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检索条件"机构=Key Lab of Computing Intelligent and Signal Processing"

共 2010 条记录，以下是171-180 订阅

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Linear-to-Circular Polarization Converter Based on Transmission Metamaterial 4

Linear-to-Circular Polarization Converter Based on Transmiss...

引用

4th International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2021

作者： Li, Huixiang Wu, Xianliang Zhang, Liang Anhui University Key Laboratory of Intelligent Computing Signal Processing Hefei China Anhui Province Key Laboratory of Simulation and Design for Electronic Information Key Laboratory of Intelligent Computing Signal Processing Hefei Normal University Hefei China

ISBN: (纸本)9781733509619

A broadband transmissive linear-to-circular polarization conversion metamaterial composed of 5-layer '-shaped units was designed, and it was placed directly above the ordinary linear polarization antenna so that the antenna can radiate circular polarization waves. The 3dB axial ratio bandwidth is 11.5GHz-14.5GHz, and the relative axial ratio bandwidth reaches 23%, which proves that the polarization conversion surface has good polarization conversion characteristics. © 2021 Applied Computational Electromagnetics Society (ACES).

关键词： Circular polarization

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Adversarial Example Generation Method Based on Probability Histogram Equalization

Adversarial Example Generation Method Based on Probability H...

引用

Chinese Control Conference (CCC)

作者： Youqing Fang Jingwen Jia Yuhai Yang Wan-Li Lyu Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

CNNs (Convolutional Neural Networks) have a good performance on most classification tasks, but they are vulnerable when meeting adversarial examples. Research and design of highly aggressive adversarial examples can help enhance the security and robustness of CNNs. The transferability of adversarial examples is still low in black-box settings. Therefore, an adversarial example method based on probability histogram equalization, namely HE-MI-FGSM (Histogram Equalization Momentum Iterative Fast Gradient Sign Method) is proposed. In each iteration of the adversarial example generation process, the original input image is randomly histogram equalized, and then the gradient is calculated to generate adversarial perturbations to mitigate overfitting in the adversarial example. The effectiveness of the method is verified on the ImageNet dataset. Compared with the advanced method I-FGSM (Iterative Fast Gradient Sign Method) and MI-FGSM (Momentum I-FGSM), the attack success rate in the adversarial training network increased by 27.9% and 7.7% on average, respectively.

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GenRec: Unifying Video Generation and Recognition with Diffusion Models 38

GenRec: Unifying Video Generation and Recognition with Diffu...

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38th Conference on Neural Information processing Systems, NeurIPS 2024

作者： Weng, Zejia Yang, Xitong Xing, Zhen Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Department of Computer Science University of Maryland United States

Video diffusion models are able to generate high-quality videos by learning strong spatial-temporal priors on large-scale datasets. In this paper, we aim to investigate whether such priors derived from a generative process are suitable for video recognition, and eventually joint optimization of generation and recognition. Building upon Stable Video Diffusion, we introduce GenRec, the first unified framework trained with a random-frame conditioning process so as to learn generalized spatial-temporal representations. The resulting framework can naturally supports generation and recognition, and more importantly is robust even when visual inputs contain limited information. Extensive experiments demonstrate the efficacy of GenRec for both recognition and generation. In particular, GenRec achieves competitive recognition performance, offering 75.8% and 87.2% accuracy on SSV2 and K400, respectively. GenRec also performs the best on class-conditioned image-to-video generation, achieving 46.5 and 49.3 FVD scores on SSV2 and EK-100 datasets. Furthermore, GenRec demonstrates extraordinary robustness in scenarios that only limited frames can be observed. Code will be available at https://***/wengzejia1/GenRec. © 2024 Neural information processing systems foundation. All rights reserved.

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Fast Near-field Computational Imaging with Deep Neural Networks

Fast Near-field Computational Imaging with Deep Neural Netwo...

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2021 CIE International Conference on Radar, Radar 2021

作者： Zhao, Fafa Wu, Zhenhua Pan, Xueli Yang, Lixia Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Hefei China

ISBN: (纸本)9781665498142

With the capability to sculpt complex radiative wavefronts and couple energy from the reference wave to the desired radiation pattern, metasurface antennas have gained tremendous progress in recent years. The flexibility in antenna design gives rise to a bunch of metasurface antennas, while there is still a comprise between the needed operation band and radiation field pattern coherence. Furthermore, the relatively low sampling ratio and overlap between frequency measurement modes would inevitably pose a great challenge for matched filter and other advanced sparsity-driven post-processing imaging algorithms. In this paper, we design a deep neural network to achieve fast scene image reconstruction. The trained network could fastly reconstruct the imaged object in both low scene compressed ratio and high coherence of the measurement modes. The imaging experiments with measured radiation field data are conducted to verify the effectiveness of our method. © 2021 IEEE.

关键词： computational imaging deep neural network metasurface antennas

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A Wideband Millimeter-wave Bidirectional OAM Antenna using sequential rotation feeding 4

A Wideband Millimeter-wave Bidirectional OAM Antenna using s...

引用

4th International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2021

作者： Wu, Jie Feng, Jian Huang, Zhixiang Wu, Xianliang Key Laboratory of Intelligent Computing Signal Processing Anhui University Ministry of Education Hefei China

ISBN: (纸本)9781733509619

A wideband bidirectional circularly polarized (CP) orbital angular momentum (OAM) antenna array is proposed for millimeter-wave frequency. The designed antenna array is composed of four bidirectional radiating dual-CP antenna units that rotate 90 degrees sequentially to each other in the clockwise direction. Results indicate that the left-hand circular polarization (LHCP) with a mode l-{OAM}=-1 in +z direction and right-hand circular polarization (RHCP) with a mode l-{OAM}=+1 in -z direction, over a wide impedance and 3-dB axial ratio (AR) relative bandwidth of 32.3% from 26 to 36 GHz. © 2021 Applied Computational Electromagnetics Society (ACES).

关键词： Antenna arrays

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ZTrans: A new Transformer for Speech Enhancement 4

ZTrans: A new Transformer for Speech Enhancement

引用

4th International Conference on Information Communication and signal processing, ICICSP 2021

作者： Guo, Yucheng Yu, Weiwei Zhou, Jian Key Laboratory of Intelligent Computing and Signal Processing Anhui University Anhui University HeFei China

ISBN: (纸本)9781665407571

At present, Transformer architectures based on the self-attention mechanism have attracted wide attention due to their excellent performance in many fields, such as natural language processing and computer vision. However, in the field of speech processing, the potential of Transformer architectures has not been fully utilized. The main reason is that the position embedding mechanism in the Transformer cannot effectively perform sequence modeling in speech tasks, and the Transformer itself is challenging to train and converge. Inspired by recent research, we propose a new speech enhancement in this paper. Specifically, a recurrent neural network is used to replace the position embedding in Transformer, and the fully connected layer in the Transformer feed forward layer is replaced with one-dimensional convolution. In addition, the model also introduces a weighted residual connection mechanism. Experiment results show that compared with baseline models, the new model performs better in speech quality and speech intelligibility. At the same time, the convergence and inference speed are also much improved. Compared with the baseline model, the processing speed of the proposed method is increased by 4.27 times. © 2021 IEEE.

关键词： Recurrent neural networks

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Structure-constrain Source Free Domain Adaptation with Regularized Knowledge Distillation of Source Model 13

Structure-constrain Source Free Domain Adaptation with Regul...

引用

13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Yang, Dan Peng, Xin Su, Cheng Tan, Dayu Chen, Chaoyang Zhong, Weimin Ministry of Education East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Shanghai200237 China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing Anhui Hefei230601 China School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (纸本)9798350361674

Transfer learning aims to leverage the knowledge of the source domain to help the learning of unlabeled target domain models. However, all conventional transfer learning methods assume that samples from source and target domains are always freely accessible. However, this assumption is rarely plausible in real-world due to data privacy. To combat this challenge, we introduce Structure-constrain Source Free Domain Adaptation with regularized Knowledge Distillation of source model (SSFDA-KD). Our method is based on the observations that a deterministic classification result can be obtained when the target data distribution aligns with the source data distribution, and a well-trained model not only yields a deterministic classification for a sample but also has clear category boundaries, which implies samples that are close together belong to the same category. From those, we first generate the source model with labeled source data, which is regarded as a well-trained model. Then, we identify target prototypes with lower self-entropy and assign the pseudo-labels to other samples according to the neighborhood structure. Finally, we refine the target model by minimizing prediction loss, constraining internal structure via triplet loss, and reducing knowledge distillation loss. Experiments of fault diagnosis based on WWTPs are conducted to validate its effectiveness. © 2024 IEEE.

关键词： Domain Knowledge

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Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection 61

Learn over Past, Evolve for Future: Forecasting Temporal Tre...

引用

61st Annual Meeting of the Association for Computational Linguistics, ACL 2023

作者： Hu, Beizhe Sheng, Qiang Cao, Juan Zhu, Yongchun Wang, Danding Wang, Zhengjia Jin, Zhiwei Key Lab of Intelligent Information Processing Chinese Academy of Sciences Institute of Computing Technology China University of Chinese Academy of Sciences China ZhongKeRuijian Technology Co. Ltd. China

ISBN: (纸本)9781959429685

Fake news detection has been a critical task for maintaining the health of the online news ecosystem. However, very few existing works consider the temporal shift issue caused by the rapidly-evolving nature of news data in practice, resulting in significant performance degradation when training on past data and testing on future data. In this paper, we observe that the appearances of news events on the same topic may display discernible patterns over time, and posit that such patterns can assist in selecting training instances that could make the model adapt better to future data. Specifically, we design an effective framework FTT (Forecasting Temporal Trends), which could forecast the temporal distribution patterns of news data and then guide the detector to fast adapt to future distribution. Experiments on the real-world temporally split dataset demonstrate the superiority of our proposed framework. The code is available at https://***/ICTMCG/FTT-ACL23. © ACL *** rights reserved.

关键词： Forecasting

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T-GraphDTA: A Drug-Target Binding Affinity Prediction Framework Based on Protein Pre-training Model and Hybrid Graph Neural Network 1st

T-GraphDTA: A Drug-Target Binding Affinity Prediction Framew...

引用

1st International Conference on Applied Intelligence, ICAI 2023

作者： Wu, Yijia Lin, Yanmei Peng, Yuzhong Zhang, Ru Cai, Li Key Lab of Scientific Computing and Intelligent Information Processing Nanning Normal University Guangxi Nanning530000 China Guangxi Academy of Sciences Guangxi Nanning530007 China School of Software Yunnan University Yunnan Kunming650000 China

ISBN: (纸本)9789819709021

Drug-target affinity (DTA) prediction is an important task in computer-aided drug design and drug repositioning, which can speed up drug development and reduce resource consumption. Researchers have explored some deep learning-based methods to improve DTA prediction in recent years, demonstrating the great potential of deep learning in DTA prediction. They have developed several molecular representation learning methods for drug compounds in deep learning-based DTA prediction methods. However, most of the existing deep learning-based DTA prediction models use one-hot encoding-based methods for protein representation learning, or use recursive neural network-based methods for learning feature representations from raw protein sequences. These may affect the ability of the DTA prediction model to learn the potential features of the protein, thus weakening the predictive power of the model. To tackle this problem, we developed a novel protein pre-training method (PTR) for protein representation learning, then proposed a DTA prediction framework, called Transformer-Graph drug-target affinity prediction (T-GraphDTA), based on PTR and hybrid graph neural network. The hybrid graph neural network is mainly responsible for molecular presentation learning of drugs. Extensive experiments were conducted on four benchmark datasets of drug-target binding affinity, comparing T-GraphDTA against state-of-the-art models. The experimental results show that T-GraphDTA achieves significantly better performance than state-of-the-art models on all four benchmark datasets. It indicates that T-GraphDTA is expected to be an excellent practical tool for predicting the affinity of drug-target pairs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Determination of Continuous Dielectric Properties of Clothing Materials Using Hybrid Method

Determination of Continuous Dielectric Properties of Clothin...

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2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023

作者： Chen, Manni Chung, Kwok L. Huang, Zhining Wen, Jiating Li, Yingsong Huizhou University School of Computer Science and Engineering Guangdong516007 China Huizhou University Glorious Sun Guangdong School of Fashion Guangdong516007 China Ministry of Education Anhui University Key Laboratory of Intelligent Computing and Signal Processing China

ISBN: (纸本)9798350336740

This paper presents a hybrid method for determination of continuous dielectric properties of clothing materials. The dielectric constant and loss tangent of three types of materials are firstly investigated using open-ended coaxial probe from 0.5 to 20 GHz. The average measured values are input into the CST simulation of two CPW lines to obtain the two-port S-parameters. Finally, more reasonable yet reliable results are obtained by using two retrieval algorithms (TRACE and ABCD) for validation and comparative purposes. © 2023 IEEE.

关键词： Dielectric properties

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