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Application of VGIS Digital Twin Platform for Enhanced Oilfield Disaster Management and Environmental Protection

Informatica (Slovenia) 2024年第14期48卷 27-42页

作者： Alabbasi, Awab Aljabery, Maalim A. Bölük, Pınar Sarısaray Faculty of Engineering and Natural Sciences Electrical and Computer Engineering Dept. Bahcesehir University Istanbul34353 Turkey Faculty of Computer Science and Information Technology Computer Science Dept. University of Basrah Basrah61004 Iraq Faculty of Engineering and Natural Sciences Software Engineering Dept. Bahçeşehir University Istanbul34353 Turkey

VGIS (Virtual Geographic Information System) Platform is a unified oilfield operations management platform based on MaaS (Management as a Service) that integrates advanced technologies such as AIoT (Artificial Intelligence & Internet of Things), GIS (Geographic Information System), Digital Twin, and AR (Augmented Reality)/ VR (Virtual Reality). The VGIS platform connects, collects, integrates, analyses, and acts on disparate data sources to provide the oilfield with a centralized view to help make informed decisions fast, bring operational visibility across all assets to improve safety and operational efficiency, reduce costs and improve margins, provide comprehensive analytics and decision support, increase business agility, and reduce management complexity. This is a case study of the application of the VGIS platform Majnoon oilfield, Basrah, Iraq. VGIS digital transformation, production process, and KPIs (Key performance indicators) are highly managed taking oilfield management to a higher level globally, in this study, the objective is to address and resolve the issue of high costs associated with using cloud services by utilising a local Data Center (a large group of networked computer servers typically used by organizations for the remote storage, processing, or distribution of large amounts of data). This approach involves directly uploading 3D maps to ensure faster service on the intranet, eliminating the need to pay for cloud *** aims providing a platform for researching and implementing monitoring and controlling processes in a more convenient and remote manner. The vGIS platform has emerged as a crucial instrument in the realm of disaster response and crisis management. This technology enables responders to rapidly evaluate, analyse, and respond to crucial information in real-time. This research examined the application of vGIS technology in disaster response and analyse the potential advantages and obstacles associated with its utilisation. © 2

关键词： Information management

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A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand

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Chinese Journal of Electronics 2024年第6期33卷 1383-1398页

作者： Xueqing YAN Yongming LI Sanjiang LI School of Computer Science Shaanxi Normal University School of Mathematics and Statistics Shaanxi Normal University Centre for Quantum Software and Information Faculty of Engineering and Information TechnologyUniversity of Technology Sydney

The tile-based multiplayer game Mahjong is widely played in Asia and has also become increasingly popular worldwide. Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tiles in turn until they complete a winning hand. An important notion in Mahjong is the deficiency number(*** number in Japanese Mahjong) of a hand, which estimates how many tile changes are necessary to complete the hand into a winning hand. The deficiency number plays an essential role in major decision-making tasks such as selecting a tile to discard. This paper proposes a fast algorithm for computing the deficiency number of a Mahjong hand. Compared with the baseline algorithm, the new algorithm is usually 100 times faster and, more importantly,respects the agent's knowledge about available tiles. The algorithm can be used as a basic procedure in all Mahjong variants by both rule-based and machine learning-based Mahjong AI.

关键词： Machine learning algorithms Knowledge based systems Decision making Asia Games

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Optimizing crowd evacuation: evaluation of strategies for safety and efficiency

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Journal of Reliable Intelligent Environments 2025年第1期11卷 1-19页

作者： Oliveira, Hugo S. Faculty of Sciences University of Porto Porto Portugal Institute for Systems and Computer Engineering Technology and Science INESC TEC Porto Portugal

Predicting and controlling crowd dynamics in emergencies is one of the main objectives of simulated emergency exercises. However, during emergency exercises, there is often a lack of sense of danger by the actors involved and concerns about exposing real people to potentially dangerous environments. These problems impose limitations in running an emergency drill, harming the collection of valuable information for posterior analysis and decision-making. This work aims to mitigate these problems by using Agent Based Modelling (ABM) simulator to deepen the comprehension of human actions when exposed to a sudden variation in extensive crowded environmental conditions and how evacuation strategies affect evacuation performance. To assess the impact of the evacuation strategy employed, we propose a modified informed leader-flowing approach and compare it with common evacuation strategies in a simulated environment, replicating stadium benches with narrow corridors leading to different exit points. The objective is to determine the impact of each set of configurations and evacuation strategies and compare them against other established ones. Our experiments determined that agents following the crowd generally lead to a higher number of victims due to the rise of herding phenomena near the exits, which was significantly reduced when agents were guided towards the exit via knowing the exit beforehand or following leader agent with real-time information regarding exit location and exit current state, proving that relevant and controlled information in combination with Follow Leader strategies can be crucial in an emergency evacuation scenario with limited evacuation exit capabi and distribution. © The Author(s) 2024.

关键词： Decision making

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Adaptive Decentralized Federated Learning in LEO Satellite Networks 24

Adaptive Decentralized Federated Learning in LEO Satellite N...

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24th IEEE International Conference on Communication technology, ICCT 2024

作者： Li, Dong Yan, Zhigang Hao, Yuyu Niyato, Dusit Quek, Tony Q. S. Macau University of Science and Technology School of Computer Science and Engineering Macau China Nanyang Technological University School of Computer Science and Engineering Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore

ISBN: (纸本)9798350363760

In Federated Learning (FL) for the low Earth orbit (LEO) satellite networks with parameters aggregated by a ground station, the communication overhead is a substantial concern. To circumvent this limitation and alleviate the latency within the FL for the satellite-ground communication, Decentralized Federated Learning (DFL) has appeared as an effective solution. However, considering the heterogeneity of satellites, the problem on how to efficiently leverage the limited resources available to enhance the model performance of DFL has drawn little attention, which motivates this work. Specifically, the proposed solution involves optimizing the number of local training rounds across diverse satellites with varying resource budgets to enhance the DFL performance by utilizing the resource more efficiently. Simulation results show that, the proposed scheme achieves a much better performance than the conventional schemes with fixed rounds of local training. © 2024 IEEE.

关键词： Federated learning

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Offshore Software Maintenance Outsourcing Process Model Validation:A Case Study Approach

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computers, Materials & Continua 2023年第3期74卷 5035-5048页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Adel Sulaiman Muhammad Akram Ahmad Salman Khan Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan

The successful execution and management of Offshore Software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO *** a lot of research on software outsourcing is going on,most of the existing literature on offshore outsourcing deals with the outsourcing of software development *** frameworks have been developed focusing on guiding software systemmanagers concerning offshore software ***,none of these studies delivered comprehensive guidelines for managing the whole process of *** is a considerable lack of research working on managing OSMO from a vendor’s ***,to find the best practices for managing an OSMO process,it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s *** study validated the preliminary OSMO process model via a case study research *** results showed that the OSMO process model is applicable in an industrial setting with few *** industrial data collected during the case study enabled this paper to extend the preliminary OSMO process *** refined version of the OSMO processmodel has four major phases including(i)Project Assessment,(ii)SLA(iii)Execution,and(iv)Risk.

关键词： Offshore outsourcing process model model validation vendor challenges case study

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Development of Technologies for Prevention of Wild-Animal Related Losses to Agricultural Fields

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Journal of The Institution of Engineers (India): Series B 2025年第2期106卷 635-646页

作者： Doss, Srinath Kumar, Rajiv Paranthaman, Jothi Faculty of Engineering and Technology Botho University Gaborone Botswana School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Faculty of Engineering Gaborone University College of Law and Professional studies Gaborone Botswana

Over the past decades, integration of wireless sensor networks (WSNs) and computer vision (CV) technology has shown promising results in mitigating crop losses caused by wild animal attacks. Studies have demonstrated the effectiveness of these technologies in providing real-time monitoring and early detection of animal intrusions into agricultural fields. By deploying WSNs equipped with motion sensors and cameras, farmers can receive instant alerts when wild animals enter their fields, allowing for timely intervention to prevent crop damage. Furthermore, advancements in CV algorithms possess made possible to automatically detect and classify the animal species, facilitating targeted response strategies. For example, sophisticated image processing techniques can differentiate between harmless birds and destructive mammals, allowing farmers to focus their efforts on deterring the most damaging species. Field trials and pilot projects implementing WSN-CV systems have reported significant reductions in crop losses attributed to wild animal raids. By leveraging data collected through sensor networks and analyzed using computer vision algorithms, farmers can make informed decisions regarding pest and insect management strategies. This data-driven approach has led to more efficient utilization of resources, such as targeted application of insecticides and pesticides, resulting in both economic and environmental benefits. Moreover, the integration of WSN-CV technology has enabled the development of innovative deterrent systems that leverage artificial intelligence and automation. These systems can deploy non-lethal methods, such as sound or light-based repellents, to deter wild animals without causing harm to the environment or wildlife populations. Overall, the combination of wireless sensor networks and computer vision technology provides the promising resolution to the long-standing issue of wild animal-related losses in agriculture. By harnessing the power of data and a

关键词： Wireless sensor networks

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Introduction to the Special Issue on Machine Learning-Guided Intelligent Modeling with Its Industrial Applications

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computer Modeling in engineering & sciences 2024年第10期141卷 7-11页

作者： Xiong Luo Yongqiang Cheng Zhifang Liao School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Faculty of Technology University of SunderlandSunderlandSR60DDUK School of Computer Science and Engineering Central South UniversityChangsha410083China

With the advancement of Artificial Intelligence(AI)technology,traditional industrial systems are undergoing an intelligent transformation,bringing together advanced computing,communication and control technologies,Machine Learning(ML)-based intelligentmodelling has become a newparadigm for solving problems in the industrial domain[1–3].With numerous applications and diverse data types in the industrial domain,algorithmic and data-driven ML techniques can intelligently learn potential correlations between complex data and make efficient decisions while reducing human ***,in real-world application scenarios,existing algorithms may have a variety of limitations,such as small data volumes,small detection targets,low efficiency,and algorithmic gaps in specific application domains[4].Therefore,many new algorithms and strategies have been proposed to address the challenges in industrial applications[5–8].

关键词： Intelligence bringing intelligent

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MLMO-HSM: Multi-label Multi-output Hybrid Sequential Model for multi-resident smart home activity recognition

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Journal of Ambient Intelligence and Humanized Computing 2023年第3期14卷 2313-2325页

作者： Ramanujam, E. Perumal, Thinagaran Department of Computer Science and Engineering National Institute of Technology Silchar Assam 788010 India Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang India

Smart home automation is protective and preventive measures that are taken to monitor elderly people in a non-intrusive manner using simple and pervasive sensors termed Ambient Assistive Living. The smart home produces a large volume of sensor activations to predict an elder’s health status to improve the quality of life and independent living. Machine learning techniques are very familiar and popular in recognizing single resident activities using such a sensor reading. However, multi-resident activities are more complex, and no correlation exists between sensor readings and activities. Recently deep learning and graphical models have been proposed to solve this problem, but it consumes more time to train the model. Moreover, models are primarily executed in parallel, or multi-resident activity recognition has been converted to single for classifying events. This paper proposes a Multi-label Multi-output Hybrid Sequential Model (MLMO-HSM), a feature engineering approach with a hybrid sequential model to recognize the multi-resident activities concurrently in the shortest time. Experimentation has been performed with various machine learning, graphical and deep learning models at the different sizes of the ARAS dataset to validate the efficiency of the proposed model in terms of activity recognition and computation time. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Automation

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Dual modality prompt learning for visual question-grounded answering in robotic surgery

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 316-328页

作者： Yue Zhang Wanshu Fan Peixi Peng Xin Yang Dongsheng Zhou Xiaopeng Wei National and Local Joint Engineering Laboratory of Computer Aided Design School of Software EngineeringDalian UniversityDalian 116622LiaoningChina School of Computer Science and Technology Dalian University of TechnologyDalian 116081LiaoningChina

With recent advancements in robotic surgery,notable strides have been made in visual question answering(VQA).Existing VQA systems typically generate textual answers to questions but fail to indicate the location of the relevant content within the *** limitation restricts the interpretative capacity of the VQA models and their abil-ity to explore specific image *** address this issue,this study proposes a grounded VQA model for robotic surgery,capable of localizing a specific region during answer *** inspiration from prompt learning in language models,a dual-modality prompt model was developed to enhance precise multimodal information ***,two complementary prompters were introduced to effectively integrate visual and textual prompts into the encoding process of the model.A visual complementary prompter merges visual prompt knowl-edge with visual information features to guide accurate *** textual complementary prompter aligns vis-ual information with textual prompt knowledge and textual information,guiding textual information towards a more accurate inference of the ***,a multiple iterative fusion strategy was adopted for comprehensive answer reasoning,to ensure high-quality generation of textual and grounded *** experimental results vali-date the effectiveness of the model,demonstrating its superiority over existing methods on the EndoVis-18 and End-oVis-17 datasets.

关键词： Prompt learning Visual prompt Textual prompt Grounding-answering Visual question answering

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Hybrid Deep Learning Based Attack Detection for Imbalanced Data Classification

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Intelligent Automation & Soft Computing 2023年第1期35卷 297-320页

作者： Rasha Almarshdi Laila Nassef Etimad Fadel Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science Faculty of Computer Science and EngineeringUniversity of HailHailSaudi Arabia

Internet of Things(IoT)is the most widespread and fastest growing technology *** to the increasing of IoT devices connected to the Internet,the IoT is the most technology under security *** IoT devices are not designed with security because they are resource constrained ***,having an accurate IoT security system to detect security attacks is *** Detection Systems(IDSs)using machine learning and deep learning techniques can detect security attacks *** paper develops an IDS architecture based on Convolutional Neural Network(CNN)and Long Short-Term Memory(LSTM)deep learning *** implement our model on the UNSW-NB15 dataset which is a new network intrusion dataset that cate-gorizes the network traffic into normal and attacks *** this work,interpolation data preprocessing is used to compute the missing ***,the imbalanced data problem is solved using a synthetic data generation *** experiments have been implemented to compare the performance results of the proposed model(CNN+LSTM)with a basic model(CNN only)using both balanced and imbalanced ***,with some state-of-the-art machine learning classifiers(Decision Tree(DT)and Random Forest(RF))using both balanced and imbalanced *** results proved the impact of the balancing *** proposed hybrid model with the balance technique can classify the traffic into normal class and attack class with reasonable accuracy(92.10%)compared with the basic CNN model(89.90%)and the machine learning(DT 88.57%and RF 90.85%)***,comparing the proposed model results with the most related works shows that the proposed model gives good results compared with the related works that used the balance techniques.

关键词： IoT IDS deep learning machine learning CNN LSTM

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