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Development of a Modified Dummy Gas Lift Valve for Subsurface dept.-Resolved Microbiological Sampling and 3D Mapping

Development of a Modified Dummy Gas Lift Valve for Subsurfac...

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2024 SPE Water Lifecycle Management Conference and Exhibition, WLMC 2024

作者： McElhinney, J.M.W.R. Gomes, J.S. Hasan, A. Dias, J. BME Department Khalifa University Abu Dhabi United Arab Emirates Centre for Membranes and Advanced Water Technology Arzanah Building Khalifa University SAN Campus Abu Dhabi United Arab Emirates Computer and Electrical Engineering Dept. Khalifa University Abu Dhabi United Arab Emirates KU Center for Autonomous Robotic Systems

ISBN: (纸本)9781959025320

Objectives/Scope: This study outlines the development and use of a modified dummy gas lift valve designed to obtain subsurface microbiological samples from a defined dept. series across a production well. An overview of the initial field pilot is presented wherein microbiological data from the modified valve will be contrasted with topside samples to evaluate the information yielded from sampling downhole biofilm communities in place of planktonic populations from the well head. Methods, Procedures, Process: In this approach, the modified dummy gas lift valve, preloaded topside with carbonate plugs and coupons, is deployed at various dept.s in the well's completion for downhole incubation. This modification allows production fluids to wet the payloads, enabling in situ biofilm colonization by reservoir communities across dept. and temperature gradients. Post retrieval using a slickline tool, sample DNA is extracted for metagenomic sequencing, assessing microbial composition and metabolic capabilities (including souring and MIC-related activities). Concurrent production fluid samples from the wellhead will be analyzed for comparison to evaluate any information gained from biofilm over planktonic population sampling. Results, Observations, Conclusions: Samples from the modified valve are expected to provide sufficient yields of DNA, uncontaminated by non-resident microbes from outside of the producer. Microbial data from downhole biofilms is anticipated to provide a more diverse community makeup than production fluid communities sampled at the wellhead, with a corresponding increase in problematic species associated with MIC and souring. Corrosion coupon analyses will enable conclusions regarding proactive decision-making enablement and extrapolations of 3D projections of microbiological communities between modified valves across the completion column will provide the relationship between in situ conditions, microbial colonization in the context of pore space and risk

关键词： Decision making

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Auto Disturbance Rejection Control Based on A Neural Network Predictive State Observer

Auto Disturbance Rejection Control Based on A Neural Network...

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International Conference on Intelligent Human-Machine systems and Cybernetics, IHMSC

作者： Shengzhi Du BJ van Wyk NFS Nahri Chunling Tu Dept. Electrical Engineering Tshwane University of Technology Pretoria South Africa Dept. Computer Systems Engineering Tshwane University of Technology Pretoria South Africa

This paper addresses the auto disturbance rejection control (ADRC) for systems with time delay. ADRC is an effective control for most linear or nonlinear systems with noises and disturbances, because of its advantage on disturbance estimation and compensation. The extended state observer (ESO) is a critical component of ADRC. However, this observer is usually designed for systems without time delay. The time delay in a system could lead the ADRC unstable when the phase delay of the response cannot be ignored. This paper proposes a neural network based predictive state observer (NNPSO) to predict the response of systems with time delay. The simulation results validate the proposed method when systems with time delay are controlled.

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Power Loss Analysis of Energy Management System with Model Predictive Control in Mae Hong Son Microgrid

Power Loss Analysis of Energy Management System with Model P...

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IEEE International Conference on Power Electronics, Intelligent Control and Energy systems (ICPEICES)

作者： Nattakit Prapanwattana Donlapat Dechawatanapaisal David Banjerdpongchai Abhishek Rajan Dept. of Electrical Engineering Faculty of Engineering Center of Excellence in Intelligent Control Automation for Process Systems Chulalongkorn University Bangkok Thailand Dept. of Electrical Engineering National Institute of Technology Sikkim South Sikkim India

ISBN: (数字)9798350387193

ISBN: (纸本)9798350387209

Energy management system (EMS) is an important tool for energy efficiency and reliability of the power system. The optimal power dispatch of energy resources can be obtained using the nonlinear model predictive control (NMPC). It is formulated as an optimization problem subject to linear and nonlinear constraints. Although the existing system works well under the current conditions, power loss has not been considered. Power loss in transmission lines occurs due to their resistance and a high amount of power flow to loads connecting to the system. The power loss leads to decreased efficiency in the electrical system and reduces the lifespan of equipment because of the high temperature from loss. This paper aims to analyze the power loss in the Mae Hong Son (MHS) microgrid. To achieve the research objectives, we employ the EMS of MHS with NMPC and calculate power loss in transmission lines for a period of 7 days. EMS considers three objective functions, namely, total operating cost, total carbon dioxide emission, and combined economic and emission dispatch and take into account of three seasons consisting of rainy, winter, and summer.

关键词： Resistance Power transmission lines Microgrids Propagation losses Power system reliability Reliability Energy management systems Power generation Load flow Predictive control

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Federated Learning Model for Early Detection of Dementia Using Blood Biosamples

Federated Learning Model for Early Detection of Dementia Usi...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Elsersy, Mohamed Sherif, Ahmed Imam, Ahmad Abdel-Aliem Khan Mamun, Mohammad M. R. Khalil, Kasem Haitham, Mohamed Higher Colleges of Technology Computer and Information Systems Dept. Abu Dhabi United Arab Emirates University of Southern Mississippi School of Computing Sciences and Computer Engineering MS United States William Carey University College of Osteopathic Medicine Mississippi MS United States Tennessee Technological University Electrical and Computer Engineering Department TN United States University of Mississippi Electrical and Computer Engineering Department MS United States Cairo Egypt

ISBN: (纸本)9798350322347

Alzheimer's disease (AD) is a serious, long-term health problem that causes much pain and loss for the person with it and their family. Its early and accurate detection might result in a substantial reduction of the disease outcomes and consequences. Blood biosamples are a simple and inexpensive technique in medical testing. This paper proposes diagnostic models for blood biosamples based on federated learning (FL) and its modifications to detect AD early. Our experiments used blood biosample data sets from the ADNI website to evaluate our models. Our performance analysis indicates that our algorithms are more accurate and achieve an accuracy of 87% for early detection. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Robust Active and Reactive Power Control by using Single-Phase Grid connected H-Bridge Inverter

Robust Active and Reactive Power Control by using Single-Pha...

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International Smart Grid Workshop and Certificate Program (ISGWCP)

作者： Resul Coteli Korhan Kayisli Ruhi Zafer Caglayan Dept. of Energy Systems Engineering Technology Faculty Firat University Elazig Turkiye Dept. of Electrical- Electronic Engineering Engineering Faculty Gazi University Ankara Turkiye Dept. of Computer Engineering Engineering Faculty TED University Ankara Turkiye

ISBN: (数字)9798350361612

ISBN: (纸本)9798350361629

In a single-phase power network, inverters can be used to control the active and reactive power between sources. This task can be achieved by proper control of the current of the single-phase inverter. The current controllers using dq-frame components have a wide application area because of their superior features such as zero steady-state error and using the DC quantities. In this study, the active and reactive power between sources in a single-phase power system consisting of an AC voltage source and a DC voltage source are controlled via dq-frame based super twisting sliding mode (ST-SMC) current controller. For this aim, a Matlab/Simulink model is constructed. The model includes single phase H-bridge inverter, DC voltage source, AC voltage source, AC load, LCL filter and control unit. Two super twisting sliding mode controllers are used to control d and q-axis currents. Decoupling terms are added to output of each controller for independent control of active and reactive power. The simulation studies are performed to demonstrate the controller's ability to provide/absorb active and reactive power. To demonstrate the ability to provide/absorb active power, the d-axis current command is set to 10 A up to 2 s from the start of the simulation, and then, it is set to – 10 A between 2 s to 4 s. To demonstrate the ability to provide/absorb reactive power, the q-axis current command is set to 10 A up to 2 s from the start of the simulation, and then, it is set to – 10 A between 2 s to 4 s. The simulation results show that the controller provides/absorbs the desired active and reactive power quickly and without steady-state error.

关键词： Reactive power Reactive power control Simulation Inverters Mathematical models Steady-state Smart grids

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The Goldilocks Framework: Towards Selecting the Optimal Approach to Conducting AI Projects 22

The Goldilocks Framework: Towards Selecting the Optimal Appr...

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1st International Conference on AI engineering - Software engineering for AI, CAIN 2022

作者： Dzhusupova, Rimman Bosch, Jan Olsson, Helena Holmstrom Control & Safety Systems McDermott Dept. of Electrical & Instrumentation The Hague Netherlands Chalmers University of Technology Department of Computer Science and Engineering Gothenburg Sweden Malmö University Department of Computer Science and Media Technology Malmö Sweden

ISBN: (纸本)9781450392754

Artificial intelligence is increasingly becoming important to businesses since many companies have realized the benefits of applying Machine Learning (ML) and Deep Learning (DL) into their operations. Nevertheless, ML/DL technologies' industrial development and deployment examples are still rare and generally confined within a small cluster of large international companies who are struggling to apply ML more broadly and deploy their use cases at a large scale. Meanwhile, current AI market has started offering various solutions and services. Thus, organizations must understand how to acquire AI technology based on their business strategy and available resources. This paper discusses the industrial experience of developing and deploying ML/DL use cases to support organizations in their transformation towards AI. We identify how various factors, like cost, schedule, and intellectual property, can be affected by the choice of approach towards ML/DL project development and deployment within large international engineering corporations. As a research result, we present a framework that covers the trade-offs between those various factors and can support engineering companies to choose the best approach based on their long-term business strategies and, therefore, would help to accomplish their ML/DL project deployment successfully. © 2022 ACM.

关键词： Commerce

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SAR onboard Broadband C-band Circularly Polarized Antenna for In situ Volcanic Lava Observation 8

SAR onboard Broadband C-band Circularly Polarized Antenna fo...

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8th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2023

作者： Yoshimoto, Yuki Miyazaki, Takahiro Gao, Steven Pramono, Subuh Kashihara, Hisato Ito, Koichi Sumantyo, Josaphat Tetuko Sri Takahashi, Ayaka Santosa, Cahya Edi Naito, Motoyuki Sumitomo Metal Mining Co. Ltd. Computer Aided Engineering and Development Dept. Tokyo Japan Chiba University Dept. of Environmental Remote Sensing Chiba Japan The Chinese University of Hong Kong Dept. of Electronic Engineering Hong Kong Hong Kong Sebelas Maret University Dept. of Electrical Engineering Surakarta Indonesia Chiba University Center for Environmental Remote Sensing Chiba Japan Teikyo University Dept. of Aerospace Engineering Utsunomiya Japan National Research and Innovation Agency Research Center for Aeronautics Technology Bogor Indonesia

ISBN: (纸本)9798350393590

Estimating the volcanic eruption scale is important for mitigating eruption hazards. While the eruption scale can be explained by the eruption mode and its temporal variation, the dominant factor is expected to be the formation reaction of volcanic products, but there are still many unknowns, including the relationship with the thermal history. To solve this problem, we proposed a broadband circularly polarized microwave antenna for a synthetic aperture radar (SAR) sensor that is suitable for in situ observation of volcanic materials such as lava. As a result of fabricating the proposed antenna, the antenna characteristics were almost the same as the simulation one, with a center frequency of 5.394 GHz, IBW=32.6%, ARBW=23.7%, and Gain=12.0dBic. The measurement results indicate that the proposed antenna has loss and radiation characteristics suitable for the purpose. © 2023 IEEE.

关键词： Synthetic aperture radar

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Exploiting Partial Observability and Optimized Simple State Representations in Deep Q-Learning 20

Exploiting Partial Observability and Optimized Simple State ...

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20th International Conference on Frontiers of Information technology, FIT 2023

作者： Mahmood, Danyaal Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Applications & Nanostructure BCMaterials Basque Center for Materials Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

The advent of deep Q-learning has opened up new possibilities in training autonomous agents to perform intelligently in intricate settings. This research work examines the potential of deep Q-learning in the paradigmatic Snake game, which requires an agent to navigate a grid, ingest food items, and avoid collisions. We incorporated a partially observable game state, introducing a novel level of difficulty to the task. Our proposed approach utilizes a straightforward feed-forward neural network to estimate Q-values of potential actions, given the current state. The agent progressively learns to balance between exploration and exploitation, allowing it to navigate optimally in the game scenario. Most existing studies in deep Q-learning for the Snake game overlook the complexities posed by partial observability, focusing mainly on fully observable state representations. Furthermore, the majority of the investigations delve into the efficacy of complex neural network architectures, leaving a gap in the understanding of the performance of more simplistic feed-forward networks. Our study fills this gap by offering an in-dept. exploration and analysis of deep Q-learning strategies, especially when applied to partially observable game states and simple neural networks. Simulations and results reveal that adopting simpler state representations can significantly boost the agent's performance in the Snake game. A more detailed and intricate reward function is also observed to enhance the agent's decision-making skills and overall proficiency in the game. These observations underscore the significance of optimizing state representations and reward functions for maximizing the performance of deep Q-learning agents in the Snake game. © 2023 IEEE.

关键词： Autonomous agents

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Prediction of Polycystic Ovary Syndrome Using Genetic Algorithm-driven Feature Selection

Prediction of Polycystic Ovary Syndrome Using Genetic Algori...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Faridoon, Fatima Ali, Raja Hashim Ul Abideen, Zain Shahzadi, Nazia Ijaz, Ali Zeeshan Arshad, Usama Ali, Nisar Imad, Muhammad Nabi, Said Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

PolyCystic Ovary Syndrome (PCOS) is a hormonal disorder frequently found in women of reproductive age having a significant impact on the cause of infertility. It is an endocrine condition characterized by abnormalities in female hormone levels and aberrant synthesis of male hormones. This syndrome causes ovarian malfunction, increasing the risk of miscarriage and infertility. PCOS has a wide range of symptoms, making a diagnosis difficult. In this study, we proposed a feature selection method based on genetic algorithm with logistic regression to increase the accuracy of early diagnosis. A dataset containing the clinical and biochemical characteristics of 109 patients, including 36 with PCOS, is used. The genetic algorithm is used to extract the most important features from the dataset. Then, a logistic regression model is used for classification. The proposed model outperformed the baseline model that used all features, which had an accuracy of 86.2%, and produced a markedly improved accuracy of 95.4%. These results show that the proposed model effectively locates key characteristics for PCOS diagnosis. © 2023 IEEE.

关键词： Feature Selection

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Investigating novel machine learning based intrusion detection models for NSL-KDD data sets

Investigating novel machine learning based intrusion detecti...

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2023 International Conference on IT and Industrial Technologies, ICIT 2023

作者： Shah, Muhammad Huzaifa Abu Bakar, Muhammad Ali, Raja Hashim Ul Abideen, Zain Arshad, Usama Ijaz, Ali Zeeshan Ali, Nisar Imad, Muhammad Nabi, Said Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350315974

This study investigates the application of the Mutual Information (MI) feature selection technique to improve the accuracy of Machine Learning (ML) models on NSL-KDD datasets, building upon prior research. Six ML models, namely Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) with different kernels (1st, 2nd, and 3rd), are implemented for classification purposes. The proposed DT model in this study shows higher accuracy than the DT model proposed in the original paper by Ingre et al. for Intrusion Detection System (IDS). Additionally, a multi-class classification model for NSL-KDD datasets is developed, considering both normalized and non-normalized features. Interestingly, it is observed that the models trained without normalized features achieve higher accuracies compared to those trained with normalized features. Moreover, the study enhances the classification performance of the DT-based IDS using the Correlation based Feature Selection (CFS) technique for feature selection. The proposed IDS is evaluated both before and after feature selection for multi-class classification (normal and various attack types) and binary classification (normal and abnormal data). © 2023 IEEE.

关键词： Feature Selection

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