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Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets

Neural Computing and Applications 2025年第5期37卷 3287-3301页

作者： Chen, Chen Xu, Enze Yang, Defu Yan, Chenggang Wei, Tao Chen, Hanning Wei, Yong Chen, Minghan Intelligent Information Processing Laboratory Hangzhou Dianzi University Hangzhou China Department of Computer Science Wake Forest University Winston-SalemNC United States Department of Chemical Engineering Howard University WashingtonDC United States Texas Advanced Computing Center University of Texas at Austin AustinTX United States Department of Computer Science & Information Systems University of North Georgia DahlonegaGA United States

This study presents a new machine learning algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C3N4) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications;however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C3N4. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development. © The Author(s) 2024.

关键词： Graphitic Carbon Nitride

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Binary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection

Binary Bleed: Fast Distributed and Parallel Method for Autom...

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2024 IEEE High Performance Extreme Computing Conference, HPEC 2024

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Boureima, Ismael Matuszek, Cynthia Alexandrov, Boian S. Theoretical Division Los Alamos National Laboratory Los Alamos United States University of Maryland Baltimore County Department of Computer Science and Electrical Engineering MD United States Advanced Research in Cyber Systems Los Alamos National Laboratory Los Alamos United States

ISBN: (纸本)9798350387131

In several Machine Learning (ML) clustering and dimensionality reduction approaches, such as nonnegative matrix factorization (NMF), RESCAL, and K-Means clustering, users must select a hyper-parameter k to define the number of clusters or components that yield an ideal separation of samples or clean clusters. This selection, while difficult, is crucial to avoid overfitting or underfitting the data. Several ML applications use scoring methods (e.g., Silhouette and Davies Boulding scores) to evaluate the cluster pattern stability for a specific k. The score is calculated for different trials over a range of k, and the ideal k is heuristically selected as the value before the model starts overfitting, indicated by a drop or increase in the score resembling an elbow curve plot. While the grid-search method can be used to accurately find a good k value, visiting a range of k can become time-consuming and computationally resource-intensive. In this paper, we introduce the Binary Bleed method based on binary search, which significantly reduces the k search space for these grid-search ML algorithms by truncating the target k values from the search space using a heuristic with thresholding over the scores. Binary Bleed is designed to work with single-node serial, single-node multi-processing, and distributed computing resources. In our experiments, we demonstrate the reduced search space gain over a naive sequential search of the ideal k and the accuracy of the Binary Bleed in identifying the correct k for NMFk, K-Means pyDNMFk, and pyDRESCALk with Silhouette and Davies Boulding scores. We make our implementation of Binary Bleed for the NMF algorithm available on GitHub. © 2024 IEEE.

关键词： Dimensionality reduction

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Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization 12

Cyber-Security Knowledge Graph Generation by Hierarchical No...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Wanna, Selma Solovyev, Nicholas Rasmussen, Kim Alexandrov, Boian S. Nicholas, Charles Matuszek, Cynthia Los Alamos National Laboratory Theoretical Division Los Alamos United States Los Alamos National Laboratory Advanced Research in Cyber Systems Los Alamos United States University of Maryland Department of Computer Science and Electrical Engineering Baltimore CountyMD United States

ISBN: (纸本)9798350330366

Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting actionable insights hidden within large text datasets. Knowledge Graphs (KGs) serve as a means to store factual information in a structured manner, providing explicit, interpretable knowledge that includes domain-specific information from the cybersecurity scientific literature. One of the challenges in constructing a KG from scientific literature is the extraction of ontology from unstructured text. In this paper, we address this topic and introduce a method for building a multi-modal KG by extracting structured ontology from scientific papers. We demonstrate this concept in the cybersecurity domain. One modality of the KG represents observable information from the papers, such as the categories in which they were published or the authors. The second modality uncovers latent (hidden) patterns of text extracted through hierarchical and semantic non-negative matrix factorization (NMF), such as named entities, topics or clusters, and keywords. We illustrate this concept by consolidating more than two million scientific papers uploaded to arXiv into the cyber-domain, using hierarchical and semantic NMF, and by building a cyber-domain-specific KG. © 2024 IEEE.

关键词： Cybersecurity

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A Chaotic Encryption Algorithm with Dynamic Key Enabled by Power-Division Multiplexed Transmission

A Chaotic Encryption Algorithm with Dynamic Key Enabled by P...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Ding, Yinchen Lei, Yi Lu, Qi Guo, Zhongyi Chen, Bin School of Computer Science and Information Engineering Hefei University of Technology Hefei China Hefei University of Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei China Shanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication Systems and Networks China

Using power-division multiplexing, a chaotic encryption method is proposed based on dynamic key generated from the instantaneously transmitted information. The influence of power allocation is analyzed and the securit... 详细信息

ISBN: (纸本)9798350312614

关键词： chaotic encryption dynamic key Optical fiber communication physical layer security power-division mul-tiplexing

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Multi-objective Feature Attribution Explanation for Explainable Machine Learning

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ACM Transactions on Evolutionary Learning and Optimization 2024年第1期4卷 1-32页

作者： Wang, Ziming Huang, Changwu Li, Yun Yao, Xin Research Institute of Trustworthy Autonomous Systems Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Departmentof Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China The Advanced Cognitive Technology Lab Huawei Technologies Co. Ltd Shanghai201206 China School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

The feature attribution-based explanation (FAE) methods, which indicate how much each input feature contributes to the model's output for a given data point, are one of the most popular categories of explainable machine learning techniques. Although various metrics have been proposed to evaluate the explanation quality, no single metric could capture different aspects of the explanations. Different conclusions might be drawn using different metrics. Moreover, during the processes of generating explanations, existing FAE methods either do not consider any evaluation metric or only consider the faithfulness of the explanation, failing to consider multiple metrics simultaneously. To address this issue, we formulate the problem of creating FAE explainable models as a multi-objective learning problem that considers multiple explanation quality metrics simultaneously. We first reveal conflicts between various explanation quality metrics, including faithfulness, sensitivity, and complexity. Then, we define the considered multi-objective explanation problem and propose a multi-objective feature attribution explanation (MOFAE) framework to address this newly defined problem. Subsequently, we instantiate the framework by simultaneously considering the explanation's faithfulness, sensitivity, and complexity. Experimental results comparing with six state-of-The-Art FAE methods on eight datasets demonstrate that our method can optimize multiple conflicting metrics simultaneously and can provide explanations with higher faithfulness, lower sensitivity, and lower complexity than the compared methods. Moreover, the results have shown that our method has better diversity, i.e., it provides various explanations that achieve different tradeoffs between multiple conflicting explanation quality metrics. Therefore, it can provide tailored explanations to different stakeholders based on their specific requirements. © 2024 Copyright held by the owner/author(s). Publication rights licensed

关键词： Machine learning

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Artificial Intelligence Techniques with Digital Twin for Fault Diagnosis in Interconnected systems: A Review

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IEEE Access 2025年 13卷 91860-91874页

作者： Nakti, Imen Mansouri, Majdi Al-Hmouz, Rami Khedher, Atef University of Sousse ENISO Laboratory of Advanced Technology and Intelligent systems Sousse4002 Tunisia Sultan Qaboos University College of Engineering Department of Electrical and Computer Engineering Muscat123 Oman

As industrial systems become more complex and interconnected, diagnosing faults accurately and in real time has become increasingly challenging. This paper explores how combining artificial intelligence with digital twin technology can address these challenges. We focus on developing hybrid Artificial intelligence models that leverage diverse data sources to enhance fault detection and diagnosis, enabling secure and distributed diagnostics. Digital twins, virtual models of physical systems, are shown to enhance predictive maintenance and decision-making by providing real-time system insights. What sets our work apart is the way we integrate these technologies to create scalable, adaptive, and context-aware diagnostic solutions. We demonstrate the potential of this approach across applications such as smart grids, manufacturing, and autonomous systems. Our goal is to provide researchers and practitioners with a practical and forward-looking framework for developing intelligent, reliable fault diagnosis systems in today’s data-rich industrial environments. © 2013 IEEE.

关键词： Deep learning

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PI-funnel and inverse hysteresis compensation cascade control of smart soft dielectric elastomer actuator

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Control Theory and Technology 2024年第4期22卷 552-567页

作者： Peng Huang Yawu Wang Yue Zhang Jundong Wu Chun-Yi Su School of Automation China University of GeosciencesWuhan430074HubeiChina Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074HubeiChina Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan430074HubeiChina Gina Cody School of Engineering and Computer Science Concordia UniversityMontrealQCH3G 1M8Canada

Smart soft dielectric elastomer actuators(SSDEAs)possess wide applications in soft robotics due to their properties similar to natural muscles,including large deformation ratio,high energy density,and fast response ***,the complicated asymmetric and rate-dependent hysteresis property,creep property and quadratic input property of the SSDEA pose enormous challenges to its dynamic modeling and motion *** this paper,first,we construct the dynamic model of the SSDEA by connecting a square module,a one-sided Prandtl–Ishlinskii(OSPI)model and a linear system in series to describe the above *** key and innovative aspect of the dynamic modeling lies in cascading the square module in series with the OSPI model to construct the asymmetric hysteresis ***,a PI-funnel and inverse hysteresis compensation(PIFIHC)cascade control method of the SSDEA is proposed to actualize its tracking control *** performing the inversion operation on the asymmetric hysteresis model,the inverse hysteresis compensation controller(IHCC)is designed to compensate the asymmetric hysteresis property and quadratic input property of the *** addition,a PI-funnel controller is designed to cascade with the IHCC to construct the PIFIHC cascade controller to obtain a good tracking ***,the stability analysis of the PIFIHC cascade control system of the SSDEA is performed to theoretically prove that the tracking error can be controlled within the performance funnel and the steady-state error converges to ***,several practical tracking control experiments of the SSDEA are conducted,and RRMSEs are less than 2.30%for all *** experimental results indicate the effectiveness and feasibility of the proposed PIFIHC cascade control method of the SSDEA.

关键词： Smart soft dielectric elastomer actuator Dynamic modeling Inverse hysteresis compensation controller PI-funnel controller Cascade controller

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A structure based on fiber-optic gyroscopes for gravity measurement 2

A structure based on fiber-optic gyroscopes for gravity meas...

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advanced Optical Manufacturing Technologies and Applications 2022, AOMTA 2022 and 2nd International Forum of Young Scientists on advanced Optical Manufacturing, YSAOM 2022

作者： Chen, Yanjun Zhu, Lanxin Wang, Wenbo He, Yan Cao, Yuwen Huang, Huimin Ma, Xiangdong Li, Zhengbin State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing100871 China School of Electronics Engineering & Computer Science Peking University Beijing100871 China

ISBN: (纸本)9781510661264

We propose a structure based on fiber-optic gyroscopes for gravity measurement in accordance with the tilt-coupling effect, where Sagnac effect can remove the coupling between translational and rotational motion in the tilt-coupling effect. A fiber-optic gyroscope with a sensitivity of 2 × 10−11 rad/s2/√Hz in sub-millihertz frequencies is reported, which can be utilized to measure the rotational motion in gravity measurement. The application of dual-polarization configuration improves the adaptability of exploration environment. © 2023 SPIE. All rights reserved.

关键词： Fiber optics

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Improved Particle Swarm Optimization Algorithm for EIT Image Reconstruction 20

Improved Particle Swarm Optimization Algorithm for EIT Image...

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20th International Multi-Conference on systems, Signals and Devices, SSD 2023

作者： Kahouli, Oumayma Hafsa, Mariem Hellara, Hiba Bennour, Imed Eddine Essoukri Ben Amarax, Najoua Kanoun, Olfa University of Sousse National Engineering School of Sousse Department of Computer Science Sousse4023 Tunisia National Engineering School of Sousse Laboratory of Advanced Technology and Intelligent Systems Tunisia Technische Universität Chemnitz Laboratory of Measurement and Sensor Technology Chemnitz Germany University of Sfax Laboratory of Signals Systems Artificial Intelligence and Networks Digital Research Center of Sfax Tunisia

ISBN: (纸本)9798350332568

Electrical Impedance Tomography (EIT) is a clinical imaging technique that gained a lot of attention because it is a non-invasive and radiation-free method. To obtain an inner image of the domain under study, a specific number of electrodes is attached to the surface of the object, then through a pair of electrodes, a low alternative current is injected. Potentials produced by this injection are measured with the remaining electrodes. Based on these boundary voltages measurements, the inner conductivity can be inferred, thus the image is reconstructed. The focus of this paper is to overcome the major weakness of the EIT image reconstruction technique, which is the low spatial resolution of the reconstructed images caused by the non-linearity of EIT inverse problem. This paper considers this problem, an optimization problem that is solved using the Particle Swarm Optimization (PSO) meta-heuristic. A variant of the standard PSO is proposed and validated, first on a circular mesh composed of 686 triangles then on a lung mesh composed of 2707 triangles. © 2023 IEEE.

关键词： Electrodes

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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