As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic e...As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.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
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
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
A real-time non-uniform rational B-spline （NURBS） surface interpolator is proposed and 5-axis machining method with a flat-end cutter is discussed. With the Taylor expansion and the coordinate transformation, the al...A real-time non-uniform rational B-spline （NURBS） surface interpolator is proposed and 5-axis machining method with a flat-end cutter is discussed. With the Taylor expansion and the coordinate transformation, the algorithms of NURBS interpolation, cutter effective machining radius, cutter offsetting and.inverse kinematics are deduced and implemented, respectively. Different from the conventional free-form surface machining, the proposed interpolator can real-time generate the motion commands of computer numerical control （CNC） machines with CC feedrate, rather than that of CL. An example part surface is demonstrated and the results of simulation show that the proposed method can be applied in actual 5-axis surface machining.展开更多
In the foundry industries,process design has traditionally relied on manuals and complex theoretical calculations.With the advent of 3D design in casting,computer-aided design(CAD)has been applied to integrate the fea...In the foundry industries,process design has traditionally relied on manuals and complex theoretical calculations.With the advent of 3D design in casting,computer-aided design(CAD)has been applied to integrate the features of casting process,thereby expanding the scope of design options.These technologies use parametric model design techniques for rapid component creation and use databases to access standard process parameters and design specifications.However,3D models are currently still created through inputting or calling parameters,which requires numerous verifications through calculations to ensure the design rationality.This process may be significantly slowed down due to repetitive modifications and extended design time.As a result,there are increasingly urgent demands for a real-time verification mechanism to address this issue.Therefore,this study proposed a novel closed-loop model and software development method that integrated contextual design with real-time verification,dynamically verifying relevant rules for designing 3D casting components.Additionally,the study analyzed three typical closed-loop scenarios of agile design in an independent developed intelligent casting process system.It is believed that foundry industries can potentially benefit from favorably reduced design cycles to yield an enhanced competitive product market.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
The principle of real-time look-ahead was introduced and analysed. An adaptive parametric curve interpolator with a real-time look-ahead function was developed for non-uniform rational B-spline (NURBS) curves interpol...The principle of real-time look-ahead was introduced and analysed. An adaptive parametric curve interpolator with a real-time look-ahead function was developed for non-uniform rational B-spline (NURBS) curves interpolation, which considering the maximum acceleration/deceleration of the machine tool. In order to deal with the acceleration/deceleration around the feedrate sensitive corners, the look-ahead function was designed and illustrated. It can detect and adjust the feedrate adaptively. With the help of real-time look-ahead, the acceleration/deceleration can be limited to the range of the machine tool capacity. Thus, feedrate fluctuation is reduced. A NURBS curve interpolation experiment was provided to verify the feasibility and advantages of the proposed interpolator with a real-time look-ahead function.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
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
Purpose–This research aims to monitor seismic intensity along railway lines,study methods for calculating the extent of earthquake impact on railways and address practical challenges in estimating intensity distribut...Purpose–This research aims to monitor seismic intensity along railway lines,study methods for calculating the extent of earthquake impact on railways and address practical challenges in estimating intensity distribution along railway routes,thereby achieving graded post-earthquake response measures.Design/methodology/approach–The seismic intensity monitoring system for railways adopts a two-level architecture,namely the seismic intensity monitoring equipment and the seismic intensity rapid reporting information center processing platform.The platform obtains measured instrumental intensity through the seismic intensity monitoring equipment deployed along railways and combines it with the National Seismic Network Earthquake Catalog to generate real-time railway seismic intensity distribution maps using the Kriging interpolation algorithm.A calculation method for railway seismic impact intervals is designed to calculate the mileage intervals where the intensity area corresponding to each contour line in the seismic intensity distribution map intersects with the railway line.Findings–The system was deployed for practical earthquake monitoring demonstration applications on the Nanjiang Railway Line in Xinjiang.During the operational period,the seismic intensity monitoring equipment calculated and uploaded instrumental intensity values to the seismic intensity rapid reporting information center processing platform a total of nine times.Among these,earthquakes triggering the Kriging interpolation algorithm occurred twice.The system operated stably throughout the application period and successfully visualized relevant seismic impact data,such as earthquake intensity distribution maps and affected railway mileage sections.These results validate the system’s practicality and effectiveness.Originality/value–The seismic intensity monitoring for the railway system designed in this study can integrate the measured instrumental intensity data along railways and the earthquake catalog of the National Seismic Network.It uses the Kriging interpolation method to calculate the intensity distribution and determine the seismic impact scope,thereby addressing the issue that the seismic intensity distribution calculated by traditional attenuation formulas deviates from reality.The system can provide clear graded interval recommendations for post-earthquake disposal,effectively improve the efficiency of post-earthquake recovery and inspection and offer a decision-making basis for restoring railway operations quickly.展开更多
Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation...Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy.Therefore,balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation.To address these challenges,this paper proposes a lightweight bilateral dual-residual network.By introducing a novel residual structure combined with feature extraction and fusion modules,the proposed network significantly enhances representational capacity while reducing computational costs.Specifically,an improved compound residual structure is designed to optimize the efficiency of information propagation and feature extraction.Furthermore,the proposed feature extraction and fusion module enables the network to better capture multi-scale information in images,improving the ability to detect both detailed and global semantic features.Experimental results on the publicly available Cityscapes dataset demonstrate that the proposed lightweight dual-branch network achieves outstanding performance while maintaining low computational complexity.In particular,the network achieved a mean Intersection over Union(mIoU)of 78.4%on the Cityscapes validation set,surpassing many existing semantic segmentation models.Additionally,in terms of inference speed,the network reached 74.5 frames per second when tested on an NVIDIA GeForce RTX 3090 GPU,significantly improving real-time performance.展开更多
Here,a novel real-time monitoring sensor that integrates the oxidation of peroxymonosulfate(PMS)and the in situ monitoring of the pollutant degradation process is proposed.Briefly,FeCo@carbon fiber(FeCo@CF)was utilize...Here,a novel real-time monitoring sensor that integrates the oxidation of peroxymonosulfate(PMS)and the in situ monitoring of the pollutant degradation process is proposed.Briefly,FeCo@carbon fiber(FeCo@CF)was utilized as the anode electrode,while graphite rods served as the cathode electrode in assembling the galvanic cell.The FeCo@CF electrode exhibited rapid reactivity with PMS,generating reactive oxygen species that efficiently degrade organic pollutants.The degradation experiments indicate that complete bisphenol A(BPA)degradation was achieved within 10 min under optimal conditions.The real-time electrochemical signal was measured in time during the catalytic reaction,and a linear relationship between BPA concentration and the real-time charge(Q)was confirmed by the equation ln(C0/C)=4.393Q(correlation coefficients,R^(2)=0.998).Furthermore,experiments conducted with aureomycin and tetracycline further validated the effectiveness of the monitoring sensor.First-principles investigation confirmed the superior adsorption energy and improved electron transfer in FeCo@CF.The integration of pollutant degradation with in situ monitoring of catalytic reactions offers promising prospects for expanding the scope of the monitoring of catalytic processes and making significant contributions to environmental purification.展开更多
In this study,a high-confining pressure and real-time large-displacement shearing-flow setup was developed.The test setup can be used to analyze the injection pressure conditions that increase the hydro-shearing perme...In this study,a high-confining pressure and real-time large-displacement shearing-flow setup was developed.The test setup can be used to analyze the injection pressure conditions that increase the hydro-shearing permeability and injection-induced seismicity during hot dry rock geothermal extraction.For optimizing injection strategies and improving engineering safety,real-time permeability,deformation,and energy release characteristics of fractured granite samples driven by injected water pressure under different critical sliding conditions were evaluated.The results indicated that:(1)A low injection water pressure induced intermittent small-deformation stick-slip behavior in fractures,and a high injection pressure primarily caused continuous high-speed large-deformation sliding in fractures.The optimal injection water pressure range was defined for enhancing hydraulic shear permeability and preventing large injection-induced earthquakes.(2)Under the same experimental conditions,fracture sliding was deemed as the major factor that enhanced the hydraulic shear-permeability enhancement and the maximum permeability increased by 36.54 and 41.59 times,respectively,in above two slip modes.(3)Based on the real-time transient evolution of water pressure during fracture sliding,the variation coefficients of slip rate,permeability,and water pressure were fitted,and the results were different from those measured under quasi-static conditions.(4)The maximum and minimum shear strength criteria for injection-induced fracture sliding were also determined(μ=0.6665 andμ=0.1645,respectively,μis friction coefficient).Using the 3D(three-dimensional)fracture surface scanning technology,the weakening effect of injection pressure on fracture surface damage characteristics was determined,which provided evidence for the geological markers of fault sliding mode and sliding nature transitions under the fluid influence.展开更多
The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability...The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.展开更多
With the rapid expansion of multimedia data,protecting digital information has become increasingly critical.Reversible data hiding offers an effective solution by allowing sensitive information to be embedded in multi...With the rapid expansion of multimedia data,protecting digital information has become increasingly critical.Reversible data hiding offers an effective solution by allowing sensitive information to be embedded in multimedia files while enabling full recovery of the original data after extraction.Audio,as a vital medium in communication,entertainment,and information sharing,demands the same level of security as images.However,embedding data in encrypted audio poses unique challenges due to the trade-offs between security,data integrity,and embedding capacity.This paper presents a novel interpolation-based reversible data hiding algorithm for encrypted audio that achieves scalable embedding capacity.By increasing sample density through interpolation,embedding opportunities are significantly enhanced while maintaining encryption throughout the process.The method further integrates multiple most significant bit(multi-MSB)prediction and Huffman coding to optimize compression and embedding efficiency.Experimental results on standard audio datasets demonstrate the proposed algorithm’s ability to embed up to 12.47 bits per sample with over 9.26 bits per sample available for pure embedding capacity,while preserving full reversibility.These results confirm the method’s suitability for secure applications that demand high embedding capacity and perfect reconstruction of original audio.This work advances reversible data hiding in encrypted audio by offering a secure,efficient,and fully reversible data hiding framework.展开更多
Blended acquisition offers efficiency improvements over conventional seismic data acquisition, at the cost of introducing blending noise effects. Besides, seismic data often suffers from irregularly missing shots caus...Blended acquisition offers efficiency improvements over conventional seismic data acquisition, at the cost of introducing blending noise effects. Besides, seismic data often suffers from irregularly missing shots caused by artificial or natural effects during blended acquisition. Therefore, blending noise attenuation and missing shots reconstruction are essential for providing high-quality seismic data for further seismic processing and interpretation. The iterative shrinkage thresholding algorithm can help obtain deblended data based on sparsity assumptions of complete unblended data, and it characterizes seismic data linearly. Supervised learning algorithms can effectively capture the nonlinear relationship between incomplete pseudo-deblended data and complete unblended data. However, the dependence on complete unblended labels limits their practicality in field applications. Consequently, a self-supervised algorithm is presented for simultaneous deblending and interpolation of incomplete blended data, which minimizes the difference between simulated and observed incomplete pseudo-deblended data. The used blind-trace U-Net (BTU-Net) prevents identity mapping during complete unblended data estimation. Furthermore, a multistep process with blending noise simulation-subtraction and missing traces reconstruction-insertion is used in each step to improve the deblending and interpolation performance. Experiments with synthetic and field incomplete blended data demonstrate the effectiveness of the multistep self-supervised BTU-Net algorithm.展开更多
基金supported by the National Natural Science Foundation of China 62402171.
文摘As a fundamental component in computer vision,edges can be categorized into four types based on discontinuities in reflectance,illumination,surface normal,or depth.While deep CNNs have significantly advanced generic edge detection,real-time multi-class semantic edge detection under resource constraints remains challenging.To address this,we propose a lightweight framework based on PiDiNet that enables fine-grained semantic edge detection.Our model simultaneously predicts background and four edge categories from full-resolution inputs,balancing accuracy and efficiency.Key contributions include:a multi-channel output structure expanding binary edge prediction to five classes,supported by a deep supervision mechanism;a dynamic class-balancing strategy combining adaptive weighting with physical priors to handle extreme class imbalance;and maintained architectural efficiency enabling real-time inference.Extensive evaluations on BSDS-RIND show our approach achieves accuracy competitive with state-of-the-art methods while operating in real time.
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.
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A real-time non-uniform rational B-spline （NURBS） surface interpolator is proposed and 5-axis machining method with a flat-end cutter is discussed. With the Taylor expansion and the coordinate transformation, the algorithms of NURBS interpolation, cutter effective machining radius, cutter offsetting and.inverse kinematics are deduced and implemented, respectively. Different from the conventional free-form surface machining, the proposed interpolator can real-time generate the motion commands of computer numerical control （CNC） machines with CC feedrate, rather than that of CL. An example part surface is demonstrated and the results of simulation show that the proposed method can be applied in actual 5-axis surface machining.
基金the financial support of the Natural Science Foundation of Hubei Province,China (Grant No.2022CFB770)。
文摘In the foundry industries,process design has traditionally relied on manuals and complex theoretical calculations.With the advent of 3D design in casting,computer-aided design(CAD)has been applied to integrate the features of casting process,thereby expanding the scope of design options.These technologies use parametric model design techniques for rapid component creation and use databases to access standard process parameters and design specifications.However,3D models are currently still created through inputting or calling parameters,which requires numerous verifications through calculations to ensure the design rationality.This process may be significantly slowed down due to repetitive modifications and extended design time.As a result,there are increasingly urgent demands for a real-time verification mechanism to address this issue.Therefore,this study proposed a novel closed-loop model and software development method that integrated contextual design with real-time verification,dynamically verifying relevant rules for designing 3D casting components.Additionally,the study analyzed three typical closed-loop scenarios of agile design in an independent developed intelligent casting process system.It is believed that foundry industries can potentially benefit from favorably reduced design cycles to yield an enhanced competitive product market.
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.
文摘The principle of real-time look-ahead was introduced and analysed. An adaptive parametric curve interpolator with a real-time look-ahead function was developed for non-uniform rational B-spline (NURBS) curves interpolation, which considering the maximum acceleration/deceleration of the machine tool. In order to deal with the acceleration/deceleration around the feedrate sensitive corners, the look-ahead function was designed and illustrated. It can detect and adjust the feedrate adaptively. With the help of real-time look-ahead, the acceleration/deceleration can be limited to the range of the machine tool capacity. Thus, feedrate fluctuation is reduced. A NURBS curve interpolation experiment was provided to verify the feasibility and advantages of the proposed interpolator with a real-time look-ahead function.
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funded by the Research and Development Fund Project of China Academy of Railway Science Group Co.,Ltd.,(No:2023YJ259)the Science and Technology Research and Development Program Project of China State Railway Group Co.,Ltd.(No:J2024G008).
文摘Purpose–This research aims to monitor seismic intensity along railway lines,study methods for calculating the extent of earthquake impact on railways and address practical challenges in estimating intensity distribution along railway routes,thereby achieving graded post-earthquake response measures.Design/methodology/approach–The seismic intensity monitoring system for railways adopts a two-level architecture,namely the seismic intensity monitoring equipment and the seismic intensity rapid reporting information center processing platform.The platform obtains measured instrumental intensity through the seismic intensity monitoring equipment deployed along railways and combines it with the National Seismic Network Earthquake Catalog to generate real-time railway seismic intensity distribution maps using the Kriging interpolation algorithm.A calculation method for railway seismic impact intervals is designed to calculate the mileage intervals where the intensity area corresponding to each contour line in the seismic intensity distribution map intersects with the railway line.Findings–The system was deployed for practical earthquake monitoring demonstration applications on the Nanjiang Railway Line in Xinjiang.During the operational period,the seismic intensity monitoring equipment calculated and uploaded instrumental intensity values to the seismic intensity rapid reporting information center processing platform a total of nine times.Among these,earthquakes triggering the Kriging interpolation algorithm occurred twice.The system operated stably throughout the application period and successfully visualized relevant seismic impact data,such as earthquake intensity distribution maps and affected railway mileage sections.These results validate the system’s practicality and effectiveness.Originality/value–The seismic intensity monitoring for the railway system designed in this study can integrate the measured instrumental intensity data along railways and the earthquake catalog of the National Seismic Network.It uses the Kriging interpolation method to calculate the intensity distribution and determine the seismic impact scope,thereby addressing the issue that the seismic intensity distribution calculated by traditional attenuation formulas deviates from reality.The system can provide clear graded interval recommendations for post-earthquake disposal,effectively improve the efficiency of post-earthquake recovery and inspection and offer a decision-making basis for restoring railway operations quickly.
文摘Real-time semantic segmentation tasks place stringent demands on network inference speed,often requiring a reduction in network depth to decrease computational load.However,shallow networks tend to exhibit degradation in feature extraction completeness and inference accuracy.Therefore,balancing high performance with real-time requirements has become a critical issue in the study of real-time semantic segmentation.To address these challenges,this paper proposes a lightweight bilateral dual-residual network.By introducing a novel residual structure combined with feature extraction and fusion modules,the proposed network significantly enhances representational capacity while reducing computational costs.Specifically,an improved compound residual structure is designed to optimize the efficiency of information propagation and feature extraction.Furthermore,the proposed feature extraction and fusion module enables the network to better capture multi-scale information in images,improving the ability to detect both detailed and global semantic features.Experimental results on the publicly available Cityscapes dataset demonstrate that the proposed lightweight dual-branch network achieves outstanding performance while maintaining low computational complexity.In particular,the network achieved a mean Intersection over Union(mIoU)of 78.4%on the Cityscapes validation set,surpassing many existing semantic segmentation models.Additionally,in terms of inference speed,the network reached 74.5 frames per second when tested on an NVIDIA GeForce RTX 3090 GPU,significantly improving real-time performance.
基金supported by the National Natural Science Foundation of China(No.22306076)the Natural Science Foundation of Jiangsu Province(No.BK20230676)the Natural Science Foundation of Jiangsu Higher Education Institutions of China(No.22KJB610011).
文摘Here,a novel real-time monitoring sensor that integrates the oxidation of peroxymonosulfate(PMS)and the in situ monitoring of the pollutant degradation process is proposed.Briefly,FeCo@carbon fiber(FeCo@CF)was utilized as the anode electrode,while graphite rods served as the cathode electrode in assembling the galvanic cell.The FeCo@CF electrode exhibited rapid reactivity with PMS,generating reactive oxygen species that efficiently degrade organic pollutants.The degradation experiments indicate that complete bisphenol A(BPA)degradation was achieved within 10 min under optimal conditions.The real-time electrochemical signal was measured in time during the catalytic reaction,and a linear relationship between BPA concentration and the real-time charge(Q)was confirmed by the equation ln(C0/C)=4.393Q(correlation coefficients,R^(2)=0.998).Furthermore,experiments conducted with aureomycin and tetracycline further validated the effectiveness of the monitoring sensor.First-principles investigation confirmed the superior adsorption energy and improved electron transfer in FeCo@CF.The integration of pollutant degradation with in situ monitoring of catalytic reactions offers promising prospects for expanding the scope of the monitoring of catalytic processes and making significant contributions to environmental purification.
基金supported by the National Natural Science Foundation of China (Grant No.52122405)Science and Technology Major Project of Shanxi Province,China (Grant No.202101060301024)Science and Technology Major Project of Xizang Autonomous Region,China (Grant No.XZ202201ZD0004G0204).
文摘In this study,a high-confining pressure and real-time large-displacement shearing-flow setup was developed.The test setup can be used to analyze the injection pressure conditions that increase the hydro-shearing permeability and injection-induced seismicity during hot dry rock geothermal extraction.For optimizing injection strategies and improving engineering safety,real-time permeability,deformation,and energy release characteristics of fractured granite samples driven by injected water pressure under different critical sliding conditions were evaluated.The results indicated that:(1)A low injection water pressure induced intermittent small-deformation stick-slip behavior in fractures,and a high injection pressure primarily caused continuous high-speed large-deformation sliding in fractures.The optimal injection water pressure range was defined for enhancing hydraulic shear permeability and preventing large injection-induced earthquakes.(2)Under the same experimental conditions,fracture sliding was deemed as the major factor that enhanced the hydraulic shear-permeability enhancement and the maximum permeability increased by 36.54 and 41.59 times,respectively,in above two slip modes.(3)Based on the real-time transient evolution of water pressure during fracture sliding,the variation coefficients of slip rate,permeability,and water pressure were fitted,and the results were different from those measured under quasi-static conditions.(4)The maximum and minimum shear strength criteria for injection-induced fracture sliding were also determined(μ=0.6665 andμ=0.1645,respectively,μis friction coefficient).Using the 3D(three-dimensional)fracture surface scanning technology,the weakening effect of injection pressure on fracture surface damage characteristics was determined,which provided evidence for the geological markers of fault sliding mode and sliding nature transitions under the fluid influence.
基金funded by the Ongoing Research Funding Program(ORF-2025-890)King Saud University,Riyadh,Saudi Arabia and was supported by the Competitive Research Fund of theUniversity of Aizu,Japan.
文摘The exponential expansion of the Internet of Things(IoT),Industrial Internet of Things(IIoT),and Transportation Management of Things(TMoT)produces vast amounts of real-time streaming data.Ensuring system dependability,operational efficiency,and security depends on the identification of anomalies in these dynamic and resource-constrained systems.Due to their high computational requirements and inability to efficiently process continuous data streams,traditional anomaly detection techniques often fail in IoT systems.This work presents a resource-efficient adaptive anomaly detection model for real-time streaming data in IoT systems.Extensive experiments were carried out on multiple real-world datasets,achieving an average accuracy score of 96.06%with an execution time close to 7.5 milliseconds for each individual streaming data point,demonstrating its potential for real-time,resourceconstrained applications.The model uses Principal Component Analysis(PCA)for dimensionality reduction and a Z-score technique for anomaly detection.It maintains a low computational footprint with a sliding window mechanism,enabling incremental data processing and identification of both transient and sustained anomalies without storing historical data.The system uses a Multivariate Linear Regression(MLR)based imputation technique that estimates missing or corrupted sensor values,preserving data integrity prior to anomaly detection.The suggested solution is appropriate for many uses in smart cities,industrial automation,environmental monitoring,IoT security,and intelligent transportation systems,and is particularly well-suited for resource-constrained edge devices.
基金funded by theNational Science and Technology Council of Taiwan under the grant number NSTC 113-2221-E-035-058.
文摘With the rapid expansion of multimedia data,protecting digital information has become increasingly critical.Reversible data hiding offers an effective solution by allowing sensitive information to be embedded in multimedia files while enabling full recovery of the original data after extraction.Audio,as a vital medium in communication,entertainment,and information sharing,demands the same level of security as images.However,embedding data in encrypted audio poses unique challenges due to the trade-offs between security,data integrity,and embedding capacity.This paper presents a novel interpolation-based reversible data hiding algorithm for encrypted audio that achieves scalable embedding capacity.By increasing sample density through interpolation,embedding opportunities are significantly enhanced while maintaining encryption throughout the process.The method further integrates multiple most significant bit(multi-MSB)prediction and Huffman coding to optimize compression and embedding efficiency.Experimental results on standard audio datasets demonstrate the proposed algorithm’s ability to embed up to 12.47 bits per sample with over 9.26 bits per sample available for pure embedding capacity,while preserving full reversibility.These results confirm the method’s suitability for secure applications that demand high embedding capacity and perfect reconstruction of original audio.This work advances reversible data hiding in encrypted audio by offering a secure,efficient,and fully reversible data hiding framework.
基金supported by the National Natural Science Foundation of China(42374134,42304125,U20B6005)the Science and Technology Commission of Shanghai Municipality(23JC1400502)the Fundamental Research Funds for the Central Universities.
文摘Blended acquisition offers efficiency improvements over conventional seismic data acquisition, at the cost of introducing blending noise effects. Besides, seismic data often suffers from irregularly missing shots caused by artificial or natural effects during blended acquisition. Therefore, blending noise attenuation and missing shots reconstruction are essential for providing high-quality seismic data for further seismic processing and interpretation. The iterative shrinkage thresholding algorithm can help obtain deblended data based on sparsity assumptions of complete unblended data, and it characterizes seismic data linearly. Supervised learning algorithms can effectively capture the nonlinear relationship between incomplete pseudo-deblended data and complete unblended data. However, the dependence on complete unblended labels limits their practicality in field applications. Consequently, a self-supervised algorithm is presented for simultaneous deblending and interpolation of incomplete blended data, which minimizes the difference between simulated and observed incomplete pseudo-deblended data. The used blind-trace U-Net (BTU-Net) prevents identity mapping during complete unblended data estimation. Furthermore, a multistep process with blending noise simulation-subtraction and missing traces reconstruction-insertion is used in each step to improve the deblending and interpolation performance. Experiments with synthetic and field incomplete blended data demonstrate the effectiveness of the multistep self-supervised BTU-Net algorithm.
