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Polypropylene Glycol-Modified Anode Interface for High-Performance Perovskite Solar Cells

Chinese Journal of Chemistry 2022年第22期40卷 2694-2700页

作者： Fei Wu Kangrong Yan Haotian Wu Yuanhang Guo Shiqi Shan Tianyi Chen Weifei Fu Lijian Zuo Hongzheng Chen State Key Laboratory of Silicon Materials MOE Key Laboratory of Macromolecular Synthesis and FunctionalizationDepartment of Polymer Scienceand EngineeringZhejiang UniversityHangzhouZhejiang310027China Zhejiang University-Hangzhou Global Scientific and Technological Innovation Center HangzhouZhejiang 310014China

The surface properties and chemical interactions are critical for perovskite solar cells(PVSCs).In this work,we show that the polypropylene glycol(PPG)can simultaneously passivate the NiOx surface and grain boundaries of perovskite films,allowing more efficient charge transfer at the anode interface and reducing the recombination of *** a result,the open-circuit voltage(Voc)of MAPbI3 based inverted PVSCs increases from 1.087 V to 1.127 V,and the short-circuit current density(Jsc)is increased from 20.87 mA·cm^(–2) to 22.32 mA·cm^(–2),thereby realizing the improvement of the device power conversion efficiency(PCE)from 18.34%to 20.12%.Moreover,the steady-state output of the PVSCs is remarkably improved by incorporating *** analysis of surface properties suggests that part of the PPG at the interface can permeate into the precursor solution with the help of DMF solvent and remain in the perovskite layer to form a concentration *** ether bond of PPG and the uncoordinated Pb2+in the perovskite are coordinated to achieve passivation effects and improve device performance.

关键词： Nanoparticles Polymers Interfaces Passivation effects Inverted perovskitesolarcells

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A hierarchical access control and sharing model for healthcare data with on-chain-off-chain collaboration

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Journal of Intelligent and Fuzzy Systems 2025年第3期48卷 215-231页

作者： Dong, Jianhang Jiang, Rong Zhang, Yi Tian, Shenghu Wu, Bin Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Institute of Intelligence Applications Yunnan University of Finance and Economics Kunming China Yunnan Academy of Scientific and Technical Information Kunming China

Healthcare data is growing rapidly and data sharing is becoming increasingly important among hospitals. However, traditional methods are prone to data leakage and threaten patient privacy. To ensure data security and realize sharing and access control, combining blockchain technology, attribute-based encryption and ciphertext policy (CP-ABE) and AES symmetric encryption algorithm, this paper proposes a hierarchical attribute-based encryption algorithm based on master-sidechain (MSC-CP-ABE) and a collaborative access control and sharing model with on-chain and off-chain (AC-CSS). The algorithm encrypts hierarchically based on data sensitivity, the ciphertext is stored in the interplanetary file system (IPFS), and the encrypted address and key are stored in the side chain, which is convenient for indexing in the main chain. The vectorized access policy implements policy hiding, combines on-chain and off-chain, saves blockchain space, achieves hierarchical access control, and improves security and efficiency. Simulation experiments show that the main sidechain model proposed in this paper has better performance in terms of transaction delay and throughput. Compared with existing models, the algorithm proposed in this paper has certain advantages in data encryption and decryption time and key generation time. © 2025 – IOS Press. All rights reserved.

关键词： Authorization

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ERROR ANALYSIS OF FRACTIONAL COLLOCATION METHODS FOR VOLTERRA INTEGRO-DIFFERENTIAL EQUATIONS WITH NONCOMPACT OPERATORS

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Journal of Computational Mathematics 2025年第3期43卷 690-707页

作者： Zheng Ma Chengming Huang Anatoly A.Alikhanov School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China North-Caucasus Center for Mathematical Research North-Caucasus Federal UniversityStavropol 355017Russia North-Eastern Federal University Yakutsk 677000Russia

This paper is concerned with the numerical solution of Volterra integro-differential equations with noncompact *** focus is on the problems with weakly singular *** handle the initial weak singularity of the solution,a fractional collocation method is applied.A rigorous hp-version error analysis of the numerical method under a weighted H1-norm is carried *** result shows that the method can achieve high order convergence for such *** experiments are also presented to confirm the effectiveness of the proposed method.

关键词： Volterra integro-differential equation Noncompact operator Nonsmooth solution Collocation method Fractional polynomial hp-version error analysis

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Model-based Visual 3D Pose Tracking of Non-cooperative Spacecraft in Close Range 74

Model-based Visual 3D Pose Tracking of Non-cooperative Space...

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74th International Astronautical Congress, IAC 2023

作者： Liu, Chang Tang, Hao Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen518055 China Shenzhen Key Laboratory for Exascale Engineering and Scientific Computing Shenzhen China

This work proposes a new monocular algorithm to track the 3D pose of non-cooperative spacecraft for proximity operations in real time with the 3D boundaries and 3D contour of the spacecraft as features. The 3D boundary segments of the spacecraft are automatically extracted from the 3D spacecraft model a priori. At the beginning instant of tracking, the tracking system is initialized by a pose retrieval method. At the following time instant, with the initial pose and its covariance provided by the extended Kalman filter (EKF), the 3D contour segments are rapidly extracted from the 3D model. The boundary segments and contour segments are taken as 3D features. In the input image, we search the image data corresponding to the 3D features within the range decided by the initial pose and its covariance. Then, the spacecraft pose is determined in real time by minimizing the geometric distances of the 3D features to the back-projection lines of their corresponding image data. The covariance of the pose is estimated via the first-order optimal condition of the minimization. Eventually, the EKF is derived from the second-order autoregression (2nd-AR) to generate the final pose estimate, and the initial pose and its covariance for next time instant. The synthetic trials quantitatively indicate that our method can track the 3D pose of spacecraft in real time with high accuracy and has outstanding robustness to the dark occlusion, cluster background, large image distance, and image noise. The real trial qualitatively validates the effectiveness of our tracking method. Copyright © 2023 by the International Astronautical Federation (IAF). All rights reserved.

关键词： Kalman filter Pose determination Spacecraft Tracking

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DLE: Document Illumination Correction with Dynamic Light Estimation

DLE: Document Illumination Correction with Dynamic Light Est...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Quan, Jiahao Wang, Hailing Wu, Chunwei Cao, Guitao East China Normal University Shanghai Key Laboratory of Trustworthy Computing Shanghai China MoE Engineering Research Center of SW/HW Co-design Technology and Application East China Normal University Shanghai China

ISBN: (纸本)9781665410205

Document images captured through mobile devices in natural environments are often affected by various types of illumination degradation. The degradation diminishes the clarity and readability of document images, thereby complicating their application to OCR downstream tasks. Existing methods typically address only one or a limited number of degradation types and do not consider the diversity of image degradation types. Additionally, these methods typically involve a pre-trained fixed sub-network to estimate background light or shadows, which lacks flexibility and adaptability. To overcome these challenges, this study proposes a novel framework named DLE, which comprises a two-loop generative adversarial network and a multi-modal discriminator. Specifically, to improve the quality of image representation, a mask extractor is embedded before the image input generator. This forces the model to focus on the distinct features in the image, enhancing the representation of illumination anomalous and degraded regions. The mask extractor generates a luminance mask to evaluate the difference in illumination between the input and target images. Subsequently, the consistency loss computation incorporates a dynamic optimization of the mask extractor, strengthening its ability to estimate the illumination degradation part. Moreover, a pre-trained visual-language model is introduced into the multi-modal discriminator, leveraging its robust cross-modal alignment capability to improve the semantic consistency of the generated images with the preset input text. Extensive experiments demonstrate that our approach achieves the SOTA performance in terms of edit distance (ED) and character error rate (CER). © 2024 IEEE.

关键词： Visual languages

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A NEW ADAPTIVE SUBSPACE MINIMIZATION THREE-TERM CONJUGATE GRADIENT ALGORITHM FOR UNCONSTRAINED OPTIMIZATION

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Journal of Computational Mathematics 2021年第2期39卷 159-177页

作者： Keke Zhang Hongwei Liu Zexian Liu School of Mathematics and Statistics Xidian UniversityXi’an 710126China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering computingAMSSChinese Academy of SciencesBeijing 100190China

A new adaptive subspace minimization three-term conjugate gradient algorithm with nonmonotone line search is introduced and analyzed in this *** search directions are computed by minimizing a quadratic approximation of the objective function on special subspaces,and we also proposed an adaptive rule for choosing different searching directions at each *** obtain a significant conclusion that the each choice of the search directions satisfies the sufficient descent *** the used nonmonotone line search,we prove that the new algorithm is globally convergent for general nonlinear functions under some mild *** experiments show that the proposed algorithm is promising for the given test problem set.

关键词： Conjugate gradient method Nonmonotone line search Subspace minimization Sufficient descent condition Global convergence

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MVESF: Multi-View Enhanced Semantic Fusion for Controllable Text-to-Image Generation

MVESF: Multi-View Enhanced Semantic Fusion for Controllable ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhang, Hai Cao, Guitao Wang, Xinke Quan, Jiahao East China Normal University Shanghai Key Laboratory of Trustworthy Computing Shanghai China MoE Engineering Research Center of SW/HW Co-design Technology and Application East China Normal University Shanghai China

ISBN: (纸本)9781665410205

Text-to-image(T2I) synthesis aims to generate semantically consistent images with texts. Currently, existing methods merely use the shallow semantics of the text to crudely guide image generation. They cannot fully integrate rich textual semantics with image features, leading to an inability to control image generation through text finely. To address this issue, we propose a GAN-based method named MVESF (Multi-View Enhanced Semantic Fusion), which enhances the semantic fusion of text and images from multiple perspectives for fine-grained controllable text-to-image synthesis. In multi-view, we introduce Multi-domain Semantic Guidance, Local Semantic Attention, and Visual-textual Consistency Loss to enhance the semantic fusion of text and images in image generation, image discrimination, and image supervision, respectively. Our method promotes the consistent alignment between text and images, allowing for fine-grained variations in the generated images when subtle changes in the input text without affecting unrelated regions. Extensive experimental results have demonstrated the effectiveness of our approach. © 2024 IEEE.

关键词： Semantics

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Class-specific Prompts in Vision Transformer for Continual Learning of New Diseases

Class-specific Prompts in Vision Transformer for Continual L...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhao, Defeng Ye, Zejun Zheng, Wei-Shi Wang, Ruixuan Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Peng Cheng Laboratory Department of Network Intelligence Shenzhen China Moe Key Laboratory of Machine Intelligence and Advanced Computing China

ISBN: (纸本)9798350337488

Current intelligent diagnosis systems are often trained to diagnose a small number of diseases and lack the ability of continually learning new disease knowledge. To have such continual learning ability, the deployed intelligent system needs to be continually updated based on training data of only new diseases, without accessing old data of previously learned diseases due to privacy concerns and challenges in data sharing across medical centers. In this study, a novel and effective prompt learning strategy is proposed to help a pretrained and fixed Vision Transformer (ViT) continually learn new diseases. In particular, a set of unique prompts for each disease are effectively learned such that discriminative disease features can be well extracted from the fixed ViT under the instructions of prompts during feature extraction, even though the ViT feature extractor is pretrained in the natural image domain. Extensive empirical evaluations on two medical image datasets and one natural image dataset demonstrate the superior performance of the proposed method. The source code is available at https://***/zhaoedf/CSPrompt. © 2023 IEEE.

关键词： Continual learning Disease diagnosis Prompt learning

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Convergence of Physics-Informed Neural Networks Applied to Linear Second-Order Elliptic Interface Problems

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Communications in Computational Physics 2023年第2期33卷 596-627页

作者： Sidi Wu Aiqing Zhu Yifa Tang Benzhuo Lu State Key Laboratory of Scientific and Engineering Computing National Center for Mathematics and Interdisciplinary SciencesAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China.

With the remarkable empirical success of neural networks across diverse scientific disciplines,rigorous error and convergence analysis are also being developed and ***,there has been little theoretical work focusing on neural networks in solving interface *** this paper,we perform a convergence analysis of physics-informed neural networks(PINNs)for solving second-order elliptic interface ***,we consider PINNs with domain decomposition technologies and introduce gradient-enhanced strategies on the interfaces to deal with boundary and interface jump *** is shown that the neural network sequence obtained by minimizing a Lipschitz regularized loss function converges to the unique solution to the interface problem in H2 as the number of samples *** experiments are provided to demonstrate our theoretical analysis.

关键词： Elliptic interface problems generalization errors convergence analysis neural networks.

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Interpretable Drug Response Prediction through Molecule Structure-aware and Knowledge-Guided Visible Neural Network

Interpretable Drug Response Prediction through Molecule Stru...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xie, Jiancong Zhang, Zhe Li, Youyou Rao, Jiahua Yang, Yuedong Sun Yat-sen University School of Computer Science and Engineering China University of Chicago Department of Chemistry IL United States Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing of Moe China

ISBN: (纸本)9798350386226

Precise prediction of anti-cancer drug responses has become a crucial obstruction in anti-cancer drug design and clinical applications. In recent years, various deep learning methods have been applied to drug response prediction and become more accurate. However, they are still criticized as being non-transparent. To offer reliable drug response prediction in real-world applications, there is still a pressing demand to develop a model with high predictive performance as well as interpretability. In this study, we propose DrugVNN, an end-to-end interpretable drug response prediction framework, which extracts gene features of cell lines through a knowledge-guided visible neural network (VNN) and learns drug representation through a node-edge communicative message passing network (CMPNN). Additionally, between these two networks, a novel drug-aware gene attention gate is designed to direct the drug representation to VNN to simulate the effects of drugs. By evaluating on the GDSC dataset, DrugVNN achieved state-of-the-art performance. Moreover, DrugVNN can identify active genes and relevant signaling pathways for specific drug-cell line pairs with supporting evidence in the literature, implying the interpretability of our model. © 2024 IEEE.

关键词： Prediction models

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