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

作者： Chen, Fan Shi, Hao He, Jingfei Yin, Yifei Meng, Fanyu Chen, Liang Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

Ship classification using Synthetic Aperture Radar (SAR) images is a crucial component in marine monitoring. With the rise of deep neural networks (DNN), the abstract feature maps obtained by CNN-based methods have significantly improved classification performance. However, most current DNN-based methods are predominantly inherited from optical imagery, designed only for SAR intensity images, which may present challenges for further improving recognition performance. Therefore, this paper proposes using the point Scattering Center (SC) model for scattering feature extraction combined with graph convolutional networks for ship classification tasks. Comprehensive ablation experiments were carried out to demonstrate the efficacy of the proposed approach. Finally, experimental results on OpenSARShip1.0 dataset verify that the performance of the suggested approach notably outperforms DNN-driven techniques. © 2024 IEEE.

关键词： Deep neural networks

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Advanced Scale-aware Attention for Enhanced Tiny Object Detection in Remote Sensing 2

Advanced Scale-aware Attention for Enhanced Tiny Object Dete...

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

作者： Qiao, Tingting Chen, He Chai, Bingqian Liu, Wenchao Qi, Guijie Xie, Yizhuang Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

Recent advancements in remote sensing object detection have progressed, but distinguishing objects from complex backgrounds remains difficult, especially for tiny objects. To address this, we introduce the Advanced Scale-Aware Attention (ASA) module, which retains key channel information while minimizing computational cost. This module reorganizes channels into the batch dimension and splits them into multiple sub-features. The ASA module contains three branches: the first encodes weights based on scale and channel relationships. The second and third branches use different convolution kernel sizes to capture features across various receptive fields. These branches collaborate to enhance pixel-level relationships, improving foreground-background contrast. On the AI-TOD dataset, our method improved AP50 by 8.6% over state-of-the-art approaches. © 2024 IEEE.

关键词： Object recognition

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An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 518-532页

作者： Tian, Ye Ji, Luke Hu, Yiwei Ma, Haiping Wu, Le Zhang, Xingyi Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Hefei230029 China

Represented by evolutionary algorithms and swarm intelligence algorithms, nature-inspired metaheuristics have been successfully applied to recommender systems and amply demonstrated effectiveness, in particular, for multiobjective recommendation. Owing to the population-based search paradigm, these algorithms can produce a number of recommendation lists, making diverse tradeoffs between multiple metrics and meeting the requirements of accuracy, novelty, diversity, and other user preferences. However, these algorithms are criticized for the low efficiency of the optimization process, especially when the number of users is large. To address this issue, this article proposes an evolutionary multitasking-based recommendation method, where each task corresponds to a user and all the tasks are optimized simultaneously, thus highly improving the efficiency of recommendation. To enhance the convergence speed, all the users are divided into multiple populations according to the similarity between their preferences, where each population evolves with internal knowledge transfer between users, and all the populations evolve with external knowledge transfer between populations. Experimental results on various datasets verify that the proposed method can better balance between multiple metrics than classical and deep neural network-based recommendation methods and exhibits significantly higher efficiency than evolutionary multiobjective optimization-based recommendation methods. © 2024 IEEE. All rights reserved.

关键词： Multiobjective optimization

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Leveraging Knowledge Distillation for Improved Event Extraction in QA Models 6

Leveraging Knowledge Distillation for Improved Event Extract...

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6th International Conference on Frontier Technologies of information and Computer, ICFTIC 2024

作者： Zhou, Peiyao Hu, Zhikui Zi, Kangli Zhang, Dawei School of Computer Science Jiangsu University of Science and Technology Zhenjiang China Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Beijing China

ISBN: (纸本)9798331541750

Event extraction is an important task in natural language processing, and it is widely utilized in intelligence domains such as business and military for information extraction. Recently, many works have successfully transformed document-level event extraction into Question-answering (QA) tasks with remarkable results. This approach embeds event argument information within the questions, introducing prior knowledge into the process. Jin et al. highlighted the importance of high-quality QA pairs for practical QA tasks, emphasizing that constructing these pairs remains a key challenge[1]. In this study, we propose a Prompt-based question generation method to automatically generate questions containing event arguments, which converts event extraction into a QA task. We introduce an event ontology-based information retrieval module to enhance answer accuracy and select the most relevant document segments as input text. Additionally, we employ knowledge distillation to build the QA model, transferring knowledge from a large pre-trained model to a more compact and efficient one, improving the student model's performance. Experiments show that our model performs strongly in mainstream benchmarks, with 4.8 improvements on WikiEvents. © 2024 IEEE.

关键词： Question answering

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A Generalized Non-convex Surrogated Framework for Anomaly Detection on Blurred Hyperspectral Images

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IEEE Transactions on Image processing 2025年 34卷 3108-3122页

作者： Wang, Yinjian Li, Wei Gui, Yuanyuan Xie, Haijun Zhang, Lianbo National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Guangdong Zhuhai519088 China

Hyperspectral imaging is endowed with outstanding discriminability between different land types by its comprehensive sensing of the spectrum, thus favored applying to anomaly detection. However, blurring effect, as a critical cause for quality deterioration of hyperspectral imaging, has been omitted by previous hyperspectral anomaly detection models. On one hand, given that anomalies are sparsely distributed in nature, such blurring effect entangling neighboring pixels severely weighs those detection models down. On the other hand, abnormal objects jeopardize the low-dimensional structure of the image, thus deblurring those images with anomalies is more challenging than normal ones. Hence, it is of much significance to investigate anomaly detection using blurred hyperspectral images. To this end, this paper proposes a generalized nonconvex surrogated tensor framework that is able to perform anomaly detection robustly to blurring effects on hyperspectral images. The proposed framework is featured to be a unified paradigm which guarantees convergence for a broad class of non-convex surrogates. Through treating the spatial and spectral low-rankness adaptively via Block Term Decomposition, the unevenness in the multi-linear low-rankness of hyperspectral image is comprehensively considered, which together with the non-convex surrogates results in a tighter modeling of the low-dimensional prior of hyperspectral images. Extensive experiments demonstrate the superiority of the proposed method compared with the state-of-the-art methods on both hyperspectral image deblurring and anomaly detection. © 1992-2012 IEEE.

关键词： Principal component analysis

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Lightweight Food Image Recognition With Global Shuffle Convolution

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 392-402页

作者： Sheng, Guorui Min, Weiqing Yao, Tao Song, Jingru Yang, Yancun Wang, Lili Jiang, Shuqiang Ludong University Department of Information and Electrical Engineering Yantai264025 China Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China

Consumer behaviors and habits in food choices impact their physical health and have implications for climate change and global warming. Efficient food image recognition can assist individuals in making more environmentally friendly and healthier dietary choices using end devices, such as smartphones. Simultaneously, it can enhance the efficiency of server-side training, thereby reducing carbon emissions. We propose a lightweight deep neural network named Global Shuffle Net (GSNet) that can efficiently recognize food images. In GSNet, we develop a novel convolution method called global shuffle convolution, which captures the dependence between long-range pixels. Merging global shuffle convolution with classic local convolution yields a framework that works as the backbone of GSNet. Through GSNet's ability to capture the dependence between long-range pixels at the start of the network, by restricting the number of layers in the middle and rear, the parameters and floating operation operations (FLOPs) can be minimized without compromising the performance, thus permitting a lightweight goal to be achieved. Experimental results on four popular food recognition datasets demonstrate that our approach achieves state-of-the-art performance with higher accuracy and fewer FLOPs and parameters. For example, in comparison to the current state-of-the-art model of MobileViTv2, GSNet achieved 87.9% accuracy of the top-1 level on the Eidgenössische Technische Hochschule Zürich (ETHZ) Food-101 dataset with 28% reduction in the parameters, 37% reduction in the FLOPs, but a 0.7% more accuracy. © 2024 IEEE.

关键词： Carbon dioxide

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Hybrid particle swarm optimization with adaptive learning strategy

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Soft computing 2024年第17-18期28卷 9759-9784页

作者： Wang, Lanyu Tian, Dongping Gou, Xiaorui Shi, Zhongzhi School of Mathematics and Information Sciences Baoji University of Arts and Science Shaanxi Baoji721007 China Institute of Computer Software Baoji University of Arts and Sciences Shaanxi Baoji721007 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Particle swarm optimization (PSO) is a meta-heuristic algorithm that can be used to solve different real-world optimization problems. However, PSO suffers from some intrinsic drawbacks, including falling into local optimal and slow convergence in the latter evolution process. To deal with these issues, a hybrid PSO with adaptive learning strategy (abbreviated as HPSO-ALS) is proposed in this paper. To start with, the chaotic opposition-learning method is employed to generate the uniformly distributed particles so as to enhance the quality of the initial swarm. Next, a self-logical mapping-based nonlinear decreasing inertia weight is formulated to trade off the global search and local search abilities. Meanwhile, different adaptive position updating strategies are leveraged to achieve better convergence according to different evolution status. In addition, inspired by the cask effect, the global worst particle (gworst) is introduced into the velocity updating to make full use of the potential information of the particles. Finally, extensive experiments on sixteen benchmark functions and CEC’13 test suite have verified the promising performance of HPSO-ALS proposed in this paper. Alternatively, HPSO-ALS is also applied to the classical traveling salesman problem (TSP) and the experimental results have further validated the effectiveness and efficiency of our proposal. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Traveling salesman problem

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TransCGAN-based Parameter Extraction Framework for SAR Image Simulation 2

TransCGAN-based Parameter Extraction Framework for SAR Image...

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

作者： Deng, Sidan He, Jingfei Mao, Yongfei Zhao, Liangbo Chen, Liang Shi, Hao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Chongqing Innovation Center Chongqing China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Chongqing Innovation Center Beijing China

ISBN: (纸本)9798331515669

Synthetic Aperture Radar (SAR) images have a wide range of applications due to their all-weather and all-day working conditions. However, SAR images with different scenarios and imaging conditions are insufficient or even rare, which is required in specific SAR image tasks. Fortunately, SAR image simulation technology can provide ample simulated images under these conditions at a low cost, addressing the scarcity of specific real SAR data. Accurate parameters are crucial for obtaining high-quality simulated images. However, it is time-consuming and labour-intensive to adjust parameters manually, and it often fails to achieve accurate simulation parameters. To tackle this problem, this paper proposes a TransCGAN-based SAR image simulation method that combines deep learning with traditional methods. By training TransCGAN, a conditional generative adversarial network (CGAN) integrated with Transformer architecture, a mapping between SAR images and simulation parameters is established. This enables the extraction of simulation parameters directly from the real SAR image, guided by the corresponding real SAR image. Ultimately, the parameters are subsequently converted into simulated SAR images via simulation software. Experimental results demonstrate that our TransCGAN-based method can effectively extract accurate simulation parameters from real SAR images, resulting in simulated images holding high similarity to real images. © 2024 IEEE.

关键词： Generative adversarial networks

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Adaptive Spatial Modeling and Multi-Scale Attention Aggregation for Semantic Segmentation of Remote Sensing Images 2

Adaptive Spatial Modeling and Multi-Scale Attention Aggregat...

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

作者： Yang, Wenying Zhang, Yuchuan Jin, Yuxuan Wang, Yupei Chen, Liang Beijing Institute of Technology Chongqing Innovation Center National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Chongqing China Jianghan University School of Artificial Intelligence Wuhan China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China Beijing Institute of Technology Chongqing Innovation Center Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

As remote sensing technology advances and high-resolution sensors are deployed, the analysis of high-resolution remote sensing images encounters challenging issues, such as intra-class variability, and inter-class similarity, due to the unobvious appearance and complicated background. The high-resolution remote sensing images pose critical issues for precise semantic segmentation due to these fundamental issues. To this end, we propose an adaptive hierarchical aggregation network. Different from the previous approach of modeling semantic interdependencies solely within a single feature map in the channel dimension, we introduce multi-scale channel-wise feature aggregation to capture global contextual dependencies. Meanwhile, we design an adaptive detail-aware module to model long-range spatial dependencies, adaptively extracting effective details from different levels as compensation. Additionally, this module utilizes depth-wise separable convolution to selectively filter out irrelevant spatial details from lower-level feature maps. The outputs of the two modules are fused layer by layer to achieve refined feature representation, thereby generating precise pixel-level segmentation results. Our experiments are conducted on the Vaihingen dataset and the Potsdam dataset. The experimental results demonstrate that the proposed algorithm surpasses several existing state-of-the-art approaches. © 2024 IEEE.

关键词： Semantic Segmentation

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Optical and Electrical Analysis of an Advanced 4T All-perovskite Tandem Solar Cell

Optical and Electrical Analysis of an Advanced 4T All-perovs...

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2024 Photonics and Electromagnetics Research Symposium, PIERS 2024

作者： Xia, Zhaosheng Ren, Xingang Wei, Shengyang Huang, Zhixiang Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province Institute of Physical Science and Information Technology Hefei230039 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Hefei230601 China

ISBN: (纸本)9798350375909

The investigation of the optoelectronic characteristics of all-perovskite tandem solar cells holds pivotal significance in surpassing the Shockley-Queisser limit of single-junction perovskite solar cells. Initially, we established two distinct four-terminal tandem solar cells (4T TSCs) composed of different perovskite materials: FA0.8Cs0.2Pb(I0.7Br0.3)3 (Eg = 1.75 eV) and MASnI3 (Eg = 1.3 eV). Through numerical simulations of the optoelectronic characteristics of 4T TSCs, we evaluated the performance of the TSCs at varying perovskite layer thicknesses. Following optimization, the power conversion efficiency (PCE) of the 4T TSCs increased from the initial 30.34% to 33.54%. Through an in-depth analysis of the cell and energy distribution in TSCs, it was observed that the light-coupling layer significantly influenced the PCE of the 4T TSCs. Upon optimizing the light-coupling layer, the PCE of the TSC increased from the initial 30.34% to 34.7%. This study provides crucial theoretical and experimental foundations for optimizing the performance of all-perovskite TSCs. © 2024 IEEE.

关键词： Perovskite solar cells

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