Academic literature on the topic 'Deployment error estimation'
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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 (June 21, 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 WSN, to characterize the moisture behavior for spatio-temporal analysis of soil moisture in the crop. Finally, the implementation cost of this strategy is shown, considering the number of nodes and the corresponding margin of error.
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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 (October 6, 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 process stage. To this end, a consensus-based sequential estimation (CSE) framework is proposed which utilizes the co-design of IWSN and parameter state estimation. First, a group-based network deployment strategy, together with a TDMA (Time division multiple access)-based scheduling scheme is provided to track and sample the moving workpiece. Then, by matching to the tailored IWSN, the sequential estimation algorithm, which is based on the consensus-based Kalman estimation, is developed, and the optimal estimator that minimizes the mean-square error (MSE) is derived under the uncertain wireless communications. Finally, a case study on temperature estimation during the hot milling process is provided. The results show that the estimation error can be reduced to less than 3 ∘ C within a limited time period, although the measurement error can be more than 100 ∘ C in existing systems with a single-point temperature sensor.
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Rolling, Craig A., Yuhong Yang, and Dagmar Velez. "COMBINING ESTIMATES OF CONDITIONAL TREATMENT EFFECTS." Econometric Theory 35, no. 6 (November 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. However, combining estimates from the candidate procedures often provides a more accurate estimate than the selection of a single procedure. This article proposes a method of model combination that targets accurate estimation of the treatment effect conditional on covariates. We provide a risk bound for the resulting estimator under squared error loss and illustrate the method using data from a labor skills training program.
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Hsiao, Chiu-Han, Frank Yeong-Sung Lin, Hao-Jyun Yang, Yennun Huang, Yu-Fang Chen, Ching-Wen Tu, and Si-Yao Zhang. "Optimization-Based Approaches for Minimizing Deployment Costs for Wireless Sensor Networks with Bounded Estimation Errors." Sensors 21, no. 21 (October 27, 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 a given threshold. Moreover, the results significantly ensure the accuracy of the gathered information while minimizing the cost of deployment and maximizing the lifetime of the WSN. Furthermore, the proposed solution can be readily applied to sensor distribution problems in various fields.
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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 (April 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 travel-time forecasting, and (iv) corridor / route travel-time forecasting. This study applied cross validated mean square error (CVMSE) model for the link and route travel-time estimations, and a forecasting mean square error (FMSE) model for the link and corridor / route travel-time forecasting. These models were applied to loop detector data obtained from the Kyeongbu expressway in Korea. It was found that the optimal aggregation sizes for the travel-time estimation and forecasting were 3 to 5 min and 10 to 20 min, respectively.
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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 (May 28, 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. Inaccuracy with the WLAN estimator comes from a need to filter out mobile devices outside the bus and to map the number of detected devices to a number of people. The comparison is performed based on data collected from a real-life deployment in a medium sized German city. The comparison shows the trade-off in selecting either of the two methods. Furthermore, a novel approach for fusion of the light sensor and WLAN estimators is proposed which has a big potential in improving accuracy of both estimators. A fusion approach is proposed that utilizes the different error characteristics for error compensation by calculating compensation terms. The knowledge of Ground Truth is not required as part of this fusion approach for calibration; results show that the approach can find the optimal parameter settings and that it makes this occupancy estimation approach scalable and automated.
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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 (February 1, 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 estimation in the context of respiration rate estimation, and model compactness in terms of deployment in wearable platforms. In this direction, we propose a multifunctional framework, which includes the combination of an attention mechanism, an uncertainty estimation functionality, and a knowledge distillation framework. We evaluated the performance of our framework on two datasets containing ambulatory movement. The attention mechanism visually and quantitatively improved instantaneous respiration rate estimation. Using Monte Carlo dropouts to embed the network with inferential uncertainty estimation resulted in the rejection of 3.7% of windows with high uncertainty, which consequently resulted in an overall reduction of 7.99% in the mean absolute error. The attention-aware knowledge distillation mechanism reduced the model’s parameter count and inference time by 49.5% and 38.09%, respectively, without any increase in error rates. Through experimentation, ablation, and visualization, we demonstrated the efficacy of the proposed framework in addressing practical challenges, thus taking a step towards deployment in wearable edge devices.
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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 (July 13, 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 Localisation Extended Kalman Filter which fuses Pozyx Ultra-wideband range measurements with odometry to obtain an optimal position estimation, all whilst using the path generated from the remote tracking feature of the Robotic Total Station as a ground truth metric. Experiments show that the proposed method yields an improved positional estimation compared to the Pozyx systems’ native firmware algorithm as well as producing a smoother trajectory.
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Ukani, Neema Amish, and Saurabh S. Chakole. "Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective." Journal of Physics: Conference Series 2327, no. 1 (August 1, 2022): 012026. http://dx.doi.org/10.1088/1742-6596/2327/1/012026.
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Abstract Modelling of accurate detection & estimation soil moisture sensors requires integration of various signal processing, filtering, segmentation, and pattern analysis methods. Sensing of moisture is generally performed via use of resistive, or capacitive materials, which change their parametric characteristics w.r.t. changes in moisture levels. These sensors are further classified depending upon capabilities of measurements, which include, volumetric sensors, soil water tensor sensors, electromagnetic sensors, time domain reflectometry (TDR) sensors, Neutron probe sensors, tensiometer-based sensors, etc. Each of these sensors are connected to a series of processing blocks, which assist in improving their measurement performance. This performance includes parameters like, accuracy of measurement, cost of deployment, measurement delay, average measurement error, etc. This wide variation in measurement performance increases ambiguity of sensor selection for a particular soil type. Due to this, researchers & soil engineers are required to test & validate performance of different moisture sensors for their application scenario, which increases time & cost needed for model deployment. To overcome this limitation, and reduce ambiguity in selection of optimum moisture sensing interfaces, this text reviews various state-of-the-art models proposed by researchers for performing this task. This review discusses various nuances, advantages, limitations & future research scopes for existing moisture sensing interfaces and evaluates them in terms of statistical parameters like accuracy of detection, sensing & measurement delay, cost of deployment, deployment complexity, scalability, & type of usage applications. This text also compares the reviewed models in terms of these parameters, which will assist researchers & soil engineers to identify most optimum models for their deployments. Based on this research, it was observed that machine learning models are highly recommended for error reduction during moisture analysis. Machine learning prediction models that utilize Neural Networks (NNs) outperform other models in terms of error performance, and must be deployed for high-accuracy & low-cost moisture sensing applications. Based on similar observations, this text also recommends fusion of different sensing interfaces for improving accuracy, while optimizing cost & complexity of deployment. These recommendations are also based on context of the application for which the sensing interface is being deployed. These recommendations must be used to further improve overall sensing performance under multiple deployment scenarios.
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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 (May 13, 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 consider a weighted norm of residual measurement model, which might hinder masked gross errors contained in the null-space of the Jacobian matrix. Towards the solution of this, a distributed innovation-based model is presented. Measurement innovation is used towards error composition. The measurement error is an independent random variable, where the residual is not. Thus, the masked component is recovered through measurement innovation. Model solution is obtained through an Alternating Direction Method of Multipliers (ADMM), which requires minimal information communication. The presented framework is validated using the IEEE 14 and IEEE 118 bus systems. Easy-to-implement model, build-on the classical weighted norm of the residual solution, and without hard-to-design parameters highlight potential aspects towards real-life implementation.
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 l’augmentation de données pour rendre les réseaux de neurones plus robustes à divers glissements de données synthétiques et naturels. Un glissement de données étant simplement une violation de l’hypothèse i.i.d. Cependant, les résultats de cette approche se sont révélés mitigés. En effet, nous avons observé que dans certains cas l’augmentation de données pouvait donner lieu à des bonds de performance sur le jeu de déploiement. Mais ce phénomène ne se produisait pas à chaque fois. Dans certains cas, augmenter les données pouvait même réduire les performances sur le jeu de déploiement. Nous proposons une explication granulaire à ce phénomène dans nos conclusions. Une meilleure utilisation de l’augmentation des données pour la robustesse des réseaux de neurones consiste à générer des tests de résistance ou "stress test" pour observer le comportement d’un modèle lorsque divers glissements de données surviennent. Ensuite, ces informations sur le comportement du modèle sont utilisées pour estimer l’erreur sur l’ensemble de déploiement même sans étiquettes, nous appelons cela l’estimation de l’erreur de déploiement. Par ailleurs, nous montrons que l’utilisation d’augmentation de données indépendantes peut améliorer l’estimation de l’erreur de déploiement. Nous croyons que cet usage de l’augmentation de données permettra de mieux cerner quantitativement la fiabilité des réseaux de neurones lorsqu’ils seront déployés sur de nouveaux jeux de données inconnusIn this thesis, we considered the problem of the robustness of neural networks. That is, we have considered the case where the learning set and the deployment set are not independently and identically distributed from the same source. This hypothesis is called : the i.i.d hypothesis. Our main research axis has been data augmentation. Indeed, an extensive literature review and preliminary experiments showed us the regularization potential of data augmentation. Thus, as a first step, we sought to use data augmentation to make neural networks more robust to various synthetic and natural dataset shifts. A dataset shift being simply a violation of the i.i.d assumption. However, the results of this approach have been mixed. Indeed, we observed that in some cases the augmented data could lead to performance jumps on the deployment set. But this phenomenon did not occur every time. In some cases, the augmented data could even reduce performance on the deployment set. In our conclusion, we offer a granular explanation for this phenomenon. Better use of data augmentation toward neural network robustness is to generate stress tests to observe a model behavior when various shift occurs. Then, to use that information to estimate the error on the deployment set of interest even without labels, we call this deployment error estimation. Furthermore, we show that the use of independent data augmentation can improve deployment error estimation. We believe that this use of data augmentation will allow us to better quantify the reliability of neural networks when deployed on new unknown datasets
Book chapters on the topic "Deployment error estimation"
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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, 459–78. 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 the achievable Bit Error-Rate (BER) performance of these adaptive MUDs and of the ideal MMSE-MUD, which requires ideal knowledge about the UWB channels and the signature sequences of all active users. Both the advantages and disadvantages of the various adaptive MUDs are analyzed when communicating over indoor UWB channels modeled by the Saleh-Valenzuela (S-V) channel model. Furthermore, the complexity of the adaptive MUDs is analyzed and compared with that of the single-user RAKE receiver and also with that of the ideal MMSE-MUD. The study and simulation results show that the considered adaptive MUDs constitute feasible detection techniques for deployment in practical UWB systems. It can be shown that, with the aid of a training sequence of reasonable length, an adaptive MUD is capable of achieving a similar BER performance as the ideal MMSE-MUD while requiring a complexity that is even lower than that of a corresponding RAKE receiver.
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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, 273–89. 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 estimation using signal strength is one of the foremost. In indoor environments, the authors found that the signal strength based localization methods are usually not accurate, since signal strength fluctuates. The fluctuation in signal strength is due to deficient antenna coverage and multi-path interference. Thus, localization algorithms usually fail to achieve good accuracy. The authors propose to solve this problem by combining multiple receivers in a body area sensor network to estimate the location with a higher accuracy. This method mitigates the errors caused by antenna orientations and beam forming properties. The chapter evaluates the performance of the solution with experiments. It is tested with both range-based and range-free localization algorithm that we developed. The chapter shows that with spatial diversity, the localization accuracy is improved compared to using single receiver alone. Moreover, the authors observe that range-based algorithm has a better performance.
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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, 1–25. 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, miniaturization and low-power are more important to meet the requirements to facilitate wearing and long-running operation. As there are strong correlations between sensory data collected from different sensors, data fusion is employed to reduce the redundant data and the load in body sensor networks. To accomplish the complex task, more than one kind of node must be equipped or transplanted to monitor multi-targets, which makes the fusion process become sophisticated. In this chapter, a new BSNs system is designed to complete online diagnosis function. Based on the principle of data fusion in BSNs, we measure and investigate its performance in the efficiency of saving energy. Furthermore, the authors discuss the detection and rectification of errors in sensory data. Then a data evaluation method based on Bayesian estimation is proposed. Finally, the authors verify the performance of the designed system and the validity of the proposed data evaluation method. The chapter is concluded by identifying some open research issues on this topic.
Conference papers on the topic "Deployment error estimation"
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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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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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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 approach exploits a Genetic Algorithm (GA) in combination with Artificial Neural Networks (ANNs) for SOC estimation. Specifically, the training parameters of a Nonlinear Auto-Regressive with Exogenous inputs (NARX) ANN are chosen by the GA-based optimization. As a consequence of the GA-based optimization, the ANN-based SOC estimator architecture is defined. Then, the proposed SOC estimation algorithm is trained and validated with experimental datasets recorded during real driving missions performed by a heavy-duty vehicle. An equivalent circuit model representing the retained lead-acid battery is used to collect the training, validation and testing datasets that replicates the recorded experimental data related to electrical consumers and the cabin systems or during overnight stops in heavy-duty vehicles. This article illustrates the architecture of the proposed SOC estimation algorithm along with the identification procedure of the ANN parameters with GA. The method is able to estimate SOC with a low estimation error, being suitable for deployment on common on-board Battery Management Systems (BMS).
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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 effective ways to obtain accurate power curves in the presence of outliers. In this paper, a robust density-based clustering technique (DBSCAN) to first identify outliers in the dataset is proposed, followed by artificial neural network (ANN) models that are trained using the outlier-free data to obtain accurate power curve estimates. ANNs are trained using a range of optimization methods and are compared in this study. Preliminary results show the proposed method is superior to probabilistic models that use error-functions to generate accurate power curves and that the proposed hybrid model can generate more accurate power output estimations in the presence of outliers compared to deterministic models such as integrated curve fitting models that are known to be robust.
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Booncharoen, Pichita, Thananya Rinsiri, Pakawat Paiboon, Supaporn Karnbanjob, Sonchawan Ackagosol, Prateep Chaiwan, and Ouraiwan Sapsomboon. "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 pressure. From 2018 initiative model in Satun-Funan, the classification model was obtained by using mud gas, porosity, water saturation, net sand thickness, net-hydrocarbon-pore thickness and neutron-density separation. However, the limitation is drilling parameters could not account by classifier, and accurate only original pressure category. So, this study has expanded scope to include other reservoir properties and drilling parameters then applied with machine learning on offset well dataset by using three regressors such quantile, ridge and XGBoost regressors. The pore pressure estimation model aims to improve efficiency for making decision in execution phase, increasing confidence in perforation strategy. The model parameters, pay thickness, porosity, water saturation, original pressure from local pressure profile and total gas show are accounted into this model. As of regressor assumption, some facts are conducted to logarithm and perform 2nd polynomial feature for model flexibility. There are three steps for building model such as data manipulation, analysis and deployment. Two purposes of pressure prediction impact algorithm selection, for operational phase, quantile regressor is implemented to provide conservative prediction while Ridge or XGBoost regressors are alternatives for perforation strategy, provide mid case result of pressure prediction. Overall model performance was measured using root mean square error (RMSE) on train & test dataset which show approximately 1.2 and 1.5 ppg range of accuracy respectively from total 12 drilling projects in Pattani basin. Overall model fitting is within reasonable range of generalization capacity to apply with unknown data point (test set). The future model will continue to improve accuracy and manage imbalanced dataset between original pressure and depleted sands.
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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 existence of these procedures, within the control strategy, increases the size of the algorithms, limiting the system performance in real-time. The research presented has as a main objective to create an intelligent controller, based on feedback error learning, which is capable of extracting performance information from precision large membrane deployables, and subsequently using this information to achieve maximum surface precision. This paper presents a method to spatially discretize a doubly-curved membrane model into N = m × n spring-mass-damper cells. A recursive algorithm is developed and used in a simulator to predict the surface profile of the membrane. Each cell is connected to a feedback error learning controller in order to extract local state estimations. Simulation results are then compared to finite element predictions. The discrete cell model is shown to be simple enough for real-time control strategies, and potential methods for sensing/actuating to close the loop are discussed.
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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 the development and deployment of an end-to-end cost-effective real-time personnel location system suitable for both indoor and outdoor hazardous and safe areas. It leverages on facility wireless communication systems, wearable technologies such as smart helmets and wearable tags, and machine learning. Personnel carries the client device which collects location-related information and sends it to the localization algorithm in the cloud. When the personnel moves, the tracking dashboard shows client location in real-time. The proposed localization algorithm relies on wireless signal fingerprinting and machine learning algorithms to estimate the location. The machine learning algorithm is a mix of clustering and classification that was designed to scale well with bigger target areas and is suitable for cloud deployment. The system was tested in both office and industrial process environments using consumer-grade handphones and intrinsically safe wearable devices. It achieved an average distance error of less than 2 meters in 3D space.
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Ramos Gurjao, Kildare George, Eduardo Gildin, Richard Gibson, and Mark Everett. "Estimation of Far-Field Fiber Optics Distributed Acoustic Sensing DAS Response Using Spatio-Temporal Machine Learning Schemes and Improvement of Hydraulic Fracture Geometric Characterization." In SPE Hydraulic Fracturing Technology Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/209119-ms.
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Abstract Distributed Acoustic Sensing (DAS) is a fiber optics method that is revolutionizing the unconventional reservoir monitoring technology with substantial spatial coverage, high frequency data acquisition, and broad cable deployment options including hazardous/harsh environments compared to traditional geophysical methods such as point sensors (i.e., geophones). However, a single well equipped with fiber cannot acquire the far-field strain response since the sensitivity of this technique is restricted to a region near the monitor well. In this paper, we develop an Artificial Intelligence (AI) algorithm to estimate the magnitude of the far-field DAS response for any spatio-temporal input. Moreover, we identify a discontinuity in displacement results following fracture hit, which is interpreted as an effect of rock plastic deformation, and for the first time we demonstrate that it may be related to fracture width. Therefore, the output of our algorithm is used to estimate such geometric property along time in multiple locations. We generate the tangent displacement component (uy) (parallel to monitor well) using an in-house code based on Displacement Discontinuity Method (DDM). Several monitor wells are incorporated in the simulation of physical scenarios characterized by single and multiple hydraulic fractures. For each specific scenario we train and test an Artificial Neural Network (ANN) with position and time as input variables, and axial displacement as output. The Machine Learning (ML) model is designed with 7 hidden layers, 100 the maximum number of neurons per layer and hyperbolic tangent as activation function. Finally, predicted uy is used to: (1) obtain Distributed Acoustic Sensing (DAS) data deriving it sequentially in space and time; and (2) estimate fracture width based on discontinuity magnitude. Training stage is performed avoiding overfitting and minimizing ANN loss function. In the testing phase, error between true and predicted variables is negligible in the entire waterfall plot region, except at initial time steps where fracture treatment starts at operation well and magnitude of axial displacement collected at monitor well is very small on the order of 10-6 or even lower. In this case, we suspect that these tiny supervisor values may have minimal impact on the loss function, and consequently weights and biases of regression model are barely updated to consider the effect of such outputs. Regarding fracture width estimation, error reduces consistently along time at all locations reaching values near 0%. To the best of our knowledge this is the first work that creates a ML algorithm able to estimate strain fields generated during hydraulic fracturing treatments merely based on position and time inputs. The model developed with synthetic data is an incentive for the deployment of multiple monitor wells in the field to enhance beyond the near wellbore region geometric characterization of created fracture systems, and possibly identify critical patterns associated with fracture propagation that ultimately can lead to production optimization.
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Jenkins, C. H., V. D. Kalanovic, S. M. Faisal, K. Padmanabhan, and M. Tampi. "Adaptive Shape Control of Precision Membrane Antennae and Reflectors." In ASME 1998 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/imece1998-0951.
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Abstract A resurgence of interest in membrane structures in space is developing after a brief period of activity about two decades ago. This renewal is motivated in large part by the great potential for reduced launch mass and stowed volume that membrane structures can afford. Applications for such structures range from planar configurations in solar sails, concentrators, and shields, to inflatable lenticulars for radar, radio, and optical purposes. Three key factors are paramount for the success and user acceptance of this technology: deployment, longevity, and performance. Performance hinges critically on the precision of the membrane surface. Nonlinear controllers developed to improve performance of such systems are often dependent on state estimation and parameter identification procedures. The existence of these procedures, within the control strategy, increases the size of the algorithms, limiting the system performance in real-time. The research presented has as a main objective to create an intelligent controller, based on feedback error learning, which is capable of extracting performance information from precision large membrane deployables, and subsequently using this information to achieve maximum surface precision. This paper addresses the problem of modeling and controlling a class of nonlinear systems that can be considered as highly compliant structures, specifically planar and inflatable membranes, which are represented by complex nonlinear multi-variable models. Methods of noncontact local state estimation are considered in order to provide feedback on the membrane shape, which would then be coupled with a mathematical model of boundary perturbation and/or thermal effects for control efforts.
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Wani, Ankit, Jyotsana Singh, Deepa Kumari, Avinash Ithape, and Govind Rapanwad. "“FEV’s ‘CogniSafe’: An Innovative Deep Learning-Based AI Driver Monitoring System for the Future of Mobility”." In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2024. http://dx.doi.org/10.4271/2024-01-2012.
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<div class="section abstract"><div class="htmlview paragraph">Driver state monitoring is a crucial technology for enhancing road safety and preventing human error-caused accidents in the era of autonomous vehicles. This paper presents CogniSafe, a comprehensive driver monitoring system that uses deep learning and computer vision methods to detect various types of driver distractions and fatigue. CogniSafe consists of four modules: <b><i>Driver anomaly detection and classification</i></b>: A novel two-phase network that proposes and recognizes driver anomalies, such as texting, drinking, and adjusting radios, using multimodal and multiview input. <b><i>Gaze estimation</i></b>: A video-based neural network that jointly learns head pose and gaze dynamics, achieving robust and efficient gaze estimation across different head poses. <b><i>Eye state analysis</i></b>: A multi-tasking CNN that encodes features from both eye and mouth regions, predicting the percentage of eye closure (PERCLOS) and the frequency of mouth opening (FOM). <b><i>Head pose estimation</i></b>: A CNN-based method that estimates the head pose from a single face image, providing additional information for driver attention assessment. CogniSafe integrates the outputs of each module into a specific driver status measurement and produces the driver's level of alertness on a scale from 0 to 5. The paper evaluates the performance of each module on benchmark datasets and discusses the practicality, technicality, and pivotal role of CogniSafe in the development and deployment of autonomous vehicles. The paper contributes to the field of driver monitoring by providing a novel and comprehensive system that covers the most common types of driver distractions and fatigue using deep learning and computer vision methods. The paper also provides a comprehensive overview of the current state-of-the-art and the future challenges of driver monitoring systems for a safer, more connected future of mobility.</div></div>