Academic literature on the topic 'Linear Prediction Formula'
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Journal articles on the topic "Linear Prediction Formula"
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Lee, Kwang-Ho, and Yong-Hwan Cho. "Simple Breaker Index Formula Using Linear Model." Journal of Marine Science and Engineering 9, no. 7 (July 1, 2021): 731. http://dx.doi.org/10.3390/jmse9070731.
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Breaking waves generated by wave shoaling in coastal areas have a close relationship with various physical phenomena in coastal regions. Therefore, it is crucial to accurately predict breaker indexes such as breaking wave height and breaking depth when designing coastal structures. Many studies on wave breaking have been carried out, and many experimental data have been documented. Representative studies on wave breaking provide many empirical formulas for the prediction of breaking index, mainly through hydraulic model experiments. However, the existing empirical formulas for breaking index determine the coefficients of the assumed equation through statistical analysis of data under the assumption of a specific equation. This study presents an alternative method to estimate breaker index using representative linear-based supervised machine learning algorithms that show high predictive performance in various research fields related to regression or classification problems. Based on the used machine learning methods, a new simple linear equation for the prediction of breaker index is presented. The newly proposed breaker index formula showed similar predictive performance compared to the existing empirical formula, although it was a simple linear equation.
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Ngô, Trọng Hữu. "Dự đoán khả năng chịu uốn của tiết diện dầm bê tông cốt thép bằng công thức thực hành." Vietnam Institute for Building Science and Technology 2023, vi.vol2 (June 2023): 14–21. http://dx.doi.org/10.59382/j-ibst.2023.vi.vol2-2.
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This paper presents the process of developing a practical formula for predicting the ultimate bending moment of rectangular reinforced concrete (RC) beams through regression analysis. The data used for regression analysis was generated by using the fiber method to analyze a non-linear batch of commonly encountered RC beam cross-sections. The practical formula was obtained by fitting a linear regression model to the training set and then making predictions on the test set. The coefficient of determination, R2, between the bending moment values calculated from the formula and the results of the non-linear analysis was 0.9948, indicating a good predictive capability of the formula.
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Mohammadi, Mohammad, and Adel Mohammadpour. "On the Prediction of α-Stable Time Series." Fluctuation and Noise Letters 15, no. 04 (September 29, 2016): 1650021. http://dx.doi.org/10.1142/s0219477516500218.
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This paper addresses the point prediction of [Formula: see text]-stable time series. Our key idea is to define a new Hilbert space that contains [Formula: see text]-stable processes. Then, we apply the advantage of Hilbert space theory for finding the best linear prediction. We show how to use the presented predictor practically for [Formula: see text]-stable linear processes. The implementation of the presented method is easier than the implementation of the minimum dispersion method. We reveal the appropriateness of the presented method through an empirical study on predicting the natural logarithms of the volumes of SP500 market.
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Di, Yu, Ying Li, and Yan Luo. "Prediction of Implantable Collamer Lens Vault Based on Preoperative Biometric Factors and Lens Parameters." Journal of Refractive Surgery 39, no. 5 (May 2023): 332–39. http://dx.doi.org/10.3928/1081597x-20230207-03.
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Purpose: To establish and validate the accuracy of implantable collamer lens (ICL) vault size prediction formula based on preoperative biometric factors and lens parameters. Methods: This study included 300 patients (300 eyes) with Visian ICL V4c (STAAR Surgical) implantation. They were randomly divided into the formula establishment group and formula validation group. Anterior segment measurements, ICL V4c size and power, and vault 1 week postoperatively were collected from all patients. Multiple linear regression analysis was performed to establish the prediction formula. Mean absolute error (MAE), median absolute error (MedAE), root mean square error (RMSE), and Bland-Altman diagrams were used to evaluate the prediction formula. Results: Anterior chamber depth (ACD) had the greatest influence on vault 1 week after ICL V4c implantation, followed by ICL V4c size and angle-to-angle distance (ATA). The prediction formula was obtained according to the partial regression coefficient, which was vault (mm) = −1.279 + 0.291 × ACD (mm) + 0.210 × ICL V4c size (mm) – 0.144 × ATA (mm) ( R 2 = 0.661). In the formula validation group, the mean predictive vault, MAE, MedAE, and RMSE were 628.10, 135.09, 130.42, and 150.46 µm, respectively. The Bland-Altman diagram showed the predictive vault was in good agreement with the actual vault. Conclusions: A novel ICL V4c vault prediction formula was developed and shown to be an effective method for predicting the vault to reduce surgical complications. [ J Refract Surg . 2023;39(5):332–339.]
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HINTON-BAYRE, ANTON. "Reliable Change formula query." Journal of the International Neuropsychological Society 6, no. 3 (March 2000): 362–63. http://dx.doi.org/10.1017/s1355617700633118.
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In a recent article, Temkin et al. (1999) contrasted four models for detecting significant change in individual performance on neuropsychological tests. Two of these models relied on the calculation of the Reliable Change Index (RCI) by Jacobson and Truax (1991), with and without a correction for practice associated with repeated testing. The other two models were based on simple linear regression and multiple regression, respectively. The models were contrasted based on the width of 90% prediction intervals (PI) and normal-distribution-based prediction accuracy of classifying unusual cases. Participants were tested twice (Time 1 and Time 2), on seven common neuropsychological measures. Prediction accuracy was based on the discrepancy between obtained and predicted Time 2 scores.
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Engel, B., W. G. Buist, P. Walstra, E. Olsen, and G. Daumas. "Accuracy of prediction of percentage lean meat and authorization of carcass measurement instruments: adverse effects of incorrect sampling of carcasses in pig classification." Animal Science 76, no. 2 (April 2003): 199–209. http://dx.doi.org/10.1017/s1357729800053455.
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AbstractClassification of pig carcasses in the European Community is based on the lean meat percentage of the carcass. The lean meat percentage is predicted from instrumental carcass measurements, such as fat and muscle depth measurements, obtained in the slaughter-line. The prediction formula employed is derived from the data of a dissection experiment and has to meet requirements for authorization as put down in EC regulations. Requirements involve the sampling procedure and sample size for the dissected carcasses and the accuracy of prediction. Formulae are often derived by linear regression. In this paper we look at a particular type of sampling scheme. This involves selection of carcasses on the basis of carcass measurements not all of which are intended to be used as prediction variables. This sampling scheme frequently appears in requests for authorization of carcass measurement instruments and accompanying prediction formulae, despite the fact that it lacks formal statistical justification when used in conjunction with linear regression. The objective of this work was to assess the performance of the prediction formula that follows from this potentially faulty combination of sampling scheme and linear regression in relation to the requirements in the EC regulations. We show that this sampling scheme may produce poor predictions for lean meat percentage compared with proper sampling procedures with selection on prediction variables only or random sampling. We do so by computer simulation. Initially, simulated data were based on recent and historic data from The Netherlands. Prediction variables are fat and muscle depth measurements. The additional variable involved in sampling, but not included in the regression, was carcass weight. We also show that due to this faulty sampling scheme there is a serious risk that a new measurement instrument may not be authorized because performance criteria in the EC-regulations are not met.
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Xie, Liusen, and William W. Hsieh. "Predicting the Return Migration Routes of the Fraser River Sockeye Salmon (Oncorhynchus nerka)." Canadian Journal of Fisheries and Aquatic Sciences 46, no. 8 (August 1, 1989): 1287–92. http://dx.doi.org/10.1139/f89-165.
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The Johnstone Strait diversion rate (i.e. the percentage of homeward migrating Fraser River sockeye salmon (Oncorhynchus nerka) travelling around Vancouver Island via the northern route of Johnstone Strait) is statistically predicted using the March values of the Kains Island sea surface temperature T and the Fraser River runoff R. The prediction formula incorporates nonlinear terms such as T2 and RT, as well as the diversion rate 2 yr ago. We tested the forecasting performance by constructing a prediction formula using only data from 1953–78, and making predictions for 1979–88. The mean absolute error of our prediction of the diversion rate was 8% which compared favourably with the prediction by a linear temperature scheme and a linear runoff scheme where the errors were respectively 13 and 29%. Unlike the latter two schemes where T and R data from April–June are needed, our scheme with the use of data no later than March allows much earlier forecasts to be made.
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Yuan, Shasha, Weidong Zhou, and Liyan Chen. "Epileptic Seizure Prediction Using Diffusion Distance and Bayesian Linear Discriminate Analysis on Intracranial EEG." International Journal of Neural Systems 28, no. 01 (December 20, 2017): 1750043. http://dx.doi.org/10.1142/s0129065717500435.
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Epilepsy is a chronic neurological disorder characterized by sudden and apparently unpredictable seizures. A system capable of forecasting the occurrence of seizures is crucial and could open new therapeutic possibilities for human health. This paper addresses an algorithm for seizure prediction using a novel feature — diffusion distance (DD) in intracranial Electroencephalograph (iEEG) recordings. Wavelet decomposition is conducted on segmented electroencephalograph (EEG) epochs and subband signals at scales 3, 4 and 5 are utilized to extract the diffusion distance. The features of all channels composing a feature vector are then fed into a Bayesian Linear Discriminant Analysis (BLDA) classifier. Finally, postprocessing procedure is applied to reduce false prediction alarms. The prediction method is evaluated on the public intracranial EEG dataset, which consists of 577.67[Formula: see text]h of intracranial EEG recordings from 21 patients with 87 seizures. We achieved a sensitivity of 85.11% for a seizure occurrence period of 30[Formula: see text]min and a sensitivity of 93.62% for a seizure occurrence period of 50[Formula: see text]min, both with the seizure prediction horizon of 10[Formula: see text]s. Our false prediction rate was 0.08/h. The proposed method yields a high sensitivity as well as a low false prediction rate, which demonstrates its potential for real-time prediction of seizures.
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Castillo-Sanchez, L. E., J. R. Canul-Solís, D. Pozo-Leyva, E. Camacho-Perez, J. M. Lugo-Quintal, A. L. Chaves-Gurgel, G. T. Santos, L. C. V. Ítavo, and A. J. Chay-Canul. "Prediction of live weight in beef heifers using a body volume formula." Arquivo Brasileiro de Medicina Veterinária e Zootecnia 74, no. 6 (December 2022): 1127–33. http://dx.doi.org/10.1590/1678-4162-12886.
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ABSTRACT The objective of this study was to develop and evaluate linear, quadratic, and allometric models to predict live weight (LW) using the body volume formula (BV) in crossbred heifers raised in southeastern Mexico. The LW (426.25±117.49kg) and BV (338.05±95.38 dm3) were measured in 360 heifers aged between 3 and 30 months. Linear and non-linear regression were used to construct prediction models. The goodness-of-fit of the models was evaluated using the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), coefficient of determination (R2), mean squared error (MSE), and root MSE (RMSE). In addition, the developed models were evaluated through cross-validation (k-folds). The ability of the fitted models to predict the observed values was evaluated based on the RMSEP, R2, and mean absolute error (MAE). The quadratic model had the lowest values of AIC (2688.39) and BIC (2700.05). On the other hand, the linear model showed the lowest values of MSE (7954.74) and RMSE (89.19), and the highest values of AIC (2709.70) and BIC (2717.51). Despite this, all models presented the same R2 value (0.87). The cross-validation (k-folds) evaluation of fit showed that the quadratic model had better values of MSEP (41.49), R2 (0.85), and MAE (31.95). We recommend the quadratic model to predictive of the crossbred beef heifers' live weight using the body volume as the predictor.
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KARNA, DILLIP KUMAR, ADITYA PRASAD ACHARYA, BHABESH CHANDRA DAS, GANGADHAR NAYAK, and M. R. DIBYADARSHINI. "Comparison of regression methods and Shaeffer’s formula in prediction of Live Body Weight of Ganjam Goats." Indian Journal of Animal Sciences 92, no. 6 (March 21, 2022): 770–75. http://dx.doi.org/10.56093/ijans.v92i6.108921.
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Linear regression and polynomial regression of order two and three were utilized to predict the live weight ofGanjam goats across five age groups using chest girth as predictor and their accuracies were compared with theprediction of weight made by Shaeffer’s formula. Live body weight of Ganjam goat recorded by electronic weighingbalance was used as standard for calculating the error of prediction. The body weights of 1014 Ganjam goats (329males and 685 females) were estimated by each technique during 2015 to 2017. Compared with electronic weighingscale, the body weight estimates in Ganjam goat exceeded in all age groups for Shaeffer’s formula whereas predicted body weight estimates by linear regression and second order polynomial regression were close to the live body weights. The estimates of linear regression and second order polynomial regression were significantly different from the electronic weighing scale for all age groups. The study concluded that polynomial regression of order two had better predictive value for live body weight of Ganjam goat, followed by third order polynomial regression, linear regression and Shaeffer’s formula, in order
Dissertations / Theses on the topic "Linear Prediction Formula"
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Nguyen, Cu Ngoc. "Stochastic differential equations with long-memory input." Thesis, Queensland University of Technology, 2001.
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Sehnalová, Pavla. "Stabilita a konvergence numerických výpočtů." Doctoral thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-261248.
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Tato disertační práce se zabývá analýzou stability a konvergence klasických numerických metod pro řešení obyčejných diferenciálních rovnic. Jsou představeny klasické jednokrokové metody, jako je Eulerova metoda, Runge-Kuttovy metody a nepříliš známá, ale rychlá a přesná metoda Taylorovy řady. V práci uvažujeme zobecnění jednokrokových metod do vícekrokových metod, jako jsou Adamsovy metody, a jejich implementaci ve dvojicích prediktor-korektor. Dále uvádíme generalizaci do vícekrokových metod vyšších derivací, jako jsou např. Obreshkovovy metody. Dvojice prediktor-korektor jsou často implementovány v kombinacích modů, v práci uvažujeme tzv. módy PEC a PECE. Hlavním cílem a přínosem této práce je nová metoda čtvrtého řádu, která se skládá z dvoukrokového prediktoru a jednokrokového korektoru, jejichž formule využívají druhých derivací. V práci je diskutována Nordsieckova reprezentace, algoritmus pro výběr proměnlivého integračního kroku nebo odhad lokálních a globálních chyb. Navržený přístup je vhodně upraven pro použití proměnlivého integračního kroku s přístupe vyšších derivací. Uvádíme srovnání s klasickými metodami a provedené experimenty pro lineární a nelineární problémy.
Books on the topic "Linear Prediction Formula"
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Healey, Richard. Interlude: Some Alternative Interpretations. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198714057.003.0007.
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An understanding of quantum theory is manifested by the ability successfully and unproblematically to use it to further the scientific goals of prediction, explanation, and control of natural phenomena. An Interpretation seeks further to formulate or reformulate it as a fundamental theory that provides a self-contained description of the world. I critically review three prominent but radically different Interpretations of quantum theory (Bohmian mechanics, non-linear theories, Everettian quantum mechanics) and give my reasons for rejecting each as a way of understanding quantum theory. These include problems associated with non-locality, failure of relativistic invariance, empirical inaccessibility, and decision-making. We can achieve a satisfactory understanding of quantum theory and how it successfully advances the goals of science without providing an Interpretation of the theory.
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Little, Max A. Machine Learning for Signal Processing. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198714934.001.0001.
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Digital signal processing (DSP) is one of the ‘foundational’ engineering topics of the modern world, without which technologies such the mobile phone, television, CD and MP3 players, WiFi and radar, would not be possible. A relative newcomer by comparison, statistical machine learning is the theoretical backbone of exciting technologies such as automatic techniques for car registration plate recognition, speech recognition, stock market prediction, defect detection on assembly lines, robot guidance and autonomous car navigation. Statistical machine learning exploits the analogy between intelligent information processing in biological brains and sophisticated statistical modelling and inference. DSP and statistical machine learning are of such wide importance to the knowledge economy that both have undergone rapid changes and seen radical improvements in scope and applicability. Both make use of key topics in applied mathematics such as probability and statistics, algebra, calculus, graphs and networks. Intimate formal links between the two subjects exist and because of this many overlaps exist between the two subjects that can be exploited to produce new DSP tools of surprising utility, highly suited to the contemporary world of pervasive digital sensors and high-powered and yet cheap, computing hardware. This book gives a solid mathematical foundation to, and details the key concepts and algorithms in, this important topic.
Book chapters on the topic "Linear Prediction Formula"
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Fukui, Tetsuo, and Shizuka Shirai. "Predictive Algorithm for Converting Linear Strings to General Mathematical Formulae." In Human Interface and the Management of Information: Supporting Learning, Decision-Making and Collaboration, 15–28. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58524-6_2.
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Fukui, Tetsuo. "Algorithm for Predicting Mathematical Formulae from Linear Strings for Mathematical Inputs." In Applications of Computer Algebra, 137–48. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56932-1_9.
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Shirai, Shizuka, and Tetsuo Fukui. "Evaluation of a Predictive Algorithm for Converting Linear Strings to Mathematical Formulae for an Input Method." In Mathematical Aspects of Computer and Information Sciences, 421–25. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32859-1_36.
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Wang, Yong, Chen Xu, Changchun Li, Xiaofei Yao, Xingbo Xiang, and Haoxuan Huang. "Surface Vibration of Throw-Type Blast in an Open-Pit Mine." In Lecture Notes in Civil Engineering, 123–36. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2532-2_11.
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AbstractIn the process of Open-pit mining in China, throwing blasting is an important method, which is very likely to cause serious damage to the slope of the discharge field under the action of vibration load of throwing blasting. With the background of throwing blasting process in Heidaigou open-pit mine in Ordos, vibration velocity data were collected from the discharge field near the throwing blast, and the vibration signal of throwing blast was analyzed by means of Fourier transform to obtain the characteristics of throwing blast vibration velocity wave and the attenuation law and prediction formula in the process of propagation. The results show that: 100 ~ 300 m away from the blasting area, the radial direction (X direction) of the blasting area produces the largest vibration velocity of 26.8 cm/s, but at the same time, the decay rate of the peak vibration velocity of the survey line 2 in each direction is small, and the decay percentages of 56, 75 and 70% are smaller than that of the survey line 1 and survey line 3 in the lateral direction of the blasting area, and the decay rate of the velocity is smaller as the propagation As the distance increases, the decay rate of the velocity decreases. The curve gradually tends to flatten and the vibration velocity of the three directions gradually close. The frequency band of the blast vibration is distributed within 200 Hz and the frequency and energy are mainly distributed in the low frequency stage (0–20 Hz), accounting for more than 50% of the total energy.
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Hansen, Lars Peter, and Thomas J. Sargent. "Linear Stochastic Difference Equations." In Recursive Models of Dynamic Linear Economies. Princeton University Press, 2013. http://dx.doi.org/10.23943/princeton/9780691042770.003.0002.
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This chapter describes the vector first-order linear stochastic difference equation. It is first used to represent information flowing to economic agents, then again to represent competitive equilibria. The vector first-order linear stochastic difference equation is associated with a tidy theory of prediction and a host of procedures for econometric application. Ease of analysis has prompted the adoption of economic specifications that cause competitive equilibria to have representations as vector first-order linear stochastic difference equations. Because it expresses next period's vector of state variables as a linear function of this period's state vector and a vector of random disturbances, a vector first-order vector stochastic difference equation is recursive. Disturbances that form a “martingale difference sequence” are basic building blocks used to construct time series. Martingale difference sequences are easy to forecast, a fact that delivers convenient recursive formulas for optimal predictions of time series.
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Park, Mincheol, Heuisoo Han, and Yoonhwa Jin. "Integrated Analysis Method for Stability Analysis and Maintenance of Cut-Slope in Urban." In Slope Engineering. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.94252.
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In the process of constructing roads for the development of the city, cut-slopes are made by excavating mountains. However, these cut-slopes are degraded in strength by time-deterioration phenomenon, and progressive slope failure is caused. This study developed an integrated analysis method for stability analysis and maintenance of cut-slopes in urban. The slope stability analysis was performed using the finite element model, and the progressive slope failure by time-dependent deterioration was quantified by using the strength parameters of soil applying the strength reduction factor (SRF). The displacements until the slope failure by slope stability analysis were quantified by cumulative displacement curve, velocity curve, and inverse velocity curve and, applied to the slope maintenance method. The inverse-velocity curve applied to the prediction of the time of slope failure was regressed to the 1st linear equation in the brittle material and the 3rd polynomial equation in the ductile material. This is consistent with the proposed formula of Fukuzono and also shows similar behavior to the failure case in literature. In the future, integrated analysis method should be improved through additional research. And it should be applied to cut-slope to prevent disasters.
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Anderson, Raymond A. "Predictive Modelling Techniques." In Credit Intelligence & Modelling, 503–46. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192844194.003.0014.
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This chapter covers commonly-used supervised-learning techniques and combinations, with guidance on choices. (1) A view from on high—i) caveats—data dependency of predictive modelling; ii) language—in academic literature for representation of formulae and concepts. (2) Parametric—make assumptions about the relationships between predictors and predicted, and their distributions {linear regression, linear probability modelling (LPM), probit/logit, discriminant analysis, linear programming}. (3) Non-parametric—require few or no assumptions {k-nearest neighbours, Decisions Trees and Random Forests (RF)s, support vector machines, artificial neural networks, genetic algorithms}. (4) Conglomerations—of models and approaches, whether i) practical—for business reasons; ii) parallel—developed using the same data, and then fused; iii) residual—subsequent models predict what prior models could not. Machine learning is introduced. (5) Making the choice—factors affecting the choice {regulatory/compliance, transparency/opacity, suitability to the statistical and business problems, skills availability, longer-term maintenance, development and implementation timeframes, speed of execution once implemented}.
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Carstensen, Bendix. "Regression models." In Epidemiology with R, 65–92. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198841326.003.0005.
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This chapter evaluates regression models, focusing on the normal linear regression model. The normal linear regression model establishes a relationship between a quantitative response (also called outcome or dependent) variable, assumed to be normally distributed, and one or more explanatory (also called regression, predictor, or independent) variables about which no distributional assumptions are made. The model is usually referred to as 'the general linear model'. The chapter then differentiates between simple linear regression and multiple regression. The term 'simple linear regression' covers the regression model where there is one response variable and one explanatory variable, assuming a linear relationship between the two. The chapter also discusses the model formulae in R; generalized linear models; collinearity and aliasing; and logarithmic transformations.
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Deng, Jin, and Shutong Li. "Correlation Fitted of Loess Microscopic Parameter Statistics with Shear Strength C and φ." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220854.
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The microstructure of loess mainly reflects the size distribution, orientation, contact mode and pore distribution of particles. The six original loess (low clayey silt loess) on the same site in Lanzhou is selected by the direct shear test, high power electron microscope and energy spectrum test. To simulated calculation the cohesion (C) and internal friction angle (φ), used by the parameters data, including to the mineral element (Fe/Al/Si, Al/Si, K) ratio, microstructure parameters (Long axis of the particles, long axis’ skewness, kurtosis and orientation degree. The results indicated that the (φ) value was linearly correlated with the mean value of the Angle, and the correlation coefficient of the fitting formula was 0.82. The cohesion C value was linearly correlated with the kurtosis, Skewness, Fe/Si, Al/Si and K/Al of the long axis, and the maximum correlation coefficient after fitting was 0.99. The fitting formula of microcosmic parameters and shear strength can provide a new research method for predicting the shear strength of loess samples.
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Costa, Rian de Carvalho da, and Francisco Carpegiani Medeiros Borges. "Discrete mathematical modeling of biological phenomena: Case of drug concentration in the organism." In DEVELOPMENT AND ITS APPLICATIONS IN SCIENTIFIC KNOWLEDGE. Seven Editora, 2023. http://dx.doi.org/10.56238/devopinterscie-237.
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At first, mathematical modeling is a tool used to understand the occurrence of phenomena, and through the existence of a pattern in the recurrences, it is possible to formulate a recurrence expression where the phenomenon is modeled in such a way, understanding its behavior and predicting certain situations. In the study of biological phenomena, there is a need to understand a certain action in such a way, thus, to understand the process is given by Mathematical Modeling applying the equations of differences, in this way resulting in linear or non-linear functions, because of this modeling with N applications in the biological field, the study of the research project at UFDPar will be discussed as an example.
Conference papers on the topic "Linear Prediction Formula"
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Shuai, Jian, Chun’e Zhang, Fulai Chen, and Renyang He. "Prediction of Failure Pressure of Corroded Pipelines Based on Finite Element Analysis." In 2008 7th International Pipeline Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/ipc2008-64260.
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A numerical model for predicting the burst failure of corroded pipeline is constructed using the non-linear finite element method, in which the technical points including element mesh, materials model, non-linear solution and failure criterion are recommended. Using this model, the full-size pipe burst experiments in different material, size and defect was analyzed and computed. The proposed FEM model was validated. Based on the calculation result using the model, a new formula predicting failure pressure is proposed, in which depth, length and width of a defect was involved. Comparison of the formula with the other assessment method and experiments show the formula had a satisfactory precision.
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Zhang, Jiancheng, Donghai Jin, Zefeng Li, and Xingmin Gui. "Improvement of a Tip Clearance Loss Prediction Model Based on Geometric Variation." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-14613.
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Abstract Tip leakage flow is an important factor affecting the efficiency and stability of compressors. It is important to study the tip leakage flow model in the design and performance prediction process of turbomachinery. This work analyzes the existing tip clearance loss prediction model and refines it to predict the tip clearance loss considering more blade geometric parameters. After analyzing the Yaras tip leakage loss model derivation process, it is found that the discharge coefficient (taken as constants by Yaras) will change with the gap geometry, namely, it is related to the maximum thickness and the gap size of the blade. In this paper the discharge coefficient of tip gap is revised to better predict the effect of gap loss with regard to the maximum thickness of the blade. In this paper, the research objects are linear cascades with NACA65 profile and numerical experiments were carried out by a CFD package Numeca under the condition of Mach number equal to 0.45, where the variables are the gap sizes (1%, 2%, 3%, 4%, 6% of the axial chord length) and the maximum thickness of blades (3%, 5%, 7%, 9%, 11% of the axial chord length). Combined with the numerical calculation results, according to Yaras loss prediction formula and using the similar characteristics to the discharge coefficient of the variation trend and the Planck blackbody radiation formula, the relationship between the discharge coefficient and the maximum thickness of the blade and the tip clearance is summarized, which is integrated with the Yaras loss prediction formula to obtain the final formula. This prediction formula fits the NACA65 blade calculation fairly. The average error of 24 calculation points is 2.59%. Then the improved model was compared with several existing models. Besides the CDA062, C4 and polynomial thickness distribution blade tip clearance loss is predicted using the refined prediction formula and the biggest prediction error is 5.46%. Therefore, the improved formula still has a good prediction effect when the blade type change. It is considered that within a certain range of maximum thickness of the blade type and tip gap sizes, the improved formula can give good predictions even though the blade type is different.
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Lin, Bo, Molong Duan, Chinedum E. Okwudire, and Jason S. Wou. "An Improved Analytical Model of Friction and Ball Motion in Linear Ball Bearings With Application to Ball-to-Ball Contact Prediction." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-88033.
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The friction behavior of rolling ball machine components like linear ball bearings is very important to their functionality. For instance, differences in linear velocity of balls induces ball-to-ball contact in certain circumstances, resulting in significant increases and variations in friction. In this paper, an improved analytical formula for determining the linear velocity of balls in four-point-contact linear ball bearings is derived as a function of contact angle deviations and contact forces. The analytical formula is validated against a comprehensive friction model in the literature and shown to be in good agreement, while an oversimplified analytical model proposed by the authors in prior work is shown to be inaccurate. A case study is presented where insights gained from the derived analytical formula are used to mitigate velocity difference of balls in a linear ball bearing which otherwise would experience ball-to-ball contact.
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Shinomoto, Kyohei, Sadaoki Matsui, Kei Sugimoto, and Shinsaku Ashida. "Development of Closed Formula of Wave Load Based Upon Long-Term Prediction: Heave Acceleration and Pitch Angle." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-18558.
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Abstract In order to ensure the structural safety of a ship, the most severe sea states she is expected to encounter throughout her service life need to be given consideration. This is the reason why the maximum loads corresponding to such sea states are typically specified in classification society structural rules such as the Common Structural Rules (CSR) of the International Association of Classification Societies (IACS). The maximum loads used for the structural design of a ship can have a significant impact on not only her structural safety, but also her hull construction cost; therefore, it is very important that the loads be accurately estimated. The linear term of the maximum loads typically specified in some classification society rules is equivalent to a long-term predicted value with an exceedance probability of 10−8. Since the maximum loads specified in classification society rules such as the CSR were developed specifically for specific ship types, their effective application to other ship types may be somewhat limited. Aim of our larger study is to develop a closed formula of long-term prediction for maximum loads. The formula has high accuracy and can be applied to any ship size and type. This paper focused on the heave acceleration and pitch angle, which are used for the calculation of internal loads and so on. A formula which takes into account such as the standard deviation of the hull response in irregular waves and the directional distribution of irregular waves was proposed. Main ship parameters such as ship length L, breadth B, draft d, block coefficient Cb, and water line area coefficient Cw were used for formulating the long-term prediction. The accuracy and effectiveness of the proposed formula were confirmed through various numerical calculations using a linear seakeeping analysis code developed by ClassNK. The calculation covers 154 ship models (77 existing ships × 2 loading conditions per ship).
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Bai, Lu, and Dingyü Xue. "A Numerical Algorithm to Initial Value Problem of Linear Caputo Fractional-Order Differential Equation." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46668.
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A numerical algorithm is presented to solve the initial value problem of linear Caputo fractional-order differential equations. Error analysis has been done to Taylor series algorithm, the reason has been found why the error of the algorithm is large, the condition of using Taylor series algorithm is presented. A new algorithm called exponential function algorithm is proposed based on the analysis. Nonzero initial value problem could be transformed into zero initial value problem. The obtained fractional-order differential equation is transformed into difference equation, the numerical solution can be found with closed form solution formula. The error of the numerical solution can be modified with prediction-correction algorithm.
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Tao, Liang, Yin Qi, Meirong Tang, Kai Ye, Deyu Wang, Mirinuer Halifu, and Yuhang Zhao. "A New Approach for Multi-Fractured Horizontal Wells Productivity Prediction in Shale Oil Reservoirs." In International Petroleum Technology Conference. IPTC, 2023. http://dx.doi.org/10.2523/iptc-23019-ea.
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Abstract The continental shale oil reservoirs usually have strong heterogeneity, which make the law of fracture propagation extremely complex, and the quantitative characterization of fracture network swept volume brings great challenges. In this paper, firstly, the grey correlation analysis method is used to calculate the correlation coefficient between different parameters and microseismic monitoring volume (SRV), and the key factors affecting SRV are identified. Secondly, the relationship between key geological engineering parameters and SRV is established by using the method of multiple linear regression, and the relationship is further corrected by productivity numerical simulation method, and the empirical formula for quantitative characterization of fracture network swept volume(FSV) is established. Finally, according to the field production of big data, the fitting chart of the accumulated oil production and the FSV is established, and the production of horizontal well is further predicted according to the fitting formula. The study results shown that the main factors affecting the SRV were fracturing fluid volume, fracture density, brittleness index, pump rate, horizontal stress difference, net pay thickness and proppant amount.The FSV in the study area was positively correlated with the cumulative oil production of the horizontal well. With the increase of the FSV, the accumulated oil production increased at first and then tended to be stable, and the optimal FSV was 760 ~ 850*104m3. The prediction method was verified by the typical platform in the field to be accurate and reliable. It can provide scientific basis for the productivity prediction of horizontal wells in shale oil reservoirs.
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Lee, Yu-Tai, Theodore M. Farabee, and William K. Blake. "Wall Pressure Fluctuations in the Reattachment Region of a Backward Facing Step." In ASME/JSME 2007 5th Joint Fluids Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/fedsm2007-37142.
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Steady mean flow fields and turbulent flow characteristics obtained from solving the Reynolds Averaged Navier Stokes (RANS) equations with a k-ε isotropic turbulence model are used to predict the frequency spectrum of wall-pressure fluctuations for flow past a backward facing step. The linear source term (LST) of the governing fluctuating-pressure equation is used in deriving the final double integration formula for the fluctuating wall pressure. The integrand of the solution formula includes the mean-flow velocity gradient, modeled turbulence normal fluctuation, Green’s function and the spectral model for the interplane correlation. An anisotropic distribution of the turbulent kinetic energy is implemented using a function named anisotropic factor. This function represents a ratio of the turbulent normal Reynolds stress to the turbulent kinetic energy and is developed based on an equilibrium turbulent flow or flows with zero streamwise pressure gradient. The spectral correlation model for predicting the wall-pressure fluctuations is obtained through modeling of the streamwise and spanwise wavenumber spectra. The nonlinear source term (NST) in the original fluctuating-pressure equation is considered following the conclusion of Kim’s direct numerical simulation (DNS) study of channel flow. Predictions of frequency spectra for the reattachment flow past a backward facing step (BFS) are investigated to verify the validity of the current modeling. Detailed turbulence features and wall-pressure spectra for the flow in the reattachment region of the BFS are predicted and discussed. DNS and experimental data for BFSs are used to develop and validate these calculations. The prediction results based on different modeling characteristics and flow physics agree with the observed turbulence field.
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Lee, Yu-Tai, Theodore M. Farabee, and William K. Blake. "Predictions on Wall Pressure Fluctuations for a Backward-Facing Step." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-79342.
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Time-mean flow fields and turbulent flow characteristics obtained from solving the Reynolds averaged Navier Stokes (RANS) equations with a k-ε turbulence model are used to predict the frequency spectrum of wall-pressure fluctuations for flow past a backward facing step. The linear source term of the governing fluctuating pressure equation is used in deriving the final double integration formula for the fluctuating wall pressure. The integrand includes the RANS mean-velocity gradient, modeled turbulence normal fluctuation, Green’s function and the spectral model for the interplane correlation. An anisotropic distribution of the turbulent kinetic energy is implemented using a function named anisotropic factor. This function represents a ratio of the turbulent normal Reynolds stress to the turbulent kinetic energy and is developed based on an equilibrium turbulent flow or flows with zero streamwise pressure gradient. The spectral correlation model for predicting the wall-pressure fluctuations is obtained through modeling of the streamwise and spanwise wavenumber spectra. The non-linear source term in the original governing equation is considered following the conclusion of Kim’s direct numerical simulation (DNS) study. Predictions of frequency spectra for the reattachment flow past a backward facing step (BFS) are investigated to verify the validity of the current modeling. Detailed turbulence features and wall-pressure spectra for the flow in the reattachment region of the BFS are predicted and discussed. The prediction results based on different modeling characteristics and flow physics agree with the observed turbulence field.
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Warren, Peter, Nandhini Raju, Milos Krsmanovic, Hossein Ebrahimi, Jayanta Kapat, Ramesh Subramanian, and Ranajay Ghosh. "Shrinkage Prediction Using Machine Learning for Additively Manufactured Ceramic and Metallic Components for Gas Turbine Applications." In ASME Turbo Expo 2022: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/gt2022-83418.
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Abstract Additive manufacturing of metallic and ceramic components can provide several benefits for gas turbines. This technology can help to decrease time and cost required for prototyping of both operational components and assistive tooling equipment. There are currently many methodologies for metallic and ceramic material printing, and these processes typically involve a sintering phase during the manufacturing procedure. The amount of shrinkage that occurs is dependent upon the sintering oven conditions, the printed material, and the geometry of the printed component. These factors can be broken down into many subfactors leading to an equation that is too complex and with too many variables to be solved in an analytical matter. Additive manufacturing can provide the ability to rapidly manufacture geometrically specific components for turbomachinery operations. To increase the accuracy of the geometric dimensions of the final product, the sintering shrinkage must be accurately predicted. Machine learning can assist by using data-driven approaches to ensure accurate prediction of shrinkage. This ultimately will increase the dimensional accuracy of the printed components. If there is an accurate shrinkage prediction formula that accounts for the material type, sintering oven conditions, and geometric specifications, the components can be scaled up in CAD (computer aided design) software prior to 3D printing and entering the sintering oven. This predictive capability will allow the end user to create dimensionally accurate parts at a rate that has not yet been possible. In this work, a machine learning approach is developed for the prediction of shrinkage during sintering of additive manufactured components. This paper uses a combination of experimental data and artificially generated data to create a framework for implementing linear regression tools to predict sintering trajectories. An accurate knowledge of the sintering trajectory for a given material can allow for more user control over the final properties and geometrical accuracy of a given component.
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Liu, Xiaoben, Hong Zhang, and Yanfei Chen. "Strain Prediction for X80 Steel Pipeline Subjected to Strike-Slip Fault Under Compression Combined With Bending." In ASME 2015 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/pvp2015-45173.
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Strike-slip fault is one main kind of PGD faced by long distance gas pipelines. Based on non-linear finite element method, a numerical model for buried pipeline under strike-slip fault was proposed. The model was proven to be reasonable by comparing the numerical results with previous researcher’s experiment results. By using the FE model, peak compressive strain of X80 steel pipeline subjected to strike-slip fault under compression combined with bending was studied. The sensitivities of the diameter, wall thickness, soil rigidity, fault displacement and crossing angle on the peak compressive strain of the pipeline are examined in detail. Furthermore, based on numerous numerical results, a regression equation for predicting peak compressive strain of X80 steel pipeline is proposed. The applicable range of the formula is given. 15 true design cases in the Second West to East pipeline Project in China were investigated to demonstrate the accuracy and applicability of the proposed methodology by comparing the predicting peak compressive strain results with FEM results. The proposed method can be referred in the strain-based and reliability-based design for X80 steel pipelines subjected to strike-slip fault.
Reports on the topic "Linear Prediction Formula"
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Warrick, Arthur W., Gideon Oron, Mary M. Poulton, Rony Wallach, and Alex Furman. Multi-Dimensional Infiltration and Distribution of Water of Different Qualities and Solutes Related Through Artificial Neural Networks. United States Department of Agriculture, January 2009. http://dx.doi.org/10.32747/2009.7695865.bard.
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The project exploits the use of Artificial Neural Networks (ANN) to describe infiltration, water, and solute distribution in the soil during irrigation. It provides a method of simulating water and solute movement in the subsurface which, in principle, is different and has some advantages over the more common approach of numerical modeling of flow and transport equations. The five objectives were (i) Numerically develop a database for the prediction of water and solute distribution for irrigation; (ii) Develop predictive models using ANN; (iii) Develop an experimental (laboratory) database of water distribution with time; within a transparent flow cell by high resolution CCD video camera; (iv) Conduct field studies to provide basic data for developing and testing the ANN; and (v) Investigate the inclusion of water quality [salinity and organic matter (OM)] in an ANN model used for predicting infiltration and subsurface water distribution. A major accomplishment was the successful use of Moment Analysis (MA) to characterize “plumes of water” applied by various types of irrigation (including drip and gravity sources). The general idea is to describe the subsurface water patterns statistically in terms of only a few (often 3) parameters which can then be predicted by the ANN. It was shown that ellipses (in two dimensions) or ellipsoids (in three dimensions) can be depicted about the center of the plume. Any fraction of water added can be related to a ‘‘probability’’ curve relating the size of the ellipse (or ellipsoid) that contains that amount of water. The initial test of an ANN to predict the moments (and hence the water plume) was with numerically generated data for infiltration from surface and subsurface drip line and point sources in three contrasting soils. The underlying dataset consisted of 1,684,500 vectors (5 soils×5 discharge rates×3 initial conditions×1,123 nodes×20 print times) where each vector had eleven elements consisting of initial water content, hydraulic properties of the soil, flow rate, time and space coordinates. The output is an estimate of subsurface water distribution for essentially any soil property, initial condition or flow rate from a drip source. Following the formal development of the ANN, we have prepared a “user-friendly” version in a spreadsheet environment (in “Excel”). The input data are selected from appropriate values and the output is instantaneous resulting in a picture of the resulting water plume. The MA has also proven valuable, on its own merit, in the description of the flow in soil under laboratory conditions for both wettable and repellant soils. This includes non-Darcian flow examples and redistribution and well as infiltration. Field experiments were conducted in different agricultural fields and various water qualities in Israel. The obtained results will be the basis for the further ANN models development. Regions of high repellence were identified primarily under the canopy of various orchard crops, including citrus and persimmons. Also, increasing OM in the applied water lead to greater repellency. Major scientific implications are that the ANN offers an alternative to conventional flow and transport modeling and that MA is a powerful technique for describing the subsurface water distributions for normal (wettable) and repellant soil. Implications of the field measurements point to the special role of OM in affecting wettability, both from the irrigation water and from soil accumulation below canopies. Implications for agriculture are that a modified approach for drip system design should be adopted for open area crops and orchards, and taking into account the OM components both in the soil and in the applied waters.