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Precipitation Retrieval of FY-3D MWHTS and MWRI Data Based on Light Gradient Boosting Model

Precipitation Retrieval of FY-3D MWHTS and MWRI Data Based o...

引用

Progress in Electromagnetic Research Symposium (PIERS)

作者： Yanfang Lv Lanjie Zhang Yifan Zhang Biao Zhang Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

As an essential component of the earth system, precipitation plays a crucial role in the regional and global water cycle. This paper uses observations from the Fengyun-3D microwave humidity and temperature sounder and microwave radiation imager, as well as Integrated Multi-satellite Retrievals for GPM (IMERG), to establish a precipitation retrieval model based on the light gradient boosting model (LGBM). Using IMERG as the comparison data, the performance of precipitation identification with the LGBM-based precipitation retrieval model is as follows: accuracy is 0.9302, probability of detection is 0.7683, false alarm rate is 0.3529, and critical success index is 0.5414. The performance of precipitation estimation is also evaluated, with a correlation coefficient of 0.7536, a mean absolute error of 0.9119, and a mean squared error of 3.9506. In summary, this study achieves good performance in precipitation identification and estimation by the LGBM model.

关键词： Earth Correlation coefficient Precipitation Estimation Humidity Boosting Data models

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Contrastive Learning-Based Generic Audio-to-Lyrics Alignment 21

Contrastive Learning-Based Generic Audio-to-Lyrics Alignment

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Jin, Zhenkun Huang, Shuai Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business UIniversity Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Hubei Key Laboratory of Distributed System Security Wuhan China Hubei Engineering Research Center on BigData Security Wuhan China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350329223

In this research, we tackled the complex task of aligning audio and lyrics content automatically. This task entails the precise matching of lyrics with the corresponding audio segments in songs, necessitating the coordination of two distinct modalities: text and audio. To surmount this challenge, we employed a contrastive learning approach that generates cross-modal embeddings capable of establishing connections between the audio and text domains. Furthermore, we adopted a hierarchical and cascading design. Initially, the system predicts synchronization at the sentence level and subsequently refines the alignment at the word level. This design equips the system to handle extended sequences effectively, as cross-correlations leverage secondary memory that corresponds to sequence length. Through this innovative approach, we developed a trainable end-to-end system that can harness weakly annotated training data, concurrently learning a robust text model optimized for alignment tasks. Our experiments demonstrated substantial enhancements in average alignment accuracy when utilizing our proposed system, underscoring its robustness compared to previous state-of-the-art models. © 2023 IEEE.

关键词： Alignment

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Typhoon Wind Speed Retrieval from HY-2C Microwave Radiometer Based on LightGBM

Typhoon Wind Speed Retrieval from HY-2C Microwave Radiometer...

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Progress in Electromagnetic Research Symposium (PIERS)

作者： Xuchen Dai Biao Zhang Yifan Zhang Lanjie Zhang Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

In this paper, the sea surface wind speed (SSWS) retrieval accuracy for HaiYang-2C (HY-2C) passive microwave radiometer under the severe weather such as typhoon is evaluated. Based on the observation data of HY-2C, this study explores the influence of brightness temperature (TB) observed by microwave radiometer on sea surface wind speed retrieval under typhoon conditions. In the case of typhoon precipitation, the attenuation of the atmosphere to the high frequency channel is stronger, which makes the sea surface information observed by the microwave radiometer very weak. Therefore, we mainly select the TB of horizontal polarization and vertical polarization observation of 6.9GHz and 10.7GHz channels to carry out the study of typhoon wind speed retrieval. This paper constructs a matching data set based on the TB observed by HY-2C passive microwave radiometer, HY-2C L2B wind product and H*wind reanalysis wind field data in October and November 2020. The maximum differences of spatial and temporal were set as 25km and 30minutes, respectively. Then, the typhoon wind speed retrieval model based on LightGBM is established. We use the data of October 2020 to train the model and the data of November 2020 to test model performance. Comparing with the H*wind reanalysis wind field data, the bias and root-mean-square error of retrieved wind speed are 0.15m/s and 1.26m/s, respectively. In addition, the correlation coefficient is 0.90, indicating that the retrieved wind speed has a good consistency with H*wind data.

关键词： Microwave measurement Sea surface Tropical cyclones Wind speed Microwave theory and techniques Data models Brightness temperature

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Research on the Wirtinger Flow algorithm based on quadratic distribution initial value 1

Research on the Wirtinger Flow algorithm based on quadratic ...

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1st Advanced Imaging and information Processing Conference, AIIP 2023

作者： Xie, Zhenfei Yu, Xuelian Wang, Zhengxian Li, Heng Heilongjiang Provincial Key Laboratory of Quantum Control School of Measurement and Communication Engineering Harbin University of Science and Technology Harbin150080 China Shenzhen Engineering Laboratory of Aerospace Detection and Imaging College of Electronic and Information Engineering Harbin Institute of Technology Shenzhen518055 China

ISBN: (纸本)9781510671904

Phase retrieval algorithms, such as the Wirtinger Flow (WF) algorithm, are widely used in various fields. As a non-convex optimization algorithm for phase retrieval, WF is commonly employed in the reconstruction of holograms in holographic image projection. These types of algorithms typically involve two stages: an initialization stage and an iterative optimization stage. In the initialization stage, an initial value is provided, and a spectral method is used to calculate an approximate solution as the initial guess. The iterative optimization stage then utilizes the Wirtinger gradient to iteratively compute and converge the initial guess to a nearby real solution, thereby obtaining the global optimal solution. However, due to the random nature of the initial values, the computed results often exhibit significant instability. To address this issue, this paper proposes an approach based on a quadratic distribution for improving the stability of the results. In the initialization stage, the initial value is set as the quadratic distribution initial value. Then, the spectral method is applied again to calculate the initial guess. Since the quadratic distribution initial value is artificially assigned, it enhances the stability of the computed results. To validate this method, the paper applies the quadratic distribution initial value to both the initialization stage of the WF algorithm and the Truncated Amplitude Flow (TAF) algorithm. A comparison is made between the results obtained using random initial value and those obtained using the quadratic distribution initial values. The results demonstrate that compared to random initial values, the quadratic distribution initial values can achieve faster and equally accurate computation results with higher stability. Finally, this method is applied to simulation experiments of in-line digital holography, and the reconstruction results from the experiments further confirm the effectiveness of our approach. © 2023 SPIE.

关键词： Convex optimization

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Enhancing privacy protection in intelligent and connected vehicles: a styleGAN3-based image desensitization dataset and framework 4

Enhancing privacy protection in intelligent and connected ve...

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4th International Conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Wang, Jingyan Wang, Liyong Ji, Haojie Fang, Junzhe Guo, Teng Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology University No.12 Xiaoying East Road Haidian District Beijing100192 China Zaozhuang Network Security Center No. 2621 Guangming Avenue Xuecheng District Shandong Province 277020 China

ISBN: (纸本)9781510686106

Intelligent and connected vehicles (ICVs) collect vast video footage, inadvertently capturing privacy-sensitive data. To address this, we introduce the IDD-ICV dataset, featuring diverse in-vehicle camera footage. To protect privacy, we propose a styleGAN3 based framework that integrates MHA (Multi-Head Attention) for detecting and desensitizing facial data in datasets. This approach ensures compliance with data security standards and enhances ICVs' privacy protection capabilities during the life cycle of data. Our study contributes to the development of secure and trustworthy autonomous transportation systems by demonstrating the effectiveness of our framework in safeguarding privacy-sensitive information. © 2024 SPIE.

关键词： Differential privacy

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Viewpoint Rotation Prediction based Resource-Efficient Holographic Type communication in EON enabled 6G RAN

Viewpoint Rotation Prediction based Resource-Efficient Holog...

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International Conference on Optical communications and Networks (ICOCN)

作者： Xin Wang Chengyuan Zhang Yafei Wang Xuehua Li Key Laboratory of Information and Communication Systems Ministry of Information Industry and Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University (BISTU) Beijing China

A LSTM based viewpoint rotation prediction enabled resource-efficient holographic type communication is investigated in EON enabled 6G RAN. The CensNet enhanced PPO is used for feature extraction based DU-CU deploymen... 详细信息

ISBN: (数字)9798350367652

ISBN: (纸本)9798350367669

关键词： 6G mobile communication Simulation Benchmark testing Prediction algorithms Feature extraction Optical fiber communication Long short term memory

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Pruning CNN based on Combinational Filter Deletion 50

Pruning CNN based on Combinational Filter Deletion

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Huang, Wenli Wang, Xiujie Zhao, Zhihong Su, Li Sui, Shuai Wang, Jinjun School of Electronic and Information Engineering Ningbo University of Technology Ningbo China Science and Technology on Communication Networks Laboratory Shijiazhuang China Research Institute of Intelligent Control and Systems Harbin Institute of Technology Harbin China Liaoning University of Technology Jinzhou China Institute of Artificial Intelligence and Robotic Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9781665464543

Network pruning is a figurative model compression technique designed to lighten and accelerate neural network models. Most existing pruning methods prioritize the selection of filters by their importance or apply regularization based on the properties of individual filters, neglecting the internal connections within combinations of multiple filters. This work introduces a pruning method termed Combinational Filter Deletion (CFD), which incorporates a straightforward yet effective evaluation metric based on the diversity of filter combination distributions to reveal the characteristics inherent to multiple filter interactions. CFD enables the exploration of an expanded search space, offering a greater array of choices and leveraging the intrinsic information of conventional layers. Moreover, this method is both general and non-exclusive, capable of enhancing the efficacy of other single-filter-based pruning techniques. © 2024 IEEE.

关键词： Wiener filtering

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Dwell Time Allocation for Decentralized Phased Array Radar Sensing Networks with Target Tracking

Dwell Time Allocation for Decentralized Phased Array Radar S...

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IEEE SENSORS

作者： Ye Yuan Zishi Zhang Lei Zhu Jing He Wei Yi School of Information and Communication Engineering University of Electronic Science and Technology of China National Key Laboratory of Electromagnetic Space Security Chengdu China

ISBN: (数字)9798350363517

ISBN: (纸本)9798350363524

This paper proposes a parallel strategy-based dwell time allocation method for a decentralized phased array radar sensing network (PA-RSN) for target tracking. Firstly, the dwell time allocation of PA-RSN is formulated as an optimization problem that aims to maximize the overall tracking utility. To make the solution applicable to PA-RSN, the utility function is approximated as a linear combination of multiple local utility functions, allowing for the update of them at each radar without fusion center. Upon reaching a consensus through a sufficient number of iterations, a global solution to the optimization problem is achieved. Numerical simulations verify the effectiveness of the proposed approach in a decentralized PA-RSN.

关键词： Phased arrays Target tracking Radar Radar tracking Numerical simulation Sensors Resource management Iterative methods Optimization Sensor arrays

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Rapid Prediction of EM Scattering from Finite Periodic Structures Using CBFM with ANNs 12

Rapid Prediction of EM Scattering from Finite Periodic Struc...

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12th IEEE Asia-Pacific Conference on Antennas and Propagation, APCAP 2024

作者： Wang, Mingxin Chen, Xinlei Gao, Fan Meng, Hao Yang, Wu Lu, Weibing Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Key Laboratory of Radar Imaging and Microwave Photonics Key Laboratory of Inlet and Exhaust System Technology Nanjing211106 China Southeast University State Key Laboratory of Millimeter Waves Nanjing210096 China Southeast University State Key Laboratory of Millimeter Waves School of Information Science and Engineering Nanjing210096 China Southeast University Center for Flexible Rf Technology Moe Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Purple Mountain Laboratories Nanjing210096 China

ISBN: (纸本)9798350351019

In this article, the artificial neural networks (ANNs) are combined with the characteristic basis function method (CBFM) to fast predict the electromagnetic scattering from finite periodic structures. The amplitude and the phase of the expansion coefficients of the characteristic basis functions (CBFs) are predicted by two different ANNs. Then, the predicted expansion coefficients are used to calculated the radar cross sections (RCSs) of the finite periodic structures. Numerical results are given to show the performance of the ANN-assisted CBFM. © 2024 IEEE.

关键词： Radar cross section

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Self-supervised Visual Odometry Based on Geometric Consistency

Self-supervised Visual Odometry Based on Geometric Consisten...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Rujun Song Jiaqi Liu Kaisheng Liao Zhuoling Xiao Bo Yan School of Information and Communication Engineering University of Electronic Science and Technology of China Science and Technology on Electronic Information Control Laboratory Chengdu China

Learning-based monocular visual odometry (VO) has lately drawn significant attention for its robustness to camera parameters and environmental variations. Unlike most self-supervised learning-based methods, our approach simultaneously focuses on the adjacent and interval co-visibility correspondence to improve the pose estimation. To handle different pixel displacements, we apply the Multi-scale Feature Fusion component for the full exploration of latent motion features. Besides, the Interval Feature Guided Refinement component is incorporated to adaptively exploit the continuity of camera motions and steer the network for retaining pose consistency in the time domain. Extensive experiments on the KITTI and Malaga datasets have demonstrated the promising performance of our approaches. The proposed method produces competitive results against classic algorithms and outperform state-of-the-art methods by up to 23.9 % and 15.4 % on average translational and rotational evaluation.

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