Relevant bibliographies by topics / Deployment error estimation

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Deployment error estimation'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Deployment error estimation"

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Gutierrez, Edgar Andres, Ivan Fernando Mondragon, Julian D. Colorado, and Diego Mendez Ch. "Optimal Deployment of WSN Nodes for Crop Monitoring Based on Geostatistical Interpolations." Plants 11, no. 13 (2022): 1636. http://dx.doi.org/10.3390/plants11131636.

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This paper proposes an integrated method for the estimation of soil moisture in potato crops that uses a low-cost wireless sensor network (WSN). Soil moisture estimation maps were created by applying the Kriging technique over a WSN composed of 11×11 nodes. Our goal is to estimate the soil moisture of the crop with a small-scale WSN. Using a perfect mesh approach on a potato crop, experimental results demonstrated that 25 WSN nodes were optimal and sufficient for soil moisture characterization, achieving estimations errors <2%. We provide a strategy to select the number of nodes to use in a
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Lin, Feilong, Wenbai Li, and Liyong Yuan. "Consensus-Based Sequential Estimation of Process Parameters via Industrial Wireless Sensor Networks." Sensors 18, no. 10 (2018): 3338. http://dx.doi.org/10.3390/s18103338.

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Process parameter estimation, to a large extent, determines the industrial production quality. However, limited sensors can be deployed in a traditional wired manner, which results in poor process parameter estimation in hostile environments. Industrial wireless sensor networks (IWSNs) are techniques that enrich sampling points by flexible sensor deployment and then purify the target by collaborative signal denoising. In this paper, the process industry scenario is concerned, where the workpiece is transferred on the belt and the parameter estimate is required before entering into the next pro
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Rolling, Craig A., Yuhong Yang, and Dagmar Velez. "COMBINING ESTIMATES OF CONDITIONAL TREATMENT EFFECTS." Econometric Theory 35, no. 6 (2018): 1089–110. http://dx.doi.org/10.1017/s0266466618000397.

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Estimating a treatment’s effect on an outcome conditional on covariates is a primary goal of many empirical investigations. Accurate estimation of the treatment effect given covariates can enable the optimal treatment to be applied to each unit or guide the deployment of limited treatment resources for maximum program benefit. Applications of conditional treatment effect estimation are found in direct marketing, economic policy, and personalized medicine. When estimating conditional treatment effects, the typical practice is to select a statistical model or procedure based on sample data. Howe
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Liu, Yu-Sun, Shingchern You, and Yu-Chun Lai. "Machine Learning-Based Channel Estimation Techniques for ATSC 3.0." Information 15, no. 6 (2024): 350. http://dx.doi.org/10.3390/info15060350.

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Channel estimation accuracy significantly affects the performance of orthogonal frequency-division multiplexing (OFDM) systems. In the literature, there are quite a few channel estimation methods. However, the performances of these methods deteriorate considerably when the wireless channels suffer from nonlinear distortions and interferences. Machine learning (ML) shows great potential for solving nonparametric problems. This paper proposes ML-based channel estimation methods for systems with comb-type pilot patterns and random pilot symbols, such as ATSC 3.0. We compare their performances wit
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Hsiao, Chiu-Han, Frank Yeong-Sung Lin, Hao-Jyun Yang, et al. "Optimization-Based Approaches for Minimizing Deployment Costs for Wireless Sensor Networks with Bounded Estimation Errors." Sensors 21, no. 21 (2021): 7121. http://dx.doi.org/10.3390/s21217121.

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As wireless sensor networks have become more prevalent, data from sensors in daily life are constantly being recorded. Due to cost or energy consumption considerations, optimization-based approaches are proposed to reduce deployed sensors and yield results within the error tolerance. The correlation-aware method is also designed in a mathematical model that combines theoretical and practical perspectives. The sensor deployment strategies, including XGBoost, Pearson correlation, and Lagrangian Relaxation (LR), are determined to minimize deployment costs while maintaining estimation errors below
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Park, Dongjoo, Soyoung You, Jeonghyun Rho, Hanseon Cho, and Kangdae Lee. "Investigating optimal aggregation interval sizes of loop detector data for freeway travel-time estimation and prediction." Canadian Journal of Civil Engineering 36, no. 4 (2009): 580–91. http://dx.doi.org/10.1139/l08-129.

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With recent increases in the deployment of intelligent transportation system (ITS) technologies, traffic management centers have the ability to obtain and archive large amounts of data regarding the traffic system. These data can then be employed in estimations of current conditions and the prediction of future conditions on the roadway network. In this paper, we propose a general solution methodology for the identification of the optimal aggregation interval sizes of loop detector data for four scenarios (i) link travel-time estimation, (ii) corridor / route travel-time estimation, (iii) link
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Madsen, Tatiana, Hans-Peter Schwefel, Lars Mikkelsen, and Annelore Burggraf. "Comparison of WLAN Probe and Light Sensor-Based Estimators of Bus Occupancy Using Live Deployment Data." Sensors 22, no. 11 (2022): 4111. http://dx.doi.org/10.3390/s22114111.

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Bus company operators are interested in obtaining knowledge about the number of passengers on their buses—preferably doing so at low deployment costs and in an automated manner, while keeping accuracy high. One solution, widely used in practice, involves deploying a light sensor-based system, counting the people entering and leaving the bus. The light sensor system is simple, but errors accumulate over time, because it is not capable of error correcting. For this reason, the light sensor-based system is compared to a WLAN probe-based system, which has entirely different characteristics. Inaccu
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McLoughlin, Benjamin, Harry Pointon, John McLoughlin, Andy Shaw, and Frederic Bezombes. "Uncertainty Characterisation of Mobile Robot Localisation Techniques using Optical Surveying Grade Instruments." Sensors 18, no. 7 (2018): 2274. http://dx.doi.org/10.3390/s18072274.

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Recent developments in localisation systems for autonomous robotic technology have been a driving factor in the deployment of robots in a wide variety of environments. Estimating sensor measurement noise is an essential factor when producing uncertainty models for state-of-the-art robotic positioning systems. In this paper, a surveying grade optical instrument in the form of a Trimble S7 Robotic Total Station is utilised to dynamically characterise the error of positioning sensors of a ground based unmanned robot. The error characteristics are used as inputs into the construction of a Localisa
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Rathore, Kapil Singh, Sricharan Vijayarangan, Preejith SP, and Mohanasankar Sivaprakasam. "A Multifunctional Network with Uncertainty Estimation and Attention-Based Knowledge Distillation to Address Practical Challenges in Respiration Rate Estimation." Sensors 23, no. 3 (2023): 1599. http://dx.doi.org/10.3390/s23031599.

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Respiration rate is a vital parameter to indicate good health, wellbeing, and performance. As the estimation through classical measurement modes are limited only to rest or during slow movements, respiration rate is commonly estimated through physiological signals such as electrocardiogram and photoplethysmography due to the unobtrusive nature of wearable devices. Deep learning methodologies have gained much traction in the recent past to enhance accuracy during activities involving a lot of movement. However, these methods pose challenges, including model interpretability, uncertainty estimat
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Aljohani, Nader, Tierui Zou, Arturo S. Bretas, and Newton G. Bretas. "Multi-Area State Estimation: A Distributed Quasi-Static Innovation-Based Model with an Alternative Direction Method of Multipliers." Applied Sciences 11, no. 10 (2021): 4419. http://dx.doi.org/10.3390/app11104419.

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In the modern power system networks, grid observability has greatly increased due to the deployment of various metering technologies. Such technologies enhanced the real-time monitoring of the grid. The collection of observations are processed by the state estimator in which many applications have relied on. Traditionally, state estimation on power grids has been done considering a centralized architecture. With grid deregulation, and awareness of information privacy and security, much attention has been given to multi-area state estimation. Considering such, state-of-the-art solutions conside
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Dissertations / Theses on the topic "Deployment error estimation"

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Sarr, Jean Michel Amath. "Étude de l’augmentation de données pour la robustesse des réseaux de neurones profonds." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS072.

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Dans cette thèse, nous avons considéré le problème de robustesse des réseaux de neurones. C’est-à-dire que nous avons considéré le cas où le jeu d’apprentissage et le jeu de déploiement ne sont pas indépendamment et identiquement distribués suivant la même source. On appelle cette hypothèse : l’hypothèse i.i.d. Notre principal outil de travail a été l’augmentation de données. En effet, une revue approfondie de la littérature et des expériences préliminaires nous ont montré le potentiel de régularisation de l’augmentation des données. Ainsi, dans un premier temps, nous avons cherché à utiliser
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Book chapters on the topic "Deployment error estimation"

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Agarwal, Krishnav. "Transforming Agricultural Management: A Multimodal Deep Learning Approach to Shelf Life Estimation." In Future Frontiers: Mastering the Latest in Computer Technology. QTanalytics India, 2025. https://doi.org/10.48001/978-81-980647-0-7-10.

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The UN reports that almost 700 million people can't afford food, while 1.3 billion tons of food is wasted yearly. The waste happens throughout the process and many factors contribute to it. The solution presented in this study utilizes a multimodal deep learning network to predict the decay of agricultural produce several days in advance and enables timely interventions to reduce spoilage and enhance food accessibility and affordability. A new set of data was collected to analyze the decay pattern of the selected agricultural produce. Raw tomatoes and strawberries were used for the study and w
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Ahmed, Qasim Zeeshan, and Lie-Liang Yang. "Comparative Study of Adaptive Multiuser Detections in Hybrid Direct-Sequence Time-Hopping Ultrawide Bandwidth Systems." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-5170-8.ch018.

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This chapter considers low-complexity detection in hybrid Direct-Sequence Time-Hopping (DS-TH) Ultrawide Bandwidth (UWB) systems. A range of Minimum Mean-Square Error (MMSE) assisted Multiuser Detection (MUD) schemes are comparatively investigated with emphasis on the low-complexity adaptive MMSE-MUDs, which are free from channel estimation. In this contribution, three types of adaptive MUDs are considered, which are derived based on the principles of Least Mean-Square (LMS), Normalized Least Mean-Square (NLMS), and Recursive Least-Square (RLS), respectively. The authors study comparatively th
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Guo, Cheng, R. Venkatesha Prasad, Jing Wang, Vijay Sathyanarayana Rao, and Ignas Niemegeers. "Localizing Persons Using Body Area Sensor Network." In Developments in Wireless Network Prototyping, Design, and Deployment. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1797-1.ch013.

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Context awareness is an important aspect in many ICT applications. For example, in an intelligent home network, location of the user enables session transfer, lighting, and temperature control, et cetera. In fact, in a body area sensor network (BASN), location estimation of a user helps in realizing realtime monitoring of the person (especially those who require help) for better health supervision. In this chapter the authors first introduce many localization methods and algorithms from the literature in BASNs. They also present classification of these methods. Amongst them, location estimatio
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Lin, Kai, Min Chen, Joel J. P. C. Rodrigues, and Hongwei Ge. "System Design and Data Fusion in Body Sensor Networks." In Advances in Healthcare Information Systems and Administration. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0888-7.ch001.

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Body Sensor Networks (BSNs) are formed by the equipped or transplanted sensors in the human body, which can sense the physiology and environment parameters. As a novel e-health technology, BSNs promote the deployment of innovative healthcare monitoring applications. In the past few years, most of the related research works have focused on sensor design, signal processing, and communication protocol. This chapter addresses the problem of system design and data fusion technology over a bandwidth and energy constrained body sensor network. Compared with the traditional sensor network, miniaturiza
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Conference papers on the topic "Deployment error estimation"

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Khalid, Md Meraj, Luisa Peterson, Edgar Ivan Sanchez Medina, and Kai Sundmacher. "Physics-Informed Graph Neural Networks for Modeling Spatially Distributed Dynamically Operated Processes." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.101576.

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Modeling process systems by use of partial differential equations is often complex and computationally expensive, especially for inverse problems such as optimization, state identification, or parameter estimation. Data-driven methods typically provide efficient alternatives with lower computational cost. One such method is Graph Neural Networks (GNNs), which can be used to model dynamical systems as graphs. However, dynamic GNNs often face challenges with extrapolation and representability. Integrating mechanistic insights in surrogate models can improve both prediction accuracy and interpret
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Guihen, Damien, and Peter King. "A model Of AUV survey feature resolution and error estimation for deployment optimization." In 2016 IEEE/OES Autonomous Underwater Vehicles (AUV). IEEE, 2016. http://dx.doi.org/10.1109/auv.2016.7778672.

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Wang, Yifan, Jannes Iatropoulos, Silvia Thal, and Roman Henze. "Enhancing Urban AEB Systems: Simulation-Based Analysis of Error Tolerance in Distance Estimation and Road-Tire Friction Coefficients." In 2024 Stuttgart International Symposium. SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2992.

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<div class="section abstract"><div class="htmlview paragraph">AEB systems are critical in preventing collisions, yet their effectiveness hinges on accurately estimating the distance between the vehicle and other road users, as well as understanding road conditions. Errors in distance estimation can result in premature or delayed braking and varying road conditions alter road-tire friction coefficients, affecting braking distances. The integration of advanced sensors like LiDARs has significantly enhanced distance estimation. Cameras and deep neural networks are also employed to est
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Mahboubi, Hamid, Mojtaba Vaezi, and Fabrice Labeau. "Distributed deployment algorithms in a network of nonidentical mobile sensors subject to location estimation error." In 2014 IEEE Sensors. IEEE, 2014. http://dx.doi.org/10.1109/icsens.2014.6985374.

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Figueroa, J., P. Baraldi, I. Chouybat, F. Ursini, E. Vignati, and E. Zio. "Estimation of Real-Time Bottomhole Parameters in CO2 Injection Wells During Operations by Means of an Ensemble of Neural Networks." In SPE Europe Energy Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/220048-ms.

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Abstract Effective management of CO2 storage operations in subsurface reservoirs demands real-time estimation of well performance to identify deviations from expected operating conditions and ensure compliance with the requirements of Carbon Capture and Storage monitoring plans. To overcome the limitations of analytical tools in accurately estimating bottomhole flowing parameters, particularly during two-phase flow, this work aims to develop an empirical mapping between monitored parameters at various well sections and the bottomhole flowing parameters. Specifically, an ensemble of neural netw
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Luciani, Sara, Stefano Feraco, Angelo Bonfitto, Andrea Tonoli, Nicola Amati, and Maurizio Quaggiotto. "A Machine Learning Method for State of Charge Estimation in Lead-Acid Batteries for Heavy-Duty Vehicles." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-68469.

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Abstract In the automotive framework, an accurate assessment of the State of Charge (SOC) in lead-acid batteries of heavy-duty vehicles is of major importance. SOC is a crucial battery state that is non-observable. Furthermore, an accurate estimation of the battery SOC can prevent system failures and battery damage due to a wrong usage of the battery itself. In this context, a technique based on machine learning for SOC estimation is presented in this study. Thus, this method could be used for safety and performance monitoring purposes in electric subsystem of heavy-duty vehicles. The proposed
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Banerjee, Amit, Issam Abu-Mahfouz, Jianyan Tian, and A. H. M. Esfakur Rahman. "A Robust Hybrid Machine Learning-Based Modeling Technique for Wind Power Production Estimates." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-94173.

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Abstract The need to accurately estimate wind power is essential to the design and deployment of individual wind turbines and wind farms. The estimation problem is framed as wind power curve modeling. Lately, machine learning techniques have been used to model power curves and provide power estimates. Such models rely on the fact that all outliers are removed from the raw wind data before they are used in modeling and estimation since outliers can adversely affect performance. However, generating outlier-free data is not always possible. Robust models and robust objective functions can be two
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Kalanovic, V. D., K. Padmanabhan, and C. H. Jenkins. "A Discrete Cell Model for Shape Control of Precision Membrane Antennae and Reflectors." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-0548.

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Abstract The potential for placing large precision reflectors in space is currently being investigated through the use of inflatable membrane structures. Their capacity for reducing launch mass and stowed volume is being exploited. However, on-orbit performance will require an understanding of the various influences on the deployment, inflation, and service of the membrane, and the associated effects on reflector surface precision. Nonlinear controllers developed to improve performance of such systems are often dependent on state estimation and parameter identification procedures. The existenc
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Booncharoen, Pichita, Thananya Rinsiri, Pakawat Paiboon, et al. "Pore Pressure Estimation by Using Machine Learning Model." In International Petroleum Technology Conference. IPTC, 2021. http://dx.doi.org/10.2523/iptc-21490-ms.

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Abstract In the past few years, over hundreds of wells were drilled in Gulf of Thailand, had faced with the depletion and lost circulation issues resulted from a lack of pressure data. A prior research of reservoir depletion pressure (Fangming, 2009) in oil field, China was obtained from multivariate statistic and regression by using density and neutron porosity log curves in logging-while-drilling data. However, the relative errors are 7.5% from the actual formation pressure. Thus, there are several latent variables in the model like drilling parameters (Rehm, 1971) which part of formation pr
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AL-Qutami, Tareq Aziz, and Fatin Awina Awis. "Personnel Real Time Tracking in Hazardous Areas Using Wearable Technologies and Machine Learning." In International Petroleum Technology Conference. IPTC, 2021. http://dx.doi.org/10.2523/iptc-21426-ms.

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Abstract Real-time location information is essential in the hazardous process and construction areas for safety and emergency management, security, search and rescue, and even productivity tracking. It's also crucial during pandemics such as the COVID-19 pandemic for contact tracing to isolate those who came to the proximity of infected individuals. While global positioning systems (GPS), can address the demand for location awareness in outdoor environments, another accurate location estimation technology for indoor environments where GPS doesn't perform well is required. This paper presents t
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