Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze we...Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.展开更多
With the rapid development of wearable electronic skin technology, flexible strain sensors have shown great application prospects in the fields of human motion and physiological signal detection, medical diagnostics, ...With the rapid development of wearable electronic skin technology, flexible strain sensors have shown great application prospects in the fields of human motion and physiological signal detection, medical diagnostics, and human-computer interaction owing to their outstanding sensing performance. This paper reports a strain sensor with synergistic conductive network, consisting of stable carbon nanotube dispersion (CNT) layer and brittle MXene layer by dip-coating and electrostatic self-assembly method, and breathable three-dimensional (3D) flexible substrate of thermoplastic polyurethane (TPU) fibrous membrane prepared through electrospinning technology. The MXene/CNT@PDA-TPU (MC@p-TPU) flexible strain sensor had excellent air permeability, wide operating range (0–450 %), high sensitivity (Gauge Factor, GFmax = 8089.7), ultra-low detection limit (0.05 %), rapid response and recovery times (40 ms/60 ms), and excellent cycle stability and durability (10,000 cycles). Given its superior strain sensing capabilities, this sensor can be applied in physiological signals detection, human motion pattern recognition, and driving exoskeleton robots. In addition, MC@p-TPU fibrous membrane also exhibited excellent photothermal conversion performance and can be used as a wearable photo-heater, which has far-reaching application potential in the photothermal therapy of human joint diseases.展开更多
The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also e...The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.展开更多
This study presents an energy-efficient Internet of Things(IoT)-based wireless sensor network(WSN)framework for autonomous data validation in remote environmental monitoring.We address two critical challenges in WSNs:...This study presents an energy-efficient Internet of Things(IoT)-based wireless sensor network(WSN)framework for autonomous data validation in remote environmental monitoring.We address two critical challenges in WSNs:ensuring data reliability and optimizing energy consumption.Our novel approach integrates an artificial neural network(ANN)-based multi-fault detection algorithm with an energy-efficient IoT-WSN architecture.The proposed ANN model is designed to simultaneously detect multiple fault types,including spike faults,stuckat faults,outliers,and out-of-range faults.We collected sensor data at 5-minute intervals over three months,using temperature and humidity sensors.The ANN was trained on 70%of the 26,280 data points per sensor,with 15%each for validation and testing.Our framework demonstrated a 97.1%improvement in fault detection accuracy(measured by F1 score)compared to existing methods,including rule-based,moving average,and statistical outlier detection approaches.The energy efficiency of the system was evaluated through 24-h power consumption tests,showing significant savings over traditional WSN architectures.Key contributions include a multi-fault detection ANN model balancing accuracy and computational efficiency,an energy-optimized IoTWSN architecture for remote deployments,and a comprehensive performance evaluation framework.While our approach offers improvements in both data validation and energy efficiency,we acknowledge limitations such as potential scalability issues and the need for further real-world testing.This research advances the field of remote environmental monitoring by providing a robust,energy-efficient solution for ensuring data reliability in challenging deployment scenarios.Future work will explore more advanced machine learning techniques and extended field testing to further validate and improve the system’s performance.展开更多
Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approac...Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization Algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection algorithm.Initially,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural condition.Subsequently,the Relief-F algorithm is applied for signal feature extraction,culminating in the formation of a feature matrix.This matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model globally.The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network algorithm.The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities.The effect of different sensor-receiver path signals on the model damage recognition results is also discussed.The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area.Consequently,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.展开更多
Zn vapour is easily generated on the surface by fusion welding galvanized steel sheet,resulting in the formation of defects.Rapidly developing computer vision sensing technology collects weld images in the welding pro...Zn vapour is easily generated on the surface by fusion welding galvanized steel sheet,resulting in the formation of defects.Rapidly developing computer vision sensing technology collects weld images in the welding process,then obtains laser fringe information through digital image processing,identifies welding defects,and finally realizes online control of weld defects.The performance of a convolutional neural network is related to its structure and the quality of the input image.The acquired original images are labeled with LabelMe,and repeated attempts are made to determine the appropriate filtering and edge detection image preprocessing methods.Two-stage convolutional neural networks with different structures are built on the Tensorflow deep learning framework,different thresholds of intersection over union are set,and deep learning methods are used to evaluate the collected original images and the preprocessed images separately.Compared with the test results,the comprehensive performance of the improved feature pyramid networks algorithm based on the basic network VGG16 is lower than that of the basic network Resnet101.Edge detection of the image will significantly improve the accuracy of the model.Adding blur will reduce the accuracy of the model slightly;however,the overall performance of the improved algorithm is still relatively good,which proves the stability of the algorithm.The self-developed software inspection system can be used for image preprocessing and defect recognition,which can be used to record the number and location of typical defects in continuous welds.展开更多
Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiv...Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiveness of sinter quality prediction,an intelligent flare monitoring system for sintering machine tails that combines hybrid neural networks integrating convolutional neural network with long short-term memory(CNN-LSTM)networks was proposed.The system utilized a high-temperature thermal imager for image acquisition at the sintering machine tail and employed a zone-triggered method to accurately capture dynamic feature images under challenging conditions of high-temperature,high dust,and occlusion.The feature images were then segmented through a triple-iteration multi-thresholding approach based on the maximum between-class variance method to minimize detail loss during the segmentation process.Leveraging the advantages of CNN and LSTM networks in capturing temporal and spatial information,a comprehensive model for sinter quality prediction was constructed,with inputs including the proportion of combustion layer,porosity rate,temperature distribution,and image features obtained from the convolutional neural network,and outputs comprising quality indicators such as underburning index,uniformity index,and FeO content of the sinter.The accuracy is notably increased,achieving a 95.8%hit rate within an error margin of±1.0.After the system is applied,the average qualified rate of FeO content increases from 87.24%to 89.99%,representing an improvement of 2.75%.The average monthly solid fuel consumption is reduced from 49.75 to 46.44 kg/t,leading to a 6.65%reduction and underscoring significant energy saving and cost reduction effects.展开更多
Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring(SHM).However,traditional methods struggle to address the reconstruction of acceleration response...Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring(SHM).However,traditional methods struggle to address the reconstruction of acceleration responses with complex features,resulting in a lower reconstruction accuracy.This paper addresses this challenge by leveraging the advanced feature extraction and learning capabilities of fully convolutional networks(FCN)to achieve precise reconstruction of acceleration responses.In the designed network architecture,the incorporation of skip connections preserves low-level details of the network,greatly facilitating the flow of information and improving training efficiency and accuracy.Dropout techniques are employed to reduce computational load and enhance feature extraction.The proposed FCN model automatically extracts high-level features from the input data and establishes a nonlinearmapping relationship between the input and output responses.Finally,the accuracy of the FCN for structural response reconstructionwas evaluated using acceleration data from an experimental arch rib and comparedwith several traditional methods.Additionally,this approach was applied to reconstruct actual acceleration responses measured by an SHM system on a long-span bridge.Through parameter analysis,the feasibility and accuracy of aspects such as available response positions,the number of available channels,and multi-channel response reconstruction were explored.The results indicate that this method exhibits high-precision response reconstruction capability in both time and frequency domains.,with performance surpassing that of other networks,confirming its effectiveness in reconstructing responses under various sensor data loss scenarios.展开更多
Passive source imaging can reconstruct body wave reflections similar to those of active sources through seismic interferometry(SI).It has become a low-cost,environmentally friendly alternative to active source seismic...Passive source imaging can reconstruct body wave reflections similar to those of active sources through seismic interferometry(SI).It has become a low-cost,environmentally friendly alternative to active source seismic,showing great potential.However,this method faces many challenges in practical applications,including uneven distribution of underground sources and complex survey environments.These situations seriously affect the reconstruction quality of virtual shot records,resulting in unguaranteed imaging results and greatly limiting passive source seismic exploration applications.In addition,the quality of the reconstructed records is directly related to the time length of the noise records,but in practice it is often difficult to obtain long-term,high-quality noise segments containing body wave events.To solve the above problems,we propose a deep learning method for reconstructing passive source virtual shot records and apply it to passive source time-lapse monitoring.This method combines the UNet network and the BiLSTM(Bidirectional Long Short-Term Memory)network for extracting spatial features and temporal features respectively.It introduces the spatial attention mechanism to establish a hybrid SUNet-BiLSTM-Attention(SBA)network for supervised training.Through pre-training and fine-tuning training,the network can accurately reconstruct passive source virtual shot records directly from short-time noisy segments containing body wave events.The experimental results of theoretical data show that the virtual shot records reconstructed by the network have high resolution and signal to noise ratio(SNR),providing high-quality data for subsequent monitoring and imaging.Finally,to further validate the effectiveness of proposed method,we applied it to field data collected from gas storage in northwest China.The reconstruction results of field data effectively improve the quality of virtual records and obtain more reliable time-lapse imaging monitoring results,which have significant practical value.展开更多
Unmanned Aerial Vehicles(UAVs)integrated with Wireless Sensor Networks(WSNs)present a transformative approach to environmental monitoring by enabling real-time,low power,wide-area,and high-resolution data collection.T...Unmanned Aerial Vehicles(UAVs)integrated with Wireless Sensor Networks(WSNs)present a transformative approach to environmental monitoring by enabling real-time,low power,wide-area,and high-resolution data collection.This paper proposes a UAV-based WSN framework designed for efficient ecological data acquisition,including parameters such as temperature,humidity,various gases,detection of motion of a material,and safety features.The system leverages UAVs for dynamic deployment and data retrieval from distributed sensor nodes in remote or inaccessible areas,reducing the reliance on fixed infrastructure.Long Range Communication(LoRa)technology is also integrated with a WSN to enhance network coverage and adaptability issues.The proposed system covers vast areas through LoRa communication ensuring minimal energy consumption and cost-effective sensing capabilities.Field tests and simulation findings show how well the system captures spatiotemporal environmental fluctuations,making it an invaluable tool for monitoring climate change,ecological research,and disaster response.展开更多
Given the swift proliferation of structural health monitoring(SHM)technology within tunnel engineering,there is a demand on proficiently and precisely imputing the missing monitoring data to uphold the precision of di...Given the swift proliferation of structural health monitoring(SHM)technology within tunnel engineering,there is a demand on proficiently and precisely imputing the missing monitoring data to uphold the precision of disaster prediction.In contrast to other SHM datasets,the monitoring data specific to tunnel engineering exhibits pronounced spatiotemporal correlations.Nevertheless,most methodologies fail to adequately combine these types of correlations.Hence,the objective of this study is to develop spatiotemporal recurrent neural network(ST-RNN)model,which exploits spatiotemporal information to effectively impute missing data within tunnel monitoring systems.ST-RNN consists of two moduli:a temporal module employing recurrent neural network(RNN)to capture temporal dependencies,and a spatial module employing multilayer perceptron(MLP)to capture spatial correlations.To confirm the efficacy of the model,several commonly utilized methods are chosen as baselines for conducting comparative analyses.Furthermore,parametric validity experiments are conducted to illustrate the efficacy of the parameter selection process.The experimentation is conducted using original raw datasets wherein various degrees of continuous missing data are deliberately introduced.The experimental findings indicate that the ST-RNN model,incorporating both spatiotemporal modules,exhibits superior interpolation performance compared to other baseline methods across varying degrees of missing data.This affirms the reliability of the proposed model.展开更多
Monitoring the condition of road infrastructure is crucial for maintaining its structural integrity and ensuring safe transportation.This study proposes a deep learning framework based on Temporal Convolutional Networ...Monitoring the condition of road infrastructure is crucial for maintaining its structural integrity and ensuring safe transportation.This study proposes a deep learning framework based on Temporal Convolutional Networks(TCN)integrated with Adaptive Parametric Rectified Linear Unit(APReLU)to predict future road subbase strain trends.Our model leverages time-series strain data collected from embedded triaxial sensors within a national highway,spanning August 2021 to June 2022,to forecast strain dynamics critical for proactive maintenance planning.The TCN-APReLU architecture combines dilated causal convolutions to capture long-termdependencies and APReLU activation functions to adaptively model nonlinear strain patterns,addressing limitations of traditional ReLU in handling bidirectional strain signals(compressive and tensile).Comparative experiments demonstrate TCN-APReLU’s superior performance.These improvements highlight its enhanced accuracy in predicting strain accumulation under cyclic traffic loads,enabling maintenance teams to prioritize interventions 5-7 days before critical thresholds(e.g.,>100με)are exceeded.This work provides a robust data-driven solution for urban road health monitoring,emphasizing scalability through parallelizable convolutions and adaptability to sensor noise.Future extensions will integrate multi-modal data to further generalize predictions across diverse infrastructure scenarios.展开更多
Mining-related seismicity poses significant challenges in underground coal mining due to its complex rupture mechanisms and associated hazards.To bridge gaps in understanding these intricate processes,this study emplo...Mining-related seismicity poses significant challenges in underground coal mining due to its complex rupture mechanisms and associated hazards.To bridge gaps in understanding these intricate processes,this study employed a multi-local seismic monitoring network,integrating both in-mine and local instruments at overlapping length scales.We specifically focused on a damaging local magnitude(ML)2.6 event and its aftershocks that occurred on 10 September 2022 in the vicinity of the 3308 working face of the Yangcheng coal mine in Shandong Province,China.Moment tensor(MT)inversion revealed a complex cascading rupture mechanism:an initial moment magnitude(M_(w))2.2 normal fault slip along the DF60 fault in an ESEeWNW direction,transitioning to a M_(w)3.0 event as the FD24 and DF60 faults unclamped.The scale-independent self-similarity and stress heterogeneity of mining-related seismicity were investigated through source parameter calculations,providing valuable insights into the driving mechanism of these seismic sequences.The in-mine network,constrained by its low dynamic changes,captured only the nucleation phase of the DF60 fault.Furthermore,standard decomposition of the MT solution from the seismic network proved inadequate for accurately identifying the complex nature of the rupture.To enhance safety and risk management in mining environments,we examined the implications of source reactivation within the cluster area post-stress-adjustment.This comprehensive multiscale analysis offers crucial insights into the complex rupture mechanisms and hazards associated with mining-related seismicity.The results underscore the importance of continuous multi-local network monitoring and advanced analytical techniques for improved disaster assessment and risk mitigation in mining operations.展开更多
A case study of seismic interferometry applied to a small microseismic monitoring network is here presented.The main objectives of this study are(i)to quantify the lateral variability of shear-wave ve-locities in the ...A case study of seismic interferometry applied to a small microseismic monitoring network is here presented.The main objectives of this study are(i)to quantify the lateral variability of shear-wave ve-locities in the studied area,and(ii)to investigate the bias produced by noise directionality and non-stationarity in the velocity estimate.Despite the limited number of stations and the short-period char-acter of the seismic sensors,the empirical Green's functions were retrieved for all station pairs using two years of passive data.Both group and phase velocities were derived,the former using the widespread frequency-time analysis,the latter through the analysis of the real part of the cross-spectra.The main advantage of combining these two methods is a more accurate identification of higher modes,resulting in a reduction of ambiguity during picking and data interpretation.Surface wave tomography was run to obtain the spatial distribution of group and phase velocities for the same wavelengths.The low standard deviation of the results suggests that the sparse character of the network does not limit the applicability of the method,for this specific case.The obtained maps highlight the presence of a lower velocity area that extends from the centre of the network towards southeast.Group and phase velocity dispersion curves have been jointly inverted to retrieve as many shear-wave velocity profiles as selected station pairs.While the average model can be used for a more accurate location of the local natural seismicity,the associated standard deviations give us an indication of the lateral heterogeneity of seismic velocities as a function of depth.Finally,the same velocity analysis was repeated for different time windows in order to quantify the error associated to variations in the noise field.Errors as large as 4%have been found,related to the unfavorable orientation of the receiver pairs with respect to strongly directional noise sources,and to the very short time widows.It was shown that using a one-year time window these errors arereduced to 0.3%.展开更多
The Belt and Road global navigation satellite system(B&R GNSS)network is the first large-scale deployment of Chinese GNSS equipment in a seismic system.Prior to this,there have been few systematic assessments of t...The Belt and Road global navigation satellite system(B&R GNSS)network is the first large-scale deployment of Chinese GNSS equipment in a seismic system.Prior to this,there have been few systematic assessments of the data quality of Chinese GNSS equipment.In this study,data from four representative GNSS sites in different regions of China were analyzed using the G-Nut/Anubis software package.Four main indicators(data integrity rate,data validity ratio,multi-path error,and cycle slip ratio)used to systematically analyze data quality,while evaluating the seismic monitoring capabilities of the network based on earthquake magnitudes estimated from high-frequency GNSS data are evaluated by estimating magnitude based on highfrequency GNSS data.The results indicate that the quality of the data produced by the three types of Chinese receivers used in the network meets the needs of earthquake monitoring and the new seismic industry standards,which provide a reference for the selection of equipment for future new projects.After the B&R GNSS network was established,the seismic monitoring capability for earthquakes with magnitudes greater than M_(W)6.5 in most parts of the Sichuan-Yunnan region improved by approximately 20%.In key areas such as the Sichuan-Yunnan Rhomboid Block,the monitoring capability increased by more than 25%,which has greatly improved the effectiveness of regional comprehensive earthquake management.展开更多
Multifunctional flexible sensors as wearable electronic systems have attracted considerable attention for mimicking human skin to sense ambient stimuli.However,sensors need to have high resolution,stability and sensit...Multifunctional flexible sensors as wearable electronic systems have attracted considerable attention for mimicking human skin to sense ambient stimuli.However,sensors need to have high resolution,stability and sensitivity to realize fully biomimetic skin.Here,an assembled and welded Ag/W composite nanowire flexible electrode was prepared for body motion monitoring and flexible heaters.This Ag/W composite nanowire flexible electrode has a high transmittance of 90.1%(at 121Ω·sq^(−1) sheet resistance)and a low sheet resistance of 27Ω·sq^(−1)(at 60.1%transmittance).Although the transparency of this electrode is not high,the fluctuation in relative resistance change rate at 10%strain is only 5%after 1000 tensile cycles.It can be employed to monitor human body motions,including bending of fingers,arms,wrists,and throat action.Meanwhile,the Ag/W nanowires composite film heater achieves a steady-state temperature of up to 100℃ at a constant voltage of 3.5 V and an instantaneous heating rate of up to 36.5℃·s^(−1).展开更多
The operational state of distillation columns significantly impacts product quality and production efficiency.However,due to the complex operation and diverse influencing factors,ensuring the safety and efficient oper...The operational state of distillation columns significantly impacts product quality and production efficiency.However,due to the complex operation and diverse influencing factors,ensuring the safety and efficient operation of the distillation columns becomes paramount.This research combines passive acoustic monitoring with artificial intelligence techniques,proposed a technology based on residual network(ResNet),which involves the transformation of the acoustic signals emitted by three distillation columns under different operating states.The acoustic signals were initially in one-dimensional waveform format and then converted into two-dimensional Mel-Frequency Cepstral Coefficients spectrogram database using fast Fourier transform.Ultimately,this database was employed to train a ResNet for the purpose of identifying the operational states of the distillation columns.Through this approach,the operational states of distillation columns were monitored.Various faults,including flooding,entrainment,dry-tray,etc.,were diagnosed with an accuracy of 98.91%.Moreover,an intermediate transitional state between normal operation and fault was identified and accurately recognized by the proposed method.Under the transitional state,the acoustic signals achieved an accuracy of 97.85%on the ResNet,which enables early warnings before faults occur,enhancing the safety of chemical production processes.The approach presents a powerful tool for the monitoring and diagnosis of chemical equipment,particularly distillation columns,ensuring the safety and efficiency.展开更多
Passive microseismic monitoring(PMM)serves as a fundamental technology for assessing hydraulic fracturing(HF)effectiveness,with a key focus on accurate and efficient phase detection/arrival picking and source location...Passive microseismic monitoring(PMM)serves as a fundamental technology for assessing hydraulic fracturing(HF)effectiveness,with a key focus on accurate and efficient phase detection/arrival picking and source location.In PMM data processing,the data-driven paradigm(deep learning based)outperforms the model-driven paradigm in characteristic extraction but lacks quality control and uncertainty quantification.Monte Carlo Dropout,a Bayesian uncertainty quantification technique,performs stochastic neuron deactivation through multiple forward propagation samplings.Therefore,this study proposes a deep learning neural network incorporating uncertainty quantification with manual quality control integration,establishing an optimized workflow spanning automated phase detection to robust source location.The methodology implementation comprises two principal components:(1)The MDNet employing Monte Carlo Dropout strategy enabling simultaneous phase detection/arrival picking and unce rtainty estimation;(2)an integrated hybrid-driven workflow with a traveltime-based inve rsion method for source location.Validation with field data demonstrates that MD-Net achieves superior performance under low signal-to-noise ratio conditions,maintaining detection accuracy exceeding 99%for both P-and S-waves.The phase arrival picking precision shows significant improvement,with a 40%reduction in standard deviation compared to the baseline model(P-S time difference decreasing from12.0 ms to 7.1 ms),while providing quantifiable uncertainty metrics for manual calibration.Source location results further reveal that our hybrid-driven workflow produces more physically plausible event distributions,with 100%of microseismic eve nts clustering along the primary fracture expanding direction.This performance surpasses traditional cross-correlation methods and single/multi-trace data-driven me thods in spatial rationality.This study establishes an inte rpretable,high-pre cision automated framework for HF-PMM applications,demonstrating potential for extension to diverse geological settings and monitoring configurations.展开更多
This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge compon...This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge components are prone to cracks and other defects,severely compromising structural safety and service life.Traditional inspection methods relying on manual visual assessment or vehicle-mounted sensors suffer from low efficiency,strong subjectivity,and high costs,while conventional image processing techniques and early deep learning models(e.g.,UNet,Faster R-CNN)still performinadequately in complex environments(e.g.,varying illumination,noise,false cracks)due to poor perception of fine cracks andmulti-scale features,limiting practical application.To address these challenges,this paper proposes CACNN-Net(CBAM-Augmented CNN),a novel dual-encoder architecture that innovatively couples a CNN for local detail extraction with a CBAM-Transformer for global context modeling.A key contribution is the dedicated Feature FusionModule(FFM),which strategically integratesmulti-scale features and focuses attention on crack regions while suppressing irrelevant noise.Experiments on bridge crack datasets demonstrate that CACNNNet achieves a precision of 77.6%,a recall of 79.4%,and an mIoU of 62.7%.These results significantly outperform several typical models(e.g.,UNet-ResNet34,Deeplabv3),confirming their superior accuracy and robust generalization,providing a high-precision automated solution for bridge crack detection and a novel network design paradigm for structural surface defect identification in complex scenarios,while future research may integrate physical features like depth information to advance intelligent infrastructure maintenance and digital twin management.展开更多
With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance s...With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.展开更多
基金supported by the National Natural Science Foundation of China(No.51605054).
文摘Environmentalmonitoring systems based on remote sensing technology have a wider monitoringrange and longer timeliness, which makes them widely used in the detection andmanagement of pollution sources. However, haze weather conditions degrade image qualityand reduce the precision of environmental monitoring systems. To address this problem,this research proposes a remote sensing image dehazingmethod based on the atmosphericscattering model and a dark channel prior constrained network. The method consists ofa dehazing network, a dark channel information injection network (DCIIN), and a transmissionmap network. Within the dehazing network, the branch fusion module optimizesfeature weights to enhance the dehazing effect. By leveraging dark channel information,the DCIIN enables high-quality estimation of the atmospheric veil. To ensure the outputof the deep learning model aligns with physical laws, we reconstruct the haze image usingthe prediction results from the three networks. Subsequently, we apply the traditionalloss function and dark channel loss function between the reconstructed haze image and theoriginal haze image. This approach enhances interpretability and reliabilitywhile maintainingadherence to physical principles. Furthermore, the network is trained on a synthesizednon-homogeneous haze remote sensing dataset using dark channel information from cloudmaps. The experimental results show that the proposed network can achieve better imagedehazing on both synthetic and real remote sensing images with non-homogeneous hazedistribution. This research provides a new idea for solving the problem of decreased accuracyof environmental monitoring systems under haze weather conditions and has strongpracticability.
基金supported by the National Natural Science Foundation of China(Nos.52373093 and 12072325)the Outstanding Youth Fund of Henan Province(No.242300421062)+1 种基金National Key R&D Program of China(No.2019YFA0706802)the 111 project(No.D18023).
文摘With the rapid development of wearable electronic skin technology, flexible strain sensors have shown great application prospects in the fields of human motion and physiological signal detection, medical diagnostics, and human-computer interaction owing to their outstanding sensing performance. This paper reports a strain sensor with synergistic conductive network, consisting of stable carbon nanotube dispersion (CNT) layer and brittle MXene layer by dip-coating and electrostatic self-assembly method, and breathable three-dimensional (3D) flexible substrate of thermoplastic polyurethane (TPU) fibrous membrane prepared through electrospinning technology. The MXene/CNT@PDA-TPU (MC@p-TPU) flexible strain sensor had excellent air permeability, wide operating range (0–450 %), high sensitivity (Gauge Factor, GFmax = 8089.7), ultra-low detection limit (0.05 %), rapid response and recovery times (40 ms/60 ms), and excellent cycle stability and durability (10,000 cycles). Given its superior strain sensing capabilities, this sensor can be applied in physiological signals detection, human motion pattern recognition, and driving exoskeleton robots. In addition, MC@p-TPU fibrous membrane also exhibited excellent photothermal conversion performance and can be used as a wearable photo-heater, which has far-reaching application potential in the photothermal therapy of human joint diseases.
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.62371187the Hunan Provincial Natural Science Foundation of China under Grant Nos.2024JJ8309 and 2023JJ50495.
文摘The integration of cloud computing into traditional industrial control systems is accelerating the evolution of Industrial Cyber-Physical System(ICPS),enhancing intelligence and autonomy.However,this transition also expands the attack surface,introducing critical security vulnerabilities.To address these challenges,this article proposes a hybrid intrusion detection scheme for securing ICPSs that combines system state anomaly and network traffic anomaly detection.Specifically,an improved variation-Bayesian-based noise covariance-adaptive nonlinear Kalman filtering(IVB-NCA-NLKF)method is developed to model nonlinear system dynamics,enabling optimal state estimation in multi-sensor ICPS environments.Intrusions within the physical sensing system are identified by analyzing residual discrepancies between predicted and observed system states.Simultaneously,an adaptive network traffic anomaly detection mechanism is introduced,leveraging learned traffic patterns to detect node-and network-level anomalies through pattern matching.Extensive experiments on a simulated network control system demonstrate that the proposed framework achieves higher detection accuracy(92.14%)with a reduced false alarm rate(0.81%).Moreover,it not only detects known attacks and vulnerabilities but also uncovers stealthy attacks that induce system state deviations,providing a robust and comprehensive security solution for the safety protection of ICPS.
基金supported by King Saud University through Researchers Supporting Project number(RSPD2024R1006),King Saud University,Riyadh,Saudi Arabia.
文摘This study presents an energy-efficient Internet of Things(IoT)-based wireless sensor network(WSN)framework for autonomous data validation in remote environmental monitoring.We address two critical challenges in WSNs:ensuring data reliability and optimizing energy consumption.Our novel approach integrates an artificial neural network(ANN)-based multi-fault detection algorithm with an energy-efficient IoT-WSN architecture.The proposed ANN model is designed to simultaneously detect multiple fault types,including spike faults,stuckat faults,outliers,and out-of-range faults.We collected sensor data at 5-minute intervals over three months,using temperature and humidity sensors.The ANN was trained on 70%of the 26,280 data points per sensor,with 15%each for validation and testing.Our framework demonstrated a 97.1%improvement in fault detection accuracy(measured by F1 score)compared to existing methods,including rule-based,moving average,and statistical outlier detection approaches.The energy efficiency of the system was evaluated through 24-h power consumption tests,showing significant savings over traditional WSN architectures.Key contributions include a multi-fault detection ANN model balancing accuracy and computational efficiency,an energy-optimized IoTWSN architecture for remote deployments,and a comprehensive performance evaluation framework.While our approach offers improvements in both data validation and energy efficiency,we acknowledge limitations such as potential scalability issues and the need for further real-world testing.This research advances the field of remote environmental monitoring by providing a robust,energy-efficient solution for ensuring data reliability in challenging deployment scenarios.Future work will explore more advanced machine learning techniques and extended field testing to further validate and improve the system’s performance.
基金funded by the Key Program of the National Natural Science Foundation of China(U2341235)Youth Fund for Basic Research Program of Jiangnan University(JUSRP123003)+2 种基金Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1237)the National Key R&D Program of China(2018YFA0702800)Key Technologies R&D Program of CNBM(2023SJYL01).
文摘Fatigue damage is a primary contributor to the failure of composite structures,underscoring the critical importance of monitoring its progression to ensure structural safety.This paper introduces an innovative approach to fatigue damage monitoring in composite structures,leveraging a hybrid methodology that integrates the Whale Optimization Algorithm(WOA)-Backpropagation(BP)neural network with an ultrasonic guided wave feature selection algorithm.Initially,a network of piezoelectric ceramic sensors is employed to transmit and capture ultrasonic-guided waves,thereby establishing a signal space that correlates with the structural condition.Subsequently,the Relief-F algorithm is applied for signal feature extraction,culminating in the formation of a feature matrix.This matrix is then utilized to train the WOA-BP neural network,which optimizes the fatigue damage identification model globally.The proposed model’s efficacy in quantifying fatigue damage is tested against fatigue test datasets,with its performance benchmarked against the traditional BP neural network algorithm.The findings demonstrate that the WOA-BP neural network model not only surpasses the BP model in predictive accuracy but also exhibits enhanced global search capabilities.The effect of different sensor-receiver path signals on the model damage recognition results is also discussed.The results of the discussion found that the path directly through the damaged area is more accurate in modeling damage recognition compared to the path signals away from the damaged area.Consequently,the proposed monitoring method in the fatigue test dataset is adept at accurately tracking and recognizing the progression of fatigue damage.
基金the National Natural Science Foundation of China(No.12064027)。
文摘Zn vapour is easily generated on the surface by fusion welding galvanized steel sheet,resulting in the formation of defects.Rapidly developing computer vision sensing technology collects weld images in the welding process,then obtains laser fringe information through digital image processing,identifies welding defects,and finally realizes online control of weld defects.The performance of a convolutional neural network is related to its structure and the quality of the input image.The acquired original images are labeled with LabelMe,and repeated attempts are made to determine the appropriate filtering and edge detection image preprocessing methods.Two-stage convolutional neural networks with different structures are built on the Tensorflow deep learning framework,different thresholds of intersection over union are set,and deep learning methods are used to evaluate the collected original images and the preprocessed images separately.Compared with the test results,the comprehensive performance of the improved feature pyramid networks algorithm based on the basic network VGG16 is lower than that of the basic network Resnet101.Edge detection of the image will significantly improve the accuracy of the model.Adding blur will reduce the accuracy of the model slightly;however,the overall performance of the improved algorithm is still relatively good,which proves the stability of the algorithm.The self-developed software inspection system can be used for image preprocessing and defect recognition,which can be used to record the number and location of typical defects in continuous welds.
基金founded by the Open Project Program of Anhui Province Key Laboratory of Metallurgical Engineering and Resources Recycling(Anhui University of Technology)(No.SKF21-06)Research Fund for Young Teachers of Anhui University of Technology in 2020(No.QZ202001).
文摘Real-time prediction and precise control of sinter quality are pivotal for energy saving,cost reduction,quality improvement and efficiency enhancement in the ironmaking process.To advance,the accuracy and comprehensiveness of sinter quality prediction,an intelligent flare monitoring system for sintering machine tails that combines hybrid neural networks integrating convolutional neural network with long short-term memory(CNN-LSTM)networks was proposed.The system utilized a high-temperature thermal imager for image acquisition at the sintering machine tail and employed a zone-triggered method to accurately capture dynamic feature images under challenging conditions of high-temperature,high dust,and occlusion.The feature images were then segmented through a triple-iteration multi-thresholding approach based on the maximum between-class variance method to minimize detail loss during the segmentation process.Leveraging the advantages of CNN and LSTM networks in capturing temporal and spatial information,a comprehensive model for sinter quality prediction was constructed,with inputs including the proportion of combustion layer,porosity rate,temperature distribution,and image features obtained from the convolutional neural network,and outputs comprising quality indicators such as underburning index,uniformity index,and FeO content of the sinter.The accuracy is notably increased,achieving a 95.8%hit rate within an error margin of±1.0.After the system is applied,the average qualified rate of FeO content increases from 87.24%to 89.99%,representing an improvement of 2.75%.The average monthly solid fuel consumption is reduced from 49.75 to 46.44 kg/t,leading to a 6.65%reduction and underscoring significant energy saving and cost reduction effects.
基金National Natural Science Foundation of China(Grant Nos.52408314,52278292)Chongqing Outstanding Youth Science Foundation(Grant No.CSTB2023NSCQ-JQX0029)+1 种基金Science and Technology Project of Sichuan Provincial Transportation Department(Grant No.2023-ZL-03)Science and Technology Project of Guizhou Provincial Transportation Department(Grant No.2024-122-018).
文摘Lost acceleration response reconstruction is crucial for assessing structural conditions in structural health monitoring(SHM).However,traditional methods struggle to address the reconstruction of acceleration responses with complex features,resulting in a lower reconstruction accuracy.This paper addresses this challenge by leveraging the advanced feature extraction and learning capabilities of fully convolutional networks(FCN)to achieve precise reconstruction of acceleration responses.In the designed network architecture,the incorporation of skip connections preserves low-level details of the network,greatly facilitating the flow of information and improving training efficiency and accuracy.Dropout techniques are employed to reduce computational load and enhance feature extraction.The proposed FCN model automatically extracts high-level features from the input data and establishes a nonlinearmapping relationship between the input and output responses.Finally,the accuracy of the FCN for structural response reconstructionwas evaluated using acceleration data from an experimental arch rib and comparedwith several traditional methods.Additionally,this approach was applied to reconstruct actual acceleration responses measured by an SHM system on a long-span bridge.Through parameter analysis,the feasibility and accuracy of aspects such as available response positions,the number of available channels,and multi-channel response reconstruction were explored.The results indicate that this method exhibits high-precision response reconstruction capability in both time and frequency domains.,with performance surpassing that of other networks,confirming its effectiveness in reconstructing responses under various sensor data loss scenarios.
基金supported by the CNPC-SWPU Innovation Alliance Technology Cooperation Project(2020CX020000)the Natural Science Foundation of Sichuan Province(24NSFSC0808)the China Scholarship Council(202306440144).
文摘Passive source imaging can reconstruct body wave reflections similar to those of active sources through seismic interferometry(SI).It has become a low-cost,environmentally friendly alternative to active source seismic,showing great potential.However,this method faces many challenges in practical applications,including uneven distribution of underground sources and complex survey environments.These situations seriously affect the reconstruction quality of virtual shot records,resulting in unguaranteed imaging results and greatly limiting passive source seismic exploration applications.In addition,the quality of the reconstructed records is directly related to the time length of the noise records,but in practice it is often difficult to obtain long-term,high-quality noise segments containing body wave events.To solve the above problems,we propose a deep learning method for reconstructing passive source virtual shot records and apply it to passive source time-lapse monitoring.This method combines the UNet network and the BiLSTM(Bidirectional Long Short-Term Memory)network for extracting spatial features and temporal features respectively.It introduces the spatial attention mechanism to establish a hybrid SUNet-BiLSTM-Attention(SBA)network for supervised training.Through pre-training and fine-tuning training,the network can accurately reconstruct passive source virtual shot records directly from short-time noisy segments containing body wave events.The experimental results of theoretical data show that the virtual shot records reconstructed by the network have high resolution and signal to noise ratio(SNR),providing high-quality data for subsequent monitoring and imaging.Finally,to further validate the effectiveness of proposed method,we applied it to field data collected from gas storage in northwest China.The reconstruction results of field data effectively improve the quality of virtual records and obtain more reliable time-lapse imaging monitoring results,which have significant practical value.
文摘Unmanned Aerial Vehicles(UAVs)integrated with Wireless Sensor Networks(WSNs)present a transformative approach to environmental monitoring by enabling real-time,low power,wide-area,and high-resolution data collection.This paper proposes a UAV-based WSN framework designed for efficient ecological data acquisition,including parameters such as temperature,humidity,various gases,detection of motion of a material,and safety features.The system leverages UAVs for dynamic deployment and data retrieval from distributed sensor nodes in remote or inaccessible areas,reducing the reliance on fixed infrastructure.Long Range Communication(LoRa)technology is also integrated with a WSN to enhance network coverage and adaptability issues.The proposed system covers vast areas through LoRa communication ensuring minimal energy consumption and cost-effective sensing capabilities.Field tests and simulation findings show how well the system captures spatiotemporal environmental fluctuations,making it an invaluable tool for monitoring climate change,ecological research,and disaster response.
基金supported by the National Natural Science Foundation of China(Grant Nos.51991395 and 42293355)geological survey project of China Geological Survey:Support for Geo-hazard monitoring,early warning and prevention(Grant No.DD20230085).
文摘Given the swift proliferation of structural health monitoring(SHM)technology within tunnel engineering,there is a demand on proficiently and precisely imputing the missing monitoring data to uphold the precision of disaster prediction.In contrast to other SHM datasets,the monitoring data specific to tunnel engineering exhibits pronounced spatiotemporal correlations.Nevertheless,most methodologies fail to adequately combine these types of correlations.Hence,the objective of this study is to develop spatiotemporal recurrent neural network(ST-RNN)model,which exploits spatiotemporal information to effectively impute missing data within tunnel monitoring systems.ST-RNN consists of two moduli:a temporal module employing recurrent neural network(RNN)to capture temporal dependencies,and a spatial module employing multilayer perceptron(MLP)to capture spatial correlations.To confirm the efficacy of the model,several commonly utilized methods are chosen as baselines for conducting comparative analyses.Furthermore,parametric validity experiments are conducted to illustrate the efficacy of the parameter selection process.The experimentation is conducted using original raw datasets wherein various degrees of continuous missing data are deliberately introduced.The experimental findings indicate that the ST-RNN model,incorporating both spatiotemporal modules,exhibits superior interpolation performance compared to other baseline methods across varying degrees of missing data.This affirms the reliability of the proposed model.
基金Supported by open project fund of National Engineering Research Center of Digital Construction and Evaluation Technology of Urban Rail Transit(2024023).
文摘Monitoring the condition of road infrastructure is crucial for maintaining its structural integrity and ensuring safe transportation.This study proposes a deep learning framework based on Temporal Convolutional Networks(TCN)integrated with Adaptive Parametric Rectified Linear Unit(APReLU)to predict future road subbase strain trends.Our model leverages time-series strain data collected from embedded triaxial sensors within a national highway,spanning August 2021 to June 2022,to forecast strain dynamics critical for proactive maintenance planning.The TCN-APReLU architecture combines dilated causal convolutions to capture long-termdependencies and APReLU activation functions to adaptively model nonlinear strain patterns,addressing limitations of traditional ReLU in handling bidirectional strain signals(compressive and tensile).Comparative experiments demonstrate TCN-APReLU’s superior performance.These improvements highlight its enhanced accuracy in predicting strain accumulation under cyclic traffic loads,enabling maintenance teams to prioritize interventions 5-7 days before critical thresholds(e.g.,>100με)are exceeded.This work provides a robust data-driven solution for urban road health monitoring,emphasizing scalability through parallelizable convolutions and adaptability to sensor noise.Future extensions will integrate multi-modal data to further generalize predictions across diverse infrastructure scenarios.
基金funded by the National Natural Science Foundation of China(Grant No.51574225)Shandong Energy Group(Grant No.SNKJ2022BJ03-R28)for Caiping Lu+1 种基金the Research Team on MonitoringActivity Mechanisms of Unnatural Earthquakes of Shandong Earthquake Agency(Grant No.TD202301)for Chengyu Liu.
文摘Mining-related seismicity poses significant challenges in underground coal mining due to its complex rupture mechanisms and associated hazards.To bridge gaps in understanding these intricate processes,this study employed a multi-local seismic monitoring network,integrating both in-mine and local instruments at overlapping length scales.We specifically focused on a damaging local magnitude(ML)2.6 event and its aftershocks that occurred on 10 September 2022 in the vicinity of the 3308 working face of the Yangcheng coal mine in Shandong Province,China.Moment tensor(MT)inversion revealed a complex cascading rupture mechanism:an initial moment magnitude(M_(w))2.2 normal fault slip along the DF60 fault in an ESEeWNW direction,transitioning to a M_(w)3.0 event as the FD24 and DF60 faults unclamped.The scale-independent self-similarity and stress heterogeneity of mining-related seismicity were investigated through source parameter calculations,providing valuable insights into the driving mechanism of these seismic sequences.The in-mine network,constrained by its low dynamic changes,captured only the nucleation phase of the DF60 fault.Furthermore,standard decomposition of the MT solution from the seismic network proved inadequate for accurately identifying the complex nature of the rupture.To enhance safety and risk management in mining environments,we examined the implications of source reactivation within the cluster area post-stress-adjustment.This comprehensive multiscale analysis offers crucial insights into the complex rupture mechanisms and hazards associated with mining-related seismicity.The results underscore the importance of continuous multi-local network monitoring and advanced analytical techniques for improved disaster assessment and risk mitigation in mining operations.
基金This study is part of the project 2021RUAPON-REACT EU-Finanziamento PON“Ricerca e Innovazione”20142020,grant n.19-G-12543-2,funded by the Italian Ministry of University and Research(MUR)This study was developed in the frame of“The Geosciences for Sustainable Development”project(Budget Minis-tero dell'Universita e della Ricerca-Dipartimenti di Eccellenza 2023-2027,code n.C93C23002690001).
文摘A case study of seismic interferometry applied to a small microseismic monitoring network is here presented.The main objectives of this study are(i)to quantify the lateral variability of shear-wave ve-locities in the studied area,and(ii)to investigate the bias produced by noise directionality and non-stationarity in the velocity estimate.Despite the limited number of stations and the short-period char-acter of the seismic sensors,the empirical Green's functions were retrieved for all station pairs using two years of passive data.Both group and phase velocities were derived,the former using the widespread frequency-time analysis,the latter through the analysis of the real part of the cross-spectra.The main advantage of combining these two methods is a more accurate identification of higher modes,resulting in a reduction of ambiguity during picking and data interpretation.Surface wave tomography was run to obtain the spatial distribution of group and phase velocities for the same wavelengths.The low standard deviation of the results suggests that the sparse character of the network does not limit the applicability of the method,for this specific case.The obtained maps highlight the presence of a lower velocity area that extends from the centre of the network towards southeast.Group and phase velocity dispersion curves have been jointly inverted to retrieve as many shear-wave velocity profiles as selected station pairs.While the average model can be used for a more accurate location of the local natural seismicity,the associated standard deviations give us an indication of the lateral heterogeneity of seismic velocities as a function of depth.Finally,the same velocity analysis was repeated for different time windows in order to quantify the error associated to variations in the noise field.Errors as large as 4%have been found,related to the unfavorable orientation of the receiver pairs with respect to strongly directional noise sources,and to the very short time widows.It was shown that using a one-year time window these errors arereduced to 0.3%.
基金supported by grants from the National Natural Science Foundation of China(No.42004010)the B&R Seismic Monitoring Network Project of the China Earthquake Networks Center(No.5007).
文摘The Belt and Road global navigation satellite system(B&R GNSS)network is the first large-scale deployment of Chinese GNSS equipment in a seismic system.Prior to this,there have been few systematic assessments of the data quality of Chinese GNSS equipment.In this study,data from four representative GNSS sites in different regions of China were analyzed using the G-Nut/Anubis software package.Four main indicators(data integrity rate,data validity ratio,multi-path error,and cycle slip ratio)used to systematically analyze data quality,while evaluating the seismic monitoring capabilities of the network based on earthquake magnitudes estimated from high-frequency GNSS data are evaluated by estimating magnitude based on highfrequency GNSS data.The results indicate that the quality of the data produced by the three types of Chinese receivers used in the network meets the needs of earthquake monitoring and the new seismic industry standards,which provide a reference for the selection of equipment for future new projects.After the B&R GNSS network was established,the seismic monitoring capability for earthquakes with magnitudes greater than M_(W)6.5 in most parts of the Sichuan-Yunnan region improved by approximately 20%.In key areas such as the Sichuan-Yunnan Rhomboid Block,the monitoring capability increased by more than 25%,which has greatly improved the effectiveness of regional comprehensive earthquake management.
基金supported by the National Natural Science Foundation of China(Nos.51905103,52275177).
文摘Multifunctional flexible sensors as wearable electronic systems have attracted considerable attention for mimicking human skin to sense ambient stimuli.However,sensors need to have high resolution,stability and sensitivity to realize fully biomimetic skin.Here,an assembled and welded Ag/W composite nanowire flexible electrode was prepared for body motion monitoring and flexible heaters.This Ag/W composite nanowire flexible electrode has a high transmittance of 90.1%(at 121Ω·sq^(−1) sheet resistance)and a low sheet resistance of 27Ω·sq^(−1)(at 60.1%transmittance).Although the transparency of this electrode is not high,the fluctuation in relative resistance change rate at 10%strain is only 5%after 1000 tensile cycles.It can be employed to monitor human body motions,including bending of fingers,arms,wrists,and throat action.Meanwhile,the Ag/W nanowires composite film heater achieves a steady-state temperature of up to 100℃ at a constant voltage of 3.5 V and an instantaneous heating rate of up to 36.5℃·s^(−1).
基金the National Natural Science Foundation of China(22308079)the Natural Science Foundation of Hebei Province,China(B2022202008,B2023202025)the Science and Technology Project of Hebei Education Department,China(BJK2022037).
文摘The operational state of distillation columns significantly impacts product quality and production efficiency.However,due to the complex operation and diverse influencing factors,ensuring the safety and efficient operation of the distillation columns becomes paramount.This research combines passive acoustic monitoring with artificial intelligence techniques,proposed a technology based on residual network(ResNet),which involves the transformation of the acoustic signals emitted by three distillation columns under different operating states.The acoustic signals were initially in one-dimensional waveform format and then converted into two-dimensional Mel-Frequency Cepstral Coefficients spectrogram database using fast Fourier transform.Ultimately,this database was employed to train a ResNet for the purpose of identifying the operational states of the distillation columns.Through this approach,the operational states of distillation columns were monitored.Various faults,including flooding,entrainment,dry-tray,etc.,were diagnosed with an accuracy of 98.91%.Moreover,an intermediate transitional state between normal operation and fault was identified and accurately recognized by the proposed method.Under the transitional state,the acoustic signals achieved an accuracy of 97.85%on the ResNet,which enables early warnings before faults occur,enhancing the safety of chemical production processes.The approach presents a powerful tool for the monitoring and diagnosis of chemical equipment,particularly distillation columns,ensuring the safety and efficiency.
基金funded by the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project(Grant No.2024ZD1002503)。
文摘Passive microseismic monitoring(PMM)serves as a fundamental technology for assessing hydraulic fracturing(HF)effectiveness,with a key focus on accurate and efficient phase detection/arrival picking and source location.In PMM data processing,the data-driven paradigm(deep learning based)outperforms the model-driven paradigm in characteristic extraction but lacks quality control and uncertainty quantification.Monte Carlo Dropout,a Bayesian uncertainty quantification technique,performs stochastic neuron deactivation through multiple forward propagation samplings.Therefore,this study proposes a deep learning neural network incorporating uncertainty quantification with manual quality control integration,establishing an optimized workflow spanning automated phase detection to robust source location.The methodology implementation comprises two principal components:(1)The MDNet employing Monte Carlo Dropout strategy enabling simultaneous phase detection/arrival picking and unce rtainty estimation;(2)an integrated hybrid-driven workflow with a traveltime-based inve rsion method for source location.Validation with field data demonstrates that MD-Net achieves superior performance under low signal-to-noise ratio conditions,maintaining detection accuracy exceeding 99%for both P-and S-waves.The phase arrival picking precision shows significant improvement,with a 40%reduction in standard deviation compared to the baseline model(P-S time difference decreasing from12.0 ms to 7.1 ms),while providing quantifiable uncertainty metrics for manual calibration.Source location results further reveal that our hybrid-driven workflow produces more physically plausible event distributions,with 100%of microseismic eve nts clustering along the primary fracture expanding direction.This performance surpasses traditional cross-correlation methods and single/multi-trace data-driven me thods in spatial rationality.This study establishes an inte rpretable,high-pre cision automated framework for HF-PMM applications,demonstrating potential for extension to diverse geological settings and monitoring configurations.
基金supported by the National Natural Science Foundation of China(No.52308332)the General Scientific Research Project of the Education Department of Zhejiang Province(No.Y202455824).
文摘This research centers on structural health monitoring of bridges,a critical transportation infrastructure.Owing to the cumulative action of heavy vehicle loads,environmental variations,and material aging,bridge components are prone to cracks and other defects,severely compromising structural safety and service life.Traditional inspection methods relying on manual visual assessment or vehicle-mounted sensors suffer from low efficiency,strong subjectivity,and high costs,while conventional image processing techniques and early deep learning models(e.g.,UNet,Faster R-CNN)still performinadequately in complex environments(e.g.,varying illumination,noise,false cracks)due to poor perception of fine cracks andmulti-scale features,limiting practical application.To address these challenges,this paper proposes CACNN-Net(CBAM-Augmented CNN),a novel dual-encoder architecture that innovatively couples a CNN for local detail extraction with a CBAM-Transformer for global context modeling.A key contribution is the dedicated Feature FusionModule(FFM),which strategically integratesmulti-scale features and focuses attention on crack regions while suppressing irrelevant noise.Experiments on bridge crack datasets demonstrate that CACNNNet achieves a precision of 77.6%,a recall of 79.4%,and an mIoU of 62.7%.These results significantly outperform several typical models(e.g.,UNet-ResNet34,Deeplabv3),confirming their superior accuracy and robust generalization,providing a high-precision automated solution for bridge crack detection and a novel network design paradigm for structural surface defect identification in complex scenarios,while future research may integrate physical features like depth information to advance intelligent infrastructure maintenance and digital twin management.
基金funded by scientific research projects under Grant JY2024B011.
文摘With the increasing complexity of industrial automation,planetary gearboxes play a vital role in largescale equipment transmission systems,directly impacting operational efficiency and safety.Traditional maintenance strategies often struggle to accurately predict the degradation process of equipment,leading to excessive maintenance costs or potential failure risks.However,existing prediction methods based on statistical models are difficult to adapt to nonlinear degradation processes.To address these challenges,this study proposes a novel condition-based maintenance framework for planetary gearboxes.A comprehensive full-lifecycle degradation experiment was conducted to collect raw vibration signals,which were then processed using a temporal convolutional network autoencoder with multi-scale perception capability to extract deep temporal degradation features,enabling the collaborative extraction of longperiod meshing frequencies and short-term impact features from the vibration signals.Kernel principal component analysis was employed to fuse and normalize these features,enhancing the characterization of degradation progression.A nonlinear Wiener process was used to model the degradation trajectory,with a threshold decay function introduced to dynamically adjust maintenance strategies,and model parameters optimized through maximum likelihood estimation.Meanwhile,the maintenance strategy was optimized to minimize costs per unit time,determining the optimal maintenance timing and preventive maintenance threshold.The comprehensive indicator of degradation trends extracted by this method reaches 0.756,which is 41.2%higher than that of traditional time-domain features;the dynamic threshold strategy reduces the maintenance cost per unit time to 55.56,which is 8.9%better than that of the static threshold optimization.Experimental results demonstrate significant reductions in maintenance costs while enhancing system reliability and safety.This study realizes the organic integration of deep learning and reliability theory in the maintenance of planetary gearboxes,provides an interpretable solution for the predictive maintenance of complex mechanical systems,and promotes the development of condition-based maintenance strategies for planetary gearboxes.
