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Assessing uncertainty in wind turbine electrical power production using Monte-Carlo simulation: A case study on Sidrap wind turbine, Sulawesi - Indonesia

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AIP Conference Proceedings 2024年第1期3039卷

作者： Leonard Lisapaly Post Graduate Program Electrical Engineering Department Universitas Kristen Indonesia (UKI) Jakarta Indonesia

As the number of populations worldwide, including Indonesia, increases annually, the need for electrical energy also increases. With fossil-based energy starting to decrease in some places, and the global need to achieve net zero-emission, renewable energy is gaining popularity as the source of electrical power generation, for example, wind energy. electrical power generated from wind turbines is one of the popular choices in many countries; among them is the Sidrap wind turbine plant in Sulawesi, Indonesia. electrical power generated by a wind turbine relies on the wind speed, which varies from time to time and season to season. Therefore, building wind turbines plant should not be based on a deterministic wind speed but should bring the wind speed uncertainty into account. Taking the Sidrap wind turbine plant in Sulawesi, Indonesia, as the case study, this work aims to predict the plant’s average power and uncertainty. This calculation is done by implementing the Monte-Carlo simulation to calculate the production uncertainty. The data used for simulation is public data set between 2004 and 2015, obtained from the EMD International website. The simulation result is then compared to the actual production in 2018 and 2019 for analysis. The result indicates that the product is still in the range estimated using the Monte Carlo simulation. This method can also estimate other electrical power potential and uncertainty in an area before a wind turbine plant is built, given enough data sets containing wind speed variation for years. It is expected that by correctly understanding the uncertainty in electrical power production, the decision-making process to build the plant can be accurately determined.

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Electrostatic Shielding for Bearing Current Mitigation - An Analysis of its Thermal Impact on the Motor

Electrostatic Shielding for Bearing Current Mitigation - An ...

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Industrial Applications Society Annual Conference on Petroleum and Chemical Industry

作者： Marco T. A. Êvo Arismar M. G. Júnior Caio Eduardo Silva Hélder De Paula Electrical Engineering Department Federal University of São João del Rei São João del Rei MG Brazil Graduate Program in Electrical Engineering Federal University of Uberlândia Uberlândia MG Brazil Faculty of Electrical Engineering Federal University of Uberlândia Uberlândia MG Brazil

This work presents a study on the use of electrostatic shields to reduce bearing currents (both Electrostatic discharge machining and circulating currents), focusing on its thermal impact on the motor operation. 2-D finite element models are utilized to analyze the temperature distribution inside the coil winding slot of motors both with and without shielding. The results show that the shielding device can be used with a negligible influence on the temperature rise inside the slot. In addition, it is demonstrated that for some shield configurations, the stator winding eddy current loss can be attenuated. In such cases, the shield presence can also lead to a temperature reduction in the stator conductors. Furthermore, the developed models can be expanded to be used in the analysis of any other motor equipped with this type of solution, aiding the design and implementation of the shielding device. Keeping in mind that the majority of the motor failures are due to bearing problems, this paper contributes to the industry by addressing issues closely related to the reliability topic, such as solutions to increase the motor availability, which is the very case of the shielding device.

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EllipsoNet: Deep-learning-enabled optical ellipsometry for complex thin films

arXiv

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arXiv 2022年

作者： Wang, Ziyang Lin, Yuxuan Cosmi Zhang, Kunyan Wu, Wenjing Huang, Shengxi Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Department of Electrical Engineering The Pennsylvania State University University ParkPA16802 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States

Optical spectroscopy is indispensable for research and development in nanoscience and nanotechnology, microelectronics, energy, and advanced manufacturing. Advanced optical spectroscopy tools often require both specifically designed high-end instrumentation and intricate data analysis techniques. Beyond the common analytical tools, deep learning methods are well suited for interpreting high-dimensional and complicated spectroscopy data. They offer great opportunities to extract subtle and deep information about optical properties of materials with simpler optical setups, which would otherwise require sophisticated instrumentation. In this work, we propose a computational ellipsometry approach based on a conventional tabletop optical microscope and a deep learning model called EllipsoNet. Without any prior knowledge about the multilayer substrates, EllipsoNet can predict the complex refractive indices of thin films on top of these nontrivial substrates from experimentally measured optical reflectance spectra with high accuracies. This task was not feasible previously with traditional reflectometry or ellipsometry methods. Fundamental physical principles, such as the Kramers-Kronig relations, are spontaneously learned by the model without any further training. This approach enables in-operando optical characterization of functional materials within complex photonic structures or optoelectronic devices. © 2022, CC BY.

关键词： Refractive index

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Deep STI: Deep Stochastic Time-series Imputation on Electronic Health Records

Deep STI: Deep Stochastic Time-series Imputation on Electron...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Ming-Che Cheng Yi-Hsien Hsieh Te-Cheng Hsu Tung-Hung Su Che Lin Institute of Communications Engineering National Tsing Hua University Hsinchu Taiwan Graduate Institute of Communication Engineering National Taiwan University (NTU) Taipei Taiwan Dept. of Internal Medicine Division of Gastroenterolgy and Hepatology National Taiwan University Hospital Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Electronic Health Records (EHRs) are a cornerstone of modern healthcare analytics, offering rich datasets for various disease analyses through advanced deep learning algorithms. However, the pervasive issue of missing values in EHRs significantly hampers the development and performance of these models. Addressing this challenge is crucial for enhancing clinical decision-making and patient care. Existing methods for handling missing data, ranging from simple imputation to more sophisticated approaches, often fall short of capturing the temporal dynamics inherent in EHRs. To bridge this gap, we introduce the Deep Stochastic Time-series Imputation (Deep STI) algorithm, an innovative end-to-end deep learning model that seamlessly integrates a sequence-to-sequence generative network with a prediction network. Deep STI is designed to leverage the observed time-series data in EHRs, learning to infer missing values from the temporal context with high accuracy. We evaluated Deep STI on the liver cancer data from the National Taiwan University Hospital (NTUH), Taiwan. Our results showed that Deep STI achieved better 5-year hepatocellular carcinoma predictions (19.21% in the area under the precision-recall curve) than extreme gradient boosting (18.15%) and Transformer (18.09%). The ablation study also illustrates the efficacy of our generative architecture design compared to regular imputations. This approach not only promises to improve the reliability of disease analysis in the presence of incomplete data but also sets a new standard for utilizing EHRs in predictive healthcare. Our work aims to advance the field of healthcare analytics and open new avenues for research in deep learning applications to EHRs.

关键词： Deep learning Time series analysis Stochastic processes Predictive models Transformers Imputation Reliability Electronic medical records Standards Diseases

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Utilizing multimodal approach to identify candidate pathways and biomarkers and predicting frailty syndrome in individuals from UK Biobank

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GeroScience 2024年第1期46卷 1211-1228页

作者： Tseng, Watson Hua-Sheng Chattopadhyay, Amrita Phan, Nam Nhut Chuang, Eric Y. Lee, Oscar K. Institute of Clinical Medicine National Yang Ming Chiao Tung University Taipei Taiwan Bioinformatics and Biostatistics Core Centers of Genomic and Precision Medicine National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics Department of Electrical Engineering National Taiwan University Taipei Taiwan Center for Translational Genomics and Regenerative Medicine China Medical University Hospital Taichung Taiwan Stem Cell Research Center National Yang Ming Chiao Tung University Taipei Taiwan Department of Orthopedics China Medical University Hospital Taichung Taiwan Biomedical Technology and Device Research Laboratories Industrial Technology Research Institute Hsinchu Taiwan

Frailty, a prevalent clinical syndrome in aging adults, is characterized by poor health outcomes, represented via a standardized frailty-phenotype (FP), and Frailty Index (FI). While the relevance of the syndrome is gaining awareness, much remains unclear about its underlying biology. Further elucidation of the genetic determinants and possible underlying mechanisms may help improve patients’ outcomes allowing healthy aging. Genotype, clinical and demographic data of subjects (aged 60–73 years) from UK Biobank were utilized. FP was defined on Fried’s criteria. FI was calculated using electronic-health-records. Genome-wide-association-studies (GWAS) were conducted and polygenic-risk-scores (PRS) were calculated for both FP and FI. Functional analysis provided interpretations of underlying biology. Finally, machine-learning (ML) models were trained using clinical, demographic and PRS towards identifying frail from non-frail individuals. Thirty-one loci were significantly associated with FI accounting for 12% heritability. Seventeen of those were known associations for body-mass-index, coronary diseases, cholesterol-levels, and longevity, while the rest were novel. Significant genes CDKN2B and APOE, previously implicated in aging, were reported to be enriched in lipoprotein-particle-remodeling. Linkage-disequilibrium-regression identified specific regulation in limbic-system, associated with long-term memory and cognitive-function. XGboost was established as the best performing ML model with area-under-curve as 85%, sensitivity and specificity as 0.75 and 0.8, respectively. This study provides novel insights into increased vulnerability and risk stratification of frailty syndrome via a multi-modal approach. The findings suggest frailty as a highly polygenic-trait, enriched in cholesterol-remodeling and metabolism and to be genetically associated with cognitive abilities. ML models utilizing FP and FI + PRS were established that identified frailty-syndrome patients with

关键词： Aging Frailty index (FI) Frailty phenotype (FP) Frailty syndrome GWAS UK-Biobank XGboost

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Full-Duplex Beamforming in the Sub-Terahertz Regime

Full-Duplex Beamforming in the Sub-Terahertz Regime

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International Conference on Infrared and Millimeter Waves

作者： Subhajit Karmakar Atsutse Kludze Jacques Doumani Andrey Baydin Junichiro Kono Yasaman Ghasempour Department of Electrical and Computer Engineering Princeton University Princeton NJ USA Department of Electrical and Computer Engineering Rice University Houston TX USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Smalley-Curl Institute Rice University Houston TX USA Department of Physics and Astronomy Rice University Houston TX USA Department of Material Science and NanoEngineering Rice University Houston TX USA

We propose a novel architecture that realizes full-duplex directional beamforming in the sub-terahertz regime. Our architecture leverages the frequency-controlled beam steering of leaky-wave antenna together with the polarization-dependent response of aligned single-wall carbon nanotubes. We evaluate the performance of this architecture via preliminary experiments.

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Control of a Smart Active Flexible Needle for Therapeutic procedures

Control of a Smart Active Flexible Needle for Therapeutic pr...

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SICE International Symposium on Control Systems (ISCS)

作者： Surender Hans Jitender Kumar Goyal Ankit Sachan Payal Bansal Sandeep Soni Mohamed Djemai Department of Electronics and Communication Engineering Poornima College of Engineering Jaipur India School of Electrical Engineering Vellore Institute of Technology Vellore Tamil nadu India Graduate School of Advanced Science and Engineering Hiroshima University Japan INSA Centre Val de Loire Universite dOrleans Bourges Cedex France IINSA Hauts-de-France LAMIH UMR CNRS 8201 UPHF Campus Mout Houy France

Minimally Invasive Surgery (MIS) is more efficient than open surgery on the grounds that the recuperation and hospitalization time of MIS is significantly lesser than that of ordinary surgical methods. Till date physicians perform MIS procedures using conventional flexible passive needles. From the literature survey, it is observed that self-actuating needle promises enhancement in target reaching accuracy thereby widening the needle steerable methodology. Considering this into account, here, we aim to develop and control a shape memory alloy actuated smart needle by state feedback controller. We started from a previously proposed passive flexible needle in the literature to the proposed smart active needle. Since only one cause of idle movement of passive needle is the bevelled-tip. The main objective of the proposed controller for this smart needle is to remain within the desired plane of interest inside the tissue region. The three control inputs, one primary and two secondary are deployed to the proposed state feedback controller. Numerical simulations are presented to evaluate the proposed control technique on the developed smart active needle.

关键词： State feedback Solid modeling Target tracking Three-dimensional displays Minimally invasive surgery Shape memory alloys Medical services

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Multimodal Spatial Attention Network for Automatic Head and Neck Tumor Segmentation in FDG-PET and CT Images 1

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2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021

作者： Cho, Minjeong Choi, Yujin Hwang, Donghwi Yie, Si Young Kim, Hanvit Lee, Jae Sung School of Electrical and Electronics Engineering Chung-Ang University Seoul06974 Korea Republic of Department of Biomedical Engineering Kyunghee University College of Electronics and Information 1732 Deogyeong-daero Giheung-gu Gyeonggi-do Yongin-si Korea Republic of Department of Nuclear Medicine Seoul National University College of Medicine Seoul03080 Korea Republic of Department of Biomedical Sciences Seoul National University College of Medicine Seoul03080 Korea Republic of Interdisciplinary Program in Bioengineering Seoul National University Graduate School Seoul Korea Republic of Integrated Major in Innovative Medical Science Seoul National University Graduate School Seoul Korea Republic of Ulsan Korea Republic of Artificial Intelligence Institute Seoul National University Seoul08826 Korea Republic of Brightonix Imaging Inc. Seoul04782 Korea Republic of

ISBN: (数字)9783030982539

ISBN: (纸本)9783030982522

Quantitative positron emission tomography/computed tomography (PET/CT), owing to the functional metabolic information and anatomical information of the human body that it presents, is useful to achieve accurate tumor delineation. However, manual annotation of a Volume Of Interest (VOI) is a labor-intensive and time-consuming task. In this study, we automatically segmented the Head and Neck (H&N) primary tumor in combined PET/CT images. Herein, we propose a convolutional neural network named Multimodal Spatial Attention Network (MSA-Net), supplemented with a Spatial Attention Module (SAM), which uses a PET image as an input. We evaluated this model on the MICCAI 2021 HEad and neCK TumOR (HECKTOR) segmentation challenge dataset. Our method delivered a competitive cross-validation efficiency, with Dice Similarity Coefficient (DSC) 0.757, precision 0.788, and recall 0.785. When we tested out method on test dataset, we achieved an average DSC and Hausdorff Distance at 95% (HD95) of 0.766 and 3.155 respectively. Our team name is ‘Heck_Uihak’. © 2022, Springer Nature Switzerland AG.

关键词： Tumors

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Negative Thermal Expansion Coefficient of Al Pnictides –A Systematic Realistic Pressure-Dependent Lattice Dynamical Study

SSRN

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SSRN 2023年

作者： Talwar, Devki N. Lin, Hao-Hsiung Department of Physics University of North Florida 1 UNF Drive JacksonvilleFL32224-7699 United States Department of Physics Indiana University of Pennsylvania 975 Oakland Avenue 56 Weyandt Hall IndianaPA15705-1087 United States Graduate Institute of Electronics Engineering Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan

In weakly and strongly bonded semiconductors, the knowledge of accurate linear thermal expansion coefficients and Grüneisen constants play crucial roles for attaining the basic understanding of lattice anharmonicity and appraising their use as thermal management materials in designing different electronic devices. By adopting a realistic rigid-ion-model (RIM), we have reported the results of a comprehensive study on phonon dispersions and thermodynamical properties of weakly bonded III-V AlX (X = P, As, and Sb) pnictides at ambient (P = 0) and high pressure (P = 8 GPa). The results are compared/contrasted with strongly bonded III-Ns (AlN, GaN, and InN) and other materials. Specifically, we have linked the simulated negative thermal expansion (NTE) coefficient in AlX to the weakness of bond-bending forces which cause large softening in making and more negative at low T. © 2023, The Authors. All rights reserved.

关键词： Thermal expansion

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Structural and Optical Properties of Inp1-Xsbx/N-Inas Epilayers Grown by Gas Source Molecular Beam Epitaxy

SSRN

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SSRN 2023年

Experimental photoluminescence (PL), synchrotron resolution extended x-ray absorption fine-structure spectroscopy (SR-EXAFS) and theoretical studies are systematically performed to assess the optical and structural properties of InP1−xSbx/n-InAs epifilms grown by gas-source molecular-beam epitaxy method. For the InP0.63Sb0.37/n-InAs sample, our photoluminescence (PL) study has revealed two A1, A2 bands. The A1 band, prevalent at low temperature, is attributed to the recombination of carriers trapped in the tail states resulting from compositional disorder. The A2 band having constant transition energy (0.45 eV – 0.46 eV) is virtually temperature independent. The peak energy of A1 band has redshifted with temperature and indicated strong disorder in InP1−xSbx alloys. The A2 band, dominant at higher temperature, with Gaussian-like line shape is ascribed to the deep-level transition as its behavior coincides with the signature of a configuration coordinate model. For InP0.63Sb0.37 alloy, our simulated results based on model dielectric functions (MDFs) approach provided the lowest energy bandgap [[EQUATION]] ~ 0.49 eV in good agreement with the PL A2 band. The composition dependent SR-EXAFS data analyses on InP1−xSbx/InAs samples has confirmed that the nearest neighbor (NN) shell (In-P, In-Sb) distances are maintained within the range of NN bond lengths of bulk materials implying the divergence from Vegard's law. © 2023, The Authors. All rights reserved.

关键词： Photoluminescence

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