Journal articles on the topic 'Predictive mathematical model'
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Chien, Wen T., and W. C. Hung. "Investigation on the Predictive Model for Burr in Laser Cutting Titanium Alloy." Materials Science Forum 526 (October 2006): 133–38. http://dx.doi.org/10.4028/www.scientific.net/msf.526.133.
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The purpose of this study is to develop two predictive models for burr height in cutting titanium alloy plates by using Nd:YAG laser. Firstly, Taguchi method has been used to arrange the experimental scheme and analyze the results via analysis of mean . The important laser cutting parameters affecting burr height can be found. It shows that the pressure of assistant gas, the focusing position and the pulsed frequency are the most important cutting parameters in order. Then they have been chosen as the input variables for response surface methodology and used to construct a mathematical equation for predicting burr height. Secondly, the laser cutting parameters and experimental results obtained from conducting the schematic arrangement using Taguchi method and response surface methodology have been treated as training patterns and recalling patterns for the back-propagation neural network. As a result, a predictive model for burr height prediction in laser cutting titanium alloy has been established. To verify the accuracy of above two prediction models, there are 9 sets of experiment have been performed. It shows that the average error for predicting burr height by the mathematical equation derived from response surface methodology is 5.52% and by the predictive model established by back-propagation neural network is 4.51%, respectively. Obviously, both predictive models are good enough for the relational research and practical applications. It can be concluded that the procedure used in this research and the obtaining predictive models can be used practically in correlate industry.
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Zhang, Cheng Long, Ping Fa Feng, Zhi Jun Wu, and Ding Wen Yu. "A Mathematical Model for Predicting Cutting Force in Rotary Ultrasonic Drilling." Advanced Materials Research 433-440 (January 2012): 2034–41. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2034.
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Rotary ultrasonic machining is a hybrid machining process that combines diamond grinding and ultrasonic machining. The mathematical predictive material removal rate models have been developed in rotary ultrasonic machining with a constant pressure. However, there is no report on mathematical predictive cutting force model in rotary ultrasonic drilling at a constant feedrate presently. Since cutting force can not only reflect the processing state, but also affect the machined surface quality, it is necessary to develop a mathematical model for predicting cutting force which can forecast the machining results. This paper presents a mathematical model to predict the cutting force in rotary ultrasonic machining. On the basis of this model, the relations between cutting force and controllable machining parameters are researched by numerical computation method. This paper also researches the influences of spindle speed and feedrate on cutting force by experiments. The results observed through the experiments agree well with the relations generated from the mathematical model, which verify the developed model.
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Leal-Enríquez, E., and A. R. Gutiérrez-Antúnez. "Indicators of Violence Levels: Questionnaires and Predictive Mathematical Model." Modelling and Simulation in Engineering 2020 (March 17, 2020): 1–9. http://dx.doi.org/10.1155/2020/5857263.
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In this paper, we present, in detail, how a mathematical model that simulates the probable scenarios of intimate partner violence is linked to the application of any questionnaire of domestic violence already in use. This questionnaire assigns a weight of severity to each proposed inquiry for the types of psychological, physical, and sexual violence. We show a numerical procedure that must be performed to obtain the probable scenarios of violence in which the victim is involved, taking as key factor the loss of control of the perpetrator. With the numerical data obtained from the application of the mathematical model, the probable levels of violence that the victim could experience month to month for two cycles of violence are plotted, as well as the behaviors of the probable states of loss of control that the perpetrator would have during the next twelve months. Based on the results obtained, we generated a help table of indicators that could be used by victim assistance centers and/or health experts for decision-making schemes.
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Everett, R. A., A. M. Packer, and Y. Kuang. "Can Mathematical Models Predict the Outcomes of Prostate Cancer Patients Undergoing Intermittent Androgen Deprivation Therapy?" Biophysical Reviews and Letters 09, no. 02 (June 2014): 173–91. http://dx.doi.org/10.1142/s1793048014300023.
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Androgen deprivation therapy is a common treatment for advanced or metastatic prostate cancer. Like the normal prostate, most tumors depend on androgens for proliferation and survival but often develop treatment resistance. Hormonal treatment causes many undesirable side effects which significantly decrease the quality of life for patients. Intermittently applying androgen deprivation in cycles reduces the total duration with these negative effects and may reduce selective pressure for resistance. We extend an existing model which used measurements of patient testosterone levels to accurately fit measured serum prostate specific antigen (PSA) levels. We test the model's predictive accuracy, using only a subset of the data to find parameter values. The results are compared with those of an existing piecewise linear model which does not use testosterone as an input. Since actual treatment protocol is to re-apply therapy when PSA levels recover beyond some threshold value, we develop a second method for predicting the PSA levels. Based on a small set of data from seven patients, our results showed that the piecewise linear model produced slightly more accurate results while the two predictive methods are comparable. This suggests that a simpler model may be more beneficial for a predictive use compared to a more biologically insightful model, although further research is needed in this field prior to implementing mathematical models as a predictive method in a clinical setting. Nevertheless, both models are an important step in this direction.
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Tedeschi, Luis O. "1 Assessing the predictive adequacy of simple and complex mathematical models." Journal of Animal Science 97, Supplement_3 (December 2019): 24. http://dx.doi.org/10.1093/jas/skz258.046.
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Abstract The establishment of credibility for a mathematical model’s (MM) predictive ability is an essential component for improving the MM because it stimulates the evolutionary thinking (i.e., the next generation of the model) of mental conceptualizations, assumptions, and boundaries of the MM. Its predictive adequacy is commonly assessed through its ability to precisely or accurately predict observed (real) values. The precision component measures how closely the model predicted values are of each other or whether a defined pattern of predictions exists. The accuracy component, on the other hand, measures how closely the average of the model predicted values are to the actual (true) average. Many statistics exist to determine precision and accuracy of MM such as mean bias, resistant coefficient of determination, coefficient of determination, modeling efficiency, concordance correlation coefficient (CCC), the mean square error of prediction, Kleijnen’s statistic (regression of the difference between predicted and observed on their sum), and Altman and Bland’s limits of agreement statistics among many more. However, for complex models that use skewed data or repeated data in which the data is not independent (e.g., multiple measurements on the same subject), simple statistics may not suffice. For instance, four methods to compute CCC exist (moment, variance components, U-statistics, and generalized estimating equations—GEE), but only the last two methods are resilient to lightly skewed data. Another type of complexity arises when meta-analytical approaches are used at the model development phase or the model evaluation phase. In general, meta-analytical approaches remove errors (i.e., variation) associated with random variables that are believed to be known. Under these circumstances, MM tends to overperform (i.e., they have greater predictive adequacy) and their future performance may be deceitful when trying to forecast at scenarios in which the random variable(s) is(are) indeterminable or unquantifiable.
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García, B., G. Rubio, C. Santamaría, J. L. Pontones, C. D. Vera, and J. F. Jimenez. "A predictive mathematical model in the recurrence of bladder cancer." Mathematical and Computer Modelling 42, no. 5-6 (September 2005): 621–34. http://dx.doi.org/10.1016/j.mcm.2004.05.013.
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Kesseler, Kevin J., Michael L. Blinov, Timothy C. Elston, William K. Kaufmann, and Dennis A. Simpson. "A predictive mathematical model of the DNA damage G2 checkpoint." Journal of Theoretical Biology 320 (March 2013): 159–69. http://dx.doi.org/10.1016/j.jtbi.2012.12.011.
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Deepa, N., K. Ganesan, and Balaji Sethuramasamyraja. "Predictive mathematical model for solving multi-criteria decision-making problems." Neural Computing and Applications 31, no. 10 (April 27, 2018): 6733–46. http://dx.doi.org/10.1007/s00521-018-3505-2.
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Tedeschi, Luís Orlindo, Danny Gene Fox, Roberto Daniel Sainz, Luís Gustavo Barioni, Sérgio Raposo de Medeiros, and Celso Boin. "Mathematical models in ruminant nutrition." Scientia Agricola 62, no. 1 (January 2005): 76–91. http://dx.doi.org/10.1590/s0103-90162005000100015.
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Mathematical models can be used to improve performance, reduce cost of production, and reduce nutrient excretion by accounting for more of the variation in predicting requirements and feed utilization in each unique production situation. Mathematical models can be classified into five or more categories based on their nature and behavior. Determining the appropriate level of aggregation of equations is a major problem in formulating models. The most critical step is to describe the purpose of the model and then to determine the appropriate mix of empirical and mechanistic representations of physiological functions, given development and evaluation dataset availability, inputs typically available and the benefits versus the risks of use associated with increased sensitivity. We discussed five major feeding systems used around the world. They share common concepts of energy and nutrient requirement and supply by feeds, but differ in structure and application of the concepts. Animal models are used for a variety of purposes, including the simple description of observations, prediction of responses to management, and explanation of biological mechanisms. Depending upon the objectives, a number of different approaches may be used, including classical algebraic equations, predictive empirical relationships, and dynamic, mechanistic models. The latter offer the best opportunity to make full use of the growing body of knowledge regarding animal biology. Continuing development of these types of models and computer technology and software for their implementation holds great promise for improvements in the effectiveness with which fundamental knowledge of animal function can be applied to improve animal agriculture and reduce its impact on the environment.
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Amry, Zul. "Bayesian Estimate of Parameters for ARMA Model Forecasting." Tatra Mountains Mathematical Publications 75, no. 1 (April 1, 2020): 23–32. http://dx.doi.org/10.2478/tmmp-2020-0002.
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AbstractThis paper presents a Bayesian approach to finding the Bayes estimator of parameters for ARMA model forecasting under normal-gamma prior assumption with a quadratic loss function in mathematical expression. Obtaining the conditional posterior predictive density is based on the normal-gamma prior and the conditional predictive density, whereas its marginal conditional posterior predictive density is obtained using the conditional posterior predictive density. Furthermore, the Bayes estimator of parameters is derived from the marginal conditional posterior predictive density.
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Fernández-Martínez, Juan Luis, Zulima Fernández-Muñiz, Ana Cernea, and Andrzej Kloczkowski. "Predictive Mathematical Models of the Short-Term and Long-Term Growth of the COVID-19 Pandemic." Computational and Mathematical Methods in Medicine 2021 (August 11, 2021): 1–14. http://dx.doi.org/10.1155/2021/5556433.
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The prediction of the dynamics of the COVID-19 outbreak and the corresponding needs of the health care system (COVID-19 patients’ admissions, the number of critically ill patients, need for intensive care units, etc.) is based on the combination of a limited growth model (Verhulst model) and a short-term predictive model that allows predictions to be made for the following day. In both cases, the uncertainty analysis of the prediction is performed, i.e., the set of equivalent models that adjust the historical data with the same accuracy. This set of models provides the posterior distribution of the parameters of the predictive model that adjusts the historical series. It can be extrapolated to the same analyzed time series (e.g., the number of infected individuals per day) or to another time series of interest to which it is correlated and used, e.g., to predict the number of patients admitted to urgent care units, the number of critically ill patients, or the total number of admissions, which are directly related to health needs. These models can be regionalized, that is, the predictions can be made at the local level if data are disaggregated. We show that the Verhulst and the Gompertz models provide similar results and can be also used to monitor and predict new outbreaks. However, the Verhulst model seems to be easier to interpret and to use.
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Oden, J. Tinsley. "Adaptive multiscale predictive modelling." Acta Numerica 27 (May 1, 2018): 353–450. http://dx.doi.org/10.1017/s096249291800003x.
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The use of computational models and simulations to predict events that take place in our physical universe, or to predict the behaviour of engineered systems, has significantly advanced the pace of scientific discovery and the creation of new technologies for the benefit of humankind over recent decades, at least up to a point. That ‘point’ in recent history occurred around the time that the scientific community began to realize that true predictive science must deal with many formidable obstacles, including the determination of the reliability of the models in the presence of many uncertainties. To develop meaningful predictions one needs relevant data, itself possessing uncertainty due to experimental noise; in addition, one must determine model parameters, and concomitantly, there is the overriding need to select and validate models given the data and the goals of the simulation.This article provides a broad overview of predictive computational science within the framework of what is often called the science of uncertainty quantification. The exposition is divided into three major parts. In Part 1, philosophical and statistical foundations of predictive science are developed within a Bayesian framework. There the case is made that the Bayesian framework provides, perhaps, a unique setting for handling all of the uncertainties encountered in scientific prediction. In Part 2, general frameworks and procedures for the calculation and validation of mathematical models of physical realities are given, all in a Bayesian setting. But beyond Bayes, an introduction to information theory, the maximum entropy principle, model sensitivity analysis and sampling methods such as MCMC are presented. In Part 3, the central problem of predictive computational science is addressed: the selection, adaptive control and validation of mathematical and computational models of complex systems. The Occam Plausibility Algorithm, OPAL, is introduced as a framework for model selection, calibration and validation. Applications to complex models of tumour growth are discussed.
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Moreno-Salinas, David, Dictino Chaos, Eva Besada-Portas, José Antonio López-Orozco, Jesús M. de la Cruz, and Joaquín Aranda. "Semiphysical Modelling of the Nonlinear Dynamics of a Surface Craft with LS-SVM." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/890120.
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One of the most important problems in many research fields is the development of reliable mathematical models with good predictive ability to simulate experimental systems accurately. Moreover, in some of these fields, as marine systems, these models play a key role due to the changing environmental conditions and the complexity and high cost of the infrastructure needed to carry out experimental tests. In this paper, a semiphysical modelling technique based on least-squares support vector machines (LS-SVM) is proposed to determine a nonlinear mathematical model of a surface craft. The speed and steering equations of the nonlinear model of Blanke are determined analysing the rudder angle, surge and sway speeds, and yaw rate from real experimental data measured from a zig-zag manoeuvre made by a scale ship. The predictive ability of the model is tested with different manoeuvring experimental tests to show the good performance and prediction ability of the model computed.
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Zeng, Xian Kui, Chang He Yang, Ze Shuai Song, and Shu Hong Zhao. "Study of the Mathematical Model for Online Predicting Mix Mooney Viscosity on the Rubber Open Mill." Key Engineering Materials 561 (July 2013): 54–58. http://dx.doi.org/10.4028/www.scientific.net/kem.561.54.
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According to studying the mechanism of open mill mixing in low temperature and its intelligent mixing theory, based on the analysis of the experimental results getting from the self-developed XK-160E type open mill, we established a mathematical model for predicting the mix Mooney viscosity. The inspection and verification of mathematical model results showed that the predicted Mooney viscosity was very close to the practical value indicating a good predictive effect．
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Senft, J. R. "A Mathematical Model for Ringbom Engine Operation." Journal of Engineering for Gas Turbines and Power 107, no. 3 (July 1, 1985): 590–95. http://dx.doi.org/10.1115/1.3239777.
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The Ringbom or hybrid Stirling engine, essentially dormant for some 70 years, is experiencing a vigorous rebirth following recent fundamental discoveries concerning its unique mode of operation. In this paper the author’s earlier mathematical model of Ringbom engine operation is extended to include volume variation effects due to the displacer rod. This injects a new parameter, the rod-to-displacer area ratio, into the theory improving its fidelity and increasing its predictive power. It is rigorously shown that the main theorem characterizing overdriven mode operation, the basic regime of stable running for hybrids, remains valid in the extended model and is presented here in a new form.
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Budkar, L. N., Tatyana Yu Obukhova, S. I. Solodushkin, A. A. Fedoruk, O. G. Shmonina, and E. A. Karpova. "MATHEMATICAL MODELING OF THE DEVELOPMENT OF CHRONIC FLUORINE INTOXICATION IN ALUMINIUM INDUSTRY WORKERS." Hygiene and sanitation 99, no. 1 (January 15, 2020): 115–19. http://dx.doi.org/10.33029/0016-9900-2020-99-1-115-119.
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Introduction. Chronic fluorine intoxication prevails among the newly discovered occupational diseases in aluminum industry workers. Mathematical modeling is one of the helpful tools in ensuring better risk management with respect to the development of occupational fluorosis. Objective. Developing a logistic regression model predicting a probability of occupational fluorosis development in an occupational staff of aluminum plants in order to suggest adequate prophylactic strategies. Material and methods. A logistic regression model predicting a probability of the development of occupational fluorosis in aluminum industry workers of the Sverdlovsk region was constructed. The model embraced the results of a univariate analysis conducted with respect to major occupational exposures and health characteristics of 201 workers. Results. Six major factors were identified as being predictive of occupational fluorosis development in aluminum industry workers: age (fluorosis risk increases with age); type 2 diabetes mellitus; atrophic gastritis; kidney cysts; X-ray examination data (fluorosis risk increases with the stage as determined by X-ray); the hydro fluoride concentration increases by more than 2 occupational exposure limits. The developed model was verified by clinical cases and showed a high predictive ability (86.2 %). Both sensitivity (true positive rate) and specificity (true negative rate) of the model amounted to 86.2 %. Conclusion. By multivariate analysis the significant, mutually independent factors were identified, their combination being associated with chronic fluorine intoxication in an occupational staff of aluminum plants. The developed mathematical model has a high predictive ability and can be recommended as a sure tool to forecast the course of occupational fluorosis development in the workers at the aluminum industry.
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Myasnikova, Ekaterina, and Konstantin N. Kozlov. "Statistical method for estimation of the predictive power of a gene circuit model." Journal of Bioinformatics and Computational Biology 12, no. 02 (April 2014): 1441002. http://dx.doi.org/10.1142/s0219720014410029.
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In this paper, a specific aspect of the prediction problem is considered: high predictive power is understood as a possibility to reproduce correct behavior of model solutions at predefined values of a subset of parameters. The problem is discussed in the context of a specific mathematical model, the gene circuit model for segmentation gap gene system in early Drosophila development. A shortcoming of the model is that it cannot be used for predicting the system behavior in mutants when fitted to wild type (WT) data. In order to answer a question whether experimental data contain enough information for the correct prediction we introduce two measures of predictive power. The first measure reveals the biologically substantiated low sensitivity of the model to parameters that are responsible for correct reconstruction of expression patterns in mutants, while the second one takes into account their correlation with the other parameters. It is demonstrated that the model solution, obtained by fitting to gene expression data in WT and Kr - mutants simultaneously, and exhibiting the high predictive power, is characterized by much higher values of both measures than those fitted to WT data alone. This result leads us to the conclusion that information contained in WT data is insufficient to reliably estimate the large number of model parameters and provide predictions of mutants.
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LOW, K. H., C. W. CHONG, CHUNLIN ZHOU, and GERALD SEET. "A PERFORMANCE PREDICTIVE MODEL FOR STEADY SWIMMING OF A FISH ROBOT." International Journal of Humanoid Robotics 08, no. 01 (March 2011): 185–203. http://dx.doi.org/10.1142/s0219843611002393.
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Swimming performance is one of the primary concerns and applications to the underwater robots, such as thrust force relating to swimming velocity. As fish's swimming involves the kinematics of its own body and the hydrodynamic interaction with the surrounding fluid, it is difficult to formulate a precise mathematical model by purely analytical approaches. In order to avoid tedious parameter studies in evaluating its performance, this paper proposes a semi-empirical method to model the steady-state swimming performance of a BCF (body and/or caudal fin) biomimetic robotic robot. By using a dimensional analysis method, the semi-empirical model for predicting the thrust force generated by a BCF-oscillating swimming mode is derived. Thereafter, the swimming velocity prediction model is established based on the predictive thrust model together with the use of fundamental theory on drag force and the regression analysis on the experimental data. The model shows a reasonable prediction capability as the resultant predicted results are in good agreement with experiment data. Therefore, the proposed modeling method can be used for a quick prediction of the swimming performance in terms of thrust and velocity. The proposed methodology can be extended to other types of fish robots in real environment, by including changes to relevant parameters.
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Samulenkov, Yu I., Ya A. Filatova, and A. D. Gruzd. "Aircraft maintenance system simulation mathematical model construction." Civil Aviation High Technologies 24, no. 4 (August 27, 2021): 38–49. http://dx.doi.org/10.26467/2079-0619-2021-24-4-38-49.
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The development of the aviation transport system is characterized by sophistication of interacting objects, the multi-criteria nature of the tasks to be solved and difficulties in making management decisions. For example, modern medium haul aircraft are fitted with up to 25,000 sensors to monitor the performance capabilities of functional systems components. Numerous ground-based instrumental methods and means of diagnosing the technical condition are used. This requires the development of methods and algorithms for determining and monitoring the criteria of limiting state of the monitored components and functional systems of aeronautical equipment. In this regard, analytical models of predictive estimate for the technical condition of aeronautical equipment, determination of aircraft maintenance modes and provision of spare parts and materials become essential. The paper proposes a scheme of the aircraft fleet maintenance system modeling algorithm and deducing the mathematical model of the optimal number of aircraft states in order to exclude secondary and subjective factors. The method of statistical modeling based on Markov processes with discrete states and continuous time is the basis of the proposed analytical model. The proposed method is reduced to the synthesis of some modeling algorithm of the investigated process that simulates the complex system components behavior and interaction as well as random perturbing factors. A distinctive feature of the presented algorithm is determination of the predominant estimated dependences of transition probabilities and intensities taking into account the requirements of the modern regulatory framework in terms of reliability of equipment and diagnosing the technical condition. The analysis of the predominant estimated dependencies study results in the conditions of operation of the aircraft maintenance system confirmed a high degree of correlation of the time duration effect on the particular states in order to diagnose the technical condition depending on the diagnostic concept. The proposed simulation model can be used for the aircraft technical condition predictive estimate, aircraft gas turbine engines and functional systems.
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Cross, Frederick R., Vincent Archambault, Mary Miller, and Martha Klovstad. "Testing a Mathematical Model of the Yeast Cell Cycle." Molecular Biology of the Cell 13, no. 1 (January 2002): 52–70. http://dx.doi.org/10.1091/mbc.01-05-0265.
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We derived novel, testable predictions from a mathematical model of the budding yeast cell cycle. A key qualitative prediction of bistability was confirmed in a strain simultaneously lacking cdc14 and G1 cyclins. The model correctly predicted quantitative dependence of cell size on gene dosage of the G1 cyclinCLN3, but it incorrectly predicted strong genetic interactions between G1 cyclins and the anaphase- promoting complex specificity factor Cdh1. To provide constraints on model generation, we determined accurate concentrations for the abundance of all nine cyclins as well as the inhibitor Sic1 and the catalytic subunit Cdc28. For many of these we determined abundance throughout the cell cycle by centrifugal elutriation, in the presence or absence of Cdh1. In addition, perturbations to the Clb-kinase oscillator were introduced, and the effects on cyclin and Sic1 levels were compared between model and experiment. Reasonable agreement was obtained in many of these experiments, but significant experimental discrepancies from the model predictions were also observed. Thus, the model is a strong but incomplete attempt at a realistic representation of cell cycle control. Constraints of the sort developed here will be important in development of a truly predictive model.
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Ding, Jun, Anthony S. Wexler, and Stuart A. Binder-Macleod. "A predictive model of fatigue in human skeletal muscles." Journal of Applied Physiology 89, no. 4 (October 1, 2000): 1322–32. http://dx.doi.org/10.1152/jappl.2000.89.4.1322.
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Fatigue is a major limitation to the clinical application of functional electrical stimulation. The activation pattern used during electrical stimulation affects force and fatigue. Identifying the activation pattern that produces the greatest force and least fatigue for each patient is, therefore, of great importance. Mathematical models that predict muscle forces and fatigue produced by a wide range of stimulation patterns would facilitate the search for optimal patterns. Previously, we developed a mathematical isometric force model that successfully identified the stimulation patterns that produced the greatest forces from healthy subjects under nonfatigue and fatigue conditions. The present study introduces a four-parameter fatigue model, coupled with the force model that predicts the fatigue induced by different stimulation patterns on different days during isometric contractions. This fatigue model accounted for 90% of the variability in forces produced by different fatigue tests. The predicted forces at the end of fatigue testing differed from those observed by only 9%. This model demonstrates the potential for predicting muscle fatigue in response to a wide range of stimulation patterns.
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Voiko, A. V. "Bankruptcy Prediction Models for Construction Companies in the Russian Federation." Finance: Theory and Practice 23, no. 5 (October 24, 2019): 62–74. http://dx.doi.org/10.26794/2587-5671-2019-23-5-62-74.
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The article is concerned with determining the main predictors of bankruptcy in construction organizations in the Russian Federation. Probabilistic prediction of bankruptcy is relevant for both individual companies and sectors of the national economy. Developed a long time ago, the existing bankruptcy prediction methods do not consider the industry specifics of organizations. The article investigates the mechanism for probabilistic prediction of bankruptcy based on logit models. Criteria affecting the bankruptcy probability were substantiated; a mathematical model was proposed to calculate the probability. The provided model was tested in a real company. Based on the sample of small and medium-sized construction companies, the author proposed a logit model reflecting the main factors affecting the financial state of construction companies in Russia and, therefore, the likelihood of their bankruptcy. Testing the model on the actual data from the construction enterprises showed its high predictive power. The study results allow predicting the bankruptcy in construction organizations by means of logit models.
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Vale, Júlia Maria de Carvalho, and Gabriela Fernanda de Azevedo. "Predictive Control applied to a mathematical model of a Flotation Column." International Journal of Advanced Engineering Research and Science 5, no. 10 (2018): 110–14. http://dx.doi.org/10.22161/ijaers.5.10.15.
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Huang, Chang Yuan, and Hai Peng Pan. "Practical Research on Predictive Fuzzy-PID Control in Reactor Temperature Control." Applied Mechanics and Materials 313-314 (March 2013): 355–58. http://dx.doi.org/10.4028/www.scientific.net/amm.313-314.355.
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Against the characteristics of the temperature in reactor such as time-delay, time-varying and difficulty to build a precise mathematical model in the chemical industry. Through the analysis of dynamic characteristics of the controlled object, the method of fuzzy-PID control was designed based on a predictive model. According to the detected temperature signal, the output deviation of the controller and the on-line identification of prediction model, this algorithm gains the predictive value which uses a generalized predictive model and the fuzzy-PID control. Then compare the predictive value with the reference trajectory to get the deviation. Finally use this deviation and the change of the deviation to optimize the PID control parameters and attain the appropriate amount of system control. The simulation results show that the fuzzy-PID control based on prediction model has strong adaptability, good robustness, control accuracy and higher practical value.
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Gogo, Kevin Otieno, Lawrence Nderu, and Makau Mutua. "Variances in knowledge-based interval type 2 Gaussian fuzzy on linear regression models." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 1807–20. http://dx.doi.org/10.3233/jifs-210568.
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Fuzzy logic is a branch of artificial intelligence that has been used extensively in developing Fuzzy systems and models. These systems usually offer artificial intelligence based on the predictive mathematical models used; in this case linear regression mathematical model. Interval type 2 Gaussian fuzzy logic is a fuzzy logic that utilizes Gaussian upper membership function and the lower membership function, with a footprint of uncertainty in between the Gaussian membership functions. The artificial intelligence solutions predicted by these interval type 2 fuzzy systems depends on the training and the resultant linear regression mathematical model developed, which usually extract their training data from the expert knowledge stored in their knowledge bases. The variances in the expert knowledge stored in these knowledge-bases usually affect the overall accuracy of the linear regression predictive models of these systems, due to the variances in the training data. This research therefore establishes the extent that these variances in knowledge bases affect the predictive accuracy of these models, with a case study on knowledge bases used to predict learners’ knowledge level abilities. The calculated linear regression predictive models show that for every variance in the knowledge base, there occurs a change in linear regression predictive model with an intercept value factor commensurate to the variances and their respective weights in the knowledge bases.
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Wang, Jun, Jian Zhong Xu, Guo Dong Wang, and Pu Wang. "Study on Predictive Model of Plate Camber." Advanced Materials Research 572 (October 2012): 137–42. http://dx.doi.org/10.4028/www.scientific.net/amr.572.137.
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The plate camber is one of the thorny problems in the plate rolling process. The characteristic variables of plate camber at the delivery and at the entry sides of the mill were illustrated based on the primary concepts of camber. The relationship between the plate characteristic variable and velocity distribution in the deformation area of the plate was also determined. This paper focuses on the features of asymmetry in the transverse direction during rolling, an elastic deformation mathematical model of four-high mill has been developed to optimize the predictive model of plate camber, which ensures the theory of influence factors of plate camber to be applied in plate rolling.
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Yamamoto, Shigeru. "A New Model-Free Predictive Control Method Using Input and Output Data." Advanced Materials Research 1042 (October 2014): 182–87. http://dx.doi.org/10.4028/www.scientific.net/amr.1042.182.
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The purpose of this paper is to present a new predictive control utilizing online data and stored data of input/output of the controlled system. The conventional predictive control methods utilize the mathematical model of the control system to predict an optimal future input to control the system. The model is usually obtained by a standard system identification method from the measured input/output data. The proposed method does not require the mathematical model to predict the optimal future control input to achieve the desired output. This control strategy, called just-in-time, was originally proposed by Inoue and Yamamoto in 2004. In this paper, we proposed a simplified version of the original just-in-time predictive control method.
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Lima, E. A. B. F., J. T. Oden, D. A. Hormuth, T. E. Yankeelov, and R. C. Almeida. "Selection, calibration, and validation of models of tumor growth." Mathematical Models and Methods in Applied Sciences 26, no. 12 (October 25, 2016): 2341–68. http://dx.doi.org/10.1142/s021820251650055x.
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This paper presents general approaches for addressing some of the most important issues in predictive computational oncology concerned with developing classes of predictive models of tumor growth. First, the process of developing mathematical models of vascular tumors evolving in the complex, heterogeneous, macroenvironment of living tissue; second, the selection of the most plausible models among these classes, given relevant observational data; third, the statistical calibration and validation of models in these classes, and finally, the prediction of key Quantities of Interest (QOIs) relevant to patient survival and the effect of various therapies. The most challenging aspects of this endeavor is that all of these issues often involve confounding uncertainties: in observational data, in model parameters, in model selection, and in the features targeted in the prediction. Our approach can be referred to as “model agnostic” in that no single model is advocated; rather, a general approach that explores powerful mixture-theory representations of tissue behavior while accounting for a range of relevant biological factors is presented, which leads to many potentially predictive models. Then representative classes are identified which provide a starting point for the implementation of OPAL, the Occam Plausibility Algorithm (OPAL) which enables the modeler to select the most plausible models (for given data) and to determine if the model is a valid tool for predicting tumor growth and morphology (in vivo). All of these approaches account for uncertainties in the model, the observational data, the model parameters, and the target QOI. We demonstrate these processes by comparing a list of models for tumor growth, including reaction–diffusion models, phase-fields models, and models with and without mechanical deformation effects, for glioma growth measured in murine experiments. Examples are provided that exhibit quite acceptable predictions of tumor growth in laboratory animals while demonstrating successful implementations of OPAL.
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Musin, Artur R. "Economic-mathematical model for predicting financial market dynamics." Statistics and Economics 15, no. 4 (September 4, 2018): 61–69. http://dx.doi.org/10.21686/2500-3925-2018-4-61-69.
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Study purpose.Existing approaches to forecasting dynamics of financial markets, as a rule, reduce to econometric calculations or technical analysis techniques, which in turn is a consequence of preferences among specialists, engaged in theoretical research and professional market participants, respectively. The main study purpose is developing a predictive economic-mathematical model that allows combining both approaches. In other words, this model should be estimated using traditional methods of econometrics and, at the same time, take into account the impact on the pricing process of the effect of clustering participants on behavioral patterns, as the basis of technical analysis. In addition, it is necessary that the created economic-mathematical model should take into account the phenomenon of existing historical trading levels and control the influence they exert on price dynamics, when it falls into local areas of these levels. Such analysis of price behavior patterns in certain areas of historical repeating levels is a popular approach among professional market participants. Besides, an important criterion of developing model’s potential applicability by a wide range of the interested specialists is its general functional form’s simplicity and, in particular, its components.Materials and methods. In the study, the market of the pound sterling exchange rate against the US dollar (GBP/USD) for the whole period of 2017 was chosen as the considered financial series, in order to forecast it. The presented economic-mathematical model was estimated by classical Kalman filter with an embedded neural network. The choice of these assessment tools can be explained by their wide capabilities in dealing with non-stationary, noisy financial market time series. In addition, applying Kalman filter is a popular technique for estimation local-level models, which principle was implemented in the newly model, proposed in article.Results. Using chosen approach of simultaneous applying Kalman filter and artificial neural network, there were obtained statistically significant estimations of all model’s coefficients. The subsequent model application on GBP/USD series from the test dataset allowed demonstrating its high predictive ability comparing with added random walk model, in particular judging by percentage of correct forecast directions. All received results have confirmed that constructed model allows effectively taking into account structural features of considered market and building good forecasts of future price dynamics.Conclusion. The study was focused on developing and improving apparatus of forecasting financial market prices dynamics. In turn, economic-mathematical model presented in that paper can be used both by specialists, carrying out theoretical studies of pricing process in financial markets, and by professional market participants, forecasting the direction of future price movements. High percentage of correct forecast directions makes it possible to use proposed model independently or as a confirmatory tool.
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Amitrano, Chiara, Giovanni Battista Chirico, Stefania De Pascale, Youssef Rouphael, and Veronica De Micco. "Crop Management in Controlled Environment Agriculture (CEA) Systems Using Predictive Mathematical Models." Sensors 20, no. 11 (May 31, 2020): 3110. http://dx.doi.org/10.3390/s20113110.
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Proximal sensors in controlled environment agriculture (CEA) are used to monitor plant growth, yield, and water consumption with non-destructive technologies. Rapid and continuous monitoring of environmental and crop parameters may be used to develop mathematical models to predict crop response to microclimatic changes. Here, we applied the energy cascade model (MEC) on green- and red-leaf butterhead lettuce (Lactuca sativa L. var. capitata). We tooled up the model to describe the changing leaf functional efficiency during the growing period. We validated the model on an independent dataset with two different vapor pressure deficit (VPD) levels, corresponding to nominal (low VPD) and off-nominal (high VPD) conditions. Under low VPD, the modified model accurately predicted the transpiration rate (RMSE = 0.10 Lm−2), edible biomass (RMSE = 6.87 g m−2), net-photosynthesis (rBIAS = 34%), and stomatal conductance (rBIAS = 39%). Under high VPD, the model overestimated photosynthesis and stomatal conductance (rBIAS = 76–68%). This inconsistency is likely due to the empirical nature of the original model, which was designed for nominal conditions. Here, applications of the modified model are discussed, and possible improvements are suggested based on plant morpho-physiological changes occurring in sub-optimal scenarios.
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BERNAERTS, KRISTEL, ELS DENS, KAREN VEREECKEN, ANNEMIE H. GEERAERD, ARNOUT R. STANDAERT, FRANK DEVLIEGHERE, JOHAN DEBEVERE, and JAN F. VAN IMPE. "Concepts and Tools for Predictive Modeling of Microbial Dynamics." Journal of Food Protection 67, no. 9 (September 1, 2004): 2041–52. http://dx.doi.org/10.4315/0362-028x-67.9.2041.
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Description of microbial cell (population) behavior as influenced by dynamically changing environmental conditions intrinsically needs dynamic mathematical models. In the past, major effort has been put into the modeling of microbial growth and inactivation within a constant environment (static models). In the early 1990s, differential equation models (dynamic models) were introduced in the field of predictive microbiology. Here, we present a general dynamic model-building concept describing microbial evolution under dynamic conditions. Starting from an elementary model building block, the model structure can be gradually complexified to incorporate increasing numbers of influencing factors. Based on two case studies, the fundamentals of both macroscopic (population) and microscopic (individual) modeling approaches are revisited. These illustrations deal with the modeling of (i) microbial lag under variable temperature conditions and (ii) interspecies microbial interactions mediated by lactic acid production (product inhibition). Current and future research trends should address the need for (i) more specific measurements at the cell and/or population level, (ii) measurements under dynamic conditions, and (iii) more comprehensive (mechanistically inspired) model structures. In the context of quantitative microbial risk assessment, complexity of the mathematical model must be kept under control. An important challenge for the future is determination of a satisfactory trade-off between predictive power and manageability of predictive microbiology models.
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Narayanan, Muthalagappan. "Configuring the Objective Function of A Model Predictive Controller for An Integrated Thermal-Electrical Decentral Renewable Energy System." International Journal of Renewable Energy Development 10, no. 2 (January 4, 2021): 317–31. http://dx.doi.org/10.14710/ijred.2021.34241.
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With the increasing integration of decentral renewable energy systems in the residential sector, the opportunity to enhance the control via model predictive control is available. In this article, the main focus is to investigate the objective function of the model predictive controller (MPC) of an integrated thermal-electrical renewable energy system consisting of photovoltaics, solar thermal collectors, fuel cell along with auxiliary gas boiler and electricity grid using electrical and thermal storage in a single-family house. The mathematical definition of the objective function and the depth of detailing the objectives are the prime focus of this particular article. Four different objective functions are defined and are investigated on a day-to-day basis in the selected six representative days of the whole year for the single-family house in Ehingen, Germany with a white-box simulation model simulated using TRNSYS and MATLAB. Using the clustering technique then the six representative days are weighted extrapolated to a whole year and the outcomes of the whole year MPC implementation are estimated. The results show that the detailing of the mathematical model, even though is time and personnel consuming, does have its advantages. With the detailed objective function, 9% more solar thermal fraction; 32% less power-to-heat at an expense of 32% more gas boiler usage; 6% more thermal system effectiveness along with 10% increased total self-consumption fraction with 16% decrease in space heating demand, 492 kWh more battery usage and 66% reduced fuel cell production is achieved by the MPC in comparison to the status quo controller. Except for the effectiveness of the thermal system with increased gas boiler usage, which occurs due to less power-to-heat, the detailed objective function in comparison to the simple mathematical definition does evidently increase the smartness of the MPC.
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Bouzoualegh, Samir, El-Hadi Guechi, and Ridha Kelaiaia. "Model Predictive Control of a Differential-Drive Mobile Robot." Acta Universitatis Sapientiae Electrical and Mechanical Engineering 10, no. 1 (December 1, 2018): 20–41. http://dx.doi.org/10.2478/auseme-2018-0002.
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Abstract This paper presents a model predictive control (MPC) for a differential-drive mobile robot (DDMR) based on the dynamic model. The robot’s mathematical model is nonlinear, which is why an input–output linearization technique is used, and, based on the obtained linear model, an MPC was developed. The predictive control law gains were acquired by minimizing a quadratic criterion. In addition, to enable better tuning of the obtained predictive controller gains, torques and settling time graphs were used. To show the efficiency of the proposed approach, some simulation results are provided.
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Kontes, Georgios, Georgios Giannakis, Víctor Sánchez, Pablo de Agustin-Camacho, Ander Romero-Amorrortu, Natalia Panagiotidou, Dimitrios Rovas, Simone Steiger, Christopher Mutschler, and Gunnar Gruen. "Simulation-Based Evaluation and Optimization of Control Strategies in Buildings." Energies 11, no. 12 (December 2, 2018): 3376. http://dx.doi.org/10.3390/en11123376.
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Over the last several years, a great amount of research work has been focused on the development of model predictive control techniques for the indoor climate control of buildings, but, despite the promising results, this technology is still not adopted by the industry. One of the main reasons for this is the increased cost associated with the development and calibration (or identification) of mathematical models of special structure used for predicting future states of the building. We propose a methodology to overcome this obstacle by replacing these hand-engineered mathematical models with a thermal simulation model of the building developed using detailed thermal simulation engines such as EnergyPlus. As designing better controllers requires interacting with the simulation model, a central part of our methodology is the control improvement (or optimisation) module, facilitating two simulation-based control improvement methodologies: one based in multi-criteria decision analysis methods and the other based on state-space identification of dynamical systems using Gaussian process models and reinforcement learning. We evaluate the proposed methodology in a set of simulation-based experiments using the thermal simulation model of a real building located in Portugal. Our results indicate that the proposed methodology could be a viable alternative to model predictive control-based supervisory control in buildings.
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Oba, Kenneth Miebaka, and Ichebadu George Amadi. "A Predictive Mathematical Model for Water Absorption of Sawdust Ash - Sand Concrete." International Journal of Engineering and Management Research 10, no. 01 (February 28, 2020): 33–41. http://dx.doi.org/10.31033/ijemr.10.1.7.
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LEE, SAMUEL C. K., JUN DING, LAURA A. PROSSER, ANTHONY S. WEXLER, and STUART A. BINDER-MACLEOD. "A predictive mathematical model of muscle forces for children with cerebral palsy." Developmental Medicine & Child Neurology 51, no. 12 (December 2009): 949–58. http://dx.doi.org/10.1111/j.1469-8749.2009.03350.x.
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Cahyaningtias, Sari, Tahiyatul Asfihani, and Subchan Subchan. "DESIGN CONTROL OF SURFACE MARINE VEHICLE USING DISTURBANCE COMPENSATING MODEL PREDICTIVE CONTROL (DC-MPC)." BAREKENG: Jurnal Ilmu Matematika dan Terapan 15, no. 1 (March 1, 2021): 167–78. http://dx.doi.org/10.30598/barekengvol15iss1pp167-178.
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This research studied ship motion control by considering four degrees of freedom (DoF): yaw, roll, sway, and surge in which comprehensive mathematical modeling forming a nonlinear differential equation. Furthermore, this research also investigated solutions for fundamental yet challenging steering problems of ship maneuvering using advanced control method: Disturbance Compensating Model Predictive Control (DC-MPC) method, which based on Model Predictive Control (MPC). The DC-MPC allows optimizing a compensated control then consider sea waves as the environmental disturbances. Those sea waves influence the control and also becomes one of the constraints for the system. The simulation compared the varying condition of Horizon Prediction (Np) and another method showing that the DC-MPC can manage well the given disturbances while maneuvering in certain Horizon Prediction. The results revealed that the ship is stable and follows the desired trajectory
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Stodola, Petr, and Jiří Stodola. "Model of Predictive Maintenance of Machines and Equipment." Applied Sciences 10, no. 1 (December 26, 2019): 213. http://dx.doi.org/10.3390/app10010213.
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This paper presents selected possibilities for mathematical models in predictive maintenance of equipment. This model includes automatic classification of machines by labor intensity, determination of labor intensity standards, and drawing up monthly and yearly maintenance plans for manufacturing lines and technical equipment in an engineering company. This model reduces human error, clarifies accounting and operational records of machines, evaluates the actual maintenance labor intensity, eliminates routine administrative work, enables the use of cloud storages, and includes automatic reporting of problems in the case of on-board diagnostic systems. It is based on differentiated machine care, can be an effective tool for the overall optimization of maintenance processes, and is a part of the digitization of these processes in engineering companies.
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Ipate, George, Gabriel Musuroi, Gabriel-Alexandru Constantin, Elena Madalina Ştefan, Bianca Zabava, and Marina Pihurov. "Experimental and numerical simulation research of sedimentation process in stationary column of aqueous suspension of solids." E3S Web of Conferences 112 (2019): 03028. http://dx.doi.org/10.1051/e3sconf/201911203028.
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Our study proposes a new mathematical model for predicting the gravitational settling velocity of sediment particles, a measure to evaluate water clarity, to provide information to environmental engineers, first responders dealing with environmental emergencies or farmers, for the most rapid and efficient management responses. New and simplified simulation program are developed to the study sedimentation process. The mathematical model proposes a first-order differential equation who is solved by a numerical method algorithm. The proposed formula has the highest degree of predictive accuracy when compared with experimental data.
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LI, DI, LORETTA M. FRIEDRICH, MICHELLE D. DANYLUK, LINDA J. HARRIS, and DONALD W. SCHAFFNER. "Development and Validation of a Mathematical Model for Growth of Pathogens in Cut Melons." Journal of Food Protection 76, no. 6 (June 1, 2013): 953–58. http://dx.doi.org/10.4315/0362-028x.jfp-12-398.
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Many outbreaks of foodborne illness associated with the consumption of fresh-cut melons have been reported. The objective of our research was to develop a mathematical model that predicts the growth rate of Salmonella on fresh-cut cantaloupe over a range of storage temperatures and to validate that model by using Salmonella and Escherichia coli O157:H7 on cantaloupe, honeydew, and watermelon, using both new data and data from the published studies. The growth of Salmonella on honeydew and watermelon and E. coli O157:H7 on cantaloupe, honeydew, and watermelon was monitored at temperatures of 4 to 25°C. The Ratkowsky (or square-root model) was used to describe Salmonella growth on cantaloupe as a function of storage temperature. Our results show that the levels of Salmonella on fresh-cut cantaloupe with an initial load of 3 log CFU/g can reach over 7 log CFU/g at 25°C within 24 h. No growth was observed at 4°C. A linear correlation was observed between the square root of Salmonella growth rate and temperature, such that , R2 = 0.9779. The model was generally suitable for predicting the growth of both Salmonella and E. coli O157:H7 on cantaloupe, honeydew, and watermelon, for both new data and data from the published literature. When compared with existing models for growth of Salmonella, the new model predicts a theoretic minimum growth temperature similar to the ComBase Predictive Models and Pathogen Modeling Program models but lower than other food-specific models. The ComBase Prediction Models results are very similar to the model developed in this study. Our research confirms that Salmonella can grow quickly and reach high concentrations when cut cantaloupe is stored at ambient temperatures, without visual signs of spoilage. Our model provides a fast and cost-effective method to estimate the effects of storage temperature on fresh-cut melon safety and could also be used in subsequent quantitative microbial risk assessments.
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Pla, María-Leonor, Sandra Oltra, María-Dolores Esteban, Santiago Andreu, and Alfredo Palop. "Comparison of Primary Models to Predict Microbial Growth by the Plate Count and Absorbance Methods." BioMed Research International 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/365025.
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The selection of a primary model to describe microbial growth in predictive food microbiology often appears to be subjective. The objective of this research was to check the performance of different mathematical models in predicting growth parameters, both by absorbance and plate count methods. For this purpose, growth curves of three different microorganisms (Bacillus cereus, Listeria monocytogenes, andEscherichia coli) grown under the same conditions, but with different initial concentrations each, were analysed. When measuring the microbial growth of each microorganism by optical density, almost all models provided quite high goodness of fit (r2>0.93) for all growth curves. The growth rate remained approximately constant for all growth curves of each microorganism, when considering one growth model, but differences were found among models. Three-phase linear model provided the lowest variation for growth rate values for all three microorganisms. Baranyi model gave a variation marginally higher, despite a much better overall fitting. When measuring the microbial growth by plate count, similar results were obtained. These results provide insight into predictive microbiology and will help food microbiologists and researchers to choose the proper primary growth predictive model.
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Levashcva, S. V., E. I. Etkina, A. A. Fazylova, G. D. Sakaeva, L. I. Babenkova, and N. A. Orlova. "Predictive model of development of atopic dermatitis in children." Russian Journal of Allergy 13, no. 2 (December 15, 2016): 27–31. http://dx.doi.org/10.36691/rja379.
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Background. To identify the set of possible predictors of atopic dermatitis forming in children. Methods. There were 440 children aged from 0 to 18 years old (315 patients with atopic dermatitis, 125 - children of the monitoring group) under investigation. Mathematical models used logistic regression method were developed. Results. On the basis of the obtained data the logistic regression equation was selected, including 16 predictors, statistical significance of which was within 5% of the Wald statistics. Conclusion. Practical application of the devised formula will help to identify children with high risk to develop atopic dermatitis.
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Gbemavo, Charlemagne Judes Dossou Sèblodo. "Mathematical prediction of the Jatropha curcas L. plant yield: comparing Multiple Linear Regression and Artificial Neural Network Multilayer Perceptron models." African Journal of Applied Statistics 7, ` (January 1, 2020): 941–31. http://dx.doi.org/10.16929/ajas/2020.929.248.
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The aim of this study was to predict the Jatropha~curcas plant yield through an Artificial Neural Network (ANN) Multi-Layer Perceptron (MLP) model. The predictive ability of the developed model was tested against the Multiple Linear Regression (MLR) using performance indexes. According to the performance indexes the use of ANN-MLP model improved J.~curcas plant yield prediction comparatively to MLR model
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Gbemavo, Charlemagne Judes Dossou Sèblodo. "Mathematical prediction of the Jatropha curcas L. plant yield: comparing Multiple Linear Regression and Artificial Neural Network Multilayer Perceptron models." African Journal of Applied Statistics 7, ` (January 1, 2020): 933–43. http://dx.doi.org/10.16929/ajas/2020.933.248.
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The aim of this study was to predict the Jatropha~curcas plant yield through an Artificial Neural Network (ANN) Multi-Layer Perceptron (MLP) model. The predictive ability of the developed model was tested against the Multiple Linear Regression (MLR) using performance indexes. According to the performance indexes the use of ANN-MLP model improved J.~curcas plant yield prediction comparatively to MLR model
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Khoroshilov, V. S. "Mathematical Modelling of Sayano-Shushenskaya Dam Displacement Process after 2009 Accident." International Journal of Engineering Research in Africa 39 (November 2018): 47–59. http://dx.doi.org/10.4028/www.scientific.net/jera.39.47.
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The research presented in the article is of cutting-edge importance because it proves the necessity to develop prognostic mathematical models with the view to studying the behavior of high-head dams for identifying the regularities of their deformations development process and thus providing quantitative definition for the set criteria values of the diagnostic indices to ensure safe operation of such structures. The paper focuses on the peculiarities of building prognostic mathematical models of the dynamic type on the basis of recurrent equations of the 1stand 2ndorders of different types depending on the order of the mathematical model, number of the principal acting factors and discreteness of the input data, with decorrelation of the input actions and sequence of transport delay introduction. It is shown that the properties of the recurrent equation solution in the form of two first conditional moment generating functions of the displacement process of the observed points in the structure delineate a prognostic model which allows predicting the displacements of the observed points. The paper describes the sequence of estimation stages during the creation of prognostic mathematical models in respect to the character of the predictive problem for various time periods of the structure operation. Different combinations of input actions and discreteness of input data, as well as their decorrelation, have been used. We also applied transport delay in order to correctly consider the inertial delay of the dam under different loads. To account for the residual part of the inertial delay, which is affected by random and unaccounted for factors, we used the autoregression model of the process development regularity. To determine the order of the autoregression model, we calculated asymptotically unbiased ratings of the correlation function for the residual error as a difference between the actual and predicted displacements. Methodological specifics of constructing prognostic models have been established in the context of the factors above. Prognostic mathematical models of different types have been developed for the selected period of the dam operation and the results of prediction have been discussed.
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Li, Shi, Xi Ju Zong, and Yan Hu. "Modeling and Control of Biochemical Reactor." Advanced Materials Research 791-793 (September 2013): 818–21. http://dx.doi.org/10.4028/www.scientific.net/amr.791-793.818.
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This paper is concerns with the study of modeling and control of biochemical reactor. Firstly, a mathematical model is established for a typical biochemical reactor, the mass balance equations are established individually for substrate concentration and biomass concentration. Then, the model is linearized at the steady-state point, two linear models are derived: state space model and transfer function model. The transfer function model is used in internal model control (IMC), where the filter parameter is selected and discussed. The state space model is applied in model predictive control (MPC), where controller parameters of control prediction horizon length and constraint of control variable variation are discussed.
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Li, Shi, Xi Ju Zong, and Yan Hu. "Modeling and Control of Sludge Pyrolysis in a Fluidized Bed Reactor." Advanced Materials Research 846-847 (November 2013): 69–72. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.69.
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This paper is concerns with the study of modeling and control of sludge pyrolysis in a fluidized bed reactor. Firstly, a mathematical model is established for sludge pyrolysis in a fluidized bed furnace, mass balance and energy equations are established. Then, the model is linearized at the steady-state point, two linear models are derived: state space model and transfer function model. The transfer function model is used in internal model control (IMC), where the filter parameter is selected and discussed. The state space model is applied in model predictive control (MPC), where controller parameters of prediction horizon length and control horizon length are discussed.
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Wang, Ning Na, and Qin Lin Zhou. "Mathematical Models for Predicting and Managing Water Resources — The Case of China in 2025." Applied Mechanics and Materials 448-453 (October 2013): 995–1001. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.995.
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An effective management of water supply is critically significant to a countrys water utilities, and accurate prediction of water supply and demand is of key importance for water supply management. The objectives of this paper are to use Grey System Model (GSM) and Linear Regression Model to forecast the water demand and water supply respectively in China 2025, and then propose a new Optimal Allocation Model (OAM) to generate solution so that analysts and decision makers can gain insight and understanding. The two predictive models take into account four major factors including domestic development, agriculture, industries and eco-environment, calculating a deficit between water demand and water supply in China 2025. Then the OAM, which considers desalinization, irrigation saving and urban recycling, provides a feasible solution to fill the gap and an effectual management of water supply.
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Asad, Bilal, and K. M. Hasan. "Laguerre Function’s Based Model Predictive Control of Three Phase Induction Motor." Applied Mechanics and Materials 229-231 (November 2012): 961–67. http://dx.doi.org/10.4028/www.scientific.net/amm.229-231.961.
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Electrical drives play a vital role in industrial and domestic applications, so these drives must be more efficient and easy to control. Conventional control methods are well established and nearly fulfill all demands of modern electrical drives but there is still a need to improve these controllers. In this paper it is proposed that the conventional field oriented controllers can be replaced by Model Predictive Controllers (MPC) which are currently being used as process controllers. These MPC controllers are preferred over conventional field oriented controllers because they can predict future behavior of system and according to that predicted value they can adjust their present input in order to minimize the error signal. The major drawback of conventional MPC is that a lot of mathematical iterations are required to predict the future behavior of system so Laguerre function’s based MPC model is used to reduce the mathematical effort and results are validated for a three phase induction motor.
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Acharyya, Rupam, Shouman Das, Ankani Chattoraj, and Md Iftekhar Tanveer. "FairyTED: A Fair Rating Predictor for TED Talk Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 338–45. http://dx.doi.org/10.1609/aaai.v34i01.5368.
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With the recent trend of applying machine learning in every aspect of human life, it is important to incorporate fairness into the core of the predictive algorithms. We address the problem of predicting the quality of public speeches while being fair with respect to sensitive attributes of the speakers, e.g. gender and race. We use the TED talks as an input repository of public speeches because it consists of speakers from a diverse community and has a wide outreach. Utilizing the theories of Causal Models, Counterfactual Fairness and state-of-the-art neural language models, we propose a mathematical framework for fair prediction of the public speaking quality. We employ grounded assumptions to construct a causal model capturing how different attributes affect public speaking quality. This causal model contributes in generating counterfactual data to train a fair predictive model. Our framework is general enough to utilize any assumption within the causal model. Experimental results show that while prediction accuracy is comparable to recent work on this dataset, our predictions are counterfactually fair with respect to a novel metric when compared to true data labels. The FairyTED setup not only allows organizers to make informed and diverse selection of speakers from the unobserved counterfactual possibilities but it also ensures that viewers and new users are not influenced by unfair and unbalanced ratings from arbitrary visitors to the ted.com website when deciding to view a talk.