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Energy-Efficient Deterministic Fault-Tolerant Scheduling for Embedded Real-Time Systems 认领 引用
1
作者 李国徽 胡方晓 +1 位作者 杜小坤 唐向红 《Journal of Southwest Jiaotong University(English Edition)》 2009年第4期283-291,共9页
By combining fault-tolerance with power management, this paper developed a new method for aperiodic task set for the problem of task scheduling and voltage allocation in embedded real-time systems. The scbedulability ... By combining fault-tolerance with power management, this paper developed a new method for aperiodic task set for the problem of task scheduling and voltage allocation in embedded real-time systems. The scbedulability of the system was analyzed through checkpointing and the energy saving was considered via dynamic voltage and frequency scaling. Simulation results showed that the proposed algorithm had better performance compared with the existing voltage allocation techniques. The proposed technique saves 51.5% energy over FT-Only and 19.9% over FT ＋ EC on average. Therefore, the proposed method was more appropriate for aperiodic tasks in embedded real-time systems. 展开更多
关键词 Embedded real-time systems Aperiodic tasks Fault tolerance Power management Task scheduling and voltageallocation
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AI-driven interpretation and prediction of embedded printability based on rheology 认领 引用 被引量：1
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作者 Xianhao Zhou Zhenrui Zhang +7 位作者 Jintian Yu Lixi Ma Sicheng Ma Bingyan Wu Zixuan Wang Ting Zhang Yongcong Fang Zhuo Xiong 《Bio-Design and Manufacturing》 SCIE EI CAS CSCD 2026年第1期63-79,I0003-I0012,共17页
Embedded printing is a highly promising approach for creating complex structures within a yield-stress support bath.However,the accurate prediction and control of printability remain fundamental challenges due to the ... Embedded printing is a highly promising approach for creating complex structures within a yield-stress support bath.However,the accurate prediction and control of printability remain fundamental challenges due to the complex interactions between inks and support baths.Here,we present an artificial intelligence(AI)-driven framework that interprets and predicts embedded printability using rheological data.Using a standardized workflow,we extracted 21 rheological descriptors and established 12 indicators to evaluate structural continuity and geometric fidelity.Interpretable machine learning models revealed that direction-dependent defects are governed by the synergistic interplay among ink yield stress,support bath zero shear viscosity,flow behavior index,and time constant.To enable the prediction of printability in a generalizable manner,we further developed a cascaded neural network,which achieved mean relative prediction errors below 15%across all indicators.Experimental validation using three-dimensional(3 D)-printed constructs and micro-computed tomography(μCT)reconstructions confirmed a strong correlation between predicted and actual fidelity.This work establishes a physics-informed,data-driven paradigm for decoding and optimizing embedded printing,offering broad applicability and providing a robust tool for the rapid pairing of suitable printable ink-support bath combinations. 展开更多
关键词 Embedded bioprinting Printability Rheology Machine learning Neural network
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Energy relief effect of real-time drilling to prevent rockburst in high-stress rock 认领 引用 被引量：1
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作者 Zhichao He Fengqiang Gong +2 位作者 Li Ren Weimin Yang Xuezhen Wu 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第2期1460-1475,共16页
To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens... To investigate the energy relief effect of real-time drilling in preventing rockburst in high-stress rock,a series of high-stress real-time drilling uniaxial compression tests were conducted on red sandstone specimens using the SG4500 drilling rig.Results showed that the mechanical behavior(i.e.peak strength and rockburst intensity)of the rock was weakened under high-stress real-time drilling and exhibited a downward trend as the drilling diameter increased.The real-time drilling energy dissipation index(ERD)was proposed to characterize the energy relief during high-stress real-time drilling.The ERD exhibited a linear increase with the real-time drilling diameter.Furthermore,the elastic strain energy of post-drilling rock showed a linear relationship with the square of stress across different stress levels,which also applied to the peak elastic strain energy and the square of peak stress.This findingreveals the intrinsic link between the weakening effect of peak elastic strain energy and peak strength due to high-stress real-time drilling,confirmingthe consistency between energy relief and pressure relief effects.By establishing relationships among rockburst proneness,peak elastic strain energy,and peak strength,it was demonstrated that high-stress real-time drilling reduces rockburst proneness through energy dissipation.Specifically,both peak elastic strain energy and rockburst proneness decreased with larger drill bit diameters,consistent with reductions in peak strength,rockburst intensity,and fractal dimensions of high-stress real-time drilled rock.These results validate the energy relief mechanism of real-time drilling in mitigating rockburst risks. 展开更多
关键词 Rock mechanics Rockburst Real-time drilling Drilling energy relief Energy storage capacity Rockburst proneness
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A Requirements Engineering Environment for Embedded Real-Time Software—SREE 认领 引用
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作者 LI Yonghua SHU Fengdi +1 位作者 WU Guoqing LIANG Zhengping 《Wuhan University Journal of Natural Sciences》 EI CAS 2006年第3期533-538,共6页
The paper presents the embedded real-time software-oriented requirements engineering environment—SREZ. It involves the whole process of software requirements engineering, including the definition, analysis and checki... The paper presents the embedded real-time software-oriented requirements engineering environment—SREZ. It involves the whole process of software requirements engineering, including the definition, analysis and checking of requirements ,specifications. We first explain the principles of the executable specification language RTRSM. Subsequently, we introduce the main functions of SREE, illustrate the methods and techniques of checking requirements specifications, especially how to perform simulation execution, combining prototyping method with RTRSM and animated representations. At last, we compare the SREE with other requirements specifications methods and make a summary for SREE＇s advantages. 展开更多
关键词 embedded real-time system requirements specification requirements verification simulation execution
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Real-time control for autoclave curing process of CFRP composites considering tool-part interaction 认领 引用
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作者 Wenyuan TANG Liang HE +4 位作者 Xinyu HUI Jianwen NIU Yingjie XU Rutong YANG Yutong LIU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第3期667-681,共15页
An integrated real-time control methodology is introduced to mitigate process-induced challenges,namely temperature overshoot,uneven cure,and interfacial shear stress,during the autoclave curing of Carbon Fiber-Reinfo... An integrated real-time control methodology is introduced to mitigate process-induced challenges,namely temperature overshoot,uneven cure,and interfacial shear stress,during the autoclave curing of Carbon Fiber-Reinforced Polymer(CFRP)composites.First,a high-fidelity Finite Element(FE)model incorporating tool-part interaction is developed to reveal the curing process of the composites,wherein the interaction is characterized by friction interface modeling with experimentally measured cure-dependent friction coefficients.The accuracy of FE model is confirmed through experimental tests on a doubly curved T-stiffened composite panel.This validated model then generates a dataset of curing temperature profile and associated defect information,which is used to train a customized Long Short-Term Memory(LSTM)neural network.We culminate in a real-time control framework that actively optimizes the curing process by integrating LSTM-based state prediction with Q-learning-driven decision logic.The optimized thermal profile demonstrates a clear performance enhancement over the traditional multi-dwell approach,achieving marked reductions in temperature difference,Degree of Cure(DoC)difference and tool-part interface shear stress,which provides more insights for intelligent composite manufacturing. 展开更多
关键词 CFRP composites Curing process Neural network Real-time optimization Tool-part interaction
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Mechanical property variation mechanisms of granite subjected to real-time high temperatures and subsequent cooling treatment 认领 引用
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作者 Rui Pang Dehao Meng +6 位作者 Thomas Frühwirt Hao Liu Yanan Zhao Qingyou Zhu Wengang Dang Mohamed Ismael Fei Wang 《Deep Underground Science and Engineering》 EI CAS CSCD 2026年第1期277-295,共19页
During geothermal resource exploitation,the potential deterioration of mechanical properties in high-temperature granite subjected to cooling poses a significant safety concern.To address this,the present study invest... During geothermal resource exploitation,the potential deterioration of mechanical properties in high-temperature granite subjected to cooling poses a significant safety concern.To address this,the present study investigates the coupled thermo-mechanical behavior of granite during heating and cooling through a combination of laboratory tests and finite difference method analysis.Initial investigations involve X-ray diffraction,thermal expansion test,thermogravimetric analysis,and uniaxial compression test.Results show the significant variations of granite properties under different thermal conditions,attributed to temperature gradients,water evaporation,and mineral phase transitions.Subsequently,a model considering temperature-dependent parameters and real-time cooling rates was employed to simulate linear heating and nonlinear cooling processes.Simulation results indicate that the thermal cracking predominantly occurs during the heating stage,with tensile failure as the primary mode.Additionally,a faster real-time cooling rate at higher temperatures intensifies the thermal cracking behavior in granite.This study effectively elucidates the thermomechanical coupling behavior of granite during heating and cooling processes,providing insights into the mechanisms of mechanical property changes with rising or decreasing temperatures. 展开更多
关键词 granite nonlinear cooling real-time high temperature thermal cracking thermo-mechanical coupling
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Real-time impedance spectroscopy of lithium-ion batteries via composite current pulses 认领 引用
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作者 Yue Yang Tongfang Fu +4 位作者 Wenbin Li Paul Thiele Chenye Song Simon Mertes Stefan Pischinger 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第6期902-914,I0021,共13页
Conventional sinusoidal electrochemical impedance spectroscopy is often impractical for real-time control or on-board diagnostics because measurements at low frequencies require long dwell time,resulting in lengthy te... Conventional sinusoidal electrochemical impedance spectroscopy is often impractical for real-time control or on-board diagnostics because measurements at low frequencies require long dwell time,resulting in lengthy test duration.To address this issue,the composite current pulse excitation is implemented in this work for real-time impedance spectrum acquisition,using the discrete Fourier transform.Pulse sequences and sampling conditions are designed to balance bandwidth and accuracy of the impedance results while satisfying hardware constraints and system relaxation requirements.To improve repeatability under noise and dynamic operating conditions,outliers are mitigated by introducing truncated singular value decomposition reconstruction.Two pulse widths(1 and 100 ms)are applied to overcome the bandwidth limitation of a single-width excitation,enabling an accurate spectrum across 1 k Hz to 1 Hz within~1 s.On a commercial 18650 lithium-ion battery,a mean relative impedance deviation of 2.1%compared with galvanostatic electrochemical impedance spectroscopy results is achieved across state of charge from 5%to 90%at 10 and 25℃.Time-domain voltage simulations using pulse-calibrated parameters reproduce the measured dynamic responses,achieving accuracy comparable to simulations parameterized from galvanostatic electrochemical impedance spectroscopy. 展开更多
关键词 Real-time impedance measurement Current pulse Electrochemical impedance spectroscopy Lithium-ion batteries
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A multi-scale fracture prediction method based on improved deep embedded clustering 认领 引用
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作者 Yu-jia Lu Chao Chen +2 位作者 Zhu-jiang Liu Fu-bin Wei Zhe-ge Liu 《Applied Geophysics》 SCIE CSCD 2026年第2期571-590,867,共20页
Accurately characterizing the distribution and scale characteristics of fractures in subsurface media is a crucial step for quantitatively evaluating shale gas sweet spots and guiding reservoir fracturing stimulation.... Accurately characterizing the distribution and scale characteristics of fractures in subsurface media is a crucial step for quantitatively evaluating shale gas sweet spots and guiding reservoir fracturing stimulation.However,a single seismic attribute is often used to identify fracture features of a specifi c scale,making it diffi cult to achieve detailed characterization of fractures across multiple scales simultaneously.Multi-attribute fusion algorithms often focus on statistical correlations,lacking in-depth exploration of the spatial topological relationships and intrinsic physical connections among fractures of diff erent scales,resulting in reduced accuracy in complex structural areas.To address this challenge,we propose a multi-scale integrated fracture prediction method based on an improved deep embedded clustering(DEC)framework,using the marine shale reservoir of the Wufeng–Longmaxi Formation in southeastern Sichuan Basin as a case study.Specifically,(1)an improved DEC objective function integrating fracture topology constraints and cluster-balancing mechanisms is developed to enhance the model’s adaptability to complex geological structures;(2)an“expand–then–contract”stacked autoencoder architecture is designed to better capture nonlinear relationships among multi-attribute data and decouple multi-scale fracture features;and(3)an integrated workfl ow from multi-attribute optimization,intelligent fusion clustering to geological interpretation is established,enabling diff erentiated and high-precision characterization of multi-scale fractures.Furthermore,based on the geological characteristics of the study area,we systematically analyze the spatial mapping relationships of the autoencoder’s multi-layer features and elucidate their implicit geophysical signifi cance.This analysis reveals the intrinsic processes through which the proposed model performs fracture attribute optimization,noise separation,and multi-scale feature extraction.Finally,by integrating intelligent fault identifi cation,micro-fracture amplitude variation with azimuth(AVAZ)inversion,and conventional geometric attributes,high-precision spatial characterization of the fracture system is achieved,spanning from large-scale faults to micro-fractures.The prediction results show strong agreement with geological understanding. 展开更多
关键词 Multi-scale fractures Deep embedded clustering Deep learning Autoencoding Marine shale
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Thermodynamic real-time coupling modeling and thermal characteristic analysis of angular contact ball bearings under combined loads 认领 引用
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作者 Wenbing Tu Yuankuo Lou +1 位作者 Chenlu Liu Wennian Yu 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第5期518-534,共17页
The distribution of heat generation and operating temperature are crucial factors affecting the performance and useful service life of angular contact ball bearings.A thermodynamic real-time coupling analytical model ... The distribution of heat generation and operating temperature are crucial factors affecting the performance and useful service life of angular contact ball bearings.A thermodynamic real-time coupling analytical model is established in this work to illustrate alterations in the bearing structure,internal load,and the sliding and rotation of the ball correlated with temperature fluctuations.The temperature rise and heat generation of angular contact ball bearings under combined loads are accurately calculated.The simulation of the temperature rise in bearings is conducted based on this model,and the effectiveness of the model is validated through simulation experiments.The simulation results of the distribution of heat generation are compared between thermodynamic real-time coupling and thermodynamic sequential coupling,with a focus on the transient thermal characteristics of bearings and the effect of load on their steady-state thermal characteristics.The precision of temperature simulation through thermodynamic real-time coupling is significantly higher than that through thermodynamic sequential coupling.Axial load mainly affects heat generated by the sliding and rotation of the ball on the inner raceway,while radial load predominantly affects the heat generation of the ball due to sliding on the inner and outer raceways.The findings of the work can guide the design and performance evaluation of angular contact ball bearings and provide support for temperature simulation. 展开更多
关键词 Angular contact ball bearings Combined loads Thermodynamic real-time coupling Thermal characteristics
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Experimental study on real-time monitoring of surrounding rock 3D wave velocity structure and failure zone in deep tunnels 认领 引用
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作者 Hongyun Yang Chuandong Jiang +4 位作者 Yong Li Zhi Lin Xiang Wang Yifei Wu Wanlin Feng 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2026年第2期423-437,共15页
An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of a... An innovative real-time monitoring method for surrounding rock damage based on microseismic time-lapse double-difference tomography is proposed for delayed dynamic damage identification and insufficient detection of adverse geological conditions in deep-buried tunnel construction.The installation techniques for microseismic sensors were optimized by mounting sensors at bolt ends which significantly improves signal-to-noise ratio(SNR)and anti-interference capability compared to conventional borehole placement.Subsequently,a 3D wave velocity evolution model that incorporates construction-induced disturbances was established,enabling the first visualization of spatiotemporal variations in surrounding rock wave velocity.It finds significant wave velocity reduction near the tunnel face,with roof and floor damage zones extending 40–50 m;wave velocities approaching undisturbed levels at 15 m ahead of the working face and on the laterally undisturbed side;pronounced spatial asymmetry in wave velocity distribution—values on the left side exceed those on the right,with a clear stress concentration or transition zone located 10–15 m;and systematically lower velocities behind the face than in front,indicating asymmetric rock damage development.These results provide essential theoretical support and practical guidance for optimizing dynamic construction strategies,enabling real-time adjustment of support parameters,and establishing safety early warning systems in deep-buried tunnel engineering. 展开更多
关键词 Deep-buried tunnel Microseismic monitoring Wave velocity tomography Surrounding rock damage zone Real-time monitoring
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In situ real-time imaging of photothermal-induced rupture of self-assembled naphthalimide nanofibers 认领 引用
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作者 Yinchan Zhang Wenting Yin +3 位作者 Guoli Han Yiyan Ruan Qinglong Qiao Zhaochao Xu 《Chinese Chemical Letters》 SCIE CAS CSCD 2026年第7期524-528,共5页
Photoresponsive supramolecular polymers hold great promise for applications in sensing,actuation,and biomedicine.However,the role of photothermal effects in regulating supramolecular assemblies and their dynamic struc... Photoresponsive supramolecular polymers hold great promise for applications in sensing,actuation,and biomedicine.However,the role of photothermal effects in regulating supramolecular assemblies and their dynamic structural evolution at the microscopic level remains poorly understood,as most studies have primarily focused on molecular photochemical mechanisms.Herein,we report a photothermal-responsive supramolecular system based on 1,8-naphthalimide building blocks.By reducing hydrogen-bonding sites through modification of the amide N-substituent of BAze from 4-(hydroxymethyl)benzyl to benzyl,an OH-free derivative,Ph Aze,was obtained.This subtle structural change endows Ph Aze assemblies with photoresponsive behavior that markedly differs from that of BAze assemblies.Upon 488 nm laser irradiation,Ph Aze assemblies undergo nanofiber rupture accompanied by~40%fluorescence enhancement,whereas BAze assemblies show only a slight fluorescence decrease without morphological change.Real-time in situ observation based on confocal laser scanning microscopy(CLSM)reveals the dynamic photoresponse of Ph Aze nanofibers.Localized fluorescence enhancement first appears along the fibers,followed by the formation of molten-like microspheres that ultimately lead to fiber rupture.Two distinct modes of microsphere formation,synchronous and asynchronous,are identified during this process.Moreover,thermal imaging further confirms that the response originates from a photothermal effect,where localized temperature rise weakens noncovalent interactions and induces partial melting of the supramolecular fibers.This study provides direct in situ insights into photothermal-driven structural evolution in supramolecular assemblies and offers a new strategy for designing photothermal-responsive supramolecular materials. 展开更多
关键词 Naphthalimide Self-assembly In situ real-time imaging Photoresponse dynamic Confocal laser scanning microscopy
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Deep learning powered real-time state of charge monitoring for vanadium flow battery energy storage system 认领 引用
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作者 Tianyu Li Jiawei Sun +8 位作者 Shengkai Xu Xijun Wang Huacheng Wu Feng Xing Zhao Ma He Jiang Zonghao Liu Tao Liu Xianfeng Li 《Journal of Energy Chemistry》 SCIE EI CAS CSCD 2026年第7期259-265,I0008,共7页
Vanadium flow batteries(VFBs)are well suitable for grid-scale energy storage owing to their long lifespan,high efficiency and safety.State of charge(SOC)monitoring is essential for battery health assessment and system... Vanadium flow batteries(VFBs)are well suitable for grid-scale energy storage owing to their long lifespan,high efficiency and safety.State of charge(SOC)monitoring is essential for battery health assessment and system management.However,accurate SOC determination during operation remains challenging due to vanadium ion crossover and side reactions that disrupt the valence and concentration balance between positive and negative electrolytes.Herein,an inverted transformer model,namely iTransformer,is employed to predict the SOC in VFB systems during charge–discharge cycles.The iTransformer-SOC model can achieve high accuracy and robustness.Even with training limited to the first three cycles,the model predicts SOC for the next 21 cycles with mean absolute percentage error(MAPE)less than 0.03.It can adapt to power variations and electrolyte rebalancing scenarios.Most importantly,an iTransformer-based SOC monitoring system was validated and confirmed by a 10 kW VFB system,demonstrating superior performance in predicting SOC of complete charge–discharge cycles(MAPE<0.05,less than 1/3 of the traditional open-circuit voltage(OCV)method's error).This datadriven approach provides a robust framework for real-time SOC monitoring in VFB systems,serving as a complementary alternative to physics-based model without requiring prior knowledge of system dynamics. 展开更多
关键词 Vanadium flow battery State of charge Data-driven iTransformer-based SOC model Real-time SOC monitoring
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Real-time early warning of landslide disaster risks on major highways in Ganzhou City,China 认领 引用
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作者 Faming Huang Yang Yang +6 位作者 Guotao Ma Mohammad Rezania Zhilu Chang Filippo Catani Bingchen Jiang Xi Chen Fei Guo 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第7期5218-5241,共24页
Extreme weather events like heavy rainfall have become more frequent recently,increasing the occurrence of landslides and slope instability along mountainous highways and threatening transportation safety.This researc... Extreme weather events like heavy rainfall have become more frequent recently,increasing the occurrence of landslides and slope instability along mountainous highways and threatening transportation safety.This research aims to develop an effective real-time early warning system for highway landslides triggered by extreme weather.Using landslides along Ganzhou's major highways as a case study,a 250-m buffer zone was established along the roads,within which 88,497 slope units were divided using multi-scale segmentation.Subsequently,1547 landslide samples and 18 conditioning factors were collected for landslide susceptibility prediction(LSP)based on random forest(RF),C5.0 decision tree(DT),and support vector machine(SVM)models.Model performance was evaluated using receiver operating characteristic(ROC)curves,the distribution characteristics of the landslide susceptibility index(LSI),and a confusion matrix.A continuous probability rainfall threshold equation was then fittedusing data from the rainfall station.Subsequently,the analytic hierarchy process(AHP)method was employed to assess highway vulnerability.Finally,by integrating LSI,rainfall thresholds,vulnerability,and disaster-bearing entities,effective early warning was achieved for two typical landslide cases.Results indicate that the RF model yielded the best LSP outcomes,with an Raux2 of 0.958,an RMSE of 0.069,and a sum of squared residuals of 0.331 for the continuous probability equation.The hazard assessment reached 90.4%accuracy,with hazard values exceeding 0.8 in both typical cases.AHP analysis,validated by expert experience and consistency tests,identifiedslope,road density,and road grade as key vulnerability factors.Ultimately,real-time risk early warning for typical landslide events was achieved by incorporating population distribution and economic value. 展开更多
关键词 Highway landslide risk Buffer selection Distribution of disaster-bearing entities Real-time early warning
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Real-time decision support for bolter recovery safety:Long short-term memory network-driven aircraft sequencing 认领 引用
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作者 Wei Han Changjiu Li +4 位作者 Xichao Su Yong Zhang Fang Guo Tongtong Yu Xuan Li 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第2期184-205,共22页
The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,th... The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations. 展开更多
关键词 Carrier-based aircraft Recovery scheduling Deep reinforcement learning Long short-term memory networks Dynamic real-time decision-making
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Perceiving the Battery Multi-Electrochemical States in Real-time Based on Model-Informed Neural Network 认领 引用
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作者 Ying Zhang Qinghao Zhang +3 位作者 Bin Duan Pingwei Gu Changlong Li Chenghui Zhang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第5期1041-1053,共13页
Accurate estimation of electrochemical states serves as a pathway to observe internal battery behaviors,effectively bridging the gap between micro mechanism and macro performance and enabling more precise control in a... Accurate estimation of electrochemical states serves as a pathway to observe internal battery behaviors,effectively bridging the gap between micro mechanism and macro performance and enabling more precise control in an advanced battery management system.Yet conventional pseudo-two-dimensional(P2D)physics methods suffer from high computational complexity and limit their online application.Thus,we develop a modelinformed neural network(MINN)framework that synergistically combines deep learning with a physics-based model to accurately monitor the battery electrochemical state(such as lithium-ion concentration,plating potential).Firstly,the MINN model is constructed with the innovative loss term containing experimentally measurable parameters and governing physical laws.Secondly,a composite framework based on a convolutional neural network(CNN)architecture is integrated to automatically extract features and enforce spatial boundary conditions,which significantly reduces the number of boundary loss terms that need to be solved and alleviates the complexity of the training process.After training,the MINN model can achieve an accurate estimation of internal states and even their spatiotemporal distributions that cannot be directly measured based on limited observable data and physical laws.At last,by incorporating dynamic current input,the well-trained basic model exhibits strong robustness and can be directly transferred to other cycling protocols with high accuracy,requiring no further retraining.MINN is a novel and promising framework to realize online and accurate micro electrochemical states monitoring,achieving at least 776 times speedup compared with the P2D model.As an innovative artificial intelligence assisted modeling for electrochemical systems,this framework enables root-cause analysis of battery behavior and failure modes,while empowering the management system with more reliable and trustworthy decision-making capabilities. 展开更多
关键词 Electrochemical state estimation lithium-ion battery model-informed neural network(MINN) real-time application
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Fabricating engineered tissues with spatially varied microenvironments via embedded 3D printing in a cell-dense suspension 认领 引用
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作者 Min Ye Jie Gao +9 位作者 Zhiyuan Zheng Di Xin Zhe Zhang Shilu Zhu Qingdong Zhang Yang Zhang Liang Xu Shuwei Shen Mingzhai Sun Ronald X Xu 《International Journal of Extreme Manufacturing》 SCIE EI CAS CSCD 2026年第3期595-615,共21页
The long-term goal of bioengineered tissues is to achieve precise cell type distribution,physiological cell density,perfusable vascular channels,and mature functionality.However,fabricating engineered tissue with the ... The long-term goal of bioengineered tissues is to achieve precise cell type distribution,physiological cell density,perfusable vascular channels,and mature functionality.However,fabricating engineered tissue with the microenvironmental features of organs with physiological cell density remains a significant challenge in this field.To address this,several key obstacles must be overcome.First,vascularization is indispensable for engineered tissues;however,disturbances may occur when introducing vascular channels within pre-fabricated tissues.Second,maintaining fabrication precision becomes increasingly difficult during high-cell-density embedded printing.Third,the suspension bath used for embedded printing often fails to provide a suitable growth environment.Herein,we modified the rheological properties of the bioactive hydrogel by incorporating a thixotropic laponite nanoclay(LPN)and demonstrated that an optimized ratio of collagen methacrylate(ColMA)to LPN forms a self-healing suspension bath,which is enhanced by hydrogen bonding interactions and is capable of in situ crosslinking.This printing strategy was generalized as the embedded 3D printing in cell-dense suspension(EPICS).The self-healing properties of the EPICS remain unaffected even when encapsulating a near-physiological cell density of 108 cells·mL-1,and it provides precise control of the printing resolution from 1 mm to 100μm.Compared with the model containing 106 cells·mL-1,the use of EPICS could create a robust hepatic model with mature liver markers and reduced apoptosis gene expression.Moreover,EPICS can efficiently fabricate spatially controlled perfusable channels,thereby mimicking the spatially varied microenvironments of hepatocellular carcinoma,highlighting its broad applications in therapeutics involving tissue and organ constructs. 展开更多
关键词 embedded 3D printing cell-dense suspension spatially varied microenvironments tissue and organ constructs
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FragSAM:Near real-time rock fragment segmentation for size distribution analysis across diverse engineering domains 认领 引用
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作者 Yudi Tang Yulin Wang +4 位作者 Jixiong Zhang Runzhe Hu Changwei Wang Joung Oh Guangyao Si 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2026年第6期1167-1188,共22页
Rock fragment size distribution(FSD)plays an important role in various engineering applications,such as mining,tunnelling,and other underground construction scenarios.While vision-based deep learning approaches have b... Rock fragment size distribution(FSD)plays an important role in various engineering applications,such as mining,tunnelling,and other underground construction scenarios.While vision-based deep learning approaches have been increasingly applied to FSD analysis,they are often case-specific,showing limited cross-site generalization despite their accuracy.To address these challenges,FragSAM,an end-to-end,fully automated framework is proposed for near real-time rock fragment segmentation and FSD analysis across diverse engineering environments.FragSAM integrates the generalization power of Segment Anything Model(SAM)with a context-aware prompting mechanism and lightweight architecture for efficient dense fragment segmentation.In Stage 1,an enhanced SAM automatically generates high-quality annotations,which are used to train a modified CenterNet for precise centroid prediction.In Stage 2,these centroids serve as prompts for EdgeSAM,a lightweight SAM variant optimized for real-time inference.This two-stage design eliminates dense grid prompting and reduces reliance on heavy postprocessing,enabling efficient and scalable segmentation.Experimental results show that FragSAM achieves competitive segmentation performance with significantly lower latency and model complexity compared to existing SAM-based methods.In comparison with supervised learning approaches,it also demonstrates superior generalization and performs better in low-quality or unseen scenarios.Furthermore,case studies on blasting fragmentation,TBM muck,and coastal rock surfaces confirm its robustness and seamless cross-site adaptability,requiring no tuning or retraining,making it highly practical for on-site applications. 展开更多
关键词 Segment anything model Rock fragment size distribution FragSAM Prompt-based vision models Near real-time segmentation
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Control strategy for secondary frequency regulation in hybrid energy storage considering real-time dispatchable power and maximum output constraints 认领 引用
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作者 Cuiping Li Liang Zhang +4 位作者 Wenbo Si Caiqi Jia Xingxu Zhu Gangui Yan Junhui Li 《Global Energy Interconnection》 EI CSCD 2026年第3期471-488,共18页
Hybrid energy storage systems(HESSs)involved in secondary frequency regulation(FR)can overcome the technical limitations of single energy storage systems(ESSs).However,coordinating the control of ESSs with differing c... Hybrid energy storage systems(HESSs)involved in secondary frequency regulation(FR)can overcome the technical limitations of single energy storage systems(ESSs).However,coordinating the control of ESSs with differing characteristics remains a major challenge.In this study,we propose a cooperative control strategy for HESSs in automatic generation control FR.First,the maximum output dynamic adjustment factor of the flywheel energy storage system(FESS)and the real-time dispatchable power of ESSs are introduced to constrain the charge/discharge power of ESSs.Subsequently,a coordinated allocation strategy of prioritizing the FESS,i.e.,battery energy storage system(BESS)supplementation,is adopted to pre-allocate the FR power of HESSs between BESSs and FESSs.Second,we minimized the energy loss and balanced the state of charge(SOC)of each ESS to redistribute the pre-allocated FR power of each ESS among the internal energy storage units.Finally,we conducted a simulation analysis using actual operational data.The findings indicate that the proposed strategy can reduce the lifetime loss of the BESS and enhance the continuous operating capability of the FESS.This system can also reduce the energy loss in each ESS,thereby effectively maintaining the SOC equilibrium of each system. 展开更多
关键词 Hybrid energy storage Dynamic adjustment factor Real-time dispatchable power Energy loss SOC balancing
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Tunnel boring machine jamming anticipation based on a new monitoring system for real-time perception of shield strain 认领 引用
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作者 Yucong Pan Quansheng Liu +3 位作者 Xinlin Wu Shaohui Tang Xing Huang Yalong Jiang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第7期5562-5572,共11页
Tunnel boring machine(TBM)jamming has become one of the critical factors restricting the tunnelling speed and construction period of squeezing tunnels.To minimize the potential risk and damage of jamming accidents,a s... Tunnel boring machine(TBM)jamming has become one of the critical factors restricting the tunnelling speed and construction period of squeezing tunnels.To minimize the potential risk and damage of jamming accidents,a series of methods have been proposed to perceive TBM jamming under the condition of soft and fractured surrounding rocks.However,most of these methods cannot predict TBM jamming accurately in advance or perceive the jamming process in real-time.In the present study,a real-time monitoring system(composed of strain gauges,data acquisition,data transmission and data storage)for shield strain was developed and implemented in a TBM at the Lanzhou Water Resource Project in China.The shield strain of the double-shielded TBM was monitored and analysed continuously.The working conditions of the TBM(such as excavation or standstill,jamming or disjamming)were identifiedby analysing the characteristics of shield strain.The perception information was compared to the excavation records.The results indicate that there is good consistency between them.Combined with monitoring information and fieldsurveys,the interaction mechanisms between the shield and surrounding rock were analysed qualitatively.The jamming mechanisms of the two accidents were revealed according to the variation in stable shield strain.A criterion for anticipation TBM jamming was proposed based on the shield strain characteristics(such as trend,amplitude,and magnification).These studies provide references for real-time perception and accurate anticipation of TBM jamming in soft and fractured surrounding rock conditions. 展开更多
关键词 Tunnel boring machine(TBM)jamming Shield strain Real-time perception Anticipation criterion Monitoring system
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Optimum Operation of Low-Voltage AC/DC Distribution Areas with Embedded DC Interconnections under Three-Phase Unbalanced Compensation Conditions 认领 引用
20
作者 Zhukui Tan Dacheng Zhou +4 位作者 Song Deng Jikai Li Zhuang Wu Qihui Feng Xuan Zhang 《Energy Engineering》 EI 2026年第3期81-95,共15页
This paper presents an optimal operation method for embedded DC interconnections based on low-voltage AC/DC distribution areas(EDC-LVDA)under three-phase unbalanced compensation conditions.It can optimally determine t... This paper presents an optimal operation method for embedded DC interconnections based on low-voltage AC/DC distribution areas(EDC-LVDA)under three-phase unbalanced compensation conditions.It can optimally determine the transmission power of the DC and AC paths to simultaneously improve voltage quality and reduce losses.First,considering the embedded interconnected,unbalanced power structure of the distribution area,a power flow calculation method for EDC-LVDA that accounts for three-phase unbalanced compensation is introduced.This method accurately describes the power flow distribution characteristics under both AC and DC power allocation scenarios.Second,an optimization scheduling model for EDC-LVDA under three-phase unbalanced conditions is developed,incorporating network losses,voltage quality,DC link losses,and unbalance levels.The proposed model employs an improved particle swarm optimization(IPSO)two-layer algorithm to autonomously select different power allocation coefficients for the DC link and AC section under various operating conditions.This enables embedded economic optimization scheduling while maintaining compensation for unbalanced conditions.Finally,a case study based on the IEEE 13-node system for EDC-LVDA is conducted and tested.The results show that the proposed optimal operation method achieves a 100%voltage compliance rate and reduces network losses by 13.8%,while ensuring three-phase power balance compensation.This provides a practical solution for the modernization and upgrading of low-voltage power grids. 展开更多
关键词 Power loss optimization low-voltage AC/DC distribution areas embedded DC interconnections
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