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International Joint Conference on Neural Networks (IJCNN)

作者： Andre Alves Portela Santos Leandro dos Santos Coelho Carlos Eduardo Klein Department of Statistics Universidad Carlos III de Madrid Spain Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba Parana Brazil CNPq National Council for Scientific and Technological Development Brazil

In this article, we propose a nonlinear forecasting model based on radial basis function neural networks (RBF-NNs) with Gaussian activation functions and robust clustering algorithms to model the conditional mean and a parametric generalized autoregressive conditional heteroskedasticity (GARCH) specification to model the conditional volatility. Instead of calibrating the parameters of the RBF-NNs via numerical simulations, we propose a novel estimation procedure by which the number of basis functions, their corresponding widths and the parameters of the GARCH model are jointly estimated via maximum likelihood along with a genetic algorithm to maximize the likelihood function. We use this model to provide hour-ahead point and direction-of-change forecasts of the Spanish electricity pool prices.

关键词： Load modeling Artificial neural networks Predictive models

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Anticipatory monitoring and control of complex energy systems using a fuzzy based fusion of support vector regressors

Anticipatory monitoring and control of complex energy system...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Miltiadis Alamaniotis Vivek Agarwal Tatjana Jevremovic Nuclear Engineering Program The University of Utah Salt Lake City UT USA Department of Human Factors Controls and Statistics Idaho National Laboratory Idaho Falls ID USA University of Utah Salt Lake City UT US

This paper places itself in the realm of anticipatory systems and envisions monitoring and control methods being capable of making predictions over system critical parameters. Anticipatory systems allow intelligent control of complex systems by predicting their future state. In the current work, an intelligent model aimed at implementing anticipatory monitoring and control in energy industry is presented and tested. More particularly, a set of support vector regressors (SVRs) are trained using both historical and observed data. The trained SVRs are used to predict the future value of the system based on current operational system parameter. The predicted values are then inputted to a fuzzy logic based module where the values are fused to obtain a single value, i.e., final system output prediction. The methodology is tested on real turbine degradation datasets. The outcome of the approach presented in this paper highlights the superiority over single support vector regressors. In addition, it is shown that appropriate selection of fuzzy sets and fuzzy rules plays an important role in improving system performance.

关键词： Monitoring Support vector machines Turbines Control systems Fuzzy logic Predictive models Degradation

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Fractional Integrable Nonlinear Soliton Equations

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Physical Review Letters 2022年第18期128卷 184101-184101页

作者： Mark J. Ablowitz Joel B. Been Lincoln D. Carr Department of Applied Mathematics University of Colorado Boulder Colorado 80309 USA Department of Applied Mathematics and Statistics Colorado School of Mines Golden Colorado 80401 USA Department of Physics Colorado School of Mines Golden Colorado 80401 USA Quantum Engineering Program Colorado School of Mines Golden Colorado 80401 USA

Nonlinear integrable equations serve as a foundation for nonlinear dynamics, and fractional equations are well known in anomalous diffusion. We connect these two fields by presenting the discovery of a new class of integrable fractional nonlinear evolution equations describing dispersive transport in fractional media. These equations can be constructed from nonlinear integrable equations using a widely generalizable mathematical process utilizing completeness relations, dispersion relations, and inverse scattering transform techniques. As examples, this general method is used to characterize fractional extensions to two physically relevant, pervasive integrable nonlinear equations: the Korteweg–deVries and nonlinear Schrödinger equations. These equations are shown to predict superdispersive transport of nondissipative solitons in fractional media.

关键词： Solitons

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Environment, krill and squid in the Monterey Bay: From fisheries to life histories and back again

Environment, krill and squid in the Monterey Bay: From fishe...

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作者： Ish, Teresa Dick, E.J. Switzer, Paul V. Mangel, Marc Graduate Program in Marine Sciences University of California Santa Cruz CA United States Dept. of Ecol. and Evol. Biology University of California Santa Cruz CA United States Department of Biological Sciences Eastern Illinois University Charleston IL United States Dept. of Appl. Math. and Statistics The Jack Baskin Sch. of Engineering University of California 95064 Santa Cruz CA United States

The fishery for market squid in California is extremely valuable and only recently subject to regulations (in that sense, it is one of the last great open-access fisheries on the western coast of North America). We briefly review the history of the fishery for Loligo opalescens, on an annual time scale. Within-season analyses suggest that cohorts of squid persist offshore and move inshore for reproduction throughout the season. In order to predict the timing of inshore movement of squid, we develop a life history model using stochastic dynamic programming. This model allows us to link squid growth and reproduction with environmental factors, particularly, upwelling, temperature, and food abundance, in a consistent Darwinian framework. Using the model, we predict the timing of squid movement inshore in cold and warm environments, and the amount of biomass inshore in weekly intervals. We compare the model predictions with unpublished fishery-dependent data subsampled from landings and discuss the implications of our work for management of the squid fishery. © 2004 Elsevier Ltd. All rights reserved.

关键词： Oceanography

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Genetic variant analysis of boys with Autism: A pilot study on linking facial phenotype to genotype

Genetic variant analysis of boys with Autism: A pilot study ...

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2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

作者： Obafemi-Ajayi, Tayo Settles, Luke Su, Yuqing Germeroth, Cynthia Olbricht, Gayla R. Wunsch, Donald C. Takahashi, T. Nicole Miles, Judith Engineering Program Missouri State University Springfield MO United States Mathematics and Statistics Dept. Missouri University of Science and Technology RollaMO United States Information Technology Missouri University of Science and Technology RollaMO United States Electrical and Computer Eng. Dept. Missouri University of Science and Technology RollaMO United States Thompson Center for Autism and Neurodevelopmental Disorders ColumbiaMO United States

ISBN: (纸本)9781509030491

This work examines the validity of facial phenotypes as Autism Spectrum Disorders (ASD) biomarkers in boys with essential autism. A family-based association analysis framework is presented that uses previously identified facially-delineated (FD) clusters to examine relationship between FD clusters and known ASD genes. The hypothesis is that there are certain genetic variants, single nucleotide polymorphisms (SNP), specific to the FD clusters. Although statistical significance was not established, the results identified some candidate SNPs unique to each of the FD clusters that could indicate an underlying etiological difference. Further, recommendations are provided for larger-scale studies that could utilize the analysis framework presented. © 2017 IEEE.

关键词： Biomarkers

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The Small-World Effect for Interferometer Networks

arXiv

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arXiv 2023年

作者： Krawciw, Benjamin Carr, Lincoln D. Behn, Cecilia Diniz Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO United States Department of Physics Colorado School of Mines GoldenCO United States Quantum Engineering Program Colorado School of Mines GoldenCO United States

Complex network theory has focused on properties of networks with real-valued edge weights. However, in signal transfer networks, such as those representing the transfer of light across an interferometer, complex-valued edge weights are needed to represent the manipulation of the signal in both magnitude and phase. These complex-valued edge weights introduce interference into the signal transfer, but it is unknown how such interference affects network properties such as small-worldness. To address this gap, we have introduced a small-world interferometer network model with complex-valued edge weights and generalized existing network measures to define the interferometric clustering coefficient, the apparent path length, and the interferometric small-world coefficient. Using high-performance computing resources, we generated a large set of small-world interferometers over a wide range of parameters in system size, nearest-neighbor count, and edge-weight phase and computed their interferometric network measures. We found that the interferometric small-world coefficient depends significantly on the amount of phase on complex-valued edge weights: for small edge-weight phases, constructive interference led to a higher interferometric small-world coefficient;while larger edge-weight phases induced destructive interference which led to a lower interferometric small-world coefficient. Thus, for the small-world interferometer model, interferometric measures are necessary to capture the effect of interference on signal transfer. This model is an example of the type of problem that necessitates interferometric measures, and applies to any wave-based network including quantum networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Interferometers

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Forgery blind inspection for detecting manipulations of gel electrophoresis images

arXiv

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arXiv 2020年

作者： Shao, Hao-Chiang Cheng, Ya-Jen Duh, Meng-Yun Lin, Chia-Wen Dept. Statistics and Information Science Fu Jen Catholic University Taiwan Office of Neuroscience Program of Academia Sinica Institute of Molecular Biology Academia Sinica Taiwan Dept. Electrical Engineering National Tsing Hua University Taiwan

Recently, falsified images have been found in papers involved in research misconducts. However, although there have been many image forgery detection methods, none of them was designed for molecular-biological experiment images. In this paper, we proposed a fast blind inquiry method, named FBIGEL, for integrity of images obtained from two common sorts of molecular experiments, i.e., western blot (WB) and polymerase chain reaction (PCR). Based on an optimized pseudo-background capable of highlighting local residues, FBIGEL can reveal traceable vestiges suggesting inappropriate local modifications on WB/PCR images. Additionally, because the optimized pseudo-background is derived according to a closed-form solution, FBIGEL is computationally efficient and thus suitable for large scale inquiry tasks for WB/PCR image integrity. We applied FBIGEL on several papers questioned by the public on PUBPEER, and our results show that figures of those papers indeed contain doubtful unnatural patterns. Copyright © 2020, The Authors. All rights reserved.

关键词： Polymerase chain reaction

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Performance Analysis of Building Integrated Photovoltaic (BIPV): MANOVA Analysis

Performance Analysis of Building Integrated Photovoltaic (BI...

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Solar Conference (MENA-SC), Middle East and North Africa

作者： Wadhah Alzahmi Alyaa Alyaarbi Tahani Alnaqbi Ayman Alzaatreh Dubai Electricity and Water Authority Dubai United Arab Emirates Department of Industrial Engineering Engineering Systems Management Program American University of Sharjah Sharjah Sharjah United Arab Emirates Department of Mathematics and Statistics College of Arts and Sciences American University of Sharjah Sharjah Sharjah United Arab Emirates

Global energy consumption has increased over the years due to population growth, economic expansion, and the pursuit of a higher quality of life. The building sector is a critical sector for high consumption, contributing to about 36% of the total global energy consumption in 2020. The global interest in reducing energy consumption allows different sectors to adopt renewable resources such as solar photovoltaics (PV). However, utilizing solar PV in buildings has limitations due to rooftop space constraints in newly designed buildings. Due to this limitation and continuous innovation in solar PV technologies, Building Integrated Photovoltaics (BIPV) have been introduced as a potential alternative for solar PV in buildings. As such, BIPV energy performance and panel temperatures are affected by many factors such as orientation setup and technology type. Therefore, this research aims to analyze the impact of technology type (indium gallium selenide (CIGS) and monocrystalline (MONO) & orientation on BIPV energy production & module temperature. The data used for this study is based on a project completed in Dubai for BIPV in building applications. The results indicate that BIPV type and system orientation during each session significantly affect BIPV energy production and BIPV module temperature.

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Emerging Neuroimaging Approach of Hybrid EEG-fNIRS Recordings: Data Collection and Analysis Challenges

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Data Science in Science 2024年第1期3卷

作者： Pinto-Orellana, Marco Antonio Khan, Haroon Ombao, Hernando Mirtaheri, Peyman Biomedical Engineering Department University of California Irvine Irvine CA United States Department of Mechanical Electronics and Chemical Engineering Oslomet–Oslo Metropolitan University Oslo Norway Statistics Program King Abdullah University of Science and Technology Thuwal Saudi Arabia

The hybrid EEG-fNIRS (electroencephalogram - functional near-infrared spectroscopy) modality provides a comprehensive understanding of brain activity by simultaneously capturing electrical and hemodynamic responses. It takes advantage of the temporal resolution in EEG with the good spatial resolution of fNIRS. This hybrid system is non-invasive, portable, and relatively affordable compared to functional magnetic resonance imaging (fMRI). Despite its inherent limitations, EEG-fNIRS systems can be a valuable tool in neuroimaging, suitable for cognitive and clinical research that requires temporal and spatial information about brain function. They are well-suited in naturalistic experimental settings, brain-computer interface applications, and disease diagnosis. This paper also explores electro-metabolic interactions in the motor cortex during left- and right-hand grip tasks. We analyzed cross-frequency coupling (CFC) between delta rhythms (0–4Hz) and oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentrations calculated from fNIRS. CFC was estimated as the correlation between HbO, HbR, and the instantaneous amplitude of delta signals. CFC cannot be studied with non-synchronous measurements and is not directly related to activation amplitudes. Based on previous fMRI-EEG studies, some categories of oscillations denote a negligible correlation with fNIRS signals. However, our results showed a contra-lateral coupling between delta-EEG and HbO with significant pattern differences in the left motor cortex. © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： cross-frequency coupling data pre-processing electrical-hemodynamic coupling Hybrid EEG-fNIRS statistical modeling synchronization

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Modeling brain connectivity with graphical models on frequency domain

arXiv

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arXiv 2018年

作者： Gao, Xu Shen, Weining Ting, Chee-Ming Cramer, Steven C. Srinivasan, Ramesh Ombao, Hernando Department of Statistics University of California IrvineCA United States School of Biomedical Engineering and Health Sciences Universiti Teknologi Malaysia Malaysia Department of Anatomy and Neurobiology University of California IrvineCA United States Department of Cognitive Sciences University of California IrvineCA United States Statistics Program King Abdullah University of Science and Technology Thuwal Saudi Arabia

Multichannel electroencephalograms (EEGs) have been widely used to study cortical connectivity during acquisition of motor skills. In this paper, we introduce copula Gaussian graphical models on spectral domain to characterize dependence in oscillatory activity between channels. To obtain a simple and robust representation of brain connectivity that can explain the most variation in the observed signals, we propose a framework based on maximizing penalized likelihood with Lasso regularization to search for the sparse precision matrix. To address the optimization problem, graphical Lasso, Ledoit-Wolf and sparse estimation of a covariance matrix (SPCOV) algorithms were modified and implemented. Simulations show the benefit of using the proposed algorithms in terms of robustness and small estimation errors. Furthermore, analysis of the EEG data in a motor skill task conducted using algorithms of modified graphical LASSO and Ledoit-Wolf, reveal a sparse pattern of brain connectivity among cortices which is consistent with the results from other work in the literature. Copyright © 2018, The Authors. All rights reserved.

关键词： Electroencephalography

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