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
Monochromatic y-rays are thought to be the smoking gun signal for identifying dark matter annihilation. However, the flux of monochromatic y-rays is usually suppressed by virtual quantum effects since dark matter shou...Monochromatic y-rays are thought to be the smoking gun signal for identifying dark matter annihilation. However, the flux of monochromatic y-rays is usually suppressed by virtual quantum effects since dark matter should be neutral and does not couple with y-rays directly. In this work, we study the detection strategy of the monochromatic y-rays in a future space-based detector. The flux of monochromatic y-rays between 50 GeV and several TeV is calculated by assuming the supersymmetric neutralino as a typical dark matter candidate. The detection both by focusing on the Galactic center and in a scan mode that detects y-rays from the whole Galactic halo are compared. The detector performance for the purpose of monochromatic y-ray detection, with different energy and angular resolution, field of view, and background rejection efficiencies, is carefully studied with both analytical and fast Monte-Carlo methods.展开更多
Understanding the dynamics and regulation of a particular ecological process requires monitoring of the process at appropriate spatial and temporal scales.Information collected at an inappropriate spatiotemporal scale...Understanding the dynamics and regulation of a particular ecological process requires monitoring of the process at appropriate spatial and temporal scales.Information collected at an inappropriate spatiotemporal scale may be insufficient for capturing spatio-temporal dynamics of fish populations and community.In this study,a Monte Carlo method was developed to evaluate the detectability performances of different sampling frequencies,sampling timings and sampling intensities on fish community indices and fish species.Species richness indices tended to decrease with an increased sampling frequency,while species diversity indices had small changes in response to changes in sampling frequency.The diversity index was more likely to be influenced by the choice of sampling timing compared to the richness index.The total number of species,especially seasonal and rare species present in the simulated sampling,increased with sampling frequency.Although sampling frequency is more important than sampling intensity,sampling intensity is also important for the detectability of fish species.This study showed that sampling frequency and intensity could greatly influence the estimation of fish community.Choices of sampling timing,sampling frequency and intensity may result in different estimates of fish species compositions and community structure.It is very necessary to consider the importance of sufficient sampling frequency and intensity in a survey program.展开更多
Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing can...Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.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
The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risk...The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risks,including floods and droughts.Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms.Observational data reveal a 20th-century shift:increased rainfall pre-1950s,followed by aridification and partial recovery post-1980s,driven by both internal variability(e.g.,Atlantic Multidecadal Oscillation)and external forcings(greenhouse gases,aerosols),while ENSO drives interannual variability through ocean-atmosphere interactions.Future projections under greenhouse forcing suggest long-term monsoon intensification,though regional disparities and model uncertainties persist.Models indicate robust trends but struggle to quantify extremes,where thermodynamic effects(warming-induced moisture rise)uniformly boost heavy rainfall,while dynamical shifts(circulation changes)create spatial heterogeneity.Volcanic eruptions and proposed solar radiation modification(SRM)further complicate predictions:tropical eruptions suppress monsoons,whereas high-latitude events alter cross-equatorial flows,highlighting unresolved feedbacks.The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons.Critical challenges remain.Model biases and sparse 20th-century observational data hinder accurate attribution.The interplay between natural variability and anthropogenic forcings,along with nonlinear extreme precipitation risks under warming,demands deeper mechanistic insights.Additionally,SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation.Addressing these gaps necessitates enhanced observational networks,refined climate models,and interdisciplinary efforts to disentangle multiscale drivers,ultimately improving resilience strategies for monsoon-dependent regions.展开更多
The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests suc...The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests such as the Mini-Mental State Examination and the Montreal Cognitive Assessment,exhibit inherent limitations with respect to accessibility,administration burden,and sensitivity to subtle cognitive decline,particularly among diverse populations.This commentary critically examines a recent study that champions a novel approach:The integration of gait and handwriting kinematic parameters analyzed via machine learning for MCI screening.The present study positions itself within the broader landscape of MCI detection,with a view to comparing its advantages against established neuropsychological batteries,advanced neuroimaging(e.g.,positron emission tomography,magnetic resonance imaging),and emerging fluid biomarkers(e.g.,cerebrospinal fluid,blood-based assays).While the study demonstrates promising accuracy(74.44%area under the curve 0.74 with gait and graphic handwriting)and addresses key unmet needs in accessibility and objectivity,we highlight its cross-sectional nature,limited sample diversity,and lack of dual-task assessment as areas for future refinement.This commentary posits that kinematic biomarkers offer a distinctive,scalable,and ecologically valid approach to widespread MCI screening,thereby complementing existing methods by providing real-world functional insights.Future research should prioritize longitudinal validation,expansion to diverse cohorts,integration with multimodal data including dual-tasking,and the development of highly portable,artificial intelligence-driven solutions to achieve the democratization of early MCI detection and enable timely interventions.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 Natural Science Foundation of China (10435070,10773011,10721140381,10099630)China Ministry of Science and Technology (2007CB16101,2010CB833000)
文摘Monochromatic y-rays are thought to be the smoking gun signal for identifying dark matter annihilation. However, the flux of monochromatic y-rays is usually suppressed by virtual quantum effects since dark matter should be neutral and does not couple with y-rays directly. In this work, we study the detection strategy of the monochromatic y-rays in a future space-based detector. The flux of monochromatic y-rays between 50 GeV and several TeV is calculated by assuming the supersymmetric neutralino as a typical dark matter candidate. The detection both by focusing on the Galactic center and in a scan mode that detects y-rays from the whole Galactic halo are compared. The detector performance for the purpose of monochromatic y-ray detection, with different energy and angular resolution, field of view, and background rejection efficiencies, is carefully studied with both analytical and fast Monte-Carlo methods.
基金The work was supported by the National Basic Research Program of China(No.2011CB111608)National Natural Science Foundation of China(No.41176110,No.41606146)+1 种基金Shanghai First-Class Program(Fisheries)Shanghai Ocean University College of Marine Sciences and International Center for Marine Sciences,The development fund of science and technology special of shanghai ocean university(A2-0203-00-100211)Ph.D early development program of Shanghai Ocean University(A2-0203-00-100353).
文摘Understanding the dynamics and regulation of a particular ecological process requires monitoring of the process at appropriate spatial and temporal scales.Information collected at an inappropriate spatiotemporal scale may be insufficient for capturing spatio-temporal dynamics of fish populations and community.In this study,a Monte Carlo method was developed to evaluate the detectability performances of different sampling frequencies,sampling timings and sampling intensities on fish community indices and fish species.Species richness indices tended to decrease with an increased sampling frequency,while species diversity indices had small changes in response to changes in sampling frequency.The diversity index was more likely to be influenced by the choice of sampling timing compared to the richness index.The total number of species,especially seasonal and rare species present in the simulated sampling,increased with sampling frequency.Although sampling frequency is more important than sampling intensity,sampling intensity is also important for the detectability of fish species.This study showed that sampling frequency and intensity could greatly influence the estimation of fish community.Choices of sampling timing,sampling frequency and intensity may result in different estimates of fish species compositions and community structure.It is very necessary to consider the importance of sufficient sampling frequency and intensity in a survey program.
文摘Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.
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supported by the National Key Research and Development Program of China(Grant No.2020YFA0608904)the International Partnership Program of the Chinese Academy of Sciences(Grant Nos.060GJHZ2023079GC and 134111KYSB20160031)+1 种基金supported by the Office of Science,U.S.Department of Energy(DOE)Biological and Environmental Research as part of the Regional and Global Model Analysis program area through the Water Cycle and Climate Extremes Modeling(WACCEM)scientific focus areaoperated for DOE by Battelle Memorial Institute under contract DE-AC05-76RL01830。
文摘The global monsoon system,encompassing the Asian-Australian,African,and American monsoons,sustains two-thirds of the world’s population by regulating water resources and agriculture.Monsoon anomalies pose severe risks,including floods and droughts.Recent research associated with the implementation of the Global Monsoons Model Intercomparison Project under the umbrella of CMIP6 has advanced our understanding of its historical variability and driving mechanisms.Observational data reveal a 20th-century shift:increased rainfall pre-1950s,followed by aridification and partial recovery post-1980s,driven by both internal variability(e.g.,Atlantic Multidecadal Oscillation)and external forcings(greenhouse gases,aerosols),while ENSO drives interannual variability through ocean-atmosphere interactions.Future projections under greenhouse forcing suggest long-term monsoon intensification,though regional disparities and model uncertainties persist.Models indicate robust trends but struggle to quantify extremes,where thermodynamic effects(warming-induced moisture rise)uniformly boost heavy rainfall,while dynamical shifts(circulation changes)create spatial heterogeneity.Volcanic eruptions and proposed solar radiation modification(SRM)further complicate predictions:tropical eruptions suppress monsoons,whereas high-latitude events alter cross-equatorial flows,highlighting unresolved feedbacks.The emergent constraint approach is booming in terms of correcting future projections and reducing uncertainty with respect to the global monsoons.Critical challenges remain.Model biases and sparse 20th-century observational data hinder accurate attribution.The interplay between natural variability and anthropogenic forcings,along with nonlinear extreme precipitation risks under warming,demands deeper mechanistic insights.Additionally,SRM’s regional impacts and hemispheric monsoon interactions require systematic evaluation.Addressing these gaps necessitates enhanced observational networks,refined climate models,and interdisciplinary efforts to disentangle multiscale drivers,ultimately improving resilience strategies for monsoon-dependent regions.
文摘The increasing global prevalence of mild cognitive impairment(MCI)necessitates a paradigm shift in early detection strategies.Conventional neuropsychological assessment methods,predominantly paper-and-pencil tests such as the Mini-Mental State Examination and the Montreal Cognitive Assessment,exhibit inherent limitations with respect to accessibility,administration burden,and sensitivity to subtle cognitive decline,particularly among diverse populations.This commentary critically examines a recent study that champions a novel approach:The integration of gait and handwriting kinematic parameters analyzed via machine learning for MCI screening.The present study positions itself within the broader landscape of MCI detection,with a view to comparing its advantages against established neuropsychological batteries,advanced neuroimaging(e.g.,positron emission tomography,magnetic resonance imaging),and emerging fluid biomarkers(e.g.,cerebrospinal fluid,blood-based assays).While the study demonstrates promising accuracy(74.44%area under the curve 0.74 with gait and graphic handwriting)and addresses key unmet needs in accessibility and objectivity,we highlight its cross-sectional nature,limited sample diversity,and lack of dual-task assessment as areas for future refinement.This commentary posits that kinematic biomarkers offer a distinctive,scalable,and ecologically valid approach to widespread MCI screening,thereby complementing existing methods by providing real-world functional insights.Future research should prioritize longitudinal validation,expansion to diverse cohorts,integration with multimodal data including dual-tasking,and the development of highly portable,artificial intelligence-driven solutions to achieve the democratization of early MCI detection and enable timely interventions.
