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Industrial Semiconductor GPT: A Question-and-Answer System that Provides Professional Advice and Problem-Solving Methods for Semiconductor and Factory Equipment and Process

Industrial Semiconductor GPT: A Question-and-Answer System t...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Yi-Cheng Yu Cheng-Fu Liou Shang-Wen Chuang Chen-Yi Lee Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Research Labs (MZAIR Labs) Meta Zen Artificial Intelligence Tainan Taiwan Artificial Intelligence and Industrial Internet of Things Research Center Advantech Co. Ltd. Taipei Taiwan Department of Electronics Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

Research on PHM(Predictive Health Management) for detecting or predicting abnormalities in semiconductors and factory equipment and processes typically focuses on improving accuracy and reducing model size. However, in actual factory settings, on-site personnel not only need to accurately detect and predict abnormalities in processes and equipment but also require an understanding of how to handle the encountered abnormal problems when they occur. This study proposes Industrial Semiconductor GPT technology, which embeds relevant professional technical documents of process equipment into a vector database and generates possible professional questions and answers. When on-site personnel inquire, a natural language processing algorithm is used to compare the generated answers with actual answers. Furthermore, this study verifies the collected expert data through prompting engineering, resulting in an approximately $9.39 \%$ increase in similarity, ensuring that factory personnel can obtain accurate and helpful answers when facing abnormal situations.

关键词： Semiconductor device modeling Accuracy Databases Search methods Production facilities Vectors Semiconductor process modeling

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Deep and shallow data science for multi-scale optical neuroscience

arXiv

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arXiv 2024年

作者： Mishne, Gal Charles, Adam Halıcıoğlu Data Science Institute Department of Electrical and Computer Engineering the Neurosciences Graduate Program UC San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Biomedical Engineering Kavli Neuroscience Discovery Institute Center for Imaging Science Department of Neuroscience Mathematical Institute for Data Science Johns Hopkins University BaltimoreMD21287 United States

Optical imaging of the brain has expanded dramatically in the past two decades. New optics, indicators, and experimental paradigms are now enabling in-vivo imaging from the synaptic to the cortex-wide scales. To match the resulting flood of data across scales, computational methods are continuously being developed to meet the need of extracting biologically relevant information. In this pursuit, challenges arise in some domains (e.g., SNR and resolution limits in micron-scale data) that require specialized algorithms. These algorithms can, for example, make use of state-of-the-art machine learning to maximally learn the details of a given scale to optimize the processing pipeline. In contrast, other methods, however, such as graph signal processing, seek to abstract away from some of the details that are scale-specific to provide solutions to specific sub-problems common across scales of neuroimaging. Here we discuss limitations and tradeoffs in algorithmic design with the goal of identifying how data quality and variability can hamper algorithm use and dissemination. © 2024, CC BY.

关键词： Neuroimaging

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Millimeter-Scale Leak Detection Using Distributed Acoustic and Temperature Gradient Sensing

Millimeter-Scale Leak Detection Using Distributed Acoustic a...

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IEEE SENSORS

作者： Guilherme H. Weber Danilo F. Gomes Eduardo N. dos Santos Ana L. B. Santana Jean C. C. da Silva Cicero Martelli Daniel R. Pipa Rigoberto E. M. Morales Sérgio T. C. Manoel F. da Silva Marco J. da Silva Graduate Program in Electrical and Computer Engineering Universidade Tecnológica Federal do Paraná Curitiba Brazil Multiphase Flow Research Center Universidade Tecnológica Federal do Paraná Curitiba Brazil Petrobras Rio de Janeiro Brazil Johannes Kepler University Linz Linz Austria

The occurrence of leaks in the transport of fluids in the oil and gas industry is a recurring problem. Compounds subject to reactions with the environment flowing under high pressure and temperature, through long pipes in places of difficult access exposed to the weather create several conditions for leaks. Distributed Acoustic Sensing (DAS) technology perfectly addresses those challenges as it provides long-range remote sensing through a dense network of sensors with high sensitivity and wide frequency response. In this work, we demonstrate the use of a DAS system simultaneously operating in acoustic and temperature gradient modes to detect a 1 mm diameter hole leak in a 1 inch-pipeline under water-air slug flow. The measurement data demonstrate the effects of the leakage in both acoustic and thermal perspectives. The results determine the system's ability to identify the integrity of the pipeline, as well as the exact moment the leak started, its location, characteristics in both time and frequency domain, and thermal effects as well. This research contributes to advance DAS technology as a sensing tool capable of detecting mm-sized leak hole in fluid transport pipelines.

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Sub-structure Characteristic Mode Analysis of Microstrip Antennas Using a Global Multi-trace Formulation

arXiv

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arXiv 2022年

作者： Zhao, Ran Lu, Yuyu Cheng, Guang Shang Zhu, Wei Hu, Jun Bagci, Hakan Chengdu611731 China The Electrical and Computer Engineering Program Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia The Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei230601 China The Anhui Province Key Laboratory of Target Recognition and Feature Extraction Lu’an237010 China Thuwal23955 Saudi Arabia

A characteristic mode (CM) method that relies on a global multi-trace formulation (MTF) of surface integral equations is proposed to compute the modes and the resonance frequencies of microstrip patch antennas with finite dielectric substrates and ground planes. Compared to the coupled formulation of electric field and Poggio-Miller-Chang-Harrington-Wu-Tsai integral equations, global MTF allows for more direct implementation of a sub-structure CM method. This is achieved by representing the coupling of the electromagnetic fields on the substrate and ground plane in the form of a numerical Green function matrix, which yields a more compact generalized eigenvalue equation. The resulting sub-structure CM method avoids the cumbersome computation of the multilayered medium Green function (unlike the CM methods that rely on mixed-potential integral equations) and the volumetric discretization of the substrate (unlike the CM methods that rely on volume-surface integral equations), and numerical results show that it is a reliable and accurate approach to predicting the modal behavior of electromagnetic fields on practical microstrip antennas. Copyright © 2022, The Authors. All rights reserved.

关键词： Microstrip antennas

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Demonstration of a polarization-agnostic geometric phase in nonlocal metasurfaces

Demonstration of a polarization-agnostic geometric phase in ...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Adam C. Overvig Y. Kasahara Andrea Alù Photonics Initiative Advanced Science Research Center City University of New York New York NY USA Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX USA Physics Program Graduate Center of the City University of New York New York NY USA

We theoretically propose and experimentally demonstrate nonlocal metasurfaces rationally designed using symmetry-breaking principles to manipulate the optical wavefront with a geometric phase selective to custom ellip... 详细信息

ISBN: (纸本)9781957171258

关键词： Circular polarization Constructive interference Fano resonance Polarization Polarized light Thermal emission

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Dish-Trend: Intervention Modeling Simulator That Accounts for Trend Influences

Dish-Trend: Intervention Modeling Simulator That Accounts fo...

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2021 Winter Simulation Conference, WSC 2021

作者： Andjelkovic, Stefan Miskov-Zivanov, Natasa Computational and Systems Biology Joint Carnegie Mellon University University of Pittsburgh Computational Biology PhD Program University of Pittsburgh PittsburghPA15213 United States Electrical and Computer Engineering Bioengineering Computational and Systems Biology University of Pittsburgh PittsburghPA15213 United States

ISBN: (纸本)9781665433112

Simulation on directed graphs is an important method for understanding the dynamics in the systems where connectivity graphs contain cycles. Discrete Stochastic Heterogeneous Simulator (DiSH) is one of the simulation tools with wide application, which uses regulator values to calculate state updates of regulated elements. Here we present a new simulation approach DiSH-trend which also takes into account the trends in regulating elements. We demonstrate the features of trend-based regulation, as well as hybrid regulation, which is a combination of the trend-and level-based approaches. The modeling capabilities are demonstrated on a small toy model, showcasing different functionalities. Real-world capabilities are demonstrated on a larger network model of food insecurity in the Ethiopian region Oromia. Adding trend-based regulation to models results in increased modeling flexibility, and hybrid regulation improves qualitative dynamic behavior prediction. With appropriate data, DiSH-trend becomes a powerful tool for exploring intervention strategies. © 2021 IEEE.

关键词： Directed graphs

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CLINICAL DETERIORATION PREDICTION IN BRAZILIAN HOSPITALS BASED ON ARTIFICIAL NEURAL NETWORKS AND TREE DECISION MODELS

arXiv

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arXiv 2022年

作者： Yazdanpanah, Hamed Silva, Augusto C.M. Guedes, Murilo Morales, Hugo M.P. Coelho, Leandro dos S. Moro, Fernando G. Department of Research Robô Laura PR Curitiba Brazil Department of Computer Science University of São Paulo SP São Paulo Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana PR Curitiba Brazil Electrical Engineering Graduate Program Federal University of Parana PR Curitiba Brazil

Early recognition of clinical deterioration (CD) has vital importance in patients' survival from exacerbation or death. Electronic health records (EHRs) data have been widely employed in Early Warning Scores (EWS) to measure CD risk in hospitalized patients. Recently, EHRs data have been utilized in Machine Learning (ML) models to predict mortality and CD. The ML models have shown superior performance in CD prediction compared to EWS. Since EHRs data are structured and tabular, conventional ML models are generally applied to them, and less effort is put into evaluating the artificial neural network's performance on EHRs data. Thus, in this article, an extremely boosted neural network (XBNet) is used to predict CD, and its performance is compared to eXtreme Gradient Boosting (XGBoost) and random forest (RF) models. For this purpose, 103,105 samples from thirteen Brazilian hospitals are used to generate the models. Moreover, the principal component analysis (PCA) is employed to verify whether it can improve the adopted models' performance. The performance of ML models and Modified Early Warning Score (MEWS), an EWS candidate, are evaluated in CD prediction regarding the accuracy, precision, recall, F1-score, and geometric mean (G-mean) metrics in a 10-fold cross-validation approach. According to the experiments, the XGBoost model obtained the best results in predicting CD among Brazilian hospitals' data. © 2022, CC BY-NC-SA.

关键词： Machine learning

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Implantable Biosensor for Continuous Serotonin Detection in Freely Moving Crayfish 22

Implantable Biosensor for Continuous Serotonin Detection in ...

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22nd International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2023

作者： Han, Jinjing Ho, Tawen Stine, Justin M. Straker, Michael A. Herberholz, Jens Ghodssi, Reza University Of Maryland Department Of Electrical And Computer Engineering College ParkMD United States University Of Maryland Institute For System Research College ParkMD United States University Of Maryland Robert E. Fischell Institute For Biomedical Devices College ParkMD United States University Of Maryland Department Of Psychology College ParkMD United States University Of Maryland Program In Neuroscience And Cognitive Science College ParkMD United States University Of Maryland Fischell Department Of Bioengineering College ParkMD United States

ISBN: (纸本)9784886864352

This work presents, for the first time, an implantable biosensor for continuous in vivo serotonin (5-HT) monitoring in freely moving crayfish. 5-HT is an important neurotransmitter that regulates animal behaviors. Conventional methods for detecting 5-HT do not provide real-time information. Our approach facilitates continuous electrochemical monitoring of 5-HT levels without impeding animal natural behavior. The sensing system shows a linear range of 0.4 - 1.2 μΜ with a sensitivity of 0.07 μA/μΜ for 5-HT detection. With a 3D-printed package, the sensor can be easily attached to crayfish dorsal carapace allowing electrodes implantation into crayfish heart to monitor neurohormonal dynamics of 5-HT. The implantable biosensor system displayed a significant increase in oxidation currents after 5-HT injections. The device successfully demonstrates the application for in vivo detection of 5-HT in the hemolymph (i.e., blood) of freely behaving crayfish underwater. © 2023 IEEJ.

关键词： Biosensing Electrochemical Device Electrochemistry Implantable Electrode In Vivo Sensing

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Investigating Skin Aging Using Jones Matrix Polarization-Sensitive Optical Coherence Tomography

Investigating Skin Aging Using Jones Matrix Polarization-Sen...

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Bio-Optics: Design and Application, BODA 2023 - Part of Optica Biophotonics Congress: Optics in the Life Sciences 2023

作者： Maloufi, Sina Zhou, Xin Mirsalehi, Mitra Louie, Daniel C. Lee, Tim K. Tang, Shuo School of Biomedical Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Department of Dermatology and Skin Science Photomedicine Institute University of British Columbia Vancouver Coastal Health Research Institute VancouverBCV5Z 4E8 Canada Cancer Control Research Program BC Cancer VancouverBCV5Z 1L3 Canada

JM-OCT was utilized to present comparison on the effects of intrinsic aging on features quantified by JM-OCT, such as epidermal thickness, local depolarization, and collagen disruption. © 2023 The Author(s)

ISBN: (纸本)9781957171210

关键词： Optical tomography

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On Representation Formulas for Optimal Control: A Lagrangian Perspective∗

arXiv

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arXiv 2022年

作者： Kim, Yeoneung Yang, Insoon Department of Electrical and Computer Engineering ASRI Education and Research program for Future ICT Pioneers Seoul National University Seoul08826 Korea Republic of Deparment of Financial Mathematics Gachon University Seongnam13120 Korea Republic of

In this paper, we study representation formulas for finite-horizon optimal control problems with or without state constraints, unifying two different viewpoints: the Lagrangian and dynamic programming (DP) frameworks. In a recent work [1], the generalized Lax formula is obtained via DP for optimal control problems with state constraints and nonlinear systems. We revisit the formula from the Lagrangian perspective to provide a unified framework for understanding and implementing the nontrivial representation of the value function. Our simple derivation makes direct use of the Lagrangian formula from the theory of Hamilton–Jacobi (HJ) equations. We also discuss a rigorous way to construct an optimal control using a δ-net, as well as a numerical scheme for controller synthesis via convex optimization. Copyright © 2022, The Authors. All rights reserved.

关键词： Lagrange multipliers

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