Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyram...An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.展开更多
In traditional sensing,each parameter is treated as a real number in the signal demodulation,whereas the electric field of light is a complex number.The real and imaginary parts obey the Kramers-Kronig relationship,wh...In traditional sensing,each parameter is treated as a real number in the signal demodulation,whereas the electric field of light is a complex number.The real and imaginary parts obey the Kramers-Kronig relationship,which is expected to help further enhance sensing precision.We propose a self-Bayesian estimate of the method,aiming at reducing measurement variance.This method utilizes the intensity and phase of the parameter to be measured,achieving statistical optimization of the estimated value through Bayesian inference,effectively reducing the measurement variance.To demonstrate the effectiveness of this method,we adopted an optical fiber heterodyne interference sensing vibration measurement system.The experimental results show that the signal-to-noise ratio is effectively improved within the frequency range of 200 to 500 kHz.Moreover,it is believed that the self-Bayesian estimation method holds broad application prospects in various types of optical sensing.展开更多
The principle of optical time-domain reflection localization limits the sensing spatial resolution of Raman distributed optical fiber sensing.We provide a solution for a Raman distributed optical fiber sensing system ...The principle of optical time-domain reflection localization limits the sensing spatial resolution of Raman distributed optical fiber sensing.We provide a solution for a Raman distributed optical fiber sensing system with kilometer-level sensing distance and submeter spatial resolution.Based on this,we propose a Raman distributed optical fiber sensing scheme based on chaotic pulse cluster demodulation.Chaotic pulse clusters are used as the probe signal,in preference to conventional pulsed or chaotic single-pulse lasers.Furthermore,the accurate positioning of the temperature variety region along the sensing fiber can be realized using chaotic pulse clusters.The proposed demodulation scheme can enhance the signal-to-noise ratio by improving the correlation between the chaotic reference and the chaotic Raman anti-Stokes scattering signals.The experiment achieved a sensing spatial resolution of 30 cm at a distributed temperature-sensing distance of∼6.0 km.Furthermore,we explored the influence of chaotic pulse width and detector bandwidth on the sensing spatial resolution.In addition,the theoretical experiments proved that the sensing spatial resolution in the proposed scheme was independent of the pulse width and sensing distance.展开更多
Efficient segmentation of oiled pixels in optical remotely sensed images is the precondition of optical identification and classification of different spilled oils,which remains one of the keys to optical remote sensi...Efficient segmentation of oiled pixels in optical remotely sensed images is the precondition of optical identification and classification of different spilled oils,which remains one of the keys to optical remote sensing of oil spills.Optical remotely sensed images of oil spills are inherently multidimensional and embedded with a complex knowledge framework.This complexity often hinders the effectiveness of mechanistic algorithms across varied scenarios.Although optical remote-sensing theory for oil spills has advanced,the scarcity of curated datasets and the difficulty of collecting them limit their usefulness for training deep learning models.This study introduces a data expansion strategy that utilizes the Segment Anything Model(SAM),effectively bridging the gap between traditional mechanism algorithms and emergent self-adaptive deep learning models.Optical dimension reduction is achieved through standardized preprocessing processes that address the decipherable properties of the input image.After preprocessing,SAM can swiftly and accurately segment spilled oil in images.The unified AI-based workflow significantly accelerates labeled-dataset creation and has proven effective for both rapid emergency intelligence during spill incidents and the rapid mapping and classification of oil footprints across China’s coastal waters.Our results show that coupling a remote sensing mechanism with a foundation model enables near-real-time,large-scale monitoring of complex surface slicks and offers guidance for the next generation of detection and quantification algorithms.展开更多
Background:Traditional imaging approaches to keratoconus(KCN)have thus far failed to produce a standardized approach for diagnosis.While many diagnostic modalities and metrics exist,none have proven robust enough to b...Background:Traditional imaging approaches to keratoconus(KCN)have thus far failed to produce a standardized approach for diagnosis.While many diagnostic modalities and metrics exist,none have proven robust enough to be considered a gold standard.This study aims to introduce novel metrics to differentiate between KCN and healthy corneas using three-dimensional(3D)measurements of surface area and volume.Methods:This retrospective observational study examined KCN patients along with healthy control patients between the ages of 20 and 79 years old at the University of Maryland,Baltimore.The selected patients underwent a nine-line raster scan anterior segment optical coherence tomography(AS-OCT).ImageJ was used to determine the central 6 mm of each image and each corneal image was then divided into six 1 mm segments.Free-D software was then used to render the nine different images into a 3D model to calculate corneal surface area and volume.A two-tailed Mann-Whitney test was used to assess statistical significance when comparing these subsets.Results:Thirty-three eyes with KCN,along with 33 healthy control,were enrolled.There were statistically significant differences between the healthy and KCN groups in the metric of anterior corneal surface area(13.927 vs.13.991 mm^(2),P=0.046),posterior corneal surface area(14.045 vs.14.173 mm^(2),P<0.001),and volume(8.430 vs.7.773 mm3,P<0.001)within the central 6 mm.Conclusions:3D corneal models derived from AS-OCT can be used to measure anterior corneal surface area,posterior corneal surface area,and corneal volume.All three parameters are statistically different between corneas with KCN and healthy corneas.Further study and application of these parameters may yield new methodologies for the detection of KCN.展开更多
Mudflat vegetation plays a crucial role in the ecological function of wetland environment,and obtaining its fine spatial distri-bution is of great significance for wetland protection and management.Remote sensing tech...Mudflat vegetation plays a crucial role in the ecological function of wetland environment,and obtaining its fine spatial distri-bution is of great significance for wetland protection and management.Remote sensing techniques can realize the rapid extraction of wetland vegetation over a large area.However,the imaging of optical sensors is easily restricted by weather conditions,and the backs-cattered information reflected by Synthetic Aperture Radar(SAR)images is easily disturbed by many factors.Although both data sources have been applied in wetland vegetation classification,there is a lack of comparative study on how the selection of data sources affects the classification effect.This study takes the vegetation of the tidal flat wetland in Chongming Island,Shanghai,China,in 2019,as the research subject.A total of 22 optical feature parameters and 11 SAR feature parameters were extracted from the optical data source(Sentinel-2)and SAR data source(Sentinel-1),respectively.The performance of optical and SAR data and their feature paramet-ers in wetland vegetation classification was quantitatively compared and analyzed by different feature combinations.Furthermore,by simulating the scenario of missing optical images,the impact of optical image missing on vegetation classification accuracy and the compensatory effect of integrating SAR data were revealed.Results show that:1)under the same classification algorithm,the Overall Accuracy(OA)of the combined use of optical and SAR images was the highest,reaching 95.50%.The OA of using only optical images was slightly lower,while using only SAR images yields the lowest accuracy,but still achieved 86.48%.2)Compared to using the spec-tral reflectance of optical data and the backscattering coefficient of SAR data directly,the constructed optical and SAR feature paramet-ers contributed to improving classification accuracy.The inclusion of optical(vegetation index,spatial texture,and phenology features)and SAR feature parameters(SAR index and SAR texture features)in the classification algorithm resulted in an OA improvement of 4.56%and 9.47%,respectively.SAR backscatter,SAR index,optical phenological features,and vegetation index were identified as the top-ranking important features.3)When the optical data were missing continuously for six months,the OA dropped to a minimum of 41.56%.However,when combined with SAR data,the OA could be improved to 71.62%.This indicates that the incorporation of SAR features can effectively compensate for the loss of accuracy caused by optical image missing,especially in regions with long-term cloud cover.展开更多
The concrete hydration heat release process of the base plate is monitored using Roman optical time domain reflectometry（ROTDR） sensing sensors. The monitoring data shows that the internal maximum temperature of the...The concrete hydration heat release process of the base plate is monitored using Roman optical time domain reflectometry（ROTDR） sensing sensors. The monitoring data shows that the internal maximum temperature of the base plate is about 54 ℃ after the concrete was cured for 120 h. The fiber Bragg grating （FBG） temperature sensors are adopted to measure the surface temperature of the concrete and the temperature results are used to compensate the data measured by the pulse-prepump Brillouin optical time-domain analyzer （PPP-BOTDA） to obtain the real concrete surface strain of the base plate. The monitoring data is analyzed to obtain a clear understanding of the strain state of the base plate under the effect of concrete hydration heat release. The monitoring results demonstrate the potential of distributed optical fibre sensing techniques as a powerful tool in real-time construction monitoring, and also provide an important insight into the design, construction and maintenance of large hydraulic structures.展开更多
Soft polymer optical fiber(SPOF)has shown great potential in optical-based wearable and implantable biosensors due to its excellent mechanical properties and optical guiding characteristics.However,the multimodality c...Soft polymer optical fiber(SPOF)has shown great potential in optical-based wearable and implantable biosensors due to its excellent mechanical properties and optical guiding characteristics.However,the multimodality characteristics of SPOF limit their integration with traditional fiber optic sensors.This article introduces for the first time a flexible fiber optic vibration sensor based on laser interference technology,which can be applied to vibration measurement under high stretch conditions.This sensor utilizes elastic optical fibers made of polydimethylsiloxane(PDMS)as sensing elements,combined with phase generating carrier technology,to achieve vibration measurement at 50−260 Hz within the stretch range of 0−42%.展开更多
This paper presents a bathymetry inversion method using single-frame fine-resolution optical remote sensing imagery based on ocean-wave refraction and shallow-water wave theory. First, the relationship among water dep...This paper presents a bathymetry inversion method using single-frame fine-resolution optical remote sensing imagery based on ocean-wave refraction and shallow-water wave theory. First, the relationship among water depth, wavelength and wave radian frequency in shallow water was deduced based on shallow-water wave theory. Considering the complex wave distribution in the optical remote sensing imagery, Fast Fourier Transform （FFT） and spatial profile measurements were applied for measuring the wavelengths. Then, the wave radian frequency was calculated by analyzing the long-distance fluctuation in the wavelength, which solved a key problem in obtaining the wave radian frequency in a single-frame image. A case study was conducted for Sanya Bay of Hainan Island, China. Single-flame fine-resolution optical remote sensing imagery from QuickBird satellite was used to invert the bathymetry without external input parameters. The result of the digital elevation model （DEM） was evaluated against a sea chart with a scale of 1：25 000. The root-mean-square error of the inverted bathymetry was 1.07 m, and the relative error was 16.2%. Therefore, the proposed method has the advantages including no requirement for true depths and environmental parameters, and is feasible for mapping the bathymetry of shallow coastal water.展开更多
Battery safety has emerged as a critical challenge for achieving carbon neutrality,driven by the increasing frequency of thermal runaway incidents in electric vehicles(EVs)and stationary energy storage systems(ESSs).C...Battery safety has emerged as a critical challenge for achieving carbon neutrality,driven by the increasing frequency of thermal runaway incidents in electric vehicles(EVs)and stationary energy storage systems(ESSs).Conventional battery monitoring technologies struggle to track multiple physicochemical parameters in real time,hindering early hazard detection.Embedded optical fiber sensors have gained prominence as a transformative solution for next-generation smart battery sensing,owing to their micrometer size,multiplexing capability,and electromagnetic immunity.However,comprehensive reviews focusing on their advancements in operando multi-parameter monitoring remain scarce,despite their critical importance for ensuring battery safety.To address this gap,this review first introduces a classification and the fundamental principles of advanced battery-oriented optical fiber sensors.Subsequently,it summarizes recent developments in single-parameter battery monitoring using optical fiber sensors.Building on this foundation,this review presents the first comprehensive analysis of multifunctional optical fiber sensing platforms capable of simultaneously tracking temperature,strain,pressure,refractive index,and monitoring battery aging.Targeted strategies are proposed to facilitate the practical development of this technology,including optimization of sensor integration techniques,minimizing sensor invasiveness,resolving the cross-sensitivity of fiber Bragg grating(FBG)through structural innovation,enhancing techno-economics,and combining with artificial intelligence(AI).By aligning academic research with industry requirements,this review provides a methodological roadmap for developing robust optical sensing systems to ensure battery safety in decarbonization-driven applications.展开更多
The distributed fiber optic sensing system,known for its high sensitivity and wide-ranging measurement capabilities,has been widely used in monitoring underground gas pipelines.It primarily serves to perceive vibratio...The distributed fiber optic sensing system,known for its high sensitivity and wide-ranging measurement capabilities,has been widely used in monitoring underground gas pipelines.It primarily serves to perceive vibration signals induced by external events and to effectively provide early warnings of potential intrusion activities.Due to the complexity and diversity of external intrusion events,traditional deep learning methods can achieve event recognition with an average accuracy exceeding 90%.However,these methods rely on large-scale datasets,leading to significant time and labor costs during the data collection process.Additionally,traditional methods perform poorly when faced with the scarcity of low-frequency event samples,making it challenging to address these rare occurrences.To address this issue,this paper proposes a small-sample learning model based on triplet learning for intrusion event recognition.The model employs a 6-way 20-shot support set configuration and utilizes the KNN clustering algorithm to assess the model's performance.Experimental results indicate that the model achieves an average accuracy of 91.6%,further validating the superior performance of the triplet learning model in classifying external intrusion events.Compared to traditional methods,this approach not only effectively reduces the dependence on large-scale datasets but also better addresses the classification of low-frequency event samples,demonstrating significant application potential.展开更多
Spaceborne synthetic remote sensing of atmospheric aerosol optical depth and vegetation reflectance is very significant, but it remains to be a question unresolved yet. Based on the property of vegetation reflectance ...Spaceborne synthetic remote sensing of atmospheric aerosol optical depth and vegetation reflectance is very significant, but it remains to be a question unresolved yet. Based on the property of vegetation reflectance spectra from near ultra violet to near infrared and the sensitivity of outgoing radiance to vegetation reflectance and atmospheric aerosol optical depth, a new method for spaceborne synthetic remote sensing of the reflectance and the depth is proposed, and an iteration correlation inversion algorithm is developed in this paper. According to numerical experiment, effects of radiance error, error in aerosol imaginary index and vegetation medium inhomogeneity on retrieved result are analyzed. Inversion results show that the effect of error in aerosol imaginary index is very important. As the error of aerosol imaginary index is within 0.01, standard errors of aerosol optical depth and vegetation reflectance solutions for 14 spectral channels from 410 nm to 900 nm are respectively less than 0.063 and 0.023. And as the radiance error is within 2%, the standard errors are less than 0.023 and 0.0056.展开更多
Since the beginning of the twenty-first century,several countries have made great efforts to develop space remote sensing for building a high-resolution earth observation system.Under the great attention of the govern...Since the beginning of the twenty-first century,several countries have made great efforts to develop space remote sensing for building a high-resolution earth observation system.Under the great attention of the government and the guidance of the major scientific and technological project of the high-resolution earth observation system,China has made continuous breakthroughs and progress in high-resolution remote sensing imaging technology.The development of domestic high-resolution remote sensing satellites shows a vigorous trend,and consequently,a relatively stable and perfect high-resolution earth observation system has been formed.The development of high-resolution remote sensing satellites has greatly promoted and enriched modern mapping technologies and methods.In this paper,the development status,along with mapping modes and applications of China’s high-resolution remote sensing satellites are reviewed,and the development trend in high-resolution earth observation system for global and ground control-free mapping is discussed,providing a reference for the subsequent development of high-resolution remote sensing satellites in China.展开更多
Despite the extensive use of distributed fiber optic sensing(DFOS)in monitoring underground structures,its potential in detecting structural anomalies,such as cracks and cavities,is still not fully understood.To contr...Despite the extensive use of distributed fiber optic sensing(DFOS)in monitoring underground structures,its potential in detecting structural anomalies,such as cracks and cavities,is still not fully understood.To contribute to the identification of defects in underground structures,this study conducted a four-point bending test of a reinforced concrete(RC)beam and uniaxial loading tests of an RC specimen with local cavities.The experimental results revealed the disparity in DFOS strain spike profiles between these two structural anomalies.The effectiveness of DFOS in the quantification of crack opening displacement(COD)was also demonstrated,even in cases where perfect bonding was not achievable between the cable and structures.In addition,DFOS strain spikes observed in two diaphragm wall panels of a twin circular shaft were also reported.The most probable cause of those spikes was identified as the mechanical behavior associated with local concrete contamination.With the utilization of the strain profiles obtained from laboratory tests and field monitoring,three types of multi-classifiers,based on support vector machine(SVM),random forest(RF),and backpropagation neural network(BP),were employed to classify strain profiles,including crack-induced spikes,non-crack-induced spikes,and non-spike strain profiles.Among these classifiers,the SVM-based classifier exhibited superior performance in terms of accuracy and model robustness.This finding suggests that the SVM-based classifier holds promise as a potential solution for the automatic detection and classification of defects in underground structures during long-term monitoring.展开更多
Er-Tm3+-Ybtri-doped BaMoOphosphors were synthesized by co-precipitation technique and characterized by X-ray diffraction analysis, absorption study and field emission scanning electron microscopy analysis. Upconversio...Er-Tm3+-Ybtri-doped BaMoOphosphors were synthesized by co-precipitation technique and characterized by X-ray diffraction analysis, absorption study and field emission scanning electron microscopy analysis. Upconversion as well as downconversion luminescence studies were performed by using near infrared(980 nm) and ultraviolet(380 nm) excitations. Energy level diagram, pump power dependence and colour coordinate study were utilized to describe the multicolor upconversion emission properties. Under single 980 nm diode laser excitation the dual mode sensing behaviour is realized via Stark sublevels and thermally coupled energy levels of the Tm3+ and Erions in the prepared tri-doped phosphors. A comparative fluorescence intensity ratio analysis for integrated emission intensities arising from the Stark sublevels {~1 G4(a)) and ~1 G4(b))} and thermally coupled energy levels {~2 Hand 4 S3/2} of the Tm3+ and Er3+ ions, respectively was carried out in the prepared tri-doped BaMoOphosphors. The maximum sensitivity for thermally coupled energy levels of the Er3+ and Stark sublevels of the Tm3+ ion was reported. The developed phosphors could be useful in the display devices and optical thermo metric applications.展开更多
The commonly used method for estimating crack opening displacement(COD)is based on analytical models derived from strain transferring.However,when large background noise exists in distributed fiber optic sensing(DFOS)...The commonly used method for estimating crack opening displacement(COD)is based on analytical models derived from strain transferring.However,when large background noise exists in distributed fiber optic sensing(DFOS)data,estimating COD through an analytical model is very difficult even if the DFOS data have been denoised.To address this challenge,this study proposes a machine learning(ML)-based methodology to complete rock's COD estimation from establishment of a dataset with one-to-one correspondence between strain sequence and COD to the optimization of ML models.The Bayesian optimization is used via the Hyperopt Python library to determine the appropriate hyper-parameters of four ML models.To ensure that the best hyper-parameters will not be missing,the configuration space in Hyperopt is specified by probability distribution.The four models are trained using DFOS data with minimal noise while being examined on datasets with different noise levels to test their anti-noise robustness.The proposed models are compared each other in terms of goodness of fit and mean squared error.The results show that the Bayesian optimization-based random forest is promising to estimate the COD of rock using noisy DFOS data.展开更多
Considering the important applications in the military and the civilian domain, ship detection and classification based on optical remote sensing images raise considerable attention in the sea surface remote sensing f...Considering the important applications in the military and the civilian domain, ship detection and classification based on optical remote sensing images raise considerable attention in the sea surface remote sensing filed. This article collects the methods of ship detection and classification for practically testing in optical remote sensing images, and provides their corresponding feature extraction strategies and statistical data. Basic feature extraction strategies and algorithms are analyzed associated with their performance and application in ship detection and classification.Furthermore, publicly available datasets that can be applied as the benchmarks to verify the effectiveness and the objectiveness of ship detection and classification methods are summarized in this paper. Based on the analysis, the remaining problems and future development trends are provided for ship detection and classification methods based on optical remote sensing images.展开更多
Structural deformation monitoring of flight vehicles based on optical fiber sensing(OFS)technology has been a focus of research in the field of aerospace.After nearly 30 years of research and development,Chinese and i...Structural deformation monitoring of flight vehicles based on optical fiber sensing(OFS)technology has been a focus of research in the field of aerospace.After nearly 30 years of research and development,Chinese and international researchers have made significant advances in the areas of theory and methods,technology and systems,and ground experiments and flight tests.These advances have led to the development of OFS technology from the laboratory research stage to the engineering application stage.However,a few problems encountered in practical applications limit the wider application and further development of this technology,and thus urgently require solutions.This paper reviews the history of research on the deformation monitoring of flight vehicles.It examines various aspects of OFS-based deformation monitoring including the main varieties of OFS technology,technical advantages and disadvantages,suitability in aerospace applications,deformation reconstruction algorithms,and typical applications.This paper points out the key unresolved problems and the main evolution paradigms of engineering applications.It further discusses future development directions from the perspectives of an evolution paradigm,standardization,new materials,intelligentization,and collaboration.展开更多
Phase-sensitive Optical Time-Domain Reflectometer(φ-OTDR)technology facilitates the real-time detection of vibration events along fiber optic cables by analyzing changes in Rayleigh scattering signals.This technology...Phase-sensitive Optical Time-Domain Reflectometer(φ-OTDR)technology facilitates the real-time detection of vibration events along fiber optic cables by analyzing changes in Rayleigh scattering signals.This technology is widely used in applications such as intrusion monitoring and structural health assessments.Traditional signal processing methods,such as Support Vector Machines(SVM)and K-Nearest Neighbors(KNN),have limitations in feature extraction and classification in complex environments.Conversely,a single deep learning model often struggles with capturing long time-series dependencies and mitigating noise interference.In this study,we propose a deep learning model that integrates Convolutional Neural Network(CNN),Long Short-Term Memory Network(LSTM),and Transformer modules,leveraging φ-OTDR technology for distributed fiber vibration sensing event recognition.The hybrid model combines the CNN's capability to extract local features,the LSTM's ability to model temporal dynamics,and the Transformer's proficiency in capturing global dependencies.This integration significantly enhances the accuracy and robustness of event recognition.In experiments involving six types of vibration events,the model consistently achieved a validation accuracy of 0.92,and maintained a validation loss of approximately 0.2,surpassing other models,such as TAM+BiLSTM and CNN+CBAM.The results indicate that the CNN+LSTM+Transformer model is highly effective in handling vibration signal classification tasks in complex scenarios,offering a promising new direction for the application of fiber optic vibration sensing technology.展开更多
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
基金supported by the National Natural Science Foundation of China(No.62241109)the Tianjin Science and Technology Commissioner Project(No.20YDTPJC01110)。
文摘An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.
基金supported by the National Key Research and Development Plan of China(Grant No.2022YFB3207402)the National Natural Science Foundation of China(Grant Nos.U1833104 and 61735011).
文摘In traditional sensing,each parameter is treated as a real number in the signal demodulation,whereas the electric field of light is a complex number.The real and imaginary parts obey the Kramers-Kronig relationship,which is expected to help further enhance sensing precision.We propose a self-Bayesian estimate of the method,aiming at reducing measurement variance.This method utilizes the intensity and phase of the parameter to be measured,achieving statistical optimization of the estimated value through Bayesian inference,effectively reducing the measurement variance.To demonstrate the effectiveness of this method,we adopted an optical fiber heterodyne interference sensing vibration measurement system.The experimental results show that the signal-to-noise ratio is effectively improved within the frequency range of 200 to 500 kHz.Moreover,it is believed that the self-Bayesian estimation method holds broad application prospects in various types of optical sensing.
基金supported by the National Natural Science Foundation of China(Grant Nos.U23A20375 and 62075151)the National Key Research and Development Program of China(Grant No.202103021223042).
文摘The principle of optical time-domain reflection localization limits the sensing spatial resolution of Raman distributed optical fiber sensing.We provide a solution for a Raman distributed optical fiber sensing system with kilometer-level sensing distance and submeter spatial resolution.Based on this,we propose a Raman distributed optical fiber sensing scheme based on chaotic pulse cluster demodulation.Chaotic pulse clusters are used as the probe signal,in preference to conventional pulsed or chaotic single-pulse lasers.Furthermore,the accurate positioning of the temperature variety region along the sensing fiber can be realized using chaotic pulse clusters.The proposed demodulation scheme can enhance the signal-to-noise ratio by improving the correlation between the chaotic reference and the chaotic Raman anti-Stokes scattering signals.The experiment achieved a sensing spatial resolution of 30 cm at a distributed temperature-sensing distance of∼6.0 km.Furthermore,we explored the influence of chaotic pulse width and detector bandwidth on the sensing spatial resolution.In addition,the theoretical experiments proved that the sensing spatial resolution in the proposed scheme was independent of the pulse width and sensing distance.
基金The National Natural Science Foundation of China under contract No.42371380the National Key Research and Development Program of China under contract No.2023YFC2811800the Fundamental Research Funds for the Central Universities under contract No.0904-14380035.
文摘Efficient segmentation of oiled pixels in optical remotely sensed images is the precondition of optical identification and classification of different spilled oils,which remains one of the keys to optical remote sensing of oil spills.Optical remotely sensed images of oil spills are inherently multidimensional and embedded with a complex knowledge framework.This complexity often hinders the effectiveness of mechanistic algorithms across varied scenarios.Although optical remote-sensing theory for oil spills has advanced,the scarcity of curated datasets and the difficulty of collecting them limit their usefulness for training deep learning models.This study introduces a data expansion strategy that utilizes the Segment Anything Model(SAM),effectively bridging the gap between traditional mechanism algorithms and emergent self-adaptive deep learning models.Optical dimension reduction is achieved through standardized preprocessing processes that address the decipherable properties of the input image.After preprocessing,SAM can swiftly and accurately segment spilled oil in images.The unified AI-based workflow significantly accelerates labeled-dataset creation and has proven effective for both rapid emergency intelligence during spill incidents and the rapid mapping and classification of oil footprints across China’s coastal waters.Our results show that coupling a remote sensing mechanism with a foundation model enables near-real-time,large-scale monitoring of complex surface slicks and offers guidance for the next generation of detection and quantification algorithms.
文摘Background:Traditional imaging approaches to keratoconus(KCN)have thus far failed to produce a standardized approach for diagnosis.While many diagnostic modalities and metrics exist,none have proven robust enough to be considered a gold standard.This study aims to introduce novel metrics to differentiate between KCN and healthy corneas using three-dimensional(3D)measurements of surface area and volume.Methods:This retrospective observational study examined KCN patients along with healthy control patients between the ages of 20 and 79 years old at the University of Maryland,Baltimore.The selected patients underwent a nine-line raster scan anterior segment optical coherence tomography(AS-OCT).ImageJ was used to determine the central 6 mm of each image and each corneal image was then divided into six 1 mm segments.Free-D software was then used to render the nine different images into a 3D model to calculate corneal surface area and volume.A two-tailed Mann-Whitney test was used to assess statistical significance when comparing these subsets.Results:Thirty-three eyes with KCN,along with 33 healthy control,were enrolled.There were statistically significant differences between the healthy and KCN groups in the metric of anterior corneal surface area(13.927 vs.13.991 mm^(2),P=0.046),posterior corneal surface area(14.045 vs.14.173 mm^(2),P<0.001),and volume(8.430 vs.7.773 mm3,P<0.001)within the central 6 mm.Conclusions:3D corneal models derived from AS-OCT can be used to measure anterior corneal surface area,posterior corneal surface area,and corneal volume.All three parameters are statistically different between corneas with KCN and healthy corneas.Further study and application of these parameters may yield new methodologies for the detection of KCN.
基金Under the auspices of the National Key Research and Development Program of China(No.2023YFC3208500)Shanghai Municipal Natural Science Foundation(No.22ZR1421500)+3 种基金National Natural Science Foundation of China(No.U2243207)National Science and Technology Basic Resources Survey Project(No.2023FY01001)Open Research Fund of State Key Laboratory of Estuarine and Coastal Research(No.SKLEC-KF202406)Project from Science and Technology Commission of Shanghai Municipality(No.22DZ1202700)。
文摘Mudflat vegetation plays a crucial role in the ecological function of wetland environment,and obtaining its fine spatial distri-bution is of great significance for wetland protection and management.Remote sensing techniques can realize the rapid extraction of wetland vegetation over a large area.However,the imaging of optical sensors is easily restricted by weather conditions,and the backs-cattered information reflected by Synthetic Aperture Radar(SAR)images is easily disturbed by many factors.Although both data sources have been applied in wetland vegetation classification,there is a lack of comparative study on how the selection of data sources affects the classification effect.This study takes the vegetation of the tidal flat wetland in Chongming Island,Shanghai,China,in 2019,as the research subject.A total of 22 optical feature parameters and 11 SAR feature parameters were extracted from the optical data source(Sentinel-2)and SAR data source(Sentinel-1),respectively.The performance of optical and SAR data and their feature paramet-ers in wetland vegetation classification was quantitatively compared and analyzed by different feature combinations.Furthermore,by simulating the scenario of missing optical images,the impact of optical image missing on vegetation classification accuracy and the compensatory effect of integrating SAR data were revealed.Results show that:1)under the same classification algorithm,the Overall Accuracy(OA)of the combined use of optical and SAR images was the highest,reaching 95.50%.The OA of using only optical images was slightly lower,while using only SAR images yields the lowest accuracy,but still achieved 86.48%.2)Compared to using the spec-tral reflectance of optical data and the backscattering coefficient of SAR data directly,the constructed optical and SAR feature paramet-ers contributed to improving classification accuracy.The inclusion of optical(vegetation index,spatial texture,and phenology features)and SAR feature parameters(SAR index and SAR texture features)in the classification algorithm resulted in an OA improvement of 4.56%and 9.47%,respectively.SAR backscatter,SAR index,optical phenological features,and vegetation index were identified as the top-ranking important features.3)When the optical data were missing continuously for six months,the OA dropped to a minimum of 41.56%.However,when combined with SAR data,the OA could be improved to 71.62%.This indicates that the incorporation of SAR features can effectively compensate for the loss of accuracy caused by optical image missing,especially in regions with long-term cloud cover.
基金The National Key Technology R&D Program during the 12th Five-Year Plan Period(No.2012BAK10B05)the State Key Program of National Natural Science of China(No.41427801)
文摘The concrete hydration heat release process of the base plate is monitored using Roman optical time domain reflectometry（ROTDR） sensing sensors. The monitoring data shows that the internal maximum temperature of the base plate is about 54 ℃ after the concrete was cured for 120 h. The fiber Bragg grating （FBG） temperature sensors are adopted to measure the surface temperature of the concrete and the temperature results are used to compensate the data measured by the pulse-prepump Brillouin optical time-domain analyzer （PPP-BOTDA） to obtain the real concrete surface strain of the base plate. The monitoring data is analyzed to obtain a clear understanding of the strain state of the base plate under the effect of concrete hydration heat release. The monitoring results demonstrate the potential of distributed optical fibre sensing techniques as a powerful tool in real-time construction monitoring, and also provide an important insight into the design, construction and maintenance of large hydraulic structures.
文摘Soft polymer optical fiber(SPOF)has shown great potential in optical-based wearable and implantable biosensors due to its excellent mechanical properties and optical guiding characteristics.However,the multimodality characteristics of SPOF limit their integration with traditional fiber optic sensors.This article introduces for the first time a flexible fiber optic vibration sensor based on laser interference technology,which can be applied to vibration measurement under high stretch conditions.This sensor utilizes elastic optical fibers made of polydimethylsiloxane(PDMS)as sensing elements,combined with phase generating carrier technology,to achieve vibration measurement at 50−260 Hz within the stretch range of 0−42%.
基金The Public Science and Technology Research Fund Project of Ocean under contract No.201105001the National Nature Science Foundation of China under contract No.41576174the Public Science and Technology Research Fund Project of Surveying,Mapping and Geoinformation under contract No.201512030
文摘This paper presents a bathymetry inversion method using single-frame fine-resolution optical remote sensing imagery based on ocean-wave refraction and shallow-water wave theory. First, the relationship among water depth, wavelength and wave radian frequency in shallow water was deduced based on shallow-water wave theory. Considering the complex wave distribution in the optical remote sensing imagery, Fast Fourier Transform （FFT） and spatial profile measurements were applied for measuring the wavelengths. Then, the wave radian frequency was calculated by analyzing the long-distance fluctuation in the wavelength, which solved a key problem in obtaining the wave radian frequency in a single-frame image. A case study was conducted for Sanya Bay of Hainan Island, China. Single-flame fine-resolution optical remote sensing imagery from QuickBird satellite was used to invert the bathymetry without external input parameters. The result of the digital elevation model （DEM） was evaluated against a sea chart with a scale of 1：25 000. The root-mean-square error of the inverted bathymetry was 1.07 m, and the relative error was 16.2%. Therefore, the proposed method has the advantages including no requirement for true depths and environmental parameters, and is feasible for mapping the bathymetry of shallow coastal water.
基金the financial supports of the National Natural Science Foundation of China(No.52372200)a project supported by the State Key Laboratory of Mechanics and Control for Aerospace Structures(No.MCAS-S-0324G01)。
文摘Battery safety has emerged as a critical challenge for achieving carbon neutrality,driven by the increasing frequency of thermal runaway incidents in electric vehicles(EVs)and stationary energy storage systems(ESSs).Conventional battery monitoring technologies struggle to track multiple physicochemical parameters in real time,hindering early hazard detection.Embedded optical fiber sensors have gained prominence as a transformative solution for next-generation smart battery sensing,owing to their micrometer size,multiplexing capability,and electromagnetic immunity.However,comprehensive reviews focusing on their advancements in operando multi-parameter monitoring remain scarce,despite their critical importance for ensuring battery safety.To address this gap,this review first introduces a classification and the fundamental principles of advanced battery-oriented optical fiber sensors.Subsequently,it summarizes recent developments in single-parameter battery monitoring using optical fiber sensors.Building on this foundation,this review presents the first comprehensive analysis of multifunctional optical fiber sensing platforms capable of simultaneously tracking temperature,strain,pressure,refractive index,and monitoring battery aging.Targeted strategies are proposed to facilitate the practical development of this technology,including optimization of sensor integration techniques,minimizing sensor invasiveness,resolving the cross-sensitivity of fiber Bragg grating(FBG)through structural innovation,enhancing techno-economics,and combining with artificial intelligence(AI).By aligning academic research with industry requirements,this review provides a methodological roadmap for developing robust optical sensing systems to ensure battery safety in decarbonization-driven applications.
基金Supported by the Scientific Research and Technology Development Project of Petrochina Southwest Oil and Gas Field Company(20230307-02)。
文摘The distributed fiber optic sensing system,known for its high sensitivity and wide-ranging measurement capabilities,has been widely used in monitoring underground gas pipelines.It primarily serves to perceive vibration signals induced by external events and to effectively provide early warnings of potential intrusion activities.Due to the complexity and diversity of external intrusion events,traditional deep learning methods can achieve event recognition with an average accuracy exceeding 90%.However,these methods rely on large-scale datasets,leading to significant time and labor costs during the data collection process.Additionally,traditional methods perform poorly when faced with the scarcity of low-frequency event samples,making it challenging to address these rare occurrences.To address this issue,this paper proposes a small-sample learning model based on triplet learning for intrusion event recognition.The model employs a 6-way 20-shot support set configuration and utilizes the KNN clustering algorithm to assess the model's performance.Experimental results indicate that the model achieves an average accuracy of 91.6%,further validating the superior performance of the triplet learning model in classifying external intrusion events.Compared to traditional methods,this approach not only effectively reduces the dependence on large-scale datasets but also better addresses the classification of low-frequency event samples,demonstrating significant application potential.
文摘Spaceborne synthetic remote sensing of atmospheric aerosol optical depth and vegetation reflectance is very significant, but it remains to be a question unresolved yet. Based on the property of vegetation reflectance spectra from near ultra violet to near infrared and the sensitivity of outgoing radiance to vegetation reflectance and atmospheric aerosol optical depth, a new method for spaceborne synthetic remote sensing of the reflectance and the depth is proposed, and an iteration correlation inversion algorithm is developed in this paper. According to numerical experiment, effects of radiance error, error in aerosol imaginary index and vegetation medium inhomogeneity on retrieved result are analyzed. Inversion results show that the effect of error in aerosol imaginary index is very important. As the error of aerosol imaginary index is within 0.01, standard errors of aerosol optical depth and vegetation reflectance solutions for 14 spectral channels from 410 nm to 900 nm are respectively less than 0.063 and 0.023. And as the radiance error is within 2%, the standard errors are less than 0.023 and 0.0056.
基金This work is supported by the National Natural Science Foundation of China[grant numbers 91738302 and 91838303]the National Science Fund for Distinguished Young Scholars[grant number 61825103]Thanks for the support of China Centre for Resources Satellite Data and Application(CRESDA).
文摘Since the beginning of the twenty-first century,several countries have made great efforts to develop space remote sensing for building a high-resolution earth observation system.Under the great attention of the government and the guidance of the major scientific and technological project of the high-resolution earth observation system,China has made continuous breakthroughs and progress in high-resolution remote sensing imaging technology.The development of domestic high-resolution remote sensing satellites shows a vigorous trend,and consequently,a relatively stable and perfect high-resolution earth observation system has been formed.The development of high-resolution remote sensing satellites has greatly promoted and enriched modern mapping technologies and methods.In this paper,the development status,along with mapping modes and applications of China’s high-resolution remote sensing satellites are reviewed,and the development trend in high-resolution earth observation system for global and ground control-free mapping is discussed,providing a reference for the subsequent development of high-resolution remote sensing satellites in China.
基金support from the Open Research Project Programme of the State Key Laboratory of Internet of Things for Smart City,University of Macao (Grant No.SKL-IoTSC (UM)-2021-2023/ORPF/A19/2022)the General Research Fund project from Research Grants Council of Hong Kong Special Administrative Region Government of China (Grant No.15214722)the Start-up Fund from The Hong Kong Polytechnic University (Grant No.BD88).
文摘Despite the extensive use of distributed fiber optic sensing(DFOS)in monitoring underground structures,its potential in detecting structural anomalies,such as cracks and cavities,is still not fully understood.To contribute to the identification of defects in underground structures,this study conducted a four-point bending test of a reinforced concrete(RC)beam and uniaxial loading tests of an RC specimen with local cavities.The experimental results revealed the disparity in DFOS strain spike profiles between these two structural anomalies.The effectiveness of DFOS in the quantification of crack opening displacement(COD)was also demonstrated,even in cases where perfect bonding was not achievable between the cable and structures.In addition,DFOS strain spikes observed in two diaphragm wall panels of a twin circular shaft were also reported.The most probable cause of those spikes was identified as the mechanical behavior associated with local concrete contamination.With the utilization of the strain profiles obtained from laboratory tests and field monitoring,three types of multi-classifiers,based on support vector machine(SVM),random forest(RF),and backpropagation neural network(BP),were employed to classify strain profiles,including crack-induced spikes,non-crack-induced spikes,and non-spike strain profiles.Among these classifiers,the SVM-based classifier exhibited superior performance in terms of accuracy and model robustness.This finding suggests that the SVM-based classifier holds promise as a potential solution for the automatic detection and classification of defects in underground structures during long-term monitoring.
基金Project supported by Council of Scientific&Industrial Research(CSIR)New Delhi,India(03(1354)/16/EMR-II)
文摘Er-Tm3+-Ybtri-doped BaMoOphosphors were synthesized by co-precipitation technique and characterized by X-ray diffraction analysis, absorption study and field emission scanning electron microscopy analysis. Upconversion as well as downconversion luminescence studies were performed by using near infrared(980 nm) and ultraviolet(380 nm) excitations. Energy level diagram, pump power dependence and colour coordinate study were utilized to describe the multicolor upconversion emission properties. Under single 980 nm diode laser excitation the dual mode sensing behaviour is realized via Stark sublevels and thermally coupled energy levels of the Tm3+ and Erions in the prepared tri-doped phosphors. A comparative fluorescence intensity ratio analysis for integrated emission intensities arising from the Stark sublevels {~1 G4(a)) and ~1 G4(b))} and thermally coupled energy levels {~2 Hand 4 S3/2} of the Tm3+ and Er3+ ions, respectively was carried out in the prepared tri-doped BaMoOphosphors. The maximum sensitivity for thermally coupled energy levels of the Er3+ and Stark sublevels of the Tm3+ ion was reported. The developed phosphors could be useful in the display devices and optical thermo metric applications.
基金The Young Scientists Fund of the National Natural Science Foundation of China(Grant No.42407250)the Fund from Research Centre for Resources Engineering towards Carbon Neutrality(RCRE)of The Hong Kong Polytechnic University(Grant No.No.1-BBEM)the Fund from Natural Science Foundation of Jiangsu Province(Grant No.BK20241211)。
文摘The commonly used method for estimating crack opening displacement(COD)is based on analytical models derived from strain transferring.However,when large background noise exists in distributed fiber optic sensing(DFOS)data,estimating COD through an analytical model is very difficult even if the DFOS data have been denoised.To address this challenge,this study proposes a machine learning(ML)-based methodology to complete rock's COD estimation from establishment of a dataset with one-to-one correspondence between strain sequence and COD to the optimization of ML models.The Bayesian optimization is used via the Hyperopt Python library to determine the appropriate hyper-parameters of four ML models.To ensure that the best hyper-parameters will not be missing,the configuration space in Hyperopt is specified by probability distribution.The four models are trained using DFOS data with minimal noise while being examined on datasets with different noise levels to test their anti-noise robustness.The proposed models are compared each other in terms of goodness of fit and mean squared error.The results show that the Bayesian optimization-based random forest is promising to estimate the COD of rock using noisy DFOS data.
文摘Considering the important applications in the military and the civilian domain, ship detection and classification based on optical remote sensing images raise considerable attention in the sea surface remote sensing filed. This article collects the methods of ship detection and classification for practically testing in optical remote sensing images, and provides their corresponding feature extraction strategies and statistical data. Basic feature extraction strategies and algorithms are analyzed associated with their performance and application in ship detection and classification.Furthermore, publicly available datasets that can be applied as the benchmarks to verify the effectiveness and the objectiveness of ship detection and classification methods are summarized in this paper. Based on the analysis, the remaining problems and future development trends are provided for ship detection and classification methods based on optical remote sensing images.
基金funded by the National Natural Science Foundation of China(51705024,51535002,51675053,61903041,61903042,and 61903041)the National Key Research and Development Program of China(2016YFF0101801)+4 种基金the National Hightech Research and Development Program of China(2015AA042308)the Innovative Equipment Pre-Research Key Fund Project(6140414030101)the Manned Space Pre-Research Project(20184112043)the Beijing Municipal Natural Science Foundation(F7202017 and 4204101)the Beijing Nova Program of Science and Technology(Z191100001119052)。
文摘Structural deformation monitoring of flight vehicles based on optical fiber sensing(OFS)technology has been a focus of research in the field of aerospace.After nearly 30 years of research and development,Chinese and international researchers have made significant advances in the areas of theory and methods,technology and systems,and ground experiments and flight tests.These advances have led to the development of OFS technology from the laboratory research stage to the engineering application stage.However,a few problems encountered in practical applications limit the wider application and further development of this technology,and thus urgently require solutions.This paper reviews the history of research on the deformation monitoring of flight vehicles.It examines various aspects of OFS-based deformation monitoring including the main varieties of OFS technology,technical advantages and disadvantages,suitability in aerospace applications,deformation reconstruction algorithms,and typical applications.This paper points out the key unresolved problems and the main evolution paradigms of engineering applications.It further discusses future development directions from the perspectives of an evolution paradigm,standardization,new materials,intelligentization,and collaboration.
基金Supported by Key Laboratory of Space Active Optical-Electro Technology of Chinese Academy of Sciences(2021ZDKF4)。
文摘Phase-sensitive Optical Time-Domain Reflectometer(φ-OTDR)technology facilitates the real-time detection of vibration events along fiber optic cables by analyzing changes in Rayleigh scattering signals.This technology is widely used in applications such as intrusion monitoring and structural health assessments.Traditional signal processing methods,such as Support Vector Machines(SVM)and K-Nearest Neighbors(KNN),have limitations in feature extraction and classification in complex environments.Conversely,a single deep learning model often struggles with capturing long time-series dependencies and mitigating noise interference.In this study,we propose a deep learning model that integrates Convolutional Neural Network(CNN),Long Short-Term Memory Network(LSTM),and Transformer modules,leveraging φ-OTDR technology for distributed fiber vibration sensing event recognition.The hybrid model combines the CNN's capability to extract local features,the LSTM's ability to model temporal dynamics,and the Transformer's proficiency in capturing global dependencies.This integration significantly enhances the accuracy and robustness of event recognition.In experiments involving six types of vibration events,the model consistently achieved a validation accuracy of 0.92,and maintained a validation loss of approximately 0.2,surpassing other models,such as TAM+BiLSTM and CNN+CBAM.The results indicate that the CNN+LSTM+Transformer model is highly effective in handling vibration signal classification tasks in complex scenarios,offering a promising new direction for the application of fiber optic vibration sensing technology.
