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Aerospace Engineering and Systems (ISAES), International Symposium on

作者： Tao Song Siyuan Zhang Xiaoyun Shen Siming Yan Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China Technical Support Department Chongqing Sub-bureau of Southwest China Reginal ATMB Chongqing China

ISBN: (数字)9798350350418

ISBN: (纸本)9798350350425

In order to prevent runway intrusion events due to the incorrect deployment of aircraft by controllers with insufficient situational awareness, a 3D visual simulation system for runway conflict detection is designed. By analyzing different types of conflict scenarios of aircraft arrival and departure, the runway conflict models are constructed, and for different conflict models, the spatial distance method is used to determine whether runway incursion of the aircraft occurs. Then the combination of spatial distance and time threshold are used for the conflict alarm. The runway protection area is constructed, the aircraft motion model of the airport surface is established based on the data of airport surveillance data. The visual simulation of the arrival and departure conflict detection algorithm is realized on the Unreal Engine 4 platform. The experimental results show that according to the arrival and departure conflict detection, when aircrafts are close to each other and less than the encounter time threshold, the system can detect the runway conflict in real time and give the alarm.

关键词： Visualization Solid modeling Analytical models Three-dimensional displays Atmospheric modeling Airports Aircraft manufacture Aircraft Detection algorithms Engines

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Block Classification of PolSAR images based on Density Peak Clustering

Block Classification of PolSAR Images based on Density Peak ...

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2021 SAR in Big Data Era, BIGSARDATA 2021

作者： Han, Binbin Han, Ping Cheng, Zheng Tianjin Key Lab of Advanced Signal Processing Civil Aviation University of China Tianjin China Engineering Techniques Training Center Civil Aviation University of China Tianjin China

ISBN: (纸本)9781665401234

Polarimetric synthetic aperture radar (PolSAR) image interpretation tends to design complex models to describe data distribution, but there is little research on simplifying the complexity of image scenes and reducing the difficulty of interpretation. This paper proposes an effective unsupervised classification algorithm by combining density peak clustering with image blocking strategy. The algorithm firstly divides the preprocessed complex large scene image into several simple local images, then carries out initial classification in local images based on h/q decomposition features and gray features of PauliRGB image, and completes optimization in local images with Wishart classifier. Finally, unsupervised classification is realized by combining clusters of small scenes to the specified cluster number by density peak clustering. The experimental results of San Francisco data collected by American AIRSAR system show that the algorithm can significantly improve the classification effect. © 2021 IEEE.

关键词： image classification

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Design of an Enhanced Turbulence Detection Process Considering Aircraft Response 8th

Design of an Enhanced Turbulence Detection Process Consideri...

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8th International Conference on Communications, signal processing, and Systems, CSPS 2019

作者： Fan, Yuandan Lu, Xiaoguang Li, Hai Wu, Renbiao Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (纸本)9789811394089

Turbulence is very hazardous to the flight safety, which generally can be detected by airborne weather radar. In newly specification DO-220A revised by Radio Technical Commission for Aeronautics (RTCA), standards of enhanced turbulence detection with airborne weather radar have been complemented. In the specification, it is stated that the characteristics of aircrafts should be taken into account in the turbulence detecting process. The aircraft response following a turbulence encounter is analysed in this paper, and then the characteristics of aircrafts are quantified by employing the load factor. Based on the quantified analysis, the vertical load factor is predicted based on both radar observation and the characteristics of aircraft. It can provide more accurate turbulence metrics for crews involved with different aircraft types. The simulation results demonstrate that the vertical load factor based turbulence detection process meets requirements of DO-220A. Furthermore, the research is important for the study of enhanced turbulence detection specifications documented in DO-220A. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Turbulence

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Speech Recognition Model of Civil Aviation Radiotelephony Communication Based on Improved Conformer

Speech Recognition Model of Civil Aviation Radiotelephony Co...

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Automation, Robotics and Computer Engineering (ICARCE), International Conference on

作者： Zewei Xiao Guimin Jia Bo Shi Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China College of Electronic Information and Automation Civil Aviation University of China Tianjin China

ISBN: (纸本)9781665475495

Radiotelephony communication has a special grammatical structure and pronunciation, and it is difficult to apply the model of generic speech recognition directly to the field of radiotelephony communication. We propose a Conv1DSlide-Conformer model for speech recognition of radiotelephony communication. The sliding-window attention mechanism is used instead of the self-attention mechanism to improve the decoding speed of the model and increase the adaptability of the model to radiotelephony communication. The convolutional module is used instead of the feedforward neural network module to make the encoder focus more on local information. The improved Conformer model processes the FBANK features of radiotelephony communication and can extract high-dimensional features that better fit the characteristics of radiotelephony communication. The use of concatenated temporal classification (CTC) combined with a data augmentation strategy assists training to speed up convergence during model training and reduce the complexity of model training. Decoding is assisted by CTC and language models to improve the performance of speech recognition. The experimental results show that the improved Conformer speech recognition model in this paper reduces the word error rate to 8.1% and 7.8% on the actual Chinese radiotelephony communication speech dataset.

关键词： Training Adaptation models Error analysis Computational modeling Speech recognition Feature extraction Data models

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Analyzing Cognitive Patterns in Gifted Children Using MRI and Morphometric Similarity Networks

Analyzing Cognitive Patterns in Gifted Children Using MRI an...

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18th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2025

作者： Han, Shuning Duan, Feng Vilaseca, Gemma Vilaró, Núria Caiafa, Cesar F. Sun, Zhe Solé-Casals, Jordi Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Catalonia Spain Image Processing Research Group RIKEN Center for Advanced Photonics RIKEN Saitama Wako-Shi Japan Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation Nankai University Tianjin China Psychological Department Oms and Prat school Fundació Catalunya- La Pedrera Catalonia Manresa Spain Oms Foundation Catalonia Manresa Spain Instituto Argentino de Radioastronomıa-CCT La Plata CONICET / CIC-PBA / UNLP Argentina Faculty of Health Data Science Juntendo University Chiba Urayasu Japan Department of Psychiatry University of Cambridge Cambridge United Kingdom

Advances in non-invasive neuroimaging, such as structural magnetic resonance imaging (sMRI), have en abled the construction of structural brain networks (SBNs), allowing in vivo mapping of anatomical connec tions. This study investigates brain network structural differences linked to different intelligence levels in children by individual morphometric similarity networks (MSNs) derived from sMRI data. Through group and individual-level analyses, we aim to uncover key topological features associated with cognitive perfor mance and to identify a suitable connection density for SBN analysis. Connection density strongly affects global and nodal topological features, with a range of p = 0.05 to 0.15 recommended for stable and optimal results. Gifted individuals exhibit stronger intra-hemispheric and intra-modular connectivity, a more balanced distribution of left-to-right intra-hemispheric connections, and lower mean versatility, supporting efficient and stable cognitive processing. Moreover, anatomical modularity analyses based on von Economo indicate that higher cognitive performance is linked to enhanced connectivity in specific areas (such as secondary sensory area, motor to association area and secondary sensory to limbic area), alongside selective reduction in cer tain modular connections (such as motor to insular area, association to secondary sensory area and motor to secondary sensory area). Furthermore, topological features, including participation coefficient and local effi ciency, are linked to cognitive performance. These findings provide valuable insights into the SBNs underlying cognitive levels in children. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Brain mapping

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Flexible Priori Proximal Bayesian Learning for High-Resolution SAR imagery

Flexible Priori Proximal Bayesian Learning for High-Resoluti...

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2021 CIE International Conference on Radar, Radar 2021

作者： Liao, Xianhua Yang, Lei Dou, Yuchen Li, Xuan Civil Aviation University of China Tianjin Key Lab for Advanced Signal Processing Tianjin China Macalester College Saint Paul United States China Greatwall Technology Group Co. Ltd Shenzhen China

ISBN: (纸本)9781665498142

The conventional high-resolution synthetic aperture radar(SAR) imagery based on Bayesian learning encounter the problems of static and inflexible of prior. In this paper, a novel Bayesian learning algorithm based on flexible prior (FP-Bayes) is proposed for high-resolution SAR imagery, in which a new flexible prior is introduced to represent the rich features of target scene. However, the model becomes complicated due to the intended prior, which is hard to be solved by the classic Markov Chain Monte Carlo (MCMC) sampling algorithms. Therefore, the Hamiltonian Monte Carlo (HMC) is applied to this problem. Considering the potential function of the posterior distribution is likely to be non-differentiable, the proximal operator is introduced, and the proximal-HMC (P-HMC) is developed, which is capable of solving the sampling problem of this posterior. To this end, the high-resolution SAR images can be obtained. In the experimental part, the raw SAR data is utilized to verify the effectiveness and superiority of the proposed algorithm. The phase transition diagram (PTD) is also adopted to analyze the imaging performance quantitatively. © 2021 IEEE.

关键词： Bayesian learning flexible prior Hamilton Monte Carlo (HMC) synthetic aperture radar (SAR)

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Clutter Suppression of Wind Farm for Airborne Radar Based on EFA

Clutter Suppression of Wind Farm for Airborne Radar Based on...

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IEEE International Conference on Radar

作者： Ying Zhang Xinyun Zhang Weikun He Xiaoliang Wang Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (纸本)9781665468893

With the rapid development of wind power generation, the clutter generated by wind farms may lead to the problems such as decreased detection probability, increased false alarm rate, and target track divergence. Research on the wind farm clutter mitigation technology for airborne radar is vital. Considering the special problems of the wind farm clutter mitigation for airborne radar, such as the less prior information and the more complicated spectrum of the echoes, it is difficult to suppress the wind farm clutter with non-Gaussian and dynamic characteristics. In this paper, the extended factored approach (EFA) algorithm is used to suppress the wind farm clutter through space-time adaptive processing of airborne array radar echoes. The EFA algorithm does not need the prior information of wind farm clutter or a large number of training data and can adaptively form null with a certain width in the Doppler domain. The null is used to suppress the wind farm clutter, which can avoid the interference of the wind farm clutter on the detection of aircraft targets and improve the detection probability of airborne radar. The experiment results verify the effectiveness of the proposed method.

关键词： Radar clutter Spaceborne radar Heuristic algorithms Airborne radar Adaptive arrays Training data Wind farms

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Design and Implementation of Flight Data processing Software for Global Flight Tracking System Based on Stored Procedure 8th

Design and Implementation of Flight Data Processing Software...

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8th International Conference on Communications, signal processing, and Systems, CSPS 2019

作者： Wang, Peng Wang, Wanwei Zhang, Zhe Chen, Min Yang, Jun Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tian Jin China

ISBN: (纸本)9789811394089

Civil Aviation Administration of China (CAAC) proposed to enhance the global tracking and monitoring capability of China civil aircraft after the MH370 incident. In order to meet the urgent needs of global tracking for civil aircraft in China, the design scheme of flight data processing software for global flight tracking system was presented in this paper. Based on the features and operational process of global flight tracking, the overall framework and interface design of the software were given in detail. In order to meet the performance needs of the real-time processing of emergency mass data, the database access technology based on stored procedure was adopted and the realization method of flight data processing software was given. The engineering application and the test results shows that the software can well meet the functionality and performance requirements for the flight data processing of global flight tracking system, which provides technical reference for improving the global tracking and monitoring capability of China civil aircraft. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Software testing

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An Insight Into Neurodegeneration: Harnessing Functional MRI Connectivity in the Diagnosis of Mild Cognitive Impairment

An Insight Into Neurodegeneration: Harnessing Functional MRI...

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17th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2024

作者： Han, Shuning Sun, Zhe Zhao, Kanhao Duan, Feng Caiafa, Cesar F. Zhang, Yu Solé-Casals, Jordi Data and Signal Processing Research Group University of Vic-Central University of Catalonia Catalonia Vic08500 Spain Image Processing Research Group RIKEN Center for Advanced Photonics Riken Saitama Wako-Shi Japan Faculty of Health Data Science Juntendo University Chiba Urayasu Japan Department of Bioengineering Lehigh University BethlehemPA18015 United States Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation Nankai University Tianjin China Instituto Argentino de Radioastronomía-CCT La Plata CONICET/ CIC-PBA/ UNLP V. Elisa 1894 Argentina Department of Electrical and Computer Engineering Lehigh University BethlehemPA18015 United States Department of Psychiatry University of Cambridge CambridgeCB20SZ United Kingdom

Alzheimer’s disease is a progressive form of memory loss that worsens over time. Detecting it early, when memory issues are mild, is crucial for effective interventions. Recent advancements in computer technology, specifically Graph Convolutional Networks (GCNs), have proven to be powerful tools for analyzing Magnetic Resonance Imaging (MRI) data comprehensively. In this study, we developed a GCN framework for diagnosing mild cognitive impairment (MCI) by examining the functional connectivity (FC) derived from resting-state functional MRI (rfMRI) data. Our research systematically explored various types and processing methods of FC, evaluating their performance on the OASIS-3 dataset. The experimental results revealed several key findings. On the one hand, the proposed GCN exhibited significantly superior performance over both the baseline GCN and the Support Vector Machine (SVM) models, with statistically significant differences. It attained the highest average accuracy of 80.3% and a peak accuracy of 88.2%. On the other hand, the GCN frame work obtained using individual FCs showed overall slightly better performance than the one using global FCs. However, it is important to note that GCNs using global networks with appropriate connectivity can achieve comparable or even better performance than individual networks in certain cases. Finally, our results also indicate that the connectivity within specific brain regions, such as VIS, DMN, SMN, VAN, and FPC, may play a more significant role in GCN-based MRI classification for MCI diagnosis. These findings significantly contribute to the understanding of neurodegenerative disorders and offer valuable insights into the diverse ap plications of GCNs in brain analysis and disease detection. © 2024 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Neurodegenerative diseases

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GAN-based data augmentation of prohibited item X-ray images in security inspection

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Optoelectronics Letters 2020年第3期16卷 225-229页

作者： ZHU Yue ZHANG Hai-gang AN Jiu-yuan YANG Jin-feng Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of ChinaTianjin 300300China Institute of Applied Articial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen PolytechnicShenzhen 518055China

Convolutional neural networks(CNNs)based methods for automatic discriminant of prohibited items in X-ray images attract attention ***,it is difficult to train a reliable CNN model using the available X-ray security image databases,since they are not enough in sample quantity and ***,generative adversarial network(GAN)has been widely used in image generation and regarded as a power model for data *** this paper,we propose a data augmentation method for X-ray prohibited item images based on ***,the network structure and loss function of the self-attention generative adversarial network(SAGAN)are improved to generate the realistic X-ray prohibited item ***,the images generated by our model are evaluated using GAN-train and *** results of GAN-train and GAN-test are 99.91%and 98.82%*** implies that our model can enlarge the X-ray prohibited item image database effectively.

关键词： networks database network

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