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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Shen, Xitai Zhou, Qiang Yu, Bingqian Zhang, Xin Wang, Yanhua Zhang, Liang Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Beijing Institute of Technology Radar Technology Research Institute Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9798331515669

The target high resolution range profile (HRRP) is the sum of the vector projected on the radar line of sight of the target scatters, which can reflect the radial structure characteristics of the target and is one of the important means of radar target recognition. Existing HRRP-based target recognition methods adopt the idea of machine learning classification, which usually require sufficient training samples to obtain satisfactory recognition performance. To solve the dilemma of insufficient radar HRRP data caused by factors such as non-cooperative target and complexity of data acquisition process, this paper uses denoising diffusion probabilistic models (DDPM) to generate HRRP data. Furthermore, to solve the problem that the DDPM generation speed is too slow to meet the actual needs, this paper designs a fast DDPM HRRP (FDH) data generation method based on progressive knowledge distillation. Finally, the experimental results show that the generation speed is increased by about 25 times compared with the initial model under the premise of losing 7.6% similarity. © 2024 IEEE.

关键词： Radar target recognition

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A novel self-attention weight transformer for air pollution smoke detection 5

A novel self-attention weight transformer for air pollution ...

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5th International Conference on Green Energy, Environment, and Sustainable Development, GEESD 2024

作者： Zhou, Chengxu Fan, Xiaojie Jiang, Yanlin Liu, Yuchen Gu, Ke Faculty of Information Technology Beijing University of Technology Beijing100083 China Engineering Research Center of Intelligent Perception and Autonomous Control of Ministry of Education Beijing102400 China Beijing Laboratory of Smart Environmental Protection Beijing100124 China Beijing Artificial Intelligence Institute Beijing100000 China School of Electronic & Information Engineering Liaoning University of Technology Liaoning Anshan114051 China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Liaoning Dalian116024 China

ISBN: (纸本)9781510683006

There is a necessary combustion facility called the flare stack to ensure production safety in every petrochemical plant, smelter, and refinery all over the world. Because of the incomplete combustion of flare gas, the flare stack discharges a large amount of smoke into the atmosphere and make it dirty. The air pollution is becoming more and more serious caused worldwide concern. Hence, there is in desperate need of an efficient and available smoke detection method to protect the atmosphere. To this end, we present a novel self-attention weight transformer (SAWT) that can detect accurately smoke, then guarantee full combustion of the flare stack. First, we are concerned about the weight relationship between channels and draw on the self-attention mechanism in the MobileViT block structure to adaptively adjust channel-wise feature weight. Second, we leverage short connections to thoroughly learn and fuse local and global features. Results of experiments on a real smoke dataset reveal that the proposed SAWT achieves superior performance to the popular deep CNNs and state-of-the-art smoke detection algorithms. © 2024 SPIE.

关键词： Flare stacks

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Traffic Pattern Sharing for Federated Traffic Flow Prediction with Personalization 24

Traffic Pattern Sharing for Federated Traffic Flow Predictio...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhou, Hang Yu, Wentao Wan, Sheng Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education China Jiangsu Key Laboratory of Image and Video Understanding for Social Security China SKLSDE Beihang University China Jinan University Engineering Research Center of Trustworthy AI Ministry of Education China

ISBN: (纸本)9798331506681

Accurate Traffic Flow Prediction (TFP) is crucial for enhancing the efficiency and safety of transportation systems, so it has attracted intensive researches by exploiting spatial-temporal dependencies within road networks. However, existing works only consider the case of centralized data collection with all traffic data observed, which may raise privacy concerns as each region of a city may have its own traffic administration department and the traffic data is not allowed to distribute. Therefore, this paper proposes to use Federated Learning (FL) to address this issue by allowing all clients (i.e., traffic administration departments in all regions in our problem) to collaboratively train TFP models without exchanging raw data, thereby offering a solution in maintaining data privacy. Nevertheless, most existing FL methods aim to learn a global model that performs well universally, so they cannot well handle the non-Independent and Identically Distributed (non-IID) traffic data naturally over different regions. To cope with this problem, this paper develops a new FL framework termed 'personalized Federated learning with Traffic Pattern Sharing' (FedTPS) to solve federated TFP problem. Our FedTPS critically exploits the underlying common traffic patterns (e.g., morning and evening rush hours) shared across different city regions and meanwhile maintaining the region-specific data characteristics in a personalized FL manner. Specifically, to extract the common traffic patterns, we decompose the traffic data in each client via using discrete wavelet transform, where the low-frequency components uncover the stable traffic dynamics of different regions and thus can be considered as the common traffic patterns. These common patterns are then shared among different clients through traffic pattern repositories on the server side to aid the global collaborative traffic flow modeling. Moreover, the model components capturing spatial-temporal dependencies in traffic data are r

关键词： Highway administration

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Multi-Terrain Radar HRRP Target Detection Using Contrastive Learning 2

Multi-Terrain Radar HRRP Target Detection Using Contrastive ...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Wang, Xinyang Yuan, Mingchen Liu, Jiaqi Li, Yang Wang, Yanhua Zhang, Liang Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Beijing Institute of Technology Radar Technology Research Institute Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9798331515669

As radar resolution improves, high-resolution radars have significantly enhanced target detection performance through the generation of high-resolution range profiles (HRRPs). However, when the range resolution is much smaller than the target, the target's energy is dispersed across multiple range cells, leading to poor detection performance with traditional energy-based detection algorithms. Furthermore, existing detection algorithms are highly sensitive to clutter characteristics and exhibit poor adaptability to varying scenarios. To address these issues, this paper proposes a Constant False Alarm Rate (CFAR) HRRP detection algorithm based on contrastive learning for multiple terrains. The algorithm improves the contrastive learning loss function to achieve well-clustered representations of clutter samples during training. Test samples are evaluated by calculating the Mahalanobis distance between them and the training samples, and compared against detection thresholds to accomplish target detection. Testing results show that the proposed algorithm achieves superior detection performance across multiple terrains. Furthermore, to reach a 90% detection probability, the proposed method requires a signal-to-clutter ratio (SCR) reduction of 1 to 4 dB compared to the competing methods. © 2024 IEEE.

关键词： Clutter (information theory)

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Structure-fused deep 3D hierarchical network: A bioluminescence tomography scheme for different imaging objects

Structure-fused deep 3D hierarchical network: A bioluminesce...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Beilei Wang Shuangchen Li Xuelei He Yizhe Zhao Heng Zhang Xiaowei He Jingjing Yu Hongbo Guo Xi'an Key Laboratory of Radiomics and Intelligent Perception and School of Information Sciences and Technology Northwest University Xi'an China Xi'an Key Laboratory of Radiomics and Intelligent Perception Network and Data Center and School of Information Sciences and Technology Northwest University Xi'an China Xi'an Key Laboratory of Radiomics and Intelligent Perception and School of Physics and Information Technology Shaanxi Normal University Xi'an China

Monte Carlo eXtreme (MCX) method has a unique advantage for deep neural network based bioluminescence tomography (BLT) reconstruction. However, this method ignores the distribution of sources energy and relies on the determined tissue structure. In this paper, a deep 3D hierarchical reconstruction network for BLT was proposed where the inputs were divided into two parts -- bioluminescence image (BLI) and anatomy of the imaged object by CT. Firstly, a parallel encoder is used to feature the original BLI & CT slices and integrate their features to distinguish the different tissue structure of imaging objects; Secondly, GRU is used to fit the spatial information of different slices and convert it into 3D features; Finally, the 3D features are decoded to the spacial and energy information of source by a symmetrical decoding structure. Our research suggested that this method can effectively compute the radiation intensity and the spatial distribution of the source for different imaging object.

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Graded Diagnosis of Nuclear Cataracts Utilizing Precision Localization and Deep Learning Algorithms 3

Graded Diagnosis of Nuclear Cataracts Utilizing Precision Lo...

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3rd International Conference on High Performance Computing and Communication Engineering, HPCCE 2023

作者： Jiang, Dexun Hao, Jianglong Chen, Yuanlong Li, Xingling Liu, Jie School of Information Engineering Harbin University Harbin150086 China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin University Harbin150086 China School of Software Harbin Institute of Technology Harbin150090 China School of Water Conservancy & Civil Engineering Northeast Agricultural University Harbin150030 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

Cataract, the leading cause of global blindness, represents a focal concern within the field of blindness prevention. Its diagnosis primarily relies on the observation of lens opacification under slit-lamp examination, coupled with best-corrected visual acuity assessment. With the rapid evolution of artificial intelligence, the ophthalmic domain has increasingly incorporated AI technologies;however, research in the realm of cataracts remains relatively limited. This study employs computer vision segmentation techniques to obtain precise images of cataractous lens nuclei and utilizes deep learning methodologies for training and validation, yielding commendable results in the realm of graded diagnostic accuracy. The application of computer vision for meticulous cataractous lens nuclear region-of-interest imaging, coupled with adept deep learning methodologies, has demonstrated notable efficacy in achieving superior diagnostic outcomes. © 2024 SPIE.

关键词： Lenses

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A Lightweight Chinese Summarization Generation Method Based on Knowledge Distillation 4

A Lightweight Chinese Summarization Generation Method Based ...

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4th International Conference on information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Liu, Shiyue Xiao, Yannan Wang, Xueyong Huang, Wenming Deng, Zhenrong School of Computer and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China School of Computer and Information Security Guilin University of Electronic Technology Guilin China Guilin Xintong Technology Co. Guilin China

ISBN: (纸本)9798350337181

In recent years, large-scale pretrained natural language processing models such as BERT, GPT3 have achieved good results in processing tasks. However, in daily applications, these large-scale language models usually exist large model size or long running time, and the model portability and application are not very convenient. To settle this problem, we come up with a lightweight summarization generation method based on knowledge distillation (LW-BERT-KD, abbreviated as LBK model) to prove that complex neural network knowledge (taking BERT model as an example) can be extracted to lightweight language processing models (taking BiLSTM model as an example), thus achieving the purpose of complex knowledge extraction and model compression. In this paper, the BERT model is regarded as the teacher model, and the BiLSTM is regarded as the student model. The knowledge distillation technology is the transfer of knowledge from teacher models to student models, so that the generated model is effective, lightweight, and easy to port. On the Chinese abstract generation dataset, the training effect of this model is significantly better than the basic Transformer baseline. The experimental results show that if the training parameters are increased by 100 times, the model achieves comparable results with complex preprocessing language models. © 2023 IEEE.

关键词： Distillation

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Transparent Four-Port MIMO DRAs Decoupled by Conformal Strips

Transparent Four-Port MIMO DRAs Decoupled by Conformal Strip...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： He, Chao Yang, Nan Leung, Kwok Wa Liaoning Technical University Liaoning Key Laboratory of Radio Frequency and Big Data for Intelligent Applications Huludao125105 China Sun Yat-sen University School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Guangzhou China City University of Hong Kong State Key Laboratory of Terahertz and Millimeter Waves Department of Electrical Engineering Kowloon Hong Kong CityU Shenzhen Research Institute Information and Communication Technology Centre Shenzhen China

ISBN: (纸本)9798350355581

A four-port multi-input multi-output (MIMO) dielectric resonator (DR) antenna (DRA) is presented in this paper. Combing four rectangular DRs together can form a single DR element. With shape modification and conformal strips, the four ports can be decoupled. To verify the design idea, a transparent prototype can be designed and fabricated for 2.4-GHz band applications. The measured results show an impedance bandwidth of 5.8% and an isolation level of 24 dB. © 2024 IEEE.

关键词： Microwave antennas

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Laplacian Welsch Regularization for Robust Semisupervised Learning

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IEEE Transactions on Cybernetics 2022年第1期52卷 164-177页

作者： Ke, Jingchen Gong, Chen Liu, Tongliang Zhao, Lin Yang, Jian Tao, Dacheng PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China State Key Laboratory of Integrated Services Networks Xidian University Xi'an China UBTECH Sydney Artificial Intelligence Centre University of Sydney DarlingtonNSW Australia

Semisupervised learning (SSL) has been widely used in numerous practical applications where the labeled training examples are inadequate while the unlabeled examples are abundant. Due to the scarcity of labeled examples, the performances of the existing SSL methods are often affected by the outliers in the labeled data, leading to the imperfect trained classifier. To enhance the robustness of SSL methods to the outliers, this article proposes a novel SSL algorithm called Laplacian Welsch regularization (LapWR). Specifically, apart from the conventional Laplacian regularizer, we also introduce a bounded, smooth, and nonconvex Welsch loss which can suppress the adverse effect brought by the labeled outliers. To handle the model nonconvexity caused by the Welsch loss, an iterative half-quadratic (HQ) optimization algorithm is adopted in which each subproblem has an ideal closed-form solution. To handle the large datasets, we further propose an accelerated model by utilizing the Nyström method to reduce the computational complexity of LapWR. Theoretically, the generalization bound of LapWR is derived based on analyzing its Rademacher complexity, which suggests that our proposed algorithm is guaranteed to obtain satisfactory performance. By comparing LapWR with the existing representative SSL algorithms on various benchmark and real-world datasets, we experimentally found that LapWR performs robustly to outliers and is able to consistently achieve the top-level results. © 2013 IEEE.

关键词： Large dataset

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Transductive Multi-Prototype Network for HRRP Recognition with Missing Aspects 2

Transductive Multi-Prototype Network for HRRP Recognition wi...

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2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Yuan, Mingchen Liu, Jiaqi Wang, Xinyang Li, Yang Wang, Yanhua Zhang, Liang Beijing Institute of Technology Zhengzhou Academy Of Intelligent Technology Zhengzhou450000 China Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9798331515669

The High-Resolution Range Profile (HRRP) plays a pivotal role in radar automatic target recognition. However, due to its aspect sensitivity, the absence of aspects within the training data can lead to a decline in recognition accuracy. To mitigate this issue, we introduce a novel Transductive Multi-Prototype Network (TMP-Net), leveraging the intrinsic similarity within test data to propagate labels along a manifold, thereby achieving classification. TMP-Net addresses the aspect sensitivity by utilizing multiple prototypes per class. Additionally, a loss function term is proposed to ensure the existence of the manifold. Experiments on the MSATR dataset demonstrate that TMP-Net outperforms existing methods by 10.22% in recognition accuracy even with only 10% of aspects represented in the training set. Furthermore, analysis reveals the advantages of employing multiple prototypes. © 2024 IEEE.

关键词： Automatic target recognition

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