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Daqing Oilfield Cable Controlled intelligent Separate Injection Technology Process Optimization 11

Daqing Oilfield Cable Controlled Intelligent Separate Inject...

引用

11th Annual international conference on Material Science and Environmental engineering, MSEE 2023

作者： Zhang, Weichao Guo, Mingri Li, Jinghui Wang, Lili Wang, Jing Daqing Oilfield Production Technology Institute Daqing Oilfield Limited Company Daqing163453 China Heilongjiang Provincial Key Laboratory of Oil and Gas Reservoir Stimulation Daqing163453 China No.2 Oil Production Plant Daqing Oilfield Limited Company Daqing163453 China

the cable controlled intelligent separate injection process realizes the continuous monitoring and real-time measurement and adjustment of downhole parameters, greatly improves the measurement and adjustment efficiency, provides sufficient data for reservoir analysis, and helps to tap the potential of remaining oil in the later stage of ultra-high water cut. However, due to the multi-disciplinary application of downhole tools and complex process, it is found in the previous field test that the number of wells with more than two years of operation of cable controlled intelligent separate injection wells accounts for only 51.9%, which can not meet the needs of large-scale application. through the fault analysis of the early test well, the key factors affecting the success rate of well completion are found. Aiming at the main problems, the flow control valve of the cable controlled intelligent water distributor, the cable sealing connection process and the well flushing valve structure through the cable packer are optimized and improved. the flow control valve of cable controlled intelligent water distributor is opened and closed smoothly under 10MPa pressure difference, and the leakage is less than 1m 3/ d;the connection between the cable and the cable controlled intelligent water distributor is sealed reliably, and the cable can pass through the upper and lower joints of the cable packer smoothly;Since the optimization of process technology until January 2023, 223 wells have been tested on site, and the number of wells with more than two years of operation has increased from 56.1% to 91.9%. the stability of the optimized cable controlled intelligent separate injection process has been improved, which lays a foundation for the large-scale application of this technology in the oilfield. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Packers

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Research on Real-time Road Crack Detection Algorithm Based on Improved Knowledge Distillation 11

Research on Real-time Road Crack Detection Algorithm Based o...

引用

11th IEEE data Driven Control and learning Systems conference, DDCLS 2022

作者： Jiang, Yanyu Wei, Haicheng Wang, Suo Tian, Siyuan School of Electrical and Information Engineering North Minzu University Yinchuan750021 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

An intelligent real-time crack detection algorithm based on lightweight and knowledge distillation is proposed for existing crack detection systems that can hardly meet the road development needs. In this paper, YOLOv4 is used as the main framework, and MobileNetV3 is used as the backbone network, based on which the model is compressed and knowledge distilled. the experimental results show that the final model has 95.58% lower parameters than the YOLOv4 model, 66.22% higher inference speed, and 0.914 detection accuracy. the improved, streamlined model has high precision, good detection effect, and fast operation speed, and is suitable for deployment on miniaturized embedded systems with in-vehicle as the platform. © 2022 IEEE.

关键词： Crack detection

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From Monolingual to Multilingual: Enhancing Hate Speech Detection with Multi-channel Language Models 28th

From Monolingual to Multilingual: Enhancing Hate Speech Dete...

引用

28th international conference on Knowledge Based and intelligent information and engineering Systems, KES 2024

作者： Kia, Mahsa Abazari Samiee, Dorsa Northeastern University London United Kingdom Royal Holloway University of London United Kingdom

the rise of social networking services (SNS) has reshaped communication dynamics in cyberspace, yet it has also exacerbated the proliferation of online hate speech due to the anonymity and fluidity these platforms offer. With manual hate speech detection by human annotators proving costly and time-intensive, the imperative for developing automated recognition algorithms is evident. Leveraging knowledge transfer through fine-tuning pre-trained language models has emerged as a promising strategy in natural language processing. Notably, Bidirectional Encoder Representations from Transformers (BERT) and HateBERT, a domain-specific variant, stand out for their ability to glean deep bidirectional representations from extensive corpora. this paper introduces a multichannel model integrating these two language models for multilingual hate speech detection. Furthermore, we explore the efficacy of augmenting input data through translation, ensuring compatibility with the English requirement of the HateBERT model. By evaluating our approach on three non-English datasets alongside an English dataset, we demonstrate achieving state-of-the-art or comparable performance, underscoring the effectiveness of our methodology in combating hate speech across linguistic barriers. © 2024 the Authors.

关键词： Deep learning

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Visualization and Prediction of Heart Disease using Big data Analytics 11

Visualization and Prediction of Heart Disease using Big Data...

引用

11th international conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Shankhdhar, Ashutosh GLA University Dept. of Computer Engineering and Applications Mathura India

ISBN: (纸本)9781665487320

Heart disease is the leading cause of deaths worldwide. According to a study published in 2016, one out of every four fatalities in India is caused by cardiovascular disease, which, if not treated properly and immediately, can cause serious health difficulties in patients. Millions of people's lives may be saved if this life-threatening condition could be detected early. this paper proposes a legitimate fitness state detection approach with a heart disease use-case combining machine learning models such as K-Nearest Neighbors, Random Forest algo, and other ensemble learning approaches. this strategy focuses on using machine learning models to predict cardiac problems using real-time data. Stacking CV Classifier with KNN as meta classifier surpasses the other classifiers used in the study with an accuracy rate of 93.39% when using the ensemble learning approach on the data. © 2022 IEEE.

关键词： data Analytics

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Object Removal System for Urban Imagery Using Image Segmentation and Inpainting with A Deep learning Approach 11

Object Removal System for Urban Imagery Using Image Segmenta...

引用

11th international conference on Advanced Informatics: Concept, theory and Application, ICAICTA 2024

作者： Amarendra, Eiffel Aqila Munir, Rinaldi School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798331520311

Digital imagery is a two-dimensional visual representation, which often holds emotional significance and crucial information. However, in images, specifically urban imagery, unwanted objects frequently appear. To address this issue, a system capable of automatically selecting areas with unwanted objects, removing these areas, and reconstructing the removed regions is essential. the object removal system is developed by implementing and integrating an image segmentation module, image inpainting module, and graphical user interface application. the pre-trained image segmentation model, DeepLabv3+, is used for the image segmentation module. On the other hand, there are seven pre-trained image inpainting models, including DeepFillv2, EdgeConnect (Places), EdgeConnect (PSV), MADF (Places), MADF (PSV), MAT, and CoModGAN, which are compared across several testing aspects to be used in the image inpainting module. Based on the analysis of the test results on the test data, the DeepLabv3+ model is proven to perform accurate segmentation with a mIoU value reaching 0.936. the CoModGAN model is chosen as the pre-trained model of the image inpainting module due to its average PSNR score of 26.59dB, SSIM of 0.8908, FID of 39.99, and subjective evaluation of 4.105. the graphical user interface application developed and integrated with the image segmentation and image inpainting modules successfully provides flexibility to users and shows increased performance compared to previous studies. © 2024 IEEE.

关键词： Image segmentation

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Why Use Evolving Neuro-Fuzzy and Spiking Neural Networks for incremental and explainable learning of time series? A case study on predictive modelling of trade imports and outlier detection 11

Why Use Evolving Neuro-Fuzzy and Spiking Neural Networks for...

引用

11th IEEE international conference on intelligent Systems, IS 2022

作者： Abouhassan, Iman Kasabov, Nikola Popov, George Trifonov, Roumen Technical University of Sofia Dept. of Information Technology in Industry Sofia Bulgaria Auckland University of Technology KEDRI- Secms Auckland New Zealand Ulster Universty United Kingdom

ISBN: (纸本)9781665456562

What algorithms to choose for an incremental, predictive, and more importantly, explainable learning and modelling of time series data, and specifically - economic and financial time series? Will these algorithms reveal and explain abnormality in time series over time? this problem is part of the challenges in the area of explainable AI and life-long learning systems. three widely used evolving connectionist systems (ECOS) that offer a solution to the above problems, are compared in the paper. the first two models, EFuNN and DENFIS, are neuro-fuzzy models that deal with vector-based data, while the spiking neural network model NeuCube deals with temporal and spatio-temporal data. the case study data used to demonstrate the qualities of these techniques is time series data related to Bulgarian petroleum oil imports from various trading partners. Graphical visualization enhances the deep analysis, pattern recognition, and knowledge extraction from the models. Conclusions are drawn in the sense that each of these techniques reveals different aspects of the data and the problem in hand and a single model solution of a complex problem is never going to be complete. Future work outlines integration of time series and on-line news to achieve a better predictive accuracy and a better understanding of the data. © 2022 IEEE.

关键词： Time series

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the Evolution of E-learning towards the Emergence of Artificial Intelligence (A Narrative Review)

The Evolution of E-Learning towards the Emergence of Artific...

引用

international conference on E-learning and E-Teaching (ICELET)

作者： Abdollah Mehrfar Zahra Zolfaghari Arash Bordbar Zahra Karimimoghadam Shiraz University of Medical Science Shiraz Iran

In recent years, the speed of adoption and implementation of artificial intelligence (AI) in higher education has increased markedly. By leveraging technologies including machine learning and language processing, improvements in learning have been achieved for learners, in line with the principles of personalized learning. this review study aims to examine the implementation of AI in university education. A search was conducted in databases between 1990 to 2022 using relevant keywords. Inclusion criteria consisted of original studies related to the implementation of AI in university education and with empirical evaluation or modeling. 42 selected articles were reviewed. the findings showed that AI is used in university education for personalized learning, intelligent tutoring systems, virtual assistants, automation, data analysis, and improving instructional design. there are also challenges such as privacy concerns, algorithmic bias, and reduction of human interactions. AI has the potential to transform learning and education in universities through an evidence-based and ethical approach. An intelligent integration of traditional pedagogies and new technologies is required.

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Development of a Risk Assessment and Early Warning System for Pharmaceutical Cold Chain Transportation Based on Big data 11th

Development of a Risk Assessment and Early Warning System fo...

引用

11th international conference on Applications and Techniques in Cyber Intelligence, ATCI 2023

作者： Liu, Juan College of Chemistry and Environment Shandong Institute of Commerce and Technology Shandong Jinan250000 China

Cold chain logistics has become a core link in ensuring drug quality and safety, especially for temperature sensitive drugs such as vaccines and biologics. this paper aims to develop a drug cold chain transportation risk assessment and early warning system based on big data, utilizing advanced technologies such as big data analysis and machine learning to achieve real-time monitoring, intelligent analysis, and early warning of various risk factors in the drug cold chain transportation process. this paper proposes a comprehensive architecture that includes data collection layer, data transmission layer, data processing and storage layer, analysis engine, warning and response module, user interface, and system integration interface. this paper collects key environmental data in real-time through sensors, such as temperature and humidity, and uses a secure data transmission layer to transmit the data to the central processing system. the construction of the risk assessment model is based on a series of quantifiable risk indicators, including temperature deviation, humidity deviation, location deviation, etc., and through data standardization and weight allocation, combined with a machine learning model to perform risk scoring and grading. the early warning mechanism implementation part includes early warning rules and threshold settings as well as real-time monitoring and dynamic warning to ensure that the system can respond to potential risks in a timely manner. the results of the system performance evaluation show that the risk evolution prediction deviation rate remains at a very low level, with a maximum of 1.4%, the false alarm rate is very low, with a maximum of 0.15% and a minimum of 0.03%, and the risk situational awareness sensitivity coefficient remains at a high level, with a minimum of 0.8. these results indicate that the developed system performs well in terms of risk prediction accuracy, warning timeliness and risk perception sensitivity. © 2024 the Autho

关键词： Risk assessment

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An intelligent Approach to Cost-effective Management of Typhoid Fever in Children 10

An Intelligent Approach to Cost-effective Management of Typh...

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10th international conference on Signal Processing and Communication, ICSC 2025

作者： Igulu, Kingsley T. Singh, thipendra P Olayinka, Tosin C. Onyijen, Ojei H. Onatuyeh, Edwin Nwankwo, Wilson Kenule Beeson Saro-Wiwa Polytechnic Bori Department of Computer Science Bori Nigeria Bennett University Greater Noida India Wellspring University Department of Software Engineering Edo State Benin City Nigeria Edo State University Department of Physics Edo State Uzairue Nigeria Delta State University of Science & Technology Department of Accounting Ozoro Nigeria Delta State University of Science & Technology Department of Cyber Security Ozoro Nigeria

ISBN: (纸本)9798331511890

Typhoid fever poses a significant health concern among children, due to its potential for severe complications and high treatment costs. this paper proposes an intelligent approach to modelling the prognosis and management of paediatric typhoid infections leveraging machine learning and data analytics to optimize healthcare resources and improve patient outcomes. the dataset was sourced from hospitals in Edo State, Nigeria. the dataset comprises laboratory tests of blood samples of children, from age zero (0) to five (5) years. Six (6) attributes that exhibit the highest information gain: widal test, malaria count, monocyte, platelets, HB, eosinophils, cost of treatment respectively, were selected and ranked thus: 0.6759, 0.4936, 0.3147, 0.2843, 0.2416, 0.2127, and 0.20. Five different classification algorithms (J48_Consolidated, LMT, RepTree, MultiBoost Decision Stump) were employed. and Random Forest) and their results were compared. Performance analysis on the five (5) classifiers shows that Multiboost Decision Stump exhibited the best accuracy of 94% and least MAE of 0.0604. the finding shows that the developed model when integrated into automated disease diagnostic workflows will provide an enhanced and flexible solution for quick prognosis of typhoid fever in children in the tropics with significant cost savings with respect to hospitalization. the finding presupposes that we can enhance the efficiency of typhoid fever management while ensuring the delivery of quality care to children in resource-limited settings. © 2025 IEEE.

关键词： Diseases

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An Education Program for data Driven Security with Machine learning Applications

An Education Program for Data Driven Security with Machine L...

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IEEE international conference on Big data Security on Cloud (BigdataSecurity), High Performance and Smart Computing (HPSC) and intelligent data and Security (IDS)

作者： Bhavani thuraisingham Kangkook Jee Latifur Khan The University of Texas Dallas

this paper discusses our plan for an education program for data driven security for machine learning applications. We discuss the motivation for such a program, details of the program, various course modules to be dev... 详细信息

ISBN: (数字)9798331595104

ISBN: (纸本)9798331595111

关键词： Education Machine learning data science Big data Security Computer crime

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