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SSRN 2022年

作者： Baccoli, Roberto Kumar, Amit Concas, Anna Gatto, Gianluca Medda, Andrea Pintus, Nicola Rodriguez, Giuseppe Department of Civil Environmental and Architectural Engineering University of Cagliari via Marengo 2 Cagliari09123 Italy Department of Electrical and Electronics Engineering University of Cagliari via Marengo 2 Cagliari09123 Italy Department of Mathematics and Computer Science University of Cagliari via Ospedale 72 Cagliari09124 Italy

An original experimental device coupled with a simulation and optimization model for determining the thermal diffusivity (αdiff) of solid materials is developed. The inverse problem solution of the classical Fourier equation in transient condition is supervised by an optimization procedure. The heat equation is numerically solved by an optimized finite difference scheme, for the initial and boundary conditions from measurements. Imperfect adiabaticity on the insulated lateral surfaces are explained by modelling heat loss correction functions with additional time dependent Robin conditions. The optimization identifies optimal values of the heat transfer coefficients and of αdiff by minimizing the residual function between the model prevision and the experimental data. Incorporating the heat loss corrections in the solution of the heat equation significantly improves the estimation of the αdiff. The time profile of the surface temperatures measured for a specimen of PPMA is reflected by the simulated curves. The estimated αdiff is in good agreement with an experimental intercomparison of eleven laboratories. Results reveal a reliable capability of the model to identify the αdiff value that explains the functional dependence underlying the experimental observations. The error lies in the range 5% or 34%, depending on whether the heat losses are accounted or not. © 2022, The Authors. All rights reserved.

关键词： Heat conduction

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Interwell Stratigraphic Correlation Detection Based On Knowledge-Enhanced Few-Shot Learning

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SSRN 2022年

作者： Chen, Bingyang Zeng, Xingjie Zhang, Baoyu Zhang, Weishan Hou, Zhaoxiang Wang, Zhenqi School of Computer Science and Technology China University of Petroleum Qingdao266580 China Department of Electrical & Computer Engineering University of Toronto TorontoM5S 2Z9 Canada Digital Research Institute ENN Group Langfang065001 China

Interwell Stratigraphic Correlations Detection (ISCD) can guide reservoir modeling and oil development. Many existing AI (artificial intelligence) methods have been proposed for ISCD. However, they have the challenge of small samples since it is difficult to generate the label of large-scale geological data. In this paper, we propose a few-shot learning-based approach to detect stratigraphic correlations using logging data for overcoming the problem of small samples. Specifically, we design a Knowledge Enhanced Few-shot Transformer ISCD model (KEFT-ISCD) for feature enhancement with different dimensional reservoir layer samples. We design a dynamically balanced marginal softmax (dbm-softmax) to further optimize the model loss for identifying edge features, which improves the stratigraphic matching effects. In addition, we design a bi-window co-sliding approach to address the cross-matching problem in practical stratigraphic matching. To the best of our knowledge, this is the first work to detect interwell stratigraphic correlations with knowledge-enhanced few-shot learning. We evaluate the proposed method with different well sections in a pair of adjacent wells from a real-world well logging dataset. Experimental results indicate that the proposed KEFT-ISCD performs well and achieves a detection accuracy rate of 91.12%. We also conduct experiments on different wells and blocks. The results further demonstrate the generalizability of the proposed approach. © 2022, The Authors. All rights reserved.

关键词： Machine learning

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TextBenDS: A generic textual data benchmark for distributed systems

arXiv

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arXiv 2021年

作者： Truicǎ, Ciprian-Octavian Apostol, Elena Darmont, Jérôme Assent, Ira Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania Department of Computer Science Aarhus University Aarhus Denmark Université de Lyon ERIC Lyon 2 EA 3083 France DIGIT Department of Computer Science Aarhus University Denmark

Extracting top-k keywords and documents using weighting schemes are popular techniques employed in text mining and machine learning for different analysis and retrieval tasks. The weights are usually computed in the data preprocessing step, as they are costly to update and keep track of all the modifications performed on the dataset. Furthermore, computation errors are introduced when analyzing only subsets of the dataset. Therefore, in a Big Data context, it is crucial to lower the runtime of computing weighting schemes, without hindering the analysis process and the accuracy of the machine learning algorithms. To address this requirement for the task of top-k keywords and documents, it is customary to design benchmarks that compare weighting schemes within various configurations of distributed frameworks and database management systems. Thus, we propose a generic document-oriented benchmark for storing textual data and constructing weighting schemes (TextBenDS). Our benchmark offers a generic data model designed with a multidimensional approach for storing text documents. We also propose using aggregation queries with various complexities and selectivities for constructing term weighting schemes, that are utilized in extracting top-k keywords and documents. We evaluate the computing performance of the queries on several distributed environments set within the Apache Hadoop ecosystem. Our experimental results provide interesting insights. As an example, MongoDB proves to have the best overall performance, while Spark's execution time remains almost the same, regardless of the weighting schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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A Koopman-based Digital Twin Approach for Fault Detection in Cable Slab Dynamics of Wafer Scanners

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IEEE Sensors Journal 2025年

作者： Pumphrey, Michael Saaideh, Mohammad Al Al-Rawashdeh, Yazan M. Alatawneh, Natheer Aljanaideh, Khaled F. Boker, Almuatazbellah Janaideh, Mohammad Al University of Guelph School of Engineering GuelphONN1G 2W1 Canada Memorial University Department of Electrical and Computer Engineering St. John’sNLA1B 3X5 Canada Al-Zaytoonah University of Jordan Department of Electrical Engineering Amman Jordan Jordan University of Science and Technology Department of Aeronautical Engineering Irbid22110 Jordan Virginia Tech Bradley Department of Electrical and Computer Engineering BlacksburgVA United States University of Jordan Amman Jordan University of Toronto TorontoONM5S 1A1 Canada Institute for Applied Optics and Precision Engineering Jena Germany

This paper presents a higher-dimensional state-space model that employs Koopman operators to predict and monitor the nonlinear dynamics of a cable slab in wafer scanners or other precision motion systems. Developed as a digital twin, our approach not only approximates the complex behavior of the cable slab but also facilitates early fault detection by comparing predicted dynamics with real-time sensor data. To address the absence of a definitive analytical model for cable slab dynamics, we systematically evaluate various Koopman observable functions to minimize tracking errors. The resulting model achieves an error margin of approximately ±1% over the specified motion range and demonstrates robust performance in predicting untrained, acyclic randomized cable slab motion and reaction forces. The system was also compared against the state-of-the-art neural network model for the cable slab, showing a reduction in reaction force error prediction of 75.4%. A sensor noise fault with a signal-to-noise of 20 was detected in 0.35 s using only the reaction force measurement. A position-based noise fault (signal-to-noise of 30) was detected in 0.04 s using position-based fault detection. A hardware malfunction fault test is also performed by removing some tubing material inside the cable slab and is detected in 0.38 s through position based fault detection with the Koopman model. Experimental results confirm that the Koopman-based framework effectively approximates the nonlinear dynamics and detects faults in the cable slab, thereby enhancing the reliability of precision motion systems. © 2001-2012 IEEE.

关键词： Precision engineering

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A Fuzzy Group Decision Making Method with Positive and Negative Comparisons in the Ahp Method

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SSRN 2022年

作者： Song, Mingli Gu, Zehao Pedrycz, Witold Liu, Wen School of Computer and Cyber Sciences Communication University of China Beijing China State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China School of Computer Science Fudan University Shanghai China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2G7 Canada Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Systems Research Institute Polish Academy of Sciences Warsaw Poland

In this article, we are concerned with developing a comprehensive fuzzy AHP (Analytic Hierarchy Process)-based Group Decision Making (GDM) framework. Traditional AHP- based GDM models assume all decision maker express their comparisons using nine integers (using the scale of integers {1, 2, 3, …, 9}) which ignores measuring the comprehensiveness of thinking of a certain decision maker since he/she only displays own positive comparisons. In contrast, negative comparisons together with positive ones compel decision makers to balance more factors thereby increasing the individual’s consistency. We construct a corresponding positive-negative comparisons based GDM framework that simultaneously incorporates fuzzy terms and weight distribution mechanisms. The key feature of this study is to develop a sound design framework by considering human beings’ variability and the ambiguity of linguistic terms in GDM and provide an initially different evaluation strategy and carry out its subsequent analysis framework. A fuzzy Geometric Consistency Index (GCI) test is designed to evaluate the consistency of fuzzy AHP problems. Meanwhile, Tucker's congruence coefficient (TCC) method is used to dynamically evaluate the importance level of each decision maker before aggregation. Some illustrative examples are provided to show the effectiveness of the proposed method. © 2022, The Authors. All rights reserved.

关键词： Decision making

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Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2021年 2卷 836-886页

作者： Alwis, Chamitha De Kalla, Anshuman Pham, Quoc-Viet Kumar, Pardeep Dev, Kapal Hwang, Won-Joo Liyanage, Madhusanka Department of Electrical and Electronic Engineering University of Sri Jayewardenepura Nugegoda10250 Sri Lanka Centre for Wireless Communications University of Oulu Oulu90570 Finland Korean Southeast Center for 4th Industrial Revolution Leader Education Pusan National University Busan46241 Korea Republic of Department of Computer Science Swansea University SwanseaSA2 8PP United Kingdom Nimbus Research Centre Munster Technological University Bishopstown CorkT12 P928 Ireland Department of Biomedical Convergence Engineering Pusan National University Yangsan50612 Korea Republic of School of Computer Science University Collage Dublin Dublin 4D04 V1W8 Ireland

Emerging applications such as Internet of Everything, Holographic Telepresence, collaborative robots, and space and deep-sea tourism are already highlighting the limitations of existing fifth-generation (5G) mobile networks. These limitations are in terms of data-rate, latency, reliability, availability, processing, connection density and global coverage, spanning over ground, underwater and space. The sixth-generation (6G) of mobile networks are expected to burgeon in the coming decade to address these limitations. The development of 6G vision, applications, technologies and standards has already become a popular research theme in academia and the industry. In this paper, we provide a comprehensive survey of the current developments towards 6G. We highlight the societal and technological trends that initiate the drive towards 6G. Emerging applications to realize the demands raised by 6G driving trends are discussed subsequently. We also elaborate the requirements that are necessary to realize the 6G applications. Then we present the key enabling technologies in detail. We also outline current research projects and activities including standardization efforts towards the development of 6G. Finally, we summarize lessons learned from state-of-the-art research and discuss technical challenges that would shed a new light on future research directions towards 6G. © 2020 IEEE.

关键词： Surveys

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Deep charge-fusion model: Advanced hybrid modelling for predicting electric vehicle charging patterns with socio-demographic considerations

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International Journal of Transportation science and Technology 2025年

作者： Cavus, Muhammed Ayan, Huseyin Bell, Margaret Oyebamiji, Oluwole K. Dissanayake, Dilum Department of Mathematics Physics and Electrical Engineering Northumbria University Newcastle Upon TyneNE1 8SA United Kingdom School of Engineering Iskenderun Technical University Hatay31200 Turkey School of Engineering Newcastle University Newcastle upon TyneNE1 7RU United Kingdom Department of Civil Engineering Istanbul University-Cerrahpaşa Istanbul34320 Turkey School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom School of Geography Earth and Environmental Sciences University of Birmingham BirminghamB15 2TT United Kingdom

This study examines electric vehicle (EV) charging behaviours across 5,898 participants from various socio-demographic backgrounds, using both statistical analysis and deep learning models. Exploratory data analysis (EDA) and Analysis of Variance (ANOVA) show that factors such as gender, education, employment status, household size, income, and housing type significantly influence charging behaviours at home, work, and fast-charging stations. Education notably impacts home charging (F = 2.54, p = 0.04), while household income strongly affects fast charging behaviour (F = 5.34, p = 0.001). A hybrid deep learning model, Deep Charge-Fusion, was developed by combining Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and eXtreme Gradient Boosting (XGBoost). CNNs capture spatial patterns for regional charging, while LSTMs model temporal dependencies in charging patterns. XGBoost adds efficiency by managing structured data and preventing overfitting through regularisation. Dropout layers were introduced in the LSTM network to reduce overfitting. The Deep Charge-Fusion model achieved an R2 value of 0.81 for predicting fast-charging behaviours, outperforming standalone models such as Gated Recurrent Unit (GRU)-based (R2 = 0.51) and LSTM-based (R2 = 0.43) models. It also achieved an R2 of 0.81 for home charging predictions. These results underscore the role of socio-demographic factors and demonstrate that hybrid models significantly improve predictive accuracy, with implications for EV infrastructure planning and energy management strategies. © 2025 Tongji University and Tongji University Press

关键词： Charging stations

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Trustworthy Cyber-Physical Power Systems Using Ai: Dueling Algorithms for Pmu Anomaly Detection and Cybersecurity

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SSRN 2024年

作者： Cali, Umit Catak, Ferhat Ozgur Halden, Ugur University of York School of Physics Engineering and Technology Heslington YorkYO10 5DD United Kingdom University of Stavanger The Faculty of Science and Technology Department of Computer and Electrical Technology Kjell Arholms gate 41 Stavanger4021 Norway Norwegian University of Science and Technology Department of Electric Energy O. S. Bragstads plass 2E Trondheim7034 Norway

Energy systems require radical changes due to the conflicting needs of combating climate change and meeting rising energy demands. These revolutionary decentralization, decarbonization, and digitalization techniques have ushered in a new global energy paradigm. Waves of disruption have been felt across the electricity industry as the digitalization journey in this sector has converged with advances in artificial intelligence (AI). However, there are risks involved. As AI becomes more established, new security threats have emerged. Among the most important is the cyber-physical protection of critical infrastructure, such as the power grid. This article focuses on dueling AI algorithms designed to investigate the trustworthiness of power systems' cyber-physical security under various scenarios using the Phasor Measurement Units (PMU) use case. Particularly in PMU operations, the focus is on areas that manage sensitive data vital to power system operators' activities. The initial stage deals with anomaly detection applied to energy systems and PMUs, while the subsequent stage examines adversarial attacks targeting AI models. At this stage, evaluations of the Madry attack, Basic Iterative Method (BIM), Momentum Iterative Method (MIM), and Projected Gradient Descend (PGD) are carried out, which are all powerful adversarial techniques that may compromise anomaly detection methods. The final stage addresses mitigation methods for AI-based cyberattacks. All these three stages represent various uses of AI and constitute the dueling AI algorithm convention that is conceptualised and demonstrated in this work. According to the findings of this study, it is essential to investigate the trade-off between the accuracy of AI-based anomaly detection models and their digital immutability against potential cyberphysical attacks in terms of trustworthiness for the critical infrastructure under consideration. © 2024, The Authors. All rights reserved.

关键词： Cybersecurity

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A fine-grained visual attention approach for fingerspelling recognition in the wild

arXiv

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arXiv 2021年

作者： Gajurel, Kamala Zhong, Cuncong Wang, Guanghui Department of Electrical Engineering and Computer Science University of Kansas LawrenceKS United States Department of Computer Science Ryerson University TorontoONM5B 2K3 Canada

Fingerspelling in sign language has been the means of communicating technical terms and proper nouns when they do not have dedicated sign language gestures. Automatic recognition of fingerspelling can help resolve communication barriers when interacting with deaf people. The main challenges prevalent in fingerspelling recognition are the ambiguity in the gestures and strong articulation of the hands. The automatic recognition model should address high inter-class visual similarity and high intra-class variation in the gestures. Most of the existing research in fingerspelling recognition has focused on the dataset collected in a controlled environment. The recent collection of a large-scale annotated fingerspelling dataset in the wild, from social media and online platforms, captures the challenges in a real-world scenario. In this work, we propose a fine-grained visual attention mechanism using the Transformer model for the sequence-to-sequence prediction task in the wild dataset. The fine-grained attention is achieved by utilizing the change in motion of the video frames (optical flow) in sequential context-based attention along with a Transformer encoder model. The unsegmented continuous video dataset is jointly trained by balancing the Connectionist Temporal Classification (CTC) loss and the maximum-entropy loss. The proposed approach can capture better fine-grained attention in a single iteration. Experiment evaluations show that it outperforms the state-of-the-art approaches. © 2021, CC BY-NC-SA.

关键词： Behavioral research

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Computational pathology: A survey review and the way forward

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Journal of Pathology Informatics 2024年 15卷 100357-100357页

作者： Hosseini, Mahdi S. Bejnordi, Babak Ehteshami Trinh, Vincent Quoc-Huy Chan, Lyndon Hasan, Danial Li, Xingwen Yang, Stephen Kim, Taehyo Zhang, Haochen Wu, Theodore Chinniah, Kajanan Maghsoudlou, Sina Zhang, Ryan Zhu, Jiadai Khaki, Samir Buin, Andrei Chaji, Fatemeh Salehi, Ala Nguyen, Bich Ngoc Samaras, Dimitris Plataniotis, Konstantinos N. Department of Computer Science and Software Engineering (CSSE) Concordia Univeristy Montreal H3H 2R9 QC Canada Qualcomm AI Research Qualcomm Technologies Netherlands B.V. Amsterdam Netherlands Institute for Research in Immunology and Cancer of the University of Montreal Montreal H3T 1J4 QC Canada The Edward S. Rogers Sr. Department of Electrical & Computer Engineering (ECE) University of Toronto Toronto M5S 3G4 ON Canada Huron Digitial Pathology St. Jacobs N0B 2N0 ON Canada Department of Electrical and Computer Engineering University of New Brunswick Fredericton E3B 5A3 NB Canada University of Montreal Hospital Center Montreal H2X 0C2 QC Canada Department of Computer Science Stony Brook University Stony Brook 11794 NY United States

Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.

关键词： Clinical pathology computer aided diagnosis (CAD) Deep learning Digital pathology Survey Whole slide image (WSI)

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