Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dua...Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dual-frequency ultrasound array.The broadband nature of electroacoustic signals requires ultrasound detector to cover both the high-frequency range(around 6MHz)signals generated by small targets and the low-frequency range(around 1MHz)signals generated by large targets.In our EAT system,we use the 6 MHz array to detect high-frequency signals from the electrodes,and the 1 MHz array for the electrical field.To test this,we conducted simulations using COMSOL Multiphysics^(®) and MATLAB's k-Wave toolbox,followed by experiments using a custom-built setup with a dual-frequency transducer and real-time data acquisition.The results demonstrated that the dual-frequency EAT system could accurately and simultaneously monitor the electroporation process,effectively showing both the treatment area and electrode placement with the application of 1 kV electric pulses with 100 ns duration.The axial resolution of the 6MHz array for EAT was 0.45 mm,significantly better than the 2mm resolution achieved with the 1MHz array.These findings validate the potential of dual-frequency EAT as a superior method for real-time electroporation monitoring.展开更多
Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements ...Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.展开更多
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
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
With the increasing integration of renewable energy,microgrids are increasingly facing stability challenges,primarily due to the lack of inherent inertia in inverter-dominated systems,which is traditionally provided b...With the increasing integration of renewable energy,microgrids are increasingly facing stability challenges,primarily due to the lack of inherent inertia in inverter-dominated systems,which is traditionally provided by synchronous generators.To address this critical issue,Virtual Synchronous Generator(VSG)technology has emerged as a highly promising solution by emulating the inertia and damping characteristics of conventional synchronous generators.To enhance the operational efficiency of virtual synchronous generators(VSGs),this study employs smallsignal modeling analysis,root locus methods,and synchronous generator power-angle characteristic analysis to comprehensively evaluate how virtual inertia and damping coefficients affect frequency stability and power output during transient processes.Based on these analyses,an adaptive control strategy is proposed:increasing the virtual inertia when the rotor angular velocity undergoes rapid changes,while strengthening the damping coefficient when the speed deviation exceeds a certain threshold to suppress angular velocity oscillations.To validate the effectiveness of the proposed method,a grid-connected VSG simulation platform was developed inMATLAB/Simulink.Comparative simulations demonstrate that the proposed adaptive control strategy outperforms conventional VSGmethods by significantly reducing grid frequency deviations and shortening active power response time during active power command changes and load disturbances.This approach enhances microgrid stability and dynamic performance,confirming its viability for renewable-dominant power systems.Future work should focus on experimental validation and real-world parameter optimization,while further exploring the strategy’s effectiveness in improvingVSG low-voltage ride-through(LVRT)capability and power-sharing applications in multi-parallel configurations.展开更多
Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning appr...Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.展开更多
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
[ Objective] This study aimed to establish a simultaneous detection method of shrimp viruses by real-time fluorescence quantitative RT-PCR, to improve the efficiency of inspection and quarantine. [ Method] A novel rea...[ Objective] This study aimed to establish a simultaneous detection method of shrimp viruses by real-time fluorescence quantitative RT-PCR, to improve the efficiency of inspection and quarantine. [ Method] A novel real-time fluorescence quantitative RT-PCR assay was established and optimized for simultaneously detecting DNA/RNA of four shrimp viruses （WSSV, IHHNV, TSV and YHV ）. [ Result] The optimized real-time fluorescence quantitative RT-PCR system gener- ated typical amplification curves with high amplification efficiencies （E = 1.06, 1.07, 0.92 and 0.92, respectively）, good hnear relationship （ r = 1 ）, uniform repeatability （ standard deviation = 0.05 - 0.46 ; variation coefficient = 0.26% - 1.62% ） and high sensitivity, exhibiting no significant differences compared with re- al-time fluorescence quantitative PCR （average error of Ct value = 0.04 -0.40; T = 0.53 -2.50; P 〉 0.05 ）. The total detection time was about 1 h. [ Conclusion] The optimized real-time fluorescence quantitative RT-PCR system can be used for rapid detection of WSSV, IHHNV, TSV and YHV.展开更多
This paper describes a real-time beam tuning method with an improved asynchronous advantage actor–critic(A3C)algorithm for accelerator systems.The operating parameters of devices are usually inconsistent with the pre...This paper describes a real-time beam tuning method with an improved asynchronous advantage actor–critic(A3C)algorithm for accelerator systems.The operating parameters of devices are usually inconsistent with the predictions of physical designs because of errors in mechanical matching and installation.Therefore,parameter optimization methods such as pointwise scanning,evolutionary algorithms(EAs),and robust conjugate direction search are widely used in beam tuning to compensate for this inconsistency.However,it is difficult for them to deal with a large number of discrete local optima.The A3C algorithm,which has been applied in the automated control field,provides an approach for improving multi-dimensional optimization.The A3C algorithm is introduced and improved for the real-time beam tuning code for accelerators.Experiments in which optimization is achieved by using pointwise scanning,the genetic algorithm(one kind of EAs),and the A3C-algorithm are conducted and compared to optimize the currents of four steering magnets and two solenoids in the low-energy beam transport section(LEBT)of the Xi’an Proton Application Facility.Optimal currents are determined when the highest transmission of a radio frequency quadrupole(RFQ)accelerator downstream of the LEBT is achieved.The optimal work points of the tuned accelerator were obtained with currents of 0 A,0 A,0 A,and 0.1 A,for the four steering magnets,and 107 A and 96 A for the two solenoids.Furthermore,the highest transmission of the RFQ was 91.2%.Meanwhile,the lower time required for the optimization with the A3C algorithm was successfully verified.Optimization with the A3C algorithm consumed 42%and 78%less time than pointwise scanning with random initialization and pre-trained initialization of weights,respectively.展开更多
Aiming at issues on multimedia communication in synchronous real-time teleteaching (SRT) systems over IP network, a 4-tuple structural mode of multimedia communication is proposed in the paper, and an SRT-oriented dis...Aiming at issues on multimedia communication in synchronous real-time teleteaching (SRT) systems over IP network, a 4-tuple structural mode of multimedia communication is proposed in the paper, and an SRT-oriented distributed MCU model is built according to the mode. Moreover, the mechanism of multicast communication across subnets is discussed. The distributed MCU model has been applied successfully in our interactive synchronous real-time telesteaching system RealClass and has shown good extendibility in operation.展开更多
Peanut,sesame and other raw materials of food are allergens for special populations.In this study,specific primers and TaqMan probes labeled by different fluorescences were designed targeting Ara h 2 gene of peanut an...Peanut,sesame and other raw materials of food are allergens for special populations.In this study,specific primers and TaqMan probes labeled by different fluorescences were designed targeting Ara h 2 gene of peanut and Ses i 1 gene of sesame.After the optimization of reaction conditions,a real-time fluorescent PCR method was established for simultaneous detection of allergenic ingredients of peanut and sesame in food.Genomic DNA samples of peanut,sesame,rice,wheat,barley,soybean,celery,maize,potato,tomato,walnut,groundnut in shell,cashew nut,sunflower seed,almond,apple,pear and strawberry,pork,beef,mutton and fish were used as templates for PCR amplification with deionized water as negative control template.Results indicated that the established real-time fluorescent PCR method could specifically identify allergenic ingredients of peanut and sesame simultaneously.Sensitivity test showed that the minimum detection limit of this method was 0.01%.Therefore,the established real-time fluorescent PCR method is a specific,sensitive and effective assay for simultaneously detecting allergenic ingredients of peanut and sesame in food.展开更多
As the popularization of dual ring fieldbus, the optimized dual ring synchronization methods are still in short. The current synchronization methods are generally established in traditional industrial fieldbus, in whi...As the popularization of dual ring fieldbus, the optimized dual ring synchronization methods are still in short. The current synchronization methods are generally established in traditional industrial fieldbus, in which transmission is commonly considered in single track, the two-way transmitting cannot take full effect, and would result in unwanted idle load on equipment lines. In stamp-transferring part, the synchronizing algorithm is not properly processed to diminish the latency, so the real-time performance of entire system cannot be ensured. To support the synchronization control of stations in the CNC system, a real-time time synchronization method for dual ring fieldbus in the CNC system is designed in this paper. In this method a synchronizing message transmission scheme based on dual ring architecture and the synchronization algorithm between master and secondary stations are integrated. In the scheme, the clock models of both master and secondary stations are optimized with corresponding modules and the stamp data transmission based on the dual ring fieldbus is devised exclusively, so the transmitting efficiency improves with less idle work. In the algorithm, all the secondary stations can accomplish the consistent state with master station by updating clock discrepancy information in one communication cycle, and it takes the advantage of two-way transmitting and makes the best use of dual ring structure, so the real-time performance of the system can be promoted while retaining the precision of synchronization. To evaluate the performance, the costs of the method and errors during synchronizing are noted and analyzed based on the actual running environment in the industrial fieldbus. The results show that it reduces communication cost and ensures the smoothness of the system with low lag effects under heavy load. The proposed time synchronization method optimizes the architecture of sync message transmission in dual ring fieldbus, and improves the efficiency of time synchronization in the stations of CNC system.展开更多
Large portions of the tunnel boring machine(TBM)construction cost are attributed to disc cutter consumption,and assessing the disc cutter's wear level can help determine the optimal time to replace the disc cutter...Large portions of the tunnel boring machine(TBM)construction cost are attributed to disc cutter consumption,and assessing the disc cutter's wear level can help determine the optimal time to replace the disc cutter.Therefore,the need to monitor disc cutter wear in real-time has emerged as a technical challenge for TBMs.In this study,real-time disc cutter wear monitoring is developed based on sound and vibration sensors.For this purpose,the microphone and accelerometer were used to record the sound and vibration signals of cutting three different types of rocks with varying abrasions on a laboratory scale.The relationship between disc cutter wear and the sound and vibration signal was determined by comparing the measurements of disc cutter wear with the signal plots for each sample.The features extracted from the signals showed that the sound and vibration signals are impacted by the progression of disc wear during the rock-cutting process.The signal features obtained from the rock-cutting operation were utilized to verify the machine learning techniques.The results showed that the multilayer perceptron(MLP),random subspace-based decision tree(RS-DT),DT,and random forest(RF)methods could predict the wear level of the disc cutter with an accuracy of 0.89,0.951,0.951,and 0.927,respectively.Based on the accuracy of the models and the confusion matrix,it was found that the RS-DT model has the best estimate for predicting the level of disc wear.This research has developed a method that can potentially determine when to replace a tool and assess disc wear in real-time.展开更多
Propelled by the rise of artificial intelligence,cloud services,and data center applications,next-generation,low-power,local-oscillator-less,digital signal processing(DSP)-free,and short-reach coherent optical communi...Propelled by the rise of artificial intelligence,cloud services,and data center applications,next-generation,low-power,local-oscillator-less,digital signal processing(DSP)-free,and short-reach coherent optical communication has evolved into an increasingly prominent area of research in recent years.Here,we demonstrate DSP-free coherent optical transmission by analog signal processing in frequency synchronous optical network(FSON)architecture,which supports polarization multiplexing and higher-order modulation formats.The FSON architecture that allows the numerous laser sources of optical transceivers within a data center can be quasi-synchronized by means of a tree-distributed homology architecture.In conjunction with our proposed pilot-tone assisted Costas loop for an analog coherent receiver,we achieve a record dual-polarization 224-Gb/s 16-QAM 5-km mismatch transmission with reset-free carrier phase recovery in the optical domain.Our proposed DSP-free analog coherent detection system based on the FSON makes it a promising solution for next-generation,low-power,and high-capacity coherent data center interconnects.展开更多
The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for he...The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for healthcare systems,particularly for identifying actions critical to patient well-being.However,challenges such as high computational demands,low accuracy,and limited adaptability persist in Human Motion Recognition(HMR).While some studies have integrated HMR with IoT for real-time healthcare applications,limited research has focused on recognizing MRHA as essential for effective patient monitoring.This study proposes a novel HMR method tailored for MRHA detection,leveraging multi-stage deep learning techniques integrated with IoT.The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions(MBConv)blocks,followed by Convolutional Long Short Term Memory(ConvLSTM)to capture spatio-temporal patterns.A classification module with global average pooling,a fully connected layer,and a dropout layer generates the final predictions.The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets,focusing on MRHA such as sneezing,falling,walking,sitting,etc.It achieves 94.85%accuracy for cross-subject evaluations and 96.45%for cross-view evaluations on NTU RGB+D 120,along with 89.22%accuracy on HMDB51.Additionally,the system integrates IoT capabilities using a Raspberry Pi and GSM module,delivering real-time alerts via Twilios SMS service to caregivers and patients.This scalable and efficient solution bridges the gap between HMR and IoT,advancing patient monitoring,improving healthcare outcomes,and reducing costs.展开更多
Permanent magnet synchronous motor(PMSM),known for their compact size and high-power density,is widely used in fields such as electric vehicles and servo drives.However,traditional PID control methods for PMSM cannot ...Permanent magnet synchronous motor(PMSM),known for their compact size and high-power density,is widely used in fields such as electric vehicles and servo drives.However,traditional PID control methods for PMSM cannot dynamically adjust parameters according to varying operating conditions.To address this issue,this paper proposes a PID control method based on a radial basis function(RBF)neural network,which adaptively tunes the PID controller parameters.First,an offline RBF neural network with optimal structural parameters is trained using the current and speed data of the PMSM,and then employed to construct the RBF-PID controller.During online training,the Jacobian information calculated via the RBF neural network is used to adaptively adjust the PID parameters.Simultaneously,the structural parameters of the RBF network are updated in reverse based on the error between the predicted and reference speed values.Finally,numerical simulations and experiments in the context of electric vehicle drive control show that the maximum speed errors of the SMC controller and the RBF-PID controller are 1.97 km/h and 0.17 km/h,respectively.Moreover,the RBF-PID controller outperforms both the SMC and traditional PID controllers in handling sudden speed changes.展开更多
Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does no...Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does not represent an important parameter.However,in critical applications,this parameter represents a crucial aspect.One important sensing device used in IoT designs is the accelerometer.In most applications,the response time of the embedded driver software handling this device is generally not analysed and not taken into account.In this paper,we present the design and implementation of a predictable real-time driver stack for a popular accelerometer and gyroscope device family.We provide clear justifications for why this response time is extremely important for critical applications in the acquisition process of such data.We present extensive measurements and experimental results that demonstrate the predictability of our solution,making it suitable for critical real-time systems.展开更多
In the real-time scheduling theory,schedulability and synchronization analyses are used to evaluate scheduling algorithms and real-time locking protocols,respectively,and the empirical synthesis experiment is one of t...In the real-time scheduling theory,schedulability and synchronization analyses are used to evaluate scheduling algorithms and real-time locking protocols,respectively,and the empirical synthesis experiment is one of the major methods to compare the performance of such analyses.However,since many sophisticated techniques have been adopted to improve the analytical accuracy,the implementation of such analyses and experiments is often time-consuming.This paper proposes a schedulability experiment toolkit for multiprocessor real-time systems(SET-MRTS),which provides a framework with infrastructures to implement the schedulability and synchronization analyses and the deployment of empirical synthesis experiments.Besides,with well-designed peripheral components for the input and output,experiments can be conducted easily and flexibly on SET-MRTS.This demonstration further proves the effectiveness of SET-MRTS in both functionality and availability.展开更多
Background Two-dimensional speckle tracking imaging （2D-STI） and real-time three-dimensional echocardiography （RT-3DE） have more advantages in evaluating left ventricular （LV） systolic dyssynchrony than traditio...Background Two-dimensional speckle tracking imaging （2D-STI） and real-time three-dimensional echocardiography （RT-3DE） have more advantages in evaluating left ventricular （LV） systolic dyssynchrony than traditional echocardiographic techniques. The study aimed to evaluate LV dyssynchrony parameters by both 2D-STI and RT-3DE, and the correlation between these two techniques. Methods A total of 43 chronic heart failure （CHF） patients and 27 healthy volunteers were enrolled. There were 23 dyssynchrony parameters selected to evaluate left ventricular systolic synchronization, involving 15 from 2D-STI and 8 from RT-3DE. Results Few of the dyssynchrony parametersshowednegative correlations with LV ejection fraction （LVEF） in the CHF group. The difference between time to peak-systolic radial strain of the anteroseptal and posterior segments at the level of papillary muscles [AS-P（RS）] from 2D-STI showed positive correlations with parts of the parameters from RT-3DE （P 〈 0.05）. Conclusions LV systolic dysfunction does not correlate with dyssynchrony. Moreover, there is a weak association between 2D-STI and RT-3DE in assessment of left ventricular dyssynchrony.展开更多
基金supported by the National Institute of Health(R37CA240806,U01CA288351,and R50CA283816)support from UCI Chao Family Comprehensive Cancer Center(P30CA062203).
文摘Electroacoustic Tomography(EAT)is an imaging technique that detects ultrasound waves induced by electrical pulses,offering a solution for real-time electroporation monitoring.This study presents EAT system using a dual-frequency ultrasound array.The broadband nature of electroacoustic signals requires ultrasound detector to cover both the high-frequency range(around 6MHz)signals generated by small targets and the low-frequency range(around 1MHz)signals generated by large targets.In our EAT system,we use the 6 MHz array to detect high-frequency signals from the electrodes,and the 1 MHz array for the electrical field.To test this,we conducted simulations using COMSOL Multiphysics^(®) and MATLAB's k-Wave toolbox,followed by experiments using a custom-built setup with a dual-frequency transducer and real-time data acquisition.The results demonstrated that the dual-frequency EAT system could accurately and simultaneously monitor the electroporation process,effectively showing both the treatment area and electrode placement with the application of 1 kV electric pulses with 100 ns duration.The axial resolution of the 6MHz array for EAT was 0.45 mm,significantly better than the 2mm resolution achieved with the 1MHz array.These findings validate the potential of dual-frequency EAT as a superior method for real-time electroporation monitoring.
基金the support of the Major Science and Technology Project of Yunnan Province,China(Grant No.202502AD080007)the National Natural Science Foundation of China(Grant No.52378288)。
文摘Vehicle-induced response separation is a crucial issue in structural health monitoring(SHM).This paper proposes a block-wise sliding recursive wavelet transform algorithm to meet the real-time processing requirements of monitoring data.To extend the separation target from a fixed dataset to a continuously updating data stream,a block-wise sliding framework is first developed.This framework is further optimized considering the characteristics of real-time data streams,and its advantage in computational efficiency is theoretically demonstrated.During the decomposition and reconstruction processes,information from neighboring data blocks is fully utilized to reduce algorithmic complexity.In addition,a delay-setting strategy is introduced for each processing window to mitigate boundary effects,thereby balancing accuracy and efficiency.Simulated signal experiments are conducted to determine the optimal delay configuration and to verify the algorithm’s superior performance,achieving a lower Root Mean Square Error(RMSE)and only 0.0249 times the average computational time compared with the original algorithm.Furthermore,strain signals from the Lieshi River Bridge are employed to validate the method.The proposed algorithm successfully separates the static trend from vehicle-induced responses in real time across different sampling frequencies,demonstrating its effectiveness and applicability in real-time bridge monitoring.
基金supported by the National Natural Science Foundation of China(Grant No.42077244)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX24_0434).
文摘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.
基金support of the National Natural Science Foundation of China(No.52274176)the Guangdong Province Key Areas R&D Program(No.2022B0101070001)+5 种基金Chongqing Elite Innovation and Entrepreneurship Leading talent Project(No.CQYC20220302517)the Chongqing Natural Science Foundation Innovation and Development Joint Fund(No.CSTB2022NSCQ-LZX0079)the National Key Research and Development Program Young Scientists Project(No.2022YFC2905700)the Chongqing Municipal Education Commission“Shuangcheng Economic Circle Construction in Chengdu-Chongqing Area”Science and Technology Innovation Project(No.KJCX2020031)the Fundamental Research Funds for the Central Universities(No.2024CDJGF-009)the Key Project for Technological Innovation and Application Development in Chongqing(No.CSTB2025TIAD-KPX0029).
文摘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.
基金financially supported by the Talent Initiation Fund of Wuxi University(550220008).
文摘With the increasing integration of renewable energy,microgrids are increasingly facing stability challenges,primarily due to the lack of inherent inertia in inverter-dominated systems,which is traditionally provided by synchronous generators.To address this critical issue,Virtual Synchronous Generator(VSG)technology has emerged as a highly promising solution by emulating the inertia and damping characteristics of conventional synchronous generators.To enhance the operational efficiency of virtual synchronous generators(VSGs),this study employs smallsignal modeling analysis,root locus methods,and synchronous generator power-angle characteristic analysis to comprehensively evaluate how virtual inertia and damping coefficients affect frequency stability and power output during transient processes.Based on these analyses,an adaptive control strategy is proposed:increasing the virtual inertia when the rotor angular velocity undergoes rapid changes,while strengthening the damping coefficient when the speed deviation exceeds a certain threshold to suppress angular velocity oscillations.To validate the effectiveness of the proposed method,a grid-connected VSG simulation platform was developed inMATLAB/Simulink.Comparative simulations demonstrate that the proposed adaptive control strategy outperforms conventional VSGmethods by significantly reducing grid frequency deviations and shortening active power response time during active power command changes and load disturbances.This approach enhances microgrid stability and dynamic performance,confirming its viability for renewable-dominant power systems.Future work should focus on experimental validation and real-world parameter optimization,while further exploring the strategy’s effectiveness in improvingVSG low-voltage ride-through(LVRT)capability and power-sharing applications in multi-parallel configurations.
文摘Online examinations have become a dominant assessment mode,increasing concerns over academic integrity.To address the critical challenge of detecting cheating behaviours,this study proposes a hybrid deep learning approach that combines visual detection and temporal behaviour classification.The methodology utilises object detection models—You Only Look Once(YOLOv12),Faster Region-based Convolutional Neural Network(RCNN),and Single Shot Detector(SSD)MobileNet—integrated with classification models such as Convolutional Neural Networks(CNN),Bidirectional Gated Recurrent Unit(Bi-GRU),and CNN-LSTM(Long Short-Term Memory).Two distinct datasets were used:the Online Exam Proctoring(EOP)dataset from Michigan State University and the School of Computer Science,Duy Tan Unievrsity(SCS-DTU)dataset collected in a controlled classroom setting.A diverse set of cheating behaviours,including book usage,unauthorised interaction,internet access,and mobile phone use,was categorised.Comprehensive experiments evaluated the models based on accuracy,precision,recall,training time,inference speed,and memory usage.We evaluate nine detector-classifier pairings under a unified budget and score them via a calibrated harmonic mean of detection and classification accuracies,enabling deployment-oriented selection under latency and memory constraints.Macro-Precision/Recall/F1 and Receiver Operating Characteristic-Area Under the Curve(ROC-AUC)are reported for the top configurations,revealing consistent advantages of object-centric pipelines for fine-grained cheating cues.The highest overall score is achieved by YOLOv12+CNN(97.15%accuracy),while SSD-MobileNet+CNN provides the best speed-efficiency trade-off for edge devices.This research provides valuable insights into selecting and deploying appropriate deep learning models for maintaining exam integrity under varying resource constraints.
基金supported by the National Natural Science Foundation of China(Grant No.62403486)。
文摘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.
文摘[ Objective] This study aimed to establish a simultaneous detection method of shrimp viruses by real-time fluorescence quantitative RT-PCR, to improve the efficiency of inspection and quarantine. [ Method] A novel real-time fluorescence quantitative RT-PCR assay was established and optimized for simultaneously detecting DNA/RNA of four shrimp viruses （WSSV, IHHNV, TSV and YHV ）. [ Result] The optimized real-time fluorescence quantitative RT-PCR system gener- ated typical amplification curves with high amplification efficiencies （E = 1.06, 1.07, 0.92 and 0.92, respectively）, good hnear relationship （ r = 1 ）, uniform repeatability （ standard deviation = 0.05 - 0.46 ; variation coefficient = 0.26% - 1.62% ） and high sensitivity, exhibiting no significant differences compared with re- al-time fluorescence quantitative PCR （average error of Ct value = 0.04 -0.40; T = 0.53 -2.50; P 〉 0.05 ）. The total detection time was about 1 h. [ Conclusion] The optimized real-time fluorescence quantitative RT-PCR system can be used for rapid detection of WSSV, IHHNV, TSV and YHV.
文摘This paper describes a real-time beam tuning method with an improved asynchronous advantage actor–critic(A3C)algorithm for accelerator systems.The operating parameters of devices are usually inconsistent with the predictions of physical designs because of errors in mechanical matching and installation.Therefore,parameter optimization methods such as pointwise scanning,evolutionary algorithms(EAs),and robust conjugate direction search are widely used in beam tuning to compensate for this inconsistency.However,it is difficult for them to deal with a large number of discrete local optima.The A3C algorithm,which has been applied in the automated control field,provides an approach for improving multi-dimensional optimization.The A3C algorithm is introduced and improved for the real-time beam tuning code for accelerators.Experiments in which optimization is achieved by using pointwise scanning,the genetic algorithm(one kind of EAs),and the A3C-algorithm are conducted and compared to optimize the currents of four steering magnets and two solenoids in the low-energy beam transport section(LEBT)of the Xi’an Proton Application Facility.Optimal currents are determined when the highest transmission of a radio frequency quadrupole(RFQ)accelerator downstream of the LEBT is achieved.The optimal work points of the tuned accelerator were obtained with currents of 0 A,0 A,0 A,and 0.1 A,for the four steering magnets,and 107 A and 96 A for the two solenoids.Furthermore,the highest transmission of the RFQ was 91.2%.Meanwhile,the lower time required for the optimization with the A3C algorithm was successfully verified.Optimization with the A3C algorithm consumed 42%and 78%less time than pointwise scanning with random initialization and pre-trained initialization of weights,respectively.
基金ThisworkwassupportedbytheNationalScienceFoundationofChina (No .60 10 3 0 2 2 ) .
文摘Aiming at issues on multimedia communication in synchronous real-time teleteaching (SRT) systems over IP network, a 4-tuple structural mode of multimedia communication is proposed in the paper, and an SRT-oriented distributed MCU model is built according to the mode. Moreover, the mechanism of multicast communication across subnets is discussed. The distributed MCU model has been applied successfully in our interactive synchronous real-time telesteaching system RealClass and has shown good extendibility in operation.
基金Supported by Scientific Research Project of Anhui Bureau of Quality and Technical Supervision(13zj370033)
文摘Peanut,sesame and other raw materials of food are allergens for special populations.In this study,specific primers and TaqMan probes labeled by different fluorescences were designed targeting Ara h 2 gene of peanut and Ses i 1 gene of sesame.After the optimization of reaction conditions,a real-time fluorescent PCR method was established for simultaneous detection of allergenic ingredients of peanut and sesame in food.Genomic DNA samples of peanut,sesame,rice,wheat,barley,soybean,celery,maize,potato,tomato,walnut,groundnut in shell,cashew nut,sunflower seed,almond,apple,pear and strawberry,pork,beef,mutton and fish were used as templates for PCR amplification with deionized water as negative control template.Results indicated that the established real-time fluorescent PCR method could specifically identify allergenic ingredients of peanut and sesame simultaneously.Sensitivity test showed that the minimum detection limit of this method was 0.01%.Therefore,the established real-time fluorescent PCR method is a specific,sensitive and effective assay for simultaneously detecting allergenic ingredients of peanut and sesame in food.
基金supported by National Projects for Science and Technology Development of China(Grant No.2011ZX04016-071)National Basic Research Program of China(973 Program,Grant No.2011CB302400-G,the mathematical mechanization method and its application in digital design and manufacturing)
文摘As the popularization of dual ring fieldbus, the optimized dual ring synchronization methods are still in short. The current synchronization methods are generally established in traditional industrial fieldbus, in which transmission is commonly considered in single track, the two-way transmitting cannot take full effect, and would result in unwanted idle load on equipment lines. In stamp-transferring part, the synchronizing algorithm is not properly processed to diminish the latency, so the real-time performance of entire system cannot be ensured. To support the synchronization control of stations in the CNC system, a real-time time synchronization method for dual ring fieldbus in the CNC system is designed in this paper. In this method a synchronizing message transmission scheme based on dual ring architecture and the synchronization algorithm between master and secondary stations are integrated. In the scheme, the clock models of both master and secondary stations are optimized with corresponding modules and the stamp data transmission based on the dual ring fieldbus is devised exclusively, so the transmitting efficiency improves with less idle work. In the algorithm, all the secondary stations can accomplish the consistent state with master station by updating clock discrepancy information in one communication cycle, and it takes the advantage of two-way transmitting and makes the best use of dual ring structure, so the real-time performance of the system can be promoted while retaining the precision of synchronization. To evaluate the performance, the costs of the method and errors during synchronizing are noted and analyzed based on the actual running environment in the industrial fieldbus. The results show that it reduces communication cost and ensures the smoothness of the system with low lag effects under heavy load. The proposed time synchronization method optimizes the architecture of sync message transmission in dual ring fieldbus, and improves the efficiency of time synchronization in the stations of CNC system.
文摘Large portions of the tunnel boring machine(TBM)construction cost are attributed to disc cutter consumption,and assessing the disc cutter's wear level can help determine the optimal time to replace the disc cutter.Therefore,the need to monitor disc cutter wear in real-time has emerged as a technical challenge for TBMs.In this study,real-time disc cutter wear monitoring is developed based on sound and vibration sensors.For this purpose,the microphone and accelerometer were used to record the sound and vibration signals of cutting three different types of rocks with varying abrasions on a laboratory scale.The relationship between disc cutter wear and the sound and vibration signal was determined by comparing the measurements of disc cutter wear with the signal plots for each sample.The features extracted from the signals showed that the sound and vibration signals are impacted by the progression of disc wear during the rock-cutting process.The signal features obtained from the rock-cutting operation were utilized to verify the machine learning techniques.The results showed that the multilayer perceptron(MLP),random subspace-based decision tree(RS-DT),DT,and random forest(RF)methods could predict the wear level of the disc cutter with an accuracy of 0.89,0.951,0.951,and 0.927,respectively.Based on the accuracy of the models and the confusion matrix,it was found that the RS-DT model has the best estimate for predicting the level of disc wear.This research has developed a method that can potentially determine when to replace a tool and assess disc wear in real-time.
基金supported by the National Natural Science Foundation of China(Grant Nos.62405250 and 62471404)the China Postdoctoral Science Foundation(Grant No.2024M762955)+1 种基金the Key Project of Westlake Institute for Optoelectronics(Grant No.2023GD003)the Optical Com-munication and Sensing Laboratory,School of Engineering,Westlake University.
文摘Propelled by the rise of artificial intelligence,cloud services,and data center applications,next-generation,low-power,local-oscillator-less,digital signal processing(DSP)-free,and short-reach coherent optical communication has evolved into an increasingly prominent area of research in recent years.Here,we demonstrate DSP-free coherent optical transmission by analog signal processing in frequency synchronous optical network(FSON)architecture,which supports polarization multiplexing and higher-order modulation formats.The FSON architecture that allows the numerous laser sources of optical transceivers within a data center can be quasi-synchronized by means of a tree-distributed homology architecture.In conjunction with our proposed pilot-tone assisted Costas loop for an analog coherent receiver,we achieve a record dual-polarization 224-Gb/s 16-QAM 5-km mismatch transmission with reset-free carrier phase recovery in the optical domain.Our proposed DSP-free analog coherent detection system based on the FSON makes it a promising solution for next-generation,low-power,and high-capacity coherent data center interconnects.
基金funded by the ICT Division of theMinistry of Posts,Telecommunications,and Information Technology of Bangladesh under Grant Number 56.00.0000.052.33.005.21-7(Tracking No.22FS15306)support from the University of Rajshahi.
文摘The Internet of Things(IoT)and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients.Recognizing Medical-Related Human Activities(MRHA)is pivotal for healthcare systems,particularly for identifying actions critical to patient well-being.However,challenges such as high computational demands,low accuracy,and limited adaptability persist in Human Motion Recognition(HMR).While some studies have integrated HMR with IoT for real-time healthcare applications,limited research has focused on recognizing MRHA as essential for effective patient monitoring.This study proposes a novel HMR method tailored for MRHA detection,leveraging multi-stage deep learning techniques integrated with IoT.The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions(MBConv)blocks,followed by Convolutional Long Short Term Memory(ConvLSTM)to capture spatio-temporal patterns.A classification module with global average pooling,a fully connected layer,and a dropout layer generates the final predictions.The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets,focusing on MRHA such as sneezing,falling,walking,sitting,etc.It achieves 94.85%accuracy for cross-subject evaluations and 96.45%for cross-view evaluations on NTU RGB+D 120,along with 89.22%accuracy on HMDB51.Additionally,the system integrates IoT capabilities using a Raspberry Pi and GSM module,delivering real-time alerts via Twilios SMS service to caregivers and patients.This scalable and efficient solution bridges the gap between HMR and IoT,advancing patient monitoring,improving healthcare outcomes,and reducing costs.
文摘Permanent magnet synchronous motor(PMSM),known for their compact size and high-power density,is widely used in fields such as electric vehicles and servo drives.However,traditional PID control methods for PMSM cannot dynamically adjust parameters according to varying operating conditions.To address this issue,this paper proposes a PID control method based on a radial basis function(RBF)neural network,which adaptively tunes the PID controller parameters.First,an offline RBF neural network with optimal structural parameters is trained using the current and speed data of the PMSM,and then employed to construct the RBF-PID controller.During online training,the Jacobian information calculated via the RBF neural network is used to adaptively adjust the PID parameters.Simultaneously,the structural parameters of the RBF network are updated in reverse based on the error between the predicted and reference speed values.Finally,numerical simulations and experiments in the context of electric vehicle drive control show that the maximum speed errors of the SMC controller and the RBF-PID controller are 1.97 km/h and 0.17 km/h,respectively.Moreover,the RBF-PID controller outperforms both the SMC and traditional PID controllers in handling sudden speed changes.
文摘Along with process control,perception represents the main function performed by the Edge Layer of an Internet of Things(IoT)network.Many of these networks implement various applications where the response time does not represent an important parameter.However,in critical applications,this parameter represents a crucial aspect.One important sensing device used in IoT designs is the accelerometer.In most applications,the response time of the embedded driver software handling this device is generally not analysed and not taken into account.In this paper,we present the design and implementation of a predictable real-time driver stack for a popular accelerometer and gyroscope device family.We provide clear justifications for why this response time is extremely important for critical applications in the acquisition process of such data.We present extensive measurements and experimental results that demonstrate the predictability of our solution,making it suitable for critical real-time systems.
基金supported by the National Natural Science Foundation of China under Grant No.61802052the Fundamental Research Funds for the Central Universities under Grant No.A030202063008085the China Postdoctoral Science Foundation Funded Project under Grant No.2017M612947。
文摘In the real-time scheduling theory,schedulability and synchronization analyses are used to evaluate scheduling algorithms and real-time locking protocols,respectively,and the empirical synthesis experiment is one of the major methods to compare the performance of such analyses.However,since many sophisticated techniques have been adopted to improve the analytical accuracy,the implementation of such analyses and experiments is often time-consuming.This paper proposes a schedulability experiment toolkit for multiprocessor real-time systems(SET-MRTS),which provides a framework with infrastructures to implement the schedulability and synchronization analyses and the deployment of empirical synthesis experiments.Besides,with well-designed peripheral components for the input and output,experiments can be conducted easily and flexibly on SET-MRTS.This demonstration further proves the effectiveness of SET-MRTS in both functionality and availability.
文摘Background Two-dimensional speckle tracking imaging （2D-STI） and real-time three-dimensional echocardiography （RT-3DE） have more advantages in evaluating left ventricular （LV） systolic dyssynchrony than traditional echocardiographic techniques. The study aimed to evaluate LV dyssynchrony parameters by both 2D-STI and RT-3DE, and the correlation between these two techniques. Methods A total of 43 chronic heart failure （CHF） patients and 27 healthy volunteers were enrolled. There were 23 dyssynchrony parameters selected to evaluate left ventricular systolic synchronization, involving 15 from 2D-STI and 8 from RT-3DE. Results Few of the dyssynchrony parametersshowednegative correlations with LV ejection fraction （LVEF） in the CHF group. The difference between time to peak-systolic radial strain of the anteroseptal and posterior segments at the level of papillary muscles [AS-P（RS）] from 2D-STI showed positive correlations with parts of the parameters from RT-3DE （P 〈 0.05）. Conclusions LV systolic dysfunction does not correlate with dyssynchrony. Moreover, there is a weak association between 2D-STI and RT-3DE in assessment of left ventricular dyssynchrony.
