Thematische Bibliographien / Machine calibration

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Auswahl der wissenschaftlichen Literatur zum Thema „Machine calibration“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 17. Februar 2022

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Zeitschriftenartikel zum Thema "Machine calibration"

Haitjema. „Calibration of Displacement Laser Interferometer Systems for Industrial Metrology“. Sensors 19, Nr. 19 (22.09.2019): 4100. http://dx.doi.org/10.3390/s19194100.

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Displacement laser interferometer systems are widely used for the calibration of machine tools and CMMs (Coordinate Measuring Machines). Additionally, they are often the workhorse in dimensional calibration laboratories, where they act as the basic metrological traceability link for many calibrations. This paper gives a review of the calibration of such systems, where several approaches, such as the calibrations of separate components or the system as a whole, are reviewed. The calibrations discussed are: the laser frequency, the counting system, software evaluation of the environmental conditions, environmental and material temperature sensor calibration and the calibration of optics that is part of the system. For these calibrations considerations are given about the ways these can be carried out and about establishing the re-calibration intervals.

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Zhuang, Hanqi, Lixin Liu und Oren Masory. „Autonomous Calibration of Hexapod Machine Tools1“. Journal of Manufacturing Science and Engineering 122, Nr. 1 (01.11.1997): 140–48. http://dx.doi.org/10.1115/1.538893.

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Hexapod machines are emerging as a new type of CNC machine tools. Among other things, stringent calibration is an important means to improve their accuracy. Traditionally, to perform system calibration, one needs to measure a number of machine poses using an external measuring device. However, this process is often labor-intensive and invasive, and difficult for on-line calibration. In this paper, a systematic way of self-calibrating a hexapod machine tool is introduced. By adding a small number of redundant internal sensors, errors of the hexapod machine tool can be measured. This approach has the potential of automatically producing high accuracy measurement data over the entire workspace of the system with an extremely fast measurement rate. Once the measurement data is available, a recursive filter is applied to estimate machine parameter errors from the predicted geometric errors, and to update the model residing in the machine controller. Thus, it is possible to dynamically calibrate and compensate for various types of machine errors including those induced by thermal and loading variations, without interrupting the normal operation of the machine tool. To verify the concept, preliminary experimental studies were conducted on a Stewart platform built at Florida Atlantic University. [S1087-1357(00)70901-4]

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Hampel, David, Kateřina Jůzová und Martina Matulíková. „Credit rating calibration“. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 60, Nr. 2 (2012): 79–84. http://dx.doi.org/10.11118/actaun201260020079.

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In this paper we deal with determination of chosen characteristics of vending business in the Czech Republic. Vending seems to be dynamically developing sector of economics. A strong competition is present in this market. This can be a reason that new ideas of improvement appear continuously. Primary data are used to characterize vending business from the perspective of consumer as well as vending operator. The data are used as input to statistical tests; results are summarized and presented in economic terms. At first, survey (about 600 respondents) is analyzed in empirical way. It is informative in such sense, that vending machines are used by majority of users, more often in school or workplace. The main reasons of using vending machines are speed of shopping and no other shopping possibility. Further part is devoted to comparison of prices under different situations. For example, there are differences among various vending machine locations. Vending machine prices are not necessarily higher than prices in shops or cafeterias. Finally, operator profitability is explored based on company internal data. Among others, hot drinks vending machines are more profitable than vending machines selling bottled beverages of packaged food in general.

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Kajima, Mariko, Tsukasa Watanabe, Makoto Abe und Toshiyuki Takatsuji. „Calibrator for 2D Grid Plate Using Imaging Coordinate Measuring Machine with Laser Interferometers“. International Journal of Automation Technology 9, Nr. 5 (05.09.2015): 541–45. http://dx.doi.org/10.20965/ijat.2015.p0541.

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A calibrator for 2D grid plates have been developed. The calibrator was based on a commercial imaging coordinate measuring machine (imaging CMM). A laser interferometer for the calibration of the x-coordinate and two laser interferometers for the calibration of the y-coordinate were attached to the imaging CMM. By applying multistep measurement method for the calibration procedure, the geometrical error in the calibrator was reduced. The calibration of a precision 2D grid plate was demonstrated, and the expanded uncertainty was estimated to be 0.2 μm (k =2).

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李, 冬. „Machine Vision Pixel Calibration“. Artificial Intelligence and Robotics Research 03, Nr. 02 (2014): 25–33. http://dx.doi.org/10.12677/airr.2014.32005.

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KUMAR, HARISH. „A CONTEMPORARY APPROACH FOR EVALUATION OF THE BEST MEASUREMENT CAPABILITY OF A FORCE CALIBRATION MACHINE“. International Journal of Modern Physics: Conference Series 24 (Januar 2013): 1360013. http://dx.doi.org/10.1142/s2010194513600136.

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The present paper discusses the procedure for evaluation of best measurement capability of a force calibration machine. The best measurement capability of force calibration machine is evaluated by a comparison through the precision force transfer standards to the force standard machines. The force transfer standards are calibrated by the force standard machine and then by the force calibration machine by adopting the similar procedure. The results are reported and discussed in the paper and suitable discussion has been made for force calibration machine of 200 kN capacity. Different force transfer standards of nominal capacity 20 kN, 50 kN and 200 kN are used. It is found that there are significant variations in the .uncertainty of force realization by the force calibration machine according to the proposed method in comparison to the earlier method adopted.

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TAKAMASU, Kiyoshi, Ryoshu FURUTANI, Ken SHIMOJIMA und Osamu SATO. „Artifact Calibration of Coordinate Measuring Machine-Kinematic Calibration-“. Journal of the Japan Society for Precision Engineering 69, Nr. 6 (2003): 851–55. http://dx.doi.org/10.2493/jjspe.69.851.

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Huang, Feng Shan, und Li Chen. „CCD Camera Calibration Technology Based on the Translation of Coordinate Measuring Machine“. Applied Mechanics and Materials 568-570 (Juni 2014): 320–25. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.320.

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A new CCD camera calibration method based on the translation of Coordinate Measuring Machine (CMM) is proposed. The CMM brings the CCD camera to produce the relative translation with respect to the center of the white ceramic standard sphere along the X, Y, Z axis, and the coordinates of the different positions of the calibration characteristic point in the probe coordinate system can be generated. Meanwhile, the camera captures the image of the white ceramic standard sphere at every position, and the coordinates of the calibration characteristic point in the computer frame coordinate system can be registered. The calibration mathematic model was established, and the calibration steps were given and the calibration system was set up. The comparing calibration result shows that precision of this method is equivalent to that of the special calibration method, and the difference between the calibrating data of these two methods is within ±1μm.

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Igarashi, Hiroshi. „Subliminal Calibration for Machine Operation“. Journal of Advanced Computational Intelligence and Intelligent Informatics 16, Nr. 1 (20.01.2012): 108–16. http://dx.doi.org/10.20965/jaciii.2012.p0108.

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This paper proposes a skill assist technique without having the operator to be aware of it. Heretofore, many operation assists in a human-machine system has added artificial force in human operation input such as reactive force from obstacles. Such an approach is suitable in a particular task as simulated by the designer, because it can improve safety and efficiency, but is simultaneously hindering human learning ability. The proposed method will correct the machine dynamics of the operation subject subliminally, meaning that the operator will not be aware that it is being altered. Henceforth, it will be possible to enhance operability, without having to prevent the human learning ability. As a result of a verification experiment on 20 test subjects, it has been clarified that it is possible to enhance the operation performance without the operators knowing of the assist.

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Xiong, Zhong Xing, Feng Li und Bin Li. „A Calibration Device and Method to Calibrate the Beam’s Direction of the Point Laser Probe“. Applied Mechanics and Materials 799-800 (Oktober 2015): 980–84. http://dx.doi.org/10.4028/www.scientific.net/amm.799-800.980.

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When using the laser displacement sensor to measure distance, there is a deflection angle which exites between the direction of the laser beam and the direction of axis, this deflection angle has an impact on the final measurement result. This paper presented a calibration method of the laser beam’s direction, designed calibration device and ceramic calibration plate. In the process of calibrating, laser displacement sensor moves along Z-axis and Y-axis of machine, CCD camera takes the image of the spot projected by laser beam on the calibration plate, then calculate the laser beam’s direction based on the movement distance of laser displacement sensor and the spot’s position on calibration plate. Depend on the Mikron five-axis CNC machine tools, this paper describes the whole calibration process in details, the feasibility and accuracy of the method has been verified by experiment. Experimental results show that when the machine tool’s accuracy is within 0.002mm, the calibration error of laser beam’s direction can be controlled within the range of 0.05°.

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Dissertationen zum Thema "Machine calibration"

Haussamer, Nicolai Haussamer. „Model Calibration with Machine Learning“. Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29451.

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This dissertation focuses on the application of neural networks to financial model calibration. It provides an introduction to the mathematics of basic neural networks and training algorithms. Two simplified experiments based on the Black-Scholes and constant elasticity of variance models are used to demonstrate the potential usefulness of neural networks in calibration. In addition, the main experiment features the calibration of the Heston model using model-generated data. In the experiment, we show that the calibrated model parameters reprice a set of options to a mean relative implied volatility error of less than one per cent. The limitations and shortcomings of neural networks in model calibration are also investigated and discussed.

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Stark, Per. „Machine vision camera calibration and robot communication“. Thesis, University West, Department of Technology, Mathematics and Computer Science, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:hv:diva-1351.

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This thesis is a part of a larger project included in the European project, AFFIX. The reason for the project is to try to develop a new method to assemble an aircraft engine part so that the weight and manufacturing costs are reduced. The proposal is to weld sheet metal parts instead of using cast parts. A machine vision system is suggested to be used in order to detect the joints for the weld assembly operation of the sheet metal. The final system aims to locate a hidden curve on an object. The coordinates for the curve are calculated by the machine vision system and sent to a robot. The robot should create and follow a path by using the coordinates. The accuracy for locating the curve to perform an approved weld joint must be within +/- 0.5 mm. This report investigates the accuracy of the camera calibration and the positioning of the robot. It also brushes the importance of good lightning when obtaining images for a vision system and the development for a robot program that receives these coordinates and transform them into robot movements are included. The camera calibration is done in a toolbox for MatLab and it extracts the intrinsic camera parameters such as the distance between the centre of the lens and the optical detector in the camera: f, lens distortion parameters and principle point. It also returns the location of the camera and orientation at each obtained image during the calibration, the extrinsic parameters. The intrinsic parameters are used when translating between image coordinates and camera coordinates and the extrinsic parameters are used when translating between camera coordinates and world coordinates. The results of this project are a transformation matrix that translates the robots position into the cameras position. It also contains a robot program that can receive a large number of coordinates, store them and create a path to move along for the weld application.

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Alvarez, Teleña S. „Systematic trading : calibration advances through machine learning“. Thesis, University College London (University of London), 2015. http://discovery.ucl.ac.uk/1461997/.

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Systematic trading in finance uses computer models to define trade goals, risk controls and rules that can execute trade orders in a methodical way. This thesis investigates how performance in systematic trading can be crucially enhanced by both i) persistently reducing the bid-offer spread quoted by the trader through optimized and realistically backtested strategies and ii) improving the out-of-sample robustness of the strategy selected through the injection of theory into the typically data-driven calibration processes. While doing so it brings to the foreground sound scientific reasons that, for the first time to my knowledge, technically underpin popular academic observations about the recent nature of the financial markets. The thesis conducts consecutive experiments across strategies within the three important building blocks of systematic trading: a) execution, b) quoting and c) risk-reward allowing me to progressively generate more complex and accurate backtested scenarios as recently demanded in the literature (Cahan et al. (2010)). The three experiments conducted are: 1. Execution: an execution model based on support vector machines. The first experiment is deployed to improve the realism of the other two. It analyses a popular model of execution: the volume weighted average price (VWAP). The VWAP algorithm targets to split the size of an order along the trading session according to the expected intraday volume's profile since the activity in the markets typically resembles convex seasonality – with more activity around the open and the closing auctions than along the rest of the day. In doing so, the main challenge is to provide the model with a reasonable expected profile. After proving in my data sample that two simple static approaches to the profile overcome the PCA-ARMA from Bialkowski et al. (2008) (a popular two-fold model composed by a dynamic component around an unsupervised learning structure) a further combination of both through an index based on supervised learning is proposed. The Sample Sensitivity Index hence successfully allows estimating the expected volume's profile more accurately by selecting those ranges of time where the model shall be less sensitive to past data through the identification of patterns via support vector machines. Only once the intraday execution risk has been defined can the quoting policy of a mid-frequency (in general, up to a week) hedging strategy be accurately analysed. 2. Quoting: a quoting model built upon particle swarm optimization. The second experiment analyses for the first time to my knowledge how to achieve the disruptive 50% bid-offer spread discount observed in Menkveld (2013) without increasing the risk profile of a trading agent. The experiment depends crucially on a series of variables of which market impact and slippage are typically the most difficult to estimate. By adapting the market impact model in Almgren et al. (2005) to the VWAP developed in the previous experiment and by estimating its slippage through its errors' distribution a framework within which the bid-offer spread can be assessed is generated. First, a full-replication spread, (that set out following the strict definition of a product in order to hedge it completely) is calculated and fixed as a benchmark. Then, by allowing benefiting from a lower market impact at the cost of assuming deviation risk (tracking error and tail risk) a non-full-replication spread is calibrated through particle swarm optimization (PSO) as in Diez et al. (2012) and compared with the benchmark. Finally, it is shown that the latter can reach a discount of a 50% with respect to the benchmark if a certain number of trades is granted. This typically occurs on the most liquid securities. This result not only underpins Menkveld's observations but also points out that there is room for further reductions. When seeking additional performance, once the quoting policy has been defined, a further layer with a calibrated risk-reward policy shall be deployed. 3. Risk-Reward: a calibration model defined within a Q-learning framework. The third experiment analyses how the calibration process of a risk-reward policy can be enhanced to achieve a more robust out-of-sample performance – a cornerstone in quantitative trading. It successfully gives a response to the literature that recently focusses on the detrimental role of overfitting (Bailey et al. (2013a)). The experiment was motivated by the assumption that the techniques underpinned by financial theory shall show a better behaviour (a lower deviation between in-sample and out-of-sample performance) than the classical data-driven only processes. As such, both approaches are compared within a framework of active trading upon a novel indicator. The indicator, called the Expectations' Shift, is rooted on the expectations of the markets' evolution embedded in the dynamics of the prices. The crucial challenge of the experiment is the injection of theory within the calibration process. This is achieved through the usage of reinforcement learning (RL). RL is an area of ML inspired by behaviourist psychology concerned with how software agents take decisions in an specific environment incentivised by a policy of rewards. By analysing the Q-learning matrix that collects the set of state/actions learnt by the agent within the environment, defined by each combination of parameters considered within the calibration universe, the rationale that an autonomous agent would have learnt in terms of risk management can be generated. Finally, by then selecting the combination of parameters whose attached rationale is closest to that of the portfolio manager a data-driven solution that converges to the theory-driven solution can be found and this is shown to successfully outperform out-of-sample the classical approaches followed in Finance. The thesis contributes to science by addressing what techniques could underpin recent academic findings about the nature of the trading industry for which a scientific explanation was not yet given: • A novel agent-based approach that allows for a robust out-of-sampkle performance by crucially providing the trader with a way to inject financial insights into the generally data-driven only calibration processes. It this way benefits from surpassing the generic model limitations present in the literature (Bailey et al. (2013b), Schorfheid and Wolpin (2012), Van Belle and Kerr (2012) or Weiss and Kulikowski (1991)) by finding a point where theory-driven patterns (the trader's priors tend to enhance out-of-sample robustness) merge with data-driven ones (those that allow to exploit latent information). • The provision of a technique that, to the best of my knowledge, explains for the first time how to reduce the bid-offer spread quoted by a traditional trader without modifying her risk appetite. A reduction not previously addressed in the literature in spite of the fact that the increasing regulation against the assumption of risk by market makers (e.g. Dodd–Frank Wall Street Reform and Consumer Protection Act) does yet coincide with the aggressive discounts observed by Menkveld (2013). As a result, this thesis could further contribute to science by serving as a framework to conduct future analyses in the context of systematic trading. • The completion of a mid-frequency trading experiment with high frequency execution information. It is shown how the latter can have a significant effect on the former not only through the erosion of its performance but, more subtly, by changing its entire strategic design (both, optimal composition and parameterization). This tends to be highly disregarded by the financial literature. More importantly, the methodologies disclosed herein have been crucial to underpin the setup of a new unit in the industry, BBVA's Global Strategies & Data Science. This disruptive, global and cross-asset team gives an enhanced role to science by successfully becoming the main responsible for the risk management of the Bank's strategies both in electronic trading and electronic commerce. Other contributions include: the provision of a novel risk measure (flowVaR); the proposal of a novel trading indicator (Expectations’ Shift); and the definition of a novel index that allows to improve the estimation of the intraday volume’s profile (Sample Sensitivity Index).

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Ulmer, Bernard C. Jr. „Fabrication and calibration of an open architecture diamond turning machine“. Thesis, Georgia Institute of Technology, 1997. http://hdl.handle.net/1853/17120.

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Parkinson, Simon. „Construction of machine tool calibration plans using domain-independent automated planning“. Thesis, University of Huddersfield, 2014. http://eprints.hud.ac.uk/id/eprint/20329/.

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The evolution in precision manufacturing has resulted in the requirement to produce and maintain more accurate machine tools. This new requirement coupled with desire to reduce machine tool downtime places emphasis on the calibration procedure during which the machine's capabilities are assessed. Machine tool downtime can be as much as $120 per hour and is significant for manufacturers because the machine will be unavailable for manufacturing use, therefore wasting the manufacturer's time and potentially increasing lead-times for clients. In addition to machine tool downtime, the uncertainty of measurement, due to the schedule of the calibration plan, has significant implications on tolerance conformance, resulting in an increased possibility of false acceptance and rejection of machined parts. Currently calibrations are planned based on expert knowledge and there are no intelligent tools aiding to produce optimal calibration plans. This thesis describes a method of intelligently constructing calibration plans, optimising to reduce machine tool downtime and the estimated uncertainty of measurement due to the plan schedule. This resulted in the production of a novel, extensible domain model that encodes the decision making capabilities of a subject expert. Encoding the knowledge in PDDL2 requires the discretization of non-linear resources, such as continuous temperature change. Empirical analysis has shown that when this model is used alongside state-of-the-art automated planning tools, it is possible to achieve a reduction in machine tool downtime greater than 10% (12:30 to 11:18) over expert generated plans. In addition, the estimated uncertainty due to the schedule of the plan can be reduced by 59% (48 µm to 20 µm). Further experiments on a PC architecture investigate the trade-o� when optimising calibration plans for both time and the uncertainty of measurement. These experiments demonstrated that it is possible to optimise both metrics reaching a compromise that is on average 5% worse that the best-known solution for each individual metric. Additional experiments using a High Performance Computing architecture show that on average optimality of calibration plans can be improved by 4%; a potential saving of 30 minutes for a single machine and 10 hours for a company with 20 machines tools. This could incur a financial saving in excess of $1200 saving.

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Nichols, Scott A. „Improvement of the camera calibration through the use of machine learning techniques“. [Gainesville, Fla.] : University of Florida, 2001. http://etd.fcla.edu/etd/uf/2001/anp1587/nichols%5Fthesis.pdf.

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Thesis (M.S.)--University of Florida, 2001.
Title from first page of PDF file. Document formatted into pages; contains vii, 45 p.; also contains graphics. Vita. Includes bibliographical references (p. 43-44).

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Herron, Christopher, und André Zachrisson. „Machine Learning Based Intraday Calibration of End of Day Implied Volatility Surfaces“. Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273419.

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The implied volatility surface plays an important role for Front office and Risk Management functions at Nasdaq and other financial institutions which require mark-to-market of derivative books intraday in order to properly value their instruments and measure risk in trading activities. Based on the aforementioned business needs, being able to calibrate an end of day implied volatility surface based on new market information is a sought after trait. In this thesis a statistical learning approach is used to calibrate the implied volatility surface intraday. This is done by using OMXS30-2019 implied volatility surface data in combination with market information from close to at the money options and feeding it into 3 Machine Learning models. The models, including Feed Forward Neural Network, Recurrent Neural Network and Gaussian Process, were compared based on optimal input and data preprocessing steps. When comparing the best Machine Learning model to the benchmark the performance was similar, indicating that the calibration approach did not offer much improvement. However the calibrated models had a slightly lower spread and average error compared to the benchmark indicating that there is potential of using Machine Learning to calibrate the implied volatility surface.
Implicita volatilitetsytor är ett viktigt vektyg för front office- och riskhanteringsfunktioner hos Nasdaq och andra finansiella institut som behöver omvärdera deras portföljer bestående av derivat under dagen men också för att mäta risk i handeln. Baserat på ovannämnda affärsbehov är det eftertraktat att kunna kalibrera de implicita volatilitets ytorna som skapas i slutet av dagen nästkommande dag baserat på ny marknadsinformation. I denna uppsats används statistisk inlärning för att kalibrera dessa ytor. Detta görs genom att uttnytja historiska ytor från optioner i OMXS30 under 2019 i kombination med optioner nära at the money för att träna 3 Maskininlärnings modeller. Modellerna inkluderar Feed Forward Neural Network, Recurrent Neural Network och Gaussian Process som vidare jämfördes baserat på data som var bearbetat på olika sätt. Den bästa Maskinlärnings modellen jämfördes med ett basvärde som bestod av att använda föregående dags yta där resultatet inte innebar någon större förbättring. Samtidigt hade modellen en lägre spridning samt genomsnittligt fel i jämförelse med basvärdet som indikerar att det finns potential att använda Maskininlärning för att kalibrera dessa ytor.

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Sousa, João Beleza Teixeira Seixas e. „Machine learning Gaussian short rate“. Doctoral thesis, Faculdade de Ciências e Tecnologia, 2013. http://hdl.handle.net/10362/12230.

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Dissertação para obtenção do Grau de Doutor em Estatística e Gestão do Risco
The main theme of this thesis is the calibration of a short rate model under the risk neutral measure. The problem of calibrating short rate models arises as most of the popular models have the drawback of not fitting prices observed in the market, in particular, those of the zero coupon bonds that define the current term structure of interest rates. This thesis proposes a risk neutral Gaussian short rate model based on Gaussian processes for machine learning regression using the Vasicek short rate model as prior. The proposed model fits not only the prices that define the current term structure observed in the market but also all past prices. The calibration is done using market observed zero coupon bond prices, exclusively. No other sources of information are needed. This thesis has two parts. The first part contains a set of self-contained finished papers, one already published, another accepted for publication and the others submitted for publication. The second part contains a set of self-contained unsubmitted papers. Although the fundamental work on papers in part two is finished as well, there are some extra work we want to include before submitting them for publication. Part I: - Machine learning Vasicek model calibration with Gaussian processes In this paper we calibrate the Vasicek interest rate model under the risk neutral measure by learning the model parameters using Gaussian processes for machine learning regression. The calibration is done by maximizing the likelihood of zero coupon bond log prices, using mean and covariance functions computed analytically, as well as likelihood derivatives with respect to the parameters. The maximization method used is the conjugate gradients. We stress that the only prices needed for calibration are market observed zero coupon bond prices and that the parameters are directly obtained in the arbitrage free risk neutral measure. - One Factor Machine Learning Gaussian Short Rate In this paper we model the short rate, under the risk neutral measure, as a Gaussian process, conditioned on market observed zero coupon bonds log prices. The model is based on Gaussian processes for machine learning, using a single Vasicek factor as prior. All model parameters are learned directly under the risk neutral measure,using zero coupon bonds log prices only. The model supports observations of zero coupon bounds with distinct maturities limited to one observation per time instant. All the supported observations are automatically fitted.
M2A/ISEL financing conference trips; ISEL - financing conference fees; ISEL/IPL the PROTEC scholarship; CMA/FCT/UNL - financing conference trips

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Solorzano, Soria Ana Maria. „Fire Detectors Based on Chemical Sensor Arrays and Machine Learning Algorithms: Calibration and Test“. Doctoral thesis, Universitat de Barcelona, 2020. http://hdl.handle.net/10803/669584.

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In some types of fire, namely, smoldering fires or involving polymers without flame, gases and volatiles appear before smoke is released. Most of the fatalities registered for fires, are caused due to the intoxication of the building occupants over the burns. Nowadays, conventional fire detectors are based on the detection of smoke or airborne particles. In smoldering fires situations, conventional fire detectors triggers the alarm after the release of toxic emissions. The early emission of gas in fires opens the possibility to build fire alarm systems with shorter response times than widespread smoke-based detectors. Actually, the sensitivity of gas sensors to combustion products has been proved for many years. However, already early works remarked the challenge of providing reliable fire detection using chemical sensors. As gas sensors are not specific, they can be calibrated to detect large variety of fire signatures. But, at the same time, they are also potentially sensitive to any activity that releases volatiles when being performed. Cross-sensitivity to water vapor and other chemical compounds make gas-based fire alarm systems prone to false positives. For that reason, the development of reliable and robust fire detectors based on gas sensors relies in pattern recognition and Machine Learning algorithms to discriminate fire from nuisance sensor signatures. The presented PhD. Thesis explore the role of pattern recognition algorithms for fire detection using detectors based exclusively in chemical sensors. Two prototypes based on different types of gas sensors were designed. The sensor selection was performed to be sensitive to combustion products and to capture other volatiles that may help to discriminate fire and nuisances. Machine Learning algorithms for the prediction of fire were trained using standard fire tests stablished in EU norm 54. Additionally to those test experiments that may induce false alarms were also performed. Two approaches of machine learning algorithms were explore. The first prediction algorithms is based on Partial Least Squares Discriminant Analysis and the second set of algorithms are based on Support Vector Machines. Additionally, two new methodologies for cost reduction are presented. The first methodology build fire detection algorithms using the combination of Standard fire test and a reduced version of those experiments. The reduced version were performed in a small chamber. The smaller setup allows the performance of experiments in a shorter period of time. In consequence, the number of experiments to test the models increase and also the robustness of the prediction algorithms. The second methodology built general calibration models using replicates of the same sensor array. The use of different units rejects the variance between sensor arrays and allows the construction of general calibration models. The use of a single model to calibrate sensor arrays systems allows the mass production and resulting in the reduction of costs production.
Les alarmes convencionals d'incendis es basen en la detecció de fums. Tanmateix, els incendis solen emetre molts volàtils abans d'emetre fum. Altres grups de recerca ja han proposat sistemes detectors d'incendis basats en sensors químics, que poden proporcionar una resposta més ràpida, però segueixen sent propensos a falses alarmes davant d'interferències. Les tècniques de reconeixement de patrons poden ser útils per mitigar aquesta limitació. En aquesta tesi, es desenvolupen dos detectors d’incendis basats exclusivament en sensors de gas, de diverses tecnologies, que proporcionen una alarma d’incendi basada en algorismes d’aprenentatge automàtic. Els detectors van ser exposats a incendis estandarditzats i a diverses interferències. La tesi presenta dos enfocaments diferents pel reconeixement de patrons: el primer es basa en una anàlisi discriminant de mínims quadrats parcials, PLS-DA, i el segon es basa en una màquina de vectors de suport, SVM. Els resultats confirmen la capacitat de detectar incendis a una fase inicial del seu desenvolupament i el rebuig de la majoria de les interferències. A més, es presenten dues metodologies per a la reducció dels costos de calibratge d'agrupacions de sensors de gas per la detecció d'incendis, tenint present que els experiments per avaluar els detectors es fan en una sala d'incendis estàndard i són molt llargs i costosos. La primera metodologia proposada combina dades procedents d'una sala d'incendis estàndard i dades d'experiments fets a petita escala, més ràpids i menys costosos. Els resultats mostren que el rendiment dels models de predicció pot millorar amb la fusió de dades. La segona metodologia de reducció de costos compensa la necessitat de models de calibratge individuals per a cada matriu de sensors (a causa de la variabilitat del sensor) rebutjant la variabilitat del sensor i proporcionant models generals de calibratge.

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Dutra, Calainho Felipe. „Evaluation of Calibration Methods to Adjust for Infrequent Values in Data for Machine Learning“. Thesis, Högskolan Dalarna, Mikrodataanalys, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:du-28134.

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The performance of supervised machine learning algorithms is highly dependent on the distribution of the target variable. Infrequent values are more di_cult to predict, as there are fewer examples for the algorithm to learn patterns that contain those values. These infrequent values are a common problem with real data, being the object of interest in many _elds such as medical research, _nance and economics, just to mention a few. Problems regarding classi_cation have been comprehensively studied. For regression, on the other hand, few contributions are available. In this work, two ensemble methods from classi_cation are adapted to the regression case. Additionally, existing oversampling techniques, namely SmoteR, are tested. Therefore, the aim of this research is to examine the inuence of oversampling and ensemble techniques over the accuracy of regression models when predicting infrequent values. To assess the performance of the proposed techniques, two data sets are used: one concerning house prices, while the other regards patients with Parkinson's Disease. The _ndings corroborate the usefulness of the techniques for reducing the prediction error of infrequent observations. In the best case, the proposed Random Distribution Sample Ensemble reduced the overall RMSE by 8.09% and the RMSE for infrequent values by 6.44% when compared with the best performing benchmark for the housing data set.

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Applications of modeling and identification to improve machine performance: Presented at the Winter Annual Meeting of the American Society of Mechanical Engineers, Atlanta, Georgia, December 1-6, 1991. New York, N.Y: ASME, 1991.

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Loser, Raimund, John Rogers und Stephen Kyle. „Kern space theodolite calibration“. In Close-Range Photogrammetry Meets Machine Vision. SPIE, 1990. http://dx.doi.org/10.1117/12.2294285.

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