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Deep learning-enabled inverse design of polarization-selective structural color based on coding metasurface

Chinese Physics B 2025年第5期34卷 311-318页

作者： Haolin Yang Bo Ni Junhong Guo Hua Zhou Jianhua Chang Jiangsu Key Laboratory of Meteorological Observation and Information Processing School of Electronic and Information EngineeringNanjing University of Information Science and TechnologyNanjing 210044China College of Electronic and Optical Engineering and College of Flexible Electronics(Future Technology) Nanjing University of Posts and TelecommunicationsNanjing 210023China

Structural colors based on metasurfaces have very promising applications in areas such as optical image encryption and color ***,we propose a deep learning-enabled reverse design of polarization-selective structural color based on coding *** this study,the long short-term memory(LSTM)neural network is presented to enable the forward and inverse mapping between coding metasurface structure and corresponding *** results show that the method can achieve 98%accuracy for the forward prediction of color and 93%accuracy for the inverse design of the ***,a cascaded architecture is adopted to train the inverse neural network model,which can solve the nonuniqueness problem of the polarization-selective color reverse *** study provides a new path for the application and development of structural colors.

关键词： deep learning inverse design coding metasurface structural color polarization-selective

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Adaptive Multi-Updating Strategy Based Particle Swarm Optimization

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Intelligent Automation & Soft Computing 2023年第9期37卷 2783-2807页

作者： Dongping Tian Bingchun Li Jing Liu Chen Liu Ling Yuan Zhongzhi Shi School of Computer Science and Technology Kashi UniversityKashi844006China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing100190China

Particle swarm optimization(PSO)is a stochastic computation tech-nique that has become an increasingly important branch of swarm intelligence ***,like other evolutionary algorithms,PSO also suffers from premature convergence and entrapment into local optima in dealing with complex multimodal *** this paper puts forward an adaptive multi-updating strategy based particle swarm optimization(abbreviated as AMS-PSO).To start with,the chaotic sequence is employed to generate high-quality initial particles to accelerate the convergence rate of the ***,according to the current iteration,different update schemes are used to regulate the particle search process at different evolution *** be specific,two different sets of velocity update strategies are utilized to enhance the exploration ability in the early evolution stage while the other two sets of velocity update schemes are applied to improve the exploitation capability in the later evolution *** by the unequal weightage of acceleration coefficients is used to guide the search for the global worst particle to enhance the swarm *** addition,an auxiliary update strategy is exclusively leveraged to the global best particle for the purpose of ensuring the convergence of the PSO ***,extensive experiments on two sets of well-known benchmark functions bear out that AMS-PSO outperforms several state-of-the-art PSOs in terms of solution accuracy and convergence rate.

关键词： Particle swarm optimization local optima acceleration coefficients swarm diversity premature convergence

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Identifying Influential Spreaders in Complex Networks Using Neighborhood Network Structure 35

Identifying Influential Spreaders in Complex Networks Using ...

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35th International Conference on Software Engineering and Knowledge Engineering, SEKE 2023

作者： Zhang, Ziyi Yan, Rong Yuan, Wei College of Computer Science Inner Mongolia University Inner Mongolia Key Laboratory of Mongolian Information Processing Technology National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot010021 China

Identifying influential spreaders is a hot topic in complex network research. While centrality-based algorithms are easy to implement, they often have lower accuracy. Topology-based algorithms are effective for identifying network center influential spreaders but may not perform well in identifying peripheral influential spreaders. To address these problems, we propose a Neighborhood Structure Centrality (NSC) algorithm, which utilizes structural embedding and clustering to collect various network structural information and calculates node influence based on both node and neighborhood structural information. We compare the NSC algorithm with twelve baseline algorithms on six public datasets and four synthetic network datasets and demonstrate its higher accuracy. © 2023 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Complex networks

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IGED:Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

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computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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An Optimization Method for Weight Computation in Hypergraph Transductive Learning Model 9

An Optimization Method for Weight Computation in Hypergraph ...

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9th International Conference on Advanced Cloud and Big Data, CBD 2021

作者： Yang, Zhe Xu, Liangkui Bu, Ziyu Chen, Yipeng Information Processing Technology Provincial Key Laboratory for Computer Soochow University School of Computer Science and Technology Suzhou China

ISBN: (纸本)9781665407458

Compared with the graph structure, the hyper-graph structure can convey more complex and multivariate relationships. In addition, hypergraphs are more capable of portraying and mining higher-order nonlinear correlations among objects. Therefore, it has been widely used in feature selection, clustering classification, and addressing other issues, and hypergraph learning has also become an important branch in the field of machine learning. Hypergraph learning, however, is a relatively high time consuming learning algorithm, because in its iterative solving process, there are lots of high-dimension matrix computations with large-scale parameters. This paper proposes an optimization of the weights, which is constructed by Gaussian kernel, in hyper-graph transductive learning model the weight matrix is replaced by fixed parameters when constructing the hypergraph laplacian matrix, which reduces the time consuming of weights computation in iterative process. Extensive experimental with semi-supervised node classification experiment results on the UCI and Kaggle datasets show that the optimized model reduces the time consuming of the hypergraph model, while ensuring classification accuracy. After model optimization, the four accuracy indicators fluctuate on average 3.03%, the time consuming is reduced by 28.99% on average, and the maximum saving is 42.84%. © 2022 IEEE.

关键词： Learning algorithms

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A Conflict-Priority-Based Variable Neighborhood Tabu Search Method for Multi-Satellite Scheduling

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Advances in Astronautics Science and technology 2024年第2期7卷 163-176页

作者： Chen, Xiaoyu Gao, Qi Peng, Shengren Song, Shihui Liu, Yufei Dai, Guangming Wang, Maocai Zhang, Changxing School of Computer China University of Geosciences Lumo Road Wuhan430074 China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Lumo Road Wuhan430074 China Qian Xuesen Laboratory China Academy of Space Technology Youyi Road Beijing100094 China

With the increasing number and capabilities of orbital satellites and their onboard sensors, satellites scheduling plays an important role in growing demands for Earth observation tasks. A conflict-priority-based variable neighborhood tabu search method is proposed in this paper. By analyzing the flexibility of resources, the conflict-priority of tasks, and the visible time window features, the indicators of the impact, the conflict, the flexibility, and the bad impact are first established. On this basis, a first-come first-served greedy algorithm and a minimum bad impact greedy algorithm are designed for the generation of the initial solution. some rule-based heuristic strategies are also adopted for optimizing. Additionally, the algorithm incorporates three types of neighborhood structures including insertion, swapping and rearrangement, and deletion, using a tabu list to avoid local optima. Experimental results indicate the validity and efficiency of the proposed method. All solutions are within 10% of the optimal solution, with some even within 5%.The proposed method is effective for obtaining optimum solutions or solutions with a very good quality in various multi-satellite scheduling scenarios of realistic planning. © Chinese Society of Astronautics 2024.

关键词： Tabu search

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Specific Emitter Identification Based on ACGAN and STFT 7

Specific Emitter Identification Based on ACGAN and STFT

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7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Wang, Bin Xie, Jun Wang, Feng The 38th Research Institute Of China Electronics Technology Group Corporation China Key Laboratory Of Aperture Array And Space Application China Key Laboratory Of Intelligent Information Processing Anhui Hefei230088 China School Of Computer And Information Hefei University Of Technology Hefei230601 China

ISBN: (纸本)9798350361445

Deep learning methodologies are gaining popularity for enhancing the efficiency of Specific Emitter Identification (SEI). Nevertheless, the lack of labeled datasets continues to pose a challenge in most non-cooperative scenarios for SEI. Addressing this challenge, a framework for SEI is proposed, leveraging Short-Time Fourier Transform (STFT) in conjunction with auxiliary classifier GAN(ACGAN). The Short-Time Fourier Transform of emitters is utilized as radio-frequency fingerprint for the emitters. The input latent layer is infused with label information to steer the training process of the Generative Adversarial Network. Through semi-supervised training, shared weight updates are implemented for the classification network. Experimental results demonstrate that our model can achieve superior or competitive performances in several datasets compared with the former models. © 2024 IEEE.

关键词： Deep learning

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An Entity Alignment Algorithm Based on Ontology Enhancement and Multi-Feature Fusion 6

An Entity Alignment Algorithm Based on Ontology Enhancement ...

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6th International Conference on Frontier Technologies of information and computer, ICFTIC 2024

作者： Song, Hao Lei, Yuxia Zi, Kangli Quan, Fuyuan Zhang, Tianhang Li, Qi School of Computer Science Qufu Normal University Shandong Rizhao China Institute of Computing Technology Chinese Academy of Sciences Key Laboratory of Intelligent Information Processing Beijing China

ISBN: (纸本)9798331541750

Entity alignment plays a crucial role in the integration of knowledge graphs. However, current knowledge graphs often suffer from inconsistent construction standards, structural heterogeneity, and imprecise semantic representation of entities, which can lead to suboptimal embedding of structural information. Moreover, most existing entity alignment methods rely heavily on structural information, which limits alignment efficiency. To address these challenges, this paper presents Onto-IMF, an ontology-enhanced entity alignment method based on multi-feature fusion. This approach aims to enhance the semantic representation of entities by incorporating ontology information, thereby reducing structural discrepancies among heterogeneous graphs and improving the embedding of structural information. The multi-feature fusion entity alignment leverages diverse semantic features, including relations, attributes, and entity names, to achieve more accurate alignment. Experimental results on various DBP15K subsets indicate that the proposed method achieves Hits@l scores of 86.6%, 93.0%, and 97.7%, respectively, representing an average improvement of 7.9% over the best-performing baseline models. © 2024 IEEE.

关键词： Knowledge graph

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Deep Representation Learning for Electron Ionization Mass Spectra Retrieval

Deep Representation Learning for Electron Ionization Mass Sp...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Shibo Zhang, Xuan Jiao, Anlei Sun, Shiwei Dian, Longyang Cui, Xuefeng Shandong University School of Computer Science and Technology Qingdao China Chinese Academy of Sciences Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China Shandong University State Key Laboratory of Microbial Technology Qingdao China

ISBN: (纸本)9798350386226

The task of retrieving and analyzing mass spectra is indispensable for the identification of compounds in mass spectrometry (MS). This methodology is of critical importance as it enables researchers to correlate observed spectra with established databases, thereby precisely determining the chemical composition of samples. The primary challenges to its efficacy lie in optimizing the balance between retrieval accuracy and processing speed. Empirical studies have demonstrated that by converting mass spectra into embeddings via deep learning, it is possible to achieve both high accuracy and speed in retrieval. Nevertheless, there are complex challenges associated with employing deep learning for spectral embedding, particularly within the domain of electron ionization mass spectrometry (EI-MS). In this paper, we introduce a novel representation learning technique termed EI-MS2VEC for EI-MS retrieval. Our spectrum retrieval methodology surpasses current state-of-the-art techniques such as FastEI. For the in-silico library, we attain hit rate@1 and hit rate@10 of 43.6% and 84.5%, respectively, compared to FastEI's 36.7% and 80.4%. Moreover, our retrieval approach operates with an order of magnitude greater speed than FastEI. The source code is available on Github (https://***/xfcui/EI-MS2VEC). © 2024 IEEE.

关键词： Embeddings

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Monotonicity and nonmonotonicity in L3-valued propositional logic

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Frontiers of computer Science 2022年第4期16卷 33-43页

作者： Wei Li Yuefei Sui State Key Laboratory of Software Development Environment Beihang UniversityBeijing100083China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing100049China

A sequent is a pair (Γ, Δ), which is true under an assignment if either some formula in Γ is false, or some formula in Δ is true. In L_(3)-valued propositional logic, a multisequent is a triple Δ∣Θ∣Γ, which is true under an assignment if either some formula in Δ has truth-value t, or some formula in Θ has truth-value m, or some formula in Γ has truth-value f. There is a sound, complete and monotonic Gentzen deduction system G for sequents. Dually, there is a sound, complete and nonmonotonic Gentzen deduction system G′ for co-sequents Δ: Θ: Γ. By taking different quantifiers some or every, there are 8 kinds of definitions of validity of multisequent Δ∣Θ∣Γ and 8 kinds of definitions of validity of co-multisequent Δ: Θ: Γ, and correspondingly there are 8 sound and complete Gentzen deduction systems for sequents and 8 sound and complete Gentzen deduction systems for co-sequents. Correspondingly their monotonicity is discussed.

关键词： sequent mulisequent gentzen deduction system monotonicity nonmonotonicity

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