A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment,flexible maneuverability,and high spatiotemporal resolution,...A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment,flexible maneuverability,and high spatiotemporal resolution,enabling the acquisition of full-process data of the fragment scattering process.However,mismatches between camera frame rates and target velocities can lead to long motion blur tails of high-speed fragment targets,resulting in low signal-to-noise ratios and rendering conventional detection algorithms ineffective in dynamic strong interference testing environments.In this study,we propose a detection framework centered on dynamic strong interference disturbance signal separation and suppression.We introduce a mixture Gaussian model constrained under a joint spatialtemporal-transform domain Dirichlet process,combined with total variation regularization to achieve disturbance signal suppression.Experimental results demonstrate that the proposed disturbance suppression method can be integrated with certain conventional motion target detection tasks,enabling adaptation to real-world data to a certain extent.Moreover,we provide a specific implementation of this process,which achieves a detection rate close to 100%with an approximate 0%false alarm rate in multiple sets of real target field test data.This research effectively advances the development of the field of damage parameter testing.展开更多
In recent years,with the development of synthetic aperture radar(SAR)technology and the widespread application of deep learning,lightweight detection of SAR images has emerged as a research direction.The ultimate goal...In recent years,with the development of synthetic aperture radar(SAR)technology and the widespread application of deep learning,lightweight detection of SAR images has emerged as a research direction.The ultimate goal is to reduce computational and storage requirements while ensuring detection accuracy and reliability,making it an ideal choice for achieving rapid response and efficient processing.In this regard,a lightweight SAR ship target detection algorithm based on YOLOv8 was proposed in this study.Firstly,the C2f-Sc module was designed by fusing the C2f in the backbone network with the ScConv to reduce spatial redundancy and channel redundancy between features in convolutional neural networks.At the same time,the Ghost module was introduced into the neck network to effectively reduce model parameters and computational complexity.A relatively lightweight EMA attention mechanism was added to the neck network to promote the effective fusion of features at different levels.Experimental results showed that the Parameters and GFLOPs of the improved model are reduced by 8.5%and 7.0%when mAP@0.5 and mAP@0.5:0.95 are increased by 0.7%and 1.8%,respectively.It makes the model lightweight and improves the detection accuracy,which has certain application value.展开更多
Top-view fisheye cameras are widely used in personnel surveillance for their broad field of view,but their unique imaging characteristics pose challenges like distortion,complex scenes,scale variations,and small objec...Top-view fisheye cameras are widely used in personnel surveillance for their broad field of view,but their unique imaging characteristics pose challenges like distortion,complex scenes,scale variations,and small objects near image edges.To tackle these,we proposed peripheral focus you only look once(PF-YOLO),an enhanced YOLOv8n-based method.Firstly,we introduced a cutting-patch data augmentation strategy to mitigate the problem of insufficient small-object samples in various scenes.Secondly,to enhance the model's focus on small objects near the edges,we designed the peripheral focus loss,which uses dynamic focus coefficients to provide greater gradient gains for these objects,improving their regression accuracy.Finally,we designed the three dimensional(3D)spatial-channel coordinate attention C2f module,enhancing spatial and channel perception,suppressing noise,and improving personnel detection.Experimental results demonstrate that PF-YOLO achieves strong performance on the challenging events for person detection from overhead fisheye images(CEPDTOF)and in-the-wild events for people detection and tracking from overhead fisheye cameras(WEPDTOF)datasets.Compared to the original YOLOv8n model,PFYOLO achieves improvements on CEPDTOF with increases of 2.1%,1.7%and 2.9%in mean average precision 50(mAP 50),mAP 50-95,and tively.On WEPDTOF,PF-YOLO achieves substantial improvements with increases of 31.4%,14.9%,61.1%and 21.0%in 91.2%and 57.2%,respectively.展开更多
In high-speed railway(HSR)wireless communication,the rapid channel changes and limited high-capacity access cause significant impact on the link performance.Meanwhile,the Doppler shift caused by high mobility leads to...In high-speed railway(HSR)wireless communication,the rapid channel changes and limited high-capacity access cause significant impact on the link performance.Meanwhile,the Doppler shift caused by high mobility leads to the inter-carrier interference.In this paper,we propose a reconfigurable intelligent surface(RIS)-assisted receive spatial modulation(SM)scheme based on the spatial-temporal correlated HSR Rician channel.The characteristics of SM and the phase shift adjustment of RIS are used to mitigate the performance degradation in high mobility scenarios.Considering the influence of channel spatial-temporal correlation and Doppler shift,the effects of different parameters on average bit error rate(BER)performance and upper bound of ergodic capacity are analyzed.Therefore,a joint antenna and RIS-unit selection algorithm based on the antenna removal method is proposed to increase the capacity performance of communication links.Numerical results show that the proposed RIS-assisted receive SM scheme can maintain high transmission capacity compared to the conventional HSR-SM scheme,whereas the degradation of BER performance can be compensated by arranging a large number of RIS-units.In addition,selecting more RIS-units has better capacity performance than activating more antennas in the low signal-to-noise ratio regions.展开更多
4-Nonylphenol(NP)is a kind of estrogen belonging to the endocrine disrupter,widely used in various agricultural and industrial goods.However,extensive use of NP with direct release to environment poses high risks to b...4-Nonylphenol(NP)is a kind of estrogen belonging to the endocrine disrupter,widely used in various agricultural and industrial goods.However,extensive use of NP with direct release to environment poses high risks to both human health and ecosystems.Herein,for the first time,we developed near-infrared(NIR)responsive upconversion luminescence nanosensor for NP detection.The Förster resonance energy transfer based upconversion nanoparticles(UCNPs)-graphene oxide sensor offers highly selective and sensitive detection of NP in linear ranges of 5−200 ng/mL and 200−1000 ng/mL under 980 nm and 808 nm excitation,respectively,with LOD at 4.2 ng/mL.The sensors were successfully tested for NP detection in real liquid milk samples with excellent recovery results.The rare-earth fluoride based upconversion luminescence nanosensor with NIR excitation wavelength,holds promise for sensing food,environmental,and biological samples due to their high sensitivity,specific recognition,low LOD,negligible autofluorescence,along with the deep penetration of NIR excitation sources.展开更多
We present the experimental demonstration of nondestructive detection of ^(171)Yb atoms in a magneto-optical trap(MOT) based on phase shift measurement induced by the atoms on a weak off-resonant laser beam. After loa...We present the experimental demonstration of nondestructive detection of ^(171)Yb atoms in a magneto-optical trap(MOT) based on phase shift measurement induced by the atoms on a weak off-resonant laser beam. After loading a green MOT of ^(171)Yb atoms, the phase shift is obtained with a two-color Mach–Zehnder interferometer by means of ±45 MHz detuning with respect to the ^(1)S_(0)–^(1)P_(1) transition. We measured a phase shift of about 100 mrad corresponding to an atom count of around 5 × 10^(5). This demonstrates that it is possible to obtain the number of atoms without direct destructive measurement compared with the absorption imaging method. This scheme could be an important approach towards a high-precision lattice clock for clock operation through suppression of the impact of the Dick effect.展开更多
This paper concerns the exponential attitude-orbit coordinated control problems for gravitational-wave detection formation spacecraft systems.Notably,the large-scale communication delays resulting from oversized inter...This paper concerns the exponential attitude-orbit coordinated control problems for gravitational-wave detection formation spacecraft systems.Notably,the large-scale communication delays resulting from oversized inter-satellite distance of space-based laser interferometers are first modeled.Subject to the delayed communication behaviors,a new delay-dependent attitude-orbit coordinated controller is designed.Moreover,by reconstructing the less conservative Lyapunov-Krasovskii functional and free-weight matrices,sufficient criteria are derived to ensure the exponential stability of the closed-loop relative translation and attitude error system.Finally,a simulation example is employed to illustrate the numerical validity of the proposed controller for in-orbit detection missions.展开更多
Intrusion detection systems play a vital role in cyberspace security.In this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusio...Intrusion detection systems play a vital role in cyberspace security.In this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusion of a recursive feature elimination(RFE)algorithm and a bidirectional gated recurrent unit(BGRU).Particularly,the RFE algorithm is employed to select features from high-dimensional data to reduce weak correlations between features and remove redundant features in the numerical feature space.Then,a neural network that combines the BGRU and multilayer perceptron(MLP)is adopted to extract deep intrusion behavior features.Finally,a support vector machine(SVM)classifier is used to classify intrusion behaviors.The proposed model is verified by experiments on the NSL-KDD dataset.The results indicate that the proposed model achieves a 90.25%accuracy and a 97.51%detection rate in binary classification and outperforms other machine learning and deep learning models in intrusion classification.The proposed method can provide new insight into network intrusion detection.展开更多
Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpe...Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism(e.g.,hybrid automatic repeat request[HARQ])becomes indispensable.In that regard,instead of discarding previously transmitted information,the incremental knowledge-based HARQ(IK-HARQ)is deemed as a more effective mechanism that could sufficiently utilize the information semantics.However,considering the possible existence of semantic ambiguity in image transmission,a simple bit-level cyclic redundancy check(CRC)might compromise the performance of IK-HARQ.Therefore,there emerges a strong incentive to revolutionize the CRC mechanism,thus more effectively reaping the benefits of both SemCom and HARQ.In this paper,built on top of swin transformer-based joint source-channel coding(JSCC)and IK-HARQ,we propose a semantic image transmission framework SC-TDA-HARQ.In particular,different from the conventional CRC,we introduce a topological data analysis(TDA)-based error detection method,which capably digs out the inner topological and geometric information of images,to capture semantic information and determine the necessity for re-transmission.Extensive numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework,especially under the limited bandwidth condition,and manifest the superiority of TDA-based error detection method in image transmission.展开更多
This research focuses on solving the fault detection and health monitoring of high-power thyristor converter.In terms of the critical role of thyristor converter in nuclear fusion system,a method based on long short-t...This research focuses on solving the fault detection and health monitoring of high-power thyristor converter.In terms of the critical role of thyristor converter in nuclear fusion system,a method based on long short-term memory(LSTM)neural network model is proposed to monitor the operational state of the converter and accurately detect faults as they occur.By sampling and processing a large number of thyristor converter operation data,the LSTM model is trained to identify and detect abnormal state,and the power supply health status is monitored.Compared with traditional methods,LSTM model shows higher accuracy and abnormal state detection ability.The experimental results show that this method can effectively improve the reliability and safety of the thyristor converter,and provide a strong guarantee for the stable operation of the nuclear fusion reactor.展开更多
A novel electromagnetic tomography(EMT)system for defect detection of high-speed rail wheel is proposed,which differs from traditional electromagnetic tomography systems in its spatial arrangements of coils.A U-shaped...A novel electromagnetic tomography(EMT)system for defect detection of high-speed rail wheel is proposed,which differs from traditional electromagnetic tomography systems in its spatial arrangements of coils.A U-shaped sensor array was designed,and then a simulation model was built with the low frequency electromagnetic simulation software.Three different algorithms were applied to perform image reconstruction,therefore the defects can be detected from the reconstructed images.Based on the simulation results,an experimental system was built and image reconstruction were performed with the measured data.The reconstructed images obtained both from numerical simulation and experimental system indicated the locations of the defects of the wheel,which verified the feasibility of the EMT system and revealed its good application prospect in the future.展开更多
The current High-Speed Railway(HSR)communications increasingly fail to satisfy the massive access services of numerous user equipment brought by the increasing number of people traveling by HSRs.To this end,this paper...The current High-Speed Railway(HSR)communications increasingly fail to satisfy the massive access services of numerous user equipment brought by the increasing number of people traveling by HSRs.To this end,this paper investigates millimeter-Wave(mmWave)extra-large scale(XL)-MIMO-based massive Internet-of-Things(loT)access in near-field HSR communications,and proposes a block simultaneous orthogonal matching pursuit(B-SOMP)-based Active User Detection(AUD)and Channel Estimation(CE)scheme by exploiting the spatial block sparsity of the XLMIMO-based massive access channels.Specifically,we first model the uplink mmWave XL-MIMO channels,which exhibit the near-field propagation characteristics of electromagnetic signals and the spatial non-stationarity of mmWave XL-MIMO arrays.By exploiting the spatial block sparsity and common frequency-domain sparsity pattern of massive access channels,the joint AUD and CE problem can be then formulated as a Multiple Measurement Vectors Compressive Sensing(MIMV-CS)problem.Based on the designed sensing matrix,a B-SOMP algorithm is proposed to achieve joint AUD and CE.Finally,simulation results show that the proposed solution can obtain a better AUD and CE performance than the conventional CS-based scheme for massive IoT access in near-field HSR communications.展开更多
A neutron flux monitor .（NFM） is a key diagnostic system in the International Ther- monuclear Experimental Reactor （ITER）, and may provide readings of a series of important parameters in fusion reaction processes....A neutron flux monitor .（NFM） is a key diagnostic system in the International Ther- monuclear Experimental Reactor （ITER）, and may provide readings of a series of important parameters in fusion reaction processes. As a valuable part of the main electronics system of the NFM, the high-speed baseline restorer we designed is an important signal conversion plug-in which can restore the input signal baseline offset to a zero level, while keeping the output pulse signal waveform from the preamplifier basically unchanged.展开更多
To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection...To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection method.Hence,we proposed an intrusion detection algorithm based on convolutional neural network(CNN)and AdaBoost algorithm.This algorithm uses CNN to extract the characteristics of network traffic data,which is particularly suitable for the analysis of continuous and classified attack data.The AdaBoost algorithm is used to classify network attack data that improved the detection effect of unbalanced data classification.We adopt the UNSW-NB15 dataset to test of this algorithm in the PyCharm environment.The results show that the detection rate of algorithm is99.27%and the false positive rate is lower than 0.98%.Comparative analysis shows that this algorithm has advantages over existing methods in terms of detection rate and false positive rate for small proportion of attack data.展开更多
The majority of the projectiles used in the hypersonic penetration study are solid flat-nosed cylindrical projectiles with a diameter of less than 20 mm.This study aims to fill the gap in the experimental and analytic...The majority of the projectiles used in the hypersonic penetration study are solid flat-nosed cylindrical projectiles with a diameter of less than 20 mm.This study aims to fill the gap in the experimental and analytical study of the evolution of the nose shape of larger hollow projectiles under hypersonic penetration.In the hypersonic penetration test,eight ogive-nose AerMet100 steel projectiles with a diameter of 40 mm were launched to hit concrete targets with impact velocities that ranged from 1351 to 1877 m/s.Severe erosion of the projectiles was observed during high-speed penetration of heterogeneous targets,and apparent localized mushrooming occurred in the front nose of recovered projectiles.By examining the damage to projectiles,a linear relationship was found between the relative length reduction rate and the initial kinetic energy of projectiles in different penetration tests.Furthermore,microscopic analysis revealed the forming mechanism of the localized mushrooming phenomenon for eroding penetration,i.e.,material spall erosion abrasion mechanism,material flow and redistribution abrasion mechanism and localized radial upsetting deformation mechanism.Finally,a model of highspeed penetration that included erosion was established on the basis of a model of the evolution of the projectile nose that considers radial upsetting;the model was validated by test data from the literature and the present study.Depending upon the impact velocity,v0,the projectile nose may behave as undistorted,radially distorted or hemispherical.Due to the effects of abrasion of the projectile and enhancement of radial upsetting on the duration and amplitude of the secondary rising segment in the pulse shape of projectile deceleration,the predicted DOP had an upper limit.展开更多
In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,e...In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,emergency communication,and real-time scheduling,demands advanced capabilities in real-time perception,automated driving,and digitized services,which accelerate the integration and application of Artificial Intelligence(AI)in the HSR system.This paper first provides a brief overview of AI,covering its origin,evolution,and breakthrough applications.A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system:mechanical manufacturing and electrical control,communication and signal control,and transportation management.The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components,forecast of railroad maintenance,optimization of energy consumption in railroads and trains,communication security,communication dependability,channel modeling and estimation,passenger scheduling,traffic flow forecasting,high-speed railway smart platform.Finally,challenges associated with the application of AI are discussed,offering insights for future research directions.展开更多
Hunting stability is an important performance criterion in railway vehicles.This study proposes an incorporation of a bio-inspired limb-like structure(LLS)-based nonlinear damping into the motor suspension system for ...Hunting stability is an important performance criterion in railway vehicles.This study proposes an incorporation of a bio-inspired limb-like structure(LLS)-based nonlinear damping into the motor suspension system for traction units to improve the nonlinear critical speed and hunting stability of high-speed trains(HSTs).Initially,a vibration transmission analysis is conducted on a HST vehicle and a metro vehicle that suffered from hunting motion to explore the effect of different motor suspension systems from on-track tests.Subsequently,a simplified lateral dynamics model of an HST bogie is established to investigate the influence of the motor suspension on the bogie hunting behavior.The bifurcation analysis is applied to optimize the motor suspension parameters for high critical speed.Then,the nonlinear damping of the bio-inspired LLS,which has a positive correlation with the relative displacement,can further improve the modal damping of hunting motion and nonlinear critical speed compared with the linear motor suspension system.Furthermore,a comprehensive numerical model of a high-speed train,considering all nonlinearities,is established to investigate the influence of different types of motor suspension.The simulation results are well consistent with the theoretical analysis.The benefits of employing nonlinear damping of the bio-inspired LLS into the motor suspension of HSTs to enhance bogie hunting stability are thoroughly validated.展开更多
The widespread adoption of blockchain technology has led to the exploration of its numerous applications in various fields.Cryptographic algorithms and smart contracts are critical components of blockchain security.De...The widespread adoption of blockchain technology has led to the exploration of its numerous applications in various fields.Cryptographic algorithms and smart contracts are critical components of blockchain security.Despite the benefits of virtual currency,vulnerabilities in smart contracts have resulted in substantial losses to users.While researchers have identified these vulnerabilities and developed tools for detecting them,the accuracy of these tools is still far from satisfactory,with high false positive and false negative rates.In this paper,we propose a new method for detecting vulnerabilities in smart contracts using the BERT pre-training model,which can quickly and effectively process and detect smart contracts.More specifically,we preprocess and make symbol substitution in the contract,which can make the pre-training model better obtain contract features.We evaluate our method on four datasets and compare its performance with other deep learning models and vulnerability detection tools,demonstrating its superior accuracy.展开更多
Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL...Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL)models find helpful in the detection and classification of anomalies.This article designs an oversampling with an optimal deep learning-based streaming data classification(OS-ODLSDC)model.The aim of the OSODLSDC model is to recognize and classify the presence of anomalies in the streaming data.The proposed OS-ODLSDC model initially undergoes preprocessing step.Since streaming data is unbalanced,support vector machine(SVM)-Synthetic Minority Over-sampling Technique(SVM-SMOTE)is applied for oversampling process.Besides,the OS-ODLSDC model employs bidirectional long short-term memory(Bi LSTM)for AD and classification.Finally,the root means square propagation(RMSProp)optimizer is applied for optimal hyperparameter tuning of the Bi LSTM model.For ensuring the promising performance of the OS-ODLSDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018,KDD-Cup 1999,and NSL-KDD datasets.展开更多
Fire detection has a great impact on people’s life safety.Fire Detection-DETR(FD-DETR)is a fire detection model based on RT-DETR for early fire identification in complex fire scenes.In this study,Adown sub-sampling m...Fire detection has a great impact on people’s life safety.Fire Detection-DETR(FD-DETR)is a fire detection model based on RT-DETR for early fire identification in complex fire scenes.In this study,Adown sub-sampling module was selected to improve the original convolution module,which improved the detection accuracy and reduced the number of parameter values.Using LSKA attention module on the backbone network further improved the detection accuracy.The experimental results showed that compared with the original RT-DETR model,the precision and mAP of FD-DETR flame detection are increased by 0.8%and 0.1%,respectively,which proves that the improved method proposed in this study effectively improves the feature extraction and feature fusion capabilities of the network.In the complex scene fire detection task,the performance of the improved RT-DETR algorithm is better than the original RT-DETR algorithm.展开更多
文摘A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment,flexible maneuverability,and high spatiotemporal resolution,enabling the acquisition of full-process data of the fragment scattering process.However,mismatches between camera frame rates and target velocities can lead to long motion blur tails of high-speed fragment targets,resulting in low signal-to-noise ratios and rendering conventional detection algorithms ineffective in dynamic strong interference testing environments.In this study,we propose a detection framework centered on dynamic strong interference disturbance signal separation and suppression.We introduce a mixture Gaussian model constrained under a joint spatialtemporal-transform domain Dirichlet process,combined with total variation regularization to achieve disturbance signal suppression.Experimental results demonstrate that the proposed disturbance suppression method can be integrated with certain conventional motion target detection tasks,enabling adaptation to real-world data to a certain extent.Moreover,we provide a specific implementation of this process,which achieves a detection rate close to 100%with an approximate 0%false alarm rate in multiple sets of real target field test data.This research effectively advances the development of the field of damage parameter testing.
文摘In recent years,with the development of synthetic aperture radar(SAR)technology and the widespread application of deep learning,lightweight detection of SAR images has emerged as a research direction.The ultimate goal is to reduce computational and storage requirements while ensuring detection accuracy and reliability,making it an ideal choice for achieving rapid response and efficient processing.In this regard,a lightweight SAR ship target detection algorithm based on YOLOv8 was proposed in this study.Firstly,the C2f-Sc module was designed by fusing the C2f in the backbone network with the ScConv to reduce spatial redundancy and channel redundancy between features in convolutional neural networks.At the same time,the Ghost module was introduced into the neck network to effectively reduce model parameters and computational complexity.A relatively lightweight EMA attention mechanism was added to the neck network to promote the effective fusion of features at different levels.Experimental results showed that the Parameters and GFLOPs of the improved model are reduced by 8.5%and 7.0%when mAP@0.5 and mAP@0.5:0.95 are increased by 0.7%and 1.8%,respectively.It makes the model lightweight and improves the detection accuracy,which has certain application value.
基金supported by National Natural Science Foundation of China(Nos.62171042,62102033,U24A20331)the R&D Program of Beijing Municipal Education Commission(No.KZ202211417048)+2 种基金the Project of Construction and Support for High-Level Innovative Teams of Beijing Municipal Institutions(No.BPHR20220121)Beijing Natural Science Foundation(Nos.4232026,4242020)the Academic Research Projects of Beijing Union University(Nos.ZKZD202302,ZK20202403)。
文摘Top-view fisheye cameras are widely used in personnel surveillance for their broad field of view,but their unique imaging characteristics pose challenges like distortion,complex scenes,scale variations,and small objects near image edges.To tackle these,we proposed peripheral focus you only look once(PF-YOLO),an enhanced YOLOv8n-based method.Firstly,we introduced a cutting-patch data augmentation strategy to mitigate the problem of insufficient small-object samples in various scenes.Secondly,to enhance the model's focus on small objects near the edges,we designed the peripheral focus loss,which uses dynamic focus coefficients to provide greater gradient gains for these objects,improving their regression accuracy.Finally,we designed the three dimensional(3D)spatial-channel coordinate attention C2f module,enhancing spatial and channel perception,suppressing noise,and improving personnel detection.Experimental results demonstrate that PF-YOLO achieves strong performance on the challenging events for person detection from overhead fisheye images(CEPDTOF)and in-the-wild events for people detection and tracking from overhead fisheye cameras(WEPDTOF)datasets.Compared to the original YOLOv8n model,PFYOLO achieves improvements on CEPDTOF with increases of 2.1%,1.7%and 2.9%in mean average precision 50(mAP 50),mAP 50-95,and tively.On WEPDTOF,PF-YOLO achieves substantial improvements with increases of 31.4%,14.9%,61.1%and 21.0%in 91.2%and 57.2%,respectively.
基金supported in part by National Natural Science Foundation of China under Grant 62461024Jiangxi Provincial Natural Science Foundation of China under Grant 20224ACB202001.
文摘In high-speed railway(HSR)wireless communication,the rapid channel changes and limited high-capacity access cause significant impact on the link performance.Meanwhile,the Doppler shift caused by high mobility leads to the inter-carrier interference.In this paper,we propose a reconfigurable intelligent surface(RIS)-assisted receive spatial modulation(SM)scheme based on the spatial-temporal correlated HSR Rician channel.The characteristics of SM and the phase shift adjustment of RIS are used to mitigate the performance degradation in high mobility scenarios.Considering the influence of channel spatial-temporal correlation and Doppler shift,the effects of different parameters on average bit error rate(BER)performance and upper bound of ergodic capacity are analyzed.Therefore,a joint antenna and RIS-unit selection algorithm based on the antenna removal method is proposed to increase the capacity performance of communication links.Numerical results show that the proposed RIS-assisted receive SM scheme can maintain high transmission capacity compared to the conventional HSR-SM scheme,whereas the degradation of BER performance can be compensated by arranging a large number of RIS-units.In addition,selecting more RIS-units has better capacity performance than activating more antennas in the low signal-to-noise ratio regions.
文摘4-Nonylphenol(NP)is a kind of estrogen belonging to the endocrine disrupter,widely used in various agricultural and industrial goods.However,extensive use of NP with direct release to environment poses high risks to both human health and ecosystems.Herein,for the first time,we developed near-infrared(NIR)responsive upconversion luminescence nanosensor for NP detection.The Förster resonance energy transfer based upconversion nanoparticles(UCNPs)-graphene oxide sensor offers highly selective and sensitive detection of NP in linear ranges of 5−200 ng/mL and 200−1000 ng/mL under 980 nm and 808 nm excitation,respectively,with LOD at 4.2 ng/mL.The sensors were successfully tested for NP detection in real liquid milk samples with excellent recovery results.The rare-earth fluoride based upconversion luminescence nanosensor with NIR excitation wavelength,holds promise for sensing food,environmental,and biological samples due to their high sensitivity,specific recognition,low LOD,negligible autofluorescence,along with the deep penetration of NIR excitation sources.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. U20A2075,11803072,and 12374467)the Innovation Program for Quantum Science and Technology (Grant No. 2021ZD0300902)the Hubei Provincial Science and Technology Major Project (Grant No. ZDZX2022000004)。
文摘We present the experimental demonstration of nondestructive detection of ^(171)Yb atoms in a magneto-optical trap(MOT) based on phase shift measurement induced by the atoms on a weak off-resonant laser beam. After loading a green MOT of ^(171)Yb atoms, the phase shift is obtained with a two-color Mach–Zehnder interferometer by means of ±45 MHz detuning with respect to the ^(1)S_(0)–^(1)P_(1) transition. We measured a phase shift of about 100 mrad corresponding to an atom count of around 5 × 10^(5). This demonstrates that it is possible to obtain the number of atoms without direct destructive measurement compared with the absorption imaging method. This scheme could be an important approach towards a high-precision lattice clock for clock operation through suppression of the impact of the Dick effect.
基金supported by the Na⁃tional Key R&D Program of China(No.2022YFC2204800)the Graduate Student Independent Exploration and Innovation Program of Central South University(No.2024ZZTS 0767).
文摘This paper concerns the exponential attitude-orbit coordinated control problems for gravitational-wave detection formation spacecraft systems.Notably,the large-scale communication delays resulting from oversized inter-satellite distance of space-based laser interferometers are first modeled.Subject to the delayed communication behaviors,a new delay-dependent attitude-orbit coordinated controller is designed.Moreover,by reconstructing the less conservative Lyapunov-Krasovskii functional and free-weight matrices,sufficient criteria are derived to ensure the exponential stability of the closed-loop relative translation and attitude error system.Finally,a simulation example is employed to illustrate the numerical validity of the proposed controller for in-orbit detection missions.
基金supported in part by the National Natural Science Foundation of China(No.62001333)the Scientific Research Project of Education Department of Hubei Province(No.D20221702).
文摘Intrusion detection systems play a vital role in cyberspace security.In this study,a network intrusion detection method based on the feature selection algorithm(FSA)and a deep learning model is developed using a fusion of a recursive feature elimination(RFE)algorithm and a bidirectional gated recurrent unit(BGRU).Particularly,the RFE algorithm is employed to select features from high-dimensional data to reduce weak correlations between features and remove redundant features in the numerical feature space.Then,a neural network that combines the BGRU and multilayer perceptron(MLP)is adopted to extract deep intrusion behavior features.Finally,a support vector machine(SVM)classifier is used to classify intrusion behaviors.The proposed model is verified by experiments on the NSL-KDD dataset.The results indicate that the proposed model achieves a 90.25%accuracy and a 97.51%detection rate in binary classification and outperforms other machine learning and deep learning models in intrusion classification.The proposed method can provide new insight into network intrusion detection.
基金supported in part by the National Key Research and Development Program of China under Grant 2024YFE0200600in part by the National Natural Science Foundation of China under Grant 62071425+3 种基金in part by the Zhejiang Key Research and Development Plan under Grant 2022C01093in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LR23F010005in part by the National Key Laboratory of Wireless Communications Foundation under Grant 2023KP01601in part by the Big Data and Intelligent Computing Key Lab of CQUPT under Grant BDIC-2023-B-001.
文摘Semantic communication(SemCom)aims to achieve high-fidelity information delivery under low communication consumption by only guaranteeing semantic accuracy.Nevertheless,semantic communication still suffers from unexpected channel volatility and thus developing a re-transmission mechanism(e.g.,hybrid automatic repeat request[HARQ])becomes indispensable.In that regard,instead of discarding previously transmitted information,the incremental knowledge-based HARQ(IK-HARQ)is deemed as a more effective mechanism that could sufficiently utilize the information semantics.However,considering the possible existence of semantic ambiguity in image transmission,a simple bit-level cyclic redundancy check(CRC)might compromise the performance of IK-HARQ.Therefore,there emerges a strong incentive to revolutionize the CRC mechanism,thus more effectively reaping the benefits of both SemCom and HARQ.In this paper,built on top of swin transformer-based joint source-channel coding(JSCC)and IK-HARQ,we propose a semantic image transmission framework SC-TDA-HARQ.In particular,different from the conventional CRC,we introduce a topological data analysis(TDA)-based error detection method,which capably digs out the inner topological and geometric information of images,to capture semantic information and determine the necessity for re-transmission.Extensive numerical results validate the effectiveness and efficiency of the proposed SC-TDA-HARQ framework,especially under the limited bandwidth condition,and manifest the superiority of TDA-based error detection method in image transmission.
基金supported by the Open Fund of Magnetic Confinement Fusion Laboratory of Anhui Province(No.2024AMF04003)the Natural Science Foundation of Anhui Province(No.228085ME142)Comprehensive Research Facility for Fusion Technology(No.20180000527301001228)。
文摘This research focuses on solving the fault detection and health monitoring of high-power thyristor converter.In terms of the critical role of thyristor converter in nuclear fusion system,a method based on long short-term memory(LSTM)neural network model is proposed to monitor the operational state of the converter and accurately detect faults as they occur.By sampling and processing a large number of thyristor converter operation data,the LSTM model is trained to identify and detect abnormal state,and the power supply health status is monitored.Compared with traditional methods,LSTM model shows higher accuracy and abnormal state detection ability.The experimental results show that this method can effectively improve the reliability and safety of the thyristor converter,and provide a strong guarantee for the stable operation of the nuclear fusion reactor.
基金Supported by the National Natural Science Foundation of China(61771041)。
文摘A novel electromagnetic tomography(EMT)system for defect detection of high-speed rail wheel is proposed,which differs from traditional electromagnetic tomography systems in its spatial arrangements of coils.A U-shaped sensor array was designed,and then a simulation model was built with the low frequency electromagnetic simulation software.Three different algorithms were applied to perform image reconstruction,therefore the defects can be detected from the reconstructed images.Based on the simulation results,an experimental system was built and image reconstruction were performed with the measured data.The reconstructed images obtained both from numerical simulation and experimental system indicated the locations of the defects of the wheel,which verified the feasibility of the EMT system and revealed its good application prospect in the future.
基金supported in part by the Natural Science Foundation of China(NSFC)under Grant 62071044 and Grant 62088101in part by the Shandong Province Natural Science Foundation under Grant ZR2022YQ62in part by the Beijing Nova Program.
文摘The current High-Speed Railway(HSR)communications increasingly fail to satisfy the massive access services of numerous user equipment brought by the increasing number of people traveling by HSRs.To this end,this paper investigates millimeter-Wave(mmWave)extra-large scale(XL)-MIMO-based massive Internet-of-Things(loT)access in near-field HSR communications,and proposes a block simultaneous orthogonal matching pursuit(B-SOMP)-based Active User Detection(AUD)and Channel Estimation(CE)scheme by exploiting the spatial block sparsity of the XLMIMO-based massive access channels.Specifically,we first model the uplink mmWave XL-MIMO channels,which exhibit the near-field propagation characteristics of electromagnetic signals and the spatial non-stationarity of mmWave XL-MIMO arrays.By exploiting the spatial block sparsity and common frequency-domain sparsity pattern of massive access channels,the joint AUD and CE problem can be then formulated as a Multiple Measurement Vectors Compressive Sensing(MIMV-CS)problem.Based on the designed sensing matrix,a B-SOMP algorithm is proposed to achieve joint AUD and CE.Finally,simulation results show that the proposed solution can obtain a better AUD and CE performance than the conventional CS-based scheme for massive IoT access in near-field HSR communications.
基金supported by State Key Laboratory of Particle Detection & Electronics of ChinaITER Plan National Major Project of China(2008GB109000)
文摘A neutron flux monitor .（NFM） is a key diagnostic system in the International Ther- monuclear Experimental Reactor （ITER）, and may provide readings of a series of important parameters in fusion reaction processes. As a valuable part of the main electronics system of the NFM, the high-speed baseline restorer we designed is an important signal conversion plug-in which can restore the input signal baseline offset to a zero level, while keeping the output pulse signal waveform from the preamplifier basically unchanged.
基金supported in part by the National Key R&D Program of China(No.2022YFB3904503)National Natural Science Foundation of China(No.62172418)。
文摘To solve the problem of poor detection and limited application range of current intrusion detection methods,this paper attempts to use deep learning neural network technology to study a new type of intrusion detection method.Hence,we proposed an intrusion detection algorithm based on convolutional neural network(CNN)and AdaBoost algorithm.This algorithm uses CNN to extract the characteristics of network traffic data,which is particularly suitable for the analysis of continuous and classified attack data.The AdaBoost algorithm is used to classify network attack data that improved the detection effect of unbalanced data classification.We adopt the UNSW-NB15 dataset to test of this algorithm in the PyCharm environment.The results show that the detection rate of algorithm is99.27%and the false positive rate is lower than 0.98%.Comparative analysis shows that this algorithm has advantages over existing methods in terms of detection rate and false positive rate for small proportion of attack data.
基金the National Natural Science Foundation of China(Grant No.12102050)the Open Fund of State Key Laboratory of Explosion Science and Technology(Grant No.SKLEST-ZZ-21-18).
文摘The majority of the projectiles used in the hypersonic penetration study are solid flat-nosed cylindrical projectiles with a diameter of less than 20 mm.This study aims to fill the gap in the experimental and analytical study of the evolution of the nose shape of larger hollow projectiles under hypersonic penetration.In the hypersonic penetration test,eight ogive-nose AerMet100 steel projectiles with a diameter of 40 mm were launched to hit concrete targets with impact velocities that ranged from 1351 to 1877 m/s.Severe erosion of the projectiles was observed during high-speed penetration of heterogeneous targets,and apparent localized mushrooming occurred in the front nose of recovered projectiles.By examining the damage to projectiles,a linear relationship was found between the relative length reduction rate and the initial kinetic energy of projectiles in different penetration tests.Furthermore,microscopic analysis revealed the forming mechanism of the localized mushrooming phenomenon for eroding penetration,i.e.,material spall erosion abrasion mechanism,material flow and redistribution abrasion mechanism and localized radial upsetting deformation mechanism.Finally,a model of highspeed penetration that included erosion was established on the basis of a model of the evolution of the projectile nose that considers radial upsetting;the model was validated by test data from the literature and the present study.Depending upon the impact velocity,v0,the projectile nose may behave as undistorted,radially distorted or hemispherical.Due to the effects of abrasion of the projectile and enhancement of radial upsetting on the duration and amplitude of the secondary rising segment in the pulse shape of projectile deceleration,the predicted DOP had an upper limit.
基金supported by the National Natural Science Foundation of China(62172033).
文摘In recent years,the global surge of High-speed Railway(HSR)revolutionized ground transportation,providing secure,comfortable,and punctual services.The next-gen HSR,fueled by emerging services like video surveillance,emergency communication,and real-time scheduling,demands advanced capabilities in real-time perception,automated driving,and digitized services,which accelerate the integration and application of Artificial Intelligence(AI)in the HSR system.This paper first provides a brief overview of AI,covering its origin,evolution,and breakthrough applications.A comprehensive review is then given regarding the most advanced AI technologies and applications in three macro application domains of the HSR system:mechanical manufacturing and electrical control,communication and signal control,and transportation management.The literature is categorized and compared across nine application directions labeled as intelligent manufacturing of trains and key components,forecast of railroad maintenance,optimization of energy consumption in railroads and trains,communication security,communication dependability,channel modeling and estimation,passenger scheduling,traffic flow forecasting,high-speed railway smart platform.Finally,challenges associated with the application of AI are discussed,offering insights for future research directions.
基金the National Natural Science Foundation of China (Nos. 52388102, 52072317 and U2268210)the State Key Laboratory of Rail Transit Vehicle System (No. 2024RVL-T12)
文摘Hunting stability is an important performance criterion in railway vehicles.This study proposes an incorporation of a bio-inspired limb-like structure(LLS)-based nonlinear damping into the motor suspension system for traction units to improve the nonlinear critical speed and hunting stability of high-speed trains(HSTs).Initially,a vibration transmission analysis is conducted on a HST vehicle and a metro vehicle that suffered from hunting motion to explore the effect of different motor suspension systems from on-track tests.Subsequently,a simplified lateral dynamics model of an HST bogie is established to investigate the influence of the motor suspension on the bogie hunting behavior.The bifurcation analysis is applied to optimize the motor suspension parameters for high critical speed.Then,the nonlinear damping of the bio-inspired LLS,which has a positive correlation with the relative displacement,can further improve the modal damping of hunting motion and nonlinear critical speed compared with the linear motor suspension system.Furthermore,a comprehensive numerical model of a high-speed train,considering all nonlinearities,is established to investigate the influence of different types of motor suspension.The simulation results are well consistent with the theoretical analysis.The benefits of employing nonlinear damping of the bio-inspired LLS into the motor suspension of HSTs to enhance bogie hunting stability are thoroughly validated.
基金supported by the National Key Research and Development Plan in China(Grant No.2020YFB1005500)。
文摘The widespread adoption of blockchain technology has led to the exploration of its numerous applications in various fields.Cryptographic algorithms and smart contracts are critical components of blockchain security.Despite the benefits of virtual currency,vulnerabilities in smart contracts have resulted in substantial losses to users.While researchers have identified these vulnerabilities and developed tools for detecting them,the accuracy of these tools is still far from satisfactory,with high false positive and false negative rates.In this paper,we propose a new method for detecting vulnerabilities in smart contracts using the BERT pre-training model,which can quickly and effectively process and detect smart contracts.More specifically,we preprocess and make symbol substitution in the contract,which can make the pre-training model better obtain contract features.We evaluate our method on four datasets and compare its performance with other deep learning models and vulnerability detection tools,demonstrating its superior accuracy.
文摘Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL)models find helpful in the detection and classification of anomalies.This article designs an oversampling with an optimal deep learning-based streaming data classification(OS-ODLSDC)model.The aim of the OSODLSDC model is to recognize and classify the presence of anomalies in the streaming data.The proposed OS-ODLSDC model initially undergoes preprocessing step.Since streaming data is unbalanced,support vector machine(SVM)-Synthetic Minority Over-sampling Technique(SVM-SMOTE)is applied for oversampling process.Besides,the OS-ODLSDC model employs bidirectional long short-term memory(Bi LSTM)for AD and classification.Finally,the root means square propagation(RMSProp)optimizer is applied for optimal hyperparameter tuning of the Bi LSTM model.For ensuring the promising performance of the OS-ODLSDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018,KDD-Cup 1999,and NSL-KDD datasets.
文摘Fire detection has a great impact on people’s life safety.Fire Detection-DETR(FD-DETR)is a fire detection model based on RT-DETR for early fire identification in complex fire scenes.In this study,Adown sub-sampling module was selected to improve the original convolution module,which improved the detection accuracy and reduced the number of parameter values.Using LSKA attention module on the backbone network further improved the detection accuracy.The experimental results showed that compared with the original RT-DETR model,the precision and mAP of FD-DETR flame detection are increased by 0.8%and 0.1%,respectively,which proves that the improved method proposed in this study effectively improves the feature extraction and feature fusion capabilities of the network.In the complex scene fire detection task,the performance of the improved RT-DETR algorithm is better than the original RT-DETR algorithm.
