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2025 SPE/IADC International Drilling Conference and Exhibition, DC 2025

作者： Soilemezidis, C. Mensah, J.S. Hollstein, W. Al Maasarani, K. Alkhawaja, A. Jamali, F. Dörfler, G. Hölzner, N. Jarbouh, K.A. Ustaoglu, D. Alukkal, A.T. Gudhane, L.A.Y. Jaeger, P. Feldmann, E. Carvajal, C.A.P. Yin, A.O. Sinkuu, L. Quayson, J. Amoah, F.B. Yahaya, H.S. Owusu, K.A. Dongoyo, C.K.N. Owusu, A. Amorin, R. Florence, F. Institute of Subsurface Energy Systems Clausthal University of Technology Lower Saxony Clausthal-Zellerfeld Germany Department of Petroleum and Natural Gas Engineering University of Mines and Technology Tarkwa Ghana Institute of Mechanical Engineering Clausthal University of Technology Lower Saxony Clausthal-Zellerfeld Germany Institute of Economics Clausthal University of Technology Lower Saxony Clausthal-Zellerfeld Germany Institute of Informatics Clausthal University of Technology Lower Saxony Clausthal-Zellerfeld Germany Department of Electrical and Electronics Engineering University of Mines and Technology Tarkwa Ghana Department of Computer Science and Engineering University of Mines and Technology Tarkwa Ghana Department of Petroleum Geoscience University of Mines and Technology Tarkwa Ghana Rig Operations AustinTX United States

ISBN: (纸本)9781959025641

SPE’s Drilling Systems Automation Technical Section (DSATS) sponsors the Drillbotics® autonomous drilling contest to promote advanced learning at participating universities. Two groups worked separately to drill a directional well hands-free and to connect to a well model to detect/close in a kick. This paper consolidates the efforts of both teams. Team A, the directional drilling team from the University of Mines and technology (UMaT) in Ghana, developed a virtual model that replicated full-scale directional drilling operations, facilitating the development and testing of innovative control schemes and automation strategies. Leveraging state-of-the-art drilling software, real-time data analytics, and advanced control systems, the project demonstrated the potential of automated drilling technologies across both the planning and execution phases of drilling projects. Team B, from Technical University Clausthal (TUC) in Germany, designed and built a mini-rig that included a working scaled version of a rotary steerable system (RSS) tool to steer a wellbore through a block of sandstone to reach two non-planer targets. The work incorporates machine learning techniques to evaluate downhole sensor data and improve steering signal input. The modular design of the second-generation surface actuation system offers a more compact and efficient solution than its predecessor. The control architecture and network protocols are analyzed, demonstrating network delays of under 2 msec using a refined second-generation translator setup. The achievements of both teams are important to the future of drilling automation, as their approaches could be used to verify and validate third-party app’s communications prior to field release on full-scale rigs. Copyright 2025, SPE/IADC International Drilling Conference and Exhibition.

关键词： Directional drilling

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Management Models for Wireless Communication Channel Interference Monitoring 8

Management Models for Wireless Communication Channel Interfe...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Pohasii, Serhii Holdobin, Serhii Brynza, Natalia Vilkhivska, Olga Bilotserkivskyi, Oleksandr Samorodov, Borys National Technical University 'Kharkiv polytechnic institute' Cybersecurity department Kharkiv Ukraine National Technical University 'Kharkiv polytechnic institute' Industrial and Biomedical electronics department Kharkiv Ukraine Kharkiv National University of Radio Electronics Department of Informatics and Computer Technology Simon Kuznets Kharkiv National University of Economics Department of Applied Mathematics Kharkiv Ukraine Simon Kuznets Kharkiv National University of Economics Department of Informatics and Computer Technology Kharkiv Ukraine National Technical University 'Kharkiv Polytechnic Institute' Business Trade and Logistics Department Kharkiv Ukraine V'N'Karazin Kharkiv National University Banking and Financial Technologies Department Kharkiv Ukraine

ISBN: (纸本)9798350354423

In modern wireless communication systems, ensuring reliable data transmission amidst interference is a critical challenge. Interference can arise from natural conditions, such as weather and terrain, or from artificial sources like other wireless devices sharing the same frequencies. This paper examines management models for monitoring interference in wireless communication channels using LoRa technology at two frequencies: 170 MHz and 433 MHz. By employing dual independent radio channels, communication redundancy is achieved, enhancing system resilience against interference. The STM32 microcontroller is utilized to provide intelligent control and real-time monitoring of the channels. Two distinct management models-feedback and cascade-are developed and compared in terms of transmission reliability, interference immunity, and resource efficiency. The findings provide insights into optimizing wireless communication systems, particularly for applications in critical sectors such as industrial sensor networks and transport systems. © 2024 IEEE.

关键词： Microcontrollers

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Linear Layouts of Bipartite Planar Graphs 18th

Linear Layouts of Bipartite Planar Graphs

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18th International Symposium on Algorithms and Data Structures, WADS 2023

作者： Förster, Henry Kaufmann, Michael Merker, Laura Pupyrev, Sergey Raftopoulou, Chrysanthi Department of Computer Science University of Tübingen Tübingen Germany Institute of Theoretical Informatics Karlsruhe Institute of Technology Karlsruhe Germany Meta Platforms Inc. Menlo ParkCA United States National Technical University of Athens Athens Greece

ISBN: (纸本)9783031389054

A linear layout of a graph G consists of a linear order, , of the vertices and a partition of the edges. A part is called a queue (stack) if no two edges nest (cross), that is, two edges (v, w) and (x, y) with v x y w (v x w y ) may not be in the same queue (stack). The best known lower and upper bounds for the number of queues needed for planar graphs are 4 [Alam et al., Algorithmica 2020] and 42 [Bekos et al., Algorithmica 2022], respectively. While queue layouts of special classes of planar graphs have received increased attention following the breakthrough result of [Dujmović et al., J. ACM 2020], the meaningful class of bipartite planar graphs has remained elusive so far, explicitly asked for by Bekos et al. In this paper we investigate bipartite planar graphs and give an improved upper bound of 28 by refining existing techniques. In contrast, we show that two queues or one queue together with one stack do not suffice;the latter answers an open question by Pupyrev [GD 2018]. We further investigate subclasses of bipartite planar graphs and give improved upper bounds;in particular we construct 5-queue layouts for 2-degenerate quadrangulations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Queueing theory

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A Simulated Annealing approach for the Circle Bin Packing Problem with Rectangular Items

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computerS & INDUSTRIAL ENGINEERING 2023年第1期176卷

作者： Tole, Kevin Moqa, Rashad Zheng, Jiongzhi He, Kun [a]School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China [b]Institute of Computing and Informatics Technical University of Mombasa Mombasa 90420-80100 Kenya

We introduce a new variant of Bin Packing Problem (BPP) called the Circular Bin Packing Problem with Rectangular Items (CBPP-RI). CBPP-RI involves the dense orthogonal packing of rectangular items into a minimum number of bins. To our knowledge, there is no existing literature addressing this NP-hard problem. To fill this gap, we study CBPP-RI and propose an algorithm consisting of two stages, an initialization stage and an improvement stage. We propose a first-fit grid search (FGS) algorithm to generate the initial solution, then we design a Simulated Annealing (SA) algorithm to explore the solution space from the initial solution by the proposed novel local neighborhood search strategy called the circular quadrant perturbation (CQP). The SA algorithm always accepts the current solution if it is better than the previous one, and otherwise accepts based on a probability function. Experimental results show that the proposed algorithm is effective for not only the introduced CBPP-RI problem, but also other related packing problems.

关键词： Circle Bin Packing Problem NP-hard First-fit Grid Search Simulated Annealing

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A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior

arXiv

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arXiv 2025年

作者： Jayasuriya, Nikil Sumanathilaka, Deshan School of Computing Informatics Institute of Technology Colombo006 Sri Lanka School of Mathematics and Computer Science Swansea University United Kingdom

This paper systematically explores the advancements in adaptive trip route planning and travel time estimation (TTE) through Artificial Intelligence (AI). With the increasing complexity of urban transportation systems, traditional navigation methods often struggle to accommodate dynamic user preferences, real-time traffic conditions, and scalability requirements. This study explores the contributions of established AI techniques, including Machine Learning (ML), Reinforcement Learning (RL), and Graph Neural Networks (GNNs), alongside emerging methodologies like Meta-Learning, Explainable AI (XAI), Generative AI, and Federated Learning. In addition to highlighting these innovations, the paper identifies critical challenges such as ethical concerns, computational scalability, and effective data integration—that must be addressed to advance the field. The paper concludes with recommendations for leveraging AI to build efficient, transparent, and sustainable navigation systems. © 2025, CC BY.

关键词： Federated learning

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Recent Trends and Challenges in Assistive Applications for Sinhala-Speaking Adults with Dyslexia: A Decade in Review 5

Recent Trends and Challenges in Assistive Applications for S...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Perera, Peshala Sumanathilaka, Deshan Informatics Institute of Technology School of Computing Colombo006 Sri Lanka Swansea University School of Mathematics and Computer Science United Kingdom

ISBN: (纸本)9798331530983

This paper discusses assistive technologies developed for Sinhala-speaking dyslexics, focusing on the challenges they face and how existing tools attempt to address them. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this review analyzed research publications from 2015 to 2024, highlighting topics such as phonetic processing, text normalization, and error detection. Of the 85 initially reviewed, 32 were included in the meta-analysis to represent major challenges posed by the lack of annotated datasets and Natural Language Processing (NLP) resources tailored to the cognitive and linguistic needs of the adult patient population. This review points out the need for the extension of low-resource language toolkits and proper integration of assistive NLP applications. The study concludes that while significant progress has been made, a collaborative, interdisciplinary approach is crucial to improving literacy, independence, and social participation for Sinhala-speaking adults with dyslexia. © 2025 IEEE.

关键词： Sentiment analysis

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Prediction with Time-Series Mixer for the S&P500 Index

Prediction with Time-Series Mixer for the S&P500 Index

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International Conference on Data Engineering Workshops

作者： Junyi Ye Jingyi Gu Ankan Dash Fadi P. Deek Guiling Grace Wang Department of Computer Science New Jersey Institute of Technology Newark USA Department of Informatics New Jersey Institute of Technology Newark USA

As an essential US economic indicator, the S&P500 Index is used to assess the current state of market performance and gauge the economy’s future course. However, stock market index prediction is challenging due to its nonlinearity and inherently volatile character. Recurrent Neural Networks (RNN) and their variants are de facto standards for sequence modeling. Recently, Convolutional Neural Networks (CNN) and attention-based networks, such as dilated casual convolutions and Transformers, have also become popular in time series forecasting. In this paper, we report on the design of a Time-Series Mixer (TS-Mixer) architecture based on MLP-Mixer, an all-MLP architecture for time series forecasting. To the best of our knowledge, this is the first implementation of MLP-Mixer-based architecture for sequence modeling. Modern deep learning models are increasingly built to handle univariate time series data. They generally pay attention to analyzing temporal dependencies while ignoring the relationship among features. The proposed architecture is specifically created for multivariate time series forecasting to capture temporal feature interactions while simultaneously learning feature correlations. To accomplish this, the proposed Time-Feature Mixer contains two types of MLP layers: feature mixer and temporal mixer. The feature mixer is applied independently to each data point to capture the correlation among features. In contrast, the temporal mixer extracts temporal dependency (trend, seasonal, cyclical, or random characteristics) of each feature across the whole input sequence. Compared to prevalent neural networks in sequence modeling, TS-Mixer exhibits competitive performance regarding S&P500 Index prediction.

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Privacy-Preserving Utilization of Distribution System Flexibility for Enhanced TSO-DSO Interoperability: A Novel Machine Learning-Based Optimal Power Flow Approach

arXiv

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arXiv 2025年

作者： Dindar, Burak Saner, Can Berk Çakmak, Hüseyin K. Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe76131 Germany Department of Electrical and Computer Engineering National University of Singapore Singapore117581 Singapore

Due to the transformation of the power system, the effective use of flexibility from the distribution system (DS) is becoming crucial for efficient network management. Leveraging this flexibility requires interoperability among stakeholders, including Transmission System Operators (TSOs) and Distribution System Operators (DSOs). However, data privacy concerns among stakeholders present significant challenges for utilizing this flexibility effectively. To address these challenges, we propose a machine learning (ML)-based method in which the technical constraints of the DSs are represented by ML models trained exclusively on non-sensitive data. Using these models, the TSO can solve the optimal power flow (OPF) problem and directly determine the dispatch of flexibility-providing units (FPUs)—in our case, distributed generators (DGs)-in a single round of communication. To achieve this, we introduce a novel neural network (NN) architecture specifically designed to efficiently represent the feasible region of the DSs, ensuring computational effectiveness. Furthermore, we incorporate various PQ charts rather than idealized ones, demonstrating that the proposed method is adaptable to a wide range of FPU characteristics. To assess the effectiveness of the proposed method, we benchmark it against the standard AC-OPF on multiple DSs with meshed connections and multiple points of common coupling (PCCs) with varying voltage magnitudes. The numerical results indicate that the proposed method achieves performant results while prioritizing data privacy. Additionally, since this method directly determines the dispatch of FPUs, it eliminates the need for an additional disaggregation step. By representing the DSs technical constraints through ML models trained exclusively on nonsensitive data, the transfer of sensitive information between stakeholders is prevented. Consequently, even if reverse engineering is applied to these ML models, no sensitive data can be extracted. This allows

关键词： Reverse engineering

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A Hybrid Computational Framework Using NLP and ML for Emotion Analysis in Sinhala Songs 5

A Hybrid Computational Framework Using NLP and ML for Emotio...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Peiris, Malinda Sumanathilaka, Deshan Informatics Institute of Technology School of Computing Colombo006 Sri Lanka Swansea University School of Mathematics and Computer Science United Kingdom

ISBN: (纸本)9798331530983

This study Rasathura explores the emotional dimensions of Sri Lankan music, specifically Sinhala songs, by developing an emotional classification model using both audio features and lyrical analysis. The research addresses the limitations in emotional classification using advanced Natural Language Processing (NLP) techniques for Sinhala music. For audio analysis, JAudio was used to extract musical data, and machine learning (ML) techniques, including Random Forest, Sequential Minimal Optimization (SMO), Naive Bayes, Decision Tree, and Logistic Regression, were evaluated for classification. Relief-based and correlation-based attribute selection were utilized to enhance classification accuracy. The SMO algorithm achieved an accuracy of 75.92% using JAudio features, which increased to 79.01% with Relief-based attribute selection. The BERT model (built on XLM-RoBERTa) was used for lyrical analysis, achieving a maximum accuracy of 61.22%. TFIDF, Word2Vec with n-grams, and BERT embeddings were evaluated for feature extraction in lyrical data. The findings could benefit the future music industry by creating personalized music recommendation systems, enhancing music therapy applications, and contributing to preserving cultural heritage through digital music archiving. © 2025 IEEE.

关键词： Natural language processing systems

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Improving fNIRS Signal Quality Using Smoothing Filtering

Improving fNIRS Signal Quality Using Smoothing Filtering

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2023 Progress in Applied Electrical Engineering, PAEE 2023

作者： Kawala-Sterniuk, Aleksandra Mikolajewski, Dariusz Leiva, Luis A. Ruotsalo, Tuukka Lysiak, Adam Grochowicz, Barbara Jacek Gorzenariczyk, Edward Luckiewicz, Adrian Wieczorek, Anna Pelc, Mariusz Automatic Control and Informatics Opole University of Technology Faculty of Electrical Engineering Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland University of Luxembourg Luxembourg Department of Computer Science Luxembourg Luxembourg University of Helsinki Denmark University of Copenhagen Finland Institute of Philosophy Kazimiez Wielki University Bydgoszcz Poland

ISBN: (纸本)9798350316254

Biomedical signals are extremely difficult to analyze, mainly due to the non-stationary nature of these signals. Filtering does not always bring the desired results, because often the desired information is filtered out. In the case of EEG signals, smoothing filters gave very good results. In this paper, various types of smoothing filters for the analysis of infrared spectroscopy signals were compared. © 2023 IEEE.

关键词： Information filtering

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