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Double-branch forgery image detection based on multi-scale feature fusion

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Optoelectronics Letters 2024年第5期20卷 307-312页

作者： ZHANG Hongying GUO Chunxing WANG Xuyong college of Electronic Information and Automation Civil Aviation University of ChinaTianjin 300300China

Most of existing methods exhibit poor performance in detecting forged images due to the small size of tampered areas and the limited pixel difference between untampered and tampered regions. To alleviate the above problem, a double-branch tampered image detection based on multi-scale features is proposed. Firstly, we introduce a fusion module based on attention mechanism in the first branch to enhance the network's sensitivity towards tampered regions. Secondly, we construct a second branch specifically designed for detection, aiming to identify subtle differences between tampered and untampered areas by utilizing rich edge information from shallow features as guidance. Compared to the existing methods on the public benchmark datasets CASIA1.0, Columbia and NIST16, the values of F-score reached 0.766, 0.900 and 0.930 on those datasets, respectively. The experimental results show that our method could significantly improve the accuracy on detecting the tampered area.

关键词： branch image utilizing

Global dust density in two-dimensional complex plasma

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Chinese Physics B 2024年第6期33卷 445-450页

作者：赵逸真刘松芬孔伟杨芳 School of Physics Nankai UniversityTianjin 300071China college of Science Civil Aviation University of ChinaTianjin 300300China

The driven-dissipative Langevin dynamics simulation is used to produce a two-dimensional(2D) dense cloud, which is composed of charged dust particles trapped in a quadratic potential. A 2D mesh grid is built to analyze the center-to-wall dust density. It is found that the local dust density in the outer region relative to that of the inner region is more nonuniform,being consistent with the feature of quadratic potential. The dependences of the global dust density on equilibrium temperature, particle size, confinement strength, and confinement shape are investigated. It is found that the particle size, the confinement strength, and the confinement shape strongly affect the global dust density, while the equilibrium temperature plays a minor effect on it. In the direction where there is a stronger confinement, the dust density gradient is bigger.

关键词： dust particles quadratic potential two-dimensional mesh grid

Modal identification based on Hilbert-Huang Transform of structural response with S VD preprocessing

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Acta Mechanica Sinica 2009年第6期25卷 883-888页

作者： Min Zheng Fan Shen Yuping Dou Xiaoyan Yan college of civil aviation,Nanjing University of Aeronautics and Astronautics,210016 Nanjing. China college of civil aviation Nanjing University of Aeronautics and Astronautics Nanjing China

In recent years, Empirical mode decomposition and Hilbert spectral analysis have been combined to identify system parameters. Singular-Value Decomposition is pro- posed as a signal preprocessing technique of Hilbert-Huang Transform to extract modal parameters for closely spaced modes and low-energy components. The proposed method is applied to a simulated airplane model built in Automatic Dynamic Analysis of Mechanical Systems software. The results demonstrate that the identified modal parameters are in good agreement with the baseline model.

关键词： Modal identification . Hilbert-Huang Transforms - Singular-value decomposition . Signal processing

Early fire detection technology based on improved transformers in aircraft cargo compartments

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Journal of Safety Science and Resilience 2024年第2期5卷 194-203页

作者： Hongzhou Ai Dong Han Xinzhi Wang Quanyi Liu Yue Wang Mengyue Li Pei Zhu college of civil aviation Safety Engineering Civil Aviation Flight University of ChinaGuanghan618307China civilAircraf Fire Science and Safety Enginring Key Laboratory of Sichuan CivilAviation Flight University of ChinaGuanghan 618307China School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Key Laboratory of civil aviation Emergency Science&Technology Nanjing University of Aeronautics and AstronauticsNanjing 210000China

The implementation of early and accurate detection of aircraft cargo compartment fire is of great significance to ensure flight safety.The current airborne fire detection technology mostly relies on single-parameter smoke detection using infrared light.This often results in a high false alarm rate in complex air transportation envi-ronments.The traditional deep learning model struggles to effectively address the issue of long-term dependency in multivariate fire information.This paper proposes a multi-technology collaborative fire detection method based on an improved transformers model.Dual-wavelength optical sensors,flue gas analyzers,and other equipment are used to carry out multi-technology collaborative detection methods and characterize various feature dimensions of fire to improve detection accuracy.The improved Transformer model which integrates the self-attention mechanism and position encoding mechanism is applied to the problem of long-time series modeling of fire information from a global perspective,which effectively solves the problem of gradient disappearance and gradient explosion in traditional RNN(recurrent neural network)and CNN(convolutional neural network).Two different multi-head self-attention mechanisms are used to classify and model multivariate fire information,respectively,which solves the problem of confusing time series modeling and classification modeling in dealing with multivariate classification tasks by a single attention mechanism.Finally,the output results of the two models are fused through the gate mechanism.The research results show that,compared with the traditional single-feature detection technology,the multi-technology collaborative fire detection method can better capture fire information.Compared with the traditional deep learning model,the multivariate fire pre-diction model constructed by the improved Transformer can better detect fires,and the accuracy rate is 0.995.

关键词： Deep learning Aircraft cargo compartment Attentionmechanism Firedetection Multi-sourcedata fusion

Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record(QAR)Data Analysis

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Advances in Atmospheric Sciences 2024年第7期41卷 1438-1449页

作者： Zibo ZHUANG Kunyun LIN Hongying ZHANG Pak-Wai CHAN Flight Academy Civil Aviation University of ChinaTianjin 300300China School of Electronic Information and Automation Civil Aviation University of ChinaTianjin 300300China aviation Weather Services Hong Kong ObservatoryHong KongChina

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

关键词： turbulence detection symbolic classifier quick access recorder data

BDSec:Security Authentication Protocol for BeiDou-Ⅱ civil Navigation Message

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China Communications 2024年第6期21卷 206-218页

作者： Wu Zhijun Zhang Yuan Yang Yiming Wang Peng Yue Meng School of Safety Science and Engineering Civil Aviation University of ChinaTianjin 300300China School of Electronic Information and Automation Civil Aviation University of ChinaTianjin 300300China

Due to the lack of authentication mechanism in BeiDou navigation satellite system(BDS),BD-Ⅱ civil navigation message(BDⅡ-CNAV) are vulnerable to spoofing attack and replay attack.To solve this problem,we present a security authentication protocol,called as BDSec,which is designed by using China’s cryptography Shangyong Mima(SM) series algorithms,such as SM2/4/9 and Zu Chongzhi(ZUC)algorithm.In BDSec protocol,both of BDⅡ-CNAV and signature information are encrypted using the SM4 algorithm(Symmetric encryption mechanism).The encrypted result is used as the subject authentication information.BDSec protocol applies SM9 algorithm(Identity-based cryptography mechanism) to protect the integrity of the BDⅡ-CNAV,adopts the SM2 algorithm(Public key cryptosystem) to guarantee the confidentiality of the important session information,and uses the ZUC algorithm(Encryption and integrity algorithm) to verify the integrity of the message authentication serial number and initial information and the information in authentication initialization sub-protocol respectively.The results of the SVO logic reasoning and performance analysis show that BDSec protocol meets security requirements for the dual user identity authentication in BDS and can realize the security authentication of BDⅡ-CNAV.

关键词： BDII civil navigation messages(BDIICNAV) BeiDou navigation satellite system(BDS) identity-based cryptography mechanism navigation message authentication protocol(BDSec)

Cemented vertebra and adjacent vertebra refractured in a chronic kidney disease-mineral and bone disorder patient: A case report

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World Journal of Clinical Cases 2024年第10期12卷 1804-1809页

作者： Ti-Dong Zhang Shuai Cao Hui-Yong Ren Yu-Min Li Yi-Ming Yuan Department of Orthopedics Civil Aviation General HospitalBeijing 100123China

BACKGROUND Although percutaneous vertebral augmentation(PVA)is a commonly used procedure for treating vertebral compression fracture(VCF),the risk of vertebral refracture should be considered.Chronic kidney disease-mineral and bone disorder(CKD-MBD)is a systemic disease of mineral and bone metabolism.It is associated with an increased risk of fracture.Few studies have reported the use of PVA in patients with CKD-MBD.We herein report a rare case wherein the cemented vertebra and the adjacent vertebra refractured simultaneously in a CKD-MBD patient after PVA.CASE SUMMARY A 74-year-old man suffered from low back pain after taking a fall about 3 wk ago.According to physical examination,imaging and laboratory findings,diagnoses of T12 VCF,CKD-MBD,and chronic kidney disease stage 5 were established.He then received percutaneous vertebroplasty at T12 vertebra.Fourteen weeks later,he presented with T12 and L1 vertebral refractures caused by lumbar sprain.Once again,he was given PVA which was optimized for the refractured vertebrae.Although the short-term postoperative effect was satisfactory,he reported chronic low back pain again at the 3-month follow-up.CONCLUSION It is necessary that patients with CKD-MBD who have received PVA are aware of the adverse effects of CKD-MBD.It may increase the risk of vertebral refracture.Furthermore,the PVA surgical technique needs to be optimized according to the condition of the patient.The medium-and long-term effects of PVA remain uncertain in patients with CKD-MBD.

关键词： Chronic kidney disease-mineral and bone disorder Percutaneous vertebral augmentation Vertebral compression fracture Vertebral refracture Cemented vertebra Adjacent vertebra Case report

Space debris environment engineering model 2019:Algorithms improvement and comparison with ORDEM 3.1 and MASTER-8

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Chinese Journal of Aeronautics 2024年第5期37卷 392-409页

作者： Yuyan LIU Runqiang CHI Baojun PANG HU Diqi Wuxiong CAO Dongfang WANG School of Astronautics Harbin Institute of TechnologyHarbin 150001China college of Safety Science&Engineering Civil Aviation University of ChinaTianjin 300300China

As an essential tool for realistic description of the current or future debris environment,the Space Debris Environment Engineering Model(SDEEM)has been developed to provide support for risk assessment of spacecraft.In contrast with SDEEM2015,SDEEM2019,the latest version,extends the orbital range from the Low Earth Orbit(LEO)to Geosynchronous Orbit(GEO)for the years 1958-2050.In this paper,improved modeling algorithms used by SDEEM2019 in propagating simulation,spatial density distribution,and spacecraft flux evaluation are presented.The debris fluxes of SDEEM2019 are compared with those of three typical models,i.e.,SDEEM2015,Orbital Debris Engineering Model 3.1(ORDEM 3.1),and Meteoroid and Space Debris Terrestrial Environment Reference(MASTER-8),in terms of two assessment modes.Three orbital cases,including the Geostationary Transfer Orbit(GTO),Sun-Synchronous Orbit(SSO)and International Space Station(ISS)orbit,are selected for the spacecraft assessment mode,and the LEO region is selected for the spatial density assessment mode.The analysis indicates that compared with previous algorithms,the variable step-size orbital propagating algorithm based on semi-major axis control is more precise,the spatial density algorithm based on the second zonal harmonic of the non-spherical Earth gravity(J_(2))is more applicable,and the result of the position-centered spacecraft flux algorithm is more convergent.The comparison shows that SDEEM2019 and MASTER-8 have consistent trends due to similar modeling processes,while the differences between SDEEM2019 and ORDEM 3.1 are mainly caused by different modeling approaches for uncatalogued debris.

关键词： SDEEM2019 Space debris propagating algorithm Spatial density algorithm ORDEM 3.1 MASTER-8

An Innovative Deep Architecture for Flight Safety Risk Assessment Based on Time Series Data

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2549-2569页

作者： Hong Sun Fangquan Yang Peiwen Zhang Yang Jiao Yunxiang Zhao civil aviation Flight Technology and Flight Safety Research Base Civil Aviation Flight University of ChinaGuanghan618307China Airport Academy Civil Aviation Flight University of ChinaGuanghan618307China School of Economics and Management Civil Aviation Flight University of ChinaGuanghan618307China China Academy of civil aviation Science and Technology Beijing101300China Flight Training Standards Branch Civil Aviation Flight University of ChinaGuanghan618307China

With the development of the integration of aviation safety and artificial intelligence,research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management,but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry.Therefore,an improved risk assessment algorithm(PS-AE-LSTM)based on long short-term memory network(LSTM)with autoencoder(AE)is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels.Firstly,based on the normal distribution characteristics of flight data,a probability severity(PS)model is established to enhance the quality of risk assessment labels.Secondly,autoencoder is introduced to reconstruct the flight parameter data to improve the data quality.Finally,utilizing the time-series nature of flight data,a long and short-termmemory network is used to classify the risk level and improve the accuracy of risk assessment.Thus,a risk assessment experimentwas conducted to analyze a fleet landing phase dataset using the PS-AE-LSTMalgorithm to assess the risk level associated with aircraft hard landing events.The results show that the proposed algorithm achieves an accuracy of 86.45%compared with seven baseline models and has excellent risk assessment capability.

关键词： Safety engineering risk assessment time series data autoencoder LSTM