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IISE Annual Conference and Expo 2021

作者： Huang, Jiajing Wu, Teresa Yoon, Hyunsoo Pradhan, Ojas Win, Jin O'Neill, Zheng School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ United States Department of Systems Science and Industrial Engineering Binghamton University Binghamton New York United States Department of Civil Architectural and Environmental Engineering Drexel University PhiladelphiaPA United States Department of Mechanical Engineering Texas A&M University College Station TX United States

ISBN: (纸本)9781713838470

Studies indicate that a large energy saving can be realized by applying automatic fault detection and diagnosis (AFDD) to building systems, which consumes more than 40% of the primary energy in the U.S. To enable AFDD, a baseline depicting the normal operation mode is needed to detect whether the building operation deviates from normality. Different from many other systems, a building system behaves differently under different weather conditions and hence needs its baseline model to reflect such weather dependence. Existing research shows baseline constructed using nonlinear mathematical models has performed well. However, determining the sample size, which is necessary to capture the totality of data space needed for accurate baseline construction, relies on trial-and-error experiments conducted offline. There is a lack of easy-to-use method that can provide guidance on whether enough sample size has been collected for baseline construction. In this research, we have developed a data-driven approach for AFDD baseline model construction based on information entropy, in conjunction with enthalpy, a measurement of outdoor air conditions to reflect weather conditions. The developed method is compared with our previously-reported baseline construction method using real building data. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.

关键词： Buildings

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A Restful Persistent Devs-Based Interaction Model For The Componentized Weap and Leap Restful Frameworks

A Restful Persistent Devs-Based Interaction Model For The Co...

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Simulation Winter Conference

作者： Mostafa D. Fard Hessam S. Sarjoughian Arizona Center for Integrative Modeling & Simulation School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA

ISBN: (纸本)9781665433129

Modeling the interactions between separate models contributes to building flexible hybrid simulation frameworks. In earlier work, an Algorithmic Interaction Model was proposed and developed to integrate the componentized WEAP and LEAP RESTful frameworks for modeling and simulating water and energy systems. However, this approach does not separate modeling and simulation protocol from each other. It also does not support flexible, structured model hierarchies. To overcome these limitations, the parallel DEVS formalism is used to develop an Interaction Model for use with the DEVS-Suite simulator. The resulting DEVS Interaction Model (DEVS-IM) is supported with a RESTful framework and MongoDB for storing the interaction models for the coupled water and energy models. The DEVS-IM, grounded in system theory and component-based modeling, offers strong support for the model reusability, flexibility, and maintainability traits essential for developing realistic simulations of coupled energy and water systems.

关键词： Visualization Protocols Computational modeling Heuristic algorithms Buildings Computational efficiency Dynamical systems

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Integrating Dynamic Correlation Shifts and Weighted Benchmarking in Extreme Value Analysis

arXiv

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arXiv 2024年

作者： Panagoulias, Dimitrios P. Sarmas, Elissaios Marinakis, Vangelis Virvou, Maria Tsihrintzis, George A. Department of Informatics University of Piraeus Karaoli ke Dimitriou 80 Piraeus18534 Greece Decision Support Systems Lab School of Electrical and Computer Engineering National Technical University of Athens Ir. Politechniou 9 Zografou Athens157 73 Greece

This paper presents an innovative approach to Extreme Value Analysis (EVA) by introducing the Extreme Value Dynamic Benchmarking Method (EVDBM). EVDBM integrates extreme value theory to detect extreme events and is coupled with the novel Dynamic Identification of Significant Correlation (DISC)Thresholding algorithm, which enhances the analysis of key variables under extreme conditions. By integrating return values predicted through EVA into the benchmarking scores, we are able to transform these scores to reflect anticipated conditions more accurately. This provides a more precise picture of how each case is projected to unfold under extreme conditions. As a result, the adjusted scores offer a forward-looking perspective, highlighting potential vulnerabilities and resilience factors for each case in a way that static historical data alone cannot capture. By incorporating both historical and probabilistic elements, the EVDBM algorithm provides a comprehensive benchmarking framework that is adaptable to a range of scenarios and contexts. The methodology is applied to real PV data, revealing critical low - production scenarios and significant correlations between variables, which aid in risk management, infrastructure design, and long-term planning, while also allowing for the comparison of different production plants. The flexibility of EVDBM suggests its potential for broader applications in other sectors where decision-making sensitivity is crucial, offering valuable insights to improve outcomes. © 2024, CC BY.

关键词： Predictive analytics

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A Self-Updating K-Contingency List for Smart Grid System

A Self-Updating K-Contingency List for Smart Grid System

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IEEE Annual computing and Communication Workshop and Conference (CCWC)

作者： Sohini Roy Arunabha Sen School of Computing Informatics and Decision System Engineering Arizona State University Tempe Arizona USA

ISBN: (数字)9781665414906

ISBN: (纸本)9781665430586

A reliable decision making by the operator in a smart grid is contingent upon correct analysis of intra-and-interdependencies between its entities and also on accurate identification of the most critical entities at a given point of time. A measurement based self-updating contingency list can provide real-time information to the operator about current system condition which can help the operator to take the required action. In this paper, the underlying intra-and-interdependencies between entities for a given power-communication network is captured using a dependency model called Modified Implicative Interdependency Model (MIIM) [1]. Given an integer K, the event-driven self-updating contingency list problem gives the list of K-most critical entities, failure of which maximizes the network damage at the current time. Owing to the problem being NP complete, a fast heuristic method to generate a real-time contingency list using system measurements is provided here. The validation of the work is done by comparing the contingency list obtained for different K values using the MIIM model on a smart grid of IEEE 14-Bus system with that obtained by simulating the smart grid using a co-simulation system formed by MATPOWER and Java Network Simulator (JNS). The results also indicate that the network damage predicted by both the ILP based solution [2] and the proposed heuristic solution using MIIM are more realistic compared to that obtained using another dependency model called Implicative Interdependency Model (IIM) [3].

关键词： Current measurement Conferences Predictive models Real-time systems Smart grids Velocity measurement Time factors

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Enhancing Malicious Url Detection: A Novel Framework Leveraging Priority Coefficient and Feature Evaluation

SSRN

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SSRN 2023年

作者： Sahban Rafsanjani, Ahmad Kamaruddin, Norshaliza Binti Behjati, Mehran Aslam, Saad Sarfaraz, Aaliya Amphawan, Angela Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor 47500 Malaysia Faculty of Technology and Informatics Razak Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia

Malicious Uniform Resource Locators (URLs) pose a significant cybersecurity threat by carrying out attacks such as phishing and malware propagation. Conventional malicious URL detection methods, relying on blacklists and heuristics, often struggle to identify new and obfuscated malicious URLs. To address this challenge, machine learning and deep learning have been leveraged to enhance detection capabilities, albeit relying heavily on large and frequently updated datasets. Furthermore, the efficacy of these methods is intrinsically tied to the quality of the training data, a requirement that becomes increasingly challenging to fulfill in real-world scenarios due to constraints such as data scarcity, privacy concerns, and the dynamic nature of evolving cyber threats. In this study, we introduce an innovative framework for malicious URL detection based on predefined static feature classification by allocating priority coefficients and feature evaluation methods. Our feature classification encompasses 42 classes, including blacklist, lexical, host-based, and content-based features. To validate our framework, we collected a dataset of 5000 real-world URLs from prominent phishing and malware websites, namely URLhaus and PhishTank. We assessed our framework's performance using three supervised machine learning methods: Support Vector Machine (SVM), Random Forest (RF), and Bayesian Network (BN). The results demonstrate that our framework outperforms these methods, achieving an impressive detection accuracy of 98.95% and a precision value of 98.60%. Furthermore, we conducted a benchmarking analysis against three comprehensive malicious URL detection methods (PDRCNN, the Li method, and URLNet), demonstrating that our proposed framework excels in terms of accuracy and precision. In conclusion, our novel malicious URL detection framework substantially enhances accuracy, significantly bolstering cybersecurity defenses against emerging threats. © 2023, The Authors. All rights res

关键词： Feature extraction

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DAFNI: A computational platform to support infrastructure systems research

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Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction 2023年第3期176卷 108-116页

作者： Matthews, Brian Hall, Jim Batty, Michael Blainey, Simon Cassidy, Nigel Choudhary, Ruchi Coca, Daniel Hallett, Stephen Harou, Julien J James, Phil Lomax, Nik Oliver, Peter Sivakumar, Aruna Tryfonas, Theodoros Varga, Liz Scientific Computing Department Science and Technology Facilities Council Didcot United Kingdom School of Geography and the Environment University of Oxford Oxford United Kingdom Centre for Advanced Spatial Analysis University College London London United Kingdom Transportation Research Group Faculty of Engineering and Physical Sciences University of Southampton Southampton United Kingdom Department of Civil Engineering University of Birmingham Birmingham United Kingdom Department of Engineering University of Cambridge Cambridge United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Centre for Environmental and Agricultural Informatics Cranfield University Cranfield United Kingdom Department of Mechanical Aerospace and Civil Engineering University of Manchester Manchester United Kingdom School of Engineering Newcastle University Newcastle United Kingdom School of Geography University of Leeds Leeds United Kingdom Department of Civil and Environmental Engineering Imperial College London London United Kingdom Department of Civil Engineering University of Bristol Bristol United Kingdom Department of Civil Environmental and Geomatic Engineering University College London London United Kingdom

Research into the engineering of infrastructure systems is increasingly data intensive. Researchers build computational models to explore scenarios such as investigating the merits of infrastructure plans, analysing historical data to inform system operations or assessing the impacts of infrastructure on the environment. Models are more complex, at higher resolution and with larger coverage. Researchers also require a 'multi-systems' approach to explore interactions between systems, such as energy and water with urban development, and across scales, from buildings and streets to regions or nations. Consequently, researchers need enhanced computational resources to support cross-institutional collaboration and sharing at scale. The Data and Analytics Facility for National Infrastructure (DAFNI) is an emerging computational platform for infrastructure systems research. It provides high-throughput compute resources so larger data sets can be used, with a data repository to upload data and share these with collaborators. Users' models can also be uploaded and executed using modern containerisation techniques, giving platform independence, scaling and sharing. Further, models can be combined into workflows, supporting multi-systems modelling and generating visualisations to present results. DAFNI forms a central resource accessible to all infrastructure systems researchers in the UK, supporting collaboration and providing a legacy, keeping data and models available beyond the lifetime of a project. © 2023 Emerald Publishing Limited: All rights reserved.

关键词： Urban growth

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Federated Learning for Enhanced ECG Signal Classification with Privacy Awareness

Federated Learning for Enhanced ECG Signal Classification wi...

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

作者： Quoc Bao Phan Linh Nguyen Ngoc Thang Bui Dinh C. Nguyen Lan Zhang Tuy Tan Nguyen School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA Department of Radiology Mayo Clinic Rochester MN USA Department of Electrical and Computer Engineering The University of Alabama in Huntsville Huntsville AL USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This paper presents a novel approach for classifying electrocardiogram (ECG) signals in healthcare applications using federated learning and stacked convolutional neural networks (CNNs). Our innovative technique leverages the distributed nature of federated learning to collaboratively train a high-performance model while preserving data privacy on local devices. We propose a stacked CNN architecture tailored for ECG data, effectively extracting discriminative features across different temporal scales. The evaluation confirms the strength of our approach, culminating in a final model accuracy of 98.6% after 100 communication rounds, significantly exceeding baseline performance. This promising result paves the way for accurate and privacy-preserving ECG classification in diverse healthcare settings, potentially leading to improved diagnosis and patient monitoring.

关键词： Data privacy Accuracy Protocols Federated learning Scalability Medical services Electrocardiography Feature extraction Robustness Convolutional neural networks

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Multiplex reconstruction with partial information

arXiv

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arXiv 2022年

作者： Kaiser, Daniel Patwardhan, Siddharth Radicchi, Filippo Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States

A multiplex is a collection of network layers, each representing a specific type of edges. This appears to be a genuine representation for many real-world systems. However, due to a variety of potential factors, such as limited budget and equipment, or physical impossibility, multiplex data can be difficult to observe directly. Often, only partial information on the layer structure of the system is available, whereas the remaining information is in the form of a single-layer network. In this work, we face the problem of reconstructing the hidden multiplex structure of an aggregated network from partial information. We propose an algorithm that leverages the layer-wise community structure that can be learned from partial observations to reconstruct the ground-truth topology of the unobserved part of the multiplex. The algorithm is characterized by a computational time that grows linearly with the network size. We perform a systematic study of reconstruction problems for both synthetic and real-world multiplex networks. We show that the ability of the proposed method to solve the reconstruction problem is affected by the heterogeneity of the individual layers and the similarity among the layers. On real-world networks, we observe that the accuracy of the reconstruction saturates quickly as the amount of available information increases. In genetic interaction and scientific collaboration multiplexes for example, we find that 10% of ground-truth information yields 70% accuracy, while 30% information allows for more than 90% accuracy. Copyright © 2022, The Authors. All rights reserved.

关键词： Network layers

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A hybrid technique for evaluating the trust of cloud services

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International Journal of Information Technology (Singapore) 2021年第2期13卷 687-695页

作者： Trabay, Doaa Asem, Azezza El-Henawy, Ibrahim Gharibi, Wajeb Department of Information Systems Faculty of Computers and Information Systems Mansoura University Mansoura Egypt Department of Information Systems Faculty of Computers and Informatics Mansoura University Mansoura Egypt Department of Computer Sciences Faculty of Computers and Informatics Systems Zagazig University Sharqiyah Shaibet an Nakareyah Zagazig 2 Egypt School of Computing and Engineering UMKC Kansas City MO United States

Cloud computing has become a common desire as an alternative for investing in new IT systems. Every day, more agencies benefit from the services reachable in the cloud. Due to the large storage of information and data in the cloud, concerns about customer confidence or trust in the cloud increase as cloud services have been increased. Given the diversity of cloud service providers, identifying the appropriate cloud services, including trust, which meets organizations requirements, is one of the main challenges in cloud computing. This paper builds a mathematical model to evaluate trust from various opinions based on performance, agility, finance, security, and usability criteria using a hybrid fuzzy logic method and multi-criteria decision-making techniques. By comparing the results obtained with others, it appears that our results are more efficient for organizations and users. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Analytical hierarchy process (AHP) Cloud computing Cloud service providers’ selection Fuzzy analytical hierarchy process (FAHP) Fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) Multi-criteria decision making (MCDM) Technique for order preference by similarity to ideal solution (TOPSIS) Trust model

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Efficient Epileptic Seizure Type Classification Using Hyperdimensional computing

Efficient Epileptic Seizure Type Classification Using Hyperd...

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IEEE Biomedical Circuits and systems (BIOCAS)

作者： Seyedeh Newsha Estiri Hisham Daoud M. Hassan Najafi Magdy Bayoumi School of Computing and Informatics University of Louisiana at Lafayette LA USA School of Computer and Cyber Sciences Augusta University GA USA Electrical Computer and Systems Engineering Department Case Western Reserve University OH USA Department of Electrical and Computer Engineering University of Louisiana at Lafayette LA USA

ISBN: (数字)9798350354959

ISBN: (纸本)9798350354966

Precise seizure identification plays a vital role in understanding cortical connectivity and informing treatment decisions. Yet, the manual diagnostic methods for epileptic seizures are both labor-intensive and highly specialized. In this study, we propose a Hyperdimensional computing (HDC) classifier for accurate and efficient multi-type seizure classification. Despite previous seizure analysis efforts using HDC being limited to binary detection (seizure or no seizure), our work breaks new ground by utilizing HDC to classify seizures into multiple distinct types. HDC offers significant advantages, such as lower memory requirements, a reduced hardware footprint for wearable devices, and decreased computational complexity. Due to these attributes, HDC can be an alternative to traditional machine learning methods, making it a practical and efficient solution, particularly in resource-limited scenarios or applications involving wearable devices. We evaluated the proposed technique on the latest version of TUH EEG Seizure Corpus (TUSZ) dataset and the evaluation result demonstrate noteworthy performance, achieving a weighted $F 1$ score of $\mathbf{9 4. 6 \%}$. This outcome is in line with, or even exceeds, the performance achieved by the state-of-the-art traditional machine learning methods.

关键词： Performance evaluation Computational modeling Memory management Machine learning Medical services Brain modeling Hardware Robustness Real-time systems Wearable devices

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