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Modelling and control of a multi-product pipeline transportation process

International Journal of Information technology (Singapore) 2025年 1-11页

作者： Jiménez-Cabas, Javier Piñeres-Espitia, Gabriel Rodelo-Serrano, Esteban Pamucar, Dragan Ramirez-Leon, Hermes Butt, Shariq Aziz Naz, Sumera Simic, Vladimir Computer Science and Electronic Department Universidad de La Costa Barranquilla Colombia Departamento de Ingeniería Mecánica Universidad Simón Bolívar Barranquilla Venezuela Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Industrial Engineering & Management Yuan Ze University Taoyuan Taiwan Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade 11010 Serbia Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea School of Systems and Technology Department of Computer Science University of Management and Technology Lahore Pakistan Department of Mathematics Division of Science and Technology University of Education Lahore Pakistan

One of the most critical processes in the petroleum industry is transporting crude oil and its derivatives. Usually, it is done, due to easiness and economic aspects, through pipelines;several products, such as gasoline, kerosene, naphtha, and liquefied petroleum gas, among others, are moved in batch processes to both distribution and storage centers or transport ships. Since these transport systems transport different products, many works have focused on developing optimization strategies for scheduling these pipelines. That is, strategies that seek to optimize the use of the pipeline by establishing at what time points certain products must be transported. These strategies would be located at the planning level in the automation pyramid. In this work, on the other hand, different control strategies are proposed for a pipeline, but at the operation level. These strategies would be located at the control level, below the planning strategies in the automation pyramid. A pipeline of three pipe sections is modeled to develop the proposed control strategies, and the mathematical model is validated with actual data. The results show that, first, the proposed model approximates the actual behavior of the pipeline quite well and, second, that the combination of PID and feedforward control is an excellent alternative to implement control strategies for the operation of liquid transportation systems through pipelines. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： control strategies liquid transportation modeling of system

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Human Activity Recognition in Smart Cities from Smart Watch Data using LSTM Recurrent Neural Networks 1

Human Activity Recognition in Smart Cities from Smart Watch ...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Kandpal, Meenakshi Sharma, Bhisham Barik, Rabindra K. Chowdhury, Subrata Patra, Sudhansu Shekhar Dhaou, Imed Ben KIIT Deemed to Be University School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India KIIT Deemed to Be University School of Computer Applications Bhubaneswar India MCA Department AP Chittoor India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia

ISBN: (纸本)9781665472753

Due to the convergence of healthcare and smart cities, information and technology are employed in health and medical procedures worldwide. Residents of smart cities now enjoy better lives and healthier bodies because to integration. The advent of smart watch technology and deep learning techniques has engendered a potential increase in performance and accuracy of such tasks. Our paper explores the application of recurrent neural networks particularly LSTM for action recognition task using smart watch sensor data, i.e., gyroscope and accelerometer data. We have taken into consideration some general activities like standing, walking, sitting, etc. and implemented a LSTM network for the action recognition job using the time sequences sensor data. This model can be useful for other health-based applications which can monitor the health conditions of a user by keeping record of his activities. © 2023 IEEE.

关键词： Watches

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Optimizing Algorithm Hyperparameters and Backbone of Single-shot Detector for Object Detection

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Sensors and Materials 2024年第1-3期36卷 337-350页

作者： Huang, Wei-Tai Chen, Yenming J. Tsai, Jinn-Tsong Ho, Wen-Hsien Department of Mechanical Engineering National Pingtung University of Science and Technology Pingtung912 Taiwan Department of Information Management National Kaohsiung University of Science and Technology Kaohsiung824 Taiwan Department of Computer Science and Artificial Intelligence National Pingtung University Pingtung900 Taiwan Department of Healthcare Administration and Medical Informatics Kaohsiung Medical University Kaohsiung807 Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung807 Taiwan College of Professional Studies National Pingtung University of Science and Technology Pingtung912 Taiwan

In this study, we explored a single-shot detector (SSD) backbone and its optimized algorithm hyperparameters for object detection, and proposed a systematic method for determining appropriate algorithm hyperparameter combinations for the SSD backbone. The VGG16 backbone for SSD has been used for object detection. The Resnet backbone won first place in the 2015 ImageNet Large Scale Visual Recognition Challenge (ILSVRC), while the VGG16 backbone ranked second place in the 2014 ILSVRC. We selected the Resnet50 backbone for SSD for vehicle image detection research because the Resnet50 backbone has a high feature extraction capability. We proposed SSD with the Resnet50 backbone and its optimized algorithm hyperparameters, called the SSD-Resnet50 model, to replace SSD with the VGG16 backbone and its optimized algorithm hyperparameters, called the SSD-VGG16 model, to enhance the vehicle image detection feature extraction capability. The Taguchi method optimized the algorithm hyperparameters of the Resnet50 and VGG16 backbones, thus improving the detection accuracies of the SSD-Resnet50 and SSD-VGG16 models, respectively. Experimental results show that the SSD-Resnet50 model using 300 × 300 × 3 input images achieved a detection accuracy of an average average precision (AP) of 97.15% in three independent experiments, outperforming the SSD-VGG16 model using 300 × 300 × 3 input images with an average AP of 86.83% on the test set of vehicle images. As a result, the SSD-Resnet50 model has a higher accuracy of vehicle detection in images from the Caltech cars 1999 and 2001 datasets. © 2024 M Y U Scientific Publishing Division. All rights reserved.

关键词： Taguchi methods

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Psychological Stress Classification Using Extreme Gradient Boosting Algorithm 15

Psychological Stress Classification Using Extreme Gradient B...

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15th International Conference on Information and Communication technology Convergence, ICTC 2024

作者： Sakanti, Maria Mahardini Siniaev, Viacheslav Amaris, Aurelia Luo, Win-Jet Suhartono Kuncoro, C. Bambang Dwi Graduate Institute of Precision Manufacturing National Chin-Yi University of Technology Taichung41170 Taiwan Institute of Applied Computer Science Itmo University St. Petersburg19710 Russia Universitas Brawijaya Faculty of Computer Science Department of Informatics Engineering Malang65145 Indonesia National Chin-Yi University of Technology Taichung41170 Taiwan National Chin-Yi University of Technology Department of Refrigeration Air Conditioning and Energy Engineering Taichung41170 Taiwan

ISBN: (纸本)9798350364637

Stress, as a reaction to threatening situations, can raise heart rate and result in serious conditions that might cause significant damage or even be life-threatening. Traditional methods for evaluating stress, which rely on subjective self-reporting and clinical assessments, often suffer from biases and inconsistencies. Artificial intelligence models have been explored to predict stress levels more accurately. This paper investigates the application of Extreme Gradient Boosting in classifying psychological stress using the WESAD dataset, which includes parameters such as acceleration, electrocardiogram, electromyography, electrodermal activity, temperature, and respiration. The dataset was balanced and sampled to create a manageable subset for experimental. Extreme Gradient Boosting was chosen for its efficiency and scalability in handling complex datasets. The model was trained and validated, achieving a 95% accuracy in predicting stress levels. This study highlights the potential of integrating Extreme Gradient Boosting models into wearable devices for real-time stress monitoring. Future work involves optimizing the model to utilize fewer sensors without decreasing accuracy, ensuring it can be integrated into portable/wearable systems using tiny microcontrollers. © 2024 IEEE.

关键词： Electromyography

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Spotted Hyena Optimizer Driven Deep Learning-Based Drug-Drug Interaction Prediction in Big Data Environment

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computer Systems Science & engineering 2023年第9期46卷 3831-3845页

作者： Mohammed Jasim Mohammed Jasim Shakir Fattah Kak Zainab Salih Ageed Subhi R.M.Zeebaree Engineering College Al-Kitab UniversityKirkukIraq Information Technology Department Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Computer Science Department College of ScienceNawroz UniversityDuhokIraq Energy Engineering Department Technical College of EngineeringDuhok Polytechnic UniversityDuhokIraq

Nowadays,smart healthcare and biomedical research have marked a substantial growth rate in terms of their presence in the literature,computational approaches,and discoveries,owing to which a massive quantity of experimental datasets was published and generated(Big Data)for describing and validating such ***-drug interaction(DDI)significantly contributed to drug administration and *** continues as the main obstacle in offering inexpensive and safe *** normally happens for patients with extensive medication,leading them to take many drugs *** may cause side effects,either mild or severe health *** reduced victims’quality of life and increased hospital healthcare expenses by increasing their recovery *** efforts were made to formulate new methods for DDI prediction to overcome this *** this aspect,this study designs a new Spotted Hyena Optimizer Driven Deep Learning based Drug-Drug Interaction Prediction(SHODL-DDIP)model in a big data *** the presented SHODL-DDIP technique,the relativity and characteristics of the drugs can be identified from different sources for *** input data is preprocessed at the primary level to improve its ***,the salp swarm optimization algorithm(SSO)is used to select *** this study,the deep belief network(DBN)model is exploited to predict the DDI *** SHO algorithm is involved in improvising the DBN model’s predictive outcomes,showing the novelty of the *** experimental result analysis of the SHODL-DDIP technique is tested using drug databases,and the results signified the improvements of the SHODLDDIP technique over other recent models in terms of different performance measures.

关键词： Drug-drug interaction deep learning spotted hyena optimization feature selection classification

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A Fused Machine Learning Approach for Intrusion Detection System

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computers, Materials & Continua 2023年第2期74卷 2607-2623页

作者： Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of *** interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and *** learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit *** intrusion detection system controls the flow of network traffic with the help of computer *** deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network *** this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network *** Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious *** proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

关键词： Fused machine learning heterogeneous network intrusion detection

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Optimization of Building Construction Control System Based on Nonlinear Image Enhancement

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computer-Aided Design and Applications 2023年 20卷 213-224页

作者： Liu, Congru Lin, Mingsen Ghafoor, Kayhan Zrar Al-Dabagh, Mustafa Zuhaer Nayef Karras, Dimitrios A. School of Urban Construction Wuhan University of Science and Technology Wuhan China Changjiang Polytechnic Wuhan China Department of Computer Science Knowledge University Erbil44001 Iraq Department of Software & Informatics Engineering Salahaddin University-Erbil Erbil44001 Iraq School of Science Dept. General Athens Greece

To reflect the nonlinear characteristics of the building structural adjustment system, an active vibration control strategy based on the nonlinear is proposed. In this method, the size of the structural control force is determined by comparing the allowable energy with the actual energy, and its direction is determined by taking the derivative of the energy. Because there is no need to solve the linear matrix inequality, the calculation is simple and the operation is convenient. Moreover, when multiple actuators with the same capacity limit are used, the control output force is consistent, thus avoiding the design of multiple control systems and simplifying the controller design. The simulation results show that the maximum controlled displacements of floor 6 to floor 8 are 1.16cm, 1.12cm, and 0.97cm, respectively. The proposed nonlinear energy control method has the advantages of simple control process and avoiding actuator saturation. © 2023 CAD Solutions, LLC.

关键词： Floors

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Design of mmWave Broadband Rotary Joint and 360° Beam-Steering Rotenna Based on Gap Waveguide technology

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IEEE Transactions on Antennas and Propagation 2025年

作者： Farahbakhsh, Ali Zarifi, Davood Mrozowski, Michal Gdansk University of Technology Faculty of Electronics Telecommunications and Informatics Department of Microwave and Antenna Engineering Gdansk80-233 Poland Graduate University of Advanced Technology Department of Electrical and Computer Engineering Kerman Iran University of Kashan School of Electrical and Computer Engineering Kashan Iran

This work presents the design and fabrication of a groove gap waveguide-based rotary joint and a rotenna (rotary antenna) with 360° mechanical beam-steering for wideband mmWave applications. The proposed rotary joint incorporates two WR-22 to groove gap waveguide transitions, connected back-to-back with an innovative transformer. A key advantage of gap waveguide technology is its ability to operate without physical contact between the rotary joint's components, allowing the rotor to rotate freely near the stator with an air gap, while wave leakage is suppressed. By replacing common coaxial connectors with standard waveguide flanges and introducing a novel transformer between the rotor and stator, we have successfully developed a wideband rotary joint suitable for mmWaves. To demonstrate the practical applicability of this concept, we designed a rotenna with 360° mechanical beam-steering. This design addresses the common limitations of electronically steerable antennas, such as limited steering range, gain degradation, and high cost and complexity. To validate the concept, prototypes of the proposed rotary joint and rotenna were fabricated and measured. The measured and simulated results show good agreement. The rotary joint exhibits a 44.4% fractional bandwidth over the frequency range of 35 to 55 GHz with an insertion loss below 0.68 dB. Additionally, the engineered rotenna demonstrates a peak realized gain of 13 dBi with no scan loss over the full 360° steering range. © 1963-2012 IEEE.

关键词： Steerable antennas

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Distributed intelligence for IoT-based smart cities: a survey

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Neural Computing and Applications 2024年第27期36卷 16621-16656页

作者： Hashem, Ibrahim Abaker Siddiqa, Aisha Alaba, Fadele Ayotunde Bilal, Muhammad Alhashmi, Saadat Mehmood Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates School of Computing College of Arts and Sciences Universiti Utara Malaysia Kedah Sintok06010 Malaysia Department of Computer Science Federal College of Education Zaria Kaduna Tudun Wada810282 Nigeria Department of Pharmaceutical Outcomes and Policy University of Florida GainesvilleFL32610 United States Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Petaling Jaya47500 Malaysia Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates

The remarkable miniaturization of Internet of Things (IoT)-based systems and the rise of distributed intelligence are promising research paradigms in the design of smart cities. IoT and distributed intelligence are conjoined. On the one hand, IoT provides a digital connection to everyday physical devices, and on the other hand, distributed intelligence allows these connected devices to work together in a distributed environment to solve complex problems through learning and reasoning. In this article, we investigate and report the recent premier advances in distributed intelligence for IoT-based smart cities. We use bibliometric techniques which provide essential insights into many studies to evaluate the literature including publication year, type, and citation status on distributed intelligence and its variations published between 2013 and 2023. We categorize and classify literature by devising a taxonomy based on essential parameters, technologies, approaches, applications, and implications with respect to distributed intelligence. Moreover, we provide recent ongoing advancements in distributed intelligence and IoT such as sustainability, blockchain, 5G technologies, AI, edge computing expansion and IoT expansion. We also highlight some of the use cases which include traffic management, energy management, waste management, public safety, and healthcare. We provide core layers and services for implementing distributed intelligence in IoT-based smart cities and present several research challenges and issues. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Smart city

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Integrating a Digital Twin Concept in the Zero Emission Sea Transporter (ZEST) Project for Sustainable Maritime Transport using Stonefish Simulator

arXiv

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arXiv 2024年

作者： Grimaldi, Michele Cernicchiaro, Carlo Rossides, George Ktoris, Angelos Yfantis, Elias Kyriakides, Ioannis Computer Vision and Robotics Institute University of Girona Girona Spain School of Engineering & Physical Sciences Heriot-Watt University United Kingdom Marine Robotics Innovation Centre Cyprus Marine and Maritime Institute Larnaca Cyprus Marine & Offshore Science Technology & Engineering Centre Cyprus Marine and Maritime Institute Larnaca Cyprus Marine Technology Division Cyprus Marine and Maritime Institute Larnaca Cyprus Computational Cognition Lab Open University of Cyprus Nicosia Cyprus Department of Engineering University of Nicosia Nicosia Cyprus

In response to stringent emission reduction targets imposed by theInternational Maritime Organization (IMO) and the European Green Deal\'s Fit for55 legislation package, the maritime industry has shifted its focus towardsdecarbonization. While significant attention has been placed on vesselsexceeding 5,000 gross tons (GT), emissions from coastal and short sea shipping,amounting to approximately 13% of global shipping transportation and 15% withinthe European Union (EU), have not received adequate consideration. Thisabstract introduces the Zero Emission Sea Transporter (ZEST) project, designedto address this issue by developing a zero-emissions multi-purpose catamaranfor short sea route © 2024, CC BY.

关键词： Emission control

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