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检索条件"机构=Tianjin Key Laboratory of Advanced Signal Processing"

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An Approach To Improve The Generalization Of DNN Using StyleGAN 5

An Approach To Improve The Generalization Of DNN Using Style...

引用

5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Jia, Yunfei Zeng, Xiaoqi Civil Aviation University of China Tianjin Key Laboratory of Advanced Signal Processing Tianjin China

ISBN: (纸本)9798350309034

The improvement of DNN model generalization relies on the availability of datasets characterized by a multitude of samples, balanced class distribution, and rich feature diversity. However, obtaining such datasets is a formidable challenge, and the problem of inadequate diversity within datasets persists. Therefore, our research aims to train the DNN model by using the StyleGAN model to expand the diversity of the data set, thereby improving the generalization of the DNN. Our approach targets specific attributes in the original image by controlling the latent space W of the StyleGAN, so that the generated image not only resembles the real-world image but also enriches the feature diversity of the dataset. Experimental results demonstrate that, in comparison to arbitrary data augmentation and class-balancing methods, the diversified datasets generated through this approach yield enhanced accuracy for VGG16 and LeNet models on the test set. Furthermore, our research employs the GD metric to quantitatively assess dataset diversity, thus substantiating the efficacy of the proposed methodology. © 2023 IEEE.

关键词： Data Augumentation Diversity DNN StyleGAN

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Research on Flight Delay Propagation Prediction Method Based on Transformer

Research on Flight Delay Propagation Prediction Method Based...

引用

2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Qu, Jingyi Zhang, Lin Wu, Shixing Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (纸本)9798350312492

In recent years, flight delay has become a problem that needs to be solved urgently. The problem of flight delay propagation prediction is studied in this thesis, which is the phenomenon of large area flight delay caused by an airport delay. The analysis and prediction of flight delay propagation in advance can assist civil aviation departments in controlling the flight delay rate and reducing the economic loss caused by flight delays. Firstly, the national flight data is cleaned and filtered to construct a dataset of flight chain affected by flight delay. At the same time, errors or missing values in the data are removed, and data related to flight delay are selected for subsequent analysis and modeling. Modeling analysis is performed on the issue of flight delay propagation, and a model of flight delay propagation is constructed to analyze the impact of flight delay propagation in airport network transmission. The model fully considers the interrelationship between flights, treating the flight chain as a whole rather than individual flights. This will be more conducive to exploring and understanding the causes and impacts of flight delay. Subsequently, in terms of constructing the dataset of flight chain structures, the method of data preprocessing and triplet flight chain dataset contains a series of flight chain data that can be used to predict the scope of flight delay propagation. Secondly, according to the features of flight delay, the Transformer model was adjusted and improved to enhance the accuracy and efficiency of prediction. The network architecture is restructured by injecting new convolutional pooling modules into the original network and adjusting the input and output dimensions of the network to make the overall model more tailored to flight delay prediction. Last but not least, the results of the experiments are showed that the modified model has low complexity, shorter time and good real-time performance. It makes prediction and decision faster,

关键词： Forecasting

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A Fast Approach for Generating Unidentifiable Adversarial Samples 5

A Fast Approach for Generating Unidentifiable Adversarial Sa...

引用

5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Jia, Yunfei Guo, Risheng Civil Aviation University of China Tianjin Key Laboratory of Advanced Signal Processing Tianjin China

ISBN: (纸本)9798350309034

In recent years, Deep Neural Networks (DNN) have been widely used to solve many complicated tasks. However, several literatures have reported small perturbations in the target can obviously injury the performance of DNN system. This hinders the wide applications of DNN system in some safety-critical scenarios. Previous studies on adversarial samples generation usually arbitrarily and blindly to change all pixels to some extent. Unfortunately, the results can be identified by naked eyes although they can fool the DNN system. But in some cases, DNN system is just an auxiliary tool, people will also be employed to validate the identified results. This paper proposed a fast approach to generating adversarial samples with slightly changed pixels, with the purpose of deceiving people's eyes. Firstly, the important pixels which makes more impact on output are identified by layer-by-layer weight analysis;secondly, these pixels are changed slightly by a fast gradient ascent method to check the output of DNN system;finally, the indicator of imperceptibility is used to valuation the change into an eye unrecognizable extent. Two well-known datasets are employed to validate the effectiveness of our approach. © 2023 IEEE.

关键词： Adversarial samples DNN Robustness

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Trajectory Optimization Methods of the OFDM-UAV Cascade Relay Communication System 11

Trajectory Optimization Methods of the OFDM-UAV Cascade Rela...

引用

2023 IEEE 11th International Conference on Information, Communication and Networks, ICICN 2023

作者： Li, Dongxia Meng, Yan Civil Aviation University of China Tianjin Key Laboratory for Advanced Signal Processing Tianjin China

ISBN: (纸本)9798350314014

Unmanned Aerial Vehicle (UAV) cascading relay communication can achieve more extensive, efficient, and secure wireless communication services. In this paper, we consider a UAV cascading relay communication system with frequency-selective fading channels, and propose two UAV trajectory optimization methods that are joint and stepwise trajectory optimization based on orthogonal frequency division multiplexing (OFDM). First, the OFDM-UAV cascade relay communication transmission model is provided, and the probability of system interruption is theoretically analyzed and an approximate calculation formula is derived. Then, based on the principle of minimizing the probability of system interruption, the joint and stepwise trajectory optimization methods are proposed. Finally, the computer simulation results demonstrate the validity and correctness of the proposed trajectory optimization methods. It is proved that the OFDM-UAV cascade relay communication system can effectively overcome frequency-selective fading, and the two proposed trajectory optimization methods have similar interruption performance. © 2023 IEEE.

关键词： Orthogonal frequency division multiplexing

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Algorithm for Urban Air Mobility Object Recognition Based on Spiking Neural Networks 4

Algorithm for Urban Air Mobility Object Recognition Based on...

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4th International Conference on signal processing and Computer Science, SPCS 2023

作者： Qu, Jingyi Li, Haohao Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (纸本)9781510672505

In recent years, a large number of intelligent unmanned aerial vehicles (UAVs) have emerged in people's vision, leading to the development of a new safe and efficient urban air mobility (UAM) system, which is suitable for both manned and cargo scenarios. Driven by the market, this service is expected to become a new trend in the development of civil aviation. The rapid development of deep learning has brought prospects to urban air traffic, but the increasing data volume and model complexity pose challenges in terms of resource consumption and model deployment. A method of applying spiking neural network (SNN) algorithms in UAM is proposed in this paper, utilizing spike-based transmission to significantly reduce energy consumption compared to deep learning algorithms. To address the issue of missing urban air traffic data sets, we build a complex urban air traffic data set comprising 10 categories such as pedestrians, vehicles, buildings, traffic signals, billboards, garbage bins, unmanned aerial vehicles, hot air balloons, cats, and dogs. In the network model, we introduce the biological neurons such as Integrate-and-Fire (IF) and Leaky Integrate-and-Fire (LIF), achieving the highest accuracy of 81.937% and 82.772%, respectively. Based on the LIF neuron, we propose the Self-Learning Leaky Integrate-and-Fire (SLLIF) neuron, which autonomously learns stimulus-input ratio relationships to better align with the brain's automatic optimization mechanism, achieving a recognition accuracy of 85.484%. Furthermore, we re-evaluate the selection of the hyper parameter time steps and choose suitable values for different neural network models to reduce resource consumption while maximizing accuracy. © 2023 SPIE.

关键词： Air mobility

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Research on UAV Image Detection Method in Urban Low-altitude Complex Background

Research on UAV Image Detection Method in Urban Low-altitude...

引用

2023 International Conference on Cyber-Physical Social Intelligence, ICCSI 2023

作者： Qu, Jingyi Huang, Yuzhuo Li, Haohao Civil Aviation University of China Tianjin Key Laboratory of Advanced Signal Processing Tianjin China

ISBN: (纸本)9798350312492

With the rapid development of urban air traffic, more and more Unmanned Aerial Vehicles are appearing in cities. The government focuses on the need for safety-oriented unmanned aviation to strengthen the regulatory capability, and to form cooperative air-ground object active surveillance, as well as passive object surveillance supplemented by visible light detection. The Unmanned Aerial Vehicle detection method for urban low-altitude complex backgrounds based on YOLOv7x is proposed and to ensure the safety of flight activities. We construct an experimental dataset which is called UAVULCB (Unmanned Aerial Vehicle dataset used in Urban Low-altitude Complex Background). Extensive experiments on the UAV-ULCB validate that this method achieves 95.8% mAP50 with an inference speed of 30 frames/second. Finally, the heat map by the Grad-CAM is used to show the most concerned parts and increase the interpretability of the model. This method provides a strong technical support for urban air traffic supervisory authorities, which can be safeguarded the safety of urban Unmanned Aerial Vehicle flight activities together with other supervisory methods. © 2023 IEEE.

关键词： Object detection

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A Three-Layer Parallel Implementation of the DPE Receiver Based on GPU 14th

A Three-Layer Parallel Implementation of the DPE Receiver Ba...

引用

14th China Satellite Navigation Conference, CSNC 2024

作者： Jia, Qiongqiong Li, Weipeng Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (纸本)9789819969319

The Popular of Global Navigation Satellite System (GNSS) is attributed to its good performance. However, the performance of a conventional GNSS receiver is severely degraded or even failed to provide services in complex environments. Direct Position Estimation (DPE) combines all the visible satellite signals for energy accumulation in the navigation domain first, and then the navigation solution is estimated directly from the accumulation results. Hence the navigation performance of GNSS can be effectively improved in complex environments by DPE. However, the signal accumulation of DPE requires calculating the correlation values of all the visible satellite signals at each candidate grid in the navigation domain, the computation load of which limits its application. To alleviate this problem, a GPU-based parallel implementation of the DPE receiver is designed, which decomposes the operations involved in the joint accumulation into three levels of parallel processing. The effectiveness of the designed parallel implementation of the DPE receiver is verified by experiment results. With the available hardware in our laboratory, the designed DPE implementation improves the efficiency to approximately 146 times on average over the conventional DPE implementation. © Aerospace Information Research Institute 2024.

关键词： Graphics processing unit

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The DME Interference Mitigation Based Wavelet Thresholding Algorithm for L-DACS Receiver 9

The DME Interference Mitigation Based Wavelet Thresholding A...

引用

9th International Conference on Intelligent Computing and signal processing, ICSP 2024

作者： Peng, Xiangqing Wang, Jiani Wang, Lei Liu, Haitao Civil Aviation University of China Tianjin Key Lab for Advanced Signal Processing Tianjin China

ISBN: (纸本)9798350376548

The L-DACS operates as an inlay system between two adjacent channels of the Distance Measuring Equipment (DME) system since the spectrum resources in the L-band are scarce and fragmented. This DME signal severely impacts the performance of L-DACS. This paper proposes an approach for mitigating DME interference by integrating spectrum aggregation technology and a wavelet thresholding algorithm. To enhance spectral efficiency, the method utilizes direct wideband aggregation technology at the transmission end to expand the bandwidth. Then, at the receiving end of Orthogonal Frequency Division Multiplexing (OFDM), the received signal is decomposed layer by layer using the wavelet threshold algorithm. The difference between the spectrum-aggregated signal and the DME signal in the wavelet domain is effectively identified to reconstruct the DME signal, thereby achieving interference elimination. Simulation results demonstrate that this method improves spectrum efficiency and data transmission rate while significantly mitigating DME interference, thereby enhancing the link transmission reliability of the L-DACS system. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Research on Unmanned Aircraft Environment Perception Methods in Urban Low Altitude Complex Backgrounds 4

Research on Unmanned Aircraft Environment Perception Methods...

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4th International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

作者： Qu, Jinyi Gao, Wenping Wang, Haoyu Huang, Daquan Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China Tianjin300300 China

ISBN: (纸本)9798400708831

With the continuous development of drone-related technologies, the application of UAV (Unmanned Aerial Vehicle) in urban low-altitude scenes is gradually increasing. Autonomous localization and perception of the surrounding environment are prerequisites for autonomous control. As a result, our team apply the VDO-SLAM algorithm to the scene of coexistence of dynamic and static obstacles in the complex background of urban low altitude. Effective segmentation of static background and moving objects is achieved by combining semantic segmentation and optical flow estimation algorithms. Since there is a lack of relevant public data for urban low-altitude scenarios, a binocular image dataset containing both static and dynamic objects is created by Airsim simulator, which is utilized for training a semantic segmentation model. In addition, a novel numbering maintenance algorithm is introduced for the semantic segmentation of continuous image sequences. The experimental results show that the algorithm can realize the autonomous perception of UAV posture and the motion state of dynamic objects in low altitude complex urban environment. This method provides the necessary self-pose information and reliable environmental data for autonomous obstacle avoidance of UAVs, and provides important support for the application of UAVs in urban low-altitude environment. © 2023 ACM.

关键词： Semantic Segmentation

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Improved Lightweight Apple Object Detection Method Based on YOLOv5s 5

Improved Lightweight Apple Object Detection Method Based on ...

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5th International Conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Jia, Yunfei Liu, Chang Civil Aviation University of China Tianjin Key Laboratory of Advanced Signal Processing Tianjin300300 China

ISBN: (纸本)9798350309034

Fast and accurate object detection is an important and challenging task in the context of automated apple harvesting. However, the current object detection models have relatively large network parameters. In this work, improvements have been made to the object detection algorithm based on YOLOv5s, by replacing the original model's backbone network with the ShuffleNetV2 lightweight network structure. To compensate for the reduction in model parameters, this paper introduces the ECA mechanism and SPPF module after the ShuffleNetV2 modules to improve the model's accuracy. Experimental results demonstrate that the improved lightweight YOLOv5s network reduces the parameter count by 85.6%, decreases computational load by 87.3%, and achieves a detection accuracy mAP@0.5 of 97.0% for apple detection, which is 0.4% higher than the original YOLOv5s model. The detection frame rate has also improved by 15 FPS compared to the original model, making it suitable for practical apple detection in real-world environments. © 2023 IEEE.

关键词： Apple Detection Attention Lightweight Model YOLOv5s

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