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检索条件"任意字段=International Conference on Measurement and Modeling of Computer Systems"

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computer Vision-Driven Precision Landing in Quadcopter Drones Using ArUco Markers 4

Computer Vision-Driven Precision Landing in Quadcopter Drone...

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4th international conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2024

作者： Putra, Karisma Trinanda Zidni, Hasan Mustar, Muhamad Yusvin Alfiansyah, Duta Fahri Ghifari, Rifqi Affan Talip, Mohamad Sofian Bin Abu Center of Artificial Intelligence and Robotics Studies Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Bantul Indonesia Universitas Negeri Yogyakarta Department of Electrical Engineering Education Yogyakarta Indonesia Universitas Muhammadiyah Yogyakarta Department of Electrical Engineering Bantul Indonesia University of Malaya Department of Electrical Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350378368

Precision landing systems for unmanned aerial vehicles (UAVs) present a significant challenge that must be addressed to prevent damage and accidents. Traditional object detection systems for precision landing often require complex computations and substantial processing power, which can be difficult to implement on the limited resources of a typical flight computer. This study proposes the use of computer vision technology, specifically ArUco markers, to detect and track a drone's landing pad. In this research, a precision landing system utilizing ArUco markers is implemented on a quadcopter drone. The system employs a webcam module to detect the ArUco marker on the landing pad and a computer vision algorithm to track it. A flight controller estimates the relative position and orientation of the quadcopter, enabling precise landing on the pad. The proposed methodology includes camera modeling and calibration, marker detection, marker pose estimation, and quadcopter control. Experimental results demonstrate a significant improvement in landing accuracy when compared to traditional GPS-based landing. The system achieved an average landing accuracy of 34.1 cm, compared to 121.1 cm with GPS alone, resulting in an 87% accuracy improvement. These findings highlight the effectiveness of the proposed ArUco marker-based precision landing system in enhancing the landing accuracy of UAVs. © 2024 IEEE.

关键词： Drones

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Enhancing Predictive modeling in High Dimensional Data Using Hybrid Feature Selection 5

Enhancing Predictive Modeling in High Dimensional Data Using...

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5th international conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Ambhika, C. Gayathri, S. Anish, T.P. Sheena, B. Gracelin Nalini, M. Siva Subramanian, R. Rmd Engineering College Dept of Aiml India S.A. Engineering College Department of Computer Science and Engineering India Rmk College of Engg and Tech Dept of Cse India Sathyabama Institute of Science and Technology India S A Engineering College Dept of Cse India

ISBN: (纸本)9798350379945

Healthcare data has become invaluable for patients care enhancement as well as outcomes which have been influenced by the expanding volume and complexity of health informatics. The current research discusses the design of a hybrid feature selection(HFS) methodology which is based on a combination of filter & wrapper methods to detect the necessary variables for predictive modeling using machine learning algorithms. The study takes the data set on Parkinson's disease as a use case. The performance of KNN, Decision Trees, and Random Forest models is assessed based on this data set. Findings reveal that integration of feature selection impacts models' efficacy, where Random Forest performs best among all techniques. KNN helped to lower the total number of errors but to increase sensitivity, while the Decision Trees (DT) applied changes not only to sensitivity but to specificity also. The findings bring feature selection into the limelight making healthcare data analysis more revealing in disease prediction and personalized medicine. The application of filter and wrapper methods by providers makes the data exploring process in a high-dimensional data easier and useful in getting actionable insights for improved patient care. Additional studies on feature selection methods and network architecture evolution should be carried out in order to increase the efficiency of medical data mining and enhance predictive model accuracy. © 2024 IEEE.

关键词： Personalized medicine

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IMC Based PID Controller for Spherical Tank Liquid Level Control 1

IMC Based PID Controller for Spherical Tank Liquid Level Con...

引用

1st international conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Kadu, C.B. Bhosale, V.P. Vikhe, P.S. Mandhare, V.V. Pravara Rural Engineering College Department of Instrumentation & Control Engineering Loni India Pravara Rural Engineering College Department of Computer Engineering Loni India

ISBN: (纸本)9798350355611

In the process industries, it's hard to control a non-linear process. Nonlinear behavior is frequently seen in real processes. The challenging problem of controlling a spherical tank is result of its nonlinearity caused by the continuously varying cross section. An IMC-based PID controller is employed to improve efficiency of controlling the level of spherical tanks which exhibit nonlinear dynamics. For processes up to second-order with dead time and first-order numerator dynamics, a single tuning parameter will be offered. The process model is determined employing mathematical modeling and standard step response-based system identification. The laboratory model of an interacting and non-interacting nonlinear system is introduced, which is capable of performing more complex tasks. To verify the effectiveness of the suggested IMC based PID controller, results of real-time experiments and a simulation study on spherical tank level control are incorporated. © 2025 IEEE.

关键词： Three term control systems

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Dynamic Monitoring of a Steel Footbridge Based on computer Vision Techniques 10th

Dynamic Monitoring of a Steel Footbridge Based on Computer V...

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10th international Operational Modal Analysis conference (IOMAC)

作者： Ponsi, Federico Buoli, Edoardo Varzaneh, Ghita Eslami Bassoli, Elisa Ghirelli, Giorgia Briseghella, Bruno Vincenzi, Loris Univ Bologna Dept Civil Chem Environm & Mat Engn I-40126 Bologna Italy Univ Modena & Reggio Emilia Dept Engn Enzo Ferrari I-41125 Modena Italy Fuzhou Univ Coll Civil Engn Joint Int Res Lab Deteriorat & Control Costal & M Fuzhou 350108 Peoples R China

ISBN: (纸本)9783031614279;9783031614255;9783031614248

The field of Structural Health Monitoring (SHM) aims to manage the built heritage, buildings and infrastructures, to first and foremost avoid collapse, detect early sign of damage and plan maintenance. As regards bridges and footbridges, structural health conditions are tightly coupled with modal properties, attainable from structural vibrations. The assessment of vibrations is traditionally obtained with measuring systems composed of sensors directly installed on the structure. Despite the large diffusion of these systems, their use may be unfeasible due to the necessity of an expensive and time-consuming installation of the acquisition system. A promising approach to characterize the footbridge dynamic behavior is represented by computer vision-based techniques, that require the installation of only one or more cameras together with, if necessary, some targets on the monitored structure. The vision-based approach is totally non-invasive, low-cost and enables the direct measurement of structural displacements. The aim of the present paper is to investigate the potential of vision-based techniques for the dynamic identification of structures, analyzing some preliminary results of dynamic tests performed on a steel footbridge. Structural vibrations caused by a jumping pedestrian were acquired from a camera as well as by an accelerometer-based monitoring system installed for comparison purposes. The paper presents and discusses the post-processing of the video frames with particular emphasis on vertical dynamic movements related to bending and torsional modes, comparing vision-based results with data obtained from accelerometers.

关键词： Vision-based monitoring Structural vibrations Steel footbridge

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Advanced Architectures and Management Strategies for Energy Efficiency in IoT systems 4

Advanced Architectures and Management Strategies for Energy ...

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4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Singh, Anupam George, Remya P. Ahmad, Nazia Hussain, Sajithunisa Khan, Rubina Liyakat Quraishi, Suhail Javed Graphic Era Hill University Department of Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to be University Department of Computer Science and Engineering Uttarakhand Dehradun India Applied College Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Mriirs Haryana Faridabad India

ISBN: (纸本)9798350387490

Internet of Things (IoT) systems penetration has brought tremendous innovations in different areas from homes to industries. But, the upsurge in the usage of IOT devices to can lead to a massive demand for energy consumptions hence call for new solutions on how this energy may be used efficiently. In the current study, the authors focus on the state of the art solutions and approaches to the energy management in IoT context. It offers an understanding of how power optimisations in hardware designs are done, including in low-power SoC solutions and in more potent STT-MRAM architectures that decrease power usage at the macro component level. Pertaining to conservation, the study also reviews the energy-savvy architectures of the network, a new system of access point, and routing protocols that employ the least energy. In addition, the methods of use of renewable energy resources for IoT operation and energy harvesting techniques are also proposed. On the management front, the paper presents overall frameworks on the methods for developing EMS, utilization of big data technology for energy use measurement and control in real-time. It also looks into techniques of demand response management and load managing that reduces fluctuations in energy demand. Discussed here are some of the simple yet effective solutions for controlling and reducing energy consumption in buildings where actual performance evaluations are made by showing marked enhancement in the systems and substantial energy saving. The following are the research propositions and avenues for future research, and the emerging technologies that could be applied in the future to influence the energy efficiency of IoT systems. © 2024 IEEE.

关键词： MRAM devices

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Increasing Detection Rate of False Data Injection Attacks Using measurement Predictors 11

Increasing Detection Rate of False Data Injection Attacks Us...

引用

11th IEEE international conference on Smart Energy Grid Engineering, SEGE 2023

作者： Senarathna, Danushka Tragoudas, Spyros Wibbenmeyer, Jason Khdeer, Nasser Southern Illinois University School of Electrical Computer and Biomedical Engineering CarbondaleIL United States Ameren Corporation Missouri St CharlesMO United States

ISBN: (纸本)9798350340716

A novel machine learning-based approach for the detection of False Data Injection Attacks (FDIAs) is presented. Existing machine learning-based FDIA detection methods exhibit a higher percentage of false negatives that may compromise the power grid's reliability. The proposed approach practically eliminates false negatives through the combination of a measurement classifier and measurement predictors. The detection rate increases drastically by detecting attacked measurement vectors in parallel using these two orthogonal mechanisms. Experimental results obtained using the IEEE 14-bus system support the effectiveness of the presented approach. © 2023 IEEE.

关键词： Long short-term memory

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Deep Learning and ANN for Forecasting of Flood in Satluj River at Kasol, Himachal Pradesh, (India) 1

Deep Learning and ANN for Forecasting of Flood in Satluj Riv...

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1st international conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart systems, ISTEMS 2024

作者： Sharma, Anshu Sharma, Abhinav Pant, Utkarsh Graphic Era Hill University Dehradun India Dehradun India Computer Science Engineering Dehradun India

ISBN: (纸本)9798350384246

In the last few decades, a significant increase in of high intensity rainfalls in India, is witnessed, leading to floods in the affecting catchment areas. This has been a motivation for researchers to develop early and accurate flood forecasting techniques to prevent the losses to the human lives, agriculture, communication networks and infrastructure etc. In the present work, one such technique has been developed for prediction of flood using the supervised statistical modeling of Rainfall-Runoff records of Satluj River, India. Statistical modeling for flood forecasting, has been proven to be an effective technique in multiple literatures, as with its use, typical hydrological relationships among the variables for any catchment area is not required to be known. Deep Learning or Deep Neural Networks (DNN) is much suitable technique for analyzing big data of high variety so suits well for flood forecasting, which makes use of continuous large data received from multiple sensors. Auto Correlation (ACF), Partial Correlation (P ACF) and Cross Correlation (CCF) of the rainfall data have been calculated for selection of the Input Vector (IV). The lead-times of 1 to 7 days are taken for performance evaluation along with the statistical indices-correlation coefficient, percentage error in peak flow estimation and root mean squared Error (RMSE). The results of deep learning (DNN) forecasting models depict quite significant improvement in the performance in terms of accuracy and precision as compared to some other conventional and simple techniques used in hydrological researches. The DNN model performance observes deterioration with increased lead time. © 2024 IEEE.

关键词： Floods

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DecMEN: Scalable measurement and Impairment Framework for Network Characterization in 5G(+)

DecMEN: Scalable Measurement and Impairment Framework for Ne...

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2024 IEEE international Performance, Computing, and Communications conference, IPCCC 2024

作者： Mondal, Niladri Niggemeyer, Mario Welzel, Simon Klingler, Florian TU Ilmenau Dept. of Computer Science Germany

ISBN: (纸本)9798350367942

The fifth generation (5G) is the current state-of-the-art mobile communication system that has features for ensuring reliability and throughput being key ingredients for many application domains. A major distinction of applications using cellular networks is that they employ different requirements regarding the QoS, e.g., latency and data rates for communication. When adapting network protocol to the underlying communication system for increasing the reliability of applications, reproducible tests and measurements of network characteristics are of utmost importance to streamline a systematic development process of those applications. In this paper, we present a framework called Decentralized measurement and Emulation of Networks (DecMEN) which is a versatile, decentralized and scalable tool for reproducible measurement and emulation of network characteristics of 5G and beyond networks. Along presenting a novel architecture for scalable measurements and network emulation, we perform a feasibility study on a private 5G campus network to showcase our developed framework in a real world setting. To assess the performance, we empirically evaluate the results of measurements of our 5G network and the emulation of network characteristics to observe differences of important networking metrics consisting of communication latency, and data rate. Our study reveals promising results, where the evaluation shows a good match of network characteristics between the real 5G network under test and the emulated 5G network. © 2024 IEEE.

关键词： 5G mobile communication systems

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Optimization of Ethanol Production Using State-Space modeling and Optimal Control Technology 13

Optimization of Ethanol Production Using State-Space Modelin...

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13th international conference on Dependable systems, Services and Technologies, DESSERT 2023

作者： Markina, Lyudmila Palchevskyi, Bogdan Hrudetskyi, Roman Smoliankin, Oleh Melnychuk, Yuliia Khrystynets, Nataliya Lutsk National Technical University Department of Automation and Computer-Integrated Technologies Lutsk Ukraine Lutsk National Technical University Department of Digital Educational Technologies Lutsk Ukraine Lutsk National Technical University Department of Computer Engineering and Cyber Security Lutsk Ukraine

ISBN: (纸本)9798350396119

in present paper we develop the automatic optimal control of the production process of dough mixture preparation, that can be used in the production of alcohol and fuel ethanol from grain raw materials. Known methods of automatic control of the dough preparation process are based on measuring a consumption of raw materials and a concentration of dough and regulating the water supply. We offer an improved method based on robust stabilization nonlinear process control that will ensure more stable operation of the system, smooth transitions of regimes and economy of used energy, improvement of a quality of the mixture dough, as well as final ethanol product. The optimal operating modes of experimental installation were investigated using state-space dynamical modelling technic and ethanol production process simulation. © 2023 IEEE.

关键词： Water supply

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Multiscan Shape Estimation for Extended Object Tracking 26

Multiscan Shape Estimation for Extended Object Tracking

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26th international conference on Information Fusion, FUSION 2023

作者： López, Michael Ernesto Brekke, Edmund Mester, Rudolf Stahl, Annette Department of Engineering Cybernetics Trondheim Norway Department of Computer Science Trondheim Norway

ISBN: (纸本)9798890344854

Extended Object Tracking (EOT) is a advantageous technique for achieving situational awareness in autonomous vehicle systems. The EOT problem is to both estimate the movement and spatial dimensions of an object using high-resolution measurements. In the case of laser measurements or other types of measurements that correspond to points on the object's boundary, the true measurement model of the EOT problem is based on an implicit equation for the measurement coordinates. This intrinsic implicity is often not addressed directly in several EOT models found in the literature. In this paper, the EOT problem is reformulated as a least square minimization problem without compromising the original implicit measurement model by introducing an extra variable for each measurement. In addition, this new least squares formulation allows considering measurements and state variables for a whole time window, and not just a single time step. An EOT algorithm based on solving the derived least squares minimization problem is proposed and tested with simulated scenarios. © 2023 international Society of Information Fusion.

关键词： Inference engines

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