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State-of-the-Art Intelligent Solenoid Valve Management System: AI-Driven Real-Time Monitoring and Predictive Maintenance

State-of-the-Art Intelligent Solenoid Valve Management Syste...

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2024 Abu Dhabi international Petroleum Exhibition and conference, ADIPEC 2024

作者： Mohamed, M. Zurix Feizal M Ghazali, M. Mokhtar, Zulkarnain Hashim, Azmi Nordin, M. Fariz Zulkifly, Muhamad Elfi Idris, Dalila M. Lumpur, Kuala Ainarappan, Victor Nagesparan PETRONAS Federal Territory of Kuala Lumpur Malaysia University College Sedaya International Kuala Lumpur Malaysia

ISBN: (纸本)9781959025498

The objective of this paper is to present the development and implementation of the Intelligent Solenoid Valve Management System, the first invention of its kind. This AI-driven, 5G-enabled solution is designed for real-time monitoring and predictive maintenance of solenoid valves (SOVs) in industrial settings, aimed at preventing spurious trips and plant upsets. This system employs non-intrusive sensors to measure the electrical loop parameters of SOV coils. These data are transmitted via a 5G module to a cloud-based platform where advanced AI algorithms analyze them to predict potential failures. The system features a web- based dashboard for real-time insights and trend analysis, providing early alerts for proactive maintenance. The process ensures seamless integration and continuous data flow from the sensors to the cloud, enabling robust monitoring and maintenance strategies (Doe et al. 2022;Johnson and Lee 2024;Smith et al. 2023;Roe et al. 2023;Brown et al. 2024;Green et al. 2024;Souza et al. 2020;Muhuri et al. 2019;Bidollahkhani and Fersheh 2024;Lima et al. 2021;Nor et al. 2022;Malek and Desai 2020). Initial deployments of this system have demonstrated significant cost avoidance and enhanced operational efficiency. The system's predictive capabilities ensure early detection of coil failures, which prevents spurious trips and plant upsets, thus maintaining operational stability and safety. Solenoid valves (SOVs) are critical components in industrial processes, used to control the flow of liquids and gases. Their failure can lead to significant disruptions in operations. The real-time data processing and analysis provided by the AI algorithms have proven to be highly accurate, leading to informed decision-making and reduced unplanned downtime (Doe et al. 2022;Roe et al. 2023;Smith et al. 2023;Brown et al. 2024;Green et al. 2024;Souza et al. 2020;Muhuri et al. 2019;Lima et al. 2021;Nor et al. 2022). The universal compatibility of this system with various SOV mak

关键词： Decision making

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RCS Weighted Filtering Method Suppresses Target Angle Flicker

RCS Weighted Filtering Method Suppresses Target Angle Flicke...

引用

Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Wei Hao Weiqiang Wang Yujie Hou Qing Liu Yangmeng Li Huayin Ordnance Weapons Test Centre Weinan China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

The relationship between RCS and target Angle flicker was established, and the calculation model of angle flicker suppression by weighted filter algorithm is established. The model is used to process and compare the simulation data and the measured data respectively, the results shows that the target RCS weighting method is suitable for suppressing target angular flicker. The angle measurement accuracy of radar can be improved by using RCS weighting method. The results show that the method can accurately determine the moment of extinction of the bottom row of projectiles. The method can also be used to determine the moment of ignition or extinction of other types of projectiles.

关键词： Weight measurement Fluctuations Radar cross-sections Accuracy Filtering Projectiles Information processing Ignition Filtering algorithms data models

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A Method for Domain Reference Model Inference Through Knowledge and data Intelligent Unifiers 19th

A Method for Domain Reference Model Inference Through Knowl...

引用

19th international conference on Research Challenges in Information Science, RCIS 2025

作者： Guimarães, Pedro ALGORITMI Research Centre University of Minho Campus de Azurém Guimarães 4800-058 Portugal CCG/ZGDV Institute University of Minho Campus de Azurém Guimarães 4800-058 Portugal

ISBN: (纸本)9783031924705

Transforming and integrating heterogeneous datasets into structured and semantically enriched data models remains a critical challenge in data and knowledge engineering. Addressing this challenge requires a systematic approach, as data and knowledge integration is often the most time-consuming phase. This work proposes a model to identify the key dimensions for establishing a method to infer Domain Reference models. Furthermore, this work also proposes how such method can be decomposed into components and steps that encompass data normalization, advanced knowledge and data alignment, and graph-based representations, in order to infer and integrate Domain Reference models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data Domain Reference Model Knowledge Knowledge Graphs

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GraphSD: A State and Dependency aware Out-of-Core Graph processing System 51

GraphSD: A State and Dependency aware Out-of-Core Graph Proc...

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51st international conference on Parallel processing (ICPP)

作者： Xu, Xianghao Jiang, Hong Wang, Fang Cheng, Yongli Fang, Peng Nanjing Univ Sci & Technol Nanjing Sch Comp Sci & Engn Nanjing Peoples R China Univ Texas Arlington Dept Comp Sci & Engn Arlington TX USA Huazhong Univ Sci & Technol Wuhan Natl Lab Optoelect Wuhan Peoples R China FuZhou Univ Coll Comp & Data Sci Fuzhou Peoples R China Zhejiang Lab Hangzhou Peoples R China

ISBN: (纸本)9781450397339

In recent years, system researchers have proposed many out-of-core graph processing systems to efficiently handle graphs that exceed the memory capacity of a single machine. Through disk-friendly graph data organizations and well-designed execution engines, existing out-of-core graph processing systems can maintain sequential locality on disk access and greatly reduce disk I/Os during processing. However, they have not fully explored the characteristics of graph data and algorithm execution to further reduce disk I/Os, leaving significant room for performance improvement. In this paper, we present a novel out-of-core graph processing system called GraphSD, which optimizes the I/O traffic by simultaneously capturing the state and dependency of graph data during computation. At the heart of GraphSD is a state- and dependency-aware update strategy that includes two adaptive update models, selective cross-iteration update (SCIU) and full cross-iteration update (FCIU). These two update models are dynamically triggered at runtime to enable active-vertex aware processing and cross-iteration vertex value computation, which avoid loading inactive edges and reduce disk I/Os in the future iterations. Moreover, an efficient sub-block based buffering scheme is proposed to further minimize I/O overheads. Our evaluation results show that GraphSD outperforms two state-of-the-art out-of-core graph processing systems HUS-Graph and Lumos by up to 2.7x and 3.9x respectively.

关键词： graph processing I/O-efficient state and dependency of graph data

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Score-Based data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance

Score-Based Data Generation for EEG Spatial Covariance Matri...

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45th Annual international conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Ju, Ce Kobler, Reinmar Josef Guan, Cuntai Nanyang Technol Univ S Lab 50 Nanyang Ave Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn 50 Nanyang Ave Singapore Singapore RIKEN Artificial Intelligence Project Tokyo Japan Adv Telecommun Res Inst Int Kyoto Japan

ISBN: (纸本)9798350324471

The efficacy of Electroencephalogram (EEG) classifiers can be augmented by increasing the quantity of available data. In the case of geometric deep learning classifiers, the input consists of spatial covariance matrices derived from EEGs. In order to synthesize these spatial covariance matrices and facilitate future improvements of geometric deep learning classifiers, we propose a generative modeling technique based on state-of-the-art score-based models. The quality of generated samples is evaluated through visual and quantitative assessments using a left/right-hand-movement motor imagery dataset. The exceptional pixel-level resolution of these generative samples highlights the formidable capacity of score-based generative modeling. Additionally, the center (Frechet mean) of the generated samples aligns with neurophysiological evidence that event-related desynchronization and synchronization occur on electrodes C3 and C4 within the Mu and Beta frequency bands during motor imagery processing. The quantitative evaluation revealed that 84.3% of the generated samples could be accurately predicted by a pre-trained classifier and an improvement of up to 8.7% in the average accuracy over ten runs for a specific test subject in a holdout experiment.

关键词： Electroencephalography

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Optimal Selection of Manufacturing Configurations Using Object-Oriented and Mathematical data models 10

Optimal Selection of Manufacturing Configurations Using Obje...

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10th international conference on Industrial Engineering and Applications, ICIEA 2023

作者： Torayev, Agajan Wang, Zi Martínez-Arellano, Giovanna Chaplin, Jack C. Sanderson, David Ratchev, Svetan Institute for Advanced Manufacturing The University of Nottingham Nottingham United Kingdom Centre for Aerospace Manufacturing The University of Nottingham Nottingham United Kingdom

ISBN: (纸本)9781643684086

In this work, we optimize manufacturing configurations, i.e., the set of necessary assets, using the known capabilities and capacities of manufacturing equipment. In particular, the work provides an Object-Oriented data model and the translation of the Object-Oriented data model into a Mathematical data model for efficiently utilizing optimization algorithms. The main contribution is developing an optimization strategy for adapting to varying demand periods with the requested capability and capacity. The proposed methodology is validated in optimizing the planning for small-box hinged product assemblies in aerospace manufacturing, which can be assembled in a reconfigurable environment with common pick and place, drilling, fastening, and inspection procedures. © 2023 The authors and IOS Press.

关键词： data models manufacturing object-oriented data models optimization resource planning

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Multi-Speaker and Wide-Band Simulated Conversations as Training data for End-To-End Neural Diarization 48

Multi-Speaker and Wide-Band Simulated Conversations as Train...

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48th IEEE international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Landini, Federico Diez, Mireia Lozano-Diez, Alicia Burget, Lukas Brno University of Technology Faculty of Information Technology Speech@FIT Czech Republic Spain

ISBN: (纸本)9781728163277

End-To-end diarization presents an attractive alternative to standard cascaded diarization systems because a single system can handle all aspects of the task at once. Many flavors of end-To-end models have been proposed but all of them require (so far non-existing) large amounts of annotated data for training. The compromise solution consists in generating synthetic data and the recently proposed simulated conversations (SC) have shown remarkable improvements over the original simulated mixtures (SM). In this work, we create SC with multiple speakers per conversation and show that they allow for substantially better performance than SM, also reducing the dependence on a fine-Tuning stage. We also create SC with wide-band public audio sources and present an analysis on several evaluation sets. Together with this publication, we release the recipes for generating such data and models trained on public sets as well as the implementation to efficiently handle multiple speakers per conversation and an auxiliary voice activity detection loss. © 2023 IEEE.

关键词： Speech recognition

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Cross-Learning-Based Sales Forecasting Using Deep Learning via Partial Pooling from Multi-level data 1

引用

24th international conference on Engineering Applications of Neural Networks (EANN)

作者： Oliveira, Jose Manuel Ramos, Patricia Univ Porto Fac Econ Rua Dr Roberto Frias P-4200464 Porto Portugal INESC TEC Inst Syst & Comp Engn Technol & Sci Rua Dr Roberto Frias P-4200465 Porto Portugal Polytech Univ Porto ISCAP Rua Jaime Lopes Amorim P-4465004 Sao Mamede de Infesta Portugal

ISBN: (数字)9783031342042

ISBN: (纸本)9783031342035;9783031342042

Sales forecasts are an important tool for inventory management, allowing retailers to balance inventory levels with customer demand and market conditions. By using sales forecasts to inform inventory management decisions, companies can optimize their inventory levels and avoid costly stockouts or excess inventory costs. The scale of the forecasting problem in the retail domain is significant and requires ongoing attention and resources to ensure accurate and effective forecasting. Recent advances in machine learning algorithms such as deep learning have made possible to build more sophisticated forecasting models that can learn from large amounts of data. These global models can capture complex patterns and relationships in the data and predict demand across multiple regions and product categories. In this paper, we investigate the cross-learning scenarios, inspired by the product hierarchy frequently utilized in retail planning, which enable global models to better capture interdependencies between different products and regions. Our empirical results obtained using M5 competition dataset indicate that the cross-learning approaches exhibit significantly superior performance compared to local forecasting benchmarks. Our findings also suggest that using partial pooling at the lowest aggregation level of the retail hierarchical allows for a more effective capture of the distinct characteristics of each group.

关键词： Deep learning Time series forecasting Cross-learning Retailing Hierarchical aggregation Intermittent data

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EXPLAINABLE AI (XAI) IN BIOMEDICAL SIGNAL AND IMAGE processing: PROMISES AND CHALLENGES 29

EXPLAINABLE AI (XAI) IN BIOMEDICAL SIGNAL AND IMAGE PROCESSI...

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IEEE international conference on Image processing (ICIP)

作者： Yang, Guang Rao, Arvind Fernandez-Maloigne, Christine Calhoun, Vince Menegaz, Gloria Imperial Coll London Natl Heart & Lung Inst London England Univ Michigan Dept Computat Med & Bioinformat Ann Arbor MI USA Univ Poitiers I3M Lab Poitiers France CNRS Poitiers France Georgia Tech TReNDS Ctr Atlanta GA USA Univ Verona Dept Comp Sci Verona Italy

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Artificial intelligence has become pervasive across disciplines and fields, and biomedical image and signal processing is no exception. The growing and widespread interest on the topic has triggered a vast research activity that is reflected in an exponential research effort. Through study of massive and diverse biomedical data, machine and deep learning models have revolutionized various tasks such as modeling, segmentation, registration, classification and synthesis, outperforming traditional techniques. However, the difficulty in translating the results into biologically/clinically interpretable information is preventing their full exploitation in the field. Explainable AI (XAI) attempts to fill this translational gap by providing means to make the models interpretable and providing explanations. Different solutions have been proposed so far and are gaining increasing interest from the community. This paper aims at providing an overview on XAI in biomedical data processing and points to an upcoming Special Issue on Deep Learning in Biomedical Image and Signal processing of the IEEE Signal processing Magazine that is going to appear in March 2022.

关键词： Explainable AI XAI Artificial Intelligence Machine Learning Deep Learning Biomedical data

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Causal Fairness-Guided dataset Reweighting using Neural Networks

Causal Fairness-Guided Dataset Reweighting using Neural Netw...

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2023 IEEE international conference on Big data, Bigdata 2023

作者： Zhao, Xuan Broelemann, Klaus Ruggieri, Salvatore Kasneci, Gjergji Schufa Holding Ag Germany University of Pisa Italy Technical University of Munich Germany

ISBN: (纸本)9798350324457

The importance of achieving fairness in machine learning models cannot be overstated. Recent research has pointed out that fairness should be examined from a causal perspective, and several fairness notions based on the on Pearl's causal framework have been proposed. In this paper, we construct a reweighting scheme of datasets to address causal fairness. Our approach aims at mitigating bias by considering the causal relationships among variables and incorporating them into the reweighting process. The proposed method adopts two neural networks, whose structures are intentionally used to reflect the structures of a causal graph and of an interventional graph. The two neural networks can approximate the causal model of the data, and the causal model of interventions. Furthermore, reweighting guided by a discriminator is applied to achieve various fairness notions. Experiments on real-world datasets show that our method can achieve causal fairness on the data while remaining close to the original data for downstream tasks. © 2023 IEEE.

关键词： adversarial reweighting causal fairness data pre-processing

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