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A Neural Network Based Modeling of Closed Room Thermal Comfort Environmental Prediction for Sensor Hub 17

A Neural Network Based Modeling of Closed Room Thermal Comfo...

17th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information technology, ECTI-CON 2020

作者： Thongkhome, Pornpawee Dejdumrong, Natasha Triamudomsuksa Pattanakarn School Science-Mathematics Program Bangkok Thailand King Mongkut's University of Technology Thonburi Dept. of Computer Engineering Bangkok Thailand

ISBN: (纸本)9781728164861

Currently, environmental protection and intelligence system are topics of interest in smart homes and intelligent buildings. This work represents an environmental prediction study of the closed room which installed a split-type air-conditioning system and a newly designed sensor hub using a feed-forward back-propagation artificial neural network-based prediction for human thermal comfort and indirectly concerned to the growth or existence of diseases, contagious diseases and allergies. The RPROP algorithm was utilized to train the closed room environmental parameters that get from sensor hub especial relative humidity in the ANN model and validate the model by using cohen's kappa and ROC curve to represents the degree of accuracy and reliability. The result obtained from the ANN trained data agreed closely with those obtained from a sensor hub with the high perfect agreement. The benefit of the ANN-based strategy in this study could be utilized for the design of a sensor hub that can realize the adaptive adjustment of environmental parameters through the air conditioning system. © 2020 IEEE.

关键词： Thermal comfort

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Artificial neural network approach for turbulence models: A local framework

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Physical Review Fluids 2021年第8期6卷 084612-084612页

作者： Chenyue Xie Xiangming Xiong Jianchun Wang Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen 518055 People's Republic of China

A local artificial neural network (LANN) framework is developed for turbulence modeling. The Reynolds-averaged Navier-Stokes (RANS) unclosed terms are reconstructed by the artificial neural network based on the local coordinate system which is orthogonal to the curved walls. We verify the proposed model in the flows over periodic hills. The correlation coefficients of the RANS unclosed terms predicted by the LANN model can be made larger than 0.96 in an a priori analysis, and the relative error of the unclosed terms can be made smaller than 18%. In an a posteriori analysis, detailed comparisons are made on the results of RANS simulations using the LANN, global artificial neural network (GANN), Spalart-Allmaras (SA), and shear stress transport (SST) k−ω models. It is shown that the LANN model performs better than the GANN, SA, and SST k−ω models in the prediction of the average velocity, wall-shear stress, and average pressure, which gives the results that are essentially indistinguishable from the direct numerical simulation data. The LANN model trained at low Reynolds number, Re=2800, can be directly applied to the cases of high Reynolds numbers, Re=5600, 10 595, 19 000, and 37 000, with accurate predictions. Furthermore, the LANN model is verified for flows over periodic hills with varying slopes. These results suggest that the LANN framework has a great potential to be applied to complex turbulent flows with curved walls.

关键词： Compressible boundary layers Turbulence Artificial neural networks

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Developing a medical artificial intelligence course for high school students

Developing a medical artificial intelligence course for high...

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International Forum on Medical Imaging in Asia 2021, IFMIA 2021

作者： Huang, Chao-Jung Wu, Tinghui Lu, Jui-Ting Lin, Beatrice Chang, Dawei Wang, Pochuan Wang, Mei-Chi Lee, Peijung Wang, Weichung National Taiwan University-Stanford Joint Program Office of AI in Biotechnology Ministry of Science and Technology Joint Research Center for Artificial Intelligence Technology and All Vista Healthcare Taipei Taiwan Institute of Applied Mathematical Sciences National Taiwan University Taipei Taiwan Department of Mathematics Ecole Normale Supérieure de Lyon Lyon France Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Mathematics National Taiwan University Taipei Taiwan

ISBN: (数字)9781510644212

ISBN: (纸本)9781510644205

Artificial intelligence (AI) training courses often require prerequisites such as calculus or statistics. It is hence challenging to design and develop an introductory AI course for students of secondary education. This research intends to develop a medical AI course, provide high school students with an overview of deep learning applications in medical image analysis, and inspire them to pursue careers in the field of medical AI. We designed a 20-hour course, including lectures and two hands-on projects based on medical image classification. The proposed courses provided medical AI disciplines and built up their knowledge from basic to advanced levels. During the ten-day online courses, all the students were fully engaged and gave us positive feedback. The students endeavored to complete the experimental study in training, testing, and hypothesis of medical images application in the course. Their performance exceeded all expectations, for they did further analysis by tuning different hyperparameters. We designed a course evaluation form, which suggested that the students found it essential and expected to interact with the instructors. The results indicate that combining lectures with hands-on sessions would lead to evidently better achievement in terms of high school students' medical AI knowledge and positive attitudes while addressing real-world problems in the projects. Through this innovative education model, high school students regained their enthusiasm and were encouraged to cultivate their medical AI skills through self-learning while finishing the project. We conclude that this course could be successfully applied to interdisciplinary education in high school. © 2021 SPIE.

关键词： Students

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3D Liver and Tumor Segmentation in CT Scans with Robust Self-Paced Learning 7

3D Liver and Tumor Segmentation in CT Scans with Robust Self...

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7th International Conference on Information science and Control engineering, ICISCE 2020

作者： Nartey, Obed Tettey Yang, Guowu Asare, Sarpong Kwadwo Wu, Jinzhao Agyapong, Dorothy Araba Yakoba Frempong, Lady Nadia University of Electronic Science and Technology of China Big Data Research Center School of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China School of Electronic Science and Engineering Chengdu611731 China Guangxi University for Nationalities School of Mathematics and Physics Nanning530006 China Kwame Nkrumah University of Science and Technology Biomedical Engineering Program Kumasi Ghana

ISBN: (纸本)9781728164069

Liver segmentation is a challenging problem where fully supervised deep learning models require large amounts of voxel-wise labels, which are usually laborious, expensive, and time consuming to obtain. However, massive non-annotated 3D CT volumes are usually easily accessible. Most deep learning based works to address semi-supervised CT medical image segmentation problems have mostly been graph-based or adversarial-based. In sharp contrast, this work investigates the potential of a semi-supervised learning method;novel self-training framework utilizing self-paced learning strategy to generate and select pseudo-labels from easy-to-transfer(well represented classes) to hard-to-transfer(less represented classes) samples. The proposed scheme introduces an effective pseudo-label generation and selection algorithm to generate highly confident pseudo-labels, jointly learn and optimize a model which can be solved by alternatively generating pseudo labels on unlabeled target data and re-training the model with the generated labels. Further, we also propose a novel class balancing framework to avoid the gradual eminence of easy-to-transfer discriminative features on pseudo-label generation utilizing class-wise normalization of confidence scores. Comprehensive experiments conducted on publicly available datasets show that the proposed method outperformed other state-of-the-art semi-supervised and some fully supervised methods on the segmentation results of tumors and achieved very competitive performance for liver segmentation. © 2020 IEEE.

关键词： Image segmentation

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Heterogeneous Oblique Double Random Forest

arXiv

引用

arXiv 2023年

作者： Ganaie, M.A. Tanveer, M. Beheshti, I. Ahmad, N. Suganthan, P.N. Department of Robotics University of Michigan Ann ArborMI United States Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Department of International Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar

The decision tree ensembles use a single data feature at each node for splitting the data. However, splitting in this manner may fail to capture the geometric properties of the data. Thus, oblique decision trees generate the oblique hyperplane for splitting the data at each non-leaf node. Oblique decision trees capture the geometric properties of the data and hence, show better generalization. The performance of the oblique decision trees depends on the way oblique hyperplanes are generate and the data used for the generation of those hyperplanes. Recently, multiple classifiers have been used in a heterogeneous random forest (RaF) classifier, however, it fails to generate the trees of proper depth. Moreover, double RaF studies highlighted that larger trees can be generated via bootstrapping the data at each non-leaf node and splitting the original data instead of the bootstrapped data recently. The study of heterogeneous RaF lacks the generation of larger trees while as the double RaF based model fails to take over the geometric characteristics of the data. To address these shortcomings, we propose heterogeneous oblique double RaF. The proposed model employs several linear classifiers at each non-leaf node on the bootstrapped data and splits the original data based on the optimal linear classifier. The optimal hyperplane corresponds to the models based on the optimized impurity criterion. The experimental analysis indicates that the performance of the introduced heterogeneous double random forest is comparatively better than the baseline models. To demonstrate the effectiveness of the proposed heterogeneous double random forest, we used it for the diagnosis of Schizophrenia disease. The proposed model predicted the disease more accurately compared to the baseline models. © 2023, CC BY-NC-ND.

关键词： Decision trees

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CAUCHY MARKOV RANDOM FIELD PRIORS FOR BAYESIAN INVERSION

arXiv

引用

arXiv 2021年

作者： Suuronen, Jarkko Chada, Neil K. Roininen, Lassi School of Engineering Science Lappeenranta-Lahti University of Technology PO Box 20 LappeenrantaFI-53851 Finland Applied Mathematics and Computational Science Program King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

The use of Cauchy Markov random field priors in statistical inverse problems can potentially lead to posterior distributions which are non-Gaussian, high-dimensional, multimodal and heavy-tailed In order to use such priors successfully, sophisticated optimization and Markov chain Monte Carlo (MCMC) methods are usually required In this paper, our focus is largely on reviewing recently developed Cauchy difference priors, while introducing interesting new variants, whilst providing a comparison We firstly propose a one-dimensional second order Cauchy difference prior, and construct new first and second order two-dimensional isotropic Cauchy difference priors Another new Cauchy prior is based on the stochastic partial differential equation approach, derived from Matérn type Gaussian presentation The comparison also includes Cauchy sheets Our numerical computations are based on both maximum a posteriori and conditional mean estimation We exploit state-of-the-art MCMC methodologies such as Metropolis-within-Gibbs, Repelling-Attracting Metropolis, and No-U-Turn sampler variant of Hamiltonian Monte Carlo We demonstrate the models and methods constructed for one-dimensional and two-dimensional deconvolution problems Thorough MCMC statistics are provided for all test cases, including potential scale reduction factors. Copyright © 2021, The Authors. All rights reserved.

关键词： Bayesian networks

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Integrated Vocal Deviation Index (IVDI): A Machine Learning Model to Classifier of the General Grade of Vocal Deviation

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Journal of Voice 2024年

作者： Lima-Filho, Luiz Medeiros Araujo Lopes, Leonardo Wanderley Filho, Telmo de Menezes e Silva Department of Statistics Member of the Graduate Program in Decision Models and Health Universidade Federal da Paraíba - UFPB Paraíba João Pessoa Brazil Department of Communication Science Disorders Member of the Graduate Program in Decision Models and Health Universidade Federal da Paraíba - UFPB Paraíba João Pessoa Brazil School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom

Objective: To develop a multiparametric index based on machine learning (ML) to predict and classify the overall degree of vocal deviation (GG). Method: The sample consisted of 300 dysphonic and non-dysphonic participants of both sexes. Two speech tasks were sustained vowel [a] and connected speech (counting numbers from 1 to 10). Five speech-language pathologists performed the auditory-perceptual judgment (APJ) of the GG and the degrees of roughness (GR), breathiness (GB), instability (GI), and strain (GS). We extracted 47 acoustic measurements from these tasks. The APJ result and the acoustic measurements were used to develop the multiparametric index. We used mean absolute error, root mean square error, and coefficient of determination (R²) to select the best model of ML to predict GG and feature importance to select the best set of variables for the index. After classifying the GG between nondysphonic, mild, moderate, and severe, the final model was validated using accuracy, sensitivity, specificity, predictive values, likelihood ratios, F1-Score, and weighted kappa. Results: The gradient boost model showed the best performance among the ML models. Eight features were selected in the model, including four acoustic measures (jitterLoc, smoothed cepstral peak prominenc, mean harmonic-to-noise ratio (HNRmean), and correlation) and four APJ measures (GR, GB, GS, and GI). The final model correctly classified 93.75% of participants and obtained a weighted kappa index of 0.9374, demonstrating the model's excellent performance. Conclusion: The Integrated Vocal Deviation Index includes four acoustic measures and four auditory-perceptual measures and showed excellent performance in classifying voices according to GG. © 2024 The Voice Foundation

关键词： Machine learning—Acoustic—Voice disorders—Dysphonia—Automatic diagnosis.

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Novel synthesis of nickel-based bimetallic compounds using ammonium tetrafluoroborate and ammonium bifluoride as structure directing agents for energy storage application

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Journal of Energy Storage 2024年 75卷

作者： Kuo, Tsung-Rong Yu, You Zher Lee, Pin-Yan Wu, Chung-Hsien Chen, Hung-Ming Husain, Sadang Kongvarhodom, Chutima Ho, Kuo-Chuan Lin, Lu-Yin International PhD Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei Taiwan Department of Chemical Engineering National Taiwan University Taipei Taiwan Gingen Technology Co. LTD. Rm. 7 10F. No.189 Sec. 2 Keelung Rd. Xinyi Dist. Taipei11054 Taiwan Department of Physics Faculty of Mathematics and Natural Science Lambung Mangkurat University Banjarmasin70124 Indonesia Department of Chemical Engineering King Mongkut's University of Technology Thonburi 126 Pracha-u-thit Toong-kru Bangkok10140 Thailand

Nickel-based compounds are widely applied as the active material for energy storage devices, owing to high theoretical capacity and easy-fabricated process. Bimetallic compounds having multiple redox states can provide abundant redox reactions for charge storage. Structure directing agent (SDA) can promote the preferable growth of active materials and to enhance energy storage abilities. In this work, NH4BF4 and NH4HF2 are firstly used as SDA to synthesize nickel-based compounds along with 2-methylmidozole in the one-step solution process for energy storage applications. The bimetallic compounds are synthesized by incorporating Al, Mn, Co, Cu and Zn salts in the precursor solution including the Ni salt. The system with Al, Cu or Zn possesses single metal compound and barely nickel compound. Mn and Co-incorporated systems present clear bimetallic components with nickel hydroxide. The highest specific capacitance (CF) of 1336.3 F/g (297.0 mAh/g) are achieved at 20 mV/s for the NiCo-based electrode, owing to the bimetallic feature and favorable two-dimensional sheet-like structures. An energy storage device is assembled using the NiCo-based compound and reduced graphene oxide electrodes. The maximum energy density of 10.5 Wh/kg at 0.3 kW/kg, and the CF retention of 78.5% and Coulombic efficiency of 85.2% are achieved after 10,000 times charge/discharge cycles. © 2023 Elsevier Ltd

关键词： Energy storage

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What we should learn from pandemic publishing

arXiv

引用

arXiv 2024年

作者： Sikdar, Satyaki Venturini, Sara Charpignon, Marie-Laure Kumar, Sagar Rinaldi, Francesco Tudisco, Francesco Fortunato, Santo Majumder, Maimuna S. Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Computer Science Loyola University Chicago ChicagoIL United States Senseable City Laboratory Massachusetts Institute of Technology CambridgeMA United States Department of Mathematics "Tullio Levi-Civita" University of Padova Padova Italy Institute for Data Systems and Society Massachusetts Institute of Technology CambridgeMA United States Network Science Institute Northeastern University BostonMA United States School of Mathematics The University of Edinburgh Edinburgh United Kingdom School of Mathematics Gran Sasso Science Institute L’Aquila Italy Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children’s Hospital BostonMA United States

Since the emergence of COVID-19, discussions of ongoing pandemic-related research have accounted for an unprecedented share of media coverage and debate in the public sphere1. The urgency of the pandemic forced researchers to operate on an accelerated timeline, as both policymakers and the public relied on the most current evidence to guide their decisions and behaviors. With high demand for rapid pandemic-related insights and lower barriers to entry via preprint servers, the volume of COVID-19 articles skyrocketed2. The pressing need for research triggered the participation of many researchers with expertise in the science of infectious disease outbreaks (‘outbreak scientists’), who were joined by researchers from other disciplines (‘bellwethers’) and more junior researchers still in training (‘newcomers’) with the common goal of advancing the frontiers of pandemic science and informing policy decisions3. Please see Box 1 for details of this taxonomy. © 2024, CC BY-NC-ND.

关键词： COVID-19

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Unveiling mitochondria as central components driving cognitive decline in alzheimer's disease through cross-transcriptomic analysis of hippocampus and entorhinal cortex microarray datasets

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Heliyon 2024年第20期10卷 e39378页

作者： Sonsungsan, Pajaree Aimauthon, Supatha Sriwichai, Nattawet Namchaiw, Poommaree Mathematics and Statistics School of Science Walailak University Nakhon Si Thammarat Thailand Chemical Engineering Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Center for Biologics Research and Development Chulabhorn Research Institute Bangkok Thailand Center for Agricultural Systems Biology Pilot Plant Development and Training Institute King Mongkut's University of Technology Thonburi (Bang Khun Thian) Bangkok Thailand Biological Engineering Program Faculty of Engineering King Mongkut's University of Technology Thonburi Bangkok Thailand Neuroscience Center for Research and Innovation Learning Institute King Mongkut's University of Technology Thonburi Bangkok Thailand

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder characterized by symptoms such as memory loss and impaired learning. This study conducted a cross-transcriptomic analysis of AD using existing microarray datasets from the hippocampus (HC) and entorhinal cortex (EC), comparing them with age-matched non-AD controls. Both of these brain regions are critical for learning and memory processing and are vulnerable areas that exhibit abnormalities in early AD. The cross-transcriptomic analysis identified 564 significantly differentially expressed genes in HC and 479 in EC. Among these, 151 genes were significantly differentially expressed in both tissues, with functions related to synaptic vesicle clustering, synaptic vesicle exocytosis/endocytosis, mitochondrial ATP synthesis, hydrogen ion transmembrane transport, and structural constituent of cytoskeleton, suggesting a potential association between cognitive decline in AD, synaptic vesicle dynamics, dysregulation of cytoskeleton organization, and mitochondrial dysfunction. Further gene ontology analysis specific to the HC revealed the gene ontology enrichment in aerobic respiration, synaptic vesicle cycle, and oxidative phosphorylation. The enrichment analysis in CA1 of HC revealed differentiation in gene expression related to mitochondrial membrane functions involved in bioenergetics, mitochondrial electron transport, and biological processes associated with microtubule-based process, while analysis in the EC region showed enrichment in synaptic vesicle dynamics which is associated with neurotransmitter release and the regulation of postsynaptic membrane potential and synaptic transmission of GABAergic and glutamatergic synapse. Protein-protein interaction analysis highlighted central hub proteins predominantly expressed in mitochondria, involved in regulation of oxidative stress and ATP synthesis. These hub proteins interact not only within the mitochondria but also with proteins in the vesicular membra

关键词： Alzheimer's disease Cytoskeleton Microarray Mitochondria Synaptic vesicle

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