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6th IEEE Information Technology and Mechatronics engineering Conference, ITOEC 2022

作者： Zhou, Liming Yang, Wencheng Du, Ying Liu, Cheng Li, Heng Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (纸本)9781665431859

The production of remote sensing products needs to go through various links of evaluation and deploy a variety of software and hardware resources. It is necessary to study how to improve the efficiency of service cluster deployment, simplify the evaluation process, and optimize its structure. This paper designs an intelligent service cluster construction method based on remote sensing applications, which realizes the accurate and efficient deployment of various complex network structures and equipment on the visual interface. The method is convenient and simple to operate. It can set various types of resource information according to the needs of users, can choose a more reasonable server cluster deployment plan, improve the efficiency of data storage and processing of remote sensing products, better serve users, and greatly promote The development of related industries. © 2022 IEEE.

关键词： Remote sensing

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Five-user quantum virtual local area network with an AlGaAs entangled photon source

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Science China(Physics,Mechanics & Astronomy) 2025年第3期68卷 61-73页

作者： Cheng Qian Hongkai Tian Xu Jing Yichen Liu Zhe Chen Huiran Luo Yuan Du Xiaodong Zheng Tangsheng Chen Yuechan Kong Hua-Lei Yin Dong Jiang Bin Niu Liangliang Lu Key Laboratory of State Manipulation and Advanced Materials in Provincial Universities School of Physical Science and TechnologyNanjing Normal UniversityNanjing210023China School of Internet Anhui UniversityHefei230039China National Key Laboratory of Solid-State Microwave Devices and Circuits Nanjing Chip Valley Industrial Technology InstituteNanjing Electronic Devices InstituteNanjing210016China Research Center for Quantum Optics and Quantum Communication School of ScienceQingdao University of TechnologyQingdao266520China Department of Physics and Beijing Key Laboratory of Opto-electronic Functional Materials and Micro-nano Devices Key Laboratory of Quantum State Construction and Manipulation(Ministry of Education)Renmin University of ChinaBeijing100872China Beijing Academy of Quantum Information Sciences Beijing100193China National Engineering Research Center of Agro-Ecological Big Data Analysis and Application Anhui UniversityHefei230601China National Laboratory of Solid-State Microstructures and School of Physics Nanjing UniversityNanjing210093China Hefei National Laboratory Hefei230088China

The promotion of quantum network applications demands the scalable connection of quantum *** is preferable to set up multiple logical networks coexisting on a single physical network infrastructure to accommodate a larger number of *** we present a quantum virtual network architecture that offers this level of scalability,without being constrained to a fixed physical-layer network relying solely on passive multiplexing *** architecture can be understood as arising from the superposition of a fully connected entanglement distribution network and port-based virtual local area network,which group multiusers by access *** terms of hardware,we leverage a semiconductor chip with a high figure-of-merit modal overlap to directly generate high-quality polarization entanglement,and a streamlined polarization analysis module,which requires only one single-photon detector for each end *** experimentally perform the BBM92 QKD protocol on the five-user quantum virtual network and demonstrate voice and image encryption on a campus area *** results may provide insights into the realization of large-scale quantum networks with integrated and cost-efficient photonic architecture.

关键词： quantum network quantum entanglement distribution integrated photonics image and voice encryption

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ATPRINPM: A single-source domain generalization method for the remaining useful life prediction of unknown bearings

ATPRINPM: A single-source domain generalization method for t...

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Sensing, Measurement & data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Juan Xu Bin Ma Yuqi Fan Xu Ding Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei University of Technology Hefei China

The remaining useful life (RUL) of bearings is critical to the proper operation of mechanical equipment, maintenance of equipment costs and availability. The existing domain adaptation methods have had great success in RUL prediction. However, when the target bearing data are unavailable or unknown to be involved in model training, the domain adaptation approaches also incapable. To solve the problem, we propose a parallel reversible instance normalization method based on adaptive threshold stage division for remaining useful life prediction of unknown bearings. First, we design an adaptive threshold method to find degradation points to divide the healthy and degradation stages. Then according to time series, we merge the original vibration data and its instance normalized data to increase the data distribution diversity. Finally, we combine instance normalization and parallel reversible normalization of the source bearing data into unified RUL learning framework to solve the uncertainty of counterfactual data and improve RUL prediction performance. The results show that the method is superior to the state-of-the-art methods for RUL prediction of unknown bearings.

关键词： Degradation Vibrations Training Adaptation models Uncertainty data analysis Time series analysis

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A new gene regulatory network reconstruction method based on single cell sequencing and transfer entropy 2

A new gene regulatory network reconstruction method based on...

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2nd International Conference on Electronic Information engineering, big data, and Computer Technology, EIBDCT 2023

作者： Zhu, Zhengtong Gao, Jing Liu, Zhenyu College of Computer and Information Engineering The Inner Mongolia Agricultural University Huhhot010018 China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research and Application of Agriculture and Animal Husbandry huhhot010018 China

ISBN: (纸本)9781510664968

In systems biology, gene regulatory networks can well reveal complex biological systems and dynamic biological processes. Traditional gene regulatory network reconstruction methods ignore dynamic biological processes. A gene regulatory network reconstruction method based on transfer entropy and single-cell dynamic time series data is proposed to solve this problem. And from a theoretical point of view, the possibility and rationality of transfer entropy in the application of big data and causal reasoning are proved in detail. Firstly, the method construct pseudo-time series dynamic gene expression data based on trajectory inference. Secondly, The transfer entropy is used to calculate the directional transfer information between paired genes, and screen the major gene regulatory relationships. Finally, remove the indirect gene regulation relationship according to the data processing inequality, and construct the gene regulation network. This method takes the single cell sequencing data of early mouse embryo blood development as an example, and selects TENET and DynGENIE3 algorithms as comparisons, which proves the feasibility and effectiveness of this method from the theoretical and experimental perspectives. The experimental results not only identify the key cell development regulatory factors and regulatory relationships, but also consume less time. It can not only provide important reference value and assumptions for biological disturbance experiment, but also reduce the time cost and research cost of biological experiment. © 2023 SPIE.

关键词： Gene expression

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Extracting Named Entity Using Entity Labeling in Geological Text Using Deep Learning Approach

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Journal of Earth Science 2023年第5期34卷 1406-1417页

作者： Qinjun Qiu Miao Tian Zhong Xie Yongjian Tan Kai Ma Qingfang Wang Shengyong Pan Liufeng Tao State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR Chinese Academy of Surveying and MappingBeijing 100036China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities MNRShanghai 20063China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Chengdu Geological Environment Monitoring Station Chengdu 610036China Wuhan Zondy Cyber Science&Technology Co. Ltd.Wuhan 430074China

Artificial intelligence(AI) is the key to mining and enhancing the value of big data, and knowledge graph is one of the important cornerstones of artificial intelligence, which is the core foundation for the integration of statistical and physical representations. Named entity recognition is a fundamental research task for building knowledge graphs, which needs to be supported by a high-quality corpus, and currently there is a lack of high-quality named entity recognition corpus in the field of geology, especially in Chinese. In this paper, based on the conceptual structure of geological ontology and the analysis of the characteristics of geological texts, a classification system of geological named entity types is designed with the guidance and participation of geological experts, a corresponding annotation specification is formulated, an annotation tool is developed, and the first named entity recognition corpus for the geological domain is annotated based on real geological reports. The total number of words annotated was 698 512 and the number of entities was 23 345. The paper also explores the feasibility of a model pre-annotation strategy and presents a statistical analysis of the distribution of technical and term categories across genres and the consistency of corpus annotation. Based on this corpus, a Lite Bidirectional Encoder Representations from Transformers(ALBERT)-Bi-directional Long Short-Term Memory(BiLSTM)-Conditional Random Fields(CRF) and ALBERT-BiLSTM models are selected for experiments, and the results show that the F1-scores of the recognition performance of the two models reach 0.75 and 0.65 respectively, providing a corpus basis and technical support for information extraction in the field of geology.

关键词： ontology geological reports named entity recognition geological corpus construction semi-automated annotation platforms deep learning

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Considering the full induction control of intersection for intelligent connected new energy vehicles

Considering the full induction control of intersection for i...

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2023 International Conference on Computer Application and Information Security, ICCAIS 2023

作者： Wang, Ruihan Liang, Zijun Li, Yuqi Deng, Ran Zhan, Xuejuan Shan, Ailin Wang, Ruipeng Niu, Yiran School of Urban Construction and Transportation Hefei University Anhui Hefei230601 China Anhui Provincial Key Laboratory of Urban Rail Transit Safety and Emergency Management Anhui Hefei230601 China Anhui Province Transportation Big Data Analysis and Application Engineering Laboratory Anhui Hefei230601 China

ISBN: (纸本)9781510675032

In response to the inadequacy of timing control methods in adapting to the random changes in traffic flow at intersections and the neglect of the impact of intelligent connected new energy vehicles (ICNEVs), we investigate the effectiveness of a full induction control logic for intersections considering ICNEVs. Firstly, vehicle headway and queue detectors are deployed at each approach of the intersection to monitor real-time traffic conditions. Subsequently, a full induction control logic and corresponding parameters are established based on the clearance and queue status of vehicles at the intersection. The study then considers the influence of different permeability of ICNEVs on the full induction control logic. Finally, a comparison is made among the current state scheme, timed optimization, and full induction control schemes using VISSIM traffic simulation software. Additionally, the study further analyzes the effects of intersection full induction control under different permeability of ICNEVs, thereby validating the effectiveness of the research. © 2024 SPIE.

关键词： Computer software

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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to data Clustering

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Computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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A Semi-supervised Bilingual Lexicon Induction Method for Distant Language Pairs Based on Bidirectional Adversarial Model 12

A Semi-supervised Bilingual Lexicon Induction Method for Dis...

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12th IEEE International Conference on big knowledge, ICBK 2021

作者： Zhi, Wenwu Zhang, Yuhong Key Laboratory of Knowledge Engineering With Big Data Hefei University of Technology Ministry of Education !!!School of Computer Science and Information Engineering @@@Hefei University of Technology Hefei China

ISBN: (纸本)9781665438582

Bilingual lexicon induction (BLI) can transfer knowledgefrom well- to under- resourced language, and has been widelyapplied to various NLP tasks. Recent work on BLI is projection-based that learns a mapping to connect source and target embedding spaces, with the isomorphism assumption. Unfortunately, the isomorphism assumption doesn't hold gener-ally, especially in typologically distant language pairs. Moreover, without supervised signals guiding, the training will further com-plicates BLI, making the performance of unsupervised methods unsatisfactory. To broke the restrict of isomorphism, we propose a semi-supervised method for distant BLI tasks, named A Semi-supervised Bilingual Lexicon Induction method in Latent Space based on Bidirectional Adversarial Model. First, two latent spaces are learned by two autoencoders for source and target domain independently to weaken the constraint of isomorphism in the embedding spaces. Then we add a few pairs of dictionary to learn the initial mapping to connect the Latent Space. Last, based on initial mapping, Cycle-Consistency is combined with Distance constraint constraint to maintain the geometry structure of both embedding spaces stable in the learning of bi-direction mapping based on adversarial model. By conducting extensive experiments, our method gets state-of-the-art results on most language pairs, especially with significant improvements on distant language pairs. © 2021 IEEE.

关键词： Mapping

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AI-Enhanced Secure data Aggregation for Smart Grids with Privacy Preservation

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Computers, Materials & Continua 2025年第1期82卷 799-816页

作者： Congcong Wang Chen Wang Wenying Zheng Wei Gu School of Software Nanjing University of Information Science and TechnologyNanjing210044China School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China School of Computer Science(School of Cyber Science and Engineering) Nanjing University of Information Science and TechnologyNanjing210044China

As smart grid technology rapidly advances,the vast amount of user data collected by smart meter presents significant challenges in data security and privacy *** research emphasizes data security and user privacy concerns within smart ***,existing methods struggle with efficiency and security when processing large-scale *** efficient data processing with stringent privacy protection during data aggregation in smart grids remains an urgent *** paper proposes an AI-based multi-type data aggregation method designed to enhance aggregation efficiency and security by standardizing and normalizing various data *** approach optimizes data preprocessing,integrates Long Short-Term Memory(LSTM)networks for handling time-series data,and employs homomorphic encryption to safeguard user *** also explores the application of Boneh Lynn Shacham(BLS)signatures for user *** proposed scheme’s efficiency,security,and privacy protection capabilities are validated through rigorous security proofs and experimental analysis.

关键词： Smart grid data security privacy protection artificial intelligence data aggregation

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Learning from Semi-Factuals: A Debiased and Semantic-Aware Framework for Generalized Relation Discovery

arXiv

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arXiv 2024年

作者： Wang, Jiaxin Zhang, Lingling Liu, Jun Guo, Tianlin Wu, Wenjun The School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China The Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Shaanxi Xi’an710049 China

We introduce a novel task, called Generalized Relation Discovery (GRD), for open-world relation extraction. GRD aims to identify unlabeled instances in existing pre-defined relations or discover novel relations by assigning instances to clusters as well as providing specific meanings for these clusters. The key challenges of GRD are how to mitigate the serious model biases caused by labeled pre-defined relations to learn effective relational representations and how to determine the specific semantics of novel relations during classifying or clustering unlabeled instances. We then propose a novel framework, SFGRD, for this task to solve the above issues by learning from semi-factuals in two stages. The first stage is semi-factual generation implemented by a tri-view debiased relation representation module, in which we take each original sentence as the main view and design two debiased views to generate semi-factual examples for this sentence. The second stage is semi-factual thinking executed by a dual-space tri-view collaborative relation learning module, where we design a cluster-semantic space and a class-index space to learn relational semantics and relation label indices, respectively. In addition, we devise alignment and selection strategies to integrate two spaces and establish a self-supervised learning loop for unlabeled data by doing semi-factual thinking across three views. Extensive experimental results show that SFGRD surpasses state-of-the-art models in terms of accuracy by 2.36% ∼5.78% and cosine similarity by 32.19%∼ 84.45% for relation label index and relation semantic quality, respectively. To the best of our knowledge, we are the first to exploit the efficacy of semi-factuals in relation extraction. Copyright © 2024, The Authors. All rights reserved.

关键词： Extraction

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