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Saudi Arabia Smart Grid Conference (SASG)

作者： Yiannis Kamarianakis Yannis Pantazis Evangelia Kalligiannaki Theodoros D. Katsaounis Konstantinos Kotsovos Issam Gereige Marwan Abdullah Aqil Jamal Athanasios Tzavaras Data Science Group Institute of Applied and Computational Mathematics Foundation for Research and Technology-Hellas Greece Dept. of Mathematics and Applied Mathematics University of Crete Greece Carbon Management Division Saudi Aramco R&D Center Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average.

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Using Heuristics to Enhance Real-Life Warehouse Replenishment Processes in the Fashion Industry

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Transportation Research Procedia 2023年 71卷 124-131页

作者： Juliana Castaneda Erika M. Herrera Julio C. Londoño Javier Panadero Angel A. Juan Logisfashion S.A. Barcelona Spain Computer Science Multimedia and Telecommunication Department Universitat Oberta de Catalunya 08018 Barcelona Spain Faculty of Engineering Universidad del Valle Cali Colombia Department of Management Universitat Politècnica de Catalunya—BarcelonaTech 08028 Barcelona Spain Dept. of Applied Statistics and Operations Research Universitat Politècnica de València Plaza Ferrandiz-Carbonell Alcoy 03801 Spain

The fashion industry is currently facing a phenomenon known as fast fashion. It is due to the high variation in product types and consumer tastes. The uncertainty generated by this phenomenon is forcing logistics warehouses in the sector to rely on innovative tools based on new technologies. These must guarantee that order preparation is performed efficiently through the autonomous and optimal management of the location of the products in the warehouses. Instead of building new areas to accommodate incoming products, companies are looking to optimize the reallocation of the existing lots. The aim is to generate savings in terms of costs and movements within the warehouse. Based on the assumption that picking at height is more costly for the warehouse when picking an order, this paper proposes a storage location assignment system based on data analysis and fast heuristics to assist in making decisions about the relocation of existing stock-keeping units, either on the floor or on shelves. This model is tested in a warehouse of a fashion company to demonstrate how the use of optimization tools can help generate operational and economic savings.

关键词： Warehouse logistics Heuristics Replenishment

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Importance of Synteny in Homology Inference

Importance of Synteny in Homology Inference

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International Conference on Emerging Technologies, ICET

作者： Ali Zeeshan Ijaz Raja Hashim Ali Asima Sarwar Talha Ali Khan Muhammad Muneeb Baig Faculty of Computer Science & Engg. AI Research Group GIK Institute of Engg. Sciences & Tech. Topi Khyber Pakhtunkhwa Pakistan Dept. of Tech & Software Engg. Univ. of Europe of Applied Sciences Berlin Germany

ISBN: (纸本)9781665459938

Homology inference is central to many Bioinformatics analyses, from phylogeny to evolutionary rates, where the goal is to identify the genes that have evolved from the same last common ancestor. An error at the stage of inferring homology may be carried down to further analysis and may cause severe errors at the later stages. For example, an incorrect pair of genes classified as homologous may lead to incorrectly changing the topology of phylogeny inferred from using the non-homologous pair or incorrect evolutionary rate inference. At the moment, most homology inference algorithms are based purely on similarity in gene contents, which is known to be inversely proportional to the rate of evolution and time since the divergence of both sequences. However, another important characteristic for homology inference is synteny - the order of genes on the homologous chromosomes, which has been rarely used in homology inference. The major reason for not using synteny for homology inference is equal or sometimes drastic affected by the rate of evolution and time since divergence of both sequences. However, using synteny in addition to gene similarity has shown promising results. In the current study, we have tried to explore the usefulness of synteny and present cases, where synteny can aid in differentiating for borderline homologs.

关键词： Correlation Inference algorithms Phylogeny Topology Classification algorithms Bioinformatics Biological cells

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Variance Reduction for Generalized Likelihood Ratio Method in Quantile Sensitivity Estimation

Variance Reduction for Generalized Likelihood Ratio Method i...

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2021 Winter Simulation Conference, WSC 2021

作者： Peng, Yijie Fu, Michael C. Hu, Jiaqiao L'Ecuyer, Pierre Tuffin, Bruno Guanghua School of Management Peking Universtiy Beijing100871 China The Robert H. Smith School of Business Universtity of Maryland Maryland20742 United States Stony Brook University Dept. of Applied Mathematics Statistics Stony BrookNY11794-3600 United States University of Montreal Dept. of Computer Science Operations Research MontrealQC Canada Univ Rennes CNRS IRISA INRIA Rennes Cedex35042 France

ISBN: (纸本)9781665433112

We apply the generalized likelihood ratio (GLR) methods in Peng et al. (2018) and Peng et al. (2021) to estimate quantile sensitivities. Conditional Monte Carlo and randomized quasi-Monte Carlo methods are used to reduce the variance of the GLR estimators. The proposed methods are applied to a toy example and a stochastic activity network example. Numerical results show that the variance reduction is significant. © 2021 IEEE.

关键词： Toys

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High pass filter based on curvelet transform coefficients 2

High pass filter based on curvelet transform coefficients

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2nd International Scientific Conference, ISC 2019

作者： Abdullah, Karam M Abdullah, Eman Abdulaziz Alsaif, Khalil I Computer Science Dept. College of Pure Science University of Mosul Mosul Iraq Computer Science Dept. College of Computer Science and Math University of Mosul Mosul Iraq

High pass filter are widely used in image processing for different purpose, due to that use many researchers provide this field with so many type for those filters with different techniques. This research provide a new approach to design high pass filter by adopting the properties of the curvelet transformation coefficients (fine, coarse and detail). Studying the effect of that coefficient by dropping some of them, then reconstructed the image from the remaining coefficients. The proposed technique applied on different type of images, which hold different amount of frequencies, dropping one level of high frequencies starting from level two up to last level of image decomposition. The result shows that dropping cells at last level will produce fine edges and thicker edges can be got by dropping cells at level 2. The benefits that were appear from the approach that this type of high frequency does not need kernel to reject low pass frequencies but depend upon the image itself. Using different images with different high frequencies support users to get wild field in using edge detection. © 2019 Published under licence by IOP Publishing Ltd.

关键词： High pass filters

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Time-resolved temperature mapping leveraging the strong thermo-optic effect in phase-change devices

arXiv

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arXiv 2022年

作者： Nobile, Nicholas A. Erickson, John R. Ríos, Carlos Zhang, Yifei Hu, Juejun Vitale, Steven A. Xiong, Feng Youngblood, Nathan Univ. of Pittsburgh Dept. of Electrical & Computer Engineering PittsburghPA15261 United States Univ. of Maryland Dept. of Materials Science & Engineering College ParkMD United States Univ. of Maryland Institute for Research in Electronics & Applied Physics College ParkMD United States MIT Dept. of Materials Science & Engineering CambridgeMA United States Advanced Materials and Microsystems Group MIT Lincoln Laboratory LexingtonMA United States

Optical phase-change materials are highly promising for emerging applications such as tunable metasurfaces, reconfigurable photonic circuits, and non-von Neumann computing. However, these materials typically require both high melting temperatures and fast quenching rates to reversibly switch between their crystalline and amorphous phases—a significant challenge for large-scale integration. Here, we present an experimental technique which leverages the thermo-optic effect in GST to enable both spatial and temporal thermal measurements of two common electro-thermal microheater designs currently used by the phase-change community. Our approach shows excellent agreement between experimental results and numerical simulations and provides a non-invasive method for rapid characterization of electrically programmable phase-change devices. © 2022, CC BY.

关键词： Numerical methods

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Federated Learning for Distributed Optimal Power Flow Solutions in Smart Grids

Federated Learning for Distributed Optimal Power Flow Soluti...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： BK Aishwarya K Shekar Revathi V Jay Singh Navdeep Singh Hayder Saadoon Abdulaali Institute of Aeronautical Engineering Hyderabad Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Department of Applied Sciences New Horizon College of Engineering Bangalore Dept. of EEE GL Bajaj Institute of Technology & Management Greater Noida U.P. India Lovely Professional University Phagwara College of Engineering Technology National University of Science and Technology Dhi Qar Iraq

The rapid evolution of smart grids has necessitated the development of advanced computational techniques to ensure efficient and reliable power distribution. This paper introduces a novel approach to solving the Optimal Power Flow (OPF) problem by leveraging Federated Learning (FL) - a decentralized machine learning paradigm. Traditional centralized methods for OPF solutions often face scalability issues and data privacy concerns when applied to large-scale, distributed smart grids. The proposed FL-based method addresses these challenges by allowing individual grid nodes to locally compute model updates without sharing raw data. This not only preserves data privacy but also reduces the communication overhead. The paper presents a comprehensive mathematical formulation of the FL-based OPF problem, followed by a detailed analysis of its convergence properties and computational efficiency. Comparative studies with conventional methods demonstrate the superiority of the proposed approach in terms of accuracy, speed, and scalability. The findings suggest that FL can serve as a robust framework for distributed OPF solutions, paving the way for more resilient and efficient smart grid operations.

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Bridging the Data Gap in AI Reliability Research and Establishing DR-AIR, a Comprehensive Data Repository for AI Reliability

arXiv

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arXiv 2025年

作者： Zheng, Simin Clark, Jared M. Salboukh, Fatemeh Silva, Priscila da Mata, Karen Pan, Fenglian Min, Jie Lian, Jiayi King, Caleb B. Fiondella, Lance Liu, Jian Deng, Xinwei Hong, Yili Department of Statistics Virginia Tech BlacksburgVA24061 United States Dept. of Engineering & Applied Science Univ. of Massachusetts DartmouthMA02747 United States Dept. of Electrical & Computer Engineering Univ. of Massachusetts DartmouthMA02747 United States Department of Systems & Industrial Engineering University of Arizona TucsonAZ85721 United States Department of Mathematics & Statistics University of South Florida TampaFL33620 United States Quantitative Analytics Wells Fargo CharlotteNC28262 United States JMP Division SAS CaryNC27513 United States

Artificial intelligence (AI) technology and systems have been advancing rapidly. However, ensuring the reliability of these systems is crucial for fostering public confidence in their use. This necessitates the modeling and analysis of reliability data specific to AI systems. A major challenge in AI reliability research, particularly for those in academia, is the lack of readily available AI reliability data. To address this gap, this paper focuses on conducting a comprehensive review of available AI reliability data and establishing DR-AIR: a data repository for AI reliability. Specifically, we introduce key measurements and data types for assessing AI reliability, along with the methodologies used to collect these data. We also provide a detailed description of the currently available datasets with illustrative examples. Furthermore, we outline the setup of the DR-AIR repository and demonstrate its practical applications. This repository provides easy access to datasets specifically curated for AI reliability research. We believe these efforts will significantly benefit the AI research community by facilitating access to valuable reliability data and promoting collaboration across various academic domains within AI. We conclude our paper with a call to action, encouraging the research community to contribute and share AI reliability data to further advance this critical field of study. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Limited-view Cone Beam CT reconstruction using 3D Patch-based Supervised and Adversarial Learning

Limited-view Cone Beam CT reconstruction using 3D Patch-base...

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Digital Holography and Three-Dimensional Imaging, DH 2021 - Part of OSA Imaging and applied Optics Congress 2021

作者： Lahiri, Anish Klasky, Marc L. Fessler, Jeffrey A. Ravishankar, Saiprasad Dept. of Electrical and Computer Engineering University of Michigan Ann ArborMI48109 United States Los Alamos National Laboratory Los AlamosNM87545 United States Depts. of Computational Math Science and Engineering and Biomedical Engineering Michigan State University East LansingMI48824 United States

We present a novel multi-stage algorithm for CBCT reconstruction from very limited projections. Our proposed method uses 3D patch-based supervised and adversarial learning from scarce training data, combined with phys... 详细信息

ISBN: (纸本)9781557528209

关键词： Image reconstruction

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Terrain prickliness: theoretical grounds for high complexity viewsheds

arXiv

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arXiv 2021年

作者： Acharyya, Ankush Löffler, Maarten Meijer, Gert G.T. Saumell, Maria Silveira, Rodrigo I. Staals, Frank Department of Computer Science and Engineering National Institute of Technology Durgapur India Dept. of Information and Computing Sciences Utrecht University Netherlands Department of Computer Science Tulane University United States Academy of ICT and Creative Technologies NHL Stenden University of Applied Sciences Netherlands Dept. of Theoretical Computer Science Faculty of Information Technology Czech Technical University in Prague Czech Republic Dept. de Matemàtiques Universitat Politècnica de Catalunya Spain

MSC Codes 68U05An important task in terrain analysis is computing viewsheds. A viewshed is the union of all the parts of the terrain that are visible from a given viewpoint or set of viewpoints. The complexity of a viewshed can vary significantly depending on the terrain topography and the viewpoint position. In this work we study a new topographic attribute, the prickliness, that measures the number of local maxima in a terrain from all possible angles of view. We show that the prickliness effectively captures the potential of 2.5D TIN terrains to have high complexity viewsheds. We present optimal (for 1.5D terrains) and near-optimal (for 2.5D terrains) algorithms to compute it for TIN terrains, and efficient approximate algorithms for raster DEMs. We validate the usefulness of the prickliness attribute with experiments in a large set of real terrains. Copyright © 2021, The Authors. All rights reserved.

关键词： Topography

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