A novel stochastic resonance algorithm was employed to enhance the signal-to-noise ratio (SNR) of signals of analytical chemistry. By using a gas chromatographic data set, it was proven that the SNR was greatly impro...A novel stochastic resonance algorithm was employed to enhance the signal-to-noise ratio (SNR) of signals of analytical chemistry. By using a gas chromatographic data set, it was proven that the SNR was greatly improved and the quantitative relationship between concentrations and chromatographic responses remained simultaneously. The linear range was extended beyond the instrumental detection limit.展开更多
In SPECT, noise is one of the major limitations that degrade image quality. To suppress the noisy signals in an image, digital filters are most commonly applied. However, in SPECT image reconstruction, selection of an...In SPECT, noise is one of the major limitations that degrade image quality. To suppress the noisy signals in an image, digital filters are most commonly applied. However, in SPECT image reconstruction, selection of an appropriate filter and its functions has always remained a difficult task. In this work an attempt was made to investigate the effects of varying cut-off frequencies and in keeping the order of Butterworth filter constant on detectability and contrast of hot and cold re-gions images. A new insert simulating hot and cold regions which provides similar views in a reconstructed image was placed in the phantom’s cylindrical source tank and imaged. Tc-99m radionuclide was distributed uniformly in the phantom. SPECT data were collected in a 20% energy window centered at 140 keV by a Philips ADAC Forte dual head gamma camera mounted with a LEHR collimator. Images were generated by using the filtered backprojection technique. A Butterworth filter of order 5 with cut-off frequencies 0.35 and 0.45 cycles·cm<sup>-1</sup> was applied. Images were examined in terms of hot and cold regions, detectability and contrast. Results show that the hot and cold regions’ detectability and contrast vary with the change of cut-off frequency. With a 0.45 cycles·cm<sup>-1</sup> cut-off frequency, a significant enhancement in contrast of cold regions was achieved as compared to a 0.35 cycles·cm<sup>-1</sup> cut-off frequency. Furthermore, the detectability of hot and cold regions improved with the use of a 0.45 cycles·cm<sup>-1</sup> cut-off frequency. In conclusion, image quality of hot and cold regions affected in a different way with a change of cut-off frequency. Thus, care should be taken in selecting the filter cut-off frequency prior to reconstruction of images;particularly, when both types of regions are expected in the reconstructed image.展开更多
The behavior of resistive short defects in FPGA interconnects is investigated through simulation and theoretical analysis.The results show that these defects result in timing failures and even Boolean faults for small...The behavior of resistive short defects in FPGA interconnects is investigated through simulation and theoretical analysis.The results show that these defects result in timing failures and even Boolean faults for small defect resistance values.The best detection situations for large resistance defect happen when the path under test makes a v-to-v′ transition and another path causing short faults remains at value v.Small defects can be detected easily through static analysis.Under the best test situations,the effects of supply voltage and temperature on test results are evaluated.The results verify that lower voltage helps to improve detectability.If short material has positive temperature coefficient,low temperature is better;otherwise,high temperature is better.展开更多
Rotating stall and surge are two violent unstable phenomena of an aero-engine compressor.The early detection of rotating stall is a critical and difficult issue in the operation of a compressor.Recently,a deterministi...Rotating stall and surge are two violent unstable phenomena of an aero-engine compressor.The early detection of rotating stall is a critical and difficult issue in the operation of a compressor.Recently,a deterministic learning based stall inception detection approach(SIDA)has been developed for modeling and detecting stall inception in aero-engine compressors.This paper considers the derivation of analytical results on the detection capabilities for the SIDA based on deterministic learning.First,by utilizing the input/output stability of the residual system,a detectability condition of the SIDA is presented,and how to choose the parameters of the diagnostic system is also analyzed.Second,based on the relationship between NN approximation capabilities and radial basis function(RBF)network structures,the influence of RBF network structures on the performance properties of the SIDA is analyzed.Finally,a simulation study is presented,in which the Mansoux-C2 compressor model is utilized to verify the effectiveness of the proposed SIDA.展开更多
The detectability and reliability analysis for the local seismic network is performed employing by Bungum and Husebye technique. The events were relocated using standard computer codes for hypocentral locations. The d...The detectability and reliability analysis for the local seismic network is performed employing by Bungum and Husebye technique. The events were relocated using standard computer codes for hypocentral locations. The detectability levels are estimated from the twenty-five years of recorded data in terms of 50%, 90% and 100% cumulative detectability thresholds, which were derived from frequency-magnitude distribution. From this analysis the 100% level of detectability of the network is M L=1.7 for events which occur within the network. The accuracy in hypocentral solutions of the network is investigated by considering the fixed real hypocenter within the network. The epicentral errors are found to be less than 4 km when the events occur within the network. Finally, the problems faced during continuous operation of the local network, which effects its detectability, are discussed.展开更多
This paper mainly discusses stabilizatbility, exact observability and exact detectability of discrete stochastic systems with both static and control dependent noise via the spectrum technique. The authors put forward...This paper mainly discusses stabilizatbility, exact observability and exact detectability of discrete stochastic systems with both static and control dependent noise via the spectrum technique. The authors put forward a definition of the spectrum and give some theorems based on the spectrum. Then the relation between discrete generalized Lyapunov equation and discrete generalized algebraic Riccati equation is also analyzed.展开更多
Phase identification procedures for teleseismic events at Syowa Station (69.0°S, 39.6°E;SYO), East Antarctica have been carried out since 1967 after the International Geophysical Year (IGY;1957-1958). Since ...Phase identification procedures for teleseismic events at Syowa Station (69.0°S, 39.6°E;SYO), East Antarctica have been carried out since 1967 after the International Geophysical Year (IGY;1957-1958). Since the development of INTELSAT telecommunication link, digital waveform data have been transmitted to the National Institute of Polar Research (NIPR) for the utilization of phase identification. Arrival times of teleseismic phases, P, PKP, PP, S, SKS have been detected manually and reported to the International Seismological Centre (ISC), and published by “JARE Data Reports” from NIPR. In this paper, hypocentral distribution and time variations for detected earthquakes are demonstrated over the last four decades in 1967-2010. Characteristics of detected events, magnitude dependency, spatial distributions, seasonal variations, together with classification by focal depth are investigated. Besides the natural increase in the occurrence of teleseismic events on the globe, a technical advance in the observing system and station infrastructure, as well as the improvement of procedures for reading seismic phases, could all combine to produce the increase in detection of events in last few decades. Variations in teleseismic detectability for longer terms may be possible by association with the meteorological environment and seaice spreading area around the Antarctic continent. Recorded teleseismic and local seismic signals have sufficient quality for many analyses on dynamics and structure of the Earth as viewed from Antarctica. The continuously recorded data are applied not only to lithospheric studies but also to the Earth’s deep interiors, as a significant contribution to the Federation of Digital Seismological Networks (FDSN) from high southern latitude.展开更多
The hidden water-bearing structures near the roadway tunnelling face are very likely to cause water seepage accidents in coal mines.Currently,transient electromagnetic(EM)technology has be-come an important method to ...The hidden water-bearing structures near the roadway tunnelling face are very likely to cause water seepage accidents in coal mines.Currently,transient electromagnetic(EM)technology has be-come an important method to detect water damage in advance of roadway excavation.In this paper,the time-domain finite element algorithm based on unstructured tetrahedron grids is used to accurate-ly simulate the geological body in front of the roadway excavation face and analyze its response.The authors detect the distance between the roadway excavation face and the low-resistivity water-bearing body,the resistivity difference between the low-resistivity body and surrounding rock,and the influence of the size of the low-resistivity body on the transient EM response.Furthermore,the common types of low-resistivity bodies in the roadway drivage process are used for modeling to analyze the attenuation of the detected EM response when there are low-resistivity bodies in front of the roadway.The research in this paper can help effectively detecting the water-bearing low-resistivity body in front of the roadway drivage and lay a foundation for reducing the risk of water seepage accidents.展开更多
The authors consider the property of detectability of discrete event systems in the presence of sensor attacks in the context of cyber-security.The authors model the system using an automaton and study the general not...The authors consider the property of detectability of discrete event systems in the presence of sensor attacks in the context of cyber-security.The authors model the system using an automaton and study the general notion of detectability where a given set of state pairs needs to be(eventually or periodically)distinguished in any estimate of the state of the system.The authors adopt the ALTER sensor attack model from previous work and formulate four notions of CA-detectability in the context of this attack model based on the following attributes:strong or weak;eventual or periodic.The authors present verification methods for strong CA-detectability and weak CA-detectability.The authors present definitions of strong and weak periodic CA-detectability that are based on the construction of a verifier automaton called the augmented CA-observer.The development also resulted in relaxing assumptions in prior results on D-detectability,which is a special case of CA-detectability.展开更多
This paper presents the notions of exact observability and exact detectability for Markov jump linear stochastic systems of Ito type with multiplieative noise （for short, MJLSS）. Stochastic Popov-Belevith-Hautus （...This paper presents the notions of exact observability and exact detectability for Markov jump linear stochastic systems of Ito type with multiplieative noise （for short, MJLSS）. Stochastic Popov-Belevith-Hautus （PBH） Criterions for exact observability and exact detectability are respectively obtained. As an application, stochastic H2/H∞ control for such MJLSS is discussed under exact detectability.展开更多
This paper mainly studies observability and detectability for continuous-time stochastic Markov jump systems.Two concepts called W-observability and W-detectability for such systems are introduced,which are shown to c...This paper mainly studies observability and detectability for continuous-time stochastic Markov jump systems.Two concepts called W-observability and W-detectability for such systems are introduced,which are shown to coincide with various notions of observability and detectability reported recently in literature,such as exact observability,exact detectability and detectability.Besides,by introducing an accumulated energy function,some efficient criteria and interesting properties for both W-observability and W-detectability are obtained.展开更多
Aiming at the drawbacks of low contrast and high noise in the thermal images, a novel method based on the combination of the thermal image sequence reconstruction and the first-order differential processing is propose...Aiming at the drawbacks of low contrast and high noise in the thermal images, a novel method based on the combination of the thermal image sequence reconstruction and the first-order differential processing is proposed in this work, which is comprised of the following procedures. Firstly, the specimen with four fabricated defects with different sizes is detected by using pulsed infrared thennography. Then, a piecewise fitting based method is proposed to reconstruct the thermal image sequence to compress the data and remove the temporal noise of each pixel in the thermal image. Finally, the first-order differential processing based method is proposed to enhance the contrast. An experimental investigation into the specimen containing de-bond defects between the steel and the heat insulation layer is carried out to validate the effectiveness of the proposed method via the above procedures. The obtained results show that the proposed method can remove the noise, enhance the contrast, and even compress the data reaching at 99.1%, thus improving the detectability of pulsed infrared thermography on metal defects.展开更多
Aims to determine the detectability of a global weedy perennial weed Hypochaeris radicata and its relationship with five common observer,species and environmental variables.Methods trained independent observers conduc...Aims to determine the detectability of a global weedy perennial weed Hypochaeris radicata and its relationship with five common observer,species and environmental variables.Methods trained independent observers conducted time-limited repeat sur-veys of H.radicata during autumn in an endangered grassy box-gum woodland ecosystem in south-east australia.single-species single-season site-occupancy modelling was used to determine if detectability of H.radicata was altered by five covariates,observer,litter height,grazing,maximum plant height and flowering state.Important Findings Detectability for H.radicata varied significantly with observer,litter height,plant maximum height and flowering state,but not with graz-ing.Despite significant observer-specific variation,there was a con-sistent increase in detectability with plant height and when plants are in flower for all observers.Detectability generally decreased as litter height increases.Perfect or constant detection rates cannot be assumed in plant surveys,even for easily recognizable plants in simple survey conditions.understanding how detectability is influ-enced by common survey variables can help improve the efficacy of plant monitoring programs by quantifying the extent of uncertainty in inferences made from survey data,or by determining optimal sur-vey conditions to increase the reliability of collected data.For plants with traits similar to H.radicata,surveying when most plants are at maximum height or in flower,increasing search intensity when litter levels are high and minimizing observer-related heterogeneity are potentially simple and effective ways to reduce detection errors.We speculate that detection rates may be lower,more variable and involve additional covariates when surveying during the peak flow-ering spring season with the presence of more warm season and taller annual species.展开更多
Seed traits play an important role in affecting seed preference and hoarding behaviors of small rodents.Despite greatly affected by seed traits,seed detectability of competitors represents pilfering risks and may also...Seed traits play an important role in affecting seed preference and hoarding behaviors of small rodents.Despite greatly affected by seed traits,seed detectability of competitors represents pilfering risks and may also modify seed hoarding preference of animals.However,whether seed traits and seed detectability show consistent effects on seed hoarding preference of animals remain largely unknown.Here,we explored how seed traits and seed detectability correlate with seed hoarding preference of Leopoldamys edwardsi and Apodemus chevrieri in a subtropical forest.Despite the effects of seed coat thickness and caloric value on hoarding preference of L.edwardsi,we detected no significant effects of other seed traits on hording preference of the 2 rodent species.There was no correlation between larder-hoarding preference and inter-or intra-specific seed detectability of L.edwardsi;however,seed detectability of L.edwardsi was negatively correlated with its own scatter-hoarding preference.Although scatter-hoarding preference of A.chevrieri was not correlated with inter-and intra-specific seed detectability,larder-hoarding preference of A.chevrieri was positively correlated with intra-specific seed detectability.Our study may provide evidence that intra-specific seed detectability rather than seed traits and inter-specific pilfering risks play an important role in modifying seed hoarding preference of rodents.展开更多
Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still st...Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.展开更多
Underwater pipeline inspection plays a vital role in the proactive maintenance and management of critical marine infrastructure and subaquatic systems.However,the inspection of underwater pipelines presents a challeng...Underwater pipeline inspection plays a vital role in the proactive maintenance and management of critical marine infrastructure and subaquatic systems.However,the inspection of underwater pipelines presents a challenge due to factors such as light scattering,absorption,restricted visibility,and ambient noise.The advancement of deep learning has introduced powerful techniques for processing large amounts of unstructured and imperfect data collected from underwater environments.This study evaluated the efficacy of the You Only Look Once(YOLO)algorithm,a real-time object detection and localization model based on convolutional neural networks,in identifying and classifying various types of pipeline defects in underwater settings.YOLOv8,the latest evolution in the YOLO family,integrates advanced capabilities,such as anchor-free detection,a cross-stage partial network backbone for efficient feature extraction,and a feature pyramid network+path aggregation network neck for robust multi-scale object detection,which make it particularly well-suited for complex underwater environments.Due to the lack of suitable open-access datasets for underwater pipeline defects,a custom dataset was captured using a remotely operated vehicle in a controlled environment.This application has the following assets available for use.Extensive experimentation demonstrated that YOLOv8 X-Large consistently outperformed other models in terms of pipe defect detection and classification and achieved a strong balance between precision and recall in identifying pipeline cracks,rust,corners,defective welds,flanges,tapes,and holes.This research establishes the baseline performance of YOLOv8 for underwater defect detection and showcases its potential to enhance the reliability and efficiency of pipeline inspection tasks in challenging underwater environments.展开更多
【Objective】This study aimed to establish a quintuple PCR method for rapid and simultaneous detection of Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provided tech...【Objective】This study aimed to establish a quintuple PCR method for rapid and simultaneous detection of Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provided technical support for early diagnosis of various soil-borne diseases on ginger.【Method】For five types of soil-borne pathogens causing ginger bacterial wilt and rhizome rot,specific primer combinations were designed and screened,the optimal quintuple reaction system was established by exploring optimal primer concentrations,annealing temperature,and sensitivity,and was applied to detect field plant samples to verify its utility.【Result】Specific primers pairs Rs1F/Rs1R,En1F/En1R,and Py1F/Py1R were designed according to flic gene of Ralstonia solanacearum,rpoB gene of Enterobacter spp.,and 18S rDNA of Pythium spp.,and combined with reported Fusarium spp.specific primers Fu3/Fu4 and specific primers 23SPecF/23SPecR of Pectobacterium spp.,a quintuple PCR reaction system for ginger soil-borne pathogens has been established(25.00μL):above primer dosage was 1.20,0.20,0.60,1.60,and 0.15μL respectively;2×PCR Mix 12.50μL;DNA templates of different pathogens were 1.00μL each;added ddH_(2)O to 25.00μL.Annealing temperature was optimized to 55.4℃.The specific fragments with sizes of 516,370,266,207,and 159 bp could be amplified simultaneously in the established quintuple PCR system,and the detection limit of this system for Ralstonia solanacearum,Enterobacter spp.and Pythium spp.reached 10^(-1)pg/μL,for Fusarium spp.and Pectobacterium spp.was 1 pg/μL,and for detecting five pathogens simultaneously was 10^(3)pg/μL.The multiplex PCR system established in this study could successfully detect the diseased plant samples from the field.【Conclusion】The quintuple PCR system established is able to rapid ly and accurately detect Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provides a useful tool for timely diagnosis and epidemic monitoring of various soil-borne diseases of ginger.展开更多
In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detec...In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detection of Vibrio parahaemolyticus.The nanozyme integrated magnetic separation,peroxidase-like catalytic activity,and specific target recognition through an aptamer-based strategy.Upon binding to V.parahaemolyticus,the catalytic oxidation of tetra-aminophenylethylene(TPE-4A)by the nanozyme was selectively inhibited,resulting in distinct colorimetric and fluorescent signals that significantly enhanced the detection accuracy and reliability.The proposed method exhibited high sensitivity,with limits of detection(LOD)of 21 and 7 CFU/mL for the colorimetric and fluorescent assays,respectively.The performance of this method was validated using real seafood samples,including Penaeus vannamei,Mytilus coruscus,and Crassostrea gigas,which showed high recovery rates(101.11%-107.30%)and excellent reproducibility.The system also demonstrated strong specificity and accuracy under various conditions,confirming its robustness and practical applicability.Collectively,this innovative platform presents a promising solution for the rapid,versatile,and sensitive detection of V.parahaemolyticus in seafood,with considerable potential to advance food safety diagnosis and on-site monitoring.展开更多
Diesel accounts for over 60%of the products derived from direct coal liquefaction(DCL).Compared to petroleum-based diesel,DCL diesel exhibits a cetane number ranging from 30 to 40,which fails to meet the automotive di...Diesel accounts for over 60%of the products derived from direct coal liquefaction(DCL).Compared to petroleum-based diesel,DCL diesel exhibits a cetane number ranging from 30 to 40,which fails to meet the automotive diesel standard requirement of≥45.Therefore,rapid and accurate analysis of its chemical composition is crucial for property optimization to meet fuel specifications by component blending.Thought traditional methods like gas chromatography offer high accuracy,they are unsuitable for rapid online analysis under industrial conditions.Near-infrared(NIR)spectroscopy can provide advantages in rapid,non-destructive analysis.Its application however,is limited by the complexity of spectral data interpretation.Machine learning(ML)is effective method for extracting valuable information from spectra and establishing high-precision prediction models.This study integrates NIR spectroscopy with ML to construct a spectral-composition database for DCL diesel.Feature extraction was performed using the correlation coefficient and mutual information methods to screen key wavelength variables and reduce data dimensionality.Subsequently,the predictive performance of three ML models—Lasso,SVR and XGBoost—was compared.Results indicate that excluding spectral data with absorbance greater than 1 significantly enhances model accuracy,increasing the test set R^(2) from 0.85 to 0.96.After feature extraction,the optimal number of wavelength variables was reduced to 177,substantially improving computational efficiency.Among the models evaluated,the SVR-MI-0.9 model,based on mutual information feature selection,demonstrated the best performance,achieving training and test set R^(2) values both exceeding 0.98.This model enables precise prediction of paraffin,naphthene,and aromatic hydrocarbon contents.This research provides a robust methodology for intelligent online quality monitoring.An intelligent NIR spectroscopy data analysis software was independently developed based on the established model.Compared with comprehensive two-dimensional gas chromatography,the software reduced the analysis time by over 98%,with an absolute prediction error below 0.2%.Thus,rapid analysis of DCL diesel components was successfully realized.展开更多
基金supported by the National Natural Science Foundation of China (No.20075024).
文摘A novel stochastic resonance algorithm was employed to enhance the signal-to-noise ratio (SNR) of signals of analytical chemistry. By using a gas chromatographic data set, it was proven that the SNR was greatly improved and the quantitative relationship between concentrations and chromatographic responses remained simultaneously. The linear range was extended beyond the instrumental detection limit.
文摘In SPECT, noise is one of the major limitations that degrade image quality. To suppress the noisy signals in an image, digital filters are most commonly applied. However, in SPECT image reconstruction, selection of an appropriate filter and its functions has always remained a difficult task. In this work an attempt was made to investigate the effects of varying cut-off frequencies and in keeping the order of Butterworth filter constant on detectability and contrast of hot and cold re-gions images. A new insert simulating hot and cold regions which provides similar views in a reconstructed image was placed in the phantom’s cylindrical source tank and imaged. Tc-99m radionuclide was distributed uniformly in the phantom. SPECT data were collected in a 20% energy window centered at 140 keV by a Philips ADAC Forte dual head gamma camera mounted with a LEHR collimator. Images were generated by using the filtered backprojection technique. A Butterworth filter of order 5 with cut-off frequencies 0.35 and 0.45 cycles·cm<sup>-1</sup> was applied. Images were examined in terms of hot and cold regions, detectability and contrast. Results show that the hot and cold regions’ detectability and contrast vary with the change of cut-off frequency. With a 0.45 cycles·cm<sup>-1</sup> cut-off frequency, a significant enhancement in contrast of cold regions was achieved as compared to a 0.35 cycles·cm<sup>-1</sup> cut-off frequency. Furthermore, the detectability of hot and cold regions improved with the use of a 0.45 cycles·cm<sup>-1</sup> cut-off frequency. In conclusion, image quality of hot and cold regions affected in a different way with a change of cut-off frequency. Thus, care should be taken in selecting the filter cut-off frequency prior to reconstruction of images;particularly, when both types of regions are expected in the reconstructed image.
文摘The behavior of resistive short defects in FPGA interconnects is investigated through simulation and theoretical analysis.The results show that these defects result in timing failures and even Boolean faults for small defect resistance values.The best detection situations for large resistance defect happen when the path under test makes a v-to-v′ transition and another path causing short faults remains at value v.Small defects can be detected easily through static analysis.Under the best test situations,the effects of supply voltage and temperature on test results are evaluated.The results verify that lower voltage helps to improve detectability.If short material has positive temperature coefficient,low temperature is better;otherwise,high temperature is better.
基金This work was supported in part by the Major Program of the National Natural Science Foundation of China(No.61890922)in part by the Major Basic Program of Shandong Provincial Natural Science Foundation(No.ZR2020ZD40).
文摘Rotating stall and surge are two violent unstable phenomena of an aero-engine compressor.The early detection of rotating stall is a critical and difficult issue in the operation of a compressor.Recently,a deterministic learning based stall inception detection approach(SIDA)has been developed for modeling and detecting stall inception in aero-engine compressors.This paper considers the derivation of analytical results on the detection capabilities for the SIDA based on deterministic learning.First,by utilizing the input/output stability of the residual system,a detectability condition of the SIDA is presented,and how to choose the parameters of the diagnostic system is also analyzed.Second,based on the relationship between NN approximation capabilities and radial basis function(RBF)network structures,the influence of RBF network structures on the performance properties of the SIDA is analyzed.Finally,a simulation study is presented,in which the Mansoux-C2 compressor model is utilized to verify the effectiveness of the proposed SIDA.
文摘The detectability and reliability analysis for the local seismic network is performed employing by Bungum and Husebye technique. The events were relocated using standard computer codes for hypocentral locations. The detectability levels are estimated from the twenty-five years of recorded data in terms of 50%, 90% and 100% cumulative detectability thresholds, which were derived from frequency-magnitude distribution. From this analysis the 100% level of detectability of the network is M L=1.7 for events which occur within the network. The accuracy in hypocentral solutions of the network is investigated by considering the fixed real hypocenter within the network. The epicentral errors are found to be less than 4 km when the events occur within the network. Finally, the problems faced during continuous operation of the local network, which effects its detectability, are discussed.
文摘This paper mainly discusses stabilizatbility, exact observability and exact detectability of discrete stochastic systems with both static and control dependent noise via the spectrum technique. The authors put forward a definition of the spectrum and give some theorems based on the spectrum. Then the relation between discrete generalized Lyapunov equation and discrete generalized algebraic Riccati equation is also analyzed.
文摘Phase identification procedures for teleseismic events at Syowa Station (69.0°S, 39.6°E;SYO), East Antarctica have been carried out since 1967 after the International Geophysical Year (IGY;1957-1958). Since the development of INTELSAT telecommunication link, digital waveform data have been transmitted to the National Institute of Polar Research (NIPR) for the utilization of phase identification. Arrival times of teleseismic phases, P, PKP, PP, S, SKS have been detected manually and reported to the International Seismological Centre (ISC), and published by “JARE Data Reports” from NIPR. In this paper, hypocentral distribution and time variations for detected earthquakes are demonstrated over the last four decades in 1967-2010. Characteristics of detected events, magnitude dependency, spatial distributions, seasonal variations, together with classification by focal depth are investigated. Besides the natural increase in the occurrence of teleseismic events on the globe, a technical advance in the observing system and station infrastructure, as well as the improvement of procedures for reading seismic phases, could all combine to produce the increase in detection of events in last few decades. Variations in teleseismic detectability for longer terms may be possible by association with the meteorological environment and seaice spreading area around the Antarctic continent. Recorded teleseismic and local seismic signals have sufficient quality for many analyses on dynamics and structure of the Earth as viewed from Antarctica. The continuously recorded data are applied not only to lithospheric studies but also to the Earth’s deep interiors, as a significant contribution to the Federation of Digital Seismological Networks (FDSN) from high southern latitude.
文摘The hidden water-bearing structures near the roadway tunnelling face are very likely to cause water seepage accidents in coal mines.Currently,transient electromagnetic(EM)technology has be-come an important method to detect water damage in advance of roadway excavation.In this paper,the time-domain finite element algorithm based on unstructured tetrahedron grids is used to accurate-ly simulate the geological body in front of the roadway excavation face and analyze its response.The authors detect the distance between the roadway excavation face and the low-resistivity water-bearing body,the resistivity difference between the low-resistivity body and surrounding rock,and the influence of the size of the low-resistivity body on the transient EM response.Furthermore,the common types of low-resistivity bodies in the roadway drivage process are used for modeling to analyze the attenuation of the detected EM response when there are low-resistivity bodies in front of the roadway.The research in this paper can help effectively detecting the water-bearing low-resistivity body in front of the roadway drivage and lay a foundation for reducing the risk of water seepage accidents.
基金supported in part by the US National Science Foundation under Grant Nos.ECCS-2146615 and ECCS-2144416.
文摘The authors consider the property of detectability of discrete event systems in the presence of sensor attacks in the context of cyber-security.The authors model the system using an automaton and study the general notion of detectability where a given set of state pairs needs to be(eventually or periodically)distinguished in any estimate of the state of the system.The authors adopt the ALTER sensor attack model from previous work and formulate four notions of CA-detectability in the context of this attack model based on the following attributes:strong or weak;eventual or periodic.The authors present verification methods for strong CA-detectability and weak CA-detectability.The authors present definitions of strong and weak periodic CA-detectability that are based on the construction of a verifier automaton called the augmented CA-observer.The development also resulted in relaxing assumptions in prior results on D-detectability,which is a special case of CA-detectability.
基金supported by National Natural Science Foundation of China under Grant Nos 60774020, 60736028,and 60821091
文摘This paper presents the notions of exact observability and exact detectability for Markov jump linear stochastic systems of Ito type with multiplieative noise （for short, MJLSS）. Stochastic Popov-Belevith-Hautus （PBH） Criterions for exact observability and exact detectability are respectively obtained. As an application, stochastic H2/H∞ control for such MJLSS is discussed under exact detectability.
基金supported by the Natural Science Foundation of China under Grant No.61174078the Research Fund for the Taishan Scholar Project of Shandong Province of China+1 种基金the SDUST Research Fund under Grant No.2011KYTD105the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources under Grant No.LAPS13018
文摘This paper mainly studies observability and detectability for continuous-time stochastic Markov jump systems.Two concepts called W-observability and W-detectability for such systems are introduced,which are shown to coincide with various notions of observability and detectability reported recently in literature,such as exact observability,exact detectability and detectability.Besides,by introducing an accumulated energy function,some efficient criteria and interesting properties for both W-observability and W-detectability are obtained.
基金the National Natural Science Foundation of China (Grant Nos.51575516 and 51605481)Xi'an Science and Technology Project(Grant No. 2017089CG/RC052 HJKC001).
文摘Aiming at the drawbacks of low contrast and high noise in the thermal images, a novel method based on the combination of the thermal image sequence reconstruction and the first-order differential processing is proposed in this work, which is comprised of the following procedures. Firstly, the specimen with four fabricated defects with different sizes is detected by using pulsed infrared thennography. Then, a piecewise fitting based method is proposed to reconstruct the thermal image sequence to compress the data and remove the temporal noise of each pixel in the thermal image. Finally, the first-order differential processing based method is proposed to enhance the contrast. An experimental investigation into the specimen containing de-bond defects between the steel and the heat insulation layer is carried out to validate the effectiveness of the proposed method via the above procedures. The obtained results show that the proposed method can remove the noise, enhance the contrast, and even compress the data reaching at 99.1%, thus improving the detectability of pulsed infrared thermography on metal defects.
文摘Aims to determine the detectability of a global weedy perennial weed Hypochaeris radicata and its relationship with five common observer,species and environmental variables.Methods trained independent observers conducted time-limited repeat sur-veys of H.radicata during autumn in an endangered grassy box-gum woodland ecosystem in south-east australia.single-species single-season site-occupancy modelling was used to determine if detectability of H.radicata was altered by five covariates,observer,litter height,grazing,maximum plant height and flowering state.Important Findings Detectability for H.radicata varied significantly with observer,litter height,plant maximum height and flowering state,but not with graz-ing.Despite significant observer-specific variation,there was a con-sistent increase in detectability with plant height and when plants are in flower for all observers.Detectability generally decreased as litter height increases.Perfect or constant detection rates cannot be assumed in plant surveys,even for easily recognizable plants in simple survey conditions.understanding how detectability is influ-enced by common survey variables can help improve the efficacy of plant monitoring programs by quantifying the extent of uncertainty in inferences made from survey data,or by determining optimal sur-vey conditions to increase the reliability of collected data.For plants with traits similar to H.radicata,surveying when most plants are at maximum height or in flower,increasing search intensity when litter levels are high and minimizing observer-related heterogeneity are potentially simple and effective ways to reduce detection errors.We speculate that detection rates may be lower,more variable and involve additional covariates when surveying during the peak flow-ering spring season with the presence of more warm season and taller annual species.
基金supported by the National Natural Science Foundation of China(32070447 and 31760156)Youth Talent Introduction and Education Program of Shandong Province(20190601).
文摘Seed traits play an important role in affecting seed preference and hoarding behaviors of small rodents.Despite greatly affected by seed traits,seed detectability of competitors represents pilfering risks and may also modify seed hoarding preference of animals.However,whether seed traits and seed detectability show consistent effects on seed hoarding preference of animals remain largely unknown.Here,we explored how seed traits and seed detectability correlate with seed hoarding preference of Leopoldamys edwardsi and Apodemus chevrieri in a subtropical forest.Despite the effects of seed coat thickness and caloric value on hoarding preference of L.edwardsi,we detected no significant effects of other seed traits on hording preference of the 2 rodent species.There was no correlation between larder-hoarding preference and inter-or intra-specific seed detectability of L.edwardsi;however,seed detectability of L.edwardsi was negatively correlated with its own scatter-hoarding preference.Although scatter-hoarding preference of A.chevrieri was not correlated with inter-and intra-specific seed detectability,larder-hoarding preference of A.chevrieri was positively correlated with intra-specific seed detectability.Our study may provide evidence that intra-specific seed detectability rather than seed traits and inter-specific pilfering risks play an important role in modifying seed hoarding preference of rodents.
基金supported by the National Natural Science Foundation of China(No.62276204)the Fundamental Research Funds for the Central Universities,China(No.YJSJ24011)+1 种基金the Natural Science Basic Research Program of Shaanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710)the China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470)。
文摘Visible and infrared(RGB-IR)fusion object detection plays an important role in security,disaster relief,etc.In recent years,deep-learning-based RGB-IR fusion detection methods have been developing rapidly,but still struggle to deal with the complex and changing scenarios captured by drones,mainly due to two reasons:(A)RGB-IR fusion detectors are susceptible to inferior inputs that degrade performance and stability.(B)RGB-IR fusion detectors are susceptible to redundant features that reduce accuracy and efficiency.In this paper,an innovative RGB-IR fusion detection framework based on global-local feature optimization,named GLFDet,is proposed to improve the detection performance and efficiency of drone-captured objects.The key components of GLFDet include a Global Feature Optimization(GFO)module,a Local Feature Optimization(LFO)module and a Channel Separation Fusion(CSF)module.Specifically,GFO calculates the information content of the input image from the frequency domain and optimizes the features holistically.Then,LFO dynamically selects high-value features and filters out low-value features before fusion,which significantly improves the efficiency of fusion.Finally,CSF fuses the RGB and IR features across the corresponding channels,which avoids the rearrangement of the channel relationships and enhances the model stability.Extensive experimental results show that the proposed method achieves the best performance on three popular RGB-IR datasets Drone Vehicle,VEDAI,and LLVIP.In addition,GLFDet is more lightweight than other comparable models,making it more appealing to edge devices such as drones.The code is available at https://github.com/lao chen330/GLFDet.
文摘Underwater pipeline inspection plays a vital role in the proactive maintenance and management of critical marine infrastructure and subaquatic systems.However,the inspection of underwater pipelines presents a challenge due to factors such as light scattering,absorption,restricted visibility,and ambient noise.The advancement of deep learning has introduced powerful techniques for processing large amounts of unstructured and imperfect data collected from underwater environments.This study evaluated the efficacy of the You Only Look Once(YOLO)algorithm,a real-time object detection and localization model based on convolutional neural networks,in identifying and classifying various types of pipeline defects in underwater settings.YOLOv8,the latest evolution in the YOLO family,integrates advanced capabilities,such as anchor-free detection,a cross-stage partial network backbone for efficient feature extraction,and a feature pyramid network+path aggregation network neck for robust multi-scale object detection,which make it particularly well-suited for complex underwater environments.Due to the lack of suitable open-access datasets for underwater pipeline defects,a custom dataset was captured using a remotely operated vehicle in a controlled environment.This application has the following assets available for use.Extensive experimentation demonstrated that YOLOv8 X-Large consistently outperformed other models in terms of pipe defect detection and classification and achieved a strong balance between precision and recall in identifying pipeline cracks,rust,corners,defective welds,flanges,tapes,and holes.This research establishes the baseline performance of YOLOv8 for underwater defect detection and showcases its potential to enhance the reliability and efficiency of pipeline inspection tasks in challenging underwater environments.
基金National Natural Science Foundation of China(32270237)Guangxi Key Research and Development Plan Project(Guike AB21238002)Basic Scientific Research Project of Guangxi Academy of Agricultural Sciences(Guinongke 2024YP082)。
文摘【Objective】This study aimed to establish a quintuple PCR method for rapid and simultaneous detection of Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provided technical support for early diagnosis of various soil-borne diseases on ginger.【Method】For five types of soil-borne pathogens causing ginger bacterial wilt and rhizome rot,specific primer combinations were designed and screened,the optimal quintuple reaction system was established by exploring optimal primer concentrations,annealing temperature,and sensitivity,and was applied to detect field plant samples to verify its utility.【Result】Specific primers pairs Rs1F/Rs1R,En1F/En1R,and Py1F/Py1R were designed according to flic gene of Ralstonia solanacearum,rpoB gene of Enterobacter spp.,and 18S rDNA of Pythium spp.,and combined with reported Fusarium spp.specific primers Fu3/Fu4 and specific primers 23SPecF/23SPecR of Pectobacterium spp.,a quintuple PCR reaction system for ginger soil-borne pathogens has been established(25.00μL):above primer dosage was 1.20,0.20,0.60,1.60,and 0.15μL respectively;2×PCR Mix 12.50μL;DNA templates of different pathogens were 1.00μL each;added ddH_(2)O to 25.00μL.Annealing temperature was optimized to 55.4℃.The specific fragments with sizes of 516,370,266,207,and 159 bp could be amplified simultaneously in the established quintuple PCR system,and the detection limit of this system for Ralstonia solanacearum,Enterobacter spp.and Pythium spp.reached 10^(-1)pg/μL,for Fusarium spp.and Pectobacterium spp.was 1 pg/μL,and for detecting five pathogens simultaneously was 10^(3)pg/μL.The multiplex PCR system established in this study could successfully detect the diseased plant samples from the field.【Conclusion】The quintuple PCR system established is able to rapid ly and accurately detect Ralstonia solanacearum,Fusarium spp.,Pectobacterium spp.,Enterobacter spp.,and Pythium spp.,which provides a useful tool for timely diagnosis and epidemic monitoring of various soil-borne diseases of ginger.
文摘In this study,a multifunctional aptamer-conjugated magnetic covalent organic framework(COF)-CuO/Au nanozyme(MCOF-CuO/Au@apt)was developed as a“three-in-one”platform for dual-signal colorimetric and fluorescent detection of Vibrio parahaemolyticus.The nanozyme integrated magnetic separation,peroxidase-like catalytic activity,and specific target recognition through an aptamer-based strategy.Upon binding to V.parahaemolyticus,the catalytic oxidation of tetra-aminophenylethylene(TPE-4A)by the nanozyme was selectively inhibited,resulting in distinct colorimetric and fluorescent signals that significantly enhanced the detection accuracy and reliability.The proposed method exhibited high sensitivity,with limits of detection(LOD)of 21 and 7 CFU/mL for the colorimetric and fluorescent assays,respectively.The performance of this method was validated using real seafood samples,including Penaeus vannamei,Mytilus coruscus,and Crassostrea gigas,which showed high recovery rates(101.11%-107.30%)and excellent reproducibility.The system also demonstrated strong specificity and accuracy under various conditions,confirming its robustness and practical applicability.Collectively,this innovative platform presents a promising solution for the rapid,versatile,and sensitive detection of V.parahaemolyticus in seafood,with considerable potential to advance food safety diagnosis and on-site monitoring.
基金Supported by National Natural Science Foundation of China(U24B6018,22178243)。
文摘Diesel accounts for over 60%of the products derived from direct coal liquefaction(DCL).Compared to petroleum-based diesel,DCL diesel exhibits a cetane number ranging from 30 to 40,which fails to meet the automotive diesel standard requirement of≥45.Therefore,rapid and accurate analysis of its chemical composition is crucial for property optimization to meet fuel specifications by component blending.Thought traditional methods like gas chromatography offer high accuracy,they are unsuitable for rapid online analysis under industrial conditions.Near-infrared(NIR)spectroscopy can provide advantages in rapid,non-destructive analysis.Its application however,is limited by the complexity of spectral data interpretation.Machine learning(ML)is effective method for extracting valuable information from spectra and establishing high-precision prediction models.This study integrates NIR spectroscopy with ML to construct a spectral-composition database for DCL diesel.Feature extraction was performed using the correlation coefficient and mutual information methods to screen key wavelength variables and reduce data dimensionality.Subsequently,the predictive performance of three ML models—Lasso,SVR and XGBoost—was compared.Results indicate that excluding spectral data with absorbance greater than 1 significantly enhances model accuracy,increasing the test set R^(2) from 0.85 to 0.96.After feature extraction,the optimal number of wavelength variables was reduced to 177,substantially improving computational efficiency.Among the models evaluated,the SVR-MI-0.9 model,based on mutual information feature selection,demonstrated the best performance,achieving training and test set R^(2) values both exceeding 0.98.This model enables precise prediction of paraffin,naphthene,and aromatic hydrocarbon contents.This research provides a robust methodology for intelligent online quality monitoring.An intelligent NIR spectroscopy data analysis software was independently developed based on the established model.Compared with comprehensive two-dimensional gas chromatography,the software reduced the analysis time by over 98%,with an absolute prediction error below 0.2%.Thus,rapid analysis of DCL diesel components was successfully realized.
