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arXiv 2023年

作者： Lyu, Deyun Song, Jiayang Zhang, Zhenya Wang, Zhijie Zhang, Tianyi Ma, Lei Zhao, Jianjun The Faculty and Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada The Department of Computer Science Purdue University West Lafayette United States

Cyber-Physical Systems (CPS) have been widely deployed in safety-critical domains such as transportation, power and energy. Recently, there comes an increasing demand in employing deep neural networks (DNNs) in CPS for more intelligent control and decision making in sophisticated industrial safety-critical conditions, giving birth to the class of DNN controllers. However, due to the inherent uncertainty and opaqueness of DNNs, concerns about the safety of DNN-enabled CPS are also surging. In this work, we propose an automated framework named AUTOREPAIR that, given a safety requirement, identifies unsafe control behavior in a DNN controller and repairs them through an optimization-based method. Having an unsafe signal of system execution, AUTOREPAIR iteratively explores the control decision space and searches for the optimal corrections for the DNN controller in order to satisfy the safety requirements. We conduct a comprehensive evaluation of AUTOREPAIR on 6 instances of industry-level DNN-enabled CPS from different safety-critical domains. Evaluation results show that AUTOREPAIR successfully repairs critical safety issues in the DNN controllers, and significantly improves the reliability of CPS. Copyright © 2023, The Authors. All rights reserved.

关键词： Cyber Physical System

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A Numerical Method to Analyze Electromagnetic Fields on Semiconductor Nanostructures

A Numerical Method to Analyze Electromagnetic Fields on Semi...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2022

作者： Uulu, Doolos Aibek Chen, Rui Chen, Liang Li, Ping Bagci, Hakan Computer Electrical and Mathematical Science and Engineering Division Electrical and Computer Engineering Program Thuwal23955-6900 Saudi Arabia Shanghai Jiao Tong University Key Lab. of Min. of Educ. of Des. and Electromagnetic Compatibility of High-Speed Electronic Systems Shanghai200240 China

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

A coupled system of volume integral and two-fluid hydrodynamic equations is solved to analyze electromagnetic field interactions with non-local dispersion effects on semiconductor nanostructures. This coupled system, where the hydrodynamic currents and the electric flux are the unknowns, is discretized using Schaubert-Wilton-Glisson basis functions. Numerical results obtained using this solver show the presence of so-called acoustic modes at the frequencies lower than the resonance frequency of localized surface plasmon (LSP) mode. These modes cannot be identified if a Drude-like model or the single-fluid hydrodynamic equation is used to represent the electrical properties of the semiconductors. © 2022 IEEE.

关键词： Electromagnetic fields

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Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

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Computational and Structural Biotechnology Journal 2024年 23卷 1864-1876页

作者： Do, Duyen Thi Yang, Ming-Ren Vo, Tran Nam Son Le, Nguyen Quoc Khanh Wu, Yu-Wei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Business Administration College of Management Lunghwa University of Science and Technology Taoyuan City Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei Taiwan

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR. © 2024 The Authors

关键词： Genes

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Federated Learning for Sleep Detection Problems

Federated Learning for Sleep Detection Problems

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International Conference on Smart Computing (SMARTCOMP)

作者： Guilherme Antonio Borges Julio Cesar Santos dos Anjos Jorge Sá Silva Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal Federal Institute Sul-rio-grandense (IFSUL) Rio Grande Sul Brazil Graduate Program Teleinformatics Engineering Federal University of Ceara Fortaleza Brazil Department of Electrical and Computer Engineering (DEEC) University of Coimbra Coimbra Portugal

ISBN: (数字)9798350349948

ISBN: (纸本)9798350349955

Human-in-the-loop cyber-physical Systems use data from various sources to provide valuable assistance to users, but privacy concerns arise when sensitive information is shared. Federated Learning is a promising solution that enables the processing of user data without sharing sensitive information. While this method holds great potential, its efficacy in detecting sleep problems remains an open question. In this way, using a real-world dataset application, our study meticulously evaluates and comprehends the impact of incorporating Federated Learning on sleep detection. Our study evaluates the impact of incorporating Federated Learning on sleep detection and compares it with traditional Machine Learning models. Our findings reveal that our approach delivers accurate sleep detection results over 84% on par with conventional techniques. Our results emphasize the critical importance of handling human error inputs, as this factor significantly influences the accuracy of results in both methods.

关键词： Data privacy Accuracy Uncertainty Data analysis Federated learning Flowering plants Data models

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Twenty years of e-learning in health science: A bibliometric 7

Twenty years of e-learning in health science: A bibliometric

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Maulana, Fairuz Iqbal Febriantono, M. Aldiki Raharja, Dwi Risza Budi Khaeruddin Herasmara, Ridho Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Indonesia Electrical Engineering Faculty of Science and Technology Raden Rahmat Islamic University Malang Indonesia

Education about health sciences has historically been limited in the curriculum of health professionals and largely inaccessible to the public. In practice, most of the health science education is still running conventionally. Supposedly with the advancement of technology and the use of the internet everywhere, learning such as e-learning can be important, especially in the health sector. Until this research was conducted, only 514 academic documents about e-learning in health sciences were found for 20 years from 2001 to 2020, obtained in searching on the Scopus database. This study presents a comprehensive overview of studies related to E-learning in the Health sciences sector. This study uses bibliometric analysis and indexed digital methods to map scientific publications throughout the world. This research employs the Scopus database to gather information, as well as the Scopus online analysis tool and Vosviewer to show the bibliometric network. The method consists with five stages: determining search keywords, initial search results, refinement of search results, initial compilation, and data analysis. Among the most published and indexed articles by Scopus, papers published by researchers in the United States have the highest number of publications (80), followed by United Kingdom (63) and Australia with 45 academic publications. The processed data shows the pattern and trend of increasing the number of international publications in E-learning in Health sciences field, which Scopus index. © 2022 Elsevier B.V.. All rights reserved.

关键词： Publishing

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Gallium Oxide as Photonic Integrated Platforms in the UV-Visible Spectra

Gallium Oxide as Photonic Integrated Platforms in the UV-Vis...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Zhou, Jingan Zhao, Xuan Xu, Rui Zhang, Xiang Fu, Kai Li, Tao Xu, Mingfei Luo, Shisong He, Ziyi Robinson, Jacob T. Zhu, Hanyu Ajayan, Pulickel M. Zhang, Xiaodong Zhao, Yuji Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States School of Electrical Computer and Energy Engineering Arizona State University TempeAZ85287 United States Nanofabrication facility Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of Sciences Jiangsu Suzhou215123 China

We report the Ga2O3 as photonic integrated platforms and its nonlinear optical effects in the UV-visible spectra. A low propagation loss of 3.7 dB/cm and second-order susceptibility of x(2)=4.89xl03pm/V were obtained.... 详细信息

ISBN: (纸本)9781957171258

关键词： Gallium compounds

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IoT for Water Quality Categorization 5

IoT for Water Quality Categorization

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Sastrohartono, Hermantoro Krisdiarto, Andreas Wahyu Uktoro, Arief Ika Rahutomo, Reza Suparyanto, Teddy Pardamean, Bens Institute of Agriculture Stiper Faculty of Agricultural Technology Agricultural Engineering Department Yogyakarta Indonesia Bina Nusantara University School of Information Systems Information System Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics & Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350369489

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results. © 2023 IEEE.

关键词： Internet of things

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Machine Learning based on Association rules about automatic course data Mining 5

Machine Learning based on Association rules about automatic ...

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5th International Conference on Advanced Electronic Materials, computers and Software engineering, AEMCSE 2022

作者： Li, Xuemei Xu, Jiajia De la Salle University -Dasmariñas City of Dasmariñas College of Electronic and Electrical Engineering Anhui Sanlian University Teacher College of Science and Computer Studies - Graduate Studies Cavite Philippines

ISBN: (数字)9781665484749

ISBN: (纸本)9781665484749

There is an intersection of knowledge between two different courses in automation major, and there is a knowledge graph between several courses. This paper takes nine courses of 127 undergraduate automation majors in a university in China as the research object. This paper attempts to find out the interrelationship and correlation intensity between courses, to guide students' self-study and teachers' lesson preparation. The research study employs the quantitative method of research. K-DD (Knowledge Discovery in Database) is used in this study. Relevance rules are used in this paper. First, we use the correlation coefficient method to calculate the correlation coefficient between the courses, and then use the correlation rule method to get the relationship between the nine courses. The algorithms in the association rules used in this paper are FP growth and Apriori algorithms. The correlation coefficient between automatic control theory and electronic measurement technology is up to 0.816. when the technical state of electronic measurement is true, the possibility that the automatic control theory is true is 0.867. When the automatic control theory state is true, the probability that the electronic measurement technology is true is 0.839. The correlation between automatic control theory and electronic measurement technology is the highest. The fact shows that there is indeed a strong correlation between the two courses. © 2022 IEEE.

关键词： Association rules

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Optimized Transmission Strategy for UAV-RIS 2.0 Assisted Communications Using Rate Splitting Multiple Access

arXiv

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arXiv 2023年

作者： Huroon, Aamer Mohamed Huang, Yu-Chih Wang, Li-Chun Department of Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Taiwan Institute of Communications Engineering National Yang Ming Chiao Tung University Taiwan Department of Electrical and Computer Engineering National Yang Ming Chiao Tung University Taiwan

In this paper, we study the transmission strategy of a ground-based beyond diagonal reconfigurable intelligent surface (BD-RIS), a.k.a RIS 2.0, in a network where multiple unmanned aerial vehicles (UAVs) simultaneously transmit signals to the respective groups of users. It is assumed that each group is assigned subcarriers orthogonal to those assigned to other groups and rate splitting multiple access (RSMA) is adopted within each group. A corresponding mixed integer nonlinear programming problem (MINLP) is formulated, which aims to jointly optimize 1) allocation of BD-RIS elements to groups, 2) BD-RIS phase rotations, 3) rate allocation in RSMA, and 4) precoders. To solve the problem, we propose using generalized benders decomposition (GBD) augmented with a manifold-based algorithm. GBD splits the MINLP problem into two sub-problems, namely the primal and the relaxed master problem, which are solved alternately and iteratively. In the primal problem, we apply block coordinate descent (BCD) to manage the coupling of variables effectively. Moreover, we recognize the manifold structure in the phase rotation constraint of BD-RIS, enabling the Riemannian conjugate gradient (RCG). Simulation results demonstrate the effectiveness of the proposed approach in maximizing spectral efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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Ensemble Deep Learning for Human-Object Interaction Detection 2

Ensemble Deep Learning for Human-Object Interaction Detectio...

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2nd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2022

作者： Mansour, Ahmed E. Mohammed, Ammar Elsayed, Hussein Abd El Atty Ramly, Salwa H. El Ain Shams University Faculty of Engineering Electronics and Electrical Communication Department Cairo Egypt Cairo University Faculty of Graduate Studies of Statistical Researches Computer Science Department Cairo Egypt

ISBN: (纸本)9781665466776

Human-object interaction (HOI) detection is the task of predicting the visual relationships between humans and their surroundings in images and videos by locating and inferring the interactions between human-object pairs. The majority of existing models have approached this task by detecting human and object instances and predicting interactions between them using a single model and relying on visual features to differentiate between different actions. In this article, we propose a novel method for solving the HOI detection problem by employing ensemble voting on simple models that use only spatial features to predict human-object pair actions. Additionally, we solve the false-positive pairs generated by mis-grouping and non-interaction objects in the image using a bipartite matching. Our proposed approach outperforms many state-of-the-art models on the V-COCO dataset while requiring less inference time. © 2022 IEEE.

关键词： Visualization Computational modeling Object detection Predictive models Feature extraction Ubiquitous computing Prediction algorithms

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