Relevant bibliographies by topics / Sensors selection

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Sensors selection'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Sensors selection"

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Wijaya, Tomi, Wahyu Caesarendra, Tegoeh Tjahjowidodo, Bobby K. Pappachan, Arthur Wee, and Muhammad Izzat Roslan. "A Review on Sensors for Real-time Monitoring and Control Systems on Machining and Surface Finishing Processes." MATEC Web of Conferences 159 (2018): 02034. http://dx.doi.org/10.1051/matecconf/201815902034.

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One of the key components in real-time monitoring and control on machining and surface finishing processes are sensors. The advances of such system have triggered interesting questions on sensor selection that act as the fundamental before starting a project. This paper is made to review and answer the questions surrounding sensor selection. The paper first explains on the type of sensors commonly used in practice for real-time monitoring and control systems. After which, the paper discusses on how often the sensors are used on several machining and surface finishing processes and what are the reasons for the sensor selection. Thereafter, a review on the type features commonly analysed through these sensors is discussed. The paper expects reader would decide better upon selecting sensors and has a better direction in their project. Thus the paper works to guide reader to improve based on what has been completed before.
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Juboor, Saed Sa’deh, Sook-Ling Chua, and Lee Kien Foo. "Informative sensor selection on clustered sensors." Journal of Physics: Conference Series 1192 (March 2019): 012057. http://dx.doi.org/10.1088/1742-6596/1192/1/012057.

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Wentao, Shi, Chen Dong, Zhou Lin, Bai Ke, and Jin Yong. "Sensor Selection Scheme considering Uncertainty Disturbance." Journal of Sensors 2022 (February 16, 2022): 1–11. http://dx.doi.org/10.1155/2022/2488907.

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In multisensor cooperative detection network, some random disturbances, energy carried by sensor, distance between target and sensor node, and so on all affect the sensor selection scheme. To effectively select some sensors for detecting the target, a novel sensor selection method considering uncertainty disturbance is proposed under constraints of estimation accuracy and energy consumption. Firstly, the sensor selection problem is modeled as a binary form optimization problem with a penalty term to minimize the number of sensors. Secondly, some factors (precision, energy, and distance, etc.) affecting the sensor selection scheme are analyzed and quantified, and energy consumption matrix and estimation precision threshold are given by matrix tra‘nsformation. Finally, the problem of minimizing sensor number after relaxation is solved by convex optimization method, obtaining sensor selection scheme by discretization and legitimization of the suboptimal solution after convex relaxation. Simulation results show that the proposed algorithm can ensure the minimum number of sensors, improving accuracy of state estimation and saving network energy.
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Reeves, J., R. Remenyte-Prescott, and J. Andrews. "Sensor selection for fault diagnostics using performance metric." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 233, no. 4 (October 10, 2018): 537–52. http://dx.doi.org/10.1177/1748006x18804690.

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As technology advances, modern systems are becoming increasingly complex, consisting of large numbers of components, and therefore large numbers of potential component failures. These component failures can result in reduced system performance, or even system failure. The system performance can be monitored using sensors, which can help to detect faults and diagnose failures present in the system. However, sensors increase the weight and cost of the system, and therefore, the number of sensors may be limited, and only the sensors that provide the most useful system information should be selected. In this article, a novel sensor performance metric is introduced. This performance metric is used in a sensor selection process, where the sensors are chosen based on their ability to detect faults and diagnose failures of components, as well as the effect the component failures have on system performance. The proposed performance metric is a suitable solution for the selection of sensors for fault diagnostics. In order to model the outputs that would be measured by the sensors, a Bayesian Belief Network is developed. Sensors are selected using the performance metric, and sensor readings can be introduced in the Bayesian Belief Network. The results of the Bayesian Belief Network can then be used to rank the component failures in order of likelihood of causing the sensor readings. To illustrate the proposed approach, a simple flow system is used in this article.
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Mehmood, Zahid, Ibraheem Haneef, and Florin Udrea. "Material selection for optimum design of MEMS pressure sensors." Microsystem Technologies 26, no. 9 (October 30, 2019): 2751–66. http://dx.doi.org/10.1007/s00542-019-04601-1.

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Abstract Choice of the most suitable material out of the universe of engineering materials available to the designers is a complex task. It often requires a compromise, involving conflicts between different design objectives. Materials selection for optimum design of a Micro-Electro-Mechanical-Systems (MEMS) pressure sensor is one such case. For optimum performance, simultaneous maximization of deflection of a MEMS pressure sensor diaphragm and maximization of its resonance frequency are two key but totally conflicting requirements. Another limitation in material selection of MEMS/Microsystems is the lack of availability of data containing accurate micro-scale properties of MEMS materials. This paper therefore, presents a material selection case study addressing these two challenges in optimum design of MEMS pressure sensors, individually as well as simultaneously, using Ashby’s method. First, data pertaining to micro-scale properties of MEMS materials has been consolidated and then the Performance and Material Indices that address the MEMS pressure sensor’s conflicting design requirements are formulated. Subsequently, by using the micro-scale materials properties data, candidate materials for optimum performance of MEMS pressure sensors have been determined. Manufacturability of pressure sensor diaphragm using the candidate materials, pointed out by this study, has been discussed with reference to the reported devices. Supported by the previous literature, our analysis re-emphasizes that silicon with 110 crystal orientation [Si (110)], which has been extensively used in a number of micro-scale devices and applications, is also a promising material for MEMS pressure sensor diaphragm. This paper hence identifies an unexplored opportunity to use Si (110) diaphragm to improve the performance of diaphragm based MEMS pressure sensors.
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Kulkarni, Amol, Janis Terpenny, and Vittaldas Prabhu. "Sensor Selection Framework for Designing Fault Diagnostics System." Sensors 21, no. 19 (September 28, 2021): 6470. http://dx.doi.org/10.3390/s21196470.

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In a world of rapidly changing technologies, reliance on complex engineered systems has become substantial. Interactions associated with such systems as well as associated manufacturing processes also continue to evolve and grow in complexity. Consider how the complexity of manufacturing processes makes engineered systems vulnerable to cascading and escalating failures; truly a highly complex and evolving system of systems. Maintaining quality and reliability requires considerations during product development, manufacturing processes, and more. Monitoring the health of the complex system while in operation/use is imperative. These considerations have compelled designers to explore fault-mechanism models and to develop corresponding countermeasures. Increasingly, there has been a reliance on embedded sensors to aid in prognosticating failures, to reduce downtime, during manufacture and system operation. However, the accuracy of estimating the remaining useful life of the system is highly dependent on the quality of the data obtained. This can be enhanced by increasing the number of sensors used, according to information theory. However, adding sensors increases total costs with the cost of the sensors and the costs associated with information-gathering procedures. Determining the optimal number of sensors, associated operating and data acquisition costs, and sensor-configuration are nontrivial. It is also imperative to avoid redundant information due to the presence of additional sensors and the efficient display of information to the decision-maker. Therefore, it is necessary to select a subset of sensors that not only reduce the cost but are also informative. While progress has been made in the sensor selection process, it is limited to either the type of the sensor, number of sensors or both. Such approaches do not address specifications of the required sensors which are integral to the sensor selection process. This paper addresses these shortcomings through a new method, OFCCaTS, to avoid the increased cost associated with health monitoring and to improve its accuracy. The proposed method utilizes a scalable multi-objective framework for sensor selection to maximize fault detection rate while minimizing the total cost of sensors. A wind turbine gearbox is considered to demonstrate the efficacy of the proposed framework.
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Abbas, Jabbar, Amin Al-Habaibeh, and Dai Zhong Su. "Sensor Fusion for Condition Monitoring System of End Milling Operations." Key Engineering Materials 450 (November 2010): 267–70. http://dx.doi.org/10.4028/www.scientific.net/kem.450.267.

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This paper describes the utilisation of multi sensor fusion model using force, vibration, acoustic emission, strain and sound sensors for monitoring tool wear in end milling operations. The paper applies the ASPS approach (Automated Sensor and Signal Processing Selection) method for signal processing and sensor selection [1]. The sensory signals were processed using different signal processing methods to create a wide range of Sensory Characteristic Features (SCFs). The sensitivity of these SCFs to tool wear is investigated. The results indicate that the sensor fusion system is capable of detecting machining faults in comparison to a single sensor using the suggested approach.
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Chodorek, Agnieszka, Robert Ryszard Chodorek, and Paweł Sitek. "Response Time and Intrinsic Information Quality as Criteria for the Selection of Low-Cost Sensors for Use in Mobile Weather Stations." Electronics 11, no. 15 (August 7, 2022): 2448. http://dx.doi.org/10.3390/electronics11152448.

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Smart-city management systems use information about the environment, including the current values of weather factors. The specificity of the urban sites requires a high density of weather measurement points, which forces the use of low-cost sensors. A typical problem of devices using low-cost sensors is the lack of legalization of the sensors and the resulting inaccuracy and uncertainty of measurement, which one can attempt to solve by additional sensor calibration. In this paper, we propose a different approach to this problem, i.e., the two-stage selection of sensors, carried out on the basis of both the literature (pre-selection) and experiments (actual selection). We formulated the criteria of the sensor selection for the needs of the sources of weather information: the major one, which is the fast response time of a sensor in a cyber-physical subsystem and two minor ones, which are based on the intrinsic information quality dimensions related to measurement information. These criteria were tested by using a set of twelve weather sensors from different manufacturers. Results show that the two-stage sensor selection allows us to choose the least energy consuming (due to the major criterion) and the most accurate (due to the minor criteria) set of weather sensors, and is able to replace some methods of sensor selection reported in the literature. The proposed method is, however, more versatile and can be used to select any sensors with a response time comparable to electric ones, and for the application of low-cost sensors that are not related to weather stations.
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Su, Shen, Yanbin Sun, Xiangsong Gao, Jing Qiu, and Zhihong Tian. "A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection." Applied Sciences 9, no. 3 (January 28, 2019): 437. http://dx.doi.org/10.3390/app9030437.

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Selecting the right features for further data analysis is important in the process of equipment anomaly detection, especially when the origin data source involves high dimensional data with a low value density. However, existing researches failed to capture the fact that the sensor data are usually correlated (e.g., duplicated deployed sensors), and the correlations would be broken when anomalies occur with happen to the monitored equipment. In this paper, we propose to capture such sensor data correlation changes to improve the performance of IoT (Internet of Things) equipment anomaly detection. In our feature selection method, we first cluster correlated sensors together to recognize the duplicated deployed sensors according to sensor data correlations, and we monitor the data correlation changes in real time to select the sensors with correlation changes as the representative features for anomaly detection. To that end, (1) we conducted curve alignment for the sensor clustering; (2) we discuss the appropriate window size for data correlation calculation; (3) and adopted MCFS (Multi-Cluster Feature Selection) into our method to adapt to the online feature selection scenario. According to the experiment evaluation derived from real IoT equipment, we prove that our method manages to reduce the false negative of IoT equipment anomaly detection of 30% with almost the same level of false positive.
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Guan, Fei, Wei-Wei Cui, Lian-Feng Li, and Jie Wu. "A Comprehensive Evaluation Method of Sensor Selection for PHM Based on Grey Clustering." Sensors 20, no. 6 (March 19, 2020): 1710. http://dx.doi.org/10.3390/s20061710.

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Sensor selection plays an essential and fundamental role in prognostics and health management technology, and it is closely related to fault diagnosis, life prediction, and health assessment. The existing methods of sensor selection do not have an evaluation standard, which leads to different selection results. It is not helpful for the selection and layout of sensors. This paper proposes a comprehensive evaluation method of sensor selection for prognostics and health management (PHM) based on grey clustering. The described approach divides sensors into three grey classes, and defines and quantifies three grey indexes based on a dependency matrix. After a brief introduction to the whitening weight function, we propose a combination weight considering the objective data and subjective tendency to improve the effectiveness of the selection result. Finally, the clustering result of sensors is obtained by analyzing the clustering coefficient, which is calculated based on the grey clustering theory. The proposed approach is illustrated by an electronic control system, in which the effectiveness of different methods of sensor selection is compared. The result shows that the technique can give a convincing analysis result by evaluating the selection results of different methods, and is also very helpful for adjusting sensors to provide a more precise result. This approach can be utilized in sensor selection and evaluation for prognostics and health management.
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Dissertations / Theses on the topic "Sensors selection"

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Jean, Paul Bambanza. "iSEE:A Semantic Sensors Selection System for Healthcare." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-59635.

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The massive use of Internet-based connectivity of devices such as smartphones and sensors has led to the emergence of Internet of Things(IoT). Healthcare is one of the areas that IoT-based applications deployment is becoming more successful. However, the deployment of IoT in healthcare faces one major challenge, the selection of IoT devices by stakeholders (for example, patients, caregivers, health professionals and other government agencies) given an amount of available IoT devices based on a disease(for ex-ample, Asthma) or various healthcare scenarios (for example, disease management, prevention and rehabilitation). Since healthcare stakeholders currently do not have enough knowledge about IoT, the IoT devices selection process has to proceed in a way that it allows users to have more detailed information about IoT devices for example, Quality of Service (QoS) parameters, cost, availability(manufacturer), device placement and associated disease. To address this challenge, this thesis work proposes, develops and validates a novel Semantic sEnsor sElection system(iSEE) for healthcare. This thesis also develops iSEE system prototype and Smart Healthcare Ontology(SHO). A Java application is built to allow users for querying our developed SHO in an efficient way.The iSEE system is evaluated based on query response time and the result-set for the queries. Further, we evaluate SHO using Competency Questions(CQs). The conducted evaluations show that our iSEE system can be used efficiently to support stakeholders within the healthcare domain.
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De, Mel Geeth R. "Intelligent resource selection for sensor-task assignment : a knowledge-based approach." Thesis, University of Aberdeen, 2014. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=215104.

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Today, sensing resources play a crucial role in the success of critical tasks such as border monitoring and surveillance. Although there are various types of resources available, each with different capabilities, only a subset of these resources are useful for a specific task. This is due to the dynamism in tasks' environment and the heterogeneity of the resources. Thus, an effective mechanism to select resources for tasks is needed so that the selected resources cater for the needs of the tasks. Though a considerable amount of research has already been done in different communities to efficiently allocate resources to tasks, we argue that there is little work done to guarantee the effectiveness of the section with respect to the context of operation. In this thesis, we propose a knowledge-based approach in which the context of operation is introduced to the resource selection process. First, we present a formalism to represent a sensor domain. We then introduce sound and complete mechanisms through which effective resource solutions for tasks are discovered. An extension to the representation is then proposed so that the agility in resource selection is increased. Finally, we present an architecture whereby a multitude of such knowledge bases are exposed as services so that a coalition can fully benefit from its networked resources; a query language – and its semantics – to discover appropriate service collections for user requirements are also presented. We have evaluated our work through controlled experiments and critical arguments. Through these evaluations, we have shown that our approach can indeed improve the resource selection process and can augment resource allocation mechanisms. Our approach is general in that, it can be applied in many other domains.
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Riedmann, Michael. "Band selection using hyperspectral data from airborne and satellite sensors." Thesis, University of Southampton, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398728.

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Ferré, Baldrich Joan. "Experimental design applied to the selection of samples and sensors in multivariate calibration." Doctoral thesis, Universitat Rovira i Virgili, 1998. http://hdl.handle.net/10803/9020.

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Els models de calibratge multivariant relacionen respostes instrumentals (per exemple, espectres) d'un conjunt de mostres de calibratge amb quantitats de variables físiques o químiques tals com concentració d'analit, o índexs (per exemple, el nombre d'octà en gasolines). Aquesta relació es fa servir per predir aquestes quantitats a partir de les respostes instrumentals de noves mostres desconegudes, mesurades de la mateixa manera.

La predicció emprant models de calibratge multivariants està esdevenint un pas comú en els procediments analítics. Per tant, l'habilitat del model de donar prediccions precises i no esbiaixades té una influència decisiva en la qualitat del resultat analític. És important que les mostres de calibratge i els sensors es triïn adequadament de manera que els models pugin representar adequadament el fenomen en estudi i assegurar la qualitat de les prediccions.

En aquesta tesi s'ha estudiat la selecció de mostres de calibratge d'un a llista de mostres candidates en regressió sobre components principals (PCR) i la selecció de longituds d'ona en el model de mínims quadrats clàssics (CLS). El fonament l'ha donat la teoria del disseny estadístic d'experiments.

En PCR, el nombre mínim de mostres de calibratge es tria emprant les respostes instrumentals de les mostres candidates. La concentració d'analit només cal determinar-la en les mostres seleccionades. S'han proposat diferents usos del criteri d'optimalitat D.

En CLS, s'han interpretat diferents criteris per la selecció de longituds d'ona des del punt de vista de l'el·lipsoide de confiança de les concentracions predites. Els criteris també s'han revisat de manera crítica d'acord amb el seu efecte en la precisió, exactitud i veracitat (que s'han revisat d'acord amb les definicions ISO). Basat en la teoria del disseny d'experiments, s'han donat les regles per a la selecció de sensors. A demés, s'ha proposat un nou mètode per a detectar i reduir el biaix en les prediccions de noves mostres predites mitjançant CLS.

Conclusions
1. Criteris d'optimalitat del disseny d'experiment en MLR s'han aplicat per triar longituds d'ona de calibratge en CLS i el nombre mínim de mostres de calibratge en MLR i PCR a partir de les respostes instrumentals o scores de components principals d'una llista de candidats. Aquests criteris són un alternativa a (i/o complementen) el criteri subjectiu de l'experimentador. Els models construïts amb els punts triats per aquests criteris tenen una menor variància dels coeficients o concentracions i una millor habilitat de predicció que els models construïts amb mostres triades aleatòriament.

2. El criteri D s'ha emprat amb èxit per triar mostres de calibratge en PCR i MLR, per triar un grup reduït de mostres per a comprovar la validesa de models de PCR abans d'estandarditzar-los i per triar longituds d'ona en CLS a partir de la matriu de sensibilitats. Les mostres de calibratge que són D òptimes generalment donen models de PCR i MLR amb una millor habilitat de predicció que quan les mostres de calibratge es trien aleatòriament o emprant l'algorisme de Kennard-Stone

3. Cal emprar algorismes d'optimització per trobar, els subconjunts de I punts òptims entre una llista de N candidats. En aquest treball es van emprar els algorismes de Fedorov, de Kennard-Stone i algorismes genètics.

4. L'el·lipsoide de confiança de les concentracions estimades i la teoria del disseny d'experiments proporcionen el marc per interpretar l'efecte dels sensors triats amb aquests criteris en els resultats de predicció del model i per definir noves regles per triar longituds d'ona.

5. L'eficàcia dels criteris de selecció en CLS basats en la matriu de calibratge necessiten que no hi hagi biaix en la resposta dels sensors triats. La qualitat de les dades s'ha de comprovar abans de que s'empri el mètode de selecció de longituds d'ona.

6. La senyal analítica neta (NAS) és important pera comprendre el procés de quantificació en CLS i la propagació dels errors a les concentracions predites. S'han emprat diagnòstics tals com la sensibilitat, selectivitat i el gràfic de regressió del senyal analític net (NASRP), que es basen en el NAS d'un analit particular. S'ha vist que la norma del NAS està relacionada amb l'error de predicció.

7. El NASRP és una eina per a detectar gràficament si la resposta mesurada de la mostra desconeguda segueix el model calculat. La concentració estimada és el pendent de la recta ajustada als punts de gràfic. plot. Els sensors amb biaix es poden detectar i els sensors que segueixen el model es poden triar emprant la funció indicador d'Error i un mètode de finestres mòbils.
Multivariate calibration models relate instrumental responses (e.g. spectra) of a set of calibration samples to the quantities of chemical or physical variables such as analyte concentrations, or indexes (e.g. octane number in fuels). This relationship is used to predict these quantities from the instrumental response data of new unknown samples measured in the same manner.

Prediction using multivariate calibration models is becoming one common step in the analytical procedure. Therefore, the ability of the model to give precise and unbiased predictions has a decisive influence on the quality of the analytical result. It is important that the calibration samples and sensors be carefully selected so that the models can properly represent the phenomenon under study and assure the quality of the predictions.

We have studied the selection of calibration samples from the list of all the available samples in principal component regression (PCR) and the selection of wavelengths in classical least squares (CLS). The underlying basis has been given by experimental design theory.

In PCR, the minimum number of calibration samples are selected using the instrumental responses of the candidate samples. The analyte concentration is only determined in the selected samples. Different uses of the D-criterion have also been proposed.

In CLS, different criteria for wavelength selection have been interpreted from the point of view of the experimental design using the confidence hyperellipsoid of the predicted concentrations. The criteria have also been critically reviewed according to their effect on precision, accuracy and trueness (which are revised following ISO definitions). Based on the experimental design theory, new guidelines for sensor selection have been given. Moreover, a new method for detecting and reducing bias in unknown samples to be analyzed using CLS.

Conclusions
1. Optimality criteria derived from experimental design in MLR have been applied to select calibration wavelengths in CLS and the minimum number of calibration samples in MLR and PCR from the instrumental responses or principal component scores of a list of candidates. These criteria are an alternative (and/or a complement) to the experimenter's subjective criterion. The models built with the points selected with the proposed criteria had a smaller variance of the coefficients or concentrations and better predictive ability than the models built with the samples selected randomly

2. The D-criterion has been successfully used for selecting calibration samples in PCR and MLR, for selecting a reduced set of samples to assess the validity of PCR models before standardization and for selecting wavelengths in CLS from the matrix of sensitivities. D optimal calibration samples generally give PCR and MLR models with a better predictive ability than calibration samples selected randomly or using the Kennard-Stone algorithm.

3. Optimization algorithms are needed to find the optimal subsets of I points from a list of N candidates. Fedorov's algorithm, Kennard-Stone algorithm and Genetic Algorithms were studied here.

4. The confidence ellipsoid of the estimated concentrations and the experimental design theory provide the framework for interpreting the effect of the sensors selected with these criteria on the prediction results of the model and for deriving new guidelines for wavelength selection.

5. The efficacy of the selection criteria in CLS based on the calibration matrix requires there to be no bias in the response at the selected sensors. The quality of the data must be checked before a wavelength selection method is used.

6. The net analyte signal (NAS) is important to understand the quantification process in CLS and the propagation of errors to the predicted concentrations. Diagnostics such as sensitivity, selectivity and net analyte signal regression plots (NASRP) which are based on the NAS for each particular analyte have been used. The norm of the NAS has been found to be related to the prediction error .

7. The NASRP is a tool for graphically detecting whether the measured response of the unknown sample follows the calculated model. The estimated concentration is the slope of the straight line fitted to the points in this plot. The sensors with bias can be detected and the sensors that best follow the model can be selected using the Error Indicator function and a moving window method.
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Johnson, Jeremy Ryan. "Fault propagation timing analysis to aid in the selection of sensors for health management systems." Diss., Rolla, Mo. : University of Missouri--Rolla i.e. [Missouri University of Science and Technology], 2008. http://scholarsmine.mst.edu/thesis/pdf/Johnson_09007dcc804bcda7.pdf.

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Thesis (M.S.)--Missouri University of Science and Technology, 2008.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed May 19, 2008) Degree granted by Missouri University of Science and Technology, formerly known as University of Missouri--Rolla. Includes bibliographical references (p. 39-41).
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Polyzos, Dimitrios. ""Measuring System Properties & Structured Diagnostics for the Selection of Sensors, Actuators Placement & Eigenstructure Assignment"." Thesis, City University London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.524712.

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Akpolat, Hacer. "Improvement of Tomato Breeding Selection Capabilities using Vibrational Spectroscopy and Prediction Algorithms." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1574812034661898.

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Lei, Hua. "Modeling and Data Analysis of Conductive Polymer Composite Sensors." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1577.pdf.

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Segal, Aleksandr V. "Iterative Local Model Selection for tracking and mapping." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:8690e0e0-33c5-403e-afdf-e5538e5d304f.

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The past decade has seen great progress in research on large scale mapping and perception in static environments. Real world perception requires handling uncertain situations with multiple possible interpretations: e.g. changing appearances, dynamic objects, and varying motion models. These aspects of perception have been largely avoided through the use of heuristics and preprocessing. This thesis is motivated by the challenge of including discrete reasoning directly into the estimation process. We approach the problem by using Conditional Linear Gaussian Networks (CLGNs) as a generalization of least-squares estimation which allows the inclusion of discrete model selection variables. CLGNs are a powerful framework for modeling sparse multi-modal inference problems, but are difficult to solve efficiently. We propose the Iterative Local Model Selection (ILMS) algorithm as a general approximation strategy specifically geared towards the large scale problems encountered in tracking and mapping. Chapter 4 introduces the ILMS algorithm and compares its performance to traditional approximate inference techniques for Switching Linear Dynamical Systems (SLDSs). These evaluations validate the characteristics of the algorithm which make it particularly attractive for applications in robot perception. Chief among these is reliability of convergence, consistent performance, and a reasonable trade off between accuracy and efficiency. In Chapter 5, we show how the data association problem in multi-target tracking can be formulated as an SLDS and effectively solved using ILMS. The SLDS formulation allows the addition of additional discrete variables which model outliers and clutter in the scene. Evaluations on standard pedestrian tracking sequences demonstrates performance competitive with the state of the art. Chapter 6 applies the ILMS algorithm to robust pose graph estimation. A non-linear CLGN is constructed by introducing outlier indicator variables for all loop closures. The standard Gauss-Newton optimization algorithm is modified to use ILMS as an inference algorithm in between linearizations. Experiments demonstrate a large improvement over state-of-the-art robust techniques. The ILMS strategy presented in this thesis is simple and general, but still works surprisingly well. We argue that these properties are encouraging for wider applicability to problems in robot perception.
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Martins, Juliano Araújo. "Dados hiperespectrais para predição do teor foliar de nitrogênio em cana-de-açúcar." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/11/11152/tde-03052016-191304/.

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Uma das alternativas bastante abordada na literatura para a melhoria do gerenciamento da adubação nitrogenada nas culturas é o sensoriamento remoto, tendo destaque a utilização de sensores espectrais na região do visível e infravermelho. Neste trabalho, buscou-se estabelecer as relações existentes entre variações no teor foliar de nitrogênio (TFN) e a resposta espectral da folha de cana-de-açúcar, utilizando um sensor hiperespectral, com avaliações em três áreas experimentais do estado de São Paulo, com diferentes solos e variedades. Cada experimento foi alocado em blocos ao acaso, com parcelas subdividas e quatro repetições. Foram aplicadas doses de 0, 50, 100 e 150 kg de nitrogênio por hectare. A análise espectral foi realizada na folha \"+1\" em laboratório, sendo coletadas 10 folhas por subparcela, estas foram posteriormente submetidas a análise química para o TFN. Observou-se que existe correlação significativa entre o TFN e as variações na resposta espectral da cana-de-açúcar, sendo que a região do verde e de transição entre o vermelho e o infravermelho próximo (\"red-edge\") foram as mais consistentes e estáveis entre as áreas em estudo e safras avaliadas. A análise de componentes principais permitiu reforçar estes resultados, uma vez que as pontuações (\"scores\") dos componentes que apresentaram correlações significativas com o TFN, tiveram maiores pesos (\"loadings\") nas regiões espectrais citadas anteriormente. A partir das curvas espectrais foram também realizados os cálculos dos índices de vegetação já descritos em literatura, e estes submetidos a análise de regressão simples para predição do TFN, sendo os modelos calibrados com dados da safra 2012/13 e validados com os dados da safra 2013/14. Índices espectrais calculados com a combinação dos comprimentos de onda do verde e/ou \"red-edge\" com comprimentos de onda do infravermelho próximo tiveram bom desempenho na fase de validação, sendo que os cinco mais estáveis foram os índices BNi (500, 705 e 750 nm), GNDVI (550 e 780 nm), NDRE (790 e 720 nm), RI-1db (735 e 720 nm) e VOGa (740 e 720 nm). A variedade SP 81 3250 foi cultivada nas três áreas experimentais, o que permitiu a comparação do potencial de modelos calibrados por área, com um modelo generalista para uma mesma variedade cultivada em diferentes condições edáficas. Observou-se que embora o modelo generalista apresente parâmetros estatísticos significativos, existe redução expressiva da sensibilidade de predição quando comparado aos modelos calibrados por área experimental. Empregou-se também nesta pesquisa a análise de regressão linear múltipla por \"stepwise\" (RLMS) que gerou modelos com boa precisão na estimativa do TFN, mesmo quando calibrados por área experimental, independentes da variedade, utilizando de 5 a 6 comprimentos de onda. Concluímos com a presente pesquisa que comprimentos de onda específicos estão associados a variação do TFN em cana-de-açúcar, e estes são reportados na região do verde (próximos a 550 nm) e na região de transição entre os comprimentos de onda do vermelho e infravermelho próximo (680 a 720 nm). Apesar da baixa correlação entre a região do infravermelho próximo com o TFN, índices de vegetação calculados a partir destes comprimentos de onda ou a inserção destes na geração de modelos lineares foram importantes para melhorar a precisão da predição.
An alternative method, quite cited in literature to improve nitrogen fertilization management on crops is the remote sensing, highlighted with the use of spectral sensors in the visible and infrared region. In this work, we sought to establish the relationship between variations in leaf nitrogen content and the spectral response of sugarcane leaf using a hyperspectral sensor, with assessments in three experimental areas of São Paulo state, Brazil, with evaluations in different soils and varieties. Each experimental area was allocated in randomized block, with splitted plots and four repetition, hence, receiving doses of 0, 50, 100 and 150 kg of nitrogen per hectare. Spectral analysis was performed on the \"+1\" leaf in laboratory; we collected 10 leaves per subplots; which were subsequently subjected to chemical analysis to leaf nitrogen content determination. We observed a significant correlation between leaf nitrogen content and variations in sugarcane spectral response, we noticed that the region of the green light and red-edge were the most consistent and stable among the studied area and the crop seasons evaluated. The principal component analysis allowed to reinforce these results, since that the scores for principal components showed significant correlations with the leaf nitrogen content, had higher loadings values for the previous spectral regions mentioned. From the spectral curves were also performed calculations of spectral indices previously described in literature, being these submitted to simple regression analysis to direct prediction of leaf nitrogen content. The models were calibrated with 2012/13 and validated with 2013/14 crop season data. Spectral indices that were calculated with green and/or red-edge, combined with near-infrared wavelengths performed well in the validation phase, and the five most stable were the BNi (500, 705 and 750 nm), GNDVI (550 and 780 nm), NDRE (790 and 720 nm), IR-1dB (735 and 720 nm) and VOGa (740 and 720 nm). The variety SP 81 3250 was cultured in the three experimental areas, allowing to compare the performance of a specific site model with a general model for the same variety growing on different soil conditions. Although the general model presents meaningful statistical parameters, there is a significant reduction in sensitivity to predict leaf nitrogen content of sugarcane when compared with specific site calibrated models. We also used on this research the stepwise multiple linear regression (SMLR) that generated models with good precision to estimate the leaf nitrogen content, even when models are calibrated for an experimental area, regardless of spectral differences between varieties, using 5 to 6 wavelengths. This study shows that specific wavelengths are associated with variation in leaf nitrogen content of sugarcane, and these are reported in the region of green (near to 550 nm) and red-edge (680 to 720nm). Despite the low correlation observed between the infrared wavelengths to the leaf nitrogen content of sugarcane, vegetation indices calculated from these wavelengths, or its insertion on linear models generation were important to improve prediction accuracy.
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Books on the topic "Sensors selection"

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Pressure sensors: Selection and application. New York: M. Dekker, 1991.

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Juds, Scott M. Photoelectric sensors and controls: Selection and application. New York: M. Dekker, 1988.

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Norton, Harry N. Sensor and transducer selection guide. Oxford, UK: Elsevier Advanced Technology, 1990.

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Ryan, Margaret A., Abhijit V. Shevade, Charles J. Taylor, M. L. Homer, Mario Blanco, and Joseph R. Stetter, eds. Computational Methods for Sensor Material Selection. New York, NY: Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-73715-7.

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Suzuki, Yasuo. Preparation and application of ion sensors. Kawasaki-shi: Meiji Daigaku Kagaku Gijutsu Kenkyūjo, 1987.

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Gaver, Donald Paul. Asymptotic properties of a sensor allocation model. Monterey, Calif: Naval Postgraduate School, 1995.

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Russotti, J. S. Sensor-operated headset selection for Virginia class submarine consoles (C3I). Groton, CT: Naval Submarine Medical Research Laboratory, 2001.

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Coșofreț, Vasile V. Pharmaceutical applications of membrane sensors. Boca Raton: CRC Press, 1992.

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Drost, Ulrich C. Sensory-motor coupling in musicians. Göttingen: Cuvillier Verlag, 2005.

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Electroanalysis na h'Éireann (International Conference) (1986 Dublin). Electrochemistry, sensors and analysis: Proceedings of the International Conference "Electroanalysis na h'Éireann", Dublin, Ireland, June 10-12, 1986. Amsterdam: Elsevier, 1986.

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Book chapters on the topic "Sensors selection"

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Gölz, Jacqueline, and Christian Hatzfeld. "Sensor Design." In Springer Series on Touch and Haptic Systems, 431–516. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04536-3_10.

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AbstractMultiple sensors are applied in haptic devices designs. Even if they are not closed-loop controlled in a narrow sense of force or torque generation, they are used to detect movement ranges and limits or the detection of the presence of a user and its type of interaction with an object or human-machine-interface (HMI). Almost any type of technical sensor had been applied in the context of haptic devices. The emerging market of gesture based user interaction and integration of haptics due to ergonomic reasons extends the range of sensors potentially relevant for haptic devices. However, what exactly is a sensor? Which is the right one for your purpose and is there a systematic way to choose it? To support you answering these fundamental questions, classification of sensors is helpful. This chapter starts with a definition and classifications according to measurand and sensing principles. Constraints, you will have to focus on, are discussed and selection criteria are deduced. An introduction in technologies and design principles for mechanical sensors serves as an overview for your selection process. Common types of force/torque, positioning, velocity and acceleration sensors are presented. Furthermore, imaging and temperature sensors are addressed briefly in this section.
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Sajid, Memoon, Jahan Zeb Gul, and Kyung Hyun Choi. "Selection of Sensors, Transducers, and Actuators." In Functional Reverse Engineering of Machine Tools, 29–51. Boca Raton, FL : CRC Press/Taylor & Francis Group, 2019. | Series: Computers in engineering design and manufacturing: CRC Press, 2019. http://dx.doi.org/10.1201/9780429022876-3.

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Olthuis, W., S. Böhm, G. R. Langereis, and P. Bergveld. "Selection in System and Sensor." In Chemical and Biological Sensors for Environmental Monitoring, 60–85. Washington, DC: American Chemical Society, 2000. http://dx.doi.org/10.1021/bk-2000-0762.ch005.

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Best, Lincoln, Ernest Foo, Hui Tian, and Zahra Jadidi. "Client Selection Frameworks Within Federated Machine Learning: The Current Paradigm." In Smart Sensors, Measurement and Instrumentation, 61–83. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-29845-5_3.

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Leschke, André. "Second Degree of Freedom: Selection of Sensors." In Algorithm Concept for Crash Detection in Passenger Cars, 159–75. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-29392-5_10.

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Ben Said, Ahmed, Abdelkarim Erradi, Azadeh Gharia Neiat, and Athman Bouguettaya. "Mobile Crowdsourced Sensors Selection for Journey Services." In Service-Oriented Computing, 463–77. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03596-9_33.

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Xu, Li, Xia Luo, and Huanzhu Wang. "Efficient Cluster Head Selection for Multimode Sensors in Wireless Sensor Network." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 98–108. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67514-1_8.

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Pirttikangas, Susanna, Kaori Fujinami, and Tatsuo Nakajima. "Feature Selection and Activity Recognition from Wearable Sensors." In Ubiquitous Computing Systems, 516–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11890348_39.

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Subramanian, Arun, Kishan G. Mehrotra, Chilukuri K. Mohan, Pramod K. Varshney, and Thyagaraju Damarla. "Feature Selection and Occupancy Classification Using Seismic Sensors." In Trends in Applied Intelligent Systems, 605–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13025-0_62.

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Yunus, M. A. Md, S. C. Mukhopadhyay, M. S. A. Rahman, N. S. Zahidin, and S. Ibrahim. "The Selection of Novel Planar Electromagnetic Sensors for the Application of Nitrate Contamination Detection." In Smart Sensors, Measurement and Instrumentation, 171–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37006-9_8.

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Conference papers on the topic "Sensors selection"

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Wendt, James B., and Miodrag Potkonjak. "Medical diagnostic-based sensor selection." In 2011 IEEE Sensors. IEEE, 2011. http://dx.doi.org/10.1109/icsens.2011.6127188.

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Khokhlov, Igor, Akshay Pudage, and Leon Reznik. "Sensor Selection Optimization with Genetic Algorithms." In 2019 IEEE SENSORS. IEEE, 2019. http://dx.doi.org/10.1109/sensors43011.2019.8956579.

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Chuprov, Sergei, Leon Reznik, Igor Khokhlov, and Karan Manghi. "Multi-Modal Sensor Selection with Genetic Algorithms." In 2022 IEEE Sensors. IEEE, 2022. http://dx.doi.org/10.1109/sensors52175.2022.9967296.

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Sowers, T. Shane, George Kopasakis, and Donald L. Simon. "Application of the Systematic Sensor Selection Strategy for Turbofan Engine Diagnostics." In ASME Turbo Expo 2008: Power for Land, Sea, and Air. ASMEDC, 2008. http://dx.doi.org/10.1115/gt2008-50525.

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The data acquired from available system sensors forms the foundation upon which any health management system is based, and the available sensor suite directly impacts the overall diagnostic performance that can be achieved. While additional sensors may provide improved fault diagnostic performance there are other factors that also need to be considered such as instrumentation cost, weight, and reliability. A systematic sensor selection approach is desired to perform sensor selection from a holistic system-level perspective as opposed to performing decisions in an ad hoc or heuristic fashion. The Systematic Sensor Selection Strategy is a methodology that optimally selects a sensor suite from a pool of sensors based on the system fault diagnostic approach, with the ability of taking cost, weight and reliability into consideration. This procedure was applied to a large commercial turbofan engine simulation. In this initial study, sensor suites tailored for improved diagnostic performance are constructed from a prescribed collection of candidate sensors. The diagnostic performance of the best performing sensor suites in terms of fault detection and identification are demonstrated, with a discussion of the results and implications for future research.
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Nemarich, Christopher P., Henry R. Hegner, and Marjorie Ann E. Natishan. "The Selection of Diagnostic Technologies and Sensors for Condition Based Maintenance Systems." In ASME 1996 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1996. http://dx.doi.org/10.1115/imece1996-0865.

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Abstract The effective implementation of Condition Based Maintenance (CBM) systems generally requires the use of sensors selected specifically to detect, isolate and diagnose machinery component faults. In order for CBM systems to operate reliably, the diagnostic technologies and sensors used to monitor the machinery must provide repeatable and accurate measurements. The ability of a CBM system to effectively detect and correctly identify component faults at their inception depends largely on the appropriate selection of the fault parameters to be measured and the sensors used to measure them. Furthermore, the incorrect selection, inappropriate application, or inattention to the sensor performance requirements may lead to missed failure detects and false alarms, resulting in the general lack of acceptance of the entire CBM system. This paper presents an overview of a methodology for the selection of applicable diagnostic technologies and sensors for machinery health monitoring systems.
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Severini, Fabio, Vincenzo Sesta, Francesca Madonini, Alfonso Incoronato, Federica Villa, and Franco Zappa. "dTOF SPAD array with Region-Of-Interest selection and dynamic TDC routing." In Optical Sensors. Washington, D.C.: OSA, 2021. http://dx.doi.org/10.1364/sensors.2021.sf1a.6.

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Pengfei, Zhang, Teo Keng Boon, and Wang Yixin. "Sensor Selection in Wireless Sensor Networks for Structural Health Monitoring." In 2019 IEEE SENSORS. IEEE, 2019. http://dx.doi.org/10.1109/sensors43011.2019.8956873.

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Arar, Malath I. "Gas Turbine Corrected Parameters Control: Humidity Correction – Sensors Evaluation and Selection." In ASME Turbo Expo 2002: Power for Land, Sea, and Air. ASMEDC, 2002. http://dx.doi.org/10.1115/gt2002-30047.

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Gas Turbine, GT, control methodology applied to power generation is being evaluated. Corrected parameter control methodology has been adopted for this purpose. This method uses the corrected physical ambient conditions such as pressure, temperature and humidity in controlling the GT operations. Humidity correction becomes increasingly important in this control scheme. The following are the reasons for accurate and robust humidity measurement: (1) Humidity measurement is important to the operation control of the dry low NOX, DLN, combustor system. (2) GT inlet performance enhancing devices, such as evaporative coolers and inlet foggers, depend upon the accurate humidity measurement to determine the amount of water needed for inlet temperature depression. (3) Humidity measurement is used to determine the amount of water to be injected in the combustor for NOX abatement when running on liquid fuel as an alternative to natural gas fuel. In order to obtain accurate and reliable humidity readings, several commercially available humidity sensors were extensively tested and evaluated in a controlled laboratory environment. The sensors were tested for their measurement accuracy, saturation conditions, power interruption and surge, sudden temperature changes and medium air speed. Test ambient temperature ranges from −30 °C to 50 °C. This covers the operating ambient conditions range for the Gas Turbine. The test criterion is that the error in the response of the sensor shall not exceed ±1 °C from the test reference for all the tests conducted on the sensors. The combustion requirements for Dry Low NOX operations and mode transfer dictate this criterion. Also, as a DLN requirement, error in specific humidity shall not exceed 0.904 g/g of air. This test criterion also satisfies the water injection requirements for NOX abatement and inlet performance enhancing devices. The results show that for ±1 °C error in the sensor measurement, the resulting error in NOX calculation is less than 0.2 ppm. The test results show that all sensors except the current one in use have met the test criterion. The current sensor, General Eastern DT-2, has a large measurement error in the order of ±5 °C. Programs have been launched to field test and evaluate these sensors in order to replace the current one.
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Roehm, Benjamin. "Concept for the Selection and Positioning of Sensor Technology in the Development of Advanced Systems." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002517.

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This paper contains criteria for evaluating suitable measurement principles of sensors on existing products. The paper's core is a concept with evaluation criteria for sensor selection and the prototypical implementation of a graphical user interface to select a suitable sensor technology. Furthermore, interfering influences on the measurement, energy supply of sensors, data transmission and communication are dealt with to design the concept. The evaluation criteria guide the user through the sensor selection process and recommend suitable sensor measurement principles.
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Zhang, Liang, Ping Lu, and Deming Liu. "Pulse selection technique in fiber sensing." In Asia Pacific Optical Sensors Conference 2013, edited by Minghong Yang, Dongning Wang, and Yun-Jiang Rao. SPIE, 2013. http://dx.doi.org/10.1117/12.2031244.

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Reports on the topic "Sensors selection"

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Hegarty-Craver, Meghan, Hope Davis-Wilson, Pooja Gaur, Howard Walls, David Dausch, and Dorota Temple. Wearable Sensors for Service Members and First Responders: Considerations for Using Commercially Available Sensors in Continuous Monitoring. RTI Press, February 2024. http://dx.doi.org/10.3768/rtipress.2024.op.0090.2402.

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Wearable sensors (“wearables”) provide a mechanism to monitor the health of service members and first responders continuously and remotely. Several wearables are commercially available, with different configurations, sensors, algorithms, and forms of communication. Choosing the “best” wearable depends on the information you need to make decisions, how often you need this information, and the level of accuracy required. In this article, we review six use cases for wearables that are relevant to the military and first responders. We examine the metrics measured and the wearables used. We conclude with recommendations for requirements and wearable selection given the constraints imposed by various use cases.
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Ratmanski, Kiril, and Sergey Vecherin. Resilience in distributed sensor networks. Engineer Research and Development Center (U.S.), October 2022. http://dx.doi.org/10.21079/11681/45680.

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With the advent of cheap and available sensors, there is a need for intelligent sensor selection and placement for various purposes. While previous research was focused on the most efficient sensor networks, we present a new mathematical framework for efficient and resilient sensor network installation. Specifically, in this work we formulate and solve a sensor selection and placement problem when network resilience is also a factor in the optimization problem. Our approach is based on the binary linear programming problem. The generic formulation is probabilistic and applicable to any sensor types, line-of-site and non-line-of-site, and any sensor modality. It also incorporates several realistic constraints including finite sensor supply, cost, energy consumption, as well as specified redundancy in coverage areas that require resilience. While the exact solution is computationally prohibitive, we present a fast algorithm that produces a near-optimal solution that can be used in practice. We show how such formulation works on 2D examples, applied to infrared (IR) sensor networks designed to detect and track human presence and movements in a specified coverage area. Analysis of coverage and comparison of sensor placement with and without resilience considerations is also performed.
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Shavlik, Jude. Selection, Combination, and Evaluation of Effective Software Sensors for Detecting Abnormal Usage of Computers Running Windows NT/2000. Fort Belvoir, VA: Defense Technical Information Center, April 2002. http://dx.doi.org/10.21236/ada406316.

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Olsen. PR-179-07200-R01 Evaluation of NOx Sensors for Control of Aftertreatment Devices. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), June 2008. http://dx.doi.org/10.55274/r0010985.

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Emissions reduction through exhaust aftertreatment is becoming more common. It is likely to play an important role in meeting new emissions regulations in the future. Currently, the predominate aftertreatment technology for NOX reduction in lean burn natural gas engines appears to be selective catalytic reduction (SCR). In SCR, a reducing agent is injected into the exhaust upstream of a catalyst. Supplying the optimal quantity of reagent is critical to effective application of SCR. If too little reagent is supplied then the NOx reduction efficiency may be too low. If too much reagent is provided then the ammonia slip may be too high. Control of reagent injection is an area where improvements could be made. In many current SCR systems, the rate of reagent injection is determined by engine loading. The relationship between engine loading and engine out NOX emission is determined during SCR system commissioning, and assumed to remain constant. Ideally, NOX emissions would be measured and used as feedback to the SCR system. It may also be advantageous to employ transient reagent injection based on time dependent variations in NOX mass flow in the exhaust. This would be possible with a fast response NOx sensor. Close loop engine control is an area of increasing importance. As regulatory emissions levels are reduced, compliance margins generally decrease. Precise control of air/fuel ratio and ignition timing become more critical. Cylinder-to-cylinder control of air/fuel ratio, ignition timing, and IMEP are also important. Advanced sensors are an enabling technology for more precise engine control. Ion sensing is an example of a technology that potentially can improve cylinder balancing and ignition timing. Cylinder-to-cylinder air/fuel ratio can be accomplished in several different ways. One approach would be to install individual sensors in the exhaust manifold, one for each cylinder. Ceramic based sensors (O2 and NOx) may be reliable enough at exhaust port temperatures. They are typically used in the exhaust of 4-stroke cycle engines, which have higher exhaust temperatures than 2-stroke cycle engines. Ceramic based NOx sensors have been under development for use, primarily, in Lean NOx Traps (LNTs). This technology is expected to be used on over-the-road Diesel truck engines in 2010. Therefore, the research effort has momentum. This provides an opportunity to capitalize on the efforts of another industry. In this project a NOx sensor will be evaluated using the SCR slipstream system on the GMV-4TF. The basic tasks are: 1. Identify commercial NOx sensors and procure most promising sensor 2. Design and modification of SCR slipstream system to accept sensors 3. Installation of sensors, sensor electronics, and data logging hardware and software 4. Sensor evaluation during SCR slipstream testing.
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Nenoff, Tina M., and Leo J. Small. Tunable Impedance Spectroscopy Sensors via Selective Nanoporous Materials. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1396079.

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Allendorf, Mark D., Aaron Michael Katzenmeyer, Vitalie Stavilla, Joanne V. Volponi, Louise Jacqueline Criscenti, Jeffery A. Greathouse, Terry Rae Guilinger, et al. Selective stress-based microcantilever sensors for enhanced surveillance. Office of Scientific and Technical Information (OSTI), November 2012. http://dx.doi.org/10.2172/1057255.

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Beebe, Kenneth R., and Bruce R. Kowalski. Wavelength or Sensor Selection by Minimization of Prediction Error. Fort Belvoir, VA: Defense Technical Information Center, July 1988. http://dx.doi.org/10.21236/ada197231.

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Solanki, Pranshoo, Haiyan Xie, John Awaitey, and Tejaswi Reddy. State-of-the-Practice Review of Field-Curing Methods for Evaluating the Strength of Concrete Test Specimens. Illinois Center for Transportation, April 2023. http://dx.doi.org/10.36501/0197-9191/23-003.

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The American Association of State Highway and Transportation Officials (AASHTO) T 23 standard provides instructions for making and curing concrete test specimens in the field. However, further research is needed to compare the strength of the field-cured specimen with the strength of the actual in-place concrete item. The purpose of this research is to build an understanding of the current state of the practice for field-curing methods. Specifically, the research team conducted a literature review and questionnaire survey to identify the selection criteria and details of field-curing methods. The results of the literature data and survey outcomes indicate most transportation agencies use field-cured cylinders, followed by the maturity method, to decide when to open pavement to traffic or remove formwork or falsework. The most commonly used field method found among transportation agencies was curing the test specimens near (or on) the cast concrete in the same manner as the concrete item represented. The cylindrical specimens are mostly field cured in insulated boxes such as a cooler or under burlap/insulation near the concrete item. In contrast, beams are mostly field cured in a damp sandpit or under burlap/insulation near the concrete item. Other field-curing technologies used by agencies are match curing, SureCureTM cylinder-mold system, piezoelectric sensors, calorimetry, and penetration-resistance tests.
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Russotti, Joseph S., and Derek W. Schwaller. Sensor-Operated Headset Selection for Virginia Class Submarine Consoles (3CI). Fort Belvoir, VA: Defense Technical Information Center, October 2001. http://dx.doi.org/10.21236/ada408227.

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Ramschak, Thomas. Efficient Gathering, Storing, Distributing and Validation of Data. IEA SHC Task 68, January 2024. http://dx.doi.org/10.18777/ieashc-task68-2024-0001.

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This report focuses on efficient data gathering, storage, distribution, and validation, covering data management topics- from sensor selection to permanent data storage. The report is mainly targeted at system designers and plant operators, aiming to provide checklists and recommendations on these topics.
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