With the rapid advancement of visual generative models such as Generative Adversarial Networks(GANs)and stable Diffusion,the creation of highly realistic Deepfake through automated forgery has significantly progressed...With the rapid advancement of visual generative models such as Generative Adversarial Networks(GANs)and stable Diffusion,the creation of highly realistic Deepfake through automated forgery has significantly progressed.This paper examines the advancements inDeepfake detection and defense technologies,emphasizing the shift from passive detection methods to proactive digital watermarking techniques.Passive detection methods,which involve extracting features from images or videos to identify forgeries,encounter challenges such as poor performance against unknown manipulation techniques and susceptibility to counter-forensic tactics.In contrast,proactive digital watermarking techniques embed specificmarkers into images or videos,facilitating real-time detection and traceability,thereby providing a preemptive defense againstDeepfake content.We offer a comprehensive analysis of digitalwatermarking-based forensic techniques,discussing their advantages over passivemethods and highlighting four key benefits:real-time detection,embedded defense,resistance to tampering,and provision of legal evidence.Additionally,the paper identifies gaps in the literature concerning proactive forensic techniques and suggests future research directions,including cross-domain watermarking and adaptive watermarking strategies.By systematically classifying and comparing existing techniques,this review aims to contribute valuable insights for the development of more effective proactive defense strategies in Deepfake forensics.展开更多
An in-pixel histogramming time-to-digital converter(hTDC)based on octonary search and 4-tap phase detection is presented,aiming to improve frame rate while ensuring high precicion.The proposed hTDC is a 12-bit two-ste...An in-pixel histogramming time-to-digital converter(hTDC)based on octonary search and 4-tap phase detection is presented,aiming to improve frame rate while ensuring high precicion.The proposed hTDC is a 12-bit two-step converter consisting of a 6-bit coarse quantization and a 6-bit fine quantization,which supports a time resolution of 120 ps and multiphoton counting up to 2 GHz without a GHz reference frequency.The proposed hTDC is designed in 0.11μm CMOS process with an area consumption of 6900μm^(2).The data from a behavioral-level model is imported into the designed hTDC circuit for simulation verification.The post-simulation results show that the proposed hTDC achieves 0.8%depth precision in 9 m range for short-range system design specifications and 0.2%depth precision in 48 m range for long-range system design specifications.Under 30×10^(3) lux background light conditions,the proposed hTDC can be used for SPAD-based flash LiDAR sensor to achieve a frame rate to 40 fps with 200 ps resolution in 9 m range.展开更多
Passive microseismic monitoring(PMM)serves as a fundamental technology for assessing hydraulic fracturing(HF)effectiveness,with a key focus on accurate and efficient phase detection/arrival picking and source location...Passive microseismic monitoring(PMM)serves as a fundamental technology for assessing hydraulic fracturing(HF)effectiveness,with a key focus on accurate and efficient phase detection/arrival picking and source location.In PMM data processing,the data-driven paradigm(deep learning based)outperforms the model-driven paradigm in characteristic extraction but lacks quality control and uncertainty quantification.Monte Carlo Dropout,a Bayesian uncertainty quantification technique,performs stochastic neuron deactivation through multiple forward propagation samplings.Therefore,this study proposes a deep learning neural network incorporating uncertainty quantification with manual quality control integration,establishing an optimized workflow spanning automated phase detection to robust source location.The methodology implementation comprises two principal components:(1)The MDNet employing Monte Carlo Dropout strategy enabling simultaneous phase detection/arrival picking and unce rtainty estimation;(2)an integrated hybrid-driven workflow with a traveltime-based inve rsion method for source location.Validation with field data demonstrates that MD-Net achieves superior performance under low signal-to-noise ratio conditions,maintaining detection accuracy exceeding 99%for both P-and S-waves.The phase arrival picking precision shows significant improvement,with a 40%reduction in standard deviation compared to the baseline model(P-S time difference decreasing from12.0 ms to 7.1 ms),while providing quantifiable uncertainty metrics for manual calibration.Source location results further reveal that our hybrid-driven workflow produces more physically plausible event distributions,with 100%of microseismic eve nts clustering along the primary fracture expanding direction.This performance surpasses traditional cross-correlation methods and single/multi-trace data-driven me thods in spatial rationality.This study establishes an inte rpretable,high-pre cision automated framework for HF-PMM applications,demonstrating potential for extension to diverse geological settings and monitoring configurations.展开更多
Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,...Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,given the challenges faced by health specialists to carry out continuous monitoring,the development of an intelligent anomaly detection system is proposed.Unlike other authors,where they use supervised techniques,this work proposes using unsupervised techniques due to the advantages they offer.These advantages include the lack of prior labeling of data,and the detection of anomalies previously not contemplated,among others.In the present work,an individualized methodology consisting of two phases is developed:characterizing the normal sitting pattern and determining abnormal samples.An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis.It can be concluded,among other aspects,that the utilization of dimensionality reduction techniques leads to improved results.Moreover,the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities.Additionally,employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.展开更多
The rapid evolution of malware presents a critical cybersecurity challenge,rendering traditional signature-based detection methods ineffective against novel variants.This growing threat affects individuals,organizatio...The rapid evolution of malware presents a critical cybersecurity challenge,rendering traditional signature-based detection methods ineffective against novel variants.This growing threat affects individuals,organizations,and governments,highlighting the urgent need for robust malware detection mechanisms.Conventional machine learning-based approaches rely on static and dynamicmalware analysis and often struggle to detect previously unseen threats due to their dependency on predefined signatures.Although machine learning algorithms(MLAs)offer promising detection capabilities,their reliance on extensive feature engineering limits real-time applicability.Deep learning techniques mitigate this issue by automating feature extraction but may introduce computational overhead,affecting deployment efficiency.This research evaluates classical MLAs and deep learningmodels to enhance malware detection performance across diverse datasets.The proposed approach integrates a novel text and imagebased detection framework,employing an optimized Support Vector Machine(SVM)for textual data analysis and EfficientNet-B0 for image-based malware classification.Experimental analysis,conducted across multiple train-test splits over varying timescales,demonstrates 99.97%accuracy on textual datasets using SVM and 96.7%accuracy on image-based datasets with EfficientNet-B0,significantly improving zero-day malware detection.Furthermore,a comparative analysis with existing competitive techniques,such as Random Forest,XGBoost,and CNN-based(Convolutional Neural Network)classifiers,highlights the superior performance of the proposed model in terms of accuracy,efficiency,and robustness.展开更多
Recently, the rapid progress of quantum sensing research reveals that Rydberg atoms have great potential in becoming high-precision centimeter-scale antennas for low-frequency fields. In order to facilitate efficient ...Recently, the rapid progress of quantum sensing research reveals that Rydberg atoms have great potential in becoming high-precision centimeter-scale antennas for low-frequency fields. In order to facilitate efficient and reliable detection of low-frequency fields via Rydberg atoms, we designed and implemented a heterodyne method based on the linear response to external signals under the condition of Rydberg electromagnetically induced transparency(EIT). Instead of relying on observing changes in the absorption of light by Rydberg atoms, our method focuses on the phase modulation effect on the probe laser induced by low-frequency fields via the Rydberg EIT mechanism and utilizes a special demodulation process to accurately retrieve signals including both amplitude and phase. The general principles of our method apply to both electric and magnetic fields, and it is even possible to realize a combination of both functionalities in the same apparatus. In particular, we experimentally demonstrate the full cycle of operations with respect to both cases. In measuring low-frequency electric fields,we discover that the Rydberg dipole–dipole interaction among atoms induces a linear superposition of Rydberg states with different angular momentum, generating a first-order response corresponding to the signature of the linear Stark effect. As Rydberg atoms have excellent coupling strengths with electric fields, our results indicate that our method can hopefully achieve high-precision performance for practical tasks in the future.展开更多
With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comp...With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.展开更多
A novel method based on independent component analysis and phase congruency is proposed for detecting defects in textile fabric images. By independent component, we can obtain textile structural features of fabric-fre...A novel method based on independent component analysis and phase congruency is proposed for detecting defects in textile fabric images. By independent component, we can obtain textile structural features of fabric-free images. By phase congru- ency, structure information is reduced, which can distinguish the defect region from the defect-free regions. Finally, we have the detecting result from binary image which is obtained by a thresh- old step, Compared with other algorithms, the proposed method not only has robustness with high detection rate, but also detects various types of defects quite well.展开更多
Aim to detect the characteristic weak magnetic field signal against the strong noises background. Methods In combination with a low-pass-filter, the correlation output of magne-* tic sensors between the magnetic field...Aim to detect the characteristic weak magnetic field signal against the strong noises background. Methods In combination with a low-pass-filter, the correlation output of magne-* tic sensors between the magnetic field and reference current was utilized to provide a DC output voltage proportional to the applied magnetic induction, computer simulation was* done to investigate the correlation output of the Hall-effect sensors. Results Some analysis results concerning the noise property, harmonic supppression and the sensitivity were given. Conclsion The minimum detection signal of the equipment evolved from the mentioned cor-* relation theory can be 10-6 T. In addition to the DC output, such sensors can also measure the phase of the detected magnetic induction and has good harmonic suppression as well as* noise elimination.展开更多
As an important guarantee for the prevention and control of animal diseases,veterinary drugs have important functions in improving animal production performance and product quality and maintaining ecological balance.T...As an important guarantee for the prevention and control of animal diseases,veterinary drugs have important functions in improving animal production performance and product quality and maintaining ecological balance.They are an important guarantee for the healthy development of animal husbandry,food safety and public health.However,the irrational use and abuse of veterinary drugs and feed pharmaceutical additives are widespread,causing harmful substances in animal foods and damage to human health,and threatening the sustainable development of the environment and animal husbandry as well.In order to ensure human health,it is urgent to develop a simple,rapid,high-sensitivity,high-throughput and low-cost veterinary drug residue detection technology.In this paper,the sample pretreatment methods and detection techniques for the analysis of veterinary drug residues in animal foods were reviewed.展开更多
A cued search algorithm with uncertain detection performance is proposed for phased array radars. Firstly, a target search model based on the information gain criterion is presented with known detection performance, a...A cued search algorithm with uncertain detection performance is proposed for phased array radars. Firstly, a target search model based on the information gain criterion is presented with known detection performance, and the statistical characteristic of the detection probability is calculated by using the fluctuant model of the target radar cross section （RCS）. Secondly, when the detection probability is completely unknown, its probability density function is modeled with a beta distribution, and its posterior probability distribution with the radar observation is derived based on the Bayesian theory. Finally simulation results show that the cued search algorithm with a known RCS fluctuant model can achieve the best performance, and the algorithm with the detection probability modeled as a beta distribution is better than that with a random selected detection probability because the model parameters can be updated by the radar observation to approach to the real value of the detection probability.展开更多
Hollow-fiber liquid-phase microextraction(HF-LPME)and electromembrane extraction(EME)are miniaturized extraction techniques,and have been coupled with various analytical instruments for trace analysis of heavy metals,...Hollow-fiber liquid-phase microextraction(HF-LPME)and electromembrane extraction(EME)are miniaturized extraction techniques,and have been coupled with various analytical instruments for trace analysis of heavy metals,drugs and other organic compounds,in recent years.HF-LPME and EME provide high selectivity,efficient sample cleanup and enrichment,and reduce the consumption of organic sol-vents to a few micro-liters per sample.HF-LPME and EME are compatible with different analytical in-struments for chromatography,electrophoresis,atomic spectroscopy,mass spectrometry,and electrochemical detection.HF-LPME and EME have gained significant popularity during the recent years.This review focuses on hollow fiber based techniques(especially HF-LPME and EME)of heavy metals and pharmaceuticals(published 2017 to May 2019),and their combinations with atomic spectroscopy,UV-VIS spectrophotometry,high performance liquid chromatography,gas chromatography,capillary elec-trophoresis,and voltammetry.展开更多
LiMn_(0.5)Fe_(0.5)PO_(4) has attracted great interest due to its good electrochemical performance and higher operating voltages.This has led to a greater than 30 percent higher energy density than for commercial Li Fe...LiMn_(0.5)Fe_(0.5)PO_(4) has attracted great interest due to its good electrochemical performance and higher operating voltages.This has led to a greater than 30 percent higher energy density than for commercial Li Fe PO4 olivine cathodes.Understanding the phase transition behaviors and kinetics of this material will help researchers to design and develop next generation cathodes for Li-ion batteries.In this study,we investigated non-equilibrium phase transition behaviors in a LiMn_(0.5)Fe_(0.5)PO_(4) cathode material during charge–discharge processes by varying current rates(C-rates)using synchrotron in-situ X-ray techniques.These methods included wide angle X-ray scattering(in-situ WAXS)and X-ray absorption spectroscopy(in-situ XAS).The WAXS spectra indicate that the phase transition of LiMn_(0.5)Fe_(0.5)PO_(4) material at slow C-rates is induced by a two-phase reaction.In contrast,at a high C-rate(5 C),the formation of an intermediate phase upon discharge is clearly observed.Concurrently,the oxidation numbers of the redox reactions of Fe^(2+)/Fe^(3+)and Mn^(2+)/Mn^(3+)were evaluated using in-situ XAS.This combination of synchrotron in-situ X-ray techniques gives clear insights into the non-equilibrium phase transition behavior of a LiMn_(0.5)Fe_(0.5)PO_(4) cathode material.This new understanding will be useful for further developments of this highly promising cathode material for practical commercialization.展开更多
Detection of small cancer biomarkers with low molecular weight and a low concentration range has always been challenging yet urgent in many clinical applications such as diagnosing early-stage cancer,monitoring treatm...Detection of small cancer biomarkers with low molecular weight and a low concentration range has always been challenging yet urgent in many clinical applications such as diagnosing early-stage cancer,monitoring treatment and detecting relapse.Here,a highly enhanced plasmonic biosensor that can overcome this challenge is developed using atomically thin two-dimensional phase change nanomaterial.By precisely engineering the configuration with atomically thin materials,the phase singularity has been successfully achieved with a significantly enhanced lateral position shift effect.Based on our knowledge,it is the first experimental demonstration of a lateral position signal change>340μm at a sensing interface from all optical techniques.With this enhanced plasmonic effect,the detection limit has been experimentally demonstrated to be 10^(-15) mol L^(−1) for TNF-α cancer marker,which has been found in various human diseases including inflammatory diseases and different kinds of cancer.The as-reported novel integration of atomically thin Ge_(2)Sb_(2)Te_(5) with plasmonic substrate, which results in a phase singularity and thus a giant lateral position shift, enables the detection of cancer markers with low molecular weight at femtomolar level. These results will definitely hold promising potential in biomedical application and clinical diagnostics.展开更多
In order to improve the performance of line spectrum detection,according to the feature that the underwater target radiated noise containing stable line spectrum,the differences of the phase difference between line sp...In order to improve the performance of line spectrum detection,according to the feature that the underwater target radiated noise containing stable line spectrum,the differences of the phase difference between line spectrum and background noise,a weighted line spectrum detection algorithm based on the phase variance is proposed in frequency domain.After phase difference alignment,the phase variance of line spectrum and the phase of background noise,respectively,are small and big in frequency domain,this method utilizes the weighted statistical algorithm to cumulate the frequency spectrum based on the phase variance,which can restrain the background noise disturbance,and enhance the signal to noise ratio（SNR）.The theory analysis and experimental results both verify that the proposed method can well enhance the energy of line spectrum,restrain the energy of background noise,and have better detection performance under lower SNR.展开更多
Surface plasmon resonance (SPR) sensing is an optical method based on evanescent wave.SPR biosensor can detect interaction of label-free biomolecules in real-time.With further development,it can become a research ins...Surface plasmon resonance (SPR) sensing is an optical method based on evanescent wave.SPR biosensor can detect interaction of label-free biomolecules in real-time.With further development,it can become a research instrument in proteomics.SPR biosensor can be divided intensity measurement and phase measurement,and the latter possesses higher sensitivity than the former one.This paper attempts to summarize the SPR phase detection theory,discuss the major developments,compare the merits and deficiencies of various methods,and look forward to future prospects.展开更多
Adenoma detection rate(ADR) is a key component of colonoscopy quality assessment, with a direct link between itself and future mortality from colorectal cancer. There are a number of potential factors, both modifiable...Adenoma detection rate(ADR) is a key component of colonoscopy quality assessment, with a direct link between itself and future mortality from colorectal cancer. There are a number of potential factors, both modifiable and non-modifiable that can impact upon ADR. As methods, understanding and technologies advance, so should our ability to improve ADRs, and thus, reduce colorectal cancer mortality. This article will review new technologies and techniques that improve ADR, both in terms of the endoscopes themselves and adjuncts to current systems. In particular it focuses on effective techniques and behaviours, developments in image enhancement, advancement in endoscope design and developments in accessories that may improve ADR. It also highlights the key role that continued medical education plays in improving the quality of colonoscopy and thus ADR. The review aims to present a balanced summary of the evidence currently available and does not propose to serve as a guideline.展开更多
For the first time, mass spectrometric (MS) techniques were employed to rapidly detect the pathogen Chalara fraxinea in-vitro and directly in-vivo in tissues of diseased ash trees caused by C. fraxinea, using a range ...For the first time, mass spectrometric (MS) techniques were employed to rapidly detect the pathogen Chalara fraxinea in-vitro and directly in-vivo in tissues of diseased ash trees caused by C. fraxinea, using a range of characteristic novel secondary metabolites of C. fraxinea as chemical markers for the presence of the pathogen. We have found an evident correlation between the presence and amount of these-only for C. fraxinea characteristic and novel-secondary metabolites (named chalarafraxinines) and the degree of disease of respective infected ash seedlings. As demonstrated in this work, the MS based high-throughput-screening approach constitute an alternative to the time consuming and expensive micro biological isolation procedures for detection of the pathogen C. fraxinea and furthermore, can be used to rapidly test ash genotypes for resistance / susceptibility to C. fraxinea infection.展开更多
Crack detection in an aerospace turbine disk is essential for aircraft-quality detection.With the unique circular stepped structure and superalloy material properties of aerospace turbine disk,it is difficult for the ...Crack detection in an aerospace turbine disk is essential for aircraft-quality detection.With the unique circular stepped structure and superalloy material properties of aerospace turbine disk,it is difficult for the traditional ultrasonic testing method to perform efficient and accurate testing.In this study,ultrasound phased array detection technology was applied to the non-destructive testing of aviation turbine disks:(i)A phased array ultrasonic c-scan device for detecting aerospace turbine disk cracks(PAUDA)was developed which consists of phased array ultrasonic,transducers,a computer,a displacement encoder,and a rotating scanner;(ii)The influence of the detection parameters include frequency,wave-type,and elements number of the ultrasonic phased array probe on the detection results on the near-surface and the far surface of the aerospace turbine disk is analyzed;(iii)Specimens with flat-bottom-hole(FBH)defects were scanned by the developed PAUDA and the results were analyzed and compared with the conventional single probe ultrasonic water immersion testing.The experiment shows that by using the ultrasonic phased array c-scan to scan the turbine disk the accuracy of the detection can be significantly improved which is of greater accuracy and higher efficiency than traditional immersion testing.展开更多
Most multiphase flow separation detection methods used commonly in oilfields are low in efficiency and accuracy,and have data delay.An online multiphase flow detection method is proposed based on magnetic resonance te...Most multiphase flow separation detection methods used commonly in oilfields are low in efficiency and accuracy,and have data delay.An online multiphase flow detection method is proposed based on magnetic resonance technology,and its supporting device has been made and tested in lab and field.The detection technology works in two parts:measure phase holdup in static state and measure flow rate in flowing state.Oil-water ratio is first measured and then gas holdup.The device is composed of a segmented magnet structure and a dual antenna structure for measuring flowing fluid.A highly compact magnetic resonance spectrometer system and intelligent software are developed.Lab experiments and field application show that the online detection system has the following merits:it can measure flow rate and phase holdup only based on magnetic resonance technology;it can detect in-place transient fluid production at high frequency and thus monitor transient fluid production in real time;it can detect oil,gas and water in a full range at high precision,the detection isn’t affected by salinity and emulsification.It is a green,safe and energy-saving system.展开更多
基金supported by the National Fund Cultivation Project from China People’s Police University(Grant Number:JJPY202402)National Natural Science Foundation of China(Grant Number:62172165).
文摘With the rapid advancement of visual generative models such as Generative Adversarial Networks(GANs)and stable Diffusion,the creation of highly realistic Deepfake through automated forgery has significantly progressed.This paper examines the advancements inDeepfake detection and defense technologies,emphasizing the shift from passive detection methods to proactive digital watermarking techniques.Passive detection methods,which involve extracting features from images or videos to identify forgeries,encounter challenges such as poor performance against unknown manipulation techniques and susceptibility to counter-forensic tactics.In contrast,proactive digital watermarking techniques embed specificmarkers into images or videos,facilitating real-time detection and traceability,thereby providing a preemptive defense againstDeepfake content.We offer a comprehensive analysis of digitalwatermarking-based forensic techniques,discussing their advantages over passivemethods and highlighting four key benefits:real-time detection,embedded defense,resistance to tampering,and provision of legal evidence.Additionally,the paper identifies gaps in the literature concerning proactive forensic techniques and suggests future research directions,including cross-domain watermarking and adaptive watermarking strategies.By systematically classifying and comparing existing techniques,this review aims to contribute valuable insights for the development of more effective proactive defense strategies in Deepfake forensics.
基金National Key Research and Development Program of China(2022YFB2804401)。
文摘An in-pixel histogramming time-to-digital converter(hTDC)based on octonary search and 4-tap phase detection is presented,aiming to improve frame rate while ensuring high precicion.The proposed hTDC is a 12-bit two-step converter consisting of a 6-bit coarse quantization and a 6-bit fine quantization,which supports a time resolution of 120 ps and multiphoton counting up to 2 GHz without a GHz reference frequency.The proposed hTDC is designed in 0.11μm CMOS process with an area consumption of 6900μm^(2).The data from a behavioral-level model is imported into the designed hTDC circuit for simulation verification.The post-simulation results show that the proposed hTDC achieves 0.8%depth precision in 9 m range for short-range system design specifications and 0.2%depth precision in 48 m range for long-range system design specifications.Under 30×10^(3) lux background light conditions,the proposed hTDC can be used for SPAD-based flash LiDAR sensor to achieve a frame rate to 40 fps with 200 ps resolution in 9 m range.
基金funded by the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project(Grant No.2024ZD1002503)。
文摘Passive microseismic monitoring(PMM)serves as a fundamental technology for assessing hydraulic fracturing(HF)effectiveness,with a key focus on accurate and efficient phase detection/arrival picking and source location.In PMM data processing,the data-driven paradigm(deep learning based)outperforms the model-driven paradigm in characteristic extraction but lacks quality control and uncertainty quantification.Monte Carlo Dropout,a Bayesian uncertainty quantification technique,performs stochastic neuron deactivation through multiple forward propagation samplings.Therefore,this study proposes a deep learning neural network incorporating uncertainty quantification with manual quality control integration,establishing an optimized workflow spanning automated phase detection to robust source location.The methodology implementation comprises two principal components:(1)The MDNet employing Monte Carlo Dropout strategy enabling simultaneous phase detection/arrival picking and unce rtainty estimation;(2)an integrated hybrid-driven workflow with a traveltime-based inve rsion method for source location.Validation with field data demonstrates that MD-Net achieves superior performance under low signal-to-noise ratio conditions,maintaining detection accuracy exceeding 99%for both P-and S-waves.The phase arrival picking precision shows significant improvement,with a 40%reduction in standard deviation compared to the baseline model(P-S time difference decreasing from12.0 ms to 7.1 ms),while providing quantifiable uncertainty metrics for manual calibration.Source location results further reveal that our hybrid-driven workflow produces more physically plausible event distributions,with 100%of microseismic eve nts clustering along the primary fracture expanding direction.This performance surpasses traditional cross-correlation methods and single/multi-trace data-driven me thods in spatial rationality.This study establishes an inte rpretable,high-pre cision automated framework for HF-PMM applications,demonstrating potential for extension to diverse geological settings and monitoring configurations.
基金FEDER/Ministry of Science and Innovation-State Research Agency/Project PID2020-112667RB-I00 funded by MCIN/AEI/10.13039/501100011033the Basque Government,IT1726-22+2 种基金by the predoctoral contracts PRE_2022_2_0022 and EP_2023_1_0015 of the Basque Governmentpartially supported by the Italian MIUR,PRIN 2020 Project“COMMON-WEARS”,N.2020HCWWLP,CUP:H23C22000230005co-funding from Next Generation EU,in the context of the National Recovery and Resilience Plan,through the Italian MUR,PRIN 2022 Project”COCOWEARS”(A framework for COntinuum COmputing WEARable Systems),N.2022T2XNJE,CUP:H53D23003640006.
文摘Detecting sitting posture abnormalities in wheelchair users enables early identification of changes in their functional status.To date,this detection has relied on in-person observation by medical specialists.However,given the challenges faced by health specialists to carry out continuous monitoring,the development of an intelligent anomaly detection system is proposed.Unlike other authors,where they use supervised techniques,this work proposes using unsupervised techniques due to the advantages they offer.These advantages include the lack of prior labeling of data,and the detection of anomalies previously not contemplated,among others.In the present work,an individualized methodology consisting of two phases is developed:characterizing the normal sitting pattern and determining abnormal samples.An analysis has been carried out between different unsupervised techniques to study which ones are more suitable for postural diagnosis.It can be concluded,among other aspects,that the utilization of dimensionality reduction techniques leads to improved results.Moreover,the normality characterization phase is deemed necessary for enhancing the system’s learning capabilities.Additionally,employing an individualized approach to the model aids in capturing the particularities of the various pathologies present among subjects.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2504).
文摘The rapid evolution of malware presents a critical cybersecurity challenge,rendering traditional signature-based detection methods ineffective against novel variants.This growing threat affects individuals,organizations,and governments,highlighting the urgent need for robust malware detection mechanisms.Conventional machine learning-based approaches rely on static and dynamicmalware analysis and often struggle to detect previously unseen threats due to their dependency on predefined signatures.Although machine learning algorithms(MLAs)offer promising detection capabilities,their reliance on extensive feature engineering limits real-time applicability.Deep learning techniques mitigate this issue by automating feature extraction but may introduce computational overhead,affecting deployment efficiency.This research evaluates classical MLAs and deep learningmodels to enhance malware detection performance across diverse datasets.The proposed approach integrates a novel text and imagebased detection framework,employing an optimized Support Vector Machine(SVM)for textual data analysis and EfficientNet-B0 for image-based malware classification.Experimental analysis,conducted across multiple train-test splits over varying timescales,demonstrates 99.97%accuracy on textual datasets using SVM and 96.7%accuracy on image-based datasets with EfficientNet-B0,significantly improving zero-day malware detection.Furthermore,a comparative analysis with existing competitive techniques,such as Random Forest,XGBoost,and CNN-based(Convolutional Neural Network)classifiers,highlights the superior performance of the proposed model in terms of accuracy,efficiency,and robustness.
基金supported by the Science and Technology Commission of Shanghai Municipality (Grant No.24DP2600202)the National Key R&D Program of China (Grant No.2024YFB4504002)+2 种基金Industrial Technology Development Research Program of Shanghai Institute of Optics and Fine Mechanicsthe National Natural Science Foundation of China (Grant No.92165107)the China Postdoctoral Science Foundation (Grant Nos.2024M753359 for S.J.and2022M723270 for X.W.)。
文摘Recently, the rapid progress of quantum sensing research reveals that Rydberg atoms have great potential in becoming high-precision centimeter-scale antennas for low-frequency fields. In order to facilitate efficient and reliable detection of low-frequency fields via Rydberg atoms, we designed and implemented a heterodyne method based on the linear response to external signals under the condition of Rydberg electromagnetically induced transparency(EIT). Instead of relying on observing changes in the absorption of light by Rydberg atoms, our method focuses on the phase modulation effect on the probe laser induced by low-frequency fields via the Rydberg EIT mechanism and utilizes a special demodulation process to accurately retrieve signals including both amplitude and phase. The general principles of our method apply to both electric and magnetic fields, and it is even possible to realize a combination of both functionalities in the same apparatus. In particular, we experimentally demonstrate the full cycle of operations with respect to both cases. In measuring low-frequency electric fields,we discover that the Rydberg dipole–dipole interaction among atoms induces a linear superposition of Rydberg states with different angular momentum, generating a first-order response corresponding to the signature of the linear Stark effect. As Rydberg atoms have excellent coupling strengths with electric fields, our results indicate that our method can hopefully achieve high-precision performance for practical tasks in the future.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS-2023-00235509Development of security monitoring technology based network behavior against encrypted cyber threats in ICT convergence environment).
文摘With the increasing emphasis on personal information protection,encryption through security protocols has emerged as a critical requirement in data transmission and reception processes.Nevertheless,IoT ecosystems comprise heterogeneous networks where outdated systems coexist with the latest devices,spanning a range of devices from non-encrypted ones to fully encrypted ones.Given the limited visibility into payloads in this context,this study investigates AI-based attack detection methods that leverage encrypted traffic metadata,eliminating the need for decryption and minimizing system performance degradation—especially in light of these heterogeneous devices.Using the UNSW-NB15 and CICIoT-2023 dataset,encrypted and unencrypted traffic were categorized according to security protocol,and AI-based intrusion detection experiments were conducted for each traffic type based on metadata.To mitigate the problem of class imbalance,eight different data sampling techniques were applied.The effectiveness of these sampling techniques was then comparatively analyzed using two ensemble models and three Deep Learning(DL)models from various perspectives.The experimental results confirmed that metadata-based attack detection is feasible using only encrypted traffic.In the UNSW-NB15 dataset,the f1-score of encrypted traffic was approximately 0.98,which is 4.3%higher than that of unencrypted traffic(approximately 0.94).In addition,analysis of the encrypted traffic in the CICIoT-2023 dataset using the same method showed a significantly lower f1-score of roughly 0.43,indicating that the quality of the dataset and the preprocessing approach have a substantial impact on detection performance.Furthermore,when data sampling techniques were applied to encrypted traffic,the recall in the UNSWNB15(Encrypted)dataset improved by up to 23.0%,and in the CICIoT-2023(Encrypted)dataset by 20.26%,showing a similar level of improvement.Notably,in CICIoT-2023,f1-score and Receiver Operation Characteristic-Area Under the Curve(ROC-AUC)increased by 59.0%and 55.94%,respectively.These results suggest that data sampling can have a positive effect even in encrypted environments.However,the extent of the improvement may vary depending on data quality,model architecture,and sampling strategy.
基金Supported by the National Natural Science Foundation of China(61072135)
文摘A novel method based on independent component analysis and phase congruency is proposed for detecting defects in textile fabric images. By independent component, we can obtain textile structural features of fabric-free images. By phase congru- ency, structure information is reduced, which can distinguish the defect region from the defect-free regions. Finally, we have the detecting result from binary image which is obtained by a thresh- old step, Compared with other algorithms, the proposed method not only has robustness with high detection rate, but also detects various types of defects quite well.
文摘Aim to detect the characteristic weak magnetic field signal against the strong noises background. Methods In combination with a low-pass-filter, the correlation output of magne-* tic sensors between the magnetic field and reference current was utilized to provide a DC output voltage proportional to the applied magnetic induction, computer simulation was* done to investigate the correlation output of the Hall-effect sensors. Results Some analysis results concerning the noise property, harmonic supppression and the sensitivity were given. Conclsion The minimum detection signal of the equipment evolved from the mentioned cor-* relation theory can be 10-6 T. In addition to the DC output, such sensors can also measure the phase of the detected magnetic induction and has good harmonic suppression as well as* noise elimination.
基金Supported by National Beef Industrial Technology System(CARS-38)Basic Science Research Fund(1610322018002)
文摘As an important guarantee for the prevention and control of animal diseases,veterinary drugs have important functions in improving animal production performance and product quality and maintaining ecological balance.They are an important guarantee for the healthy development of animal husbandry,food safety and public health.However,the irrational use and abuse of veterinary drugs and feed pharmaceutical additives are widespread,causing harmful substances in animal foods and damage to human health,and threatening the sustainable development of the environment and animal husbandry as well.In order to ensure human health,it is urgent to develop a simple,rapid,high-sensitivity,high-throughput and low-cost veterinary drug residue detection technology.In this paper,the sample pretreatment methods and detection techniques for the analysis of veterinary drug residues in animal foods were reviewed.
基金supported by the National Natural Science Foundation of China (61372165)the Postdoctoral Science Foundation of China (201150M15462012T50874)
文摘A cued search algorithm with uncertain detection performance is proposed for phased array radars. Firstly, a target search model based on the information gain criterion is presented with known detection performance, and the statistical characteristic of the detection probability is calculated by using the fluctuant model of the target radar cross section （RCS）. Secondly, when the detection probability is completely unknown, its probability density function is modeled with a beta distribution, and its posterior probability distribution with the radar observation is derived based on the Bayesian theory. Finally simulation results show that the cued search algorithm with a known RCS fluctuant model can achieve the best performance, and the algorithm with the detection probability modeled as a beta distribution is better than that with a random selected detection probability because the model parameters can be updated by the radar observation to approach to the real value of the detection probability.
基金supported by the Higher education commission of Pakistan(NRPU No.20-3925/R&D/NRPU/HEC/2014)PAK-US science and technology cooperation(Pak-US No6-4/PAK-US/HEC/2015/04)Pakistan science foundation joint research projects with MSRT,Iran(No.PSF-MSRT/Env/KP-AWKUM)。
文摘Hollow-fiber liquid-phase microextraction(HF-LPME)and electromembrane extraction(EME)are miniaturized extraction techniques,and have been coupled with various analytical instruments for trace analysis of heavy metals,drugs and other organic compounds,in recent years.HF-LPME and EME provide high selectivity,efficient sample cleanup and enrichment,and reduce the consumption of organic sol-vents to a few micro-liters per sample.HF-LPME and EME are compatible with different analytical in-struments for chromatography,electrophoresis,atomic spectroscopy,mass spectrometry,and electrochemical detection.HF-LPME and EME have gained significant popularity during the recent years.This review focuses on hollow fiber based techniques(especially HF-LPME and EME)of heavy metals and pharmaceuticals(published 2017 to May 2019),and their combinations with atomic spectroscopy,UV-VIS spectrophotometry,high performance liquid chromatography,gas chromatography,capillary elec-trophoresis,and voltammetry.
基金the Science Achievement Scholarship of Thailand(SAST)for financial supportpartially supported by the Institute of Nanomaterials Research and Innovation for Energy(IN-RIE)+1 种基金the Research and Graduate Studies,Khon Kaen University(KKU)Synchrotron Light Research Institute(SLRI),Thailand。
文摘LiMn_(0.5)Fe_(0.5)PO_(4) has attracted great interest due to its good electrochemical performance and higher operating voltages.This has led to a greater than 30 percent higher energy density than for commercial Li Fe PO4 olivine cathodes.Understanding the phase transition behaviors and kinetics of this material will help researchers to design and develop next generation cathodes for Li-ion batteries.In this study,we investigated non-equilibrium phase transition behaviors in a LiMn_(0.5)Fe_(0.5)PO_(4) cathode material during charge–discharge processes by varying current rates(C-rates)using synchrotron in-situ X-ray techniques.These methods included wide angle X-ray scattering(in-situ WAXS)and X-ray absorption spectroscopy(in-situ XAS).The WAXS spectra indicate that the phase transition of LiMn_(0.5)Fe_(0.5)PO_(4) material at slow C-rates is induced by a two-phase reaction.In contrast,at a high C-rate(5 C),the formation of an intermediate phase upon discharge is clearly observed.Concurrently,the oxidation numbers of the redox reactions of Fe^(2+)/Fe^(3+)and Mn^(2+)/Mn^(3+)were evaluated using in-situ XAS.This combination of synchrotron in-situ X-ray techniques gives clear insights into the non-equilibrium phase transition behavior of a LiMn_(0.5)Fe_(0.5)PO_(4) cathode material.This new understanding will be useful for further developments of this highly promising cathode material for practical commercialization.
基金We thank Shiyue Liu from School of Life Sciences in The Chinese University of Hong Kong for helpful discussions.This work is supported under the PROCORE-France/Hong Kong Joint Research Scheme(F-CUHK402/19)the Research Grants Council,Hong Kong Special Administration Region(AoE/P-02/12,14210517,14207419,N_CUHK407/16)the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.798916.Y.Wang is supported under the Hong Kong PhD Fellowship Scheme.
文摘Detection of small cancer biomarkers with low molecular weight and a low concentration range has always been challenging yet urgent in many clinical applications such as diagnosing early-stage cancer,monitoring treatment and detecting relapse.Here,a highly enhanced plasmonic biosensor that can overcome this challenge is developed using atomically thin two-dimensional phase change nanomaterial.By precisely engineering the configuration with atomically thin materials,the phase singularity has been successfully achieved with a significantly enhanced lateral position shift effect.Based on our knowledge,it is the first experimental demonstration of a lateral position signal change>340μm at a sensing interface from all optical techniques.With this enhanced plasmonic effect,the detection limit has been experimentally demonstrated to be 10^(-15) mol L^(−1) for TNF-α cancer marker,which has been found in various human diseases including inflammatory diseases and different kinds of cancer.The as-reported novel integration of atomically thin Ge_(2)Sb_(2)Te_(5) with plasmonic substrate, which results in a phase singularity and thus a giant lateral position shift, enables the detection of cancer markers with low molecular weight at femtomolar level. These results will definitely hold promising potential in biomedical application and clinical diagnostics.
基金supported by the National Natural Science Foundation of China(61372180)the Young Talent Frontier Project of Institute of Acoustics of Chinese Academy of Sciences(Y454341261)
文摘In order to improve the performance of line spectrum detection,according to the feature that the underwater target radiated noise containing stable line spectrum,the differences of the phase difference between line spectrum and background noise,a weighted line spectrum detection algorithm based on the phase variance is proposed in frequency domain.After phase difference alignment,the phase variance of line spectrum and the phase of background noise,respectively,are small and big in frequency domain,this method utilizes the weighted statistical algorithm to cumulate the frequency spectrum based on the phase variance,which can restrain the background noise disturbance,and enhance the signal to noise ratio（SNR）.The theory analysis and experimental results both verify that the proposed method can well enhance the energy of line spectrum,restrain the energy of background noise,and have better detection performance under lower SNR.
文摘Surface plasmon resonance (SPR) sensing is an optical method based on evanescent wave.SPR biosensor can detect interaction of label-free biomolecules in real-time.With further development,it can become a research instrument in proteomics.SPR biosensor can be divided intensity measurement and phase measurement,and the latter possesses higher sensitivity than the former one.This paper attempts to summarize the SPR phase detection theory,discuss the major developments,compare the merits and deficiencies of various methods,and look forward to future prospects.
文摘Adenoma detection rate(ADR) is a key component of colonoscopy quality assessment, with a direct link between itself and future mortality from colorectal cancer. There are a number of potential factors, both modifiable and non-modifiable that can impact upon ADR. As methods, understanding and technologies advance, so should our ability to improve ADRs, and thus, reduce colorectal cancer mortality. This article will review new technologies and techniques that improve ADR, both in terms of the endoscopes themselves and adjuncts to current systems. In particular it focuses on effective techniques and behaviours, developments in image enhancement, advancement in endoscope design and developments in accessories that may improve ADR. It also highlights the key role that continued medical education plays in improving the quality of colonoscopy and thus ADR. The review aims to present a balanced summary of the evidence currently available and does not propose to serve as a guideline.
文摘For the first time, mass spectrometric (MS) techniques were employed to rapidly detect the pathogen Chalara fraxinea in-vitro and directly in-vivo in tissues of diseased ash trees caused by C. fraxinea, using a range of characteristic novel secondary metabolites of C. fraxinea as chemical markers for the presence of the pathogen. We have found an evident correlation between the presence and amount of these-only for C. fraxinea characteristic and novel-secondary metabolites (named chalarafraxinines) and the degree of disease of respective infected ash seedlings. As demonstrated in this work, the MS based high-throughput-screening approach constitute an alternative to the time consuming and expensive micro biological isolation procedures for detection of the pathogen C. fraxinea and furthermore, can be used to rapidly test ash genotypes for resistance / susceptibility to C. fraxinea infection.
基金This work was funded by the National Natural Science Foundation of China[Grant Nos.11664027,11374134]The National Natural Science Foundation of Jiangxi Province[Grant No.20161BAB216101]+1 种基金Key Laboratory of Non-Destructive Testing and Monitoring Technology for High-Speed Transport Facilities of the Ministry of Industry and Information Technology,Nanjing University of Aeronautics and AstronauticsThe Key Laboratory of Nondestructive Testing of Ministry of Education Nanchang Hang Kong University,Nanchang,China.
文摘Crack detection in an aerospace turbine disk is essential for aircraft-quality detection.With the unique circular stepped structure and superalloy material properties of aerospace turbine disk,it is difficult for the traditional ultrasonic testing method to perform efficient and accurate testing.In this study,ultrasound phased array detection technology was applied to the non-destructive testing of aviation turbine disks:(i)A phased array ultrasonic c-scan device for detecting aerospace turbine disk cracks(PAUDA)was developed which consists of phased array ultrasonic,transducers,a computer,a displacement encoder,and a rotating scanner;(ii)The influence of the detection parameters include frequency,wave-type,and elements number of the ultrasonic phased array probe on the detection results on the near-surface and the far surface of the aerospace turbine disk is analyzed;(iii)Specimens with flat-bottom-hole(FBH)defects were scanned by the developed PAUDA and the results were analyzed and compared with the conventional single probe ultrasonic water immersion testing.The experiment shows that by using the ultrasonic phased array c-scan to scan the turbine disk the accuracy of the detection can be significantly improved which is of greater accuracy and higher efficiency than traditional immersion testing.
基金Supported by the National Natural Science Foundation of China(51704327)
文摘Most multiphase flow separation detection methods used commonly in oilfields are low in efficiency and accuracy,and have data delay.An online multiphase flow detection method is proposed based on magnetic resonance technology,and its supporting device has been made and tested in lab and field.The detection technology works in two parts:measure phase holdup in static state and measure flow rate in flowing state.Oil-water ratio is first measured and then gas holdup.The device is composed of a segmented magnet structure and a dual antenna structure for measuring flowing fluid.A highly compact magnetic resonance spectrometer system and intelligent software are developed.Lab experiments and field application show that the online detection system has the following merits:it can measure flow rate and phase holdup only based on magnetic resonance technology;it can detect in-place transient fluid production at high frequency and thus monitor transient fluid production in real time;it can detect oil,gas and water in a full range at high precision,the detection isn’t affected by salinity and emulsification.It is a green,safe and energy-saving system.
