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TechRxiv 2023年

作者： Li, Yingsong Fu, Yonglin Miao, Yongchun Huang, Zhixiang Diniz, Paulo S.R. The Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei230601 China The Universidade Federal do Rio de Janeiro Rio de Janeiro21941-972 Brazil

A unified affine-projection-like adaptive (UAPLA) algorithm is deivised and verified for system identification. The UAPLA algorithm uses a generalized cost function encompassing some data-reusing methods to cope with colored input signals. Furthermore, the UAPLA algorithm is derived based on the new cost function to generalize multiple affine-projection algorithms. As a result, the proposed UAPLA algorithm includes some classical adaptive filters as exceptional cases while allowing flexibility to achieve low estimation errors under impulsive noises. The obtained results conducted by simulations help to corroborate the superiority of the proposed UAPLA algorithm over other popular AP algorithms. © 2023, CC BY.

关键词： Adaptive filters

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

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

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Data Driven Control and Learning Systems (DDCLS)

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

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

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.

关键词： Fault diagnosis Learning systems Adaptation models Data privacy Transfer learning Prototypes Predictive models

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A Simple Broadband Slotted Circularly Polarization Antenna

A Simple Broadband Slotted Circularly Polarization Antenna

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IEEE International Workshop on Electromagnetics, Applications and Student Innovation (iWEM)

作者： Lihua Wang Yingsong Li Zhixiang Huang Tian Hong Loh College of Information and Communication Engineering Harbin Engineering University Harbin China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Electromagnetic and Eletrochemical Technologies Department National Physical Laboratory Middlesex UK

A simple broadband slotted circularly-polarized (CP) antenna (SBS-CP) is presented, which mainly consists of a ground structure with a modified slot and an asymmetric feeding structure. The two structures can give different CP characteristics in low and high frequencies. By etching rectangular slots in the ground structure, the coupling effect between feeding and ground structure will be improved and the bandwidth will be broadened. By modeling and optimizing the antenna, the proposed antenna impedance bandwidth (IBW) and axial ratio bandwidth (ARBW) are 113% (2.08~7.46 GHz) and 97% (2.5~7.24 GHz), respectively.

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DMNER: Biomedical Named Entity Recognition by Detection and Matching

DMNER: Biomedical Named Entity Recognition by Detection and ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Bian, Junyi Jiang, Rongze Zhai, Weiqi Huang, Tianyang Huang, Xiaodi Zhou, Hong Zhu, Shanfeng Fudan University School of Computer Science Shanghai China Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Shanghai China Charles Sturt University School of Computing Mathematics and Engineering Nsw Australia Atypon Systems Llc United Kingdom Zhangjiang Fudan International Innovation Center Fudan University Institute of Science and Technology for Brain-Inspired Intelligence Moe Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Shanghai China

ISBN: (纸本)9798350386226

Biomedical Named Entity Recognition (NER) is a crucial task in extracting information from biomedical texts. However, the diversity of professional terminology, semantic complexity, and the widespread presence of synonyms pose significant challenges. Traditional methods that rely on sequence labeling training datasets often struggle to handle these complexities. To address this, we introduce a novel framework for BioNER, termed DMNER, which leverages external knowledge and operates in two steps: entity boundary detection and entity category identification through matching. The core of DMNER is its second step, which determines the entity category by retrieving similar entities and their categories from a knowledge dictionary using semantic similarity matching. Our experiments on 10 biomedical datasets demonstrate that DMNER outperforms baselines across these tasks, proving its effectiveness and adaptability. DMNER is versatile and can be applied to various NER tasks, including supervised NER, distantly supervised NER, and NER on multiple datasets with disjoint label sets. The DMNER code is publicly available. © 2024 IEEE.

关键词： Semantics

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Ptpfusion: A Progressive Infrared and Visible Image Fusion Network Based on Texture Preserving

SSRN

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SSRN 2024年

作者： Lu, Yixiang Zhang, Weijian Zhao, Dawei Qian, Yucheng Maksim, Davydau Gao, Qingwei The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei230601 China The Belarusian State University of Informatics and Radioelectronics Minsk Belarus State Grid Anhui Electric Power Research Institute China

Infrared and visible image fusion is to provide a more comprehensive image for downstream tasks by highlighting the main tar get and maintaining rich texture information. Image fusion methods based on deep learning suffer from insufficient multimodal information extraction and texture loss. In this paper, we propose a texture preserving progressive fusion network to extract comple mentary information from multimodal images (PTPFusion) to solve these issues. To reduce image texture loss, we design multiple consecutive texture-preserving blocks (TPB) to enhance fused texture. The TPB can enhance the features by using a parallel ar chitecture consisting with a residual block and a derivative operators. In addition, a novel cross-channel attention (CCA) fusion module is developed to obtain complementary information by modeling global feature interactions via cross-queries mechanism, followed by information fusion to highlight the feature of salient target. To avoid information loss, the extracted features at different stages are merged as the output of TPB. Finally, the fused image will be generated by the decoder. Extensive experiments on three datasets show that our proposed fusion algorithm is better than existing state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Image fusion

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Extended Coprime Arrays with Sum-Difference Coarray for DOA Estimation 5

Extended Coprime Arrays with Sum-Difference Coarray for DOA ...

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5th IEEE International Conference on Electronic Information and Communication Technology, ICEICT 2022

作者： Xin, Wei Xu, Lin Gao, Jincheng Li, Yingsong Huang, Zhixiang College of Information and Communication Engineering Harbin Engineering University Harbin150001 China Wuhan Second Ship Design and Research Institute Wuhan China Zhaojin Mining Industry Co. Ltd. Zhaoyuan China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China

ISBN: (数字)9781665472111

ISBN: (纸本)9781665472111

A modified coprime array (CA) with a name of extended CA (ECA) is presented and constructed to reduce over-lapping sensors between sum and difference coarray using conventional CA (CCA) to get a sum-difference coarray with high degrees- of-freedoms (DOFs). The devised ECA is to shift a sub array of CCA to reduce redundancy and then to flip part of shifted sub array around zero point that is regarded as symmetric center. The onstructed ECA expands the inter-element separations with lower mutual coupling. Numerical simulation are provided to verify superiority of the devised ECA. © 2022 IEEE.

关键词： Direction of arrival

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Lifetime Prediction of Topcon Modules Based on Accelerated Damp Heat Life Tests

SSRN

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SSRN 2025年

作者： Ren, Xingang Duan, Junwei Yang, Lixia Xu, Tao Yu, Yuanjie Chen, Zhiliang Lv, Ruirui Ye, Feihong Wang, Gang Ahmed, Rida Jiang, Hao Yang, Lixia Huang, Zhixiang Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei230601 China Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei230601 China CSI Cells Co. Ltd. Suzhou215129 China Anhui University of Chinese Medicine China Hong Kong Polytechnic University Hong Kong

Tunnel Oxide Passivation Contacts (TOPCon) solar cells have achieved high efficiency and become mainstream product in the global photovoltaic (PV) market. However, their long-term reliability, especially under high-temperature and high-humidity conditions, lacks sufficient field validation, and there is a lack of lifetime prediction models for TOPCon modules. To address this, we developed a robust accelerated aging model by integrating an improved Peck model with a sigmoidal degradation function, incorporating internal humidity and temperature as critical factors. The model employs a particle swarm optimization (PSO) algorithm to calibrate activation energy and humidity sensitivity parameters, enabling precise degradation trajectory simulation. Validated through a "fitting + testing" framework, the model achieves high accuracy (RMSE = 0.7%) in predicting power degradation under Damp Heat (DH), Highly Accelerated Stress Test (HAST), and modified Pressure Cooker Test (mPCT) conditions. Accelerated aging tests reveal that DH-induced degradation primarily reduces fill factor (FF) due to finger corrosion, with minimal impact on Voc and Isc. Experimental validation using a non-commercial corrosion-sensitive BOM combination shows that polyolefin elastomer (POE)-encapsulated modules exhibit superior corrosion resistance, projecting a service lifetime 23 years longer than EVA/EVA encapsulation in high-humidity regions like Hainan. This work bridges the gap in TOPCon module lifetime prediction under extreme environments, offering actionable insights for optimizing encapsulation materials and enhancing reliability. The model provides a scalable framework for evaluating module performance under site-specific climatic conditions, accelerating the development of durable PV technologies. © 2025, The Authors. All rights reserved.

关键词： Corrosion resistance

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Ptpfusion: A Progressive Infrared and Visible Image Fusion Network Based on Texture Preserving

SSRN

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SSRN 2024年

关键词： Image fusion

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Research on Data Augmentation for Garbage Classification with Target Detection Algorithm

Research on Data Augmentation for Garbage Classification wit...

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2022 International Applied Computational Electromagnetics Society Symposium, ACES-China 2022

作者： Chen, Jiansong Zuo, Shikai Lin, Yun Tang, Kai Zou, Jianhang Feng, Naixing Cai, Yijun Xiamen University of Technology Smart Sensing Integrated Circuit Engineering Research Center of Universities in Fujian Province Xiamen361024 China Xiamen Wish Information Technology Co. Ltd Xiamen361006 China Anhui University Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Hefei China

ISBN: (纸本)9781665452366

Garbage classification is an essential work in daily life. With the development of artificial intelligence (AI), we have begun to use object detection to achieve garbage classification. However, the stacking and occlusion of garbage often make the detection performance unsatisfactory. In this paper, YOLOv5 algorithm is used for garbage detection and classification. YOLOv5 has three detection heads, which can detect objects at multiple scales. At the same time, Mosaic and Coarse Dropout image augmentation techniques are used to process our private data set of garbage, which improves the diversity of images. In this way, the target classification accuracy of the detection model can reach 75.7%, which is significantly improved compared with 69.9% without data augmentation. This paper provides a new technical support for the scene classification of stacked garbage based on deep learning. © 2022 IEEE.

关键词： Classification (of information)

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A Fractional-Order Logarithmic Adaptive Filtering Algorithm Under $\alpha$-Stable Noise

A Fractional-Order Logarithmic Adaptive Filtering Algorithm ...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Dazhi Sun Canping Yu Li Li Yingsong Li Wudang Xiao Yong Chen Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University China Aircraft Airworthiness Institute China Academy of Civil Aviation Science and Technology (Center of Aviation Safety Technology CAST) Beijing China

In practical applications, noise is more appropriately modelled as an a-stable random process than as a Gaussian process. To cope with a-stable noise, a fractional-order stochastic gradient descent (SGD) method is invented to adaptive filtering (AF) algorithms. When both input signal and noise follow α-stable random processes, the AF algorithms based on mean square error (MSE) criteria can be affected. Nevertheless, the fractional-order AF (FoAF) algorithm can effectively address this issue. For the purpose of better stability performance of the FoAF algorithm, different cost functions (CFs) are introduced into fractional-order SGD method. Based on the fractional-order derivative of a logarithmic CF (LCF), a fractional-order logarithmic adaptive filtering (FoLAF) algorithm is formed in this condition. Trials show that the FoLAF algorithm achieves faster convergence, excellent steady-state performance and superior tracking performance.

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