework are identified as essential in ensuring the practical deployment of CDPRs in real-world scenarios.This research paper offers a comprehensive overview of the theories and advancements in CDPRs,identifying critical areas for further re-search and development to unlock the full potential of these versatile and high-performance robotic systems. 展开更多
关键词 Cable-driven parallel robots Optimisation Path generation Statics Machine learning
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Optimal design of a novel dual-axis parallel solar tracker 认领 引用
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作者 Bin Zhu Liping Wang +2 位作者 Jun Wu Hengchun Cui Yanling Tian 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第2期167-179,共13页
Being renewable and readily available,solar energy has gained significant attention in addressing the global energy crisis and climate change.The efficiency of solar-energy harvesting using a concentrator depends on t... Being renewable and readily available,solar energy has gained significant attention in addressing the global energy crisis and climate change.The efficiency of solar-energy harvesting using a concentrator depends on the angle between the incident sunlight and the solar concentrator.Therefore,a solar-energy collection system equipped with a solar tracker that follows the apparent motion of the sun offers the highest collection efficiency.In this study,a novel solar tracker with a parallel mechanism is proposed based on the line graph method.The proposed parallel solar tracker(PST) features a main column with passive movements and two UPU chains that share a common constraint.This design enhances the rotational workspace,stiffness,and load-bearing capacity of the system.To solve the forward kinematics problem of the PST efficiently and accurately,a geometric elimination method is employed,converting the three-dimensional kinematics into a simpler planar problem.This allows the forward kinematics problem to be solved analytically using planar equations.By considering key performance indices,such as the effective workspace,transmission,and manipulability,the structural parameters of the PST are optimized in two steps,thereby identifying the optimal region in the design space.Finally,the computational efficiency and accuracy of both the forward and inverse kinematic solutions for the PST with optimized structural parameters are validated,demonstrating their potential for use in real-time control systems.The proposed novel solar tracker has high stiffness and load-bearing capacity.The study provides a solid foundation for improving the efficiency of solar energy utilization. 展开更多
关键词 Solar tracker Parallel mechanism Forward kinematics Effective workspace Transmissibility Manipulability
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Large-scale integrated photonic accelerators for ultralow-latency and universal AI computing 认领 引用
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作者 Xiangyan Meng Junshen Li +4 位作者 Kangwei Fei Yu Wang Wei Li Nuannuan Shi Ming Li 《Journal of Semiconductors》 EI CAS CSCD 2026年第6期12-15,共4页
Integrated silicon photonics has emerged as a transformative technology for post-Moore’s law computing,offering intrinsic advantages of high bandwidth,ultralow latency and low energy consumption that far exceed tradi... Integrated silicon photonics has emerged as a transformative technology for post-Moore’s law computing,offering intrinsic advantages of high bandwidth,ultralow latency and low energy consumption that far exceed traditional electronic computing architectures[1−4].As artificial intelligence(AI)models continue to grow in complexity and scale,the demand for high-speed,energy-efficient computing has spurred intensive research into photonic computing as a promising alternative to electronic accelerators[5−7].Matrix multiply−accumulate(MAC)operations,the core of deep learning and combinatorial optimization algorithms,are particularly amenable to photonic implementation,as light enables parallel multiplication and accumulation with minimal data movement[8,9].However,the practical application of photonic computing has long been hindered by critical challenges including large-scale integration of photonic components,electro-optical co-packaging,guaranteed computation accuracy of analog photonic systems,and compatibility with mainstream AI models and algorithms[10,11]. 展开更多
关键词 photonic computing integrated silicon photonics electronic computing ultralow latency matrix multiply accumulate analog photonic systems silicon photonics electronic accelerators matrix
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A 32‑channel charge‑sensitive amplifier for delay‑line readout of parallel plate avalanche counter array 认领 引用
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作者 Yue‑Zhao Zhang Peng Ma +8 位作者 Zhuang‑Yu Lin Zhen‑Fei Tan Xing‑Chi Han Chen Liu Shuo Wang Da‑Peng Sun Zhi‑Quan Li En‑Hong Wang Shou‑Yu Wang 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2026年第1期164-179,共16页
A 32-channel charge-sensitive amplifier(CSA)is designed for fast timing in the delay-line readout of a parallel plate avalanche counter(PPAC)array.It is realized on a PCB with operational amplifiers and other discrete... A 32-channel charge-sensitive amplifier(CSA)is designed for fast timing in the delay-line readout of a parallel plate avalanche counter(PPAC)array.It is realized on a PCB with operational amplifiers and other discrete components.Each channel consists of an integrator,a pole-zero cancellation net,and a linear amplification stage,which can be adapted to accommodate either positive or negative input signals.The RMS equivalent input noise charges are 3.3 fC,the conversion gains are approximately±2 mV∕fC,and the intrinsic time resolution reaches 32 ps.In the prototype PPAC application,the CSA performs as well as the commercial FTA820A amplifier,providing a position resolution as good as 0.17 mm,and exhibiting reliable stability during several hours of continuous data acquisition. 展开更多
关键词 Charge-sensitive amplifier Fast timing Parallel plate avalanche counter Delay-line Discrete components
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Stiffness evaluation and experimental test of a novel redundantly actuated parallel machining robot 认领 引用
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作者 Hanliang Fang Jian Wang +2 位作者 Shuyi Ge Fufu Yang Jun Zhang 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第1期413-422,共10页
Parallel machining robot is a new type of robotized equipment for high-efficiency machining structural com-ponents with complex geometries.Terminal rigidity is of great importance index for such type of equipment,whic... Parallel machining robot is a new type of robotized equipment for high-efficiency machining structural com-ponents with complex geometries.Terminal rigidity is of great importance index for such type of equipment,which affects their load capacity and working accuracy.Before a parallel machining robot can be used for heavy-load and high-efficiency machining,its terminal rigidity should be evaluated systematically.The present study is to quantitatively reveal the stiffness properties of a previously invented Z4 redundantly actuated parallel ma-chining robot(RAPMR).For this purpose,two critical issues,i.e.,stiffness modelling and index construction,are clarified to carry out stiffness evaluation of the Z4 RAPMR.Firstly,drawing on the screw theory,a semi-analytic stiffness model of the proposed RAPMR is established at a component level.Secondly,a set of virtual work-based stiffness indices is constructed to evaluate the terminal rigidity of parallel robots.Those indices have a consistent physical unit in describing linear and angular terminal rigidity.With these indices,the local and the global stiffness performance of the Z4 RAPMR are predicted.Thirdly,a laboratory prototype of the proposed RAPMR is fabricated.And the experimental test is performed to verify the correctness of the established stiffness model.The present work is expected to provide fundamental information for further light-weight design and rigidity enhancement. 展开更多
关键词 Parallel machining robot Redundantly actuated Terminal rigidity Stiffness model Experimental test
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Computing-centric computing-in-memory and memory-centric in-/near-memory computing for DNNs and transformer based LLMs 认领 引用
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作者 Xin Si Xing Wang Jun Yang 《Journal of Semiconductors》 EI CAS CSCD 2026年第7期7-11,共5页
Introduction.With the rapid development of transformer-based large language models(LLMs)and deep neural networks(DNNs),the demand for both high computational throughput and massive memory capacity has grown exponentia... Introduction.With the rapid development of transformer-based large language models(LLMs)and deep neural networks(DNNs),the demand for both high computational throughput and massive memory capacity has grown exponentially[1-4].In response,the 2D/3D hybrid integration of computing-centric computing-in-memory(CIM)and memory-centric in-ear-memory computing(INMC)circuits has emerged as a transformative technology.Unlike conventional von Neumann architectures,these memory-computing hybrid designs offer systematic advantages including high energy efficiency,high memory bandwidth,and sufficient on-device memory capacity[1-13]. 展开更多
关键词 von neumann architecturesthese near memory computing transformer based large language models large language models llms memory computing deep neural networks dnns memory centric computing centric
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A low-thermal-budget MOSFET-based reservoir computing for temporal data classification 认领 引用
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作者 Yanqing Li Feixiong Wang +5 位作者 Heyi Huang Yadong Zhang Xiangpeng Liang Shuang Liu Jianshi Tang Huaxiang Yin 《Journal of Semiconductors》 EI CAS CSCD 2026年第1期42-48,共7页
Neuromorphic devices have garnered significant attention as potential building blocks for energy-efficient hardware systems owing to their capacity to emulate the computational efficiency of the brain.In this regard,r... Neuromorphic devices have garnered significant attention as potential building blocks for energy-efficient hardware systems owing to their capacity to emulate the computational efficiency of the brain.In this regard,reservoir computing(RC)framework,which leverages straightforward training methods and efficient temporal signal processing,has emerged as a promising scheme.While various physical reservoir devices,including ferroelectric,optoelectronic,and memristor-based systems,have been demonstrated,many still face challenges related to compatibility with mainstream complementary metal oxide semiconductor(CMOS)integration processes.This study introduced a silicon-based schottky barrier metal-oxide-semiconductor field effect transistor(SB-MOSFET),which was fabricated under low thermal budget and compatible with back-end-of-line(BEOL).The device demonstrated short-term memory characteristics,facilitated by the modulation of schottky barriers and charge trapping.Utilizing these characteristics,a RC system for temporal data processing was constructed,and its performance was validated in a 5×4 digital classification task,achieving an accuracy exceeding 98%after 50 training epochs.Furthermore,the system successfully processed temporal signal in waveform classification and prediction tasks using time-division multiplexing.Overall,the SB-MOSFET's high compatibility with CMOS technology provides substantial advantages for large-scale integration,enabling the development of energy-efficient reservoir computing hardware. 展开更多
关键词 schottky barrier MOSFET back-end-of-line integration reservoir computing
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