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Manipulating Piezoelectric and Electro-Strain Properties of BiFeO3-BaTiO3-Based Ceramics Through Chemical Doping-Controlled Domain-Size Engineering

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SSRN 2025年

作者： Lin, Jiamin Liu, Bing Liu, Mengxiang Yang, Shan Zhou, Linming Hong, Zijian Zhu, Xiaoli Wu, Yongjun Li, Juan Huang, Yuhui School of Materials Science and Engineering State Key Laboratory of Silicon and Advanced Semiconductor Materials Cyrus Tang Center for Sensor Materials and Applications Institute of Fundamental and Transdisciplinary Research Zhejiang University Hangzhou310027 China Nanhu Brain-computer Interface Institute Zhejiang Hangzhou311100 China College of Electronic Information and Engineering Hangzhou Dianzi University Zhejiang Hangzhou310018 China Zhejiang Key Laboratory of Advanced Solid State Energy Storage Technology and Applications Taizhou Institute of Zhejiang University Zhejiang Taizhou318000 China School of Engineering Hangzhou City University Zhejiang Hangzhou310015 China College of Materials and Science Engineering Zhejiang University of Technology Zhejiang Hangzhou310006 China

Ferroelectric domains are crucial for the performance of piezoelectric ceramics, as the size and switching dynamics affect polarization response directly, manipulating both ferroelectric and piezoelectric properties. In this study, we achieved BiFeO3-BaTiO3 ceramics with domain sizes from macroscopic to nanoscale by incorporating a small amount of antiferroelectric phase. Contrary to the long-standing belief that smaller ferroelectric domains own lower polarization switching barriers and yield higher piezoelectricity, we found that in chemical doping-controlled domain size engineering, the smaller the domain size, the more difficult it is for polarization switching. Ceramics with the largest domain exhibited superior positive piezoelectric performance due to the sufficient polarization switching and reduced domain wall pinning, achieving a piezoelectric coefficient (d33) of 460 pC/N at 350°C. Middle-sized domains achieved optimal electro-strain performance determined by the trade-off between the domain walls pinning and the increased local tetragonal or orthorhombic symmetries, with a strain (0.29%) and an excellent piezoelectric strain coefficient (d33*) of 726.4 pm/V at 160°C. This work provides novel insights into how domain-size engineering affects domain switching and local distortion, offering guidance for performance optimization of piezoelectric ceramics. © 2025, The Authors. All rights reserved.

关键词： Piezoelectricity

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BOOTSTRAPPING FOR EXTRACTING RELATIONS FROM LARGE CORPORA

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Journal of Electronics(China) 2008年第1期25卷 89-96页

作者： Li Weigang Liu Ting Li Sheng Information Retrieval Laboratory School of Computer Science and Technology Harbin Institute of Technology Harbin 150001 China

A new approach of relation extraction is described in this paper. It adopts a bootstrap- ping model with a novel iteration strategy, which generates more precise examples of specific relation. Compared with previous methods, the proposed method has three main advantages: first, it needs less manual intervention; second, more abundant and reasonable information are introduced to represent a relation pattern; third, it reduces the risk of circular dependency occurrence in bootstrapping. Scalable evaluation methodology and metrics are developed for our task with comparable techniques over TianWang 100G corpus. The experimental results show that it can get 90% precision and have excellent expansibility.

关键词： Relation extraction Bootstrapping Patterns Tuples

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A fault recognition method based on clustering linear regression

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High technology Letters 2023年第4期29卷 406-415页

作者：陈雷 SHI Jiaqi ZHANG Ting Computer School Beijing Information Science and Technology UniversityBeijing 100101P.R.China Faculty of Information Technology Beijing University of TechnologyBeijing 100124P.R.China

Aiming at the problems of low accuracy,long time consumption,and failure to obtain quantita-tive fault identification results of existing automatic fault identification technic,a fault recognition method based on clustering linear regression is ***,Hough transform is used to detect the line segment of the enhanced image obtained by the coherence cube ***,the endpoint of the line segment detected by Hough transform is taken as the key point,and the adaptive clustering linear regression algorithm is used to cluster the key points adaptively according to the lin-ear relationship between ***,a fault is generated from each category of key points based on least squares curve fitting method to realize fault *** verify the feasibility and pro-gressiveness of the proposed method,it is compared with the traditional method and the latest meth-od on the actual seismic data through experiments,and the effectiveness of the proposed method is verified by the experimental results on the actual seismic data.

关键词： fault recognition clustering linear regression curve fitting seismic interpreta-tion

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Unleashing the power of pseudo-code for binary code similarity analysis

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Cybersecurity 2023年第2期6卷 44-61页

作者： Weiwei Zhang Zhengzi Xu Yang Xiao Yinxing Xue School of Computer Science and Engineering University of Science and Technology of ChinaHefeiChina School of Computer Science and Engineering Nanyang Technological UniversitySingaporeSingapore Institute of Information Engineering Chinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina

Code similarity analysis has become more popular due to its significant applicantions,including vulnerability detection,malware detection,and patch *** the source code of the software is difficult to obtain under most circumstances,binary-level code similarity analysis(BCSA)has been paid much attention *** recent years,many BCSA studies incorporating Al techniques focus on deriving semantic information from binary functions with code representations such as assembly code,intermediate representations,and control flow graphs to measure the ***,due to the impacts of different compilers,architectures,and obfuscations,binaries compiled from the same source code may vary considerably,which becomes the major obstacle for these works to obtain robust *** this paper,we propose a solution,named UPPC(Unleashing the Power of Pseudo-code),which leverages the pseudo-code of binary function as input,to address the binary code similarity analysis challenge,since pseudocode has higher abstraction and is platform-independent compared to binary *** selectively inlines the functions to capture the full function semantics across different compiler optimization levels and uses a deep pyramidal convolutional neural network to obtain the semantic embedding of the *** evaluated UPPC on a data set containing vulnerabilities and a data set including different architectures(X86,ARM),different optimization options(O0-O3),different compilers(GCC,Clang),and four obfuscation *** experimental results show that the accuracy of UPPC in function search is 33.2%higher than that of existing methods.

关键词： Binary code similarity Machine learning Software security Pseudo-code

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TagCombine： Recommending Tags to Contents in Software information Sites

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Journal of computer science & technology 2015年第5期30卷 1017-1035页

作者：王新宇夏鑫 David Lo 1.College of Computer Science and Technology Zhejiang University Hangzhou 310027 China 2.School of Information Systems Singapore Management University Singapore Singapore

Nowadays, software engineers use a variety of online media to search and become informed of new and interesting technologies, and to learn from and help one another. We refer to these kinds of online media which help software engineers improve their performance in software development, maintenance, and test processes as software information sites. In this paper, we propose TagCombine, an automatic tag recommendation method which analyzes objects in software information sites. TagCombine has three different components： 1） multi-label ranking component which considers tag recommendation as a multi-label learning problem; 2） similarity-based ranking component which recommends tags from similar objects; 3） tag-term based ranking component which considers the relationship between different terms and tags, and recommends tags after analyzing the terms in the objects. We evaluate TagCombine on four software information sites, Ask Different, Ask Ubuntu, Feecode, and Stack Overflow. On averaging across the four projects, TagCombine achieves recall@5 and recallS10 to 0.619 8 and 0.762 5 respectively, which improves TagRec proposed by Al-Kofahi et al. by 14.56% and 10.55% respectively, and the tag recommendation method proposed by Zangerle et al. by 12.08% and 8.16% respectively.

关键词： software information site online media tag recommendation

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Threshold quantum secure direct communication without entanglement

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science China(Physics,Mechanics & Astronomy) 2008年第2期51卷 176-183页

作者： YANG YuGuang1,3 & WEN QiaoYan2 1 College of computer science and technology, Beijing University of technology, Beijing 100022, China 2 school of science, Beijing University of Posts and Telecommunications, Beijing 100876, China 3 State Key Laboratory of information Security (Graduate school of Chinese Academy of sciences), Beijing 100049, China College of Computer Science and Technology Beijing University of Technology Beijing China State Key Laboratory of Information Security (Graduate School of Chinese Academy of Sciences) Beijing China School of Science Beijing University of Posts and Telecommunications Beijing China

For the first time, a threshold quantum secure direct communication (TQSDC) scheme is presented. Similar to the classical Shamir's secret sharing scheme, the sender makes n shares, S1, …, Sn of secret key K and each receiver keeps a share secretly. If the sender wants to send a secret message M to the receivers, he en-codes the information of K and M on a single photon sequence and sends it to one of the receivers. According to the secret shares, the t receivers sequentially per-form the corresponding unitary operations on the single photon sequence and ob-tain the secret message M. The shared shares may be reusable if it can be judged that there is no eavesdropper in line. We discuss that our protocol is feasible with current technology.

关键词： quantum secure direct communication threshold secure direct communication quantum secret sharing

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Towards effective bug triage with software data reduction techniques

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IEEE Transactions on Knowledge and Data Engineering 2015年第1期27卷 264-280页

作者： Xuan, Jifeng Jiang, He Hu, Yan Ren, Zhilei Zou, Weiqin Luo, Zhongxuan Wu, Xindong School of Software Dalian University of Technology Dalian China INRIA Lille-Nord Europe Lille France Jiangxi University of Science and Technology Nanchang China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science University of Vermont United States

Software companies spend over 45 percent of cost in dealing with software bugs. An inevitable step of fixing bugs is bug triage, which aims to correctly assign a developer to a new bug. To decrease the time cost in manual work, text classification techniques are applied to conduct automatic bug triage. In this paper, we address the problemof data reduction for bug triage, i.e., how to reduce the scale and improve the quality of bug data. We combine instance selection with feature selection to simultaneously reduce data scale on the bug dimension and the word dimension. To determine the order of applying instance selection and feature selection, we extract attributes from historical bug data sets and build a predictive model for a new bug data set. We empirically investigate the performance of data reduction on totally 600,000 bug reports of two large open source projects, namely Eclipse and Mozilla. The results show that our data reduction can effectively reduce the data scale and improve the accuracy of bug triage. Our work provides an approach to leveraging techniques on data processing to form reduced and high-quality bug data in software development and maintenance. © 2014 IEEE.

关键词： Data reduction

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Early detection of cotton Verticillium wilt based on generative adversarial networks and hyperspectral imaging technology

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Industrial Crops and Products 2025年 231卷

作者： Tan, Fei Cang, Hao Gao, Xiuwen Wu, Nianyi Di, Ruoyu Zhang, Yuan Gao, Pan Lv, Xin Zhang, Chu College of Information Science and Technology Shihezi University Shihezi832003 China College of Mechanical and Electrical Engineering Shihezi University Shihezi832003 China School of Computer Science and Technology Soochow University Suzhou215006 China College of Agriculture Shihezi University Shihezi832003 China School of Information Engineering Huzhou University Huzhou313000 China

Cotton is one of most important economic crops in the world. Cotton yield has been significantly affected by frequent infestations of Verticillium wilt (VW). Currently, most detection methods for cotton VW are implemented based on the clearly visible symptoms, leading to delayed interventions and control. Therefore, early detection of cotton VW is crucial for minimizing economic losses. However, existing early detection methods for cotton VW face substantial challenges due to the subtle nature of early-stage symptoms and the limited availability of data, which result in considerable error. To address this, an early detection method for cotton VW by integrating Generative Adversarial Networks (GANs) with hyperspectral imaging technology was proposed, focusing primarily on cotton hyperspectral data augmentation. GANS-based spectral enhancement model (Spe-GAN) and GANS-based spatial-enhancement model (Spa-GAN) were developed to capture subtle early-stage symptoms of VW under limited data from both spectral and spatial perspectives. Compared to traditional machine learning methods (RF and SVM) and deep learning methods (LSTM and ResNet18), the proposed Spe-GAN and Spa-GAN achieved better detection performance, with accuracy rates of 94.52 % and 91.78 %, respectively. Moreover, this study also explored the underlying reasons for the superiority of the proposed method from various perspectives, further enhancing the model interpretability. The data augmentation method proposed in this study provided a new perspective and opened up possibilities for achieving the early detection of cotton VW and other plant diseases. © 2025 The Authors

关键词： Cotton

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Fuzzy Knowledge Distillation Model for Incomplete Multi-modality Data and Its Application to Brain Network Analysis

Fuzzy Knowledge Distillation Model for Incomplete Multi-moda...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Qi, Xiaoyu Huang, Jiashuang Cheng, Xueyun Wang, Mingliang Ding, Weiping Nantong University School of Information Science and Technology Nantong China Nanjing University of Information Science and Technology School of Computer and Software Nanjing China

ISBN: (纸本)9798350386226

Knowledge distillation (KD) techniques have been employed to tackle the issue of incomplete multi-modality data, aiming to enhance the performance of the student model (i.e., using unimodal data) as close as possible to the teacher model (i.e., using multimodal data) through information transfer between the two models. However, directly using the traditional KD model will lead to the problem of mismatched model capacities. The teacher model typically receives a greater amount of information compared to the student model. If the learned information from the teacher model is directly transferred, it will impact the performance of the student model. To this end, this paper proposes a novel fuzzy knowledge distillation (fuzzy-KD) model for brain network analysis of incomplete multi-modality data. Specifically, we design a fuzzy-KD module to reduce the information gap between the teacher and student models through two operations: fuzzy processing and feature selection. The fuzzy processing applies an affiliation function to each channel of the feature maps learned by the teacher model to improve the generalization of representations. The feature selection employs channel compression to compel the student model to focus on crucial information within the teacher model. Experiments on real epilepsy datasets show that our method outperforms several state-of-the-art methods in the field of epilepsy recognition. © 2024 IEEE.

关键词： Feature Selection

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A resilient trust management scheme for defending against reputation time-varying attacks based on BETA distribution

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science China(information sciences) 2017年第4期60卷 60-70页

作者： Weidong FANG Wuxiong ZHANG Yang YANG Yang LIU Wei CHEN Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai Research Center for Wireless Communication School of Computer Science and Technology China University of Mining and Technology

Compared with the encryption and the authentication which can prevent the external attacks,the trust management schemes are the better approaches for defending against the internal attacks in wireless sensor network(WSN). The reputation time-varying(RTV) attacks are internal attacks. In the reputation timevarying attacks, the reputation value of nodes is manipulated to adjust dynamically by the compromised nodes or malicious attackers. Hence, these attacks have greater covert and invasive. In this paper, we propose a Timewindow-based Resilient Trust Management Scheme(TRTMS) to defend against the reputation time-varying attacks in wireless sensor network. In this scheme, based on BETA distribution, the behaviors of compromised nodes are analyzed for a period of time, and then the difference judgment and the trend analysis are utilized to identify the abnormality of nodes’ reputation value, meanwhile, the control factor Fc and the time window are introduced to verify and remove the compromised modes from the suspected malicious nodes, which refer to those nodes’ reputation value changes are caused by the wireless channel changes. The result of simulation shows that our proposed scheme can defend reputation time-varying attacks effectively and it is convenient to implement.

关键词： wireless sensor network(WSN) reputation time-varying attack trust value trust management time window

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